From ccb7c4f8f32909bf86808becfa55447cd46cadbb Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Sun, 11 Jan 2026 15:41:58 -0500
Subject: [PATCH 01/72] Add pressure_dimension_str arg to
 geopotential_thickness (#297)

---
 src/extremeweatherbench/calc.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index e207d815..2e038148 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -243,6 +243,7 @@ def geopotential_thickness(
     top_level_value: int = 300,
     bottom_level_value: int = 500,
     geopotential: bool = False,
+    pressure_dimension_str: str = "level",
 ) -> xr.DataArray:
     """Generate the geopotential thickness from the geopotential heights.
 
@@ -254,12 +255,13 @@ def geopotential_thickness(
             geopotential thickness from.
         geopotential: Whether the input DataArray is geopotential height or
             geopotential (default is geopotential height).
+        pressure_dimension_str: The name of the pressure dimension (default is "level").
 
     Returns:
         The geopotential thickness as an xarray DataArray.
     """
-    geopotential_heights = da.sel({"level": top_level_value})
-    geopotential_height_bottom = da.sel({"level": bottom_level_value})
+    geopotential_heights = da.sel({pressure_dimension_str: top_level_value})
+    geopotential_height_bottom = da.sel({pressure_dimension_str: bottom_level_value})
     if geopotential:
         geopotential_thickness = (
             geopotential_heights - geopotential_height_bottom

From d5ca07cca28975205108479d381d3b3635f1ea36 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 12 Jan 2026 20:30:14 -0500
Subject: [PATCH 02/72] `DurationMeanError` memory fix and add time resolution
 option (#296)

* update duration with handling spatial dims, remove compute, fix sparse lead time dim generation

* update name on metric in tests

* add docstring for time res arg
---
 src/extremeweatherbench/metrics.py | 68 +++++++++++++++++++++---------
 tests/test_metrics.py              |  4 +-
 2 files changed, 50 insertions(+), 22 deletions(-)

diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index e80061ed..21bed2b5 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -1192,22 +1192,30 @@ class DurationMeanError(MeanError):
         threshold_criteria: The criteria for event detection. Can be either a DataArray
             of a climatology with dimensions (dayofyear, hour, latitude, longitude) or a
             float value representing a fixed threshold.
+        reduce_spatial_dims: The spatial dimensions to reduce prior to applying threshold
+            criteria. Defaults to ["latitude", "longitude"].
         op_func: Comparison operator or string (e.g., operator.ge for >=).
         name: Name of the metric.
         preserve_dims: Dimensions to preserve during aggregation. Defaults to
             "init_time".
+        product_time_resolution_hours: Whether to product the duration by the time
+            resolution of the forecast (in hours). Defaults to False.
     """
 
     def __init__(
         self,
         threshold_criteria: xr.DataArray | float,
+        reduce_spatial_dims: list[str] = ["latitude", "longitude"],
         op_func: Union[Callable, Literal[">", ">=", "<", "<=", "==", "!="]] = ">=",
-        name: str = "duration_me",
+        name: str = "DurationMeanError",
         preserve_dims: str = "init_time",
+        product_time_resolution_hours: bool = False,
     ):
         super().__init__(name=name, preserve_dims=preserve_dims)
+        self.reduce_spatial_dims = reduce_spatial_dims
         self.threshold_criteria = threshold_criteria
         self.op_func = utils.maybe_get_operator(op_func)
+        self.product_time_resolution_hours = product_time_resolution_hours
 
     def _compute_metric(
         self,
@@ -1218,22 +1226,21 @@ def _compute_metric(
         """Compute spatially averaged duration mean error.
 
         Args:
-            forecast: Forecast dataset with dims (init_time, lead_time, valid_time)
-            target: Target dataset with dims (valid_time)
+            forecast: the forecast DataArray.
+            target: the target DataArray.
 
         Returns:
-            Mean error between forecast and target event durations
+            The mean error between forecast and target event durations.
         """
-        spatial_dims = [
-            dim
-            for dim in forecast.dims
-            if dim not in ["init_time", "lead_time", "valid_time"]
-        ]
         # Handle criteria - either climatology (xr.DataArray) or float threshold
         # Use local variable to avoid mutating self.threshold_criteria
         threshold_criteria = self.threshold_criteria
+
+        # Need to get climatology into the correct format and interpolation for
+        # comparison
         if isinstance(threshold_criteria, xr.DataArray):
-            # Climatology case, convert from dayofyear/hour to valid_time
+            # Climatology case, convert from dayofyear/hour to valid_time.
+            # Note that unintended behavior may occur if the case spans multiple years.
             threshold_criteria = utils.convert_day_yearofday_to_time(
                 threshold_criteria, forecast.valid_time.dt.year.values[0]
             )
@@ -1242,14 +1249,28 @@ def _compute_metric(
             threshold_criteria = utils.interp_climatology_to_target(
                 target, threshold_criteria
             )
-        forecast = utils.reduce_dataarray(
-            forecast, method="mean", reduce_dims=spatial_dims
-        )
-        target = utils.reduce_dataarray(target, method="mean", reduce_dims=spatial_dims)
-        forecast = forecast.compute()
-        target = target.compute()
+        # Reduce spatial dimensions if specified (default is to reduce)
+        if len(self.reduce_spatial_dims) > 0:
+            target = utils.reduce_dataarray(
+                target, method="mean", reduce_dims=self.reduce_spatial_dims, skipna=True
+            )
+            forecast = utils.reduce_dataarray(
+                forecast,
+                method="mean",
+                reduce_dims=self.reduce_spatial_dims,
+                skipna=True,
+            )
+
+            if isinstance(threshold_criteria, xr.DataArray):
+                threshold_criteria = utils.reduce_dataarray(
+                    threshold_criteria,
+                    method="mean",
+                    reduce_dims=self.reduce_spatial_dims,
+                    skipna=True,
+                )
         forecast_mask = self.op_func(forecast, threshold_criteria)
         target_mask = self.op_func(target, threshold_criteria)
+
         # Track NaN locations in forecast data
         forecast_valid_mask = ~forecast.isnull()
 
@@ -1259,10 +1280,13 @@ def _compute_metric(
             target_mask_final = target_mask.where(forecast_valid_mask)
         # If sparse, will need to expand_dims first as transpose is not supported
         except AttributeError:
-            print("target_mask is sparse")
-            target_mask_final = target_mask.expand_dims(dim={"lead_time": 41}).where(
-                forecast_valid_mask
+            logger.info(
+                "Target mask is sparse, expanding dimensions to handle unsupported "
+                "transpose operation."
             )
+            target_mask_final = target_mask.expand_dims(
+                dim={"lead_time": target.lead_time.size}
+            ).where(forecast_valid_mask)
 
         # Sum to get durations (NaN values are excluded by default)
         forecast_duration = forecast_mask_final.groupby(self.preserve_dims).sum(
@@ -1270,7 +1294,11 @@ def _compute_metric(
         )
         target_duration = target_mask_final.groupby(self.preserve_dims).sum(skipna=True)
 
-        # TODO: product of time resolution hours and duration
+        if self.product_time_resolution_hours:
+            time_resolution_hours = utils.determine_temporal_resolution(forecast)
+            forecast_duration = forecast_duration * time_resolution_hours
+            target_duration = target_duration * time_resolution_hours
+
         return super()._compute_metric(
             forecast=forecast_duration,
             target=target_duration,
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index ef964005..a7056806 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -1009,7 +1009,7 @@ def test_instantiation(self):
         climatology = self.create_climatology()
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         assert isinstance(metric, metrics.MeanError)
-        assert metric.name == "duration_me"
+        assert metric.name == "DurationMeanError"
 
     def test_base_metric_inheritance(self):
         """Test that DurationMeanError inherits from ME."""
@@ -1420,7 +1420,7 @@ def test_instantiation_with_float_threshold_criteria(self):
         criteria."""
         metric = metrics.DurationMeanError(threshold_criteria=300.0)
         assert isinstance(metric, metrics.MeanError)
-        assert metric.name == "duration_me"
+        assert metric.name == "DurationMeanError"
         assert metric.threshold_criteria == 300.0
 
     def test_me_with_float_threshold_all_forecast_exceeds(self):

From f2d8cd248443eaeebc4762c8bffa4b10055ce26e Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Tue, 13 Jan 2026 11:49:47 -0500
Subject: [PATCH 03/72] Move parallel config check outside of function (#301)

* move function out of run, move cache mkdir to init

* add tests for new func

* ruff

* update parallel_config passthrough and tests
---
 src/extremeweatherbench/evaluate.py | 91 +++++++++++++++++++----------
 tests/test_evaluate.py              | 58 +++++++++++++++++-
 2 files changed, 116 insertions(+), 33 deletions(-)

diff --git a/src/extremeweatherbench/evaluate.py b/src/extremeweatherbench/evaluate.py
index df36591b..ccc88004 100644
--- a/src/extremeweatherbench/evaluate.py
+++ b/src/extremeweatherbench/evaluate.py
@@ -72,6 +72,11 @@ def __init__(
             )
         self.evaluation_objects = evaluation_objects
         self.cache_dir = pathlib.Path(cache_dir) if cache_dir else None
+
+        # Instantiate cache dir if needed
+        if self.cache_dir:
+            if not self.cache_dir.exists():
+                self.cache_dir.mkdir(parents=True, exist_ok=True)
         self.region_subsetter = region_subsetter
 
     # Case operators as a property can be used as a convenience method for a workflow
@@ -107,40 +112,20 @@ def run(
                 Ignored if parallel_config is provided.
             parallel_config: Optional dictionary of joblib parallel configuration.
                 If provided, this takes precedence over n_jobs. If not provided and
-                n_jobs is specified, a default config with threading backend is used.
+                n_jobs is specified, a default config with loky backend is used.
 
         Returns:
             A concatenated dataframe of the evaluation results.
         """
         logger.info("Running ExtremeWeatherBench workflow...")
 
-        # Determine if running in serial or parallel mode
-        # Serial: n_jobs=1 or (parallel_config with n_jobs=1)
-        # Parallel: n_jobs>1 or (parallel_config with n_jobs>1)
-        is_serial = (
-            (n_jobs == 1)
-            or (parallel_config is not None and parallel_config.get("n_jobs") == 1)
-            or (n_jobs is None and parallel_config is None)
-        )
-        logger.debug("Running in %s mode.", "serial" if is_serial else "parallel")
-
-        if not is_serial:
-            # Build parallel_config if not provided
-            if parallel_config is None and n_jobs is not None:
-                logger.debug(
-                    "No parallel_config provided, using threading backend and %s jobs.",
-                    n_jobs,
-                )
-                parallel_config = {"backend": "threading", "n_jobs": n_jobs}
-            kwargs["parallel_config"] = parallel_config
-        else:
-            # Running in serial mode - instantiate cache dir if needed
-            if self.cache_dir:
-                if not self.cache_dir.exists():
-                    self.cache_dir.mkdir(parents=True, exist_ok=True)
-
+        # Check for serial or parallel configuration
+        parallel_config = _parallel_serial_config_check(n_jobs, parallel_config)
         run_results = _run_case_operators(
-            self.case_operators, cache_dir=self.cache_dir, **kwargs
+            self.case_operators,
+            cache_dir=self.cache_dir,
+            parallel_config=parallel_config,
+            **kwargs,
         )
 
         # If there are results, concatenate them and return, else return an empty
@@ -154,6 +139,7 @@ def run(
 def _run_case_operators(
     case_operators: list["cases.CaseOperator"],
     cache_dir: Optional[pathlib.Path] = None,
+    parallel_config: Optional[dict] = None,
     **kwargs,
 ) -> list[pd.DataFrame]:
     """Run the case operators in parallel or serial.
@@ -167,13 +153,15 @@ def _run_case_operators(
         List of result DataFrames.
     """
     with logging_redirect_tqdm():
-        # Check if parallel_config is provided
-        parallel_config = kwargs.get("parallel_config", None)
-
         # Run in parallel if parallel_config exists and n_jobs != 1
         if parallel_config is not None:
             logger.info("Running case operators in parallel...")
-            return _run_parallel(case_operators, cache_dir=cache_dir, **kwargs)
+            return _run_parallel(
+                case_operators,
+                cache_dir=cache_dir,
+                parallel_config=parallel_config,
+                **kwargs,
+            )
         else:
             logger.info("Running case operators in serial...")
             return _run_serial(case_operators, cache_dir=cache_dir, **kwargs)
@@ -910,3 +898,44 @@ def _safe_concat(
         return pd.concat(valid_dfs, ignore_index=ignore_index)
     else:
         return pd.DataFrame(columns=OUTPUT_COLUMNS)
+
+
+def _parallel_serial_config_check(
+    n_jobs: Optional[int] = None,
+    parallel_config: Optional[dict] = None,
+) -> Optional[dict]:
+    """Check if running in serial or parallel mode.
+
+    Args:
+        n_jobs: The number of jobs to run in parallel. If None, defaults to the
+            joblib backend default value. If 1, the workflow will run serially.
+        parallel_config: Optional dictionary of joblib parallel configuration. If
+            provided, this takes precedence over n_jobs. If not provided and n_jobs is
+            specified, a default config with loky backend is used.
+    Returns:
+        None if running in serial mode, otherwise a dictionary of joblib parallel
+        configuration.
+    """
+    # Determine if running in serial or parallel mode
+    # Serial: n_jobs=1 or (parallel_config with n_jobs=1)
+    # Parallel: n_jobs>1 or (parallel_config with n_jobs>1)
+    is_serial = (
+        (n_jobs == 1)
+        or (parallel_config is not None and parallel_config.get("n_jobs") == 1)
+        or (n_jobs is None and parallel_config is None)
+    )
+    logger.debug("Running in %s mode.", "serial" if is_serial else "parallel")
+
+    if not is_serial:
+        # Build parallel_config if not provided
+        if parallel_config is None and n_jobs is not None:
+            logger.debug(
+                "No parallel_config provided, using loky backend and %s jobs.",
+                n_jobs,
+            )
+            parallel_config = {"backend": "loky", "n_jobs": n_jobs}
+    # If running in serial mode, set parallel_config to None if not already
+    else:
+        parallel_config = None
+    # Return the maybe updated kwargs
+    return parallel_config
diff --git a/tests/test_evaluate.py b/tests/test_evaluate.py
index eca58b63..fe23d95c 100644
--- a/tests/test_evaluate.py
+++ b/tests/test_evaluate.py
@@ -366,10 +366,11 @@ def test_run_serial(
 
             result = ewb.run(n_jobs=1)
 
-            # Serial mode should not pass parallel_config
+            # Serial mode passes parallel_config=None
             mock_run_case_operators.assert_called_once_with(
                 [sample_case_operator],
                 cache_dir=None,
+                parallel_config=None,
             )
             assert isinstance(result, pd.DataFrame)
             assert len(result) == 1
@@ -407,7 +408,7 @@ def test_run_parallel(
             mock_run_case_operators.assert_called_once_with(
                 [sample_case_operator],
                 cache_dir=None,
-                parallel_config={"backend": "threading", "n_jobs": 2},
+                parallel_config={"backend": "loky", "n_jobs": 2},
             )
             assert isinstance(result, pd.DataFrame)
             assert len(result) == 1
@@ -2725,5 +2726,58 @@ def test_compute_case_operator_no_output_variables(self, sample_individual_case)
         assert all(result["target_variable"] == "MockDerivedVariableWithOutputs")
 
 
+class TestParallelSerialConfigCheck:
+    def test_parallel_serial_config_check_serial(self):
+        """Test that the parallel_serial_config_check returns None for serial mode.
+
+        If n_jobs == 1 in any of the arguments, parallel_config should always be
+        None."""
+        assert evaluate._parallel_serial_config_check(n_jobs=1) is None
+        assert (
+            evaluate._parallel_serial_config_check(parallel_config={"n_jobs": 1})
+            is None
+        )
+        assert (
+            evaluate._parallel_serial_config_check(
+                n_jobs=None, parallel_config={"n_jobs": 1}
+            )
+            is None
+        )
+        assert (
+            evaluate._parallel_serial_config_check(
+                n_jobs=None, parallel_config={"n_jobs": 1}
+            )
+            is None
+        )
+        assert (
+            evaluate._parallel_serial_config_check(
+                n_jobs=None, parallel_config={"backend": "threading", "n_jobs": 1}
+            )
+            is None
+        )
+
+    def test_parallel_serial_config_check_parallel(self):
+        """Test that the parallel_serial_config_check returns a dictionary for parallel mode."""
+        assert evaluate._parallel_serial_config_check(n_jobs=2) == {
+            "backend": "loky",
+            "n_jobs": 2,
+        }
+        assert evaluate._parallel_serial_config_check(
+            parallel_config={"backend": "threading", "n_jobs": 2}
+        ) == {"backend": "threading", "n_jobs": 2}
+        assert evaluate._parallel_serial_config_check(
+            n_jobs=2, parallel_config={"backend": "threading", "n_jobs": 2}
+        ) == {"backend": "threading", "n_jobs": 2}
+        assert evaluate._parallel_serial_config_check(
+            n_jobs=2, parallel_config={"backend": "threading", "n_jobs": 2}
+        ) == {"backend": "threading", "n_jobs": 2}
+        assert evaluate._parallel_serial_config_check(
+            n_jobs=2, parallel_config={"backend": "threading", "n_jobs": 2}
+        ) == {"backend": "threading", "n_jobs": 2}
+        assert evaluate._parallel_serial_config_check(
+            n_jobs=2, parallel_config={"backend": "threading", "n_jobs": 2}
+        ) == {"backend": "threading", "n_jobs": 2}
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

From d09f45661183125d67b4d7987f8b3ad4faff36fa Mon Sep 17 00:00:00 2001
From: Daniel Rothenberg <daniel@danielrothenberg.com>
Date: Tue, 13 Jan 2026 10:18:18 -0700
Subject: [PATCH 04/72] feat: Forecast wrapper for custom xarray datasets
 (#302)

* implements a new Forecast object that can wrap existing xarray datasets

* Revise per copilot review
---
 src/extremeweatherbench/inputs.py |  49 ++++
 tests/test_inputs.py              | 439 ++++++++++++++++++++++++++++++
 2 files changed, 488 insertions(+)

diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index 89763b54..eb1c8425 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -423,6 +423,55 @@ def _open_data_from_source(self) -> IncomingDataInput:
         )
 
 
+@dataclasses.dataclass
+class XarrayForecast(ForecastBase):
+    """Forecast class for datasets that were previously constructed and opened using xarray.
+
+    This class is intended for situations where the user has to manually prepare a dataset to
+    use in their evaluation. This can happen when the user is manually constructed such a
+    dataset from a collection of NetCDF or Zarr archives which need to be assembled into a
+    single, master dataset.
+
+    Attributes:
+        ds: The xarray dataset containing the forecast data.
+        source: The source of the data, defaults to "memory" for in-memory datasets.
+        name: The name of the input data source, defaults to "in-memory dataset".
+    """
+
+    #: The xarray dataset containing the forecast data. This is required for the class to be instantiated
+    #: because we inherit from ForecastBase, which has its own set of required attributes.
+    ds: Optional[xr.Dataset] = None  # type: ignore[assignment]
+    source: str = "memory"
+    name: str = "in-memory dataset"
+
+    def __post_init__(self):
+        """Validate that ds is provided and normalize None values to defaults.
+
+        This ensures backwards compatibility with the ForecastBase's __init__ behavior
+        where None values for variables and variable_mapping were converted to empty
+        containers. If the user does not provide a ds, we raise a ValueError.
+        """
+        if self.ds is None:
+            raise ValueError(
+                "The 'ds' parameter is required for XarrayForecast. "
+                "Please provide an xarray.Dataset."
+            )
+
+        # Convert None to empty containers for backwards compatibility
+        if self.variables is None:
+            object.__setattr__(self, "variables", [])
+        if self.variable_mapping is None:
+            object.__setattr__(self, "variable_mapping", {})
+
+    def _open_data_from_source(self) -> xr.Dataset:
+        """Open the input data from the source.
+
+        Returns:
+            The xarray dataset that was provided during initialization.
+        """
+        return self.ds
+
+
 @dataclasses.dataclass
 class TargetBase(InputBase):
     """An abstract base class for target data.
diff --git a/tests/test_inputs.py b/tests/test_inputs.py
index 2dc9dadd..4531d150 100644
--- a/tests/test_inputs.py
+++ b/tests/test_inputs.py
@@ -1871,6 +1871,445 @@ def test_era5_alignment_comprehensive(
         # have different time coverage
 
 
+class TestXarrayForecast:
+    """Test the XarrayForecast class for in-memory datasets."""
+
+    def test_xarray_forecast_instantiation_with_defaults(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test creating XarrayForecast with default parameters."""
+        forecast = inputs.XarrayForecast(ds=sample_forecast_with_valid_time)
+
+        assert forecast.ds is sample_forecast_with_valid_time
+        assert forecast.source == "memory"
+        assert forecast.name == "in-memory dataset"
+        assert forecast.variables == []
+        assert forecast.variable_mapping == {}
+        assert forecast.preprocess == inputs._default_preprocess
+
+    def test_xarray_forecast_instantiation_with_custom_params(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test creating XarrayForecast with custom parameters."""
+        custom_mapping = {"temp": "surface_air_temperature"}
+        custom_variables = ["surface_air_temperature"]
+
+        def custom_preprocess(ds):
+            return ds * 2
+
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            variables=custom_variables,
+            variable_mapping=custom_mapping,
+            source="custom_source",
+            name="custom_forecast",
+            preprocess=custom_preprocess,
+        )
+
+        assert forecast.ds is sample_forecast_with_valid_time
+        assert forecast.source == "custom_source"
+        assert forecast.name == "custom_forecast"
+        assert forecast.variables == custom_variables
+        assert forecast.variable_mapping == custom_mapping
+        assert forecast.preprocess == custom_preprocess
+
+    def test_xarray_forecast_open_data_from_source(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test that _open_data_from_source returns the stored dataset."""
+        forecast = inputs.XarrayForecast(ds=sample_forecast_with_valid_time)
+        result = forecast._open_data_from_source()
+
+        assert result is sample_forecast_with_valid_time
+        xr.testing.assert_identical(result, sample_forecast_with_valid_time)
+
+    def test_xarray_forecast_open_and_preprocess(self, sample_forecast_with_valid_time):
+        """Test open_and_maybe_preprocess_data_from_source with preprocessing."""
+
+        def multiply_by_two(ds):
+            return ds * 2
+
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time, preprocess=multiply_by_two
+        )
+
+        result = forecast.open_and_maybe_preprocess_data_from_source()
+
+        # Should apply preprocessing
+        assert isinstance(result, xr.Dataset)
+        # Values should be doubled
+        expected = sample_forecast_with_valid_time * 2
+        xr.testing.assert_allclose(result, expected)
+
+    def test_xarray_forecast_no_preprocessing(self, sample_forecast_with_valid_time):
+        """Test that default preprocessing does nothing."""
+        forecast = inputs.XarrayForecast(ds=sample_forecast_with_valid_time)
+        result = forecast.open_and_maybe_preprocess_data_from_source()
+
+        # Should return unchanged dataset
+        xr.testing.assert_identical(result, sample_forecast_with_valid_time)
+
+    def test_xarray_forecast_with_chunks_parameter(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test XarrayForecast accepts chunks parameter from ForecastBase."""
+        custom_chunks = {"valid_time": 10, "latitude": 45, "longitude": 90}
+
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time, chunks=custom_chunks
+        )
+
+        # ForecastBase should have chunks attribute
+        assert forecast.chunks == custom_chunks
+
+    def test_xarray_forecast_inherits_subset_data_to_case(
+        self, sample_forecast_dataset
+    ):
+        """Test that XarrayForecast inherits subset_data_to_case from ForecastBase."""
+        # Convert forecast to have init_time and lead_time like ForecastBase expects
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_dataset, variables=["surface_air_temperature"]
+        )
+
+        # Create mock case metadata
+        mock_case = mock.Mock()
+        mock_case.start_date = pd.Timestamp("2021-06-20")
+        mock_case.end_date = pd.Timestamp("2021-06-22")
+        mock_case.location.mask.return_value = sample_forecast_dataset
+
+        with (
+            mock.patch(
+                "extremeweatherbench.utils.derive_indices_from_init_time_and_lead_time"
+            ) as mock_derive,
+            mock.patch(
+                "extremeweatherbench.utils.convert_init_time_to_valid_time"
+            ) as mock_convert,
+        ):
+            # Setup mocks
+            mock_derive.return_value = (np.array([0, 1]), np.array([0, 1]))
+            result_data = xr.Dataset(
+                {
+                    "surface_air_temperature": (
+                        ["valid_time", "latitude", "longitude"],
+                        np.random.randn(3, 3, 3),
+                    )
+                },
+                coords={
+                    "valid_time": pd.date_range("2021-06-20", periods=3, freq="6h"),
+                    "latitude": [40, 41, 42],
+                    "longitude": [-100, -101, -102],
+                },
+            )
+            mock_convert.return_value = result_data
+
+            result = forecast.subset_data_to_case(sample_forecast_dataset, mock_case)
+
+            # Should use inherited method from ForecastBase
+            assert isinstance(result, xr.Dataset)
+
+    def test_xarray_forecast_with_variable_mapping(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test XarrayForecast with variable mapping."""
+        # Create dataset with original variable names
+        test_data = sample_forecast_with_valid_time.copy()
+
+        forecast = inputs.XarrayForecast(
+            ds=test_data,
+            variables=["temp"],
+            variable_mapping={"surface_air_temperature": "temp"},
+        )
+
+        # Test that variable mapping is stored
+        assert forecast.variable_mapping == {"surface_air_temperature": "temp"}
+
+        # Test that maybe_map_variable_names works
+        mapped_data = forecast.maybe_map_variable_names(test_data)
+        assert "temp" in mapped_data.data_vars
+        assert "surface_air_temperature" not in mapped_data.data_vars
+
+    def test_xarray_forecast_empty_dataset(self):
+        """Test XarrayForecast with empty dataset."""
+        empty_ds = xr.Dataset()
+        forecast = inputs.XarrayForecast(ds=empty_ds)
+
+        assert forecast.ds.equals(empty_ds)
+        result = forecast._open_data_from_source()
+        assert result.equals(empty_ds)
+
+    def test_xarray_forecast_with_multiple_variables(self):
+        """Test XarrayForecast with dataset containing multiple variables."""
+        # Create dataset with multiple variables
+        ds = xr.Dataset(
+            {
+                "surface_air_temperature": (
+                    ["valid_time", "latitude", "longitude"],
+                    np.random.randn(10, 5, 5),
+                ),
+                "surface_pressure": (
+                    ["valid_time", "latitude", "longitude"],
+                    np.random.randn(10, 5, 5),
+                ),
+                "surface_eastward_wind": (
+                    ["valid_time", "latitude", "longitude"],
+                    np.random.randn(10, 5, 5),
+                ),
+            },
+            coords={
+                "valid_time": pd.date_range("2021-06-20", periods=10, freq="6h"),
+                "latitude": [40, 41, 42, 43, 44],
+                "longitude": [-100, -101, -102, -103, -104],
+            },
+        )
+
+        forecast = inputs.XarrayForecast(
+            ds=ds,
+            variables=["surface_air_temperature", "surface_pressure"],
+            name="multi_var_forecast",
+        )
+
+        assert forecast.name == "multi_var_forecast"
+        assert set(forecast.variables) == {
+            "surface_air_temperature",
+            "surface_pressure",
+        }
+        result = forecast._open_data_from_source()
+        assert "surface_air_temperature" in result.data_vars
+        assert "surface_pressure" in result.data_vars
+        assert "surface_eastward_wind" in result.data_vars
+
+    def test_xarray_forecast_add_source_to_attrs(self, sample_forecast_with_valid_time):
+        """Test adding source name to dataset attributes."""
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time, name="test_in_memory"
+        )
+
+        result = forecast.add_source_to_dataset_attrs(sample_forecast_with_valid_time)
+
+        assert result.attrs["source"] == "test_in_memory"
+
+    def test_xarray_forecast_preserves_dataset_attrs(self):
+        """Test that XarrayForecast preserves existing dataset attributes."""
+        ds = xr.Dataset(
+            {
+                "temp": (["time", "x"], np.random.randn(5, 3)),
+            },
+            coords={"time": pd.date_range("2021-06-20", periods=5), "x": [1, 2, 3]},
+        )
+        ds.attrs["existing_attr"] = "test_value"
+        ds.attrs["description"] = "Test dataset"
+
+        forecast = inputs.XarrayForecast(ds=ds)
+        result = forecast._open_data_from_source()
+
+        # Original attributes should be preserved
+        assert result.attrs["existing_attr"] == "test_value"
+        assert result.attrs["description"] == "Test dataset"
+
+    def test_xarray_forecast_complex_preprocessing_pipeline(self):
+        """Test XarrayForecast with complex preprocessing pipeline."""
+        ds = xr.Dataset(
+            {
+                "surface_air_temperature": (
+                    ["valid_time", "latitude", "longitude"],
+                    np.random.randn(10, 5, 5) + 273.15,
+                ),
+            },
+            coords={
+                "valid_time": pd.date_range("2021-06-20", periods=10, freq="6h"),
+                "latitude": [40, 41, 42, 43, 44],
+                "longitude": [-100, -101, -102, -103, -104],
+            },
+        )
+
+        def complex_preprocess(dataset):
+            """Convert Kelvin to Celsius and add metadata."""
+            result = dataset.copy()
+            result["surface_air_temperature"] = (
+                result["surface_air_temperature"] - 273.15
+            )
+            result.attrs["units_converted"] = "K_to_C"
+            return result
+
+        forecast = inputs.XarrayForecast(ds=ds, preprocess=complex_preprocess)
+
+        result = forecast.open_and_maybe_preprocess_data_from_source()
+
+        # Check preprocessing was applied
+        assert result.attrs.get("units_converted") == "K_to_C"
+        # Check values were converted (approximately)
+        assert result["surface_air_temperature"].mean() < 100  # Should be in Celsius
+
+    def test_xarray_forecast_with_storage_options(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test XarrayForecast with storage_options parameter."""
+        storage_opts = {"anon": True, "project": "test-project"}
+
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time, storage_options=storage_opts
+        )
+
+        # Storage options should be stored (from InputBase)
+        assert forecast.storage_options == storage_opts
+
+    def test_xarray_forecast_comparison_with_zarr_forecast(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test that XarrayForecast behaves similarly to ZarrForecast."""
+        # Create XarrayForecast
+        xarray_fc = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            name="test_forecast",
+            variables=["surface_air_temperature"],
+        )
+
+        # Both should have same inherited methods
+        assert hasattr(xarray_fc, "subset_data_to_case")
+        assert hasattr(xarray_fc, "maybe_convert_to_dataset")
+        assert hasattr(xarray_fc, "maybe_map_variable_names")
+        assert hasattr(xarray_fc, "add_source_to_dataset_attrs")
+
+        # XarrayForecast should return data directly
+        result = xarray_fc._open_data_from_source()
+        assert result is sample_forecast_with_valid_time
+
+    def test_xarray_forecast_integration_with_evaluation_object(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test XarrayForecast can be used in EvaluationObject."""
+        mock_metric = mock.Mock()
+        mock_target = mock.Mock()
+
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            name="in_memory_forecast",
+            variables=["surface_air_temperature"],
+        )
+
+        # Should be able to create EvaluationObject
+        eval_obj = inputs.EvaluationObject(
+            event_type="test_event",
+            metric_list=[mock_metric],
+            target=mock_target,
+            forecast=forecast,
+        )
+
+        assert eval_obj.forecast is forecast
+        assert isinstance(eval_obj.forecast, inputs.ForecastBase)
+        assert isinstance(eval_obj.forecast, inputs.XarrayForecast)
+
+    def test_xarray_forecast_maybe_convert_to_dataset_passthrough(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test that maybe_convert_to_dataset works correctly."""
+        forecast = inputs.XarrayForecast(ds=sample_forecast_with_valid_time)
+
+        result = forecast.maybe_convert_to_dataset(sample_forecast_with_valid_time)
+
+        # Should return the dataset unchanged (already a Dataset)
+        assert isinstance(result, xr.Dataset)
+        assert result is sample_forecast_with_valid_time
+
+    def test_xarray_forecast_with_derived_variables_placeholder(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test XarrayForecast can specify derived variables (placeholder)."""
+        # This tests that the variables parameter can include derived variable
+        # specifications Note: actual derived variable calculation would happen
+        # in the pipeline
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            variables=["surface_air_temperature", "surface_wind_speed"],
+        )
+
+        assert len(forecast.variables) == 2
+        assert "surface_air_temperature" in forecast.variables
+        assert "surface_wind_speed" in forecast.variables
+
+    def test_xarray_forecast_none_handling_for_optional_params(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test that None values are properly converted to empty defaults."""
+        # Explicitly pass None to test the None handling in __init__
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time, variables=None, variable_mapping=None
+        )
+
+        # Should be converted to empty containers
+        assert forecast.variables == []
+        assert forecast.variable_mapping == {}
+
+    def test_xarray_forecast_requires_ds_parameter(self):
+        """Test that XarrayForecast raises ValueError when ds is None."""
+        with pytest.raises(
+            ValueError, match="The 'ds' parameter is required for XarrayForecast"
+        ):
+            inputs.XarrayForecast(ds=None)
+
+    def test_xarray_forecast_repr_includes_key_info(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test that XarrayForecast has proper repr from dataclass."""
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            name="test_forecast",
+            source="memory",
+        )
+
+        repr_str = repr(forecast)
+        # Should include class name and key attributes
+        assert "XarrayForecast" in repr_str
+        assert "name=" in repr_str or "test_forecast" in repr_str
+
+    def test_xarray_forecast_dataclass_equality(self, sample_forecast_with_valid_time):
+        """Test that XarrayForecast supports dataclass equality comparison."""
+        forecast1 = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            name="test",
+            source="memory",
+        )
+        forecast2 = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            name="test",
+            source="memory",
+        )
+
+        # Same dataset and parameters should be equal
+        assert forecast1 == forecast2
+
+        # Different name should be not equal
+        forecast3 = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            name="different",
+            source="memory",
+        )
+        assert forecast1 != forecast3
+
+    def test_xarray_forecast_default_source_and_name(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test that XarrayForecast provides sensible defaults for source and name."""
+        forecast = inputs.XarrayForecast(ds=sample_forecast_with_valid_time)
+
+        assert forecast.source == "memory"
+        assert forecast.name == "in-memory dataset"
+
+    def test_xarray_forecast_can_override_parent_defaults(
+        self, sample_forecast_with_valid_time
+    ):
+        """Test that XarrayForecast can override defaults from parent classes."""
+        forecast = inputs.XarrayForecast(
+            ds=sample_forecast_with_valid_time,
+            chunks={"time": 10},
+            storage_options={"anon": True},
+        )
+
+        # Should be able to override ForecastBase defaults
+        assert forecast.chunks == {"time": 10}
+        assert forecast.storage_options == {"anon": True}
+
+
 def test_default_preprocess():
     """Test default preprocess function."""
     # Import the function from inputs module since it was moved there

From 10352306b193d82f8658a796a50772ca9ea220b2 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 14 Jan 2026 14:58:38 -0500
Subject: [PATCH 05/72] Simplify IBTrACS polars subset (#303)

---
 src/extremeweatherbench/inputs.py | 25 +++++--------------------
 1 file changed, 5 insertions(+), 20 deletions(-)

diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index eb1c8425..b254dc0e 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -901,34 +901,21 @@ def subset_data_to_case(
         case_metadata: "cases.IndividualCase",
         **kwargs,
     ) -> IncomingDataInput:
-        # Note: drop parameter not applicable for polars LazyFrame data
         if not isinstance(data, pl.LazyFrame):
             raise ValueError(f"Expected polars LazyFrame, got {type(data)}")
 
-        # Get the season (year) from the case start date, cast as string as
-        # polars is interpreting the schema as strings
         season = case_metadata.start_date.year
         if case_metadata.start_date.month > 11:
             season += 1
 
-        # Create a subquery to find all storm numbers in the same season
-        matching_numbers = (
-            data.filter(pl.col("season").cast(pl.Int64) == season)
-            .select("number")
-            .unique()
-        )
-
         possible_names = utils.extract_tc_names(case_metadata.title)
 
-        # Apply the filter to get all data for storms with the same number in
-        # the same season, matching any of the possible names
-        # This maintains the lazy evaluation
-        name_filter = pl.col("tc_name").is_in(possible_names)
-        subset_target_data = data.join(
-            matching_numbers, on="number", how="inner"
-        ).filter(name_filter & (pl.col("season").cast(pl.Int64) == season))
+        # Direct filter - eliminates join, group-by, and second CSV scan
+        subset_target_data = data.filter(
+            (pl.col("season").cast(pl.Int64) == season)
+            & pl.col("tc_name").is_in(possible_names)
+        )
 
-        # Select only the columns to keep
         columns_to_keep = [
             "valid_time",
             "tc_name",
@@ -942,8 +929,6 @@ def subset_data_to_case(
 
         subset_target_data = subset_target_data.select(columns_to_keep)
 
-        # Drop rows where wind speed OR pressure are null (equivalent to pandas
-        # dropna with how="any")
         subset_target_data = subset_target_data.filter(
             pl.col("surface_wind_speed").is_not_null()
             & pl.col("air_pressure_at_mean_sea_level").is_not_null()

From 4027de82a39e0642343e26f996f25d7531936740 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 14 Jan 2026 14:59:52 -0500
Subject: [PATCH 06/72] Update `geopotential_thickness` var names and docstring
 (#306)

* update docstrings and var namings

* rename vars, add test

* ruff
---
 docs/examples/applied_tc.py         |  6 +++---
 src/extremeweatherbench/calc.py     | 32 +++++++++++++++--------------
 src/extremeweatherbench/defaults.py |  4 ++--
 tests/test_calc.py                  | 28 +++++++++++++++++++------
 4 files changed, 44 insertions(+), 26 deletions(-)

diff --git a/docs/examples/applied_tc.py b/docs/examples/applied_tc.py
index 88c88d8a..743c2163 100644
--- a/docs/examples/applied_tc.py
+++ b/docs/examples/applied_tc.py
@@ -29,7 +29,7 @@ def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
         [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
     ).astype("timedelta64[ns]")
     ds["geopotential_thickness"] = calc.geopotential_thickness(
-        ds["z"], top_level_value=300, bottom_level_value=500
+        ds["z"], top_level=300, bottom_level=500
     )
     return ds
 
@@ -46,8 +46,8 @@ def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """
     ds["geopotential_thickness"] = calc.geopotential_thickness(
         ds["geopotential"],
-        top_level_value=300,
-        bottom_level_value=500,
+        top_level=300,
+        bottom_level=500,
         geopotential=True,
     )
     return ds
diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index 2e038148..ef314349 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -240,28 +240,29 @@ def maybe_calculate_wind_speed(ds: xr.Dataset) -> xr.Dataset:
 
 def geopotential_thickness(
     da: xr.DataArray,
-    top_level_value: int = 300,
-    bottom_level_value: int = 500,
+    top_level: int = 300,
+    bottom_level: int = 500,
     geopotential: bool = False,
-    pressure_dimension_str: str = "level",
+    pressure_dim: str = "level",
 ) -> xr.DataArray:
-    """Generate the geopotential thickness from the geopotential heights.
+    """Generate the geopotential thickness from the geopotential heights. Defaults to
+    geopotential height in meters.
 
     Args:
         da: The xarray DataArray to generate the geopotential thickness from.
-        top_level_value: The value of the top level to generate the geopotential
-            thickness from.
-        bottom_level_value: The value of the bottom level to generate the
-            geopotential thickness from.
-        geopotential: Whether the input DataArray is geopotential height or
-            geopotential (default is geopotential height).
-        pressure_dimension_str: The name of the pressure dimension (default is "level").
+        top_level: The pressure value of the top level to generate the
+            geopotential thickness from in hPa.
+        bottom_level: The pressure value of the bottom level to generate the
+            geopotential thickness from in hPa.
+        geopotential: Whether the input DataArray is geopotential height (in m) or
+            geopotential (in m^2/s^2). Default is geopotential height.
+        pressure_dim: The name of the pressure dimension. Default is "level".
 
     Returns:
-        The geopotential thickness as an xarray DataArray.
+        The geopotential thickness in metersas an xarray DataArray.
     """
-    geopotential_heights = da.sel({pressure_dimension_str: top_level_value})
-    geopotential_height_bottom = da.sel({pressure_dimension_str: bottom_level_value})
+    geopotential_heights = da.sel({pressure_dim: top_level})
+    geopotential_height_bottom = da.sel({pressure_dim: bottom_level})
     if geopotential:
         geopotential_thickness = (
             geopotential_heights - geopotential_height_bottom
@@ -269,7 +270,8 @@ def geopotential_thickness(
     else:
         geopotential_thickness = geopotential_heights - geopotential_height_bottom
     geopotential_thickness.attrs = dict(
-        description="Geopotential thickness of level and 500 hPa", units="m"
+        description=f"Geopotential thickness of {top_level} hPa and {bottom_level} hPa",
+        units="m",
     )
     return geopotential_thickness
 
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index 3280fd22..461e55b4 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -102,7 +102,7 @@ def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
     # Calculate the geopotential thickness required for tropical cyclone tracks
     ds["geopotential_thickness"] = calc.geopotential_thickness(
-        ds["z"], top_level_value=300, bottom_level_value=500
+        ds["z"], top_level=300, bottom_level=500
     )
     return ds
 
@@ -126,7 +126,7 @@ def _preprocess_bb_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
     # Calculate the geopotential thickness required for tropical cyclone tracks
     ds["geopotential_thickness"] = calc.geopotential_thickness(
-        ds["geopotential"], top_level_value=300, bottom_level_value=500
+        ds["geopotential"], top_level=300, bottom_level=500
     )
     return ds
 
diff --git a/tests/test_calc.py b/tests/test_calc.py
index 8cb53b57..7ff38eef 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -536,8 +536,8 @@ def test_geopotential_thickness_custom_levels(self, sample_calc_dataset):
         """Test geopotential thickness with custom levels."""
         thickness = calc.geopotential_thickness(
             sample_calc_dataset["geopotential"],
-            top_level_value=200,
-            bottom_level_value=850,
+            top_level=200,
+            bottom_level=850,
             geopotential=True,
         )
 
@@ -555,15 +555,15 @@ def test_geopotential_thickness_multiple_calculations(self, sample_calc_dataset)
         # Calculate thickness for multiple level pairs
         thickness_200_850 = calc.geopotential_thickness(
             sample_calc_dataset["geopotential"],
-            top_level_value=200,
-            bottom_level_value=850,
+            top_level=200,
+            bottom_level=850,
             geopotential=True,
         )
 
         thickness_300_700 = calc.geopotential_thickness(
             sample_calc_dataset["geopotential"],
-            top_level_value=300,
-            bottom_level_value=700,
+            top_level=300,
+            bottom_level=700,
             geopotential=True,
         )
 
@@ -573,6 +573,22 @@ def test_geopotential_thickness_multiple_calculations(self, sample_calc_dataset)
         assert "level" not in thickness_200_850.dims
         assert "level" not in thickness_300_700.dims
 
+    def test_geopotential_thickness_different_pressure_dim_name(
+        self, sample_calc_dataset
+    ):
+        """Test geopotential thickness with different pressure dimension name."""
+
+        sample_calc_dataset = sample_calc_dataset.rename({"level": "name"})
+        thickness = calc.geopotential_thickness(
+            sample_calc_dataset["geopotential"],
+            top_level=200,
+            bottom_level=850,
+            pressure_dim="name",
+        )
+
+        # Assert calculation is performed; would KeyError if not
+        assert isinstance(thickness, xr.DataArray)
+
 
 class TestSpecificHumidityCalculations:
     """Test specific humidity calculations."""

From 1afff3582f8fb829d2b2884f3e3aad13d1f66dee Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 14 Jan 2026 15:05:59 -0500
Subject: [PATCH 07/72] Clarify default preprocess function names; geopotential
 division fix (#305)

* update naming

* default preprocess for applied_tc

* ruff

* ruff
---
 docs/examples/applied_ar.py         |  6 ++--
 docs/examples/applied_freeze.py     |  4 +--
 docs/examples/applied_tc.py         | 54 +++--------------------------
 src/extremeweatherbench/defaults.py | 29 ++++++++--------
 tests/test_defaults.py              | 10 +++---
 5 files changed, 29 insertions(+), 74 deletions(-)

diff --git a/docs/examples/applied_ar.py b/docs/examples/applied_ar.py
index d07a3edf..bc607f4d 100644
--- a/docs/examples/applied_ar.py
+++ b/docs/examples/applied_ar.py
@@ -14,7 +14,7 @@
 
 
 # Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_bb_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """An example preprocess function that renames the time coordinate to lead_time,
     creates a valid_time coordinate, and sets the lead time range and resolution not
     present in the original dataset.
@@ -75,7 +75,7 @@ def _preprocess_bb_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     ],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_bb_cira_forecast_dataset,
+    preprocess=_preprocess_cira_forecast_dataset,
 )
 
 pang_forecast = inputs.KerchunkForecast(
@@ -88,7 +88,7 @@ def _preprocess_bb_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     ],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_bb_cira_forecast_dataset,
+    preprocess=_preprocess_cira_forecast_dataset,
 )
 # Create a list of evaluation objects for atmospheric river
 ar_evaluation_objects = [
diff --git a/docs/examples/applied_freeze.py b/docs/examples/applied_freeze.py
index 70cf00d9..6505081c 100644
--- a/docs/examples/applied_freeze.py
+++ b/docs/examples/applied_freeze.py
@@ -1,7 +1,7 @@
 import logging
 import operator
 
-from extremeweatherbench import cases, evaluate, inputs, metrics, defaults
+from extremeweatherbench import cases, defaults, evaluate, inputs, metrics
 
 # Set the logger level to INFO
 logger = logging.getLogger("extremeweatherbench")
@@ -28,7 +28,7 @@
     variables=["surface_air_temperature"],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=defaults._preprocess_bb_cira_forecast_dataset,
+    preprocess=defaults._preprocess_cira_forecast_dataset,
 )
 
 # Load the climatology for DurationMeanError
diff --git a/docs/examples/applied_tc.py b/docs/examples/applied_tc.py
index 743c2163..b5365218 100644
--- a/docs/examples/applied_tc.py
+++ b/docs/examples/applied_tc.py
@@ -1,58 +1,12 @@
 import logging
 
-import numpy as np
-import xarray as xr
-
-from extremeweatherbench import calc, cases, derived, evaluate, inputs, metrics
+from extremeweatherbench import cases, defaults, derived, evaluate, inputs, metrics
 
 # Set the logger level to INFO
 logger = logging.getLogger("extremeweatherbench")
 logger.setLevel(logging.INFO)
 
 
-# Preprocessing function for CIRA data that includes geopotential thickness calculation
-# required for tropical cyclone tracks
-def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
-    """An example preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
-
-    Args:
-        ds: The forecast dataset to rename.
-
-    Returns:
-        The renamed forecast dataset.
-    """
-    ds = ds.rename({"time": "lead_time"})
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
-    ds["geopotential_thickness"] = calc.geopotential_thickness(
-        ds["z"], top_level=300, bottom_level=500
-    )
-    return ds
-
-
-# Preprocessing function for HRES data that includes geopotential thickness calculation
-# required for tropical cyclone tracks
-def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
-    """An example preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
-
-    Args:
-        ds: The forecast dataset to rename.
-    """
-    ds["geopotential_thickness"] = calc.geopotential_thickness(
-        ds["geopotential"],
-        top_level=300,
-        bottom_level=500,
-        geopotential=True,
-    )
-    return ds
-
-
 # Load the case collection from the YAML file
 case_yaml = cases.load_ewb_events_yaml_into_case_collection()
 
@@ -72,7 +26,7 @@ def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     variable_mapping=inputs.HRES_metadata_variable_mapping,
     storage_options={"remote_options": {"anon": True}},
     # Preprocess the HRES forecast to include geopotential thickness calculation
-    preprocess=_preprocess_hres_forecast_dataset,
+    preprocess=defaults._preprocess_hres_tc_forecast_dataset,
 )
 
 # Define FCNv2 forecast
@@ -83,7 +37,7 @@ def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     # Define metadata variable mapping for FCNv2 forecast
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     # Preprocess the FCNv2 forecast to include geopotential thickness calculation
-    preprocess=_preprocess_bb_cira_tc_forecast_dataset,
+    preprocess=defaults._preprocess_cira_tc_forecast_dataset,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
 )
 
@@ -96,7 +50,7 @@ def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     # Preprocess the Pangu forecast to include geopotential thickness calculation
     # which uses the same preprocessing function as the FCNv2 forecast
-    preprocess=_preprocess_bb_cira_tc_forecast_dataset,
+    preprocess=defaults._preprocess_cira_tc_forecast_dataset,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
 )
 
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index 461e55b4..a78cdcca 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -58,7 +58,7 @@
 ]
 
 
-def _preprocess_bb_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """A preprocess function for CIRA data that renames the time coordinate to
     lead_time, creates a valid_time coordinate, and sets the lead time range and
     resolution not present in the original dataset.
@@ -79,7 +79,7 @@ def _preprocess_bb_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
 # Preprocessing function for CIRA data that includes geopotential thickness calculation
 # required for tropical cyclone tracks
-def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """A preprocess function for CIRA data that includes geopotential thickness
     calculation required for tropical cyclone tracks.
 
@@ -101,15 +101,15 @@ def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     ).astype("timedelta64[ns]")
 
     # Calculate the geopotential thickness required for tropical cyclone tracks
-    ds["geopotential_thickness"] = calc.geopotential_thickness(
-        ds["z"], top_level=300, bottom_level=500
+    ds["geopotential_thickness"] = (
+        calc.geopotential_thickness(ds["z"], top_level=300, bottom_level=500) / 9.81
     )
     return ds
 
 
 # Preprocessing function for HRES data that includes geopotential thickness calculation
 # required for tropical cyclone tracks
-def _preprocess_bb_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """A preprocess function for CIRA data that includes geopotential thickness
     calculation required for tropical cyclone tracks.
 
@@ -125,14 +125,15 @@ def _preprocess_bb_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """
 
     # Calculate the geopotential thickness required for tropical cyclone tracks
-    ds["geopotential_thickness"] = calc.geopotential_thickness(
-        ds["geopotential"], top_level=300, bottom_level=500
+    ds["geopotential_thickness"] = (
+        calc.geopotential_thickness(ds["geopotential"], top_level=300, bottom_level=500)
+        / 9.81
     )
     return ds
 
 
 # Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_bb_ar_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_ar_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """An example preprocess function that renames the time coordinate to lead_time,
     creates a valid_time coordinate, and sets the lead time range and resolution not
     present in the original dataset.
@@ -160,7 +161,7 @@ def _preprocess_bb_ar_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
 
 # Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_bb_severe_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_severe_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """An example preprocess function that renames the time coordinate to lead_time,
     creates a valid_time coordinate, and sets the lead time range and resolution not
     present in the original dataset.
@@ -248,7 +249,7 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
     variables=["surface_air_temperature"],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_bb_cira_forecast_dataset,
+    preprocess=_preprocess_cira_forecast_dataset,
 )
 
 cira_freeze_forecast = inputs.KerchunkForecast(
@@ -257,7 +258,7 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
     variables=["surface_air_temperature"],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_bb_cira_forecast_dataset,
+    preprocess=_preprocess_cira_forecast_dataset,
 )
 
 cira_tropical_cyclone_forecast = inputs.KerchunkForecast(
@@ -266,7 +267,7 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
     variables=[derived.TropicalCycloneTrackVariables()],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_bb_cira_tc_forecast_dataset,
+    preprocess=_preprocess_cira_tc_forecast_dataset,
 )
 cira_atmospheric_river_forecast = inputs.KerchunkForecast(
     name="FourCastNetv2",
@@ -278,7 +279,7 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
     ],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_bb_ar_cira_forecast_dataset,
+    preprocess=_preprocess_ar_cira_forecast_dataset,
 )
 
 cira_severe_convection_forecast = inputs.KerchunkForecast(
@@ -287,7 +288,7 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
     variables=[derived.CravenBrooksSignificantSevere()],
     variable_mapping=inputs.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_bb_severe_cira_forecast_dataset,
+    preprocess=_preprocess_severe_cira_forecast_dataset,
 )
 
 
diff --git a/tests/test_defaults.py b/tests/test_defaults.py
index 092c7ced..cbbea8bd 100644
--- a/tests/test_defaults.py
+++ b/tests/test_defaults.py
@@ -10,8 +10,8 @@
 class TestDefaults:
     """Test the defaults module."""
 
-    def test_preprocess_bb_cira_forecast_dataset(self):
-        """Test the _preprocess_bb_cira_forecast_dataset function."""
+    def test_preprocess_cira_forecast_dataset(self):
+        """Test the _preprocess_cira_forecast_dataset function."""
 
         # Create a mock dataset with 'time' coordinate matching expected output size
         # The function creates lead_time with 41 values (0 to 240 by 6)
@@ -21,7 +21,7 @@ def test_preprocess_bb_cira_forecast_dataset(self):
             {"temperature": (["time"], temp_data)}, coords={"time": time_data}
         )
 
-        result = defaults._preprocess_bb_cira_forecast_dataset(mock_ds)
+        result = defaults._preprocess_cira_forecast_dataset(mock_ds)
 
         # Check that 'time' was renamed to 'lead_time'
         assert "lead_time" in result.coords
@@ -157,11 +157,11 @@ def test_cira_forecasts_have_preprocess_function(self):
         # Test that the preprocess function is the expected one
         assert (
             defaults.cira_heatwave_forecast.preprocess
-            == defaults._preprocess_bb_cira_forecast_dataset
+            == defaults._preprocess_cira_forecast_dataset
         )
         assert (
             defaults.cira_freeze_forecast.preprocess
-            == defaults._preprocess_bb_cira_forecast_dataset
+            == defaults._preprocess_cira_forecast_dataset
         )
 
     def test_get_brightband_evaluation_objects_no_exceptions(self):

From 5d239b8e52934a3b01555b9729c60470f5ea136a Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 15 Jan 2026 12:25:22 -0500
Subject: [PATCH 08/72] Remove "cases" key requirement in yamls and dicts
 (#308)

* remove cases top level of yaml and fix code to handle this

* remove old load events yaml function

* update validation precommit and formatting
---
 data_prep/ibtracs_bounds.py                   |    7 +-
 scripts/validate_events_yaml.py               |   18 +-
 src/extremeweatherbench/cases.py              |   13 +-
 src/extremeweatherbench/data/events.yaml      | 8089 ++++++++---------
 src/extremeweatherbench/data/long_events.yaml |   97 +-
 src/extremeweatherbench/utils.py              |   18 -
 tests/test_utils.py                           |   10 -
 7 files changed, 4110 insertions(+), 4142 deletions(-)

diff --git a/data_prep/ibtracs_bounds.py b/data_prep/ibtracs_bounds.py
index cf159250..9fd834ac 100644
--- a/data_prep/ibtracs_bounds.py
+++ b/data_prep/ibtracs_bounds.py
@@ -15,7 +15,7 @@
 from matplotlib.patches import Rectangle
 
 import extremeweatherbench.data
-from extremeweatherbench import inputs, regions, utils
+from extremeweatherbench import cases, inputs, regions, utils
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -537,9 +537,8 @@ def update_cases_with_storm_bounds(storm_bounds, all_storms_df):
     """
     logger.info("Updating cases with storm bounds...")
 
-    cases_all = utils.load_events_yaml()
-    cases_old = cases_all["cases"]
-    cases_new = cases_old.copy()
+    cases_all = cases.load_ewb_events_yaml_into_case_collection()
+    cases_new = cases_all.copy()
 
     # Update the yaml cases with storm bounds from IBTrACS data
     for single_case in cases_new:
diff --git a/scripts/validate_events_yaml.py b/scripts/validate_events_yaml.py
index 09815cc6..1b9c599d 100644
--- a/scripts/validate_events_yaml.py
+++ b/scripts/validate_events_yaml.py
@@ -209,24 +209,14 @@ def validate_events_yaml(file_path: Path) -> List[str]:
     except yaml.YAMLError as e:
         return [f"Invalid YAML syntax: {e}"]
 
-    if not isinstance(data, dict):
-        return ["Root element must be a dictionary"]
-
-    if "cases" not in data:
-        return ["Missing 'cases' key in root"]
-
-    cases = data["cases"]
-    if not isinstance(cases, list):
-        return ["'cases' must be a list"]
-
-    if len(cases) == 0:
-        return ["'cases' list is empty"]
+    if not isinstance(data, dict) and not isinstance(data, list):
+        return ["Root element must be a dictionary or a list"]
 
     # Validate each case
     previous_case_id = None
-    for i, case in enumerate(cases):
+    for i, case in enumerate(data):
         if not isinstance(case, dict):
-            errors.append(f"Case {i + 1}: must be a dictionary")
+            errors.append(f"Case {i + 1}: must be a dictionary: {data} {file_path}")
             continue
 
         # Check required fields
diff --git a/src/extremeweatherbench/cases.py b/src/extremeweatherbench/cases.py
index 1c10c7bf..e3afe5a6 100644
--- a/src/extremeweatherbench/cases.py
+++ b/src/extremeweatherbench/cases.py
@@ -195,7 +195,9 @@ def build_case_operators(
     return case_operators
 
 
-def load_individual_cases(cases: dict[str, list]) -> IndividualCaseCollection:
+def load_individual_cases(
+    cases: Union[dict[str, list], list],
+) -> IndividualCaseCollection:
     """Load IndividualCase metadata from a dictionary.
 
     Args:
@@ -204,6 +206,13 @@ def load_individual_cases(cases: dict[str, list]) -> IndividualCaseCollection:
     Returns:
         A collection of IndividualCase objects.
     """
+    assert isinstance(cases, dict) or isinstance(cases, list), (
+        "cases must be a dictionary or a list."
+    )
+
+    # if cases is a list, convert to a dictionary for dacite.from_dict
+    if isinstance(cases, list):
+        cases = {"cases": cases}
     case_metadata_collection = dacite.from_dict(
         data_class=IndividualCaseCollection,
         data=cases,
@@ -267,7 +276,7 @@ def load_ewb_events_yaml_into_case_collection() -> IndividualCaseCollection:
     return load_individual_cases(yaml_event_case)
 
 
-def read_incoming_yaml(input_pth: Union[str, pathlib.Path]) -> dict:
+def read_incoming_yaml(input_pth: Union[str, pathlib.Path]):
     """Read events yaml from data into a dictionary.
 
     This function is a wrapper around yaml.safe_load that reads the yaml file directly.
diff --git a/src/extremeweatherbench/data/events.yaml b/src/extremeweatherbench/data/events.yaml
index ac219b01..e1486e95 100644
--- a/src/extremeweatherbench/data/events.yaml
+++ b/src/extremeweatherbench/data/events.yaml
@@ -1,4045 +1,4044 @@
-cases:
-  - case_id_number: 1
-    title: 2021 Pacific Northwest
-    start_date: 2021-06-20 00:00:00
-    end_date: 2021-07-03 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 33.25
-        latitude_max: 68.0
-        longitude_min: 215.25
-        longitude_max: 256.0
-    event_type: heat_wave
-  - case_id_number: 2
-    title: 2022 Upper Midwest
-    start_date: 2022-05-07 00:00:00
-    end_date: 2022-05-17 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 19.5
-        latitude_max: 48.25
-        longitude_min: 252.0
-        longitude_max: 276.75
-    event_type: heat_wave
-  - case_id_number: 3
-    title: 2022 California
-    start_date: 2022-06-07 00:00:00
-    end_date: 2022-06-15 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 23.75
-        latitude_max: 44.5
-        longitude_min: 235.75
-        longitude_max: 266.25
-    event_type: heat_wave
-  - case_id_number: 4
-    title: 2022 Texas
-    start_date: 2022-06-30 00:00:00
-    end_date: 2022-07-18 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 20.5
-        latitude_max: 43.25
-        longitude_min: 254.75
-        longitude_max: 275.5
-    event_type: heat_wave
-  - case_id_number: 5
-    title: 2023 Pacific Northwest
-    start_date: 2023-05-10 00:00:00
-    end_date: 2023-05-23 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 35.25
-        latitude_max: 72.0
-        longitude_min: 219.25
-        longitude_max: 262.0
-    event_type: heat_wave
-  - case_id_number: 6
-    title: 2022 Mid-Atlantic
-    start_date: 2022-05-17 00:00:00
-    end_date: 2022-05-24 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 31.0
-        latitude_max: 42.25
-        longitude_min: 277.0
-        longitude_max: 287.75
-    event_type: heat_wave
-  - case_id_number: 7
-    title: 2023 Australia
-    start_date: 2023-11-18 00:00:00
-    end_date: 2023-11-28 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -35.0
-        latitude_max: -20.0
-        longitude_min: 113.5
-        longitude_max: 120.25
-    event_type: heat_wave
-  - case_id_number: 8
-    title: 2023 Ireland
-    start_date: 2023-09-02 00:00:00
-    end_date: 2023-09-13 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 40.75
-        latitude_max: 67.5
-        longitude_min: -9.75
-        longitude_max: 16.75
-    event_type: heat_wave
-  - case_id_number: 9
-    title: 2023 Italy
-    start_date: 2023-07-07 00:00:00
-    end_date: 2023-07-27 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 17.5
-        latitude_max: 52.25
-        longitude_min: -12.0
-        longitude_max: 32.75
-    event_type: heat_wave
-  - case_id_number: 10
-    title: 2023 SW Europe
-    start_date: 2023-08-17 00:00:00
-    end_date: 2023-08-28 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 36.0
-        latitude_max: 56.75
-        longitude_min: -9.25
-        longitude_max: 20.75
-    event_type: heat_wave
-  - case_id_number: 11
-    title: 2023 South America
-    start_date: 2023-07-29 00:00:00
-    end_date: 2023-08-04 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -44.25
-        latitude_max: -20.25
-        longitude_min: 285.75
-        longitude_max: 308.0
-    event_type: heat_wave
-  - case_id_number: 12
-    title: 2023 China (Shanghai)
-    start_date: 2023-05-24 00:00:00
-    end_date: 2023-06-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 16.75
-        latitude_max: 35.25
-        longitude_min: 99.0
-        longitude_max: 122.0
-    event_type: heat_wave
-  - case_id_number: 13
-    title: 2023 China (Yunnan Province)
-    start_date: 2023-04-14 00:00:00
-    end_date: 2023-04-23 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 12.75
-        latitude_max: 43.5
-        longitude_min: 92.25
-        longitude_max: 121.0
-    event_type: heat_wave
-  - case_id_number: 14
-    title: 2023 Algeria
-    start_date: 2023-07-05 00:00:00
-    end_date: 2023-07-27 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 16.25
-        latitude_max: 51.0
-        longitude_min: -17.0
-        longitude_max: 27.5
-    event_type: heat_wave
-  - case_id_number: 15
-    title: 2023 Iberian Peninsula
-    start_date: 2023-04-22 00:00:00
-    end_date: 2023-05-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 22.75
-        latitude_max: 46.75
-        longitude_min: -15.75
-        longitude_max: 8.75
-    event_type: heat_wave
-  - case_id_number: 16
-    title: 2023 Southeast Asia
-    start_date: 2023-04-16 00:00:00
-    end_date: 2023-04-22 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 6.0
-        latitude_max: 41.0
-        longitude_min: 84.0
-        longitude_max: 123.75
-    event_type: heat_wave
-  - case_id_number: 17
-    title: 2023 India
-    start_date: 2023-02-15 00:00:00
-    end_date: 2023-03-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 22.25
-        latitude_max: 53.0
-        longitude_min: 56.75
-        longitude_max: 101.5
-    event_type: heat_wave
-  - case_id_number: 18
-    title: 2021 Western Russia
-    start_date: 2021-06-18 00:00:00
-    end_date: 2021-06-30 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 35.5
-        latitude_max: 65.75
-        longitude_min: 13.25
-        longitude_max: 60.0
-    event_type: heat_wave
-  - case_id_number: 19
-    title: 2022 Germany
-    start_date: 2022-12-23 00:00:00
-    end_date: 2022-12-31 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 36.25
-        latitude_max: 56.25
-        longitude_min: -3.0
-        longitude_max: 21.75
-    event_type: heat_wave
-  - case_id_number: 20
-    title: 2022 UK (August)
-    start_date: 2022-08-08 00:00:00
-    end_date: 2022-08-16 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 39.25
-        latitude_max: 56.0
-        longitude_min: -9.75
-        longitude_max: 8.25
-    event_type: heat_wave
-  - case_id_number: 21
-    title: 2022 UK (July)
-    start_date: 2022-07-15 00:00:00
-    end_date: 2022-07-23 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 37.25
-        latitude_max: 58.0
-        longitude_min: -9.75
-        longitude_max: 8.25
-    event_type: heat_wave
-  - case_id_number: 22
-    title: 2022 France
-    start_date: 2022-06-09 00:00:00
-    end_date: 2022-06-21 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 29.0
-        latitude_max: 51.75
-        longitude_min: -9.75
-        longitude_max: 12.75
-    event_type: heat_wave
-  - case_id_number: 23
-    title: 2022 Japan
-    start_date: 2022-06-20 00:00:00
-    end_date: 2022-07-05 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 31.25
-        latitude_max: 46.0
-        longitude_min: 125.25
-        longitude_max: 145.25
-    event_type: heat_wave
-  - case_id_number: 24
-    title: 2022 India
-    start_date: 2022-04-24 00:00:00
-    end_date: 2022-05-04 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 18.0
-        latitude_max: 52.75
-        longitude_min: 58.0
-        longitude_max: 78.75
-    event_type: heat_wave
-  - case_id_number: 25
-    title: 2022 East Antarctica
-    start_date: 2022-03-12 00:00:00
-    end_date: 2022-03-26 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -85.5
-        latitude_max: -65.5
-        longitude_min: 99.0
-        longitude_max: 147.75
-    event_type: heat_wave
-  - case_id_number: 26
-    title: 2022 West Australia
-    start_date: 2022-01-15 00:00:00
-    end_date: 2022-01-25 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -35.0
-        latitude_max: -27.5
-        longitude_min: 114.25
-        longitude_max: 119.75
-    event_type: heat_wave
-  - case_id_number: 27
-    title: 2021 Canada Plains
-    start_date: 2021-05-30 00:00:00
-    end_date: 2021-06-09 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 38.5
-        latitude_max: 59.5
-        longitude_min: 238.0
-        longitude_max: 272.75
-    event_type: heat_wave
-  - case_id_number: 28
-    title: 2021 New Zealand
-    start_date: 2021-01-12 18:00:00
-    end_date: 2021-01-18 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -46.5
-        latitude_max: -40.75
-        longitude_min: 167.5
-        longitude_max: 175.25
-    event_type: heat_wave
-  - case_id_number: 29
-    title: 2020 Australia
-    start_date: 2020-11-25 00:00:00
-    end_date: 2020-12-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -36.75
-        latitude_max: -17.5
-        longitude_min: 126.5
-        longitude_max: 152.5
-    event_type: heat_wave
-  - case_id_number: 30
-    title: 2021 Texas
-    start_date: 2021-02-10 12:00:00
-    end_date: 2021-02-22 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.0
-        latitude_max: 54.75
-        longitude_min: 250.0
-        longitude_max: 278.75
-    event_type: freeze
-  - case_id_number: 31
-    title: 2022 Arkansas
-    start_date: 2022-02-17 18:00:00
-    end_date: 2022-03-01 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.25
-        latitude_max: 59.0
-        longitude_min: 243.25
-        longitude_max: 272.0
-    event_type: freeze
-  - case_id_number: 32
-    title: 2023 Germany
-    start_date: 2023-12-02 06:00:00
-    end_date: 2023-12-08 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 44.25
-        latitude_max: 68.5
-        longitude_min: 7.25
-        longitude_max: 22.0
-    event_type: freeze
-  - case_id_number: 33
-    title: 2023 China
-    start_date: 2023-12-15 06:00:00
-    end_date: 2023-12-26 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.75
-        latitude_max: 55.5
-        longitude_min: 101.0
-        longitude_max: 141.75
-    event_type: freeze
-  - case_id_number: 34
-    title: 2023 Afghanistan
-    start_date: 2023-01-11 06:00:00
-    end_date: 2023-01-28 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 22.25
-        latitude_max: 49.0
-        longitude_min: 50.75
-        longitude_max: 85.5
-    event_type: freeze
-  - case_id_number: 35
-    title: 2022 Colorado
-    start_date: 2022-04-06 00:00:00
-    end_date: 2022-04-16 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 35.25
-        latitude_max: 64.0
-        longitude_min: 236.75
-        longitude_max: 261.5
-    event_type: freeze
-  - case_id_number: 36
-    title: July 2024 South Dakota
-    start_date: 2024-07-13 12:00:00
-    end_date: 2024-07-14 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 38.25
-        latitude_max: 58.5
-        longitude_min: 240.86
-        longitude_max: 285.64
-    event_type: severe_convection
-  - case_id_number: 37
-    title: July 2024 Chicago
-    start_date: 2024-07-14 12:00:00
-    end_date: 2024-07-16 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 30.0
-        latitude_max: 57.75
-        longitude_min: 243.74
-        longitude_max: 289.69
-    event_type: severe_convection
-  - case_id_number: 38
-    title: July 2024 New York
-    start_date: 2024-07-16 12:00:00
-    end_date: 2024-07-17 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 31.0
-        latitude_max: 49.0
-        longitude_min: 274.31
-        longitude_max: 293.44
-    event_type: severe_convection
-  - case_id_number: 39
-    title: May 2024 Wisconsin
-    start_date: 2024-05-18 12:00:00
-    end_date: 2024-05-19 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 36.5
-        latitude_max: 55.5
-        longitude_min: 258.01
-        longitude_max: 277.99
-    event_type: severe_convection
-  - case_id_number: 40
-    title: May 2024 Kansas
-    start_date: 2024-05-19 12:00:00
-    end_date: 2024-05-20 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 30.0
-        latitude_max: 51.0
-        longitude_min: 250.79
-        longitude_max: 276.46
-    event_type: severe_convection
-  - case_id_number: 41
-    title: May 2024 Iowa + Nebraska
-    start_date: 2024-05-20 12:00:00
-    end_date: 2024-05-21 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 33.5
-        latitude_max: 49.25
-        longitude_min: 250.0
-        longitude_max: 282.75
-    event_type: severe_convection
-  - case_id_number: 42
-    title: May 2024 Iowa + Wisconsin
-    start_date: 2024-05-21 12:00:00
-    end_date: 2024-05-22 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.75
-        latitude_max: 55.75
-        longitude_min: 254.25
-        longitude_max: 278.25
-    event_type: severe_convection
-  - case_id_number: 43
-    title: May 2024 Texas
-    start_date: 2024-05-22 12:00:00
-    end_date: 2024-05-23 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.75
-        latitude_max: 50.0
-        longitude_min: 252.9
-        longitude_max: 291.35
-    event_type: severe_convection
-  - case_id_number: 44
-    title: May 2024 Iowa + Nebraska 2
-    start_date: 2024-05-23 12:00:00
-    end_date: 2024-05-24 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.75
-        latitude_max: 51.5
-        longitude_min: 251.62
-        longitude_max: 281.13
-    event_type: severe_convection
-  - case_id_number: 45
-    title: April 2024 Oklahoma
-    start_date: 2024-04-25 12:00:00
-    end_date: 2024-04-29 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.25
-        latitude_max: 50.75
-        longitude_min: 242.87
-        longitude_max: 282.12
-    event_type: severe_convection
-  - case_id_number: 46
-    title: April 2024 Midwest
-    start_date: 2024-04-02 12:00:00
-    end_date: 2024-04-03 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.0
-        latitude_max: 46.0
-        longitude_min: 262.72
-        longitude_max: 284.78
-    event_type: severe_convection
-  - case_id_number: 47
-    title: April 2024 Great Plains + Midwest
-    start_date: 2024-04-01 12:00:00
-    end_date: 2024-04-02 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.25
-        latitude_max: 45.5
-        longitude_min: 254.5
-        longitude_max: 281.0
-    event_type: severe_convection
-  - case_id_number: 48
-    title: March 2024 Midwest
-    start_date: 2024-03-14 12:00:00
-    end_date: 2024-03-15 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.75
-        latitude_max: 46.75
-        longitude_min: 254.69
-        longitude_max: 284.81
-    event_type: severe_convection
-  - case_id_number: 49
-    title: February 2024 Midwest
-    start_date: 2024-02-27 12:00:00
-    end_date: 2024-02-28 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.25
-        latitude_max: 48.75
-        longitude_min: 263.54
-        longitude_max: 285.21
-    event_type: severe_convection
-  - case_id_number: 50
-    title: January 2024 Southeast
-    start_date: 2024-01-08 12:00:00
-    end_date: 2024-01-10 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 23.25
-        latitude_max: 40.25
-        longitude_min: 257.62
-        longitude_max: 287.9
-    event_type: severe_convection
-  - case_id_number: 51
-    title: December 2023 South
-    start_date: 2023-12-09 12:00:00
-    end_date: 2023-12-10 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.0
-        latitude_max: 42.5
-        longitude_min: 259.79
-        longitude_max: 280.96
-    event_type: severe_convection
-  - case_id_number: 52
-    title: August 2023 East Coast
-    start_date: 2023-08-07 12:00:00
-    end_date: 2023-08-08 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 27.0
-        latitude_max: 47.75
-        longitude_min: 243.17
-        longitude_max: 269.83
-    event_type: severe_convection
-  - case_id_number: 53
-    title: August 2023 Minnesota
-    start_date: 2023-08-11 12:00:00
-    end_date: 2023-08-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.5
-        latitude_max: 51.75
-        longitude_min: 256.96
-        longitude_max: 281.79
-    event_type: severe_convection
-  - case_id_number: 54
-    title: July 2023 Midwest
-    start_date: 2023-07-02 12:00:00
-    end_date: 2023-07-03 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 29.0
-        latitude_max: 46.75
-        longitude_min: 264.15
-        longitude_max: 289.85
-    event_type: severe_convection
-  - case_id_number: 55
-    title: July 2023 Midwest 2
-    start_date: 2023-07-01 12:00:00
-    end_date: 2023-07-02 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 39.25
-        latitude_max: 59.75
-        longitude_min: 239.53
-        longitude_max: 265.97
-    event_type: severe_convection
-  - case_id_number: 56
-    title: June 2023 Midwest
-    start_date: 2023-06-30 12:00:00
-    end_date: 2023-07-01 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 30.75
-        latitude_max: 45.0
-        longitude_min: 248.9
-        longitude_max: 279.6
-    event_type: severe_convection
-  - case_id_number: 57
-    title: June 2023 Front Range & Texas
-    start_date: 2023-06-21 12:00:00
-    end_date: 2023-06-22 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.5
-        latitude_max: 50.25
-        longitude_min: 248.4
-        longitude_max: 266.35
-    event_type: severe_convection
-  - case_id_number: 58
-    title: June 2023 Great Plains
-    start_date: 2023-06-23 12:00:00
-    end_date: 2023-06-24 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.5
-        latitude_max: 52.5
-        longitude_min: 242.12
-        longitude_max: 271.88
-    event_type: severe_convection
-  - case_id_number: 59
-    title: June 2023 Iowa + Minnesota
-    start_date: 2023-06-24 12:00:00
-    end_date: 2023-06-25 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 34.25
-        latitude_max: 53.0
-        longitude_min: 255.64
-        longitude_max: 275.11
-    event_type: severe_convection
-  - case_id_number: 60
-    title: June 2023 South + Midwest
-    start_date: 2023-06-25 12:00:00
-    end_date: 2023-06-26 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 27.25
-        latitude_max: 50.0
-        longitude_min: 259.62
-        longitude_max: 288.88
-    event_type: severe_convection
-  - case_id_number: 61
-    title: June 2023 East Coast
-    start_date: 2023-06-26 12:00:00
-    end_date: 2023-06-27 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 29.25
-        latitude_max: 49.5
-        longitude_min: 272.34
-        longitude_max: 292.91
-    event_type: severe_convection
-  - case_id_number: 62
-    title: June 2023 Midwest + Great Plains
-    start_date: 2023-06-29 12:00:00
-    end_date: 2023-06-30 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.5
-        latitude_max: 50.5
-        longitude_min: 248.64
-        longitude_max: 279.36
-    event_type: severe_convection
-  - case_id_number: 63
-    title: June 2023 Southeast
-    start_date: 2023-06-14 12:00:00
-    end_date: 2023-06-15 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.5
-        latitude_max: 41.75
-        longitude_min: 257.55
-        longitude_max: 283.7
-    event_type: severe_convection
-  - case_id_number: 64
-    title: June 2023 OK + TX + South
-    start_date: 2023-06-15 12:00:00
-    end_date: 2023-06-16 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.0
-        latitude_max: 48.0
-        longitude_min: 251.7
-        longitude_max: 284.55
-    event_type: severe_convection
-  - case_id_number: 65
-    title: June 2023 South & Mid-Atlantic
-    start_date: 2023-06-16 12:00:00
-    end_date: 2023-06-17 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.75
-        latitude_max: 45.25
-        longitude_min: 254.5
-        longitude_max: 290.5
-    event_type: severe_convection
-  - case_id_number: 66
-    title: June 2023 Great Plains + South
-    start_date: 2023-06-17 12:00:00
-    end_date: 2023-06-18 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.25
-        latitude_max: 46.5
-        longitude_min: 250.22
-        longitude_max: 279.78
-    event_type: severe_convection
-  - case_id_number: 67
-    title: May 2023 Great Plains
-    start_date: 2023-05-10 12:00:00
-    end_date: 2023-05-11 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.75
-        latitude_max: 55.5
-        longitude_min: 248.76
-        longitude_max: 275.74
-    event_type: severe_convection
-  - case_id_number: 68
-    title: May 2023 Great Plains + South
-    start_date: 2023-05-11 12:00:00
-    end_date: 2023-05-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 23.75
-        latitude_max: 47.25
-        longitude_min: 251.98
-        longitude_max: 283.77
-    event_type: severe_convection
-  - case_id_number: 69
-    title: May 2023 Nebraska
-    start_date: 2023-05-12 12:00:00
-    end_date: 2023-05-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.75
-        latitude_max: 47.75
-        longitude_min: 254.17
-        longitude_max: 270.58
-    event_type: severe_convection
-  - case_id_number: 70
-    title: May 2024 Texas
-    start_date: 2024-05-25 00:00:00
-    end_date: 2024-05-31 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.75
-        latitude_max: 32.25
-        longitude_min: 254.25
-        longitude_max: 277.0
-    event_type: heat_wave
-  - case_id_number: 71
-    title: June 2024 Northeast US
-    start_date: 2024-06-17 00:00:00
-    end_date: 2024-06-23 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 37.0
-        latitude_max: 50.75
-        longitude_min: 272.0
-        longitude_max: 307.25
-    event_type: heat_wave
-  - case_id_number: 72
-    title: July 2024 Southwest US
-    start_date: 2024-07-04 00:00:00
-    end_date: 2024-07-10 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 30.0
-        latitude_max: 42.75
-        longitude_min: 235.5
-        longitude_max: 248.75
-    event_type: heat_wave
-  - case_id_number: 73
-    title: July 2024 Northeast US
-    start_date: 2024-07-07 00:00:00
-    end_date: 2024-07-13 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 35.75
-        latitude_max: 47.5
-        longitude_min: 281.0
-        longitude_max: 300.0
-    event_type: heat_wave
-  - case_id_number: 74
-    title: July 2024 Mid-Atlantic US
-    start_date: 2024-07-15 00:00:00
-    end_date: 2024-07-21 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 35.25
-        latitude_max: 44.75
-        longitude_min: 280.0
-        longitude_max: 292.75
-    event_type: heat_wave
-  - case_id_number: 75
-    title: August 2024 Midwest US
-    start_date: 2024-08-25 00:00:00
-    end_date: 2024-08-31 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 36.0
-        latitude_max: 46.25
-        longitude_min: 265.5
-        longitude_max: 278.25
-    event_type: heat_wave
-  - case_id_number: 76
-    title: July 2024 Antarctica
-    start_date: 2024-07-01 00:00:00
-    end_date: 2024-07-31 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -90.0
-        latitude_max: -68.75
-        longitude_min: -9.5
-        longitude_max: 39.5
-    event_type: heat_wave
-  - case_id_number: 77
-    title: August 2024 Canada
-    start_date: 2024-08-09 00:00:00
-    end_date: 2024-08-15 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 54.5
-        latitude_max: 73.5
-        longitude_min: 231.5
-        longitude_max: 272.5
-    event_type: heat_wave
-  - case_id_number: 78
-    title: August 2024 Australia
-    start_date: 2024-08-22 00:00:00
-    end_date: 2024-08-30 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -32.5
-        latitude_max: -9.5
-        longitude_min: 116.25
-        longitude_max: 144.5
-    event_type: heat_wave
-  - case_id_number: 79
-    title: July/August 2024 Japan
-    start_date: 2024-07-25 00:00:00
-    end_date: 2024-08-05 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 31.25
-        latitude_max: 43.0
-        longitude_min: 123.0
-        longitude_max: 142.75
-    event_type: heat_wave
-  - case_id_number: 80
-    title: June 2024 Saudi Arabia
-    start_date: 2024-06-16 00:00:00
-    end_date: 2024-06-22 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 19.0
-        latitude_max: 35.0
-        longitude_min: 30.0
-        longitude_max: 54.0
-    event_type: heat_wave
-  - case_id_number: 81
-    title: August 2024 Europe
-    start_date: 2024-08-10 00:00:00
-    end_date: 2024-08-16 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 35.5
-        latitude_max: 55.25
-        longitude_min: 0.0
-        longitude_max: 25.75
-    event_type: heat_wave
-  - case_id_number: 82
-    title: July 2024 Ukraine
-    start_date: 2024-07-12 00:00:00
-    end_date: 2024-07-18 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 40.0
-        latitude_max: 56.75
-        longitude_min: 14.25
-        longitude_max: 45.0
-    event_type: heat_wave
-  - case_id_number: 83
-    title: June 2024 Europe
-    start_date: 2024-06-20 00:00:00
-    end_date: 2024-06-30 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 45.5
-        latitude_max: 63.25
-        longitude_min: -4.0
-        longitude_max: 25.75
-    event_type: heat_wave
-  - case_id_number: 84
-    title: May 2024 Central Mexico
-    start_date: 2024-05-23 00:00:00
-    end_date: 2024-05-31 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 7.25
-        latitude_max: 29.75
-        longitude_min: 252.5
-        longitude_max: 283.25
-    event_type: heat_wave
-  - case_id_number: 85
-    title: May 2024 Pakistan/India
-    start_date: 2024-05-23 00:00:00
-    end_date: 2024-05-31 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 23.5
-        latitude_max: 42.5
-        longitude_min: 71.5
-        longitude_max: 98.5
-    event_type: heat_wave
-  - case_id_number: 86
-    title: August 2023 Chile
-    start_date: 2023-08-01 00:00:00
-    end_date: 2023-08-07 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -37.25
-        latitude_max: -19.0
-        longitude_min: 288.5
-        longitude_max: 305.75
-    event_type: heat_wave
-  - case_id_number: 87
-    title: January 2024 Pacific Northwest
-    start_date: 2024-01-11 12:00:00
-    end_date: 2024-01-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 41.25
-        latitude_max: 66.0
-        longitude_min: 221.25
-        longitude_max: 262.0
-    event_type: freeze
-  - case_id_number: 88
-    title: April 2022 Nevada
-    start_date: 2022-04-11 18:00:00
-    end_date: 2022-04-17 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 36.5
-        latitude_max: 65.25
-        longitude_min: 225.75
-        longitude_max: 268.5
-    event_type: freeze
-  - case_id_number: 89
-    title: February/March 2021 New England
-    start_date: 2021-03-04 18:00:00
-    end_date: 2021-03-10 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 38.5
-        latitude_max: 47.0
-        longitude_min: 283.25
-        longitude_max: 291.5
-    event_type: freeze
-  - case_id_number: 90
-    title: January 2024 Northern Europe
-    start_date: 2024-01-01 06:00:00
-    end_date: 2024-01-09 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 51.25
-        latitude_max: 70.5
-        longitude_min: 5.25
-        longitude_max: 42.5
-    event_type: freeze
-  - case_id_number: 91
-    title: December 2022 Europe
-    start_date: 2022-12-10 06:00:00
-    end_date: 2022-12-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 46.25
-        latitude_max: 70.75
-        longitude_min: -9.75
-        longitude_max: 25.75
-    event_type: freeze
-  - case_id_number: 92
-    title: April 2024 Europe
-    start_date: 2024-04-16 12:00:00
-    end_date: 2024-04-27 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 40.5
-        latitude_max: 71.0
-        longitude_min: -4.0
-        longitude_max: 26.75
-    event_type: freeze
-  - case_id_number: 93
-    title: January 2023 North China
-    start_date: 2024-01-20 00:00:00
-    end_date: 2024-02-04 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 38.25
-        latitude_max: 47.0
-        longitude_min: 118.0
-        longitude_max: 130.75
-    event_type: freeze
-  - case_id_number: 94
-    title: April 2024 Sweden
-    start_date: 2024-04-02 00:00:00
-    end_date: 2024-04-08 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 58.25
-        latitude_max: 71.0
-        longitude_min: 8.25
-        longitude_max: 31.0
-    event_type: freeze
-  - case_id_number: 95
-    title: November 2024 West Coast US
-    start_date: 2024-11-20 00:00:00
-    end_date: 2024-11-22 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 27.8
-        latitude_max: 54.2
-        longitude_min: 222.4
-        longitude_max: 245.3
-    event_type: atmospheric_river
-  - case_id_number: 96
-    title: October 2024 British Columbia
-    start_date: 2024-10-18 00:00:00
-    end_date: 2024-10-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 56.0
-        longitude_min: 217.3
-        longitude_max: 247.0
-    event_type: atmospheric_river
-  - case_id_number: 97
-    title: September 2024 SW Alaska and British Columbia
-    start_date: 2024-09-22 00:00:00
-    end_date: 2024-09-24 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 37.8
-        latitude_max: 62.2
-        longitude_min: 212.1
-        longitude_max: 247.9
-    event_type: atmospheric_river
-  - case_id_number: 98
-    title: October 2024 Bosnia
-    start_date: 2024-10-03 00:00:00
-    end_date: 2024-10-05 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 34.8
-        latitude_max: 50.5
-        longitude_min: 8.2
-        longitude_max: 27.8
-    event_type: atmospheric_river
-  - case_id_number: 99
-    title: April 2024 Eastern Australia
-    start_date: 2024-04-05 00:00:00
-    end_date: 2024-04-06 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -45.2
-        latitude_max: -17.8
-        longitude_min: 139.7
-        longitude_max: 163.1
-    event_type: atmospheric_river
-  - case_id_number: 100
-    title: April 2024 Persian Gulf
-    start_date: 2024-04-14 00:00:00
-    end_date: 2024-04-18 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 16.8
-        latitude_max: 31.2
-        longitude_min: 45.6
-        longitude_max: 65.4
-    event_type: atmospheric_river
-  - case_id_number: 101
-    title: February 2024 Pacific Northwest
-    start_date: 2024-02-28 00:00:00
-    end_date: 2024-03-04 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 35.8
-        latitude_max: 51.2
-        longitude_min: 227.1
-        longitude_max: 240.9
-    event_type: atmospheric_river
-  - case_id_number: 102
-    title: February 2024 California
-    start_date: 2024-02-18 00:00:00
-    end_date: 2024-02-21 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 49.2
-        longitude_min: 227.5
-        longitude_max: 243.3
-    event_type: atmospheric_river
-  - case_id_number: 103
-    title: February 2024 California
-    start_date: 2024-02-03 00:00:00
-    end_date: 2024-02-06 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 49.2
-        longitude_min: 227.5
-        longitude_max: 252.5
-    event_type: atmospheric_river
-  - case_id_number: 104
-    title: January 2024 Alaska to California
-    start_date: 2024-01-26 00:00:00
-    end_date: 2024-02-03 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 62.5
-        longitude_min: 202.5
-        longitude_max: 249.3
-    event_type: atmospheric_river
-  - case_id_number: 105
-    title: January 2024 California and Arizona
-    start_date: 2024-01-22 00:00:00
-    end_date: 2024-01-23 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 45.2
-        longitude_min: 232.5
-        longitude_max: 249.5
-    event_type: atmospheric_river
-  - case_id_number: 106
-    title: December 2023 East Coast Cyclone
-    start_date: 2023-12-17 00:00:00
-    end_date: 2023-12-19 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 17.8
-        latitude_max: 52.2
-        longitude_min: 267.6
-        longitude_max: 302.4
-    event_type: atmospheric_river
-  - case_id_number: 107
-    title: June 2023 Northwest Cloudband
-    start_date: 2023-06-25 00:00:00
-    end_date: 2023-06-28 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -35.2
-        latitude_max: -7.8
-        longitude_min: 113.6
-        longitude_max: 147.7
-    event_type: atmospheric_river
-  - case_id_number: 108
-    title: April 2023 Middle East
-    start_date: 2023-04-01 00:00:00
-    end_date: 2023-05-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 17.5
-        latitude_max: 40.2
-        longitude_min: 22.6
-        longitude_max: 55.1
-    event_type: atmospheric_river
-  - case_id_number: 109
-    title: March 2023 California and Arizona
-    start_date: 2023-03-09 00:00:00
-    end_date: 2023-03-15 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 45.2
-        longitude_min: 232.5
-        longitude_max: 252.5
-    event_type: atmospheric_river
-  - case_id_number: 110
-    title: March 2023 California
-    start_date: 2023-03-09 00:00:00
-    end_date: 2023-03-16 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 46.2
-        longitude_min: 227.5
-        longitude_max: 252.5
-    event_type: atmospheric_river
-  - case_id_number: 111
-    title: February 2023 Pacific Northwest
-    start_date: 2023-02-17 00:00:00
-    end_date: 2023-02-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 41.3
-        latitude_max: 55.7
-        longitude_min: 226.9
-        longitude_max: 240.8
-    event_type: atmospheric_river
-  - case_id_number: 112
-    title: January 2023 California
-    start_date: 2023-01-04 00:00:00
-    end_date: 2023-01-10 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 49.2
-        longitude_min: 227.5
-        longitude_max: 252.5
-    event_type: atmospheric_river
-  - case_id_number: 113
-    title: December 2022 California
-    start_date: 2022-12-26 00:00:00
-    end_date: 2023-01-02 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.0
-        latitude_max: 56.2
-        longitude_min: 220.0
-        longitude_max: 252.8
-    event_type: atmospheric_river
-  - case_id_number: 114
-    title: December 2022 Oregon and California
-    start_date: 2022-12-09 00:00:00
-    end_date: 2022-12-13 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 29.3
-        latitude_max: 50.0
-        longitude_min: 227.4
-        longitude_max: 243.6
-    event_type: atmospheric_river
-  - case_id_number: 115
-    title: November 2022 - December 2022 California
-    start_date: 2022-11-29 00:00:00
-    end_date: 2022-12-06 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 27.3
-        latitude_max: 42.2
-        longitude_min: 227.4
-        longitude_max: 243.3
-    event_type: atmospheric_river
-  - case_id_number: 116
-    title: October 2022 Victoria
-    start_date: 2022-10-13 00:00:00
-    end_date: 2022-10-14 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -53.2
-        latitude_max: -20.0
-        longitude_min: 116.4
-        longitude_max: 166.6
-    event_type: atmospheric_river
-  - case_id_number: 117
-    title: October 2022 Victoria
-    start_date: 2022-10-30 00:00:00
-    end_date: 2022-10-31 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -53.2
-        latitude_max: -13.8
-        longitude_min: 116.4
-        longitude_max: 166.6
-    event_type: atmospheric_river
-  - case_id_number: 118
-    title: November 2022 California
-    start_date: 2022-11-07 00:00:00
-    end_date: 2022-11-09 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 33.0
-        latitude_max: 49.2
-        longitude_min: 227.2
-        longitude_max: 247.8
-    event_type: atmospheric_river
-  - case_id_number: 119
-    title: November 2022 Pacific Northwest
-    start_date: 2022-11-03 00:00:00
-    end_date: 2022-11-06 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 33.0
-        latitude_max: 54.5
-        longitude_min: 227.2
-        longitude_max: 247.8
-    event_type: atmospheric_river
-  - case_id_number: 120
-    title: August 2022 Japan
-    start_date: 2022-08-01 00:00:00
-    end_date: 2022-09-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 27.8
-        latitude_max: 52.2
-        longitude_min: 122.4
-        longitude_max: 152.6
-    event_type: atmospheric_river
-  - case_id_number: 121
-    title: June 2022 Pacific Northwest, Wyoming, and Montana
-    start_date: 2022-06-09 00:00:00
-    end_date: 2022-06-13 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 57.2
-        longitude_min: 227.3
-        longitude_max: 262.7
-    event_type: atmospheric_river
-  - case_id_number: 122
-    title: March 2022 East Antarctica
-    start_date: 2022-03-15 00:00:00
-    end_date: 2022-03-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -71.0
-        latitude_max: -62.8
-        longitude_min: 101.3
-        longitude_max: 146.9
-    event_type: atmospheric_river
-  - case_id_number: 123
-    title: February 2022 Southeast QLD / Northern NSW
-    start_date: 2022-02-26 00:00:00
-    end_date: 2022-03-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -37.2
-        latitude_max: -17.8
-        longitude_min: 142.3
-        longitude_max: 162.7
-    event_type: atmospheric_river
-  - case_id_number: 124
-    title: February 2022 Pacific Northwest
-    start_date: 2022-02-26 00:00:00
-    end_date: 2022-03-03 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 34.8
-        latitude_max: 55.7
-        longitude_min: 227.2
-        longitude_max: 247.3
-    event_type: atmospheric_river
-  - case_id_number: 125
-    title: January 2022 Norway and Sweden
-    start_date: 2022-01-14 00:00:00
-    end_date: 2022-01-15 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 50.8
-        latitude_max: 72.2
-        longitude_min: 356.3
-        longitude_max: 28.7
-    event_type: atmospheric_river
-  - case_id_number: 126
-    title: January 2022 Colorado
-    start_date: 2022-01-04 00:00:00
-    end_date: 2022-01-08 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 29.8
-        latitude_max: 39.2
-        longitude_min: 251.3
-        longitude_max: 265.7
-    event_type: atmospheric_river
-  - case_id_number: 127
-    title: January 2022 Pacific Northwest
-    start_date: 2022-01-02 00:00:00
-    end_date: 2022-01-09 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 57.2
-        longitude_min: 227.3
-        longitude_max: 245.7
-    event_type: atmospheric_river
-  - case_id_number: 128
-    title: December 2021 - January 2022 Western US
-    start_date: 2021-12-22 00:00:00
-    end_date: 2022-01-02 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.8
-        latitude_max: 50.0
-        longitude_min: 227.5
-        longitude_max: 262.5
-    event_type: atmospheric_river
-  - case_id_number: 129
-    title: December 2021 Pacific NW
-    start_date: 2021-12-10 00:00:00
-    end_date: 2021-12-15 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 53.5
-        longitude_min: 227.3
-        longitude_max: 243.7
-    event_type: atmospheric_river
-  - case_id_number: 130
-    title: November 2021 Pacific Northwest
-    start_date: 2021-11-10 00:00:00
-    end_date: 2021-11-17 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 56.2
-        longitude_min: 227.3
-        longitude_max: 257.7
-    event_type: atmospheric_river
-  - case_id_number: 131
-    title: October 2021 California
-    start_date: 2021-10-19 00:00:00
-    end_date: 2021-10-27 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.3
-        latitude_max: 49.2
-        longitude_min: 227.4
-        longitude_max: 251.1
-    event_type: atmospheric_river
-  - case_id_number: 132
-    title: September 2021 Pacific Northwest
-    start_date: 2021-09-16 00:00:00
-    end_date: 2021-09-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 33.5
-        latitude_max: 57.2
-        longitude_min: 227.2
-        longitude_max: 257.8
-    event_type: atmospheric_river
-  - case_id_number: 133
-    title: June 2021 Bloomington Indiana
-    start_date: 2021-06-18 00:00:00
-    end_date: 2021-06-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 31.8
-        latitude_max: 47.2
-        longitude_min: 265.3
-        longitude_max: 281.7
-    event_type: atmospheric_river
-  - case_id_number: 134
-    title: June 2021 Alaska Trans-Pacific
-    start_date: 2021-06-24 00:00:00
-    end_date: 2021-06-27 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 47.8
-        latitude_max: 63.7
-        longitude_min: 181.5
-        longitude_max: 238.5
-    event_type: atmospheric_river
-  - case_id_number: 135
-    title: March 2021 Sydney
-    start_date: 2021-03-17 00:00:00
-    end_date: 2021-03-25 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -41.2
-        latitude_max: -25.8
-        longitude_min: 142.1
-        longitude_max: 159.9
-    event_type: atmospheric_river
-  - case_id_number: 136
-    title: February 2021 Pacific Northwest
-    start_date: 2021-02-21 00:00:00
-    end_date: 2021-02-24 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 34.5
-        latitude_max: 56.2
-        longitude_min: 227.2
-        longitude_max: 241.3
-    event_type: atmospheric_river
-  - case_id_number: 137
-    title: January 2021 Pacific NW
-    start_date: 2021-01-11 00:00:00
-    end_date: 2021-01-14 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 56.2
-        longitude_min: 227.3
-        longitude_max: 252.0
-    event_type: atmospheric_river
-  - case_id_number: 138
-    title: January 2021 Pacific Northwest
-    start_date: 2021-01-01 00:00:00
-    end_date: 2021-01-08 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 56.2
-        longitude_min: 227.3
-        longitude_max: 241.7
-    event_type: atmospheric_river
-  - case_id_number: 139
-    title: December 2020 Pacific Northwest
-    start_date: 2020-12-17 00:00:00
-    end_date: 2020-12-23 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 55.5
-        longitude_min: 227.3
-        longitude_max: 247.0
-    event_type: atmospheric_river
-  - case_id_number: 140
-    title: December 2020 Western US
-    start_date: 2020-12-11 00:00:00
-    end_date: 2020-12-18 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 30.0
-        latitude_max: 55.5
-        longitude_min: 227.3
-        longitude_max: 246.9
-    event_type: atmospheric_river
-  - case_id_number: 141
-    title: December 2020 Alaska
-    start_date: 2020-12-01 00:00:00
-    end_date: 2020-12-05 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 47.8
-        latitude_max: 63.2
-        longitude_min: 191.5
-        longitude_max: 238.5
-    event_type: atmospheric_river
-  - case_id_number: 142
-    title: November 2020 Western US
-    start_date: 2020-11-16 00:00:00
-    end_date: 2020-11-19 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 28.8
-        latitude_max: 54.7
-        longitude_min: 227.4
-        longitude_max: 243.9
-    event_type: atmospheric_river
-  - case_id_number: 143
-    title: October 2020 Western Alps
-    start_date: 2020-10-02 00:00:00
-    end_date: 2020-10-04 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 36.8
-        latitude_max: 54.2
-        longitude_min: 357.1
-        longitude_max: 16.9
-    event_type: atmospheric_river
-  - case_id_number: 144
-    title: September 2020 Pacific Northwest
-    start_date: 2020-09-23 00:00:00
-    end_date: 2020-09-28 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 34.8
-        latitude_max: 57.2
-        longitude_min: 227.2
-        longitude_max: 257.8
-    event_type: atmospheric_river
-  - case_id_number: 145
-    title: September 2020 Pacific Northwest
-    start_date: 2020-09-14 00:00:00
-    end_date: 2020-09-19 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 53.5
-        longitude_min: 227.3
-        longitude_max: 240.5
-    event_type: atmospheric_river
-  - case_id_number: 146
-    title: May 2020 Western US
-    start_date: 2020-05-16 00:00:00
-    end_date: 2020-05-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 28.0
-        latitude_max: 54.0
-        longitude_min: 227.4
-        longitude_max: 241.6
-    event_type: atmospheric_river
-  - case_id_number: 147
-    title: March 2020 Southwestern US
-    start_date: 2020-03-09 00:00:00
-    end_date: 2020-03-14 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 22.8
-        latitude_max: 39.2
-        longitude_min: 232.5
-        longitude_max: 262.5
-    event_type: atmospheric_river
-  - case_id_number: 148
-    title: February 2020 Southwestern US
-    start_date: 2020-02-21 00:00:00
-    end_date: 2020-02-24 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 22.8
-        latitude_max: 36.0
-        longitude_min: 235.8
-        longitude_max: 262.5
-    event_type: atmospheric_river
-  - case_id_number: 149
-    title: February 2020 Western US
-    start_date: 2020-02-04 00:00:00
-    end_date: 2020-02-09 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 38.8
-        latitude_max: 55.2
-        longitude_min: 227.0
-        longitude_max: 250.7
-    event_type: atmospheric_river
-  - case_id_number: 150
-    title: January 2020 Pacific Northwest
-    start_date: 2020-01-26 00:00:00
-    end_date: 2020-02-03 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.8
-        latitude_max: 56.7
-        longitude_min: 227.3
-        longitude_max: 250.5
-    event_type: atmospheric_river
-  - case_id_number: 151
-    title: Milton
-    start_date: 2024-10-02 18:00:00
-    end_date: 2024-10-13 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 18.6
-        latitude_max: 31.7
-        longitude_min: 262.1
-        longitude_max: 296.5
-    event_type: tropical_cyclone
-  - case_id_number: 152
-    title: Helene
-    start_date: 2024-09-21 12:00:00
-    end_date: 2024-09-30 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 15.0
-        latitude_max: 40.3
-        longitude_min: 269.5
-        longitude_max: 280.7
-    event_type: tropical_cyclone
-  - case_id_number: 153
-    title: Francine
-    start_date: 2024-09-06 18:00:00
-    end_date: 2024-09-16 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 19.2
-        latitude_max: 38.1
-        longitude_min: 261.4
-        longitude_max: 272.7
-    event_type: tropical_cyclone
-  - case_id_number: 154
-    title: Debby
-    start_date: 2024-07-31 12:00:00
-    end_date: 2024-08-12 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 18.2
-        latitude_max: 52.2
-        longitude_min: 273.2
-        longitude_max: 302.9
-    event_type: tropical_cyclone
-  - case_id_number: 155
-    title: Beryl
-    start_date: 2024-06-26 12:00:00
-    end_date: 2024-07-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 6.7
-        latitude_max: 45.3
-        longitude_min: 261.7
-        longitude_max: 322.7
-    event_type: tropical_cyclone
-  - case_id_number: 156
-    title: John
-    start_date: 2024-09-20 12:00:00
-    end_date: 2024-09-29 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.6
-        latitude_max: 20.5
-        longitude_min: 254.5
-        longitude_max: 263.9
-    event_type: tropical_cyclone
-  - case_id_number: 157
-    title: Hone
-    start_date: 2024-08-17 12:00:00
-    end_date: 2024-09-03 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.5
-        latitude_max: 28.3
-        longitude_min: 177.9
-        longitude_max: 231.5
-    event_type: tropical_cyclone
-  - case_id_number: 158
-    title: Trami
-    start_date: 2024-10-18 00:00:00
-    end_date: 2024-10-31 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.9
-        latitude_max: 19.9
-        longitude_min: 105.0
-        longitude_max: 139.8
-    event_type: tropical_cyclone
-  - case_id_number: 159
-    title: Prapiroon (Butchoy)
-    start_date: 2024-07-17 06:00:00
-    end_date: 2024-07-27 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 12.1
-        latitude_max: 24.0
-        longitude_min: 104.3
-        longitude_max: 120.2
-    event_type: tropical_cyclone
-  - case_id_number: 160
-    title: Gaemi (Carina)
-    start_date: 2024-07-17 00:00:00
-    end_date: 2024-07-30 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.4
-        latitude_max: 32.0
-        longitude_min: 110.6
-        longitude_max: 135.5
-    event_type: tropical_cyclone
-  - case_id_number: 161
-    title: Shanshan
-    start_date: 2024-08-19 00:00:00
-    end_date: 2024-09-03 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 13.8
-        latitude_max: 37.8
-        longitude_min: 127.7
-        longitude_max: 146.3
-    event_type: tropical_cyclone
-  - case_id_number: 162
-    title: Yagi (Enteng)
-    start_date: 2024-08-30 00:00:00
-    end_date: 2024-09-10 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.9
-        latitude_max: 23.8
-        longitude_min: 101.8
-        longitude_max: 128.7
-    event_type: tropical_cyclone
-  - case_id_number: 163
-    title: Bebinca (Ferdie)
-    start_date: 2024-09-07 12:00:00
-    end_date: 2024-09-20 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 7.9
-        latitude_max: 35.7
-        longitude_min: 112.4
-        longitude_max: 149.9
-    event_type: tropical_cyclone
-  - case_id_number: 164
-    title: Soulik (Gener)
-    start_date: 2024-09-13 06:00:00
-    end_date: 2024-09-22 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.3
-        latitude_max: 19.8
-        longitude_min: 102.2
-        longitude_max: 128.8
-    event_type: tropical_cyclone
-  - case_id_number: 165
-    title: Krathon
-    start_date: 2024-09-24 12:00:00
-    end_date: 2024-10-05 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 16.2
-        latitude_max: 25.0
-        longitude_min: 116.8
-        longitude_max: 129.9
-    event_type: tropical_cyclone
-  - case_id_number: 166
-    title: Asna
-    start_date: 2024-08-28 00:00:00
-    end_date: 2024-09-04 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 18.8
-        latitude_max: 26.1
-        longitude_min: 59.0
-        longitude_max: 70.7
-    event_type: tropical_cyclone
-  - case_id_number: 167
-    title: Kirrily
-    start_date: 2024-01-15 00:00:00
-    end_date: 2024-02-07 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -31.8
-        latitude_max: -11.3
-        longitude_min: 135.3
-        longitude_max: 159.0
-    event_type: tropical_cyclone
-  - case_id_number: 168
-    title: Belal
-    start_date: 2024-01-10 12:00:00
-    end_date: 2024-01-21 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -26.5
-        latitude_max: -11.1
-        longitude_min: 50.8
-        longitude_max: 68.1
-    event_type: tropical_cyclone
-  - case_id_number: 169
-    title: Alvaro
-    start_date: 2023-12-30 00:00:00
-    end_date: 2024-01-06 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -31.3
-        latitude_max: -18.5
-        longitude_min: 38.2
-        longitude_max: 59.9
-    event_type: tropical_cyclone
-  - case_id_number: 170
-    title: Franklin
-    start_date: 2023-08-17 18:00:00
-    end_date: 2023-09-11 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.6
-        latitude_max: 50.8
-        longitude_min: 286.3
-        longitude_max: 346.3
-    event_type: tropical_cyclone
-  - case_id_number: 171
-    title: Harold
-    start_date: 2020-03-30 00:00:00
-    end_date: 2020-04-13 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -37.2
-        latitude_max: -7.0
-        longitude_min: 152.2
-        longitude_max: 214.7
-    event_type: tropical_cyclone
-  - case_id_number: 172
-    title: Idalia
-    start_date: 2023-08-24 12:00:00
-    end_date: 2023-09-10 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 17.6
-        latitude_max: 47.4
-        longitude_min: 270.8
-        longitude_max: 304.9
-    event_type: tropical_cyclone
-  - case_id_number: 173
-    title: Lee
-    start_date: 2023-09-03 12:00:00
-    end_date: 2023-09-20 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.0
-        latitude_max: 55.2
-        longitude_min: 289.4
-        longitude_max: 322.7
-    event_type: tropical_cyclone
-  - case_id_number: 174
-    title: Ophelia
-    start_date: 2023-09-19 12:00:00
-    end_date: 2023-09-26 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.3
-        latitude_max: 41.1
-        longitude_min: 279.8
-        longitude_max: 287.5
-    event_type: tropical_cyclone
-  - case_id_number: 175
-    title: Tammy
-    start_date: 2023-10-16 18:00:00
-    end_date: 2023-11-02 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.7
-        latitude_max: 35.5
-        longitude_min: 293.7
-        longitude_max: 314.2
-    event_type: tropical_cyclone
-  - case_id_number: 176
-    title: Hilary
-    start_date: 2023-08-14 06:00:00
-    end_date: 2023-08-22 21:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.7
-        latitude_max: 33.6
-        longitude_min: 241.6
-        longitude_max: 261.5
-    event_type: tropical_cyclone
-  - case_id_number: 177
-    title: Lidia
-    start_date: 2023-10-01 00:00:00
-    end_date: 2023-10-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.0
-        latitude_max: 26.7
-        longitude_min: 244.9
-        longitude_max: 261.8
-    event_type: tropical_cyclone
-  - case_id_number: 178
-    title: Max
-    start_date: 2023-10-06 00:00:00
-    end_date: 2023-10-12 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.6
-        latitude_max: 20.1
-        longitude_min: 256.1
-        longitude_max: 262.4
-    event_type: tropical_cyclone
-  - case_id_number: 179
-    title: Norma
-    start_date: 2023-10-15 06:00:00
-    end_date: 2023-10-25 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.2
-        latitude_max: 26.9
-        longitude_min: 247.4
-        longitude_max: 257.9
-    event_type: tropical_cyclone
-  - case_id_number: 180
-    title: Otis
-    start_date: 2023-10-19 00:00:00
-    end_date: 2023-10-27 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 7.1
-        latitude_max: 20.8
-        longitude_min: 257.0
-        longitude_max: 266.5
-    event_type: tropical_cyclone
-  - case_id_number: 181
-    title: Mawar (Betty)
-    start_date: 2023-05-16 00:00:00
-    end_date: 2023-06-05 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 2.2
-        latitude_max: 36.3
-        longitude_min: 122.7
-        longitude_max: 153.2
-    event_type: tropical_cyclone
-  - case_id_number: 182
-    title: Talim (Dodong)
-    start_date: 2023-07-11 06:00:00
-    end_date: 2023-07-21 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 13.6
-        latitude_max: 25.6
-        longitude_min: 101.0
-        longitude_max: 125.1
-    event_type: tropical_cyclone
-  - case_id_number: 183
-    title: Doksuri (Egay)
-    start_date: 2023-07-18 06:00:00
-    end_date: 2023-08-02 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.5
-        latitude_max: 39.6
-        longitude_min: 110.5
-        longitude_max: 136.0
-    event_type: tropical_cyclone
-  - case_id_number: 184
-    title: Khanun (Falcon)
-    start_date: 2023-07-24 06:00:00
-    end_date: 2023-08-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 6.7
-        latitude_max: 41.3
-        longitude_min: 121.8
-        longitude_max: 143.6
-    event_type: tropical_cyclone
-  - case_id_number: 185
-    title: Lan
-    start_date: 2023-08-04 00:00:00
-    end_date: 2023-08-20 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 21.1
-        latitude_max: 50.0
-        longitude_min: 132.3
-        longitude_max: 152.0
-    event_type: tropical_cyclone
-  - case_id_number: 186
-    title: Saola (Goring)
-    start_date: 2023-08-20 00:00:00
-    end_date: 2023-09-06 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 13.8
-        latitude_max: 24.2
-        longitude_min: 106.8
-        longitude_max: 130.3
-    event_type: tropical_cyclone
-  - case_id_number: 187
-    title: Haikui
-    start_date: 2023-08-25 00:00:00
-    end_date: 2023-09-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 15.9
-        latitude_max: 25.9
-        longitude_min: 107.0
-        longitude_max: 146.8
-    event_type: tropical_cyclone
-  - case_id_number: 188
-    title: Koinu (Jenny)
-    start_date: 2023-09-25 12:00:00
-    end_date: 2023-10-12 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 12.0
-        latitude_max: 24.4
-        longitude_min: 109.0
-        longitude_max: 146.1
-    event_type: tropical_cyclone
-  - case_id_number: 189
-    title: Mocha
-    start_date: 2023-05-06 18:00:00
-    end_date: 2023-05-17 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 5.4
-        latitude_max: 27.3
-        longitude_min: 85.7
-        longitude_max: 100.1
-    event_type: tropical_cyclone
-  - case_id_number: 190
-    title: Biparjoy
-    start_date: 2023-06-03 00:00:00
-    end_date: 2023-06-21 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 8.1
-        latitude_max: 28.5
-        longitude_min: 63.6
-        longitude_max: 78.3
-    event_type: tropical_cyclone
-  - case_id_number: 191
-    title: Hamoon
-    start_date: 2023-10-18 06:00:00
-    end_date: 2023-10-27 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.2
-        latitude_max: 24.9
-        longitude_min: 83.5
-        longitude_max: 94.8
-    event_type: tropical_cyclone
-  - case_id_number: 192
-    title: Freddy
-    start_date: 2023-02-02 12:00:00
-    end_date: 2023-03-15 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -26.3
-        latitude_max: -8.9
-        longitude_min: 29.0
-        longitude_max: 121.6
-    event_type: tropical_cyclone
-  - case_id_number: 193
-    title: Jasper
-    start_date: 2023-11-30 12:00:00
-    end_date: 2023-12-19 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -20.0
-        latitude_max: -5.3
-        longitude_min: 139.0
-        longitude_max: 167.2
-    event_type: tropical_cyclone
-  - case_id_number: 194
-    title: Alex
-    start_date: 2022-05-31 18:00:00
-    end_date: 2022-06-08 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 19.1
-        latitude_max: 36.9
-        longitude_min: 270.1
-        longitude_max: 300.2
-    event_type: tropical_cyclone
-  - case_id_number: 195
-    title: Fiona
-    start_date: 2022-09-12 06:00:00
-    end_date: 2022-09-29 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 13.5
-        latitude_max: 66.2
-        longitude_min: 285.9
-        longitude_max: 314.4
-    event_type: tropical_cyclone
-  - case_id_number: 196
-    title: Ian
-    start_date: 2022-09-20 18:00:00
-    end_date: 2022-10-03 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.1
-        latitude_max: 37.5
-        longitude_min: 274.0
-        longitude_max: 296.0
-    event_type: tropical_cyclone
-  - case_id_number: 197
-    title: Julia
-    start_date: 2022-10-04 12:00:00
-    end_date: 2022-10-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.2
-        latitude_max: 15.9
-        longitude_min: 267.8
-        longitude_max: 295.8
-    event_type: tropical_cyclone
-  - case_id_number: 198
-    title: Nicole
-    start_date: 2022-11-04 12:00:00
-    end_date: 2022-11-13 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 18.4
-        latitude_max: 37.6
-        longitude_min: 272.9
-        longitude_max: 295.7
-    event_type: tropical_cyclone
-  - case_id_number: 199
-    title: Agatha
-    start_date: 2022-05-25 18:00:00
-    end_date: 2022-06-03 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.6
-        latitude_max: 20.5
-        longitude_min: 258.6
-        longitude_max: 267.7
-    event_type: tropical_cyclone
-  - case_id_number: 200
-    title: Kay
-    start_date: 2022-09-02 12:00:00
-    end_date: 2022-09-15 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.9
-        latitude_max: 33.4
-        longitude_min: 235.8
-        longitude_max: 261.4
-    event_type: tropical_cyclone
-  - case_id_number: 201
-    title: Roslyn
-    start_date: 2022-10-18 00:00:00
-    end_date: 2022-10-26 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 12.9
-        latitude_max: 27.8
-        longitude_min: 251.0
-        longitude_max: 261.4
-    event_type: tropical_cyclone
-  - case_id_number: 202
-    title: Megi (Agaton)
-    start_date: 2022-04-06 12:00:00
-    end_date: 2022-04-15 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 7.7
-        latitude_max: 13.5
-        longitude_min: 122.4
-        longitude_max: 130.7
-    event_type: tropical_cyclone
-  - case_id_number: 203
-    title: Hinnamnor (Henry)
-    start_date: 2022-08-25 00:00:00
-    end_date: 2022-09-10 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 19.1
-        latitude_max: 62.0
-        longitude_min: 122.2
-        longitude_max: 153.4
-    event_type: tropical_cyclone
-  - case_id_number: 204
-    title: Muifa (Inday)
-    start_date: 2022-09-02 06:00:00
-    end_date: 2022-09-18 21:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.7
-        latitude_max: 45.6
-        longitude_min: 118.0
-        longitude_max: 147.7
-    event_type: tropical_cyclone
-  - case_id_number: 205
-    title: Nanmadol (Josie)
-    start_date: 2022-09-09 06:00:00
-    end_date: 2022-09-22 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 19.5
-        latitude_max: 41.1
-        longitude_min: 128.0
-        longitude_max: 149.7
-    event_type: tropical_cyclone
-  - case_id_number: 206
-    title: Noru (Karding)
-    start_date: 2022-09-19 00:00:00
-    end_date: 2022-10-01 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 12.8
-        latitude_max: 20.2
-        longitude_min: 101.0
-        longitude_max: 136.8
-    event_type: tropical_cyclone
-  - case_id_number: 207
-    title: Nalgae (Paeng)
-    start_date: 2022-10-24 00:00:00
-    end_date: 2022-11-05 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.0
-        latitude_max: 24.3
-        longitude_min: 111.0
-        longitude_max: 136.6
-    event_type: tropical_cyclone
-  - case_id_number: 208
-    title: Sitrang
-    start_date: 2022-10-19 18:00:00
-    end_date: 2022-10-27 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.4
-        latitude_max: 26.7
-        longitude_min: 85.8
-        longitude_max: 95.6
-    event_type: tropical_cyclone
-  - case_id_number: 209
-    title: Ana
-    start_date: 2021-01-26 12:00:00
-    end_date: 2021-02-05 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -32.5
-        latitude_max: -13.3
-        longitude_min: 168.8
-        longitude_max: 191.7
-    event_type: tropical_cyclone
-  - case_id_number: 210
-    title: Batsirai
-    start_date: 2022-01-22 00:00:00
-    end_date: 2022-02-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -41.7
-        latitude_max: -7.2
-        longitude_min: 38.5
-        longitude_max: 94.0
-    event_type: tropical_cyclone
-  - case_id_number: 211
-    title: Gombe
-    start_date: 2022-03-04 00:00:00
-    end_date: 2022-03-19 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -20.7
-        latitude_max: -12.7
-        longitude_min: 34.2
-        longitude_max: 57.6
-    event_type: tropical_cyclone
-  - case_id_number: 212
-    title: Seth
-    start_date: 2021-12-21 12:00:00
-    end_date: 2022-01-09 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -30.3
-        latitude_max: -6.5
-        longitude_min: 127.3
-        longitude_max: 161.5
-    event_type: tropical_cyclone
-  - case_id_number: 213
-    title: Blas
-    start_date: 2022-06-12 06:00:00
-    end_date: 2022-06-24 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.4
-        latitude_max: 21.5
-        longitude_min: 240.9
-        longitude_max: 260.3
-    event_type: tropical_cyclone
-  - case_id_number: 214
-    title: Elsa
-    start_date: 2021-06-28 18:00:00
-    end_date: 2021-07-12 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 7.1
-        latitude_max: 49.0
-        longitude_min: 274.1
-        longitude_max: 319.5
-    event_type: tropical_cyclone
-  - case_id_number: 215
-    title: Fred
-    start_date: 2021-08-07 18:00:00
-    end_date: 2021-08-22 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.7
-        latitude_max: 44.9
-        longitude_min: 271.8
-        longitude_max: 303.6
-    event_type: tropical_cyclone
-  - case_id_number: 216
-    title: Grace
-    start_date: 2021-08-11 06:00:00
-    end_date: 2021-08-23 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 12.8
-        latitude_max: 23.0
-        longitude_min: 259.7
-        longitude_max: 315.6
-    event_type: tropical_cyclone
-  - case_id_number: 217
-    title: Henri
-    start_date: 2021-08-13 00:00:00
-    end_date: 2021-08-26 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 27.3
-        latitude_max: 44.5
-        longitude_min: 283.0
-        longitude_max: 299.9
-    event_type: tropical_cyclone
-  - case_id_number: 218
-    title: Ida
-    start_date: 2021-08-24 12:00:00
-    end_date: 2021-09-06 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.3
-        latitude_max: 51.0
-        longitude_min: 266.8
-        longitude_max: 299.9
-    event_type: tropical_cyclone
-  - case_id_number: 219
-    title: Larry
-    start_date: 2021-08-29 18:00:00
-    end_date: 2021-09-13 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.3
-        latitude_max: 57.5
-        longitude_min: 295.4
-        longitude_max: 341.7
-    event_type: tropical_cyclone
-  - case_id_number: 220
-    title: Nicholas
-    start_date: 2021-09-10 12:00:00
-    end_date: 2021-09-19 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 18.8
-        latitude_max: 33.0
-        longitude_min: 260.9
-        longitude_max: 270.8
-    event_type: tropical_cyclone
-  - case_id_number: 221
-    title: Claudette
-    start_date: 2021-06-15 18:00:00
-    end_date: 2021-06-25 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 20.3
-        latitude_max: 46.3
-        longitude_min: 265.4
-        longitude_max: 302.8
-    event_type: tropical_cyclone
-  - case_id_number: 222
-    title: Nora
-    start_date: 2021-08-22 06:00:00
-    end_date: 2021-09-01 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.0
-        latitude_max: 26.5
-        longitude_min: 250.3
-        longitude_max: 267.3
-    event_type: tropical_cyclone
-  - case_id_number: 223
-    title: Olaf
-    start_date: 2021-09-04 18:00:00
-    end_date: 2021-09-14 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.9
-        latitude_max: 26.9
-        longitude_min: 242.8
-        longitude_max: 256.6
-    event_type: tropical_cyclone
-  - case_id_number: 224
-    title: Pamela
-    start_date: 2021-10-08 06:00:00
-    end_date: 2021-10-15 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.9
-        latitude_max: 27.3
-        longitude_min: 248.4
-        longitude_max: 259.9
-    event_type: tropical_cyclone
-  - case_id_number: 225
-    title: Surigae (Bising)
-    start_date: 2021-04-09 12:00:00
-    end_date: 2021-05-02 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 3.3
-        latitude_max: 49.7
-        longitude_min: 122.5
-        longitude_max: 186.8
-    event_type: tropical_cyclone
-  - case_id_number: 226
-    title: In-fa (Fabian)
-    start_date: 2021-07-14 00:00:00
-    end_date: 2021-08-02 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.9
-        latitude_max: 42.4
-        longitude_min: 114.4
-        longitude_max: 137.6
-    event_type: tropical_cyclone
-  - case_id_number: 227
-    title: Kompasu (Maring)
-    start_date: 2021-10-05 18:00:00
-    end_date: 2021-10-16 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.8
-        latitude_max: 21.7
-        longitude_min: 103.4
-        longitude_max: 136.5
-    event_type: tropical_cyclone
-  - case_id_number: 228
-    title: Rai
-    start_date: 2021-12-09 12:00:00
-    end_date: 2021-12-23 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 3.1
-        latitude_max: 23.9
-        longitude_min: 108.3
-        longitude_max: 147.3
-    event_type: tropical_cyclone
-  - case_id_number: 229
-    title: Tauktae
-    start_date: 2021-05-11 06:00:00
-    end_date: 2021-05-21 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 7.8
-        latitude_max: 28.0
-        longitude_min: 68.6
-        longitude_max: 77.1
-    event_type: tropical_cyclone
-  - case_id_number: 230
-    title: Yaas
-    start_date: 2021-05-21 00:00:00
-    end_date: 2021-05-29 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 13.2
-        latitude_max: 26.9
-        longitude_min: 82.5
-        longitude_max: 92.3
-    event_type: tropical_cyclone
-  - case_id_number: 231
-    title: Gulab and Shaheen
-    start_date: 2021-09-21 18:00:00
-    end_date: 2021-10-06 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 15.9
-        latitude_max: 26.6
-        longitude_min: 53.7
-        longitude_max: 96.5
-    event_type: tropical_cyclone
-  - case_id_number: 232
-    title: Eloise
-    start_date: 2021-01-09 18:00:00
-    end_date: 2021-01-26 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -24.7
-        latitude_max: -9.3
-        longitude_min: 28.8
-        longitude_max: 88.1
-    event_type: tropical_cyclone
-  - case_id_number: 233
-    title: Amanda and Cristobal
-    start_date: 2020-05-28 18:00:00
-    end_date: 2020-06-02 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.0
-        latitude_max: 55.6
-        longitude_min: 265.0
-        longitude_max: 283.3
-    event_type: tropical_cyclone
-  - case_id_number: 234
-    title: Hanna
-    start_date: 2020-07-21 00:00:00
-    end_date: 2020-07-28 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 23.5
-        latitude_max: 29.4
-        longitude_min: 257.2
-        longitude_max: 274.2
-    event_type: tropical_cyclone
-  - case_id_number: 235
-    title: Isaias
-    start_date: 2020-07-26 12:00:00
-    end_date: 2020-08-07 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.3
-        latitude_max: 48.9
-        longitude_min: 277.7
-        longitude_max: 308.3
-    event_type: tropical_cyclone
-  - case_id_number: 236
-    title: Laura
-    start_date: 2020-08-18 00:00:00
-    end_date: 2020-08-31 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 12.2
-        latitude_max: 40.5
-        longitude_min: 264.3
-        longitude_max: 315.0
-    event_type: tropical_cyclone
-  - case_id_number: 237
-    title: Nana
-    start_date: 2020-08-30 06:00:00
-    end_date: 2020-09-06 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 13.6
-        latitude_max: 19.3
-        longitude_min: 266.3
-        longitude_max: 287.1
-    event_type: tropical_cyclone
-  - case_id_number: 238
-    title: Paulette
-    start_date: 2020-09-05 00:00:00
-    end_date: 2020-09-30 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.7
-        latitude_max: 49.3
-        longitude_min: 292.8
-        longitude_max: 345.7
-    event_type: tropical_cyclone
-  - case_id_number: 239
-    title: Sally
-    start_date: 2020-09-09 18:00:00
-    end_date: 2020-09-20 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 23.2
-        latitude_max: 36.3
-        longitude_min: 269.4
-        longitude_max: 283.9
-    event_type: tropical_cyclone
-  - case_id_number: 240
-    title: Teddy
-    start_date: 2020-09-10 06:00:00
-    end_date: 2020-09-26 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 8.8
-        latitude_max: 55.0
-        longitude_min: 293.4
-        longitude_max: 330.9
-    event_type: tropical_cyclone
-  - case_id_number: 241
-    title: Delta
-    start_date: 2020-10-02 18:00:00
-    end_date: 2020-10-13 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 14.2
-        latitude_max: 37.3
-        longitude_min: 263.9
-        longitude_max: 286.1
-    event_type: tropical_cyclone
-  - case_id_number: 242
-    title: Zeta
-    start_date: 2020-10-22 12:00:00
-    end_date: 2020-11-01 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 15.5
-        latitude_max: 41.7
-        longitude_min: 265.9
-        longitude_max: 290.7
-    event_type: tropical_cyclone
-  - case_id_number: 243
-    title: Eta
-    start_date: 2020-10-29 18:00:00
-    end_date: 2020-11-16 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 11.4
-        latitude_max: 37.9
-        longitude_min: 269.9
-        longitude_max: 293.1
-    event_type: tropical_cyclone
-  - case_id_number: 244
-    title: Iota
-    start_date: 2020-11-10 12:00:00
-    end_date: 2020-11-20 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.3
-        latitude_max: 17.7
-        longitude_min: 268.7
-        longitude_max: 291.4
-    event_type: tropical_cyclone
-  - case_id_number: 245
-    title: Genevieve
-    start_date: 2020-08-14 12:00:00
-    end_date: 2020-08-24 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 9.1
-        latitude_max: 29.2
-        longitude_min: 240.3
-        longitude_max: 265.8
-    event_type: tropical_cyclone
-  - case_id_number: 246
-    title: Hagupit (Dindo)
-    start_date: 2020-07-29 06:00:00
-    end_date: 2020-08-14 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 15.0
-        latitude_max: 54.5
-        longitude_min: 117.9
-        longitude_max: 182.5
-    event_type: tropical_cyclone
-  - case_id_number: 247
-    title: Maysak
-    start_date: 2020-08-24 00:00:00
-    end_date: 2020-09-08 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.6
-        latitude_max: 55.4
-        longitude_min: 120.7
-        longitude_max: 136.2
-    event_type: tropical_cyclone
-  - case_id_number: 248
-    title: Noul (Leon)
-    start_date: 2020-09-12 00:00:00
-    end_date: 2020-09-21 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 8.8
-        latitude_max: 18.7
-        longitude_min: 98.2
-        longitude_max: 129.6
-    event_type: tropical_cyclone
-  - case_id_number: 249
-    title: Linfa
-    start_date: 2020-10-05 06:00:00
-    end_date: 2020-10-14 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.3
-        latitude_max: 17.3
-        longitude_min: 104.6
-        longitude_max: 127.1
-    event_type: tropical_cyclone
-  - case_id_number: 250
-    title: Molave (Quinta)
-    start_date: 2020-10-19 18:00:00
-    end_date: 2020-10-31 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 6.5
-        latitude_max: 17.8
-        longitude_min: 101.8
-        longitude_max: 139.8
-    event_type: tropical_cyclone
-  - case_id_number: 251
-    title: Goni (Rolly)
-    start_date: 2020-10-23 06:00:00
-    end_date: 2020-11-08 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 8.4
-        latitude_max: 18.9
-        longitude_min: 105.6
-        longitude_max: 146.2
-    event_type: tropical_cyclone
-  - case_id_number: 252
-    title: Vamco (Ulysse)
-    start_date: 2020-11-06 00:00:00
-    end_date: 2020-11-18 00:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 5.8
-        latitude_max: 21.5
-        longitude_min: 101.3
-        longitude_max: 137.2
-    event_type: tropical_cyclone
-  - case_id_number: 253
-    title: Remal
-    start_date: 2024-05-23 12:00:00
-    end_date: 2024-05-29 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 16.6
-        latitude_max: 26.4
-        longitude_min: 86.0
-        longitude_max: 92.8
-    event_type: tropical_cyclone
-  - case_id_number: 254
-    title: Amphan
-    start_date: 2020-05-13 06:00:00
-    end_date: 2020-05-23 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 7.3
-        latitude_max: 27.6
-        longitude_min: 83.8
-        longitude_max: 92.4
-    event_type: tropical_cyclone
-  - case_id_number: 255
-    title: Nisarga
-    start_date: 2020-05-29 18:00:00
-    end_date: 2020-06-06 06:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 10.7
-        latitude_max: 24.4
-        longitude_min: 68.8
-        longitude_max: 79.9
-    event_type: tropical_cyclone
-  - case_id_number: 256
-    title: Damien
-    start_date: 2020-02-01 00:00:00
-    end_date: 2020-02-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -27.9
-        latitude_max: -13.8
-        longitude_min: 113.9
-        longitude_max: 131.8
-    event_type: tropical_cyclone
-  - case_id_number: 257
-    title: January 2020 Australia
-    start_date: 2020-01-19 12:00:00
-    end_date: 2020-01-20 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -39.75
-        latitude_max: -29.5
-        longitude_min: 144.01
-        longitude_max: 155.74
-    event_type: severe_convection
-  - case_id_number: 258
-    title: April 2020 Australia
-    start_date: 2020-04-02 12:00:00
-    end_date: 2020-04-03 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -41.75
-        latitude_max: -26.5
-        longitude_min: 138.03
-        longitude_max: 153.47
-    event_type: severe_convection
-  - case_id_number: 259
-    title: May 2020 Australia
-    start_date: 2020-05-30 12:00:00
-    end_date: 2020-05-31 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -39.25
-        latitude_max: -28.75
-        longitude_min: 132.78
-        longitude_max: 143.72
-    event_type: severe_convection
-  - case_id_number: 260
-    title: September 2020 Australia
-    start_date: 2020-09-24 12:00:00
-    end_date: 2020-09-25 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -38.5
-        latitude_max: -29.5
-        longitude_min: 146.03
-        longitude_max: 155.97
-    event_type: severe_convection
-  - case_id_number: 261
-    title: October 2020 Australia
-    start_date: 2020-10-30 12:00:00
-    end_date: 2020-10-31 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -38.5
-        latitude_max: -23.25
-        longitude_min: 146.13
-        longitude_max: 157.87
-    event_type: severe_convection
-  - case_id_number: 262
-    title: December 2020 Australia
-    start_date: 2020-12-04 12:00:00
-    end_date: 2020-12-05 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -36.5
-        latitude_max: -26.75
-        longitude_min: 142.1
-        longitude_max: 152.9
-    event_type: severe_convection
-  - case_id_number: 263
-    title: September 2021 Australia
-    start_date: 2021-09-29 12:00:00
-    end_date: 2021-09-30 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -39.0
-        latitude_max: -25.5
-        longitude_min: 142.59
-        longitude_max: 156.16
-    event_type: severe_convection
-  - case_id_number: 264
-    title: October 2021 Australia
-    start_date: 2021-10-13 12:00:00
-    end_date: 2021-10-14 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -39.5
-        latitude_max: -25.5
-        longitude_min: 143.83
-        longitude_max: 156.67
-    event_type: severe_convection
-  - case_id_number: 265
-    title: October 2021 Australia
-    start_date: 2021-10-17 12:00:00
-    end_date: 2021-10-19 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -34.75
-        latitude_max: -16.25
-        longitude_min: 144.02
-        longitude_max: 157.86
-    event_type: severe_convection
-  - case_id_number: 266
-    title: October 2021 Australia
-    start_date: 2021-10-27 12:00:00
-    end_date: 2021-10-28 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -39.75
-        latitude_max: -29.25
-        longitude_min: 130.77
-        longitude_max: 144.23
-    event_type: severe_convection
-  - case_id_number: 267
-    title: December 2021 Australia
-    start_date: 2021-12-02 12:00:00
-    end_date: 2021-12-03 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -38.5
-        latitude_max: -28.75
-        longitude_min: 145.3
-        longitude_max: 155.95
-    event_type: severe_convection
-  - case_id_number: 268
-    title: December 2021 Australia
-    start_date: 2021-12-08 12:00:00
-    end_date: 2021-12-09 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -33.0
-        latitude_max: -23.0
-        longitude_min: 145.45
-        longitude_max: 156.8
-    event_type: severe_convection
-  - case_id_number: 269
-    title: January 2022 Australia
-    start_date: 2022-01-14 12:00:00
-    end_date: 2022-01-15 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -41.5
-        latitude_max: -29.25
-        longitude_min: 144.24
-        longitude_max: 155.76
-    event_type: severe_convection
-  - case_id_number: 270
-    title: March 2022 Australia
-    start_date: 2022-03-03 12:00:00
-    end_date: 2022-03-04 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -31.25
-        latitude_max: -22.25
-        longitude_min: 148.23
-        longitude_max: 157.77
-    event_type: severe_convection
-  - case_id_number: 271
-    title: December 2022 Australia
-    start_date: 2022-12-07 12:00:00
-    end_date: 2022-12-08 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -30.5
-        latitude_max: -21.5
-        longitude_min: 147.99
-        longitude_max: 157.51
-    event_type: severe_convection
-  - case_id_number: 272
-    title: February 2023 Australia
-    start_date: 2023-02-13 12:00:00
-    end_date: 2023-02-14 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -32.5
-        latitude_max: -22.0
-        longitude_min: 146.97
-        longitude_max: 158.28
-    event_type: severe_convection
-  - case_id_number: 273
-    title: December 2023 Australia
-    start_date: 2023-12-11 12:00:00
-    end_date: 2023-12-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -42.0
-        latitude_max: -33.0
-        longitude_min: 135.16
-        longitude_max: 145.34
-    event_type: severe_convection
-  - case_id_number: 274
-    title: December 2023 Australia
-    start_date: 2023-12-24 12:00:00
-    end_date: 2023-12-26 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -37.25
-        latitude_max: -22.5
-        longitude_min: 146.57
-        longitude_max: 158.29
-    event_type: severe_convection
-  - case_id_number: 275
-    title: August 2024 Australia
-    start_date: 2024-08-24 12:00:00
-    end_date: 2024-08-25 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -42.5
-        latitude_max: -30.75
-        longitude_min: 138.94
-        longitude_max: 152.56
-    event_type: severe_convection
-  - case_id_number: 276
-    title: October 2024 Australia
-    start_date: 2024-10-08 12:00:00
-    end_date: 2024-10-09 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -31.75
-        latitude_max: -21.25
-        longitude_min: 143.73
-        longitude_max: 157.77
-    event_type: severe_convection
-  - case_id_number: 277
-    title: October 2024 Australia
-    start_date: 2024-10-16 12:00:00
-    end_date: 2024-10-17 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -42.0
-        latitude_max: -32.0
-        longitude_min: 136.18
-        longitude_max: 147.32
-    event_type: severe_convection
-  - case_id_number: 278
-    title: November 2024 Australia
-    start_date: 2024-10-31 12:00:00
-    end_date: 2024-11-01 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -32.5
-        latitude_max: -20.75
-        longitude_min: 147.73
-        longitude_max: 158.27
-    event_type: severe_convection
-  - case_id_number: 279
-    title: November 2024 Australia
-    start_date: 2024-11-12 12:00:00
-    end_date: 2024-11-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -37.75
-        latitude_max: -23.5
-        longitude_min: 143.38
-        longitude_max: 158.37
-    event_type: severe_convection
-  - case_id_number: 280
-    title: April 2023 Midwest
-    start_date: 2023-04-05 12:00:00
-    end_date: 2023-04-06 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 22.75
-        latitude_max: 44.75
-        longitude_min: 256.04
-        longitude_max: 282.71
-    event_type: severe_convection
-  - case_id_number: 281
-    title: March 2023 Midwest + Southeast
-    start_date: 2023-03-31 12:00:00
-    end_date: 2023-04-01 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.5
-        latitude_max: 49.0
-        longitude_min: 259.15
-        longitude_max: 284.1
-    event_type: severe_convection
-  - case_id_number: 282
-    title: March 2023 North America
-    start_date: 2023-03-02 12:00:00
-    end_date: 2023-03-03 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.5
-        latitude_max: 41.5
-        longitude_min: 254.81
-        longitude_max: 273.19
-    event_type: severe_convection
-  - case_id_number: 283
-    title: February 2023 North America
-    start_date: 2023-02-26 12:00:00
-    end_date: 2023-02-27 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 28.5
-        latitude_max: 43.0
-        longitude_min: 252.97
-        longitude_max: 271.78
-    event_type: severe_convection
-  - case_id_number: 284
-    title: July 2022 Great Plains
-    start_date: 2022-07-22 12:00:00
-    end_date: 2022-07-23 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.25
-        latitude_max: 54.25
-        longitude_min: 251.57
-        longitude_max: 280.43
-    event_type: severe_convection
-  - case_id_number: 285
-    title: June 2022 Southwest
-    start_date: 2022-06-07 12:00:00
-    end_date: 2022-06-08 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 30.25
-        latitude_max: 49.5
-        longitude_min: 249.57
-        longitude_max: 272.68
-    event_type: severe_convection
-  - case_id_number: 286
-    title: May 2022 Midwest
-    start_date: 2022-05-12 12:00:00
-    end_date: 2022-05-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 31.75
-        latitude_max: 52.75
-        longitude_min: 250.71
-        longitude_max: 277.79
-    event_type: severe_convection
-  - case_id_number: 287
-    title: April 2022 Great Plains
-    start_date: 2022-04-12 12:00:00
-    end_date: 2022-04-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.5
-        latitude_max: 50.5
-        longitude_min: 255.41
-        longitude_max: 278.09
-    event_type: severe_convection
-  - case_id_number: 288
-    title: April 2022 South
-    start_date: 2022-04-05 12:00:00
-    end_date: 2022-04-06 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.75
-        latitude_max: 45.0
-        longitude_min: 260.49
-        longitude_max: 285.51
-    event_type: severe_convection
-  - case_id_number: 289
-    title: December 2021 Midwest
-    start_date: 2021-12-15 12:00:00
-    end_date: 2021-12-16 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 32.5
-        latitude_max: 49.75
-        longitude_min: 255.01
-        longitude_max: 274.74
-    event_type: severe_convection
-  - case_id_number: 290
-    title: December 2021 Great Plains
-    start_date: 2021-12-10 12:00:00
-    end_date: 2021-12-11 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 28.25
-        latitude_max: 46.0
-        longitude_min: 258.43
-        longitude_max: 281.32
-    event_type: severe_convection
-  - case_id_number: 291
-    title: June 2021 Great Plains
-    start_date: 2021-06-18 12:00:00
-    end_date: 2021-06-19 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.0
-        latitude_max: 52.0
-        longitude_min: 258.87
-        longitude_max: 281.88
-    event_type: severe_convection
-  - case_id_number: 292
-    title: May 2021 Great Plains
-    start_date: 2021-05-03 12:00:00
-    end_date: 2021-05-05 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.0
-        latitude_max: 45.75
-        longitude_min: 250.26
-        longitude_max: 288.49
-    event_type: severe_convection
-  - case_id_number: 293
-    title: March 2021 South
-    start_date: 2021-03-27 12:00:00
-    end_date: 2021-03-28 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.5
-        latitude_max: 45.25
-        longitude_min: 259.22
-        longitude_max: 288.03
-    event_type: severe_convection
-  - case_id_number: 294
-    title: March 2021 South
-    start_date: 2021-03-25 12:00:00
-    end_date: 2021-03-26 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.5
-        latitude_max: 44.5
-        longitude_min: 264.48
-        longitude_max: 284.27
-    event_type: severe_convection
-  - case_id_number: 295
-    title: April 2020 South
-    start_date: 2020-04-22 12:00:00
-    end_date: 2020-04-23 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.0
-        latitude_max: 41.75
-        longitude_min: 256.05
-        longitude_max: 277.2
-    event_type: severe_convection
-  - case_id_number: 296
-    title: April 2020 Southeast
-    start_date: 2020-04-12 12:00:00
-    end_date: 2020-04-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 25.5
-        latitude_max: 40.75
-        longitude_min: 257.06
-        longitude_max: 285.94
-    event_type: severe_convection
-  - case_id_number: 297
-    title: April 2020 Midwest
-    start_date: 2020-04-08 12:00:00
-    end_date: 2020-04-09 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 26.25
-        latitude_max: 46.5
-        longitude_min: 262.2
-        longitude_max: 288.8
-    event_type: severe_convection
-  - case_id_number: 298
-    title: March 2020 South
-    start_date: 2020-03-28 12:00:00
-    end_date: 2020-03-29 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 27.25
-        latitude_max: 48.25
-        longitude_min: 257.9
-        longitude_max: 286.35
-    event_type: severe_convection
-  - case_id_number: 299
-    title: March 2020 Southeast
-    start_date: 2020-03-02 12:00:00
-    end_date: 2020-03-03 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 30.25
-        latitude_max: 43.5
-        longitude_min: 261.93
-        longitude_max: 280.82
-    event_type: severe_convection
-  - case_id_number: 300
-    title: February 2020 East
-    start_date: 2020-02-05 12:00:00
-    end_date: 2020-02-07 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 23.25
-        latitude_max: 41.25
-        longitude_min: 260.81
-        longitude_max: 288.16
-    event_type: severe_convection
-  - case_id_number: 301
-    title: January 2020 South
-    start_date: 2020-01-10 12:00:00
-    end_date: 2020-01-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 24.5
-        latitude_max: 44.5
-        longitude_min: 255.04
-        longitude_max: 281.51
-    event_type: severe_convection
-  - case_id_number: 302
-    title: June 2020 Canada
-    start_date: 2020-06-10 12:00:00
-    end_date: 2020-06-11 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 34.0
-        latitude_max: 50.25
-        longitude_min: 265.96
-        longitude_max: 288.54
-    event_type: severe_convection
-  - case_id_number: 303
-    title: June 2020 Canada
-    start_date: 2020-06-12 12:00:00
-    end_date: 2020-06-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 41.25
-        latitude_max: 56.25
-        longitude_min: 236.83
-        longitude_max: 253.92
-    event_type: severe_convection
-  - case_id_number: 304
-    title: June 2020 Canada
-    start_date: 2020-06-28 12:00:00
-    end_date: 2020-06-29 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 38.5
-        latitude_max: 56.0
-        longitude_min: 243.43
-        longitude_max: 274.07
-    event_type: severe_convection
-  - case_id_number: 305
-    title: July 2020 Canada
-    start_date: 2020-07-04 12:00:00
-    end_date: 2020-07-05 12:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: 31.0
-        latitude_max: 58.5
-        longitude_min: 241.83
-        longitude_max: 275.92
-    event_type: severe_convection
-  - case_id_number: 306
-    title: July 2020 Canada
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-    end_date: 2020-07-13 12:00:00
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-    title: July 2020 Canada
-    start_date: 2020-07-19 12:00:00
-    end_date: 2020-07-20 12:00:00
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-    end_date: 2020-07-24 12:00:00
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-    title: August 2020 Canada
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-    end_date: 2020-08-28 12:00:00
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-    title: June 2021 Canada
-    start_date: 2021-06-05 12:00:00
-    end_date: 2021-06-06 12:00:00
-    location:
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-        longitude_max: 274.0
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-  - case_id_number: 311
-    title: June 2021 Canada
-    start_date: 2021-06-14 12:00:00
-    end_date: 2021-06-15 12:00:00
-    location:
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-        longitude_min: 235.63
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-    title: July 2021 Canada
-    start_date: 2021-07-15 12:00:00
-    end_date: 2021-07-16 12:00:00
-    location:
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-      parameters:
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-    title: July 2021 Canada
-    start_date: 2021-07-22 12:00:00
-    end_date: 2021-07-23 12:00:00
-    location:
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-      parameters:
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-    title: August 2021 Canada
-    start_date: 2021-08-23 12:00:00
-    end_date: 2021-08-24 12:00:00
-    location:
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-    title: August 2021 Canada
-    start_date: 2021-08-31 12:00:00
-    end_date: 2021-09-01 12:00:00
-    location:
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-    title: June 2022 Canada
-    start_date: 2022-06-23 12:00:00
-    end_date: 2022-06-24 12:00:00
-    location:
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-        longitude_min: 247.12
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-    title: June 2022 Canada
-    start_date: 2022-06-29 12:00:00
-    end_date: 2022-06-30 12:00:00
-    location:
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-        longitude_min: 241.57
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-    title: July 2022 Canada
-    start_date: 2022-07-05 12:00:00
-    end_date: 2022-07-06 12:00:00
-    location:
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-        longitude_min: 241.09
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-  - case_id_number: 319
-    title: July 2022 Canada
-    start_date: 2022-07-07 12:00:00
-    end_date: 2022-07-08 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 57.5
-        longitude_min: 238.14
-        longitude_max: 266.86
-    event_type: severe_convection
-  - case_id_number: 320
-    title: July 2022 Canada
-    start_date: 2022-07-08 12:00:00
-    end_date: 2022-07-09 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 58.0
-        longitude_min: 238.59
-        longitude_max: 267.66
-    event_type: severe_convection
-  - case_id_number: 321
-    title: July 2022 Canada
-    start_date: 2022-07-17 12:00:00
-    end_date: 2022-07-18 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 57.0
-        longitude_min: 245.23
-        longitude_max: 271.27
-    event_type: severe_convection
-  - case_id_number: 322
-    title: July 2022 Canada
-    start_date: 2022-07-19 12:00:00
-    end_date: 2022-07-20 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 58.25
-        longitude_min: 239.06
-        longitude_max: 255.19
-    event_type: severe_convection
-  - case_id_number: 323
-    title: July 2022 Canada
-    start_date: 2022-07-31 12:00:00
-    end_date: 2022-08-01 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 58.5
-        longitude_min: 236.34
-        longitude_max: 258.41
-    event_type: severe_convection
-  - case_id_number: 324
-    title: May 2023 Canada
-    start_date: 2023-05-31 12:00:00
-    end_date: 2023-06-01 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-    event_type: severe_convection
-  - case_id_number: 325
-    title: June 2023 Canada
-    start_date: 2023-06-28 12:00:00
-    end_date: 2023-06-29 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 56.25
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-  - case_id_number: 326
-    title: July 2023 Canada
-    start_date: 2023-07-13 12:00:00
-    end_date: 2023-07-14 12:00:00
-    location:
-      type: bounded_region
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-        latitude_max: 54.5
-        longitude_min: 249.48
-        longitude_max: 294.02
-    event_type: severe_convection
-  - case_id_number: 327
-    title: July 2023 Canada
-    start_date: 2023-07-20 12:00:00
-    end_date: 2023-07-21 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 49.0
-        longitude_min: 265.84
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-    event_type: severe_convection
-  - case_id_number: 328
-    title: July 2023 Canada
-    start_date: 2023-07-22 12:00:00
-    end_date: 2023-07-23 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 55.5
-        longitude_min: 246.28
-        longitude_max: 279.97
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-  - case_id_number: 329
-    title: July 2023 Canada
-    start_date: 2023-07-26 12:00:00
-    end_date: 2023-07-27 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 56.5
-        longitude_min: 250.03
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-    event_type: severe_convection
-  - case_id_number: 330
-    title: August 2023 Canada
-    start_date: 2023-08-03 12:00:00
-    end_date: 2023-08-04 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 52.5
-        longitude_min: 266.29
-        longitude_max: 292.71
-    event_type: severe_convection
-  - case_id_number: 331
-    title: June 2024 Canada
-    start_date: 2024-06-12 12:00:00
-    end_date: 2024-06-13 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 57.0
-        longitude_min: 248.23
-        longitude_max: 274.52
-    event_type: severe_convection
-  - case_id_number: 332
-    title: June 2024 Canada
-    start_date: 2024-06-22 12:00:00
-    end_date: 2024-06-23 12:00:00
-    location:
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-        longitude_min: 247.71
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-    event_type: severe_convection
-  - case_id_number: 333
-    title: June 2024 Canada
-    start_date: 2024-06-23 12:00:00
-    end_date: 2024-06-24 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 58.25
-        longitude_min: 240.26
-        longitude_max: 274.24
-    event_type: severe_convection
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-    title: July 2024 Canada
-    start_date: 2024-07-11 12:00:00
-    end_date: 2024-07-12 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 50.25
-        longitude_min: 251.25
-        longitude_max: 272.5
-    event_type: severe_convection
-  - case_id_number: 335
-    title: July 2024 Canada
-    start_date: 2024-07-24 12:00:00
-    end_date: 2024-07-25 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 58.75
-        longitude_min: 236.26
-        longitude_max: 252.74
-    event_type: severe_convection
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-    title: August 2024 Canada
-    start_date: 2024-08-05 12:00:00
-    end_date: 2024-08-06 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 49.5
-        longitude_min: 259.41
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-    event_type: severe_convection
-  - case_id_number: 337
-    title: August 2024 Canada
-    start_date: 2024-08-21 12:00:00
-    end_date: 2024-08-22 12:00:00
-    location:
-      type: bounded_region
-      parameters:
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-        latitude_max: 58.25
-        longitude_min: 243.9
-        longitude_max: 265.35
-    event_type: severe_convection
+- case_id_number: 1
+  title: 2021 Pacific Northwest
+  start_date: 2021-06-20 00:00:00
+  end_date: 2021-07-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 33.25
+      latitude_max: 68.0
+      longitude_min: 215.25
+      longitude_max: 256.0
+  event_type: heat_wave
+- case_id_number: 2
+  title: 2022 Upper Midwest
+  start_date: 2022-05-07 00:00:00
+  end_date: 2022-05-17 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 19.5
+      latitude_max: 48.25
+      longitude_min: 252.0
+      longitude_max: 276.75
+  event_type: heat_wave
+- case_id_number: 3
+  title: 2022 California
+  start_date: 2022-06-07 00:00:00
+  end_date: 2022-06-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 23.75
+      latitude_max: 44.5
+      longitude_min: 235.75
+      longitude_max: 266.25
+  event_type: heat_wave
+- case_id_number: 4
+  title: 2022 Texas
+  start_date: 2022-06-30 00:00:00
+  end_date: 2022-07-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 20.5
+      latitude_max: 43.25
+      longitude_min: 254.75
+      longitude_max: 275.5
+  event_type: heat_wave
+- case_id_number: 5
+  title: 2023 Pacific Northwest
+  start_date: 2023-05-10 00:00:00
+  end_date: 2023-05-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.25
+      latitude_max: 72.0
+      longitude_min: 219.25
+      longitude_max: 262.0
+  event_type: heat_wave
+- case_id_number: 6
+  title: 2022 Mid-Atlantic
+  start_date: 2022-05-17 00:00:00
+  end_date: 2022-05-24 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 31.0
+      latitude_max: 42.25
+      longitude_min: 277.0
+      longitude_max: 287.75
+  event_type: heat_wave
+- case_id_number: 7
+  title: 2023 Australia
+  start_date: 2023-11-18 00:00:00
+  end_date: 2023-11-28 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -35.0
+      latitude_max: -20.0
+      longitude_min: 113.5
+      longitude_max: 120.25
+  event_type: heat_wave
+- case_id_number: 8
+  title: 2023 Ireland
+  start_date: 2023-09-02 00:00:00
+  end_date: 2023-09-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.75
+      latitude_max: 67.5
+      longitude_min: -9.75
+      longitude_max: 16.75
+  event_type: heat_wave
+- case_id_number: 9
+  title: 2023 Italy
+  start_date: 2023-07-07 00:00:00
+  end_date: 2023-07-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 17.5
+      latitude_max: 52.25
+      longitude_min: -12.0
+      longitude_max: 32.75
+  event_type: heat_wave
+- case_id_number: 10
+  title: 2023 SW Europe
+  start_date: 2023-08-17 00:00:00
+  end_date: 2023-08-28 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.0
+      latitude_max: 56.75
+      longitude_min: -9.25
+      longitude_max: 20.75
+  event_type: heat_wave
+- case_id_number: 11
+  title: 2023 South America
+  start_date: 2023-07-29 00:00:00
+  end_date: 2023-08-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -44.25
+      latitude_max: -20.25
+      longitude_min: 285.75
+      longitude_max: 308.0
+  event_type: heat_wave
+- case_id_number: 12
+  title: 2023 China (Shanghai)
+  start_date: 2023-05-24 00:00:00
+  end_date: 2023-06-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 16.75
+      latitude_max: 35.25
+      longitude_min: 99.0
+      longitude_max: 122.0
+  event_type: heat_wave
+- case_id_number: 13
+  title: 2023 China (Yunnan Province)
+  start_date: 2023-04-14 00:00:00
+  end_date: 2023-04-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 12.75
+      latitude_max: 43.5
+      longitude_min: 92.25
+      longitude_max: 121.0
+  event_type: heat_wave
+- case_id_number: 14
+  title: 2023 Algeria
+  start_date: 2023-07-05 00:00:00
+  end_date: 2023-07-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 16.25
+      latitude_max: 51.0
+      longitude_min: -17.0
+      longitude_max: 27.5
+  event_type: heat_wave
+- case_id_number: 15
+  title: 2023 Iberian Peninsula
+  start_date: 2023-04-22 00:00:00
+  end_date: 2023-05-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 22.75
+      latitude_max: 46.75
+      longitude_min: -15.75
+      longitude_max: 8.75
+  event_type: heat_wave
+- case_id_number: 16
+  title: 2023 Southeast Asia
+  start_date: 2023-04-16 00:00:00
+  end_date: 2023-04-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 6.0
+      latitude_max: 41.0
+      longitude_min: 84.0
+      longitude_max: 123.75
+  event_type: heat_wave
+- case_id_number: 17
+  title: 2023 India
+  start_date: 2023-02-15 00:00:00
+  end_date: 2023-03-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 22.25
+      latitude_max: 53.0
+      longitude_min: 56.75
+      longitude_max: 101.5
+  event_type: heat_wave
+- case_id_number: 18
+  title: 2021 Western Russia
+  start_date: 2021-06-18 00:00:00
+  end_date: 2021-06-30 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.5
+      latitude_max: 65.75
+      longitude_min: 13.25
+      longitude_max: 60.0
+  event_type: heat_wave
+- case_id_number: 19
+  title: 2022 Germany
+  start_date: 2022-12-23 00:00:00
+  end_date: 2022-12-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.25
+      latitude_max: 56.25
+      longitude_min: -3.0
+      longitude_max: 21.75
+  event_type: heat_wave
+- case_id_number: 20
+  title: 2022 UK (August)
+  start_date: 2022-08-08 00:00:00
+  end_date: 2022-08-16 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 39.25
+      latitude_max: 56.0
+      longitude_min: -9.75
+      longitude_max: 8.25
+  event_type: heat_wave
+- case_id_number: 21
+  title: 2022 UK (July)
+  start_date: 2022-07-15 00:00:00
+  end_date: 2022-07-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 37.25
+      latitude_max: 58.0
+      longitude_min: -9.75
+      longitude_max: 8.25
+  event_type: heat_wave
+- case_id_number: 22
+  title: 2022 France
+  start_date: 2022-06-09 00:00:00
+  end_date: 2022-06-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 29.0
+      latitude_max: 51.75
+      longitude_min: -9.75
+      longitude_max: 12.75
+  event_type: heat_wave
+- case_id_number: 23
+  title: 2022 Japan
+  start_date: 2022-06-20 00:00:00
+  end_date: 2022-07-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 31.25
+      latitude_max: 46.0
+      longitude_min: 125.25
+      longitude_max: 145.25
+  event_type: heat_wave
+- case_id_number: 24
+  title: 2022 India
+  start_date: 2022-04-24 00:00:00
+  end_date: 2022-05-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 18.0
+      latitude_max: 52.75
+      longitude_min: 58.0
+      longitude_max: 78.75
+  event_type: heat_wave
+- case_id_number: 25
+  title: 2022 East Antarctica
+  start_date: 2022-03-12 00:00:00
+  end_date: 2022-03-26 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -85.5
+      latitude_max: -65.5
+      longitude_min: 99.0
+      longitude_max: 147.75
+  event_type: heat_wave
+- case_id_number: 26
+  title: 2022 West Australia
+  start_date: 2022-01-15 00:00:00
+  end_date: 2022-01-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -35.0
+      latitude_max: -27.5
+      longitude_min: 114.25
+      longitude_max: 119.75
+  event_type: heat_wave
+- case_id_number: 27
+  title: 2021 Canada Plains
+  start_date: 2021-05-30 00:00:00
+  end_date: 2021-06-09 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 38.5
+      latitude_max: 59.5
+      longitude_min: 238.0
+      longitude_max: 272.75
+  event_type: heat_wave
+- case_id_number: 28
+  title: 2021 New Zealand
+  start_date: 2021-01-12 18:00:00
+  end_date: 2021-01-18 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -46.5
+      latitude_max: -40.75
+      longitude_min: 167.5
+      longitude_max: 175.25
+  event_type: heat_wave
+- case_id_number: 29
+  title: 2020 Australia
+  start_date: 2020-11-25 00:00:00
+  end_date: 2020-12-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -36.75
+      latitude_max: -17.5
+      longitude_min: 126.5
+      longitude_max: 152.5
+  event_type: heat_wave
+- case_id_number: 30
+  title: 2021 Texas
+  start_date: 2021-02-10 12:00:00
+  end_date: 2021-02-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.0
+      latitude_max: 54.75
+      longitude_min: 250.0
+      longitude_max: 278.75
+  event_type: freeze
+- case_id_number: 31
+  title: 2022 Arkansas
+  start_date: 2022-02-17 18:00:00
+  end_date: 2022-03-01 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.25
+      latitude_max: 59.0
+      longitude_min: 243.25
+      longitude_max: 272.0
+  event_type: freeze
+- case_id_number: 32
+  title: 2023 Germany
+  start_date: 2023-12-02 06:00:00
+  end_date: 2023-12-08 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 44.25
+      latitude_max: 68.5
+      longitude_min: 7.25
+      longitude_max: 22.0
+  event_type: freeze
+- case_id_number: 33
+  title: 2023 China
+  start_date: 2023-12-15 06:00:00
+  end_date: 2023-12-26 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 14.75
+      latitude_max: 55.5
+      longitude_min: 101.0
+      longitude_max: 141.75
+  event_type: freeze
+- case_id_number: 34
+  title: 2023 Afghanistan
+  start_date: 2023-01-11 06:00:00
+  end_date: 2023-01-28 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 22.25
+      latitude_max: 49.0
+      longitude_min: 50.75
+      longitude_max: 85.5
+  event_type: freeze
+- case_id_number: 35
+  title: 2022 Colorado
+  start_date: 2022-04-06 00:00:00
+  end_date: 2022-04-16 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.25
+      latitude_max: 64.0
+      longitude_min: 236.75
+      longitude_max: 261.5
+  event_type: freeze
+- case_id_number: 36
+  title: July 2024 South Dakota
+  start_date: 2024-07-13 12:00:00
+  end_date: 2024-07-14 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 38.25
+      latitude_max: 58.5
+      longitude_min: 240.86
+      longitude_max: 285.64
+  event_type: severe_convection
+- case_id_number: 37
+  title: July 2024 Chicago
+  start_date: 2024-07-14 12:00:00
+  end_date: 2024-07-16 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 30.0
+      latitude_max: 57.75
+      longitude_min: 243.74
+      longitude_max: 289.69
+  event_type: severe_convection
+- case_id_number: 38
+  title: July 2024 New York
+  start_date: 2024-07-16 12:00:00
+  end_date: 2024-07-17 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 31.0
+      latitude_max: 49.0
+      longitude_min: 274.31
+      longitude_max: 293.44
+  event_type: severe_convection
+- case_id_number: 39
+  title: May 2024 Wisconsin
+  start_date: 2024-05-18 12:00:00
+  end_date: 2024-05-19 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.5
+      latitude_max: 55.5
+      longitude_min: 258.01
+      longitude_max: 277.99
+  event_type: severe_convection
+- case_id_number: 40
+  title: May 2024 Kansas
+  start_date: 2024-05-19 12:00:00
+  end_date: 2024-05-20 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 30.0
+      latitude_max: 51.0
+      longitude_min: 250.79
+      longitude_max: 276.46
+  event_type: severe_convection
+- case_id_number: 41
+  title: May 2024 Iowa + Nebraska
+  start_date: 2024-05-20 12:00:00
+  end_date: 2024-05-21 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 33.5
+      latitude_max: 49.25
+      longitude_min: 250.0
+      longitude_max: 282.75
+  event_type: severe_convection
+- case_id_number: 42
+  title: May 2024 Iowa + Wisconsin
+  start_date: 2024-05-21 12:00:00
+  end_date: 2024-05-22 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.75
+      latitude_max: 55.75
+      longitude_min: 254.25
+      longitude_max: 278.25
+  event_type: severe_convection
+- case_id_number: 43
+  title: May 2024 Texas
+  start_date: 2024-05-22 12:00:00
+  end_date: 2024-05-23 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.75
+      latitude_max: 50.0
+      longitude_min: 252.9
+      longitude_max: 291.35
+  event_type: severe_convection
+- case_id_number: 44
+  title: May 2024 Iowa + Nebraska 2
+  start_date: 2024-05-23 12:00:00
+  end_date: 2024-05-24 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.75
+      latitude_max: 51.5
+      longitude_min: 251.62
+      longitude_max: 281.13
+  event_type: severe_convection
+- case_id_number: 45
+  title: April 2024 Oklahoma
+  start_date: 2024-04-25 12:00:00
+  end_date: 2024-04-29 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.25
+      latitude_max: 50.75
+      longitude_min: 242.87
+      longitude_max: 282.12
+  event_type: severe_convection
+- case_id_number: 46
+  title: April 2024 Midwest
+  start_date: 2024-04-02 12:00:00
+  end_date: 2024-04-03 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.0
+      latitude_max: 46.0
+      longitude_min: 262.72
+      longitude_max: 284.78
+  event_type: severe_convection
+- case_id_number: 47
+  title: April 2024 Great Plains + Midwest
+  start_date: 2024-04-01 12:00:00
+  end_date: 2024-04-02 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.25
+      latitude_max: 45.5
+      longitude_min: 254.5
+      longitude_max: 281.0
+  event_type: severe_convection
+- case_id_number: 48
+  title: March 2024 Midwest
+  start_date: 2024-03-14 12:00:00
+  end_date: 2024-03-15 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.75
+      latitude_max: 46.75
+      longitude_min: 254.69
+      longitude_max: 284.81
+  event_type: severe_convection
+- case_id_number: 49
+  title: February 2024 Midwest
+  start_date: 2024-02-27 12:00:00
+  end_date: 2024-02-28 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.25
+      latitude_max: 48.75
+      longitude_min: 263.54
+      longitude_max: 285.21
+  event_type: severe_convection
+- case_id_number: 50
+  title: January 2024 Southeast
+  start_date: 2024-01-08 12:00:00
+  end_date: 2024-01-10 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 23.25
+      latitude_max: 40.25
+      longitude_min: 257.62
+      longitude_max: 287.9
+  event_type: severe_convection
+- case_id_number: 51
+  title: December 2023 South
+  start_date: 2023-12-09 12:00:00
+  end_date: 2023-12-10 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.0
+      latitude_max: 42.5
+      longitude_min: 259.79
+      longitude_max: 280.96
+  event_type: severe_convection
+- case_id_number: 52
+  title: August 2023 East Coast
+  start_date: 2023-08-07 12:00:00
+  end_date: 2023-08-08 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 27.0
+      latitude_max: 47.75
+      longitude_min: 243.17
+      longitude_max: 269.83
+  event_type: severe_convection
+- case_id_number: 53
+  title: August 2023 Minnesota
+  start_date: 2023-08-11 12:00:00
+  end_date: 2023-08-12 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.5
+      latitude_max: 51.75
+      longitude_min: 256.96
+      longitude_max: 281.79
+  event_type: severe_convection
+- case_id_number: 54
+  title: July 2023 Midwest
+  start_date: 2023-07-02 12:00:00
+  end_date: 2023-07-03 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 29.0
+      latitude_max: 46.75
+      longitude_min: 264.15
+      longitude_max: 289.85
+  event_type: severe_convection
+- case_id_number: 55
+  title: July 2023 Midwest 2
+  start_date: 2023-07-01 12:00:00
+  end_date: 2023-07-02 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 39.25
+      latitude_max: 59.75
+      longitude_min: 239.53
+      longitude_max: 265.97
+  event_type: severe_convection
+- case_id_number: 56
+  title: June 2023 Midwest
+  start_date: 2023-06-30 12:00:00
+  end_date: 2023-07-01 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 30.75
+      latitude_max: 45.0
+      longitude_min: 248.9
+      longitude_max: 279.6
+  event_type: severe_convection
+- case_id_number: 57
+  title: June 2023 Front Range & Texas
+  start_date: 2023-06-21 12:00:00
+  end_date: 2023-06-22 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.5
+      latitude_max: 50.25
+      longitude_min: 248.4
+      longitude_max: 266.35
+  event_type: severe_convection
+- case_id_number: 58
+  title: June 2023 Great Plains
+  start_date: 2023-06-23 12:00:00
+  end_date: 2023-06-24 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.5
+      latitude_max: 52.5
+      longitude_min: 242.12
+      longitude_max: 271.88
+  event_type: severe_convection
+- case_id_number: 59
+  title: June 2023 Iowa + Minnesota
+  start_date: 2023-06-24 12:00:00
+  end_date: 2023-06-25 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 34.25
+      latitude_max: 53.0
+      longitude_min: 255.64
+      longitude_max: 275.11
+  event_type: severe_convection
+- case_id_number: 60
+  title: June 2023 South + Midwest
+  start_date: 2023-06-25 12:00:00
+  end_date: 2023-06-26 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 27.25
+      latitude_max: 50.0
+      longitude_min: 259.62
+      longitude_max: 288.88
+  event_type: severe_convection
+- case_id_number: 61
+  title: June 2023 East Coast
+  start_date: 2023-06-26 12:00:00
+  end_date: 2023-06-27 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 29.25
+      latitude_max: 49.5
+      longitude_min: 272.34
+      longitude_max: 292.91
+  event_type: severe_convection
+- case_id_number: 62
+  title: June 2023 Midwest + Great Plains
+  start_date: 2023-06-29 12:00:00
+  end_date: 2023-06-30 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.5
+      latitude_max: 50.5
+      longitude_min: 248.64
+      longitude_max: 279.36
+  event_type: severe_convection
+- case_id_number: 63
+  title: June 2023 Southeast
+  start_date: 2023-06-14 12:00:00
+  end_date: 2023-06-15 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.5
+      latitude_max: 41.75
+      longitude_min: 257.55
+      longitude_max: 283.7
+  event_type: severe_convection
+- case_id_number: 64
+  title: June 2023 OK + TX + South
+  start_date: 2023-06-15 12:00:00
+  end_date: 2023-06-16 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.0
+      latitude_max: 48.0
+      longitude_min: 251.7
+      longitude_max: 284.55
+  event_type: severe_convection
+- case_id_number: 65
+  title: June 2023 South & Mid-Atlantic
+  start_date: 2023-06-16 12:00:00
+  end_date: 2023-06-17 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.75
+      latitude_max: 45.25
+      longitude_min: 254.5
+      longitude_max: 290.5
+  event_type: severe_convection
+- case_id_number: 66
+  title: June 2023 Great Plains + South
+  start_date: 2023-06-17 12:00:00
+  end_date: 2023-06-18 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.25
+      latitude_max: 46.5
+      longitude_min: 250.22
+      longitude_max: 279.78
+  event_type: severe_convection
+- case_id_number: 67
+  title: May 2023 Great Plains
+  start_date: 2023-05-10 12:00:00
+  end_date: 2023-05-11 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.75
+      latitude_max: 55.5
+      longitude_min: 248.76
+      longitude_max: 275.74
+  event_type: severe_convection
+- case_id_number: 68
+  title: May 2023 Great Plains + South
+  start_date: 2023-05-11 12:00:00
+  end_date: 2023-05-12 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 23.75
+      latitude_max: 47.25
+      longitude_min: 251.98
+      longitude_max: 283.77
+  event_type: severe_convection
+- case_id_number: 69
+  title: May 2023 Nebraska
+  start_date: 2023-05-12 12:00:00
+  end_date: 2023-05-13 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.75
+      latitude_max: 47.75
+      longitude_min: 254.17
+      longitude_max: 270.58
+  event_type: severe_convection
+- case_id_number: 70
+  title: May 2024 Texas
+  start_date: 2024-05-25 00:00:00
+  end_date: 2024-05-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 9.75
+      latitude_max: 32.25
+      longitude_min: 254.25
+      longitude_max: 277.0
+  event_type: heat_wave
+- case_id_number: 71
+  title: June 2024 Northeast US
+  start_date: 2024-06-17 00:00:00
+  end_date: 2024-06-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 37.0
+      latitude_max: 50.75
+      longitude_min: 272.0
+      longitude_max: 307.25
+  event_type: heat_wave
+- case_id_number: 72
+  title: July 2024 Southwest US
+  start_date: 2024-07-04 00:00:00
+  end_date: 2024-07-10 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 30.0
+      latitude_max: 42.75
+      longitude_min: 235.5
+      longitude_max: 248.75
+  event_type: heat_wave
+- case_id_number: 73
+  title: July 2024 Northeast US
+  start_date: 2024-07-07 00:00:00
+  end_date: 2024-07-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.75
+      latitude_max: 47.5
+      longitude_min: 281.0
+      longitude_max: 300.0
+  event_type: heat_wave
+- case_id_number: 74
+  title: July 2024 Mid-Atlantic US
+  start_date: 2024-07-15 00:00:00
+  end_date: 2024-07-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.25
+      latitude_max: 44.75
+      longitude_min: 280.0
+      longitude_max: 292.75
+  event_type: heat_wave
+- case_id_number: 75
+  title: August 2024 Midwest US
+  start_date: 2024-08-25 00:00:00
+  end_date: 2024-08-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.0
+      latitude_max: 46.25
+      longitude_min: 265.5
+      longitude_max: 278.25
+  event_type: heat_wave
+- case_id_number: 76
+  title: July 2024 Antarctica
+  start_date: 2024-07-01 00:00:00
+  end_date: 2024-07-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: -68.75
+      longitude_min: -9.5
+      longitude_max: 39.5
+  event_type: heat_wave
+- case_id_number: 77
+  title: August 2024 Canada
+  start_date: 2024-08-09 00:00:00
+  end_date: 2024-08-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 54.5
+      latitude_max: 73.5
+      longitude_min: 231.5
+      longitude_max: 272.5
+  event_type: heat_wave
+- case_id_number: 78
+  title: August 2024 Australia
+  start_date: 2024-08-22 00:00:00
+  end_date: 2024-08-30 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -32.5
+      latitude_max: -9.5
+      longitude_min: 116.25
+      longitude_max: 144.5
+  event_type: heat_wave
+- case_id_number: 79
+  title: July/August 2024 Japan
+  start_date: 2024-07-25 00:00:00
+  end_date: 2024-08-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 31.25
+      latitude_max: 43.0
+      longitude_min: 123.0
+      longitude_max: 142.75
+  event_type: heat_wave
+- case_id_number: 80
+  title: June 2024 Saudi Arabia
+  start_date: 2024-06-16 00:00:00
+  end_date: 2024-06-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 19.0
+      latitude_max: 35.0
+      longitude_min: 30.0
+      longitude_max: 54.0
+  event_type: heat_wave
+- case_id_number: 81
+  title: August 2024 Europe
+  start_date: 2024-08-10 00:00:00
+  end_date: 2024-08-16 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.5
+      latitude_max: 55.25
+      longitude_min: 0.0
+      longitude_max: 25.75
+  event_type: heat_wave
+- case_id_number: 82
+  title: July 2024 Ukraine
+  start_date: 2024-07-12 00:00:00
+  end_date: 2024-07-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.0
+      latitude_max: 56.75
+      longitude_min: 14.25
+      longitude_max: 45.0
+  event_type: heat_wave
+- case_id_number: 83
+  title: June 2024 Europe
+  start_date: 2024-06-20 00:00:00
+  end_date: 2024-06-30 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 45.5
+      latitude_max: 63.25
+      longitude_min: -4.0
+      longitude_max: 25.75
+  event_type: heat_wave
+- case_id_number: 84
+  title: May 2024 Central Mexico
+  start_date: 2024-05-23 00:00:00
+  end_date: 2024-05-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 7.25
+      latitude_max: 29.75
+      longitude_min: 252.5
+      longitude_max: 283.25
+  event_type: heat_wave
+- case_id_number: 85
+  title: May 2024 Pakistan/India
+  start_date: 2024-05-23 00:00:00
+  end_date: 2024-05-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 23.5
+      latitude_max: 42.5
+      longitude_min: 71.5
+      longitude_max: 98.5
+  event_type: heat_wave
+- case_id_number: 86
+  title: August 2023 Chile
+  start_date: 2023-08-01 00:00:00
+  end_date: 2023-08-07 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -37.25
+      latitude_max: -19.0
+      longitude_min: 288.5
+      longitude_max: 305.75
+  event_type: heat_wave
+- case_id_number: 87
+  title: January 2024 Pacific Northwest
+  start_date: 2024-01-11 12:00:00
+  end_date: 2024-01-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 41.25
+      latitude_max: 66.0
+      longitude_min: 221.25
+      longitude_max: 262.0
+  event_type: freeze
+- case_id_number: 88
+  title: April 2022 Nevada
+  start_date: 2022-04-11 18:00:00
+  end_date: 2022-04-17 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.5
+      latitude_max: 65.25
+      longitude_min: 225.75
+      longitude_max: 268.5
+  event_type: freeze
+- case_id_number: 89
+  title: February/March 2021 New England
+  start_date: 2021-03-04 18:00:00
+  end_date: 2021-03-10 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 38.5
+      latitude_max: 47.0
+      longitude_min: 283.25
+      longitude_max: 291.5
+  event_type: freeze
+- case_id_number: 90
+  title: January 2024 Northern Europe
+  start_date: 2024-01-01 06:00:00
+  end_date: 2024-01-09 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 51.25
+      latitude_max: 70.5
+      longitude_min: 5.25
+      longitude_max: 42.5
+  event_type: freeze
+- case_id_number: 91
+  title: December 2022 Europe
+  start_date: 2022-12-10 06:00:00
+  end_date: 2022-12-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 46.25
+      latitude_max: 70.75
+      longitude_min: -9.75
+      longitude_max: 25.75
+  event_type: freeze
+- case_id_number: 92
+  title: April 2024 Europe
+  start_date: 2024-04-16 12:00:00
+  end_date: 2024-04-27 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.5
+      latitude_max: 71.0
+      longitude_min: -4.0
+      longitude_max: 26.75
+  event_type: freeze
+- case_id_number: 93
+  title: January 2023 North China
+  start_date: 2024-01-20 00:00:00
+  end_date: 2024-02-04 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 38.25
+      latitude_max: 47.0
+      longitude_min: 118.0
+      longitude_max: 130.75
+  event_type: freeze
+- case_id_number: 94
+  title: April 2024 Sweden
+  start_date: 2024-04-02 00:00:00
+  end_date: 2024-04-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 58.25
+      latitude_max: 71.0
+      longitude_min: 8.25
+      longitude_max: 31.0
+  event_type: freeze
+- case_id_number: 95
+  title: November 2024 West Coast US
+  start_date: 2024-11-20 00:00:00
+  end_date: 2024-11-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 27.8
+      latitude_max: 54.2
+      longitude_min: 222.4
+      longitude_max: 245.3
+  event_type: atmospheric_river
+- case_id_number: 96
+  title: October 2024 British Columbia
+  start_date: 2024-10-18 00:00:00
+  end_date: 2024-10-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 56.0
+      longitude_min: 217.3
+      longitude_max: 247.0
+  event_type: atmospheric_river
+- case_id_number: 97
+  title: September 2024 SW Alaska and British Columbia
+  start_date: 2024-09-22 00:00:00
+  end_date: 2024-09-24 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 37.8
+      latitude_max: 62.2
+      longitude_min: 212.1
+      longitude_max: 247.9
+  event_type: atmospheric_river
+- case_id_number: 98
+  title: October 2024 Bosnia
+  start_date: 2024-10-03 00:00:00
+  end_date: 2024-10-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 34.8
+      latitude_max: 50.5
+      longitude_min: 8.2
+      longitude_max: 27.8
+  event_type: atmospheric_river
+- case_id_number: 99
+  title: April 2024 Eastern Australia
+  start_date: 2024-04-05 00:00:00
+  end_date: 2024-04-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -45.2
+      latitude_max: -17.8
+      longitude_min: 139.7
+      longitude_max: 163.1
+  event_type: atmospheric_river
+- case_id_number: 100
+  title: April 2024 Persian Gulf
+  start_date: 2024-04-14 00:00:00
+  end_date: 2024-04-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 16.8
+      latitude_max: 31.2
+      longitude_min: 45.6
+      longitude_max: 65.4
+  event_type: atmospheric_river
+- case_id_number: 101
+  title: February 2024 Pacific Northwest
+  start_date: 2024-02-28 00:00:00
+  end_date: 2024-03-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.8
+      latitude_max: 51.2
+      longitude_min: 227.1
+      longitude_max: 240.9
+  event_type: atmospheric_river
+- case_id_number: 102
+  title: February 2024 California
+  start_date: 2024-02-18 00:00:00
+  end_date: 2024-02-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 49.2
+      longitude_min: 227.5
+      longitude_max: 243.3
+  event_type: atmospheric_river
+- case_id_number: 103
+  title: February 2024 California
+  start_date: 2024-02-03 00:00:00
+  end_date: 2024-02-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 49.2
+      longitude_min: 227.5
+      longitude_max: 252.5
+  event_type: atmospheric_river
+- case_id_number: 104
+  title: January 2024 Alaska to California
+  start_date: 2024-01-26 00:00:00
+  end_date: 2024-02-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 62.5
+      longitude_min: 202.5
+      longitude_max: 249.3
+  event_type: atmospheric_river
+- case_id_number: 105
+  title: January 2024 California and Arizona
+  start_date: 2024-01-22 00:00:00
+  end_date: 2024-01-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 45.2
+      longitude_min: 232.5
+      longitude_max: 249.5
+  event_type: atmospheric_river
+- case_id_number: 106
+  title: December 2023 East Coast Cyclone
+  start_date: 2023-12-17 00:00:00
+  end_date: 2023-12-19 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 17.8
+      latitude_max: 52.2
+      longitude_min: 267.6
+      longitude_max: 302.4
+  event_type: atmospheric_river
+- case_id_number: 107
+  title: June 2023 Northwest Cloudband
+  start_date: 2023-06-25 00:00:00
+  end_date: 2023-06-28 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -35.2
+      latitude_max: -7.8
+      longitude_min: 113.6
+      longitude_max: 147.7
+  event_type: atmospheric_river
+- case_id_number: 108
+  title: April 2023 Middle East
+  start_date: 2023-04-01 00:00:00
+  end_date: 2023-05-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 17.5
+      latitude_max: 40.2
+      longitude_min: 22.6
+      longitude_max: 55.1
+  event_type: atmospheric_river
+- case_id_number: 109
+  title: March 2023 California and Arizona
+  start_date: 2023-03-09 00:00:00
+  end_date: 2023-03-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 45.2
+      longitude_min: 232.5
+      longitude_max: 252.5
+  event_type: atmospheric_river
+- case_id_number: 110
+  title: March 2023 California
+  start_date: 2023-03-09 00:00:00
+  end_date: 2023-03-16 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 46.2
+      longitude_min: 227.5
+      longitude_max: 252.5
+  event_type: atmospheric_river
+- case_id_number: 111
+  title: February 2023 Pacific Northwest
+  start_date: 2023-02-17 00:00:00
+  end_date: 2023-02-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 41.3
+      latitude_max: 55.7
+      longitude_min: 226.9
+      longitude_max: 240.8
+  event_type: atmospheric_river
+- case_id_number: 112
+  title: January 2023 California
+  start_date: 2023-01-04 00:00:00
+  end_date: 2023-01-10 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 49.2
+      longitude_min: 227.5
+      longitude_max: 252.5
+  event_type: atmospheric_river
+- case_id_number: 113
+  title: December 2022 California
+  start_date: 2022-12-26 00:00:00
+  end_date: 2023-01-02 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.0
+      latitude_max: 56.2
+      longitude_min: 220.0
+      longitude_max: 252.8
+  event_type: atmospheric_river
+- case_id_number: 114
+  title: December 2022 Oregon and California
+  start_date: 2022-12-09 00:00:00
+  end_date: 2022-12-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 29.3
+      latitude_max: 50.0
+      longitude_min: 227.4
+      longitude_max: 243.6
+  event_type: atmospheric_river
+- case_id_number: 115
+  title: November 2022 - December 2022 California
+  start_date: 2022-11-29 00:00:00
+  end_date: 2022-12-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 27.3
+      latitude_max: 42.2
+      longitude_min: 227.4
+      longitude_max: 243.3
+  event_type: atmospheric_river
+- case_id_number: 116
+  title: October 2022 Victoria
+  start_date: 2022-10-13 00:00:00
+  end_date: 2022-10-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -53.2
+      latitude_max: -20.0
+      longitude_min: 116.4
+      longitude_max: 166.6
+  event_type: atmospheric_river
+- case_id_number: 117
+  title: October 2022 Victoria
+  start_date: 2022-10-30 00:00:00
+  end_date: 2022-10-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -53.2
+      latitude_max: -13.8
+      longitude_min: 116.4
+      longitude_max: 166.6
+  event_type: atmospheric_river
+- case_id_number: 118
+  title: November 2022 California
+  start_date: 2022-11-07 00:00:00
+  end_date: 2022-11-09 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 33.0
+      latitude_max: 49.2
+      longitude_min: 227.2
+      longitude_max: 247.8
+  event_type: atmospheric_river
+- case_id_number: 119
+  title: November 2022 Pacific Northwest
+  start_date: 2022-11-03 00:00:00
+  end_date: 2022-11-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 33.0
+      latitude_max: 54.5
+      longitude_min: 227.2
+      longitude_max: 247.8
+  event_type: atmospheric_river
+- case_id_number: 120
+  title: August 2022 Japan
+  start_date: 2022-08-01 00:00:00
+  end_date: 2022-09-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 27.8
+      latitude_max: 52.2
+      longitude_min: 122.4
+      longitude_max: 152.6
+  event_type: atmospheric_river
+- case_id_number: 121
+  title: June 2022 Pacific Northwest, Wyoming, and Montana
+  start_date: 2022-06-09 00:00:00
+  end_date: 2022-06-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 57.2
+      longitude_min: 227.3
+      longitude_max: 262.7
+  event_type: atmospheric_river
+- case_id_number: 122
+  title: March 2022 East Antarctica
+  start_date: 2022-03-15 00:00:00
+  end_date: 2022-03-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -71.0
+      latitude_max: -62.8
+      longitude_min: 101.3
+      longitude_max: 146.9
+  event_type: atmospheric_river
+- case_id_number: 123
+  title: February 2022 Southeast QLD / Northern NSW
+  start_date: 2022-02-26 00:00:00
+  end_date: 2022-03-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -37.2
+      latitude_max: -17.8
+      longitude_min: 142.3
+      longitude_max: 162.7
+  event_type: atmospheric_river
+- case_id_number: 124
+  title: February 2022 Pacific Northwest
+  start_date: 2022-02-26 00:00:00
+  end_date: 2022-03-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 34.8
+      latitude_max: 55.7
+      longitude_min: 227.2
+      longitude_max: 247.3
+  event_type: atmospheric_river
+- case_id_number: 125
+  title: January 2022 Norway and Sweden
+  start_date: 2022-01-14 00:00:00
+  end_date: 2022-01-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 50.8
+      latitude_max: 72.2
+      longitude_min: 356.3
+      longitude_max: 28.7
+  event_type: atmospheric_river
+- case_id_number: 126
+  title: January 2022 Colorado
+  start_date: 2022-01-04 00:00:00
+  end_date: 2022-01-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 29.8
+      latitude_max: 39.2
+      longitude_min: 251.3
+      longitude_max: 265.7
+  event_type: atmospheric_river
+- case_id_number: 127
+  title: January 2022 Pacific Northwest
+  start_date: 2022-01-02 00:00:00
+  end_date: 2022-01-09 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 57.2
+      longitude_min: 227.3
+      longitude_max: 245.7
+  event_type: atmospheric_river
+- case_id_number: 128
+  title: December 2021 - January 2022 Western US
+  start_date: 2021-12-22 00:00:00
+  end_date: 2022-01-02 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 24.8
+      latitude_max: 50.0
+      longitude_min: 227.5
+      longitude_max: 262.5
+  event_type: atmospheric_river
+- case_id_number: 129
+  title: December 2021 Pacific NW
+  start_date: 2021-12-10 00:00:00
+  end_date: 2021-12-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 53.5
+      longitude_min: 227.3
+      longitude_max: 243.7
+  event_type: atmospheric_river
+- case_id_number: 130
+  title: November 2021 Pacific Northwest
+  start_date: 2021-11-10 00:00:00
+  end_date: 2021-11-17 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 56.2
+      longitude_min: 227.3
+      longitude_max: 257.7
+  event_type: atmospheric_river
+- case_id_number: 131
+  title: October 2021 California
+  start_date: 2021-10-19 00:00:00
+  end_date: 2021-10-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.3
+      latitude_max: 49.2
+      longitude_min: 227.4
+      longitude_max: 251.1
+  event_type: atmospheric_river
+- case_id_number: 132
+  title: September 2021 Pacific Northwest
+  start_date: 2021-09-16 00:00:00
+  end_date: 2021-09-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 33.5
+      latitude_max: 57.2
+      longitude_min: 227.2
+      longitude_max: 257.8
+  event_type: atmospheric_river
+- case_id_number: 133
+  title: June 2021 Bloomington Indiana
+  start_date: 2021-06-18 00:00:00
+  end_date: 2021-06-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 31.8
+      latitude_max: 47.2
+      longitude_min: 265.3
+      longitude_max: 281.7
+  event_type: atmospheric_river
+- case_id_number: 134
+  title: June 2021 Alaska Trans-Pacific
+  start_date: 2021-06-24 00:00:00
+  end_date: 2021-06-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 47.8
+      latitude_max: 63.7
+      longitude_min: 181.5
+      longitude_max: 238.5
+  event_type: atmospheric_river
+- case_id_number: 135
+  title: March 2021 Sydney
+  start_date: 2021-03-17 00:00:00
+  end_date: 2021-03-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -41.2
+      latitude_max: -25.8
+      longitude_min: 142.1
+      longitude_max: 159.9
+  event_type: atmospheric_river
+- case_id_number: 136
+  title: February 2021 Pacific Northwest
+  start_date: 2021-02-21 00:00:00
+  end_date: 2021-02-24 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 34.5
+      latitude_max: 56.2
+      longitude_min: 227.2
+      longitude_max: 241.3
+  event_type: atmospheric_river
+- case_id_number: 137
+  title: January 2021 Pacific NW
+  start_date: 2021-01-11 00:00:00
+  end_date: 2021-01-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 56.2
+      longitude_min: 227.3
+      longitude_max: 252.0
+  event_type: atmospheric_river
+- case_id_number: 138
+  title: January 2021 Pacific Northwest
+  start_date: 2021-01-01 00:00:00
+  end_date: 2021-01-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 56.2
+      longitude_min: 227.3
+      longitude_max: 241.7
+  event_type: atmospheric_river
+- case_id_number: 139
+  title: December 2020 Pacific Northwest
+  start_date: 2020-12-17 00:00:00
+  end_date: 2020-12-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 55.5
+      longitude_min: 227.3
+      longitude_max: 247.0
+  event_type: atmospheric_river
+- case_id_number: 140
+  title: December 2020 Western US
+  start_date: 2020-12-11 00:00:00
+  end_date: 2020-12-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 30.0
+      latitude_max: 55.5
+      longitude_min: 227.3
+      longitude_max: 246.9
+  event_type: atmospheric_river
+- case_id_number: 141
+  title: December 2020 Alaska
+  start_date: 2020-12-01 00:00:00
+  end_date: 2020-12-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 47.8
+      latitude_max: 63.2
+      longitude_min: 191.5
+      longitude_max: 238.5
+  event_type: atmospheric_river
+- case_id_number: 142
+  title: November 2020 Western US
+  start_date: 2020-11-16 00:00:00
+  end_date: 2020-11-19 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 28.8
+      latitude_max: 54.7
+      longitude_min: 227.4
+      longitude_max: 243.9
+  event_type: atmospheric_river
+- case_id_number: 143
+  title: October 2020 Western Alps
+  start_date: 2020-10-02 00:00:00
+  end_date: 2020-10-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.8
+      latitude_max: 54.2
+      longitude_min: 357.1
+      longitude_max: 16.9
+  event_type: atmospheric_river
+- case_id_number: 144
+  title: September 2020 Pacific Northwest
+  start_date: 2020-09-23 00:00:00
+  end_date: 2020-09-28 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 34.8
+      latitude_max: 57.2
+      longitude_min: 227.2
+      longitude_max: 257.8
+  event_type: atmospheric_river
+- case_id_number: 145
+  title: September 2020 Pacific Northwest
+  start_date: 2020-09-14 00:00:00
+  end_date: 2020-09-19 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 53.5
+      longitude_min: 227.3
+      longitude_max: 240.5
+  event_type: atmospheric_river
+- case_id_number: 146
+  title: May 2020 Western US
+  start_date: 2020-05-16 00:00:00
+  end_date: 2020-05-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 28.0
+      latitude_max: 54.0
+      longitude_min: 227.4
+      longitude_max: 241.6
+  event_type: atmospheric_river
+- case_id_number: 147
+  title: March 2020 Southwestern US
+  start_date: 2020-03-09 00:00:00
+  end_date: 2020-03-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 22.8
+      latitude_max: 39.2
+      longitude_min: 232.5
+      longitude_max: 262.5
+  event_type: atmospheric_river
+- case_id_number: 148
+  title: February 2020 Southwestern US
+  start_date: 2020-02-21 00:00:00
+  end_date: 2020-02-24 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 22.8
+      latitude_max: 36.0
+      longitude_min: 235.8
+      longitude_max: 262.5
+  event_type: atmospheric_river
+- case_id_number: 149
+  title: February 2020 Western US
+  start_date: 2020-02-04 00:00:00
+  end_date: 2020-02-09 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 38.8
+      latitude_max: 55.2
+      longitude_min: 227.0
+      longitude_max: 250.7
+  event_type: atmospheric_river
+- case_id_number: 150
+  title: January 2020 Pacific Northwest
+  start_date: 2020-01-26 00:00:00
+  end_date: 2020-02-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.8
+      latitude_max: 56.7
+      longitude_min: 227.3
+      longitude_max: 250.5
+  event_type: atmospheric_river
+- case_id_number: 151
+  title: Milton
+  start_date: 2024-10-02 18:00:00
+  end_date: 2024-10-13 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 18.6
+      latitude_max: 31.7
+      longitude_min: 262.1
+      longitude_max: 296.5
+  event_type: tropical_cyclone
+- case_id_number: 152
+  title: Helene
+  start_date: 2024-09-21 12:00:00
+  end_date: 2024-09-30 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 15.0
+      latitude_max: 40.3
+      longitude_min: 269.5
+      longitude_max: 280.7
+  event_type: tropical_cyclone
+- case_id_number: 153
+  title: Francine
+  start_date: 2024-09-06 18:00:00
+  end_date: 2024-09-16 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 19.2
+      latitude_max: 38.1
+      longitude_min: 261.4
+      longitude_max: 272.7
+  event_type: tropical_cyclone
+- case_id_number: 154
+  title: Debby
+  start_date: 2024-07-31 12:00:00
+  end_date: 2024-08-12 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 18.2
+      latitude_max: 52.2
+      longitude_min: 273.2
+      longitude_max: 302.9
+  event_type: tropical_cyclone
+- case_id_number: 155
+  title: Beryl
+  start_date: 2024-06-26 12:00:00
+  end_date: 2024-07-13 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 6.7
+      latitude_max: 45.3
+      longitude_min: 261.7
+      longitude_max: 322.7
+  event_type: tropical_cyclone
+- case_id_number: 156
+  title: John
+  start_date: 2024-09-20 12:00:00
+  end_date: 2024-09-29 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 11.6
+      latitude_max: 20.5
+      longitude_min: 254.5
+      longitude_max: 263.9
+  event_type: tropical_cyclone
+- case_id_number: 157
+  title: Hone
+  start_date: 2024-08-17 12:00:00
+  end_date: 2024-09-03 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.5
+      latitude_max: 28.3
+      longitude_min: 177.9
+      longitude_max: 231.5
+  event_type: tropical_cyclone
+- case_id_number: 158
+  title: Trami
+  start_date: 2024-10-18 00:00:00
+  end_date: 2024-10-31 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.9
+      latitude_max: 19.9
+      longitude_min: 105.0
+      longitude_max: 139.8
+  event_type: tropical_cyclone
+- case_id_number: 159
+  title: Prapiroon (Butchoy)
+  start_date: 2024-07-17 06:00:00
+  end_date: 2024-07-27 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 12.1
+      latitude_max: 24.0
+      longitude_min: 104.3
+      longitude_max: 120.2
+  event_type: tropical_cyclone
+- case_id_number: 160
+  title: Gaemi (Carina)
+  start_date: 2024-07-17 00:00:00
+  end_date: 2024-07-30 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 9.4
+      latitude_max: 32.0
+      longitude_min: 110.6
+      longitude_max: 135.5
+  event_type: tropical_cyclone
+- case_id_number: 161
+  title: Shanshan
+  start_date: 2024-08-19 00:00:00
+  end_date: 2024-09-03 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 13.8
+      latitude_max: 37.8
+      longitude_min: 127.7
+      longitude_max: 146.3
+  event_type: tropical_cyclone
+- case_id_number: 162
+  title: Yagi (Enteng)
+  start_date: 2024-08-30 00:00:00
+  end_date: 2024-09-10 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 9.9
+      latitude_max: 23.8
+      longitude_min: 101.8
+      longitude_max: 128.7
+  event_type: tropical_cyclone
+- case_id_number: 163
+  title: Bebinca (Ferdie)
+  start_date: 2024-09-07 12:00:00
+  end_date: 2024-09-20 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 7.9
+      latitude_max: 35.7
+      longitude_min: 112.4
+      longitude_max: 149.9
+  event_type: tropical_cyclone
+- case_id_number: 164
+  title: Soulik (Gener)
+  start_date: 2024-09-13 06:00:00
+  end_date: 2024-09-22 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 14.3
+      latitude_max: 19.8
+      longitude_min: 102.2
+      longitude_max: 128.8
+  event_type: tropical_cyclone
+- case_id_number: 165
+  title: Krathon
+  start_date: 2024-09-24 12:00:00
+  end_date: 2024-10-05 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 16.2
+      latitude_max: 25.0
+      longitude_min: 116.8
+      longitude_max: 129.9
+  event_type: tropical_cyclone
+- case_id_number: 166
+  title: Asna
+  start_date: 2024-08-28 00:00:00
+  end_date: 2024-09-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 18.8
+      latitude_max: 26.1
+      longitude_min: 59.0
+      longitude_max: 70.7
+  event_type: tropical_cyclone
+- case_id_number: 167
+  title: Kirrily
+  start_date: 2024-01-15 00:00:00
+  end_date: 2024-02-07 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -31.8
+      latitude_max: -11.3
+      longitude_min: 135.3
+      longitude_max: 159.0
+  event_type: tropical_cyclone
+- case_id_number: 168
+  title: Belal
+  start_date: 2024-01-10 12:00:00
+  end_date: 2024-01-21 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -26.5
+      latitude_max: -11.1
+      longitude_min: 50.8
+      longitude_max: 68.1
+  event_type: tropical_cyclone
+- case_id_number: 169
+  title: Alvaro
+  start_date: 2023-12-30 00:00:00
+  end_date: 2024-01-06 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -31.3
+      latitude_max: -18.5
+      longitude_min: 38.2
+      longitude_max: 59.9
+  event_type: tropical_cyclone
+- case_id_number: 170
+  title: Franklin
+  start_date: 2023-08-17 18:00:00
+  end_date: 2023-09-11 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.6
+      latitude_max: 50.8
+      longitude_min: 286.3
+      longitude_max: 346.3
+  event_type: tropical_cyclone
+- case_id_number: 171
+  title: Harold
+  start_date: 2020-03-30 00:00:00
+  end_date: 2020-04-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -37.2
+      latitude_max: -7.0
+      longitude_min: 152.2
+      longitude_max: 214.7
+  event_type: tropical_cyclone
+- case_id_number: 172
+  title: Idalia
+  start_date: 2023-08-24 12:00:00
+  end_date: 2023-09-10 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 17.6
+      latitude_max: 47.4
+      longitude_min: 270.8
+      longitude_max: 304.9
+  event_type: tropical_cyclone
+- case_id_number: 173
+  title: Lee
+  start_date: 2023-09-03 12:00:00
+  end_date: 2023-09-20 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.0
+      latitude_max: 55.2
+      longitude_min: 289.4
+      longitude_max: 322.7
+  event_type: tropical_cyclone
+- case_id_number: 174
+  title: Ophelia
+  start_date: 2023-09-19 12:00:00
+  end_date: 2023-09-26 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.3
+      latitude_max: 41.1
+      longitude_min: 279.8
+      longitude_max: 287.5
+  event_type: tropical_cyclone
+- case_id_number: 175
+  title: Tammy
+  start_date: 2023-10-16 18:00:00
+  end_date: 2023-11-02 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.7
+      latitude_max: 35.5
+      longitude_min: 293.7
+      longitude_max: 314.2
+  event_type: tropical_cyclone
+- case_id_number: 176
+  title: Hilary
+  start_date: 2023-08-14 06:00:00
+  end_date: 2023-08-22 21:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 9.7
+      latitude_max: 33.6
+      longitude_min: 241.6
+      longitude_max: 261.5
+  event_type: tropical_cyclone
+- case_id_number: 177
+  title: Lidia
+  start_date: 2023-10-01 00:00:00
+  end_date: 2023-10-13 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 9.0
+      latitude_max: 26.7
+      longitude_min: 244.9
+      longitude_max: 261.8
+  event_type: tropical_cyclone
+- case_id_number: 178
+  title: Max
+  start_date: 2023-10-06 00:00:00
+  end_date: 2023-10-12 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 11.6
+      latitude_max: 20.1
+      longitude_min: 256.1
+      longitude_max: 262.4
+  event_type: tropical_cyclone
+- case_id_number: 179
+  title: Norma
+  start_date: 2023-10-15 06:00:00
+  end_date: 2023-10-25 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.2
+      latitude_max: 26.9
+      longitude_min: 247.4
+      longitude_max: 257.9
+  event_type: tropical_cyclone
+- case_id_number: 180
+  title: Otis
+  start_date: 2023-10-19 00:00:00
+  end_date: 2023-10-27 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 7.1
+      latitude_max: 20.8
+      longitude_min: 257.0
+      longitude_max: 266.5
+  event_type: tropical_cyclone
+- case_id_number: 181
+  title: Mawar (Betty)
+  start_date: 2023-05-16 00:00:00
+  end_date: 2023-06-05 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 2.2
+      latitude_max: 36.3
+      longitude_min: 122.7
+      longitude_max: 153.2
+  event_type: tropical_cyclone
+- case_id_number: 182
+  title: Talim (Dodong)
+  start_date: 2023-07-11 06:00:00
+  end_date: 2023-07-21 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 13.6
+      latitude_max: 25.6
+      longitude_min: 101.0
+      longitude_max: 125.1
+  event_type: tropical_cyclone
+- case_id_number: 183
+  title: Doksuri (Egay)
+  start_date: 2023-07-18 06:00:00
+  end_date: 2023-08-02 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.5
+      latitude_max: 39.6
+      longitude_min: 110.5
+      longitude_max: 136.0
+  event_type: tropical_cyclone
+- case_id_number: 184
+  title: Khanun (Falcon)
+  start_date: 2023-07-24 06:00:00
+  end_date: 2023-08-13 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 6.7
+      latitude_max: 41.3
+      longitude_min: 121.8
+      longitude_max: 143.6
+  event_type: tropical_cyclone
+- case_id_number: 185
+  title: Lan
+  start_date: 2023-08-04 00:00:00
+  end_date: 2023-08-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 21.1
+      latitude_max: 50.0
+      longitude_min: 132.3
+      longitude_max: 152.0
+  event_type: tropical_cyclone
+- case_id_number: 186
+  title: Saola (Goring)
+  start_date: 2023-08-20 00:00:00
+  end_date: 2023-09-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 13.8
+      latitude_max: 24.2
+      longitude_min: 106.8
+      longitude_max: 130.3
+  event_type: tropical_cyclone
+- case_id_number: 187
+  title: Haikui
+  start_date: 2023-08-25 00:00:00
+  end_date: 2023-09-12 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 15.9
+      latitude_max: 25.9
+      longitude_min: 107.0
+      longitude_max: 146.8
+  event_type: tropical_cyclone
+- case_id_number: 188
+  title: Koinu (Jenny)
+  start_date: 2023-09-25 12:00:00
+  end_date: 2023-10-12 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 12.0
+      latitude_max: 24.4
+      longitude_min: 109.0
+      longitude_max: 146.1
+  event_type: tropical_cyclone
+- case_id_number: 189
+  title: Mocha
+  start_date: 2023-05-06 18:00:00
+  end_date: 2023-05-17 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 5.4
+      latitude_max: 27.3
+      longitude_min: 85.7
+      longitude_max: 100.1
+  event_type: tropical_cyclone
+- case_id_number: 190
+  title: Biparjoy
+  start_date: 2023-06-03 00:00:00
+  end_date: 2023-06-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 8.1
+      latitude_max: 28.5
+      longitude_min: 63.6
+      longitude_max: 78.3
+  event_type: tropical_cyclone
+- case_id_number: 191
+  title: Hamoon
+  start_date: 2023-10-18 06:00:00
+  end_date: 2023-10-27 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.2
+      latitude_max: 24.9
+      longitude_min: 83.5
+      longitude_max: 94.8
+  event_type: tropical_cyclone
+- case_id_number: 192
+  title: Freddy
+  start_date: 2023-02-02 12:00:00
+  end_date: 2023-03-15 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -26.3
+      latitude_max: -8.9
+      longitude_min: 29.0
+      longitude_max: 121.6
+  event_type: tropical_cyclone
+- case_id_number: 193
+  title: Jasper
+  start_date: 2023-11-30 12:00:00
+  end_date: 2023-12-19 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -20.0
+      latitude_max: -5.3
+      longitude_min: 139.0
+      longitude_max: 167.2
+  event_type: tropical_cyclone
+- case_id_number: 194
+  title: Alex
+  start_date: 2022-05-31 18:00:00
+  end_date: 2022-06-08 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 19.1
+      latitude_max: 36.9
+      longitude_min: 270.1
+      longitude_max: 300.2
+  event_type: tropical_cyclone
+- case_id_number: 195
+  title: Fiona
+  start_date: 2022-09-12 06:00:00
+  end_date: 2022-09-29 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 13.5
+      latitude_max: 66.2
+      longitude_min: 285.9
+      longitude_max: 314.4
+  event_type: tropical_cyclone
+- case_id_number: 196
+  title: Ian
+  start_date: 2022-09-20 18:00:00
+  end_date: 2022-10-03 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.1
+      latitude_max: 37.5
+      longitude_min: 274.0
+      longitude_max: 296.0
+  event_type: tropical_cyclone
+- case_id_number: 197
+  title: Julia
+  start_date: 2022-10-04 12:00:00
+  end_date: 2022-10-12 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 9.2
+      latitude_max: 15.9
+      longitude_min: 267.8
+      longitude_max: 295.8
+  event_type: tropical_cyclone
+- case_id_number: 198
+  title: Nicole
+  start_date: 2022-11-04 12:00:00
+  end_date: 2022-11-13 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 18.4
+      latitude_max: 37.6
+      longitude_min: 272.9
+      longitude_max: 295.7
+  event_type: tropical_cyclone
+- case_id_number: 199
+  title: Agatha
+  start_date: 2022-05-25 18:00:00
+  end_date: 2022-06-03 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.6
+      latitude_max: 20.5
+      longitude_min: 258.6
+      longitude_max: 267.7
+  event_type: tropical_cyclone
+- case_id_number: 200
+  title: Kay
+  start_date: 2022-09-02 12:00:00
+  end_date: 2022-09-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 11.9
+      latitude_max: 33.4
+      longitude_min: 235.8
+      longitude_max: 261.4
+  event_type: tropical_cyclone
+- case_id_number: 201
+  title: Roslyn
+  start_date: 2022-10-18 00:00:00
+  end_date: 2022-10-26 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 12.9
+      latitude_max: 27.8
+      longitude_min: 251.0
+      longitude_max: 261.4
+  event_type: tropical_cyclone
+- case_id_number: 202
+  title: Megi (Agaton)
+  start_date: 2022-04-06 12:00:00
+  end_date: 2022-04-15 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 7.7
+      latitude_max: 13.5
+      longitude_min: 122.4
+      longitude_max: 130.7
+  event_type: tropical_cyclone
+- case_id_number: 203
+  title: Hinnamnor (Henry)
+  start_date: 2022-08-25 00:00:00
+  end_date: 2022-09-10 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 19.1
+      latitude_max: 62.0
+      longitude_min: 122.2
+      longitude_max: 153.4
+  event_type: tropical_cyclone
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+  title: Muifa (Inday)
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+  end_date: 2022-09-18 21:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 45.6
+      longitude_min: 118.0
+      longitude_max: 147.7
+  event_type: tropical_cyclone
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+  title: Nanmadol (Josie)
+  start_date: 2022-09-09 06:00:00
+  end_date: 2022-09-22 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 41.1
+      longitude_min: 128.0
+      longitude_max: 149.7
+  event_type: tropical_cyclone
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+  title: Noru (Karding)
+  start_date: 2022-09-19 00:00:00
+  end_date: 2022-10-01 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 20.2
+      longitude_min: 101.0
+      longitude_max: 136.8
+  event_type: tropical_cyclone
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+  title: Nalgae (Paeng)
+  start_date: 2022-10-24 00:00:00
+  end_date: 2022-11-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 24.3
+      longitude_min: 111.0
+      longitude_max: 136.6
+  event_type: tropical_cyclone
+- case_id_number: 208
+  title: Sitrang
+  start_date: 2022-10-19 18:00:00
+  end_date: 2022-10-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 26.7
+      longitude_min: 85.8
+      longitude_max: 95.6
+  event_type: tropical_cyclone
+- case_id_number: 209
+  title: Ana
+  start_date: 2021-01-26 12:00:00
+  end_date: 2021-02-05 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: -13.3
+      longitude_min: 168.8
+      longitude_max: 191.7
+  event_type: tropical_cyclone
+- case_id_number: 210
+  title: Batsirai
+  start_date: 2022-01-22 00:00:00
+  end_date: 2022-02-12 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: -7.2
+      longitude_min: 38.5
+      longitude_max: 94.0
+  event_type: tropical_cyclone
+- case_id_number: 211
+  title: Gombe
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+  end_date: 2022-03-19 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: -12.7
+      longitude_min: 34.2
+      longitude_max: 57.6
+  event_type: tropical_cyclone
+- case_id_number: 212
+  title: Seth
+  start_date: 2021-12-21 12:00:00
+  end_date: 2022-01-09 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: -6.5
+      longitude_min: 127.3
+      longitude_max: 161.5
+  event_type: tropical_cyclone
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+  title: Blas
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+  end_date: 2022-06-24 18:00:00
+  location:
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+    parameters:
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+      latitude_max: 21.5
+      longitude_min: 240.9
+      longitude_max: 260.3
+  event_type: tropical_cyclone
+- case_id_number: 214
+  title: Elsa
+  start_date: 2021-06-28 18:00:00
+  end_date: 2021-07-12 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 49.0
+      longitude_min: 274.1
+      longitude_max: 319.5
+  event_type: tropical_cyclone
+- case_id_number: 215
+  title: Fred
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+  end_date: 2021-08-22 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 44.9
+      longitude_min: 271.8
+      longitude_max: 303.6
+  event_type: tropical_cyclone
+- case_id_number: 216
+  title: Grace
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+  end_date: 2021-08-23 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 23.0
+      longitude_min: 259.7
+      longitude_max: 315.6
+  event_type: tropical_cyclone
+- case_id_number: 217
+  title: Henri
+  start_date: 2021-08-13 00:00:00
+  end_date: 2021-08-26 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 44.5
+      longitude_min: 283.0
+      longitude_max: 299.9
+  event_type: tropical_cyclone
+- case_id_number: 218
+  title: Ida
+  start_date: 2021-08-24 12:00:00
+  end_date: 2021-09-06 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 51.0
+      longitude_min: 266.8
+      longitude_max: 299.9
+  event_type: tropical_cyclone
+- case_id_number: 219
+  title: Larry
+  start_date: 2021-08-29 18:00:00
+  end_date: 2021-09-13 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 57.5
+      longitude_min: 295.4
+      longitude_max: 341.7
+  event_type: tropical_cyclone
+- case_id_number: 220
+  title: Nicholas
+  start_date: 2021-09-10 12:00:00
+  end_date: 2021-09-19 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 33.0
+      longitude_min: 260.9
+      longitude_max: 270.8
+  event_type: tropical_cyclone
+- case_id_number: 221
+  title: Claudette
+  start_date: 2021-06-15 18:00:00
+  end_date: 2021-06-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 46.3
+      longitude_min: 265.4
+      longitude_max: 302.8
+  event_type: tropical_cyclone
+- case_id_number: 222
+  title: Nora
+  start_date: 2021-08-22 06:00:00
+  end_date: 2021-09-01 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 26.5
+      longitude_min: 250.3
+      longitude_max: 267.3
+  event_type: tropical_cyclone
+- case_id_number: 223
+  title: Olaf
+  start_date: 2021-09-04 18:00:00
+  end_date: 2021-09-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 26.9
+      longitude_min: 242.8
+      longitude_max: 256.6
+  event_type: tropical_cyclone
+- case_id_number: 224
+  title: Pamela
+  start_date: 2021-10-08 06:00:00
+  end_date: 2021-10-15 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 27.3
+      longitude_min: 248.4
+      longitude_max: 259.9
+  event_type: tropical_cyclone
+- case_id_number: 225
+  title: Surigae (Bising)
+  start_date: 2021-04-09 12:00:00
+  end_date: 2021-05-02 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 49.7
+      longitude_min: 122.5
+      longitude_max: 186.8
+  event_type: tropical_cyclone
+- case_id_number: 226
+  title: In-fa (Fabian)
+  start_date: 2021-07-14 00:00:00
+  end_date: 2021-08-02 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 42.4
+      longitude_min: 114.4
+      longitude_max: 137.6
+  event_type: tropical_cyclone
+- case_id_number: 227
+  title: Kompasu (Maring)
+  start_date: 2021-10-05 18:00:00
+  end_date: 2021-10-16 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 21.7
+      longitude_min: 103.4
+      longitude_max: 136.5
+  event_type: tropical_cyclone
+- case_id_number: 228
+  title: Rai
+  start_date: 2021-12-09 12:00:00
+  end_date: 2021-12-23 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 23.9
+      longitude_min: 108.3
+      longitude_max: 147.3
+  event_type: tropical_cyclone
+- case_id_number: 229
+  title: Tauktae
+  start_date: 2021-05-11 06:00:00
+  end_date: 2021-05-21 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 28.0
+      longitude_min: 68.6
+      longitude_max: 77.1
+  event_type: tropical_cyclone
+- case_id_number: 230
+  title: Yaas
+  start_date: 2021-05-21 00:00:00
+  end_date: 2021-05-29 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 26.9
+      longitude_min: 82.5
+      longitude_max: 92.3
+  event_type: tropical_cyclone
+- case_id_number: 231
+  title: Gulab and Shaheen
+  start_date: 2021-09-21 18:00:00
+  end_date: 2021-10-06 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 26.6
+      longitude_min: 53.7
+      longitude_max: 96.5
+  event_type: tropical_cyclone
+- case_id_number: 232
+  title: Eloise
+  start_date: 2021-01-09 18:00:00
+  end_date: 2021-01-26 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: -9.3
+      longitude_min: 28.8
+      longitude_max: 88.1
+  event_type: tropical_cyclone
+- case_id_number: 233
+  title: Amanda and Cristobal
+  start_date: 2020-05-28 18:00:00
+  end_date: 2020-06-02 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 55.6
+      longitude_min: 265.0
+      longitude_max: 283.3
+  event_type: tropical_cyclone
+- case_id_number: 234
+  title: Hanna
+  start_date: 2020-07-21 00:00:00
+  end_date: 2020-07-28 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 29.4
+      longitude_min: 257.2
+      longitude_max: 274.2
+  event_type: tropical_cyclone
+- case_id_number: 235
+  title: Isaias
+  start_date: 2020-07-26 12:00:00
+  end_date: 2020-08-07 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 48.9
+      longitude_min: 277.7
+      longitude_max: 308.3
+  event_type: tropical_cyclone
+- case_id_number: 236
+  title: Laura
+  start_date: 2020-08-18 00:00:00
+  end_date: 2020-08-31 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 40.5
+      longitude_min: 264.3
+      longitude_max: 315.0
+  event_type: tropical_cyclone
+- case_id_number: 237
+  title: Nana
+  start_date: 2020-08-30 06:00:00
+  end_date: 2020-09-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 19.3
+      longitude_min: 266.3
+      longitude_max: 287.1
+  event_type: tropical_cyclone
+- case_id_number: 238
+  title: Paulette
+  start_date: 2020-09-05 00:00:00
+  end_date: 2020-09-30 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 49.3
+      longitude_min: 292.8
+      longitude_max: 345.7
+  event_type: tropical_cyclone
+- case_id_number: 239
+  title: Sally
+  start_date: 2020-09-09 18:00:00
+  end_date: 2020-09-20 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 36.3
+      longitude_min: 269.4
+      longitude_max: 283.9
+  event_type: tropical_cyclone
+- case_id_number: 240
+  title: Teddy
+  start_date: 2020-09-10 06:00:00
+  end_date: 2020-09-26 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 55.0
+      longitude_min: 293.4
+      longitude_max: 330.9
+  event_type: tropical_cyclone
+- case_id_number: 241
+  title: Delta
+  start_date: 2020-10-02 18:00:00
+  end_date: 2020-10-13 18:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 37.3
+      longitude_min: 263.9
+      longitude_max: 286.1
+  event_type: tropical_cyclone
+- case_id_number: 242
+  title: Zeta
+  start_date: 2020-10-22 12:00:00
+  end_date: 2020-11-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 41.7
+      longitude_min: 265.9
+      longitude_max: 290.7
+  event_type: tropical_cyclone
+- case_id_number: 243
+  title: Eta
+  start_date: 2020-10-29 18:00:00
+  end_date: 2020-11-16 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 37.9
+      longitude_min: 269.9
+      longitude_max: 293.1
+  event_type: tropical_cyclone
+- case_id_number: 244
+  title: Iota
+  start_date: 2020-11-10 12:00:00
+  end_date: 2020-11-20 12:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 17.7
+      longitude_min: 268.7
+      longitude_max: 291.4
+  event_type: tropical_cyclone
+- case_id_number: 245
+  title: Genevieve
+  start_date: 2020-08-14 12:00:00
+  end_date: 2020-08-24 06:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 29.2
+      longitude_min: 240.3
+      longitude_max: 265.8
+  event_type: tropical_cyclone
+- case_id_number: 246
+  title: Hagupit (Dindo)
+  start_date: 2020-07-29 06:00:00
+  end_date: 2020-08-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 54.5
+      longitude_min: 117.9
+      longitude_max: 182.5
+  event_type: tropical_cyclone
+- case_id_number: 247
+  title: Maysak
+  start_date: 2020-08-24 00:00:00
+  end_date: 2020-09-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 55.4
+      longitude_min: 120.7
+      longitude_max: 136.2
+  event_type: tropical_cyclone
+- case_id_number: 248
+  title: Noul (Leon)
+  start_date: 2020-09-12 00:00:00
+  end_date: 2020-09-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 18.7
+      longitude_min: 98.2
+      longitude_max: 129.6
+  event_type: tropical_cyclone
+- case_id_number: 249
+  title: Linfa
+  start_date: 2020-10-05 06:00:00
+  end_date: 2020-10-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
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+      latitude_max: 17.3
+      longitude_min: 104.6
+      longitude_max: 127.1
+  event_type: tropical_cyclone
+- case_id_number: 250
+  title: Molave (Quinta)
+  start_date: 2020-10-19 18:00:00
+  end_date: 2020-10-31 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 6.5
+      latitude_max: 17.8
+      longitude_min: 101.8
+      longitude_max: 139.8
+  event_type: tropical_cyclone
+- case_id_number: 251
+  title: Goni (Rolly)
+  start_date: 2020-10-23 06:00:00
+  end_date: 2020-11-08 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 8.4
+      latitude_max: 18.9
+      longitude_min: 105.6
+      longitude_max: 146.2
+  event_type: tropical_cyclone
+- case_id_number: 252
+  title: Vamco (Ulysse)
+  start_date: 2020-11-06 00:00:00
+  end_date: 2020-11-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 5.8
+      latitude_max: 21.5
+      longitude_min: 101.3
+      longitude_max: 137.2
+  event_type: tropical_cyclone
+- case_id_number: 253
+  title: Remal
+  start_date: 2024-05-23 12:00:00
+  end_date: 2024-05-29 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 16.6
+      latitude_max: 26.4
+      longitude_min: 86.0
+      longitude_max: 92.8
+  event_type: tropical_cyclone
+- case_id_number: 254
+  title: Amphan
+  start_date: 2020-05-13 06:00:00
+  end_date: 2020-05-23 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 7.3
+      latitude_max: 27.6
+      longitude_min: 83.8
+      longitude_max: 92.4
+  event_type: tropical_cyclone
+- case_id_number: 255
+  title: Nisarga
+  start_date: 2020-05-29 18:00:00
+  end_date: 2020-06-06 06:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.7
+      latitude_max: 24.4
+      longitude_min: 68.8
+      longitude_max: 79.9
+  event_type: tropical_cyclone
+- case_id_number: 256
+  title: Damien
+  start_date: 2020-02-01 00:00:00
+  end_date: 2020-02-12 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -27.9
+      latitude_max: -13.8
+      longitude_min: 113.9
+      longitude_max: 131.8
+  event_type: tropical_cyclone
+- case_id_number: 257
+  title: January 2020 Australia
+  start_date: 2020-01-19 12:00:00
+  end_date: 2020-01-20 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -39.75
+      latitude_max: -29.5
+      longitude_min: 144.01
+      longitude_max: 155.74
+  event_type: severe_convection
+- case_id_number: 258
+  title: April 2020 Australia
+  start_date: 2020-04-02 12:00:00
+  end_date: 2020-04-03 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -41.75
+      latitude_max: -26.5
+      longitude_min: 138.03
+      longitude_max: 153.47
+  event_type: severe_convection
+- case_id_number: 259
+  title: May 2020 Australia
+  start_date: 2020-05-30 12:00:00
+  end_date: 2020-05-31 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -39.25
+      latitude_max: -28.75
+      longitude_min: 132.78
+      longitude_max: 143.72
+  event_type: severe_convection
+- case_id_number: 260
+  title: September 2020 Australia
+  start_date: 2020-09-24 12:00:00
+  end_date: 2020-09-25 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -38.5
+      latitude_max: -29.5
+      longitude_min: 146.03
+      longitude_max: 155.97
+  event_type: severe_convection
+- case_id_number: 261
+  title: October 2020 Australia
+  start_date: 2020-10-30 12:00:00
+  end_date: 2020-10-31 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -38.5
+      latitude_max: -23.25
+      longitude_min: 146.13
+      longitude_max: 157.87
+  event_type: severe_convection
+- case_id_number: 262
+  title: December 2020 Australia
+  start_date: 2020-12-04 12:00:00
+  end_date: 2020-12-05 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -36.5
+      latitude_max: -26.75
+      longitude_min: 142.1
+      longitude_max: 152.9
+  event_type: severe_convection
+- case_id_number: 263
+  title: September 2021 Australia
+  start_date: 2021-09-29 12:00:00
+  end_date: 2021-09-30 12:00:00
+  location:
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+  end_date: 2021-10-14 12:00:00
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+- case_id_number: 322
+  title: July 2022 Canada
+  start_date: 2022-07-19 12:00:00
+  end_date: 2022-07-20 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 46.5
+      latitude_max: 58.25
+      longitude_min: 239.06
+      longitude_max: 255.19
+  event_type: severe_convection
+- case_id_number: 323
+  title: July 2022 Canada
+  start_date: 2022-07-31 12:00:00
+  end_date: 2022-08-01 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 45.5
+      latitude_max: 58.5
+      longitude_min: 236.34
+      longitude_max: 258.41
+  event_type: severe_convection
+- case_id_number: 324
+  title: May 2023 Canada
+  start_date: 2023-05-31 12:00:00
+  end_date: 2023-06-01 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 28.25
+      latitude_max: 48.25
+      longitude_min: 248.38
+      longitude_max: 261.87
+  event_type: severe_convection
+- case_id_number: 325
+  title: June 2023 Canada
+  start_date: 2023-06-28 12:00:00
+  end_date: 2023-06-29 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 33.0
+      latitude_max: 56.25
+      longitude_min: 248.09
+      longitude_max: 278.91
+  event_type: severe_convection
+- case_id_number: 326
+  title: July 2023 Canada
+  start_date: 2023-07-13 12:00:00
+  end_date: 2023-07-14 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 29.25
+      latitude_max: 54.5
+      longitude_min: 249.48
+      longitude_max: 294.02
+  event_type: severe_convection
+- case_id_number: 327
+  title: July 2023 Canada
+  start_date: 2023-07-20 12:00:00
+  end_date: 2023-07-21 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 29.75
+      latitude_max: 49.0
+      longitude_min: 265.84
+      longitude_max: 287.16
+  event_type: severe_convection
+- case_id_number: 328
+  title: July 2023 Canada
+  start_date: 2023-07-22 12:00:00
+  end_date: 2023-07-23 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 26.0
+      latitude_max: 55.5
+      longitude_min: 246.28
+      longitude_max: 279.97
+  event_type: severe_convection
+- case_id_number: 329
+  title: July 2023 Canada
+  start_date: 2023-07-26 12:00:00
+  end_date: 2023-07-27 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 34.75
+      latitude_max: 56.5
+      longitude_min: 250.03
+      longitude_max: 284.47
+  event_type: severe_convection
+- case_id_number: 330
+  title: August 2023 Canada
+  start_date: 2023-08-03 12:00:00
+  end_date: 2023-08-04 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 38.25
+      latitude_max: 52.5
+      longitude_min: 266.29
+      longitude_max: 292.71
+  event_type: severe_convection
+- case_id_number: 331
+  title: June 2024 Canada
+  start_date: 2024-06-12 12:00:00
+  end_date: 2024-06-13 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.0
+      latitude_max: 57.0
+      longitude_min: 248.23
+      longitude_max: 274.52
+  event_type: severe_convection
+- case_id_number: 332
+  title: June 2024 Canada
+  start_date: 2024-06-22 12:00:00
+  end_date: 2024-06-23 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 36.0
+      latitude_max: 57.5
+      longitude_min: 247.71
+      longitude_max: 293.54
+  event_type: severe_convection
+- case_id_number: 333
+  title: June 2024 Canada
+  start_date: 2024-06-23 12:00:00
+  end_date: 2024-06-24 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 41.25
+      latitude_max: 58.25
+      longitude_min: 240.26
+      longitude_max: 274.24
+  event_type: severe_convection
+- case_id_number: 334
+  title: July 2024 Canada
+  start_date: 2024-07-11 12:00:00
+  end_date: 2024-07-12 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 32.5
+      latitude_max: 50.25
+      longitude_min: 251.25
+      longitude_max: 272.5
+  event_type: severe_convection
+- case_id_number: 335
+  title: July 2024 Canada
+  start_date: 2024-07-24 12:00:00
+  end_date: 2024-07-25 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.75
+      latitude_max: 58.75
+      longitude_min: 236.26
+      longitude_max: 252.74
+  event_type: severe_convection
+- case_id_number: 336
+  title: August 2024 Canada
+  start_date: 2024-08-05 12:00:00
+  end_date: 2024-08-06 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 37.0
+      latitude_max: 49.5
+      longitude_min: 259.41
+      longitude_max: 293.84
+  event_type: severe_convection
+- case_id_number: 337
+  title: August 2024 Canada
+  start_date: 2024-08-21 12:00:00
+  end_date: 2024-08-22 12:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 37.25
+      latitude_max: 58.25
+      longitude_min: 243.9
+      longitude_max: 265.35
+  event_type: severe_convection
diff --git a/src/extremeweatherbench/data/long_events.yaml b/src/extremeweatherbench/data/long_events.yaml
index d1cc905b..a3191d99 100644
--- a/src/extremeweatherbench/data/long_events.yaml
+++ b/src/extremeweatherbench/data/long_events.yaml
@@ -1,49 +1,48 @@
-cases:
-  - case_id_number: 1000
-    title: 5 Year Heat Wave Test
-    start_date: 2020-01-01 00:00:00
-    end_date: 2024-12-31 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -90.0
-        latitude_max: 90.0
-        longitude_min: 0
-        longitude_max: 359.75
-    event_type: heat_wave
-  - case_id_number: 1001
-    title: 5 Year Freeze Test
-    start_date: 2020-01-01 00:00:00
-    end_date: 2024-12-31 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -90.0
-        latitude_max: 90.0
-        longitude_min: 0
-        longitude_max: 359.75
-    event_type: freeze
-  - case_id_number: 1002
-    title: 2024 Heat Wave Test
-    start_date: 2024-01-01 00:00:00
-    end_date: 2024-12-31 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -90.0
-        latitude_max: 90.0
-        longitude_min: 0
-        longitude_max: 359.75
-    event_type: heat_wave
-  - case_id_number: 1003
-    title: 2024 Freeze Test
-    start_date: 2024-01-01 00:00:00
-    end_date: 2024-12-31 18:00:00
-    location:
-      type: bounded_region
-      parameters:
-        latitude_min: -90.0
-        latitude_max: 90.0
-        longitude_min: 0
-        longitude_max: 359.75
-    event_type: freeze
\ No newline at end of file
+- case_id_number: 1000
+  title: 5 Year Heat Wave Test
+  start_date: 2020-01-01 00:00:00
+  end_date: 2024-12-31 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: 90.0
+      longitude_min: 0
+      longitude_max: 359.75
+  event_type: heat_wave
+- case_id_number: 1001
+  title: 5 Year Freeze Test
+  start_date: 2020-01-01 00:00:00
+  end_date: 2024-12-31 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: 90.0
+      longitude_min: 0
+      longitude_max: 359.75
+  event_type: freeze
+- case_id_number: 1002
+  title: 2024 Heat Wave Test
+  start_date: 2024-01-01 00:00:00
+  end_date: 2024-12-31 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: 90.0
+      longitude_min: 0
+      longitude_max: 359.75
+  event_type: heat_wave
+- case_id_number: 1003
+  title: 2024 Freeze Test
+  start_date: 2024-01-01 00:00:00
+  end_date: 2024-12-31 18:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: 90.0
+      longitude_min: 0
+      longitude_max: 359.75
+  event_type: freeze
diff --git a/src/extremeweatherbench/utils.py b/src/extremeweatherbench/utils.py
index b5e1c124..c459268d 100644
--- a/src/extremeweatherbench/utils.py
+++ b/src/extremeweatherbench/utils.py
@@ -2,7 +2,6 @@
 specialized package."""
 
 import datetime
-import importlib
 import inspect
 import logging
 import operator
@@ -153,23 +152,6 @@ def remove_ocean_gridpoints(dataset: xr.Dataset) -> xr.Dataset:
     return dataset.where(land_mask)
 
 
-def load_events_yaml():
-    """Load the events yaml file."""
-    logger.warning(
-        "This function is deprecated and will be removed in a future release. "
-        "Please use cases.load_ewb_events_yaml_into_case_collection instead."
-    )
-    import extremeweatherbench.data
-
-    events_yaml_file = importlib.resources.files(extremeweatherbench.data).joinpath(
-        "events.yaml"
-    )
-    with importlib.resources.as_file(events_yaml_file) as file:
-        yaml_event_case = read_event_yaml(file)
-
-    return yaml_event_case
-
-
 def read_event_yaml(input_pth: str | pathlib.Path) -> dict:
     """Read events yaml from data."""
     logger.warning(
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 1e11c8c0..85da431c 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -63,16 +63,6 @@ def test_remove_ocean_gridpoints():
     assert result.sizes == ds.sizes
 
 
-def test_load_events_yaml():
-    """Test loading events yaml file."""
-    result = utils.load_events_yaml()
-
-    # Should return a dictionary
-    assert isinstance(result, dict)
-    # Should contain 'cases' key (based on the existing yaml structure)
-    assert "cases" in result
-
-
 def test_read_event_yaml(tmp_path):
     """Test reading events yaml from file."""
     # Create a temporary yaml file

From 47935324e5bbfc9a09bdda1de9b349b26a5305a1 Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Thu, 15 Jan 2026 17:57:20 +0000
Subject: [PATCH 09/72] remove out-of-date notebook from docs

---
 docs/notebooks/quickstart.ipynb | 95 ---------------------------------
 1 file changed, 95 deletions(-)
 delete mode 100644 docs/notebooks/quickstart.ipynb

diff --git a/docs/notebooks/quickstart.ipynb b/docs/notebooks/quickstart.ipynb
deleted file mode 100644
index 07f66b85..00000000
--- a/docs/notebooks/quickstart.ipynb
+++ /dev/null
@@ -1,95 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quickstart\n",
-    "\n",
-    "Use this notebook as an example for a basic runthrough of ExtremeWeatherBench cases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from extremeweatherbench import config, events, evaluate\n",
-    "import logging\n",
-    "import sys\n",
-    "from distributed import Client\n",
-    "\n",
-    "# Suppress annoying logging messages\n",
-    "logging.getLogger('botocore.httpchecksum').setLevel(logging.WARNING)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the event type(s) to evaluate\n",
-    "event_list = [events.HeatWave]\n",
-    "\n",
-    "# Create a configuration to use in the evaluation\n",
-    "# Feel free to try others including HRES at \n",
-    "# gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr\n",
-    "heatwave_configuration = config.Config(\n",
-    "    event_types=event_list,\n",
-    "    forecast_dir='gs://extremeweatherbench/FOUR_v200_GFS.parq',\n",
-    "    )\n",
-    "\n",
-    "# Create a schema configuration to align the forecast data with observation data present in EWB\n",
-    "# The defaults in ForecastSchemaConfig all work for the FOUR_v200_GFS model above except for the surface_air_temperature variable\n",
-    "# which is t2 in the forecast data.\n",
-    "default_forecast_config = config.ForecastSchemaConfig(surface_air_temperature='t2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load a dask cluster to parallelize the evaluation. The larger the better, but ~10 workers on an 8 vCPU machine is a good start.\n",
-    "client = Client(n_workers=10)\n",
-    "\n",
-    "# Set logging to info to see the progress of the evaluation\n",
-    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run the evaluation given the heatwave configuration and forecast schema configuration\n",
-    "cases = evaluate.evaluate(eval_config=heatwave_configuration, forecast_schema_config=default_forecast_config)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From ed3b9e66a082a22eb4dcb89ed332f33a221b307d Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 15 Jan 2026 16:16:19 -0500
Subject: [PATCH 10/72] CIRA Icechunk store (#310)

* dependencies and generate store file started

* in-flight, added and cleaned filter funcs

* add icechunk + obstore and cira icechunk generation script

* remove cira gen script no longer used

* code cleanup

* add icechunk datatree forecast class object

* uv lock

* add documentation, group helper func, and add repository kwargs passthrough

* remove icechunk forecast object

* typo

* ruff
---
 data_prep/cira_icechunk_generation.py |  236 ++
 docs/recipes/cira_forecast.md         |  121 +
 pyproject.toml                        |    5 +-
 src/extremeweatherbench/inputs.py     |   50 +
 uv.lock                               | 4847 ++++++++++++-------------
 5 files changed, 2778 insertions(+), 2481 deletions(-)
 create mode 100644 data_prep/cira_icechunk_generation.py

diff --git a/data_prep/cira_icechunk_generation.py b/data_prep/cira_icechunk_generation.py
new file mode 100644
index 00000000..777aa80f
--- /dev/null
+++ b/data_prep/cira_icechunk_generation.py
@@ -0,0 +1,236 @@
+"""
+This script is used to generate an icechunk store for the CIRA MLWP data.
+
+Credit to CIRA for producing the AIWP model data, Tom Nicholas for VirtualiZarr,
+the Earthmover team for icechunk.
+
+To access the icechunk store, you can use the following code:
+
+    import icechunk
+    import xarray as xr
+
+    test_storage = icechunk.gcs_storage(
+        bucket="extremeweatherbench", prefix="cira-icechunk", anonymous=True
+    )
+    test_repo = icechunk.Repository.open(test_storage)
+    session = test_repo.readonly_session(branch="main")
+    dt = xr.open_datatree(session.store, engine="zarr")
+
+Which will return a DataTree object with the CIRA MLWP data for the models (except
+FCNv1, which is not compatible with this approach).
+"""
+
+import logging
+import warnings
+
+import icechunk
+import joblib
+import numpy as np
+import obstore as obs
+import pandas as pd
+import virtualizarr
+import virtualizarr.parsers
+import virtualizarr.registry
+import xarray as xr
+
+from extremeweatherbench import utils
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Try to suppress warnings about numcodecs codecs not being in the Zarr version 3
+# specification. Warnings will still output when running it in parallel in joblib as
+# there doesn't seem to be a way to suppress them in each newly spawned process.
+warnings.filterwarnings(
+    "ignore",
+    message="Numcodecs codecs are not in the Zarr version 3 specification*",
+    category=UserWarning,
+)
+
+
+def process_single_virtual_dataset(
+    path: str,
+    parser: virtualizarr.parsers.HDFParser,
+    registry: virtualizarr.registry.ObjectStoreRegistry,
+    loadable_variables: list[str] = ["time", "latitude", "longitude", "level"],
+    decode_times: bool = True,
+) -> xr.Dataset:
+    """Process a single HDF/netCDF virtual dataset from a path.
+
+    Args:
+        path: The path to the virtual dataset (e.g. "s3://this/path/to/a/netcdf.nc").
+        parser: The parser to use to parse the virtual dataset.
+        registry: The virtualizarr.registry.ObjectStoreRegistry to use to access the virtual dataset.
+
+    Returns:
+        A Virtualizarr dataset
+    """
+    vds = virtualizarr.open_virtual_dataset(
+        url=path,
+        parser=parser,
+        registry=registry,
+        loadable_variables=loadable_variables,
+        decode_times=decode_times,
+    )
+    return vds
+
+
+def process_cira_model(
+    model_key: str, model_data: list[xr.Dataset]
+) -> tuple[str, xr.Dataset | None]:
+    """Merge a list of singular virtual datasets into a single concatenated dataset.
+
+    Args:
+        model_key: The key of the model.
+        model_data: A list of singular virtual datasets.
+
+    Returns:
+        A tuple of the model key and the concatenated dataset if successful, otherwise
+            None.
+    """
+
+    # Some models e.g. FCNv1 are not compatible with this approach
+    # Also, in the scenario of a problem like variable chunking (ZEP003), concatenation
+    # will fail, so return None in that case.
+    try:
+        # Combine the virtual datasets into a single dataset. Args here are established
+        # defaults for virtualizarr that also work for this case. When creating a new
+        # dimension with concat_dim, join="override" and combine_attrs="drop_conflicts"
+        # prevents fancy indexing errors.
+        combined_vds = xr.combine_nested(
+            model_data,
+            concat_dim="init_time",
+            coords="minimal",
+            compat="override",
+            combine_attrs="drop_conflicts",
+            join="override",
+        )
+
+        # Rename the time coordinate to valid_time to be consistent with EWB conventions
+        combined_vds = combined_vds.rename({"time": "valid_time"})
+
+        # Assign the init_time attribute to the concatenated dataset
+        combined_vds = combined_vds.assign_coords(
+            init_time=[
+                pd.to_datetime(f.attrs["initialization_time"]) for f in model_data
+            ],
+        )
+
+        # Hard code lead times for now, inconsistent values in netcdf attributes
+        lead_times = np.linspace(0, 240, 41).astype("timedelta64[h]")
+
+        # Assign the lead time coordinate to the concatenated dataset
+        combined_vds = combined_vds.assign_coords(lead_time=("valid_time", lead_times))
+
+        # Swap the valid_time and lead_time dimensions to be consistent with EWB
+        # conventions
+        combined_vds = combined_vds.swap_dims({"valid_time": "lead_time"})
+
+        # Return the model key and the concatenated dataset
+        return model_key, combined_vds
+
+    # If there is an error, log it and return a dict with value being None
+    except Exception as e:
+        logger.error(f"Error processing model {model_key}: {e}, returning None")
+        return model_key, None
+
+
+def generate_cira_icechunk_store():
+    """Generate a CIRA icechunk store from the CIRA MLWP data."""
+
+    # CIRA bucket URI
+    bucket = "s3://noaa-oar-mlwp-data"
+
+    # Build the ObjectStore from the URI, knowing the region and skipping signature
+    store = obs.store.from_url(bucket, region="us-east-1", skip_signature=True)
+
+    # Subset the prefixes to only include the model directories
+    prefix_list = [
+        n for n in obs.list_with_delimiter(store)["common_prefixes"] if n.endswith("FS")
+    ]
+
+    # Build the ObjectStoreRegistry and HDFParser
+    registry = virtualizarr.registry.ObjectStoreRegistry({bucket: store})
+    parser = virtualizarr.parsers.HDFParser()
+
+    model_dict = {}
+    for model in prefix_list:
+        stream = obs.list(store, model + "/", chunk_size=1)
+        t = [n for n in stream]
+        t = [n for ns in t for n in ns]
+        stream = obs.list(store, model + "/", chunk_size=1)
+
+        with joblib.parallel_config(**{"backend": "loky", "n_jobs": -1}):
+            model_dict[model] = utils.ParallelTqdm(total=len(t))(
+                # None is the cache_dir, we can't cache in parallel mode
+                joblib.delayed(process_single_virtual_dataset)(
+                    "s3://noaa-oar-mlwp-data/" + i[0]["path"], parser, registry
+                )
+                for i in stream
+            )
+
+    # Runs starting 27 May 2025 have a different chunking scheme, which cannot be
+    # concatenated for now (ZEP003)
+    single_chunk_model_dict = {
+        n: [
+            item
+            for item in model_dict[n]
+            if item["time"][0] < pd.to_datetime("2025-05-27T00:00:00.000000000")
+        ]
+        for n in model_dict.keys()
+    }
+
+    concat_model_dict = {}
+
+    with joblib.parallel_config(**{"backend": "loky", "n_jobs": -1}):
+        results = utils.ParallelTqdm(total=len(single_chunk_model_dict))(
+            joblib.delayed(process_cira_model)(
+                model_key, single_chunk_model_dict[model_key]
+            )
+            for model_key in single_chunk_model_dict.keys()
+        )
+
+    # Filter out any None results and create a dictionary of model keys and concatenated
+    # datasets
+    concat_model_dict = {
+        model_key: result for model_key, result in results if result is not None
+    }
+
+    # Create a DataTree from the dictionary of model keys and concatenated datasets
+    cira_datatree = xr.DataTree.from_dict(concat_model_dict)
+
+    # Build the GCS storage for the icechunk repository. This will fail if you do not
+    # have the application credentials for write access to the EWB bucket. For another
+    # GCS store, run gcloud auth application-default login and find where the generated
+    # json credentials are stored, and use that path in application_credentials.
+    storage = icechunk.gcs_storage(
+        bucket="extremeweatherbench", prefix="cira-icechunk", application_credentials=""
+    )
+
+    # Build the RepositoryConfig with default config settings
+    config = icechunk.RepositoryConfig.default()
+
+    # Set the virtual chunk container to the CIRA bucket in S3
+    config.set_virtual_chunk_container(
+        icechunk.VirtualChunkContainer(
+            url_prefix="s3://noaa-oar-mlwp-data/",
+            store=icechunk.s3_store(region="us-east-1", anonymous=True),
+        ),
+    )
+
+    # Create the repository
+    repo = icechunk.Repository.create(storage, config)
+
+    # Create a writable session to the repository
+    session = repo.writable_session(branch="main")
+
+    # Convert the DataTree to icechunk and commit to the repository
+    cira_datatree.vz.to_icechunk(session.store)
+
+    # Commit the changes to the repository; required for icechunk to be used from the
+    # GCS store
+    session.commit("drop in cira icechunk store")
+
+
+if __name__ == "__main__":
+    generate_cira_icechunk_store()
diff --git a/docs/recipes/cira_forecast.md b/docs/recipes/cira_forecast.md
index e69de29b..d29e3338 100644
--- a/docs/recipes/cira_forecast.md
+++ b/docs/recipes/cira_forecast.md
@@ -0,0 +1,121 @@
+# Accessing a CIRA Forecast
+
+We have a dedicated virtual reference icechunk store for CIRA data **up to May 26th, 2025** available at `gs://extremeweatherbench/cira-icechunk`. Compared to using parquet virtual references, we have seen a speed improvements of around 2x with ~25% more memory usage.
+
+## Loading the store
+
+```python
+
+from extremeweatherbench import cases, inputs, metrics, evaluate, defaults
+import datetime
+import icechunk
+
+storage = icechunk.gcs_storage(
+    bucket="extremeweatherbench", prefix="cira-icechunk", anonymous=True
+)
+```
+
+## Accessing a CIRA Model from the store
+
+```python
+
+group_list = inputs.list_groups_in_icechunk_datatree(storage)
+```
+
+`group_list` is list of each group within the DataTree. Note that the list order will change, do not code a fixed numerical index in based on this output.
+
+```['/',
+ '/GRAP_v100_IFS',
+ '/FOUR_v200_GFS',
+ '/PANG_v100_IFS',
+ '/PANG_v100_GFS',
+ '/AURO_v100_IFS',
+ '/FOUR_v200_IFS',
+ '/GRAP_v100_GFS',
+ '/AURO_v100_GFS']
+ ```
+
+## Loading the data as an XarrayObject
+
+```python
+
+# Find FCNv2's name in the group list
+fcnv2_group = [n for n in group_list if 'FOUR_v200_GFS' in n][0]
+
+# Helper function to access the virtual dataset
+fcnv2 = inputs.open_icechunk_dataset_from_datatree(
+    storage=storage, 
+    group=fcnv2_group, 
+    authorize_virtual_chunk_access=inputs.CIRA_CREDENTIALS
+    )
+fcnv2_icechunk_forecast_object = inputs.XarrayForecast(
+    ds=fcnv2,
+    variable_mapping=inputs.CIRA_metadata_variable_mapping
+    )
+```
+
+`fcnv2_icechunk_forecast_object` is a `ForecastBase` object ready to be used within EWB's evaluation framework.
+
+## Set up metrics and target for evaluation
+
+```python
+metrics_list = [
+
+    # Assign the forecast and target variable based on EWB's variable naming
+    metrics.MaximumMeanAbsoluteError(
+        forecast_variable='surface_air_temperature', 
+        target_variable='surface_air_temperature'
+        )
+    
+    # Arbitrary thresholds to check CSI on the temperature; how did the models do
+    # spatially for the upper echelons of heat?
+    metrics.CriticalSuccessIndex(
+        forecast_variable='surface_air_temperature', 
+        target_variable='surface_air_temperature',
+        forecast_threshold=310,
+        target_threshold=310
+        )
+        
+]
+
+# Load in GHCNh target
+ghcn_target = inputs.GHCN()
+```
+
+## Load in case metadata
+
+```python
+
+# Use EWB's cases and subset to the first two heat waves
+case_vals = cases.load_ewb_events_yaml_into_case_collection()
+case_vals.select_cases('case_id_number', [1,2],inplace=True)
+```
+
+From here, all we need to do is plug in the event type, metric list, target, and forecast
+to an `EvaluationObject` and run EWB's evaluation engine:
+
+```python
+
+evaluation_object = [
+    inputs.EvaluationObject(
+        event_type="heat_wave",
+        metric_list=metrics_list,
+        target=ghcn_target,
+        forecast=fcnv2_icechunk_forecast_object,
+    ),
+]
+
+ewb = evaluate.ExtremeWeatherBench(
+    case_metadata=case_vals,
+    evaluation_objects=evaluation_object
+    )
+
+# Set up parallel configuration for the run to pass into joblib
+parallel_config = {
+    'backend':'loky',
+    'n_jobs':4,
+    'backend_params':{'timeout':1}
+    }
+
+output = ewb.run(parallel_config=parallel_config)
+```
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 218fa91f..5b822f52 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,10 +35,13 @@ dependencies = [
 [project.optional-dependencies]
 data-prep = [
     "fsspec>=2024.12.0",
+    "icechunk>=1.1.14",
     "matplotlib>=3.10.0",
+    "obstore>=0.8.2",
+    "scipy>=1.13",
     "seaborn>=0.13.2",
     "ujson>=5.10.0",
-    "scipy>=1.13",
+    "virtualizarr==2.1.2",
 ]
 multiprocessing = ["dask[complete]>=2025.1.0", "distributed>=2025.1.0"]
 
diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index b254dc0e..31376381 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -13,6 +13,7 @@
     cast,
 )
 
+import icechunk
 import numpy as np
 import pandas as pd
 import polars as pl
@@ -140,6 +141,10 @@
 
 IncomingDataInput: TypeAlias = xr.Dataset | xr.DataArray | pl.LazyFrame | pd.DataFrame
 
+CIRA_CREDENTIALS = icechunk.containers_credentials(
+    {"s3://noaa-oar-mlwp-data/": icechunk.s3_credentials(anonymous=True)}
+)
+
 
 def _default_preprocess(input_data: IncomingDataInput) -> IncomingDataInput:
     """Default forecast preprocess function that does nothing."""
@@ -1023,6 +1028,51 @@ def open_kerchunk_reference(
     return kerchunk_ds
 
 
+def list_groups_in_icechunk_datatree(
+    storage: icechunk.Storage,
+    branch: str = "main",
+) -> list[str]:
+    """List the groups in an icechunk datatree.
+
+    Args:
+        storage: The icechunk Storage object to open.
+        branch: The icechunk branch to open. Defaults to "main".
+
+    Returns:
+        A list of the groups in the icechunk datatree.
+    """
+    repo = icechunk.Repository.open(storage)
+    session = repo.readonly_session(branch=branch)
+    dt = xr.open_datatree(filename_or_obj=session.store, engine="zarr")
+    return list(dt.groups)
+
+
+def open_icechunk_dataset_from_datatree(
+    storage: icechunk.Storage,
+    group: str,
+    branch: str = "main",
+    chunks: Optional[Union[dict, str]] = "auto",
+    **repository_kwargs,
+) -> xr.Dataset:
+    """Open an icechunk datatree from a storage.
+
+    Args:
+        storage: The icechunk Storage object to open.
+        group: The group within the datatree to open.
+        branch: The icechunk branch to open. Defaults to "main".
+        chunks: The chunk pattern for the datatree. defaults to "auto".
+    Returns:
+        The dataset for the specified group.
+    """
+    repo = icechunk.Repository.open(storage, **repository_kwargs)
+    session = repo.readonly_session(branch=branch)
+    dt = xr.open_datatree(
+        filename_or_obj=session.store, engine="zarr", consolidated=False, chunks=chunks
+    )
+    # Return the dataset for the group
+    return dt[group].to_dataset()
+
+
 def zarr_target_subsetter(
     data: xr.Dataset,
     case_metadata: "cases.IndividualCase",
diff --git a/uv.lock b/uv.lock
index 883531ba..cf118970 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 2
 requires-python = ">=3.11, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -7,13 +6,16 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
+[manifest]
+constraints = [{ name = "llvm", specifier = ">0.43.0" }]
+
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 version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662 },
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 [[package]]
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     { name = "wrapt" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/4d/f8/99fa90d9c25b78292899fd4946fce97b6353838b5ecc139ad8ba1436e70c/aiobotocore-2.26.0.tar.gz", hash = "sha256:50567feaf8dfe2b653570b4491f5bc8c6e7fb9622479d66442462c021db4fadc", size = 122026 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/b7/58/3bf0b7d474607dc7fd67dd1365c4e0f392c8177eaf4054e5ddee3ebd53b5/aiobotocore-2.26.0-py3-none-any.whl", hash = "sha256:a793db51c07930513b74ea7a95bd79aaa42f545bdb0f011779646eafa216abec", size = 87333 },
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@@ -1053,10 +1041,13 @@ dependencies = [
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+    { name = "icechunk" },
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@@ -1065,6 +1056,7 @@ multiprocessing = [
 
 [package.dev-dependencies]
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From 342668ab9bc116ad3bf792533c259706360bbfb0 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 15 Jan 2026 22:04:52 -0500
Subject: [PATCH 11/72] update pyproject and uv lock

---
 pyproject.toml |    2 +-
 uv.lock        | 1431 +++++++++++++++++++++++++++++-------------------
 2 files changed, 883 insertions(+), 550 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5b822f52..4994a179 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ dependencies = [
     "pyyaml>=6.0.2",
     "regionmask>=0.13.0",
     "s3fs>=2024.12.0",
-    "scores>=2.0.0",
+    "scores>=2.4.0",
     "tqdm>=4.67.1",
     "xarray[io]>=2025.7.1",
     "zarr>=3.0.9,<4",
diff --git a/uv.lock b/uv.lock
index cf118970..266bc78b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,14 +1,14 @@
 version = 1
 requires-python = ">=3.11, <3.14"
 resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version == '3.12.*'",
-    "python_full_version < '3.12'",
+    "python_full_version >= '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and platform_machine == 'ARM64' and sys_platform == 'win32'",
+    "(python_full_version >= '3.13' and platform_machine != 'ARM64') or (python_full_version >= '3.13' and sys_platform != 'win32')",
+    "(python_full_version == '3.12.*' and platform_machine != 'ARM64') or (python_full_version == '3.12.*' and sys_platform != 'win32')",
+    "python_full_version < '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32'",
+    "(python_full_version < '3.12' and platform_machine != 'ARM64') or (python_full_version < '3.12' and sys_platform != 'win32')",
 ]
 
-[manifest]
-constraints = [{ name = "llvm", specifier = ">0.43.0" }]
-
 [[package]]
 name = "affine"
 version = "2.4.0"
@@ -20,7 +20,7 @@ wheels = [
 
 [[package]]
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-version = "2.26.0"
+version = "3.1.0"
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     { name = "aiohttp" },
@@ -31,9 +31,9 @@ dependencies = [
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     { name = "wrapt" },
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+    { url = "https://files.pythonhosted.org/packages/e4/b3/923e0bc13669f06459c39a037ca7f5c1661ce252e141798a32803288c410/aiobotocore-3.1.0-py3-none-any.whl", hash = "sha256:4801f7b996f37a48faed017d23d8eae71e5fe0c3ec6bf0d9c5ce4f4d1853fce8", size = 87661 },
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@@ -47,7 +47,7 @@ wheels = [
 
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-version = "3.13.2"
+version = "3.13.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
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@@ -1074,6 +1081,13 @@ dev = [
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 [[package]]

From 89a9bdb23a20004bbd5847769c90b64c79bb4bce Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Sat, 17 Jan 2026 08:10:57 -0500
Subject: [PATCH 12/72] add TODO

---
 src/extremeweatherbench/inputs.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index 31376381..86f99ff0 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -257,6 +257,7 @@ def add_source_to_dataset_attrs(self, ds: xr.Dataset) -> xr.Dataset:
         ds.attrs["source"] = self.name
         return ds
 
+    # TODO(314): move to sources module
     def maybe_map_variable_names(self, data: IncomingDataInput) -> IncomingDataInput:
         """Map the variable names to the data, if required.
 

From 94eeb98c9e5d78e58d0d8a3df019e36d07a25e4b Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 19 Jan 2026 09:57:24 -0500
Subject: [PATCH 13/72] update PR template

---
 .github/pull_request_template.md | 28 ++++++++++------------------
 1 file changed, 10 insertions(+), 18 deletions(-)

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index d4d340dd..afcb9192 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -6,26 +6,18 @@ Please include a summary of the change and which issue is fixed. Please also inc
 
 ## Type of change
 
-Please delete options that are not relevant.
+- [ ] Refactor
+- [ ] Bug fix
+- [ ] New feature
+- [ ] Chore
 
-- [ ] Bug fix (non-breaking change which fixes an issue)
-- [ ] New feature (non-breaking change which adds functionality)
-- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
-- [ ] This change requires a documentation update
+### If this PR is a breaking change, please explain:
 
-## How Has This Been Tested?
-
-Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration.
+What functionality is now broken?
 
+What is the new approach?
 
+Did you fix the documentation and up + downstream code accordingly?
+## How Has This Been Tested?
 
-## Checklist:
-
-- [ ] My code follows the style guidelines of this project
-- [ ] I have performed a self-review of my own code
-- [ ] I have commented my code, particularly in hard-to-understand areas
-- [ ] I have made corresponding changes to the documentation
-- [ ] My changes generate no new warnings
-- [ ] I have added tests that prove my fix is effective or that my feature works
-- [ ] New and existing unit tests pass locally with my changes
-- [ ] Any dependent changes have been merged and published in downstream modules
\ No newline at end of file
+Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration.
\ No newline at end of file

From 0fabca5e2081be86bd5292bccaf0ca3a3e9a3570 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 22 Jan 2026 15:50:21 -0500
Subject: [PATCH 14/72] Remove `IndividualCaseCollection` (#317)

* update all references to IndividualCaseCollection and convert dicts/ "cases": keys to lists

* update template

* make questions bold

* add whitespace

* remove indent error and typo from evaluate_cli

* make load_individual_cases include passthrough for existing dataclasses

* ruff

* add comment for clarification on list comp

* ruff (again)

* remove all references to collection, replace with list

* ruff

* rename collection -> list

* ruff
---
 README.md                               |    2 +-
 data_prep/ar_bounds.py                  |    8 +-
 data_prep/ibtracs_bounds.py             |   26 +-
 data_prep/severe_convection_bounds.py   |   27 +-
 docs/examples/applied_ar.py             |   11 +-
 docs/examples/applied_freeze.py         |    4 +-
 docs/examples/applied_heatwave.py       |    4 +-
 docs/examples/applied_severe.py         |    5 +-
 docs/examples/applied_tc.py             |    6 +-
 docs/examples/example_config.py         |   12 +-
 docs/parallelism.md                     |    2 +-
 docs/recipes/cira_forecast.md           |    4 +-
 docs/usage.md                           |    4 +-
 scripts/brightband_evaluation.py        |    2 +-
 src/extremeweatherbench/cases.py        |  148 +--
 src/extremeweatherbench/evaluate.py     |   25 +-
 src/extremeweatherbench/evaluate_cli.py |   16 +-
 src/extremeweatherbench/regions.py      |   39 +-
 tests/test_cases.py                     | 1178 ++++-------------------
 tests/test_evaluate.py                  |  124 ++-
 tests/test_evaluate_cli.py              |   51 +-
 tests/test_integration.py               |   72 +-
 tests/test_regions.py                   |  109 +--
 23 files changed, 477 insertions(+), 1402 deletions(-)

diff --git a/README.md b/README.md
index f7677daa..f0249f50 100644
--- a/README.md
+++ b/README.md
@@ -124,7 +124,7 @@ heatwave_evaluation_list = [
     ),
 ]
 # Load in the EWB default list of event cases
-case_metadata = cases.load_ewb_events_yaml_into_case_collection()
+case_metadata = cases.load_ewb_events_yaml_into_case_list()
 
 # Create the evaluation class, with cases and evaluation objects declared
 ewb_instance = evaluate.ExtremeWeatherBench(
diff --git a/data_prep/ar_bounds.py b/data_prep/ar_bounds.py
index a21a0c87..b642d334 100644
--- a/data_prep/ar_bounds.py
+++ b/data_prep/ar_bounds.py
@@ -810,8 +810,8 @@ def process_ar_event(
         "\nProcessing: %s (Case %s)", single_case.title, single_case.case_id_number
     )
     # Create a case object for this event
-    case_collection = cases.load_individual_cases({"cases": [single_case]})
-    case = case_collection.cases[0]
+    case_list = cases.load_individual_cases([single_case])
+    case = case_list[0]
     case.start_date = case.start_date - pd.Timedelta(days=3)
     case.end_date = case.end_date + pd.Timedelta(days=3)
 
@@ -1034,8 +1034,8 @@ def main():
     parallel = True
 
     # Load atmospheric river events from the events.yaml file
-    events_yaml = cases.load_ewb_events_yaml_into_case_collection()
-    ar_events = events_yaml.select_cases(by="event_type", value="atmospheric_river")
+    events_yaml = cases.load_ewb_events_yaml_into_case_list()
+    ar_events = [n for n in events_yaml if n.event_type == "atmospheric_river"]
     logger.info("Found %s atmospheric river events in events.yaml", len(ar_events))
 
     # Process each atmospheric river event with enhanced object-based bounds calculation
diff --git a/data_prep/ibtracs_bounds.py b/data_prep/ibtracs_bounds.py
index 9fd834ac..0a2ecc0a 100644
--- a/data_prep/ibtracs_bounds.py
+++ b/data_prep/ibtracs_bounds.py
@@ -537,13 +537,13 @@ def update_cases_with_storm_bounds(storm_bounds, all_storms_df):
     """
     logger.info("Updating cases with storm bounds...")
 
-    cases_all = cases.load_ewb_events_yaml_into_case_collection()
+    cases_all = cases.load_ewb_events_yaml_into_case_list()
     cases_new = cases_all.copy()
 
     # Update the yaml cases with storm bounds from IBTrACS data
     for single_case in cases_new:
-        if single_case["event_type"] == "tropical_cyclone":
-            storm_name = single_case["title"].upper()
+        if single_case.event_type == "tropical_cyclone":
+            storm_name = single_case.title.upper()
 
             found_bounds, storm_data, storm_names = find_storm_bounds_for_case(
                 storm_name, storm_bounds, all_storms_df
@@ -559,21 +559,21 @@ def update_cases_with_storm_bounds(storm_bounds, all_storms_df):
                     storm_name,
                 )
                 storm_data = find_best_storm_by_geography(
-                    storm_names, all_storms_df, single_case["location"]["parameters"]
+                    storm_names, all_storms_df, single_case.location.parameters
                 )
 
             if found_bounds is not None:
                 # Update the case with IBTrACS bounding box coordinates
-                single_case["location"]["parameters"]["latitude_min"] = float(
+                single_case.location.parameters.latitude_min = float(
                     found_bounds.iloc[0].latitude_min
                 )
-                single_case["location"]["parameters"]["latitude_max"] = float(
+                single_case.location.parameters.latitude_max = float(
                     found_bounds.iloc[0].latitude_max
                 )
-                single_case["location"]["parameters"]["longitude_min"] = float(
+                single_case.location.parameters.longitude_min = float(
                     found_bounds.iloc[0].longitude_min
                 )
-                single_case["location"]["parameters"]["longitude_max"] = float(
+                single_case.location.parameters.longitude_max = float(
                     found_bounds.iloc[0].longitude_max
                 )
 
@@ -586,8 +586,8 @@ def update_cases_with_storm_bounds(storm_bounds, all_storms_df):
                     start_date = pd.to_datetime(first_time) - pd.Timedelta(hours=48)
                     end_date = pd.to_datetime(last_time) + pd.Timedelta(hours=48)
 
-                    single_case["start_date"] = start_date
-                    single_case["end_date"] = end_date
+                    single_case.start_date = start_date
+                    single_case.end_date = end_date
 
                     logger.info("Updated %s with bounds", storm_names)
                     logger.info("  Start date: %s, End date: %s", start_date, end_date)
@@ -632,7 +632,7 @@ def write_updated_yaml(cases_all, cases_new):
     logger.info("Writing updated YAML file...")
 
     # Find changes between old and new cases
-    cases_old = cases_all["cases"]
+    cases_old = cases_all.copy()
     updated_cases = []
     for i, case_new in enumerate(cases_new):
         case_old = cases_old[i] if i < len(cases_old) else None
@@ -641,8 +641,8 @@ def write_updated_yaml(cases_all, cases_new):
             updated_cases.append((i, case_new))
             logger.info(
                 "Case %s (%s) has been updated",
-                case_new["case_id_number"],
-                case_new["title"],
+                case_new.case_id_number,
+                case_new.title,
             )
 
     # Replace only the updated cases in the original data
diff --git a/data_prep/severe_convection_bounds.py b/data_prep/severe_convection_bounds.py
index afdf803a..12e1bcf9 100644
--- a/data_prep/severe_convection_bounds.py
+++ b/data_prep/severe_convection_bounds.py
@@ -24,7 +24,7 @@
 
 
 def has_case_for_dates(
-    case_collection: cases.IndividualCaseCollection,
+    case_list: list[cases.IndividualCase],
     start_date: pd.Timestamp,
     end_date: pd.Timestamp,
     is_australia: bool,
@@ -32,7 +32,7 @@ def has_case_for_dates(
     """Check if there's a case covering the date range for the region.
 
     Args:
-        case_collection: IndividualCaseCollection of all events
+        case_list: list of all cases
         start_date: Start of date range to check
         end_date: End of date range to check
         is_australia: If True, look for Australia case; else US case
@@ -40,7 +40,7 @@ def has_case_for_dates(
     Returns:
         True if matching case exists
     """
-    for event in case_collection.cases:
+    for event in case_list:
         # Check date overlap (event end is exclusive)
         event_start = event.start_date
         event_end = event.end_date
@@ -95,7 +95,7 @@ def get_pph_bounding_box(
     max_distance_km: float = 2000,
     max_blob_size_km: Optional[float] = None,
     case_title: str = "",
-    all_cases: Optional[cases.IndividualCaseCollection] = None,
+    all_cases: Optional[list[cases.IndividualCase]] = None,
 ) -> Optional[Dict[str, Any]]:
     """Calculate bounding box around PPH peak with distance constraint.
 
@@ -115,7 +115,7 @@ def get_pph_bounding_box(
         max_blob_size_km: Max blob diagonal for extension (default
                          0.75*max_distance_km)
         case_title: Case title to determine hemisphere
-        all_cases: IndividualCaseCollection of all events (for
+        all_cases: list of all cases (for
                    hemisphere checking)
 
     Returns:
@@ -338,13 +338,13 @@ def _create_single_bbox(
 
 
 def match_pph_times_to_events(
-    pph_data: xr.DataArray, case_collection: cases.IndividualCaseCollection
+    pph_data: xr.DataArray, case_list: list[cases.IndividualCase]
 ) -> Dict[int, Dict[str, Any]]:
     """Match PPH valid times to severe convection events.
 
     Args:
         pph_data: xarray DataArray with PPH data
-        case_collection: IndividualCaseCollection of severe convection events
+        case_list: list of IndividualCase objects of severe convection events
 
     Returns:
         Dictionary mapping event case_id to list of valid times
@@ -353,7 +353,7 @@ def match_pph_times_to_events(
 
     event_time_map = {}
 
-    for event in case_collection.cases:
+    for event in case_list:
         case_id = event.case_id_number
         start = event.start_date
         end = event.end_date
@@ -373,7 +373,7 @@ def match_pph_times_to_events(
 def create_bounding_boxes(
     pph_data: xr.DataArray,
     event_time_map: Dict[int, Dict[str, Any]],
-    all_cases: cases.IndividualCaseCollection,
+    all_cases: list[cases.IndividualCase],
     buffer_km: float = 250,
     threshold: float = 0.01,
     max_distance_km: float = 2000,
@@ -390,8 +390,7 @@ def create_bounding_boxes(
     Args:
         pph_data: xarray DataArray with PPH data
         event_time_map: Dictionary from match_pph_times_to_events
-        all_cases: IndividualCaseCollection of all events (for
-                   hemisphere checking)
+        all_cases: list of all cases (for hemisphere checking)
         buffer_km: Buffer distance in kilometers
         threshold: Minimum PPH value to consider
         max_distance_km: Maximum distance from peak (default 2000)
@@ -518,9 +517,9 @@ def main(
 
     # Load all events and filter to severe convection
     all_cases = cases.load_individual_cases_from_yaml(events_yaml_path)
-    severe_convection_cases = all_cases.select_cases(
-        by="event_type", value="severe_convection"
-    )
+    severe_convection_cases = [
+        n for n in all_cases if n.event_type == "severe_convection"
+    ]
 
     # Match PPH times to events
     event_time_map = match_pph_times_to_events(pph_data, severe_convection_cases)
diff --git a/docs/examples/applied_ar.py b/docs/examples/applied_ar.py
index bc607f4d..75239f47 100644
--- a/docs/examples/applied_ar.py
+++ b/docs/examples/applied_ar.py
@@ -38,12 +38,11 @@ def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
 
 # Load case data from the default events.yaml
-# Users can also define their own cases_dict structure
-case_yaml = cases.load_ewb_events_yaml_into_case_collection()
-case_yaml = case_yaml.select_cases(by="case_id_number", value=114)
-
-case_yaml.cases[0].start_date = datetime.datetime(2022, 12, 27, 11, 0, 0)
-case_yaml.cases[0].end_date = datetime.datetime(2022, 12, 27, 13, 0, 0)
+# Users can also define their own cases
+case_yaml = cases.load_ewb_events_yaml_into_case_list()
+case_yaml = [n for n in case_yaml if n.case_id_number == 114]
+case_yaml[0].start_date = datetime.datetime(2022, 12, 27, 11, 0, 0)
+case_yaml[0].end_date = datetime.datetime(2022, 12, 27, 13, 0, 0)
 # Define ERA5 target
 era5_target = inputs.ERA5(
     variables=[
diff --git a/docs/examples/applied_freeze.py b/docs/examples/applied_freeze.py
index 6505081c..8b2325cd 100644
--- a/docs/examples/applied_freeze.py
+++ b/docs/examples/applied_freeze.py
@@ -8,8 +8,8 @@
 logger.setLevel(logging.INFO)
 
 # Load case data from the default events.yaml
-# Users can also define their own cases_dict structure
-case_yaml = cases.load_ewb_events_yaml_into_case_collection()
+# Users can also define their own cases
+case_yaml = cases.load_ewb_events_yaml_into_case_list()
 
 # Define targets
 # ERA5 target
diff --git a/docs/examples/applied_heatwave.py b/docs/examples/applied_heatwave.py
index e74d7cbe..7f44b081 100644
--- a/docs/examples/applied_heatwave.py
+++ b/docs/examples/applied_heatwave.py
@@ -8,8 +8,8 @@
 logger.setLevel(logging.INFO)
 
 # Load case data from the default events.yaml
-# Users can also define their own cases_dict structure
-case_yaml = cases.load_ewb_events_yaml_into_case_collection()
+# Users can also define their own cases
+case_yaml = cases.load_ewb_events_yaml_into_case_list()
 
 # Define targets
 # ERA5 target
diff --git a/docs/examples/applied_severe.py b/docs/examples/applied_severe.py
index 72cb52fd..6cdba3f9 100644
--- a/docs/examples/applied_severe.py
+++ b/docs/examples/applied_severe.py
@@ -1,6 +1,5 @@
 import logging
 
-
 from extremeweatherbench import cases, derived, evaluate, inputs, metrics
 
 # Set the logger level to INFO
@@ -9,8 +8,8 @@
 
 
 # Load case data from the default events.yaml
-case_yaml = cases.load_ewb_events_yaml_into_case_collection()
-case_yaml.select_cases("case_id_number", 305, inplace=True)
+case_yaml = cases.load_ewb_events_yaml_into_case_list()
+case_yaml = [n for n in case_yaml if n.case_id_number == 305]
 
 # Define PPH target
 pph_target = inputs.PPH(
diff --git a/docs/examples/applied_tc.py b/docs/examples/applied_tc.py
index b5365218..79d4d3b4 100644
--- a/docs/examples/applied_tc.py
+++ b/docs/examples/applied_tc.py
@@ -7,11 +7,11 @@
 logger.setLevel(logging.INFO)
 
 
-# Load the case collection from the YAML file
-case_yaml = cases.load_ewb_events_yaml_into_case_collection()
+# Load the case list from the YAML file
+case_yaml = cases.load_ewb_events_yaml_into_case_list()
 
 # Select single case (TC Ida)
-case_yaml.select_cases(by="case_id_number", value=220, inplace=True)
+case_yaml = [n for n in case_yaml if n.case_id_number == 220]
 
 # Define IBTrACS target, no arguments needed as defaults are sufficient
 ibtracs_target = inputs.IBTrACS()
diff --git a/docs/examples/example_config.py b/docs/examples/example_config.py
index eee19463..3aff5967 100644
--- a/docs/examples/example_config.py
+++ b/docs/examples/example_config.py
@@ -7,8 +7,7 @@
     ewb --config-file example_config.py
 """
 
-from extremeweatherbench import inputs, metrics
-from extremeweatherbench.cases import load_ewb_events_yaml_into_case_collection
+from extremeweatherbench import cases, inputs, metrics
 
 # Define targets (observation data)
 era5_heatwave_target = inputs.ERA5(
@@ -40,12 +39,10 @@
 ]
 
 # Load case data from the default events.yaml
-# Users can also define their own cases_dict structure
-cases_dict = load_ewb_events_yaml_into_case_collection()
-
+# Users can also define their own cases
+cases_list = cases.load_ewb_events_yaml_into_case_list()
 # Alternatively, users could define custom cases like this:
-# cases_dict = {
-#     "cases": [
+# cases_list = [
 #         {
 #             "case_id_number": 1,
 #             "title": "Custom Heat Wave Case",
@@ -62,4 +59,3 @@
 #             "event_type": "heat_wave",
 #         },
 #     ]
-# }
diff --git a/docs/parallelism.md b/docs/parallelism.md
index 60ad5603..98ceb4be 100644
--- a/docs/parallelism.md
+++ b/docs/parallelism.md
@@ -20,7 +20,7 @@ In experimentation with varying configurations, it was found `loky` was the most
 ```python
 ...
 # Load events yaml
-case_yaml = cases.load_ewb_events_yaml_into_case_collection()
+case_yaml = cases.load_ewb_events_yaml_into_case_list()
 
 # Get default EvaluationObjects
 evaluation_objects = defaults.get_brightband_evaluation_objects()
diff --git a/docs/recipes/cira_forecast.md b/docs/recipes/cira_forecast.md
index d29e3338..43a7a6ac 100644
--- a/docs/recipes/cira_forecast.md
+++ b/docs/recipes/cira_forecast.md
@@ -87,8 +87,8 @@ ghcn_target = inputs.GHCN()
 ```python
 
 # Use EWB's cases and subset to the first two heat waves
-case_vals = cases.load_ewb_events_yaml_into_case_collection()
-case_vals.select_cases('case_id_number', [1,2],inplace=True)
+case_vals = cases.load_ewb_events_yaml_into_case_list()
+case_vals = [case for case in case_vals if case.case_id_number.isin([1,2])]
 ```
 
 From here, all we need to do is plug in the event type, metric list, target, and forecast
diff --git a/docs/usage.md b/docs/usage.md
index a6c83e82..a5e67b8c 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -19,7 +19,7 @@ from extremeweatherbench import evaluate, defaults, cases
 
 eval_objects = defaults.get_brightband_evaluation_objects()
 
-cases = cases.load_ewb_events_yaml_into_case_collection()
+cases = cases.load_ewb_events_yaml_into_case_list()
 ewb = ExtremeWeatherBench(cases=cases, 
 evaluation_objects=eval_objects)
 
@@ -98,7 +98,7 @@ Plugging these all in:
 
 ```python
 from extremeweatherbench import cases, evaluate
-case_yaml = cases.load_ewb_events_yaml_into_case_collection()
+case_yaml = cases.load_ewb_events_yaml_into_case_list()
 
 
 ewb_instance = evaluate.ExtremeWeatherBench(
diff --git a/scripts/brightband_evaluation.py b/scripts/brightband_evaluation.py
index 0ce87f49..4086ef51 100644
--- a/scripts/brightband_evaluation.py
+++ b/scripts/brightband_evaluation.py
@@ -24,7 +24,7 @@ def configure_logger(level=logging.INFO):
 
 if __name__ == "__main__":
     # Load events yaml
-    case_yaml = cases.load_ewb_events_yaml_into_case_collection()
+    case_yaml = cases.load_ewb_events_yaml_into_case_list()
 
     # Initialize ExtremeWeatherBench
     ewb = evaluate.ExtremeWeatherBench(
diff --git a/src/extremeweatherbench/cases.py b/src/extremeweatherbench/cases.py
index e3afe5a6..a7800dc5 100644
--- a/src/extremeweatherbench/cases.py
+++ b/src/extremeweatherbench/cases.py
@@ -9,7 +9,7 @@
 import itertools
 import logging
 import pathlib
-from typing import TYPE_CHECKING, Literal, Sequence, Union
+from typing import TYPE_CHECKING, Any, Sequence, Union
 
 import dacite
 import yaml  # type: ignore[import]
@@ -46,84 +46,6 @@ class IndividualCase:
     event_type: str
 
 
-@dataclasses.dataclass
-class IndividualCaseCollection:
-    """A collection of IndividualCases."""
-
-    cases: list[IndividualCase]
-
-    def select_cases(
-        self,
-        by: Literal["event_type", "case_id_number", "title", "location"],
-        value: Union[str, int, regions.Region, Sequence[float], datetime.datetime],
-        inplace: bool = False,
-    ) -> "IndividualCaseCollection":
-        """Select cases from the collection based on the given criteria.
-
-        Args:
-            by: The field to select cases by.
-            value: The value to select cases by.
-                - event_type: The event type to select cases by.
-                - case_id_number: The case id number to select cases by.
-                - title: The title to select cases by.
-                - location: The location to select cases by.
-                    - Region: The Region object to select cases by.
-                    - tuple or list: The bounding coordinates in the order
-                    longitude_min, latitude_min, longitude_max, latitude_max.
-            inplace: Whether to modify the collection in place or return a new
-                collection. Defaults to False.
-
-        Returns:
-            A new IndividualCaseCollection with the cases selected by the given criteria
-        """
-        match by:
-            case "event_type":
-                cases = [case for case in self.cases if case.event_type == value]
-            case "location":
-                if isinstance(value, regions.Region):
-                    cases = [
-                        case
-                        for case in self.cases
-                        if case.location.as_geopandas()
-                        .geometry.union_all()
-                        .intersects(value.as_geopandas().geometry.union_all())
-                    ]
-                elif isinstance(value, (tuple, list)):
-                    longitude_min, latitude_min, longitude_max, latitude_max = value
-                    value_region = regions.BoundingBoxRegion(
-                        latitude_min=latitude_min,
-                        latitude_max=latitude_max,
-                        longitude_min=longitude_min,
-                        longitude_max=longitude_max,
-                    )
-                    cases = [
-                        case
-                        for case in self.cases
-                        if case.location.as_geopandas()
-                        .geometry.union_all()
-                        .intersects(value_region.as_geopandas().geometry.union_all())
-                    ]
-            case "case_id_number":
-                if isinstance(value, int):
-                    cases = [
-                        case for case in self.cases if case.case_id_number == value
-                    ]
-                elif isinstance(value, list):
-                    cases = [
-                        case for case in self.cases if case.case_id_number in value
-                    ]
-                else:
-                    raise ValueError(f"Invalid value for case_id_number: {value}")
-            case "title":
-                cases = [case for case in self.cases if case.title == value]
-            case _:
-                raise ValueError(f"Invalid field to select cases by: {by}")
-        if inplace:
-            self.cases = cases
-            return self
-        return IndividualCaseCollection(cases=cases)
-
-
 @dataclasses.dataclass
 class CaseOperator:
     """A class which stores the graph to process an individual case.
@@ -147,40 +69,23 @@ class CaseOperator:
 
 
 def build_case_operators(
-    cases: Union[dict[str, list], IndividualCaseCollection],
+    case_list: list[IndividualCase],
     evaluation_objects: list["inputs.EvaluationObject"],
 ) -> list[CaseOperator]:
     """Build a CaseOperator from the case metadata and metric evaluation objects.
 
     Args:
         cases: The case metadata to use for the case operators as a dictionary of cases
-            or an IndividualCaseCollection.
+            or a list of IndividualCases.
         evaluation_objects: The evaluation objects to apply to the case operators.
 
     Returns:
         A list of CaseOperator objects.
     """
-    # Cases are a dictionary of cases, convert to an IndividualCaseCollection
-    if isinstance(cases, dict):
-        case_metadata_collection = dacite.from_dict(
-            data_class=IndividualCaseCollection,
-            data=cases,
-            config=dacite.Config(
-                type_hooks={regions.Region: regions.map_to_create_region},
-            ),
-        )
-    elif isinstance(cases, IndividualCaseCollection):
-        # Cases are already an IndividualCaseCollection
-        case_metadata_collection = cases
-    else:
-        raise TypeError(
-            "cases must be a dictionary of cases or an IndividualCaseCollection"
-        )
-
     # build list of case operators based on information provided in case dict and
     case_operators = []
     for single_case, evaluation_object in itertools.product(
-        case_metadata_collection.cases, evaluation_objects
+        case_list, evaluation_objects
     ):
         # checks if case matches the event type provided in metric eval object
         if single_case.event_type in evaluation_object.event_type:
@@ -196,37 +101,40 @@ def build_case_operators(
 
 
 def load_individual_cases(
-    cases: Union[dict[str, list], list],
-) -> IndividualCaseCollection:
+    cases: Union[list[dict[str, Any]], list[IndividualCase]],
+) -> list[IndividualCase]:
     """Load IndividualCase metadata from a dictionary.
 
+    Will pass through existing IndividualCase objects and convert dictionaries to IndividualCase objects.
+
     Args:
         cases: A dictionary of cases based on the IndividualCase dataclass.
 
     Returns:
-        A collection of IndividualCase objects.
+        A list of IndividualCase objects.
     """
-    assert isinstance(cases, dict) or isinstance(cases, list), (
-        "cases must be a dictionary or a list."
-    )
 
-    # if cases is a list, convert to a dictionary for dacite.from_dict
-    if isinstance(cases, list):
-        cases = {"cases": cases}
-    case_metadata_collection = dacite.from_dict(
-        data_class=IndividualCaseCollection,
-        data=cases,
-        config=dacite.Config(
-            type_hooks={regions.Region: regions.map_to_create_region},
-        ),
-    )
+    # Iterate through the cases and convert dictionaries to IndividualCase objects if
+    # they are not already IndividualCase objects
+    case_list = [
+        case
+        if isinstance(case, IndividualCase)
+        else dacite.from_dict(
+            data_class=IndividualCase,
+            data=case,
+            config=dacite.Config(
+                type_hooks={regions.Region: regions.map_to_create_region},
+            ),
+        )
+        for case in cases
+    ]
 
-    return case_metadata_collection
+    return case_list
 
 
 def load_individual_cases_from_yaml(
     yaml_file: Union[str, pathlib.Path],
-) -> IndividualCaseCollection:
+) -> list[IndividualCase]:
     """Load IndividualCase metadata directly from a yaml file.
 
     This function is a wrapper around load_individual_cases that reads the yaml file
@@ -257,14 +165,14 @@ def load_individual_cases_from_yaml(
         yaml_file: A path to a yaml file containing the case metadata.
 
     Returns:
-        A collection of IndividualCase objects.
+        A list of IndividualCase objects.
     """
     yaml_event_case = read_incoming_yaml(yaml_file)
     return load_individual_cases(yaml_event_case)
 
 
-def load_ewb_events_yaml_into_case_collection() -> IndividualCaseCollection:
-    """Loads the EWB events yaml file into an IndividualCaseCollection."""
+def load_ewb_events_yaml_into_case_list() -> list[IndividualCase]:
+    """Loads the EWB events yaml file into a list of IndividualCase objects."""
     import extremeweatherbench.data
 
     events_yaml_file = importlib.resources.files(extremeweatherbench.data).joinpath(
diff --git a/src/extremeweatherbench/evaluate.py b/src/extremeweatherbench/evaluate.py
index ccc88004..ad7953eb 100644
--- a/src/extremeweatherbench/evaluate.py
+++ b/src/extremeweatherbench/evaluate.py
@@ -4,7 +4,7 @@
 import dataclasses
 import logging
 import pathlib
-from typing import TYPE_CHECKING, Optional, Sequence, Union
+from typing import TYPE_CHECKING, Any, Optional, Sequence, Union
 
 import dask.array as da
 import joblib
@@ -45,7 +45,7 @@ class ExtremeWeatherBench:
     results.
 
     Attributes:
-        case_metadata: A dictionary of cases or an IndividualCaseCollection to run.
+        case_metadata: A dictionary of cases or a list of IndividualCase objects to run.
         evaluation_objects: A list of evaluation objects to run.
         cache_dir: An optional directory to cache the mid-flight outputs of the
             workflow for serial runs.
@@ -56,20 +56,13 @@ class ExtremeWeatherBench:
 
     def __init__(
         self,
-        case_metadata: Union[dict[str, list], "cases.IndividualCaseCollection"],
+        case_metadata: Union[list[dict[str, Any]], "list[cases.IndividualCase]"],
         evaluation_objects: list["inputs.EvaluationObject"],
         cache_dir: Optional[Union[str, pathlib.Path]] = None,
         region_subsetter: Optional["regions.RegionSubsetter"] = None,
     ):
-        if isinstance(case_metadata, dict):
-            self.case_metadata = cases.load_individual_cases(case_metadata)
-        elif isinstance(case_metadata, cases.IndividualCaseCollection):
-            self.case_metadata = case_metadata
-        else:
-            raise TypeError(
-                "case_metadata must be a dictionary of cases or an "
-                "IndividualCaseCollection"
-            )
+        # Load the case metadata from the input
+        self.case_metadata = cases.load_individual_cases(case_metadata)
         self.evaluation_objects = evaluation_objects
         self.cache_dir = pathlib.Path(cache_dir) if cache_dir else None
 
@@ -87,12 +80,10 @@ def case_operators(self) -> list["cases.CaseOperator"]:
 
         # Subset the cases if a region subsetter was provided
         if self.region_subsetter:
-            subset_collection = self.region_subsetter.subset_case_collection(
-                self.case_metadata
-            )
+            subset_list = self.region_subsetter.subset_case_list(self.case_metadata)
         else:
-            subset_collection = self.case_metadata
-        return cases.build_case_operators(subset_collection, self.evaluation_objects)
+            subset_list = self.case_metadata
+        return cases.build_case_operators(subset_list, self.evaluation_objects)
 
     def run(
         self,
diff --git a/src/extremeweatherbench/evaluate_cli.py b/src/extremeweatherbench/evaluate_cli.py
index c6bc0abe..3216a1ef 100644
--- a/src/extremeweatherbench/evaluate_cli.py
+++ b/src/extremeweatherbench/evaluate_cli.py
@@ -126,15 +126,15 @@ def cli_runner(
     if default:
         click.echo("Using default Brightband evaluation objects...")
         evaluation_objects = defaults.get_brightband_evaluation_objects()
-        cases_dict = _load_default_cases()
+        case_list = _load_default_cases()
     else:
         assert config_file is not None  # for mypy
         click.echo(f"Loading evaluation objects from {config_file}...")
-        evaluation_objects, cases_dict = _load_config_file(config_file)
+        evaluation_objects, case_list = _load_config_file(config_file)
 
     # Initialize ExtremeWeatherBench
     ewb = evaluate.ExtremeWeatherBench(
-        case_metadata=cases_dict,
+        case_metadata=case_list,
         evaluation_objects=evaluation_objects,
         cache_dir=cache_dir if cache_dir else None,
     )
@@ -171,7 +171,7 @@ def cli_runner(
 def _load_default_cases():
     """Load default case data for default evaluation objects."""
 
-    return cases.load_ewb_events_yaml_into_case_collection()
+    return cases.load_ewb_events_yaml_into_case_list()
 
 
 def _load_config_file(config_path: str) -> tuple:
@@ -179,7 +179,7 @@ def _load_config_file(config_path: str) -> tuple:
 
     The config file should define:
     - evaluation_objects: List of EvaluationObject instances
-    - cases_dict: Dictionary containing case data
+    - case_list: List of case data
     """
     config_path_obj = pathlib.Path(config_path)
 
@@ -195,10 +195,10 @@ def _load_config_file(config_path: str) -> tuple:
     if not hasattr(config_module, "evaluation_objects"):
         raise click.ClickException("Config file must define 'evaluation_objects' list")
 
-    if not hasattr(config_module, "cases_dict"):
-        raise click.ClickException("Config file must define 'cases_dict' dictionary")
+    if not hasattr(config_module, "case_list"):
+        raise click.ClickException("Config file must define 'case_list' list")
 
-    return config_module.evaluation_objects, config_module.cases_dict
+    return config_module.evaluation_objects, config_module.case_list
 
 
 if __name__ == "__main__":
diff --git a/src/extremeweatherbench/regions.py b/src/extremeweatherbench/regions.py
index 48f8b81f..f2580ccb 100644
--- a/src/extremeweatherbench/regions.py
+++ b/src/extremeweatherbench/regions.py
@@ -515,27 +515,26 @@ def __init__(
         self.method = method
         self.percent_threshold = percent_threshold
 
-    def subset_case_collection(
-        self, case_collection: "cases.IndividualCaseCollection"
-    ) -> "cases.IndividualCaseCollection":
-        """Subset an IndividualCaseCollection by region.
+    def subset_case_list(
+        self, case_list: "list[cases.IndividualCase]"
+    ) -> "list[cases.IndividualCase]":
+        """Subset a list of IndividualCases by region.
 
         Args:
-            case_collection: The case collection to subset
+            case_list: The list of IndividualCases to subset
 
         Returns:
-            A new IndividualCaseCollection with cases subset to the region
+            A new list of IndividualCases with cases subset to the region
         """
         # Avoid circular import
-        from extremeweatherbench import cases
 
         filtered_cases = []
 
-        for case in case_collection.cases:
+        for case in case_list:
             if self._should_include_case(case):
                 filtered_cases.append(case)
 
-        return cases.IndividualCaseCollection(cases=filtered_cases)
+        return filtered_cases
 
     def _should_include_case(self, case: "cases.IndividualCase") -> bool:
         """Determine if a case should be included based on the subsetting criteria."""
@@ -558,18 +557,18 @@ def _should_include_case(self, case: "cases.IndividualCase") -> bool:
 
 # Convenience functions for direct usage
 def subset_cases_to_region(
-    case_collection: "cases.IndividualCaseCollection",
+    case_list: "list[cases.IndividualCase]",
     region: Union[Region, Mapping[str, float]],
     method: Literal["intersects", "percent", "all"] = "intersects",
     percent_threshold: float = 0.5,
-) -> "cases.IndividualCaseCollection":
-    """Subset an IndividualCaseCollection to a region.
+) -> "list[cases.IndividualCase]":
+    """Subset a list of IndividualCases to a region.
 
     This is a convenience function that creates a RegionSubsetter and applies it to
-    a case collection.
+    a list of IndividualCases.
 
     Args:
-        case_collection: The case collection to subset
+        case_list: The list of IndividualCases to subset
         region: The region to subset to. Can be a Region object or a
             dictionary of bounds with keys "latitude_min", "latitude_max",
             "longitude_min", and "longitude_max".
@@ -577,16 +576,16 @@ def subset_cases_to_region(
         percent_threshold: Threshold for percent overlap
 
     Returns:
-        A new IndividualCaseCollection with cases subset to the region
+        A new list of IndividualCases with cases subset to the region
     """
     subsetter = RegionSubsetter(region, method, percent_threshold)
-    return subsetter.subset_case_collection(case_collection)
+    return subsetter.subset_case_list(case_list)
 
 
 def subset_results_to_region(
     region: RegionSubsetter,
     results_df: pd.DataFrame,
-    case_collection: "cases.IndividualCaseCollection",
+    case_list: "list[cases.IndividualCase]",
 ) -> pd.DataFrame:
     """Subset results DataFrame by region using case_id_number.
 
@@ -598,15 +597,15 @@ def subset_results_to_region(
             dictionary of bounds with keys "latitude_min", "latitude_max",
             "longitude_min", and "longitude_max".
         results_df: DataFrame with results from ExtremeWeatherBench.run()
-        case_collection: The original case collection to determine which
+        case_list: The original case list to determine which
             case_id_numbers correspond to cases in the region
 
     Returns:
         Subset DataFrame containing only results for cases in the region
     """
     # Get the case IDs that should be included
-    subset_cases = region.subset_case_collection(case_collection)
-    included_case_ids = {case.case_id_number for case in subset_cases.cases}
+    subset_cases = region.subset_case_list(case_list)
+    included_case_ids = {case.case_id_number for case in subset_cases}
 
     # Filter the results DataFrame
     return results_df[results_df["case_id_number"].isin(included_case_ids)]
diff --git a/tests/test_cases.py b/tests/test_cases.py
index 8646b509..cd777ffe 100644
--- a/tests/test_cases.py
+++ b/tests/test_cases.py
@@ -9,6 +9,48 @@
 from extremeweatherbench import cases, regions
 
 
+@pytest.fixture
+def cases_list_of_dicts():
+    return [
+        {
+            "case_id_number": 1,
+            "title": "Test Case 1",
+            "start_date": datetime.datetime(2021, 1, 1),
+            "end_date": datetime.datetime(2021, 1, 15),
+            "location": {
+                "type": "centered_region",
+                "parameters": {
+                    "latitude": 40.0,
+                    "longitude": -100.0,
+                    "bounding_box_degrees": 5.0,
+                },
+            },
+            "event_type": "heat_wave",
+        },
+        {
+            "case_id_number": 2,
+            "title": "Test Case 2",
+            "start_date": datetime.datetime(2021, 6, 1),
+            "end_date": datetime.datetime(2021, 6, 15),
+            "location": {
+                "type": "bounded_region",
+                "parameters": {
+                    "latitude_min": 35.0,
+                    "latitude_max": 45.0,
+                    "longitude_min": -110.0,
+                    "longitude_max": -95.0,
+                },
+            },
+            "event_type": "drought",
+        },
+    ]
+
+
+@pytest.fixture
+def individualcase_list(cases_list_of_dicts):
+    return cases.load_individual_cases(cases_list_of_dicts)
+
+
 class TestIndividualCase:
     """Test the IndividualCase dataclass."""
 
@@ -90,48 +132,6 @@ def test_individual_case_validation(self):
         assert short_case.end_date > short_case.start_date
 
 
-class TestIndividualCaseCollection:
-    """Test the IndividualCaseCollection dataclass."""
-
-    def test_case_collection_creation(self):
-        """Test IndividualCaseCollection creation."""
-        region1 = regions.CenteredRegion.create_region(
-            latitude=40.0, longitude=-100.0, bounding_box_degrees=5.0
-        )
-        region2 = regions.CenteredRegion.create_region(
-            latitude=45.0, longitude=-120.0, bounding_box_degrees=8.0
-        )
-
-        case1 = cases.IndividualCase(
-            case_id_number=1,
-            title="Case 1",
-            start_date=datetime.datetime(2021, 1, 1),
-            end_date=datetime.datetime(2021, 1, 15),
-            location=region1,
-            event_type="heat_wave",
-        )
-
-        case2 = cases.IndividualCase(
-            case_id_number=2,
-            title="Case 2",
-            start_date=datetime.datetime(2021, 6, 1),
-            end_date=datetime.datetime(2021, 6, 15),
-            location=region2,
-            event_type="drought",
-        )
-
-        collection = cases.IndividualCaseCollection(cases=[case1, case2])
-
-        assert len(collection.cases) == 2
-        assert collection.cases[0] == case1
-        assert collection.cases[1] == case2
-
-    def test_empty_case_collection(self):
-        """Test IndividualCaseCollection with no cases."""
-        empty_collection = cases.IndividualCaseCollection(cases=[])
-        assert len(empty_collection.cases) == 0
-
-
 class TestCaseOperator:
     """Test the CaseOperator dataclass."""
 
@@ -171,112 +171,34 @@ def test_case_operator_creation(self):
 class TestLoadIndividualCases:
     """Test the load_individual_cases function."""
 
-    def test_load_individual_cases_basic(self):
+    def test_load_individual_cases_basic(self, cases_list_of_dicts):
         """Test loading individual cases from dictionary."""
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Test Case 1",
-                    "start_date": datetime.datetime(2021, 1, 1),
-                    "end_date": datetime.datetime(2021, 1, 15),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 40.0,
-                            "longitude": -100.0,
-                            "bounding_box_degrees": 5.0,
-                        },
-                    },
-                    "event_type": "heat_wave",
-                },
-                {
-                    "case_id_number": 2,
-                    "title": "Test Case 2",
-                    "start_date": datetime.datetime(2021, 6, 1),
-                    "end_date": datetime.datetime(2021, 6, 15),
-                    "location": {
-                        "type": "bounded_region",
-                        "parameters": {
-                            "latitude_min": 35.0,
-                            "latitude_max": 45.0,
-                            "longitude_min": -110.0,
-                            "longitude_max": -95.0,
-                        },
-                    },
-                    "event_type": "drought",
-                },
-            ]
-        }
 
-        collection = cases.load_individual_cases(cases_dict)
+        case_list = cases.load_individual_cases(cases_list_of_dicts)
 
-        assert isinstance(collection, cases.IndividualCaseCollection)
-        assert len(collection.cases) == 2
+        assert all(isinstance(case, cases.IndividualCase) for case in case_list)
+        assert len(case_list) == 2
 
         # Check first case
-        case1 = collection.cases[0]
+        case1 = case_list[0]
         assert case1.case_id_number == 1
         assert case1.title == "Test Case 1"
         assert case1.event_type == "heat_wave"
         assert isinstance(case1.location, regions.CenteredRegion)
 
         # Check second case
-        case2 = collection.cases[1]
+        case2 = case_list[1]
         assert case2.case_id_number == 2
         assert case2.title == "Test Case 2"
         assert case2.event_type == "drought"
         assert isinstance(case2.location, regions.BoundingBoxRegion)
 
-    def test_load_individual_cases_empty(self):
-        """Test loading empty cases list."""
-        empty_cases_dict = {"cases": []}
-
-        collection = cases.load_individual_cases(empty_cases_dict)
-
-        assert isinstance(collection, cases.IndividualCaseCollection)
-        assert len(collection.cases) == 0
-
 
 class TestBuildCaseOperators:
     """Test the build_case_operators function."""
 
-    def test_build_case_operators_basic(self):
+    def test_build_case_operators_basic(self, individualcase_list):
         """Test building case operators from cases and evaluation objects."""
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Heat Wave Case",
-                    "start_date": datetime.datetime(2021, 1, 1),
-                    "end_date": datetime.datetime(2021, 1, 15),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 40.0,
-                            "longitude": -100.0,
-                            "bounding_box_degrees": 5.0,
-                        },
-                    },
-                    "event_type": "heat_wave",
-                },
-                {
-                    "case_id_number": 2,
-                    "title": "Drought Case",
-                    "start_date": datetime.datetime(2021, 6, 1),
-                    "end_date": datetime.datetime(2021, 6, 15),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 35.0,
-                            "longitude": -105.0,
-                            "bounding_box_degrees": 8.0,
-                        },
-                    },
-                    "event_type": "drought",
-                },
-            ]
-        }
 
         # Create mock evaluation objects
         mock_eval_obj1 = mock.Mock()
@@ -293,7 +215,7 @@ def test_build_case_operators_basic(self):
 
         evaluation_objects = [mock_eval_obj1, mock_eval_obj2]
 
-        operators = cases.build_case_operators(cases_dict, evaluation_objects)
+        operators = cases.build_case_operators(individualcase_list, evaluation_objects)
 
         # Should create 3 operators:
         # - Heat wave case with eval_obj1 (matches heat_wave)
@@ -309,268 +231,37 @@ def test_build_case_operators_basic(self):
             assert hasattr(operator, "target")
             assert hasattr(operator, "forecast")
 
-    def test_build_case_operators_no_matches(self):
+    def test_build_case_operators_no_matches(self, individualcase_list):
         """Test building case operators when no event types match."""
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Storm Case",
-                    "start_date": datetime.datetime(2021, 1, 1),
-                    "end_date": datetime.datetime(2021, 1, 15),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 40.0,
-                            "longitude": -100.0,
-                            "bounding_box_degrees": 5.0,
-                        },
-                    },
-                    "event_type": "storm",
-                },
-            ]
-        }
 
         # Create evaluation object that doesn't match storm
         mock_eval_obj = mock.Mock()
-        mock_eval_obj.event_type = ["heat_wave", "drought"]
+        mock_eval_obj.event_type = ["cats_and_dogs"]
         mock_eval_obj.metric_list = mock.Mock()
         mock_eval_obj.target = mock.Mock()
         mock_eval_obj.forecast = mock.Mock()
 
         evaluation_objects = [mock_eval_obj]
 
-        operators = cases.build_case_operators(cases_dict, evaluation_objects)
+        operators = cases.build_case_operators(individualcase_list, evaluation_objects)
 
         # Should create no operators since no event types match
         assert len(operators) == 0
 
-    def test_build_case_operators_invalid_cases_dict(self):
-        """Test build_case_operators with invalid cases_dict."""
-        import dacite
+    def test_build_case_operators_invalid_case_list(self):
+        """Test build_case_operators with invalid case_list."""
 
-        invalid_cases_dict = {"cases": "not_a_list"}
+        invalid_case_list = "not_a_list"
         mock_eval_obj = mock.Mock()
 
         with pytest.raises(
-            dacite.exceptions.WrongTypeError,
-            match="wrong value type",
+            AttributeError,
         ):
-            cases.build_case_operators(invalid_cases_dict, [mock_eval_obj])
-
-    def test_build_case_operators_with_individual_case_collection_passthrough(self):
-        """Test build_case_operators with IndividualCaseCollection passthrough."""
-        # Create individual cases directly
-        region1 = regions.CenteredRegion.create_region(
-            latitude=40.0, longitude=-100.0, bounding_box_degrees=5.0
-        )
-        region2 = regions.CenteredRegion.create_region(
-            latitude=35.0, longitude=-105.0, bounding_box_degrees=8.0
-        )
+            cases.build_case_operators(invalid_case_list, [mock_eval_obj])
 
-        case1 = cases.IndividualCase(
-            case_id_number=1,
-            title="Heat Wave Case",
-            start_date=datetime.datetime(2021, 1, 1),
-            end_date=datetime.datetime(2021, 1, 15),
-            location=region1,
-            event_type="heat_wave",
-        )
-
-        case2 = cases.IndividualCase(
-            case_id_number=2,
-            title="Drought Case",
-            start_date=datetime.datetime(2021, 6, 1),
-            end_date=datetime.datetime(2021, 6, 15),
-            location=region2,
-            event_type="drought",
-        )
-
-        # Create IndividualCaseCollection directly
-        case_collection = cases.IndividualCaseCollection(cases=[case1, case2])
-
-        # Create mock evaluation objects
-        mock_eval_obj1 = mock.Mock()
-        mock_eval_obj1.event_type = ["heat_wave"]
-        mock_eval_obj1.metric_list = mock.Mock()
-        mock_eval_obj1.target = mock.Mock()
-        mock_eval_obj1.forecast = mock.Mock()
-
-        mock_eval_obj2 = mock.Mock()
-        mock_eval_obj2.event_type = ["drought", "heat_wave"]
-        mock_eval_obj2.metric_list = mock.Mock()
-        mock_eval_obj2.target = mock.Mock()
-        mock_eval_obj2.forecast = mock.Mock()
-
-        evaluation_objects = [mock_eval_obj1, mock_eval_obj2]
-
-        # Test passthrough functionality - pass IndividualCaseCollection directly
-        operators = cases.build_case_operators(case_collection, evaluation_objects)
-
-        # Should create 3 operators:
-        # - Heat wave case with eval_obj1 (matches heat_wave)
-        # - Heat wave case with eval_obj2 (matches heat_wave)
-        # - Drought case with eval_obj2 (matches drought)
-        assert len(operators) == 3
-
-        # Check that all operators are CaseOperator instances
-        for operator in operators:
-            assert isinstance(operator, cases.CaseOperator)
-            assert hasattr(operator, "case_metadata")
-            assert hasattr(operator, "metric_list")
-            assert hasattr(operator, "target")
-            assert hasattr(operator, "forecast")
-
-        # Verify the correct cases were matched
-        heat_wave_operators = [
-            op for op in operators if op.case_metadata.case_id_number == 1
-        ]
-        drought_operators = [
-            op for op in operators if op.case_metadata.case_id_number == 2
-        ]
-
-        assert len(heat_wave_operators) == 2  # Both eval objects match heat_wave
-        assert len(drought_operators) == 1  # Only eval_obj2 matches drought
-
-    def test_build_case_operators_dict_vs_collection_equivalence(self):
-        """Test dict and IndividualCaseCollection inputs produce same results."""
-        # Define cases as dictionary (traditional way)
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Test Heat Wave",
-                    "start_date": datetime.datetime(2021, 7, 1),
-                    "end_date": datetime.datetime(2021, 7, 10),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 42.0,
-                            "longitude": -95.0,
-                            "bounding_box_degrees": 6.0,
-                        },
-                    },
-                    "event_type": "heat_wave",
-                },
-                {
-                    "case_id_number": 2,
-                    "title": "Test Storm",
-                    "start_date": datetime.datetime(2021, 8, 1),
-                    "end_date": datetime.datetime(2021, 8, 5),
-                    "location": {
-                        "type": "bounded_region",
-                        "parameters": {
-                            "latitude_min": 30.0,
-                            "latitude_max": 40.0,
-                            "longitude_min": -100.0,
-                            "longitude_max": -85.0,
-                        },
-                    },
-                    "event_type": "storm",
-                },
-            ]
-        }
-
-        # Create equivalent IndividualCaseCollection
-        region1 = regions.CenteredRegion.create_region(
-            latitude=42.0, longitude=-95.0, bounding_box_degrees=6.0
-        )
-        region2 = regions.BoundingBoxRegion.create_region(
-            latitude_min=30.0,
-            latitude_max=40.0,
-            longitude_min=-100.0,
-            longitude_max=-85.0,
-        )
-
-        case1 = cases.IndividualCase(
-            case_id_number=1,
-            title="Test Heat Wave",
-            start_date=datetime.datetime(2021, 7, 1),
-            end_date=datetime.datetime(2021, 7, 10),
-            location=region1,
-            event_type="heat_wave",
-        )
-
-        case2 = cases.IndividualCase(
-            case_id_number=2,
-            title="Test Storm",
-            start_date=datetime.datetime(2021, 8, 1),
-            end_date=datetime.datetime(2021, 8, 5),
-            location=region2,
-            event_type="storm",
-        )
-
-        case_collection = cases.IndividualCaseCollection(cases=[case1, case2])
-
-        # Create evaluation objects
-        heat_wave_eval = mock.Mock()
-        heat_wave_eval.event_type = ["heat_wave"]
-        heat_wave_eval.metric_list = mock.Mock()
-        heat_wave_eval.target = mock.Mock()
-        heat_wave_eval.forecast = mock.Mock()
-
-        storm_eval = mock.Mock()
-        storm_eval.event_type = ["storm"]
-        storm_eval.metric_list = mock.Mock()
-        storm_eval.target = mock.Mock()
-        storm_eval.forecast = mock.Mock()
-
-        evaluation_objects = [heat_wave_eval, storm_eval]
-
-        # Build operators using dictionary
-        operators_from_dict = cases.build_case_operators(cases_dict, evaluation_objects)
-
-        # Build operators using IndividualCaseCollection (passthrough)
-        operators_from_collection = cases.build_case_operators(
-            case_collection, evaluation_objects
-        )
-
-        # Results should be equivalent
-        assert len(operators_from_dict) == len(operators_from_collection)
-        assert len(operators_from_dict) == 2  # One for each event type
-
-        # Sort operators by case_id_number for comparison
-        dict_ops_sorted = sorted(
-            operators_from_dict, key=lambda x: x.case_metadata.case_id_number
-        )
-        collection_ops_sorted = sorted(
-            operators_from_collection, key=lambda x: x.case_metadata.case_id_number
-        )
-
-        # Compare each operator pair
-        for dict_op, collection_op in zip(dict_ops_sorted, collection_ops_sorted):
-            # Case metadata should be equivalent
-            assert (
-                dict_op.case_metadata.case_id_number
-                == collection_op.case_metadata.case_id_number
-            )
-            assert dict_op.case_metadata.title == collection_op.case_metadata.title
-            assert (
-                dict_op.case_metadata.start_date
-                == collection_op.case_metadata.start_date
-            )
-            assert (
-                dict_op.case_metadata.end_date == collection_op.case_metadata.end_date
-            )
-            assert (
-                dict_op.case_metadata.event_type
-                == collection_op.case_metadata.event_type
-            )
-
-            # Location types should be the same
-            assert isinstance(
-                dict_op.case_metadata.location,
-                type(collection_op.case_metadata.location),
-            )
-
-            # Other attributes should reference the same mock objects
-            assert dict_op.metric_list is collection_op.metric_list
-            assert dict_op.target is collection_op.target
-            assert dict_op.forecast is collection_op.forecast
-
-    def test_build_case_operators_empty_individual_case_collection(self):
-        """Test build_case_operators with empty IndividualCaseCollection."""
-        empty_collection = cases.IndividualCaseCollection(cases=[])
+    def test_build_case_operators_empty_individual_case_list(self):
+        """Test build_case_operators with empty list of IndividualCase objects."""
+        empty_list = []
 
         mock_eval_obj = mock.Mock()
         mock_eval_obj.event_type = ["heat_wave"]
@@ -578,13 +269,13 @@ def test_build_case_operators_empty_individual_case_collection(self):
         mock_eval_obj.target = mock.Mock()
         mock_eval_obj.forecast = mock.Mock()
 
-        operators = cases.build_case_operators(empty_collection, [mock_eval_obj])
+        operators = cases.build_case_operators(empty_list, [mock_eval_obj])
 
-        # Should create no operators from empty collection
+        # Should create no operators from empty list
         assert len(operators) == 0
 
-    def test_build_case_operators_collection_with_no_matching_events(self):
-        """Test IndividualCaseCollection passthrough with no matching event types."""
+    def test_build_case_operators_list_with_no_matching_events(self):
+        """Test list of IndividualCase objects passthrough with no matching event types."""
         region = regions.CenteredRegion.create_region(
             latitude=40.0, longitude=-100.0, bounding_box_degrees=5.0
         )
@@ -598,7 +289,7 @@ def test_build_case_operators_collection_with_no_matching_events(self):
             event_type="flood",
         )
 
-        case_collection = cases.IndividualCaseCollection(cases=[flood_case])
+        case_list = [flood_case]
 
         # Evaluation object that doesn't match flood
         mock_eval_obj = mock.Mock()
@@ -607,7 +298,7 @@ def test_build_case_operators_collection_with_no_matching_events(self):
         mock_eval_obj.target = mock.Mock()
         mock_eval_obj.forecast = mock.Mock()
 
-        operators = cases.build_case_operators(case_collection, [mock_eval_obj])
+        operators = cases.build_case_operators(case_list, [mock_eval_obj])
 
         # Should create no operators since event types don't match
         assert len(operators) == 0
@@ -618,74 +309,70 @@ class TestLoadIndividualCasesFromYaml:
 
     def test_load_from_yaml_file(self, tmp_path):
         """Test loading individual cases from YAML file."""
-        yaml_content = {
-            "cases": [
-                {
-                    "case_id_number": 42,
-                    "title": "YAML Test Case",
-                    "start_date": datetime.datetime(2022, 5, 1),
-                    "end_date": datetime.datetime(2022, 5, 15),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 38.0,
-                            "longitude": -95.0,
-                            "bounding_box_degrees": 7.5,
-                        },
+        yaml_content = [
+            {
+                "case_id_number": 42,
+                "title": "YAML Test Case",
+                "start_date": datetime.datetime(2022, 5, 1),
+                "end_date": datetime.datetime(2022, 5, 15),
+                "location": {
+                    "type": "centered_region",
+                    "parameters": {
+                        "latitude": 38.0,
+                        "longitude": -95.0,
+                        "bounding_box_degrees": 7.5,
                     },
-                    "event_type": "severe_storm",
-                }
-            ]
-        }
+                },
+                "event_type": "severe_storm",
+            }
+        ]
 
         yaml_file = tmp_path / "test_cases.yaml"
         with open(yaml_file, "w") as f:
             yaml.dump(yaml_content, f)
 
-        collection = cases.load_individual_cases_from_yaml(yaml_file)
+        case_list = cases.load_individual_cases_from_yaml(yaml_file)
 
-        assert isinstance(collection, cases.IndividualCaseCollection)
-        assert len(collection.cases) == 1
+        assert all(isinstance(case, cases.IndividualCase) for case in case_list)
+        assert len(case_list) == 1
 
-        case = collection.cases[0]
-        assert case.case_id_number == 42
-        assert case.title == "YAML Test Case"
-        assert case.event_type == "severe_storm"
-        assert isinstance(case.location, regions.CenteredRegion)
+        single_case = case_list[0]
+        assert single_case.case_id_number == 42
+        assert single_case.title == "YAML Test Case"
+        assert single_case.event_type == "severe_storm"
+        assert isinstance(single_case.location, regions.CenteredRegion)
 
     def test_load_from_yaml_string_path(self, tmp_path):
         """Test loading with string path instead of Path object."""
-        yaml_content = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "String Path Test",
-                    "start_date": datetime.datetime(2023, 1, 1),
-                    "end_date": datetime.datetime(2023, 1, 10),
-                    "location": {
-                        "type": "bounded_region",
-                        "parameters": {
-                            "latitude_min": 30.0,
-                            "latitude_max": 40.0,
-                            "longitude_min": -110.0,
-                            "longitude_max": -100.0,
-                        },
+        yaml_content = [
+            {
+                "case_id_number": 1,
+                "title": "String Path Test",
+                "start_date": datetime.datetime(2023, 1, 1),
+                "end_date": datetime.datetime(2023, 1, 10),
+                "location": {
+                    "type": "bounded_region",
+                    "parameters": {
+                        "latitude_min": 30.0,
+                        "latitude_max": 40.0,
+                        "longitude_min": -110.0,
+                        "longitude_max": -100.0,
                     },
-                    "event_type": "wildfire",
-                }
-            ]
-        }
+                },
+                "event_type": "wildfire",
+            }
+        ]
 
         yaml_file = tmp_path / "test_string_path.yaml"
         with open(yaml_file, "w") as f:
             yaml.dump(yaml_content, f)
 
         # Pass as string, not Path object
-        collection = cases.load_individual_cases_from_yaml(str(yaml_file))
+        case_list = cases.load_individual_cases_from_yaml(str(yaml_file))
 
-        assert len(collection.cases) == 1
-        assert collection.cases[0].title == "String Path Test"
-        assert isinstance(collection.cases[0].location, regions.BoundingBoxRegion)
+        assert len(case_list) == 1
+        assert case_list[0].title == "String Path Test"
+        assert isinstance(case_list[0].location, regions.BoundingBoxRegion)
 
     def test_load_from_yaml_malformed_file(self, tmp_path):
         """Test error handling with malformed YAML."""
@@ -705,10 +392,10 @@ def test_load_from_yaml_missing_file(self):
 
 
 class TestLoadEventsYaml:
-    """Test the load_ewb_events_yaml_into_case_collection function."""
+    """Test the load_ewb_events_yaml_into_case_list function."""
 
     @mock.patch("importlib.resources")
-    def test_load_ewb_events_yaml_into_case_collection_success(self, mock_resources):
+    def test_load_ewb_events_yaml_into_case_list_success(self, mock_resources):
         """Test successful loading of events YAML."""
         # Mock the resource access
         mock_files = mock.Mock()
@@ -716,25 +403,23 @@ def test_load_ewb_events_yaml_into_case_collection_success(self, mock_resources)
         mock_files.joinpath.return_value = "/mock/path/events.yaml"
 
         # Mock the file content
-        mock_yaml_content = {
-            "cases": [
-                {
-                    "case_id_number": 999,
-                    "title": "Mock Event",
-                    "start_date": datetime.datetime(2021, 1, 1),
-                    "end_date": datetime.datetime(2021, 1, 5),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 0.0,
-                            "longitude": 0.0,
-                            "bounding_box_degrees": 1.0,
-                        },
+        mock_yaml_content = [
+            {
+                "case_id_number": 999,
+                "title": "Mock Event",
+                "start_date": datetime.datetime(2021, 1, 1),
+                "end_date": datetime.datetime(2021, 1, 5),
+                "location": {
+                    "type": "centered_region",
+                    "parameters": {
+                        "latitude": 0.0,
+                        "longitude": 0.0,
+                        "bounding_box_degrees": 1.0,
                     },
-                    "event_type": "test_event",
-                }
-            ]
-        }
+                },
+                "event_type": "test_event",
+            }
+        ]
 
         with mock.patch("importlib.resources.as_file") as mock_as_file:
             with mock.patch(
@@ -743,14 +428,15 @@ def test_load_ewb_events_yaml_into_case_collection_success(self, mock_resources)
                 mock_read.return_value = mock_yaml_content
                 mock_as_file.return_value.__enter__.return_value = "/mock/file"
 
-                result = cases.load_ewb_events_yaml_into_case_collection()
+                result = cases.load_ewb_events_yaml_into_case_list()
 
-                # Should return an IndividualCaseCollection, not the raw dict
-                assert isinstance(result, cases.IndividualCaseCollection)
-                assert len(result.cases) == 1
+                # Should return a list of IndividualCase objects, not the raw dict
+                assert isinstance(result, list)
+                assert all(isinstance(case, cases.IndividualCase) for case in result)
+                assert len(result) == 1
 
                 # Verify the case was loaded correctly
-                case = result.cases[0]
+                case = result[0]
                 assert case.case_id_number == 999
                 assert case.title == "Mock Event"
                 assert case.event_type == "test_event"
@@ -763,10 +449,23 @@ class TestReadIncomingYaml:
 
     def test_read_incoming_yaml_success(self, tmp_path):
         """Test successful reading of YAML file."""
-        test_data = {
-            "test_key": "test_value",
-            "cases": [{"case_id_number": 1, "title": "Test"}],
-        }
+        test_data = [
+            {
+                "case_id_number": 1,
+                "title": "Test",
+                "event_type": "heat_wave",
+                "start_date": datetime.datetime(2021, 1, 1),
+                "end_date": datetime.datetime(2021, 1, 15),
+                "location": {
+                    "type": "centered_region",
+                    "parameters": {
+                        "latitude": 40.0,
+                        "longitude": -100.0,
+                        "bounding_box_degrees": 5.0,
+                    },
+                },
+            }
+        ]
 
         yaml_file = tmp_path / "test_input.yaml"
         with open(yaml_file, "w") as f:
@@ -775,9 +474,17 @@ def test_read_incoming_yaml_success(self, tmp_path):
         result = cases.read_incoming_yaml(yaml_file)
 
         assert result == test_data
-        assert isinstance(result, dict)
-        assert "test_key" in result
-        assert result["test_key"] == "test_value"
+        assert isinstance(result, list)
+        assert len(result) == 1
+        assert result[0]["case_id_number"] == 1
+        assert result[0]["title"] == "Test"
+        assert result[0]["event_type"] == "heat_wave"
+        assert result[0]["start_date"] == datetime.datetime(2021, 1, 1)
+        assert result[0]["end_date"] == datetime.datetime(2021, 1, 15)
+        assert result[0]["location"]["type"] == "centered_region"
+        assert result[0]["location"]["parameters"]["latitude"] == 40.0
+        assert result[0]["location"]["parameters"]["longitude"] == -100.0
+        assert result[0]["location"]["parameters"]["bounding_box_degrees"] == 5.0
 
     def test_read_incoming_yaml_with_string_path(self, tmp_path):
         """Test reading YAML with string path."""
@@ -857,144 +564,55 @@ def test_case_operator_with_list_metrics(self):
 
     def test_build_case_operators_empty_evaluation_objects(self):
         """Test building case operators with empty evaluation objects."""
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Lonely Case",
-                    "start_date": datetime.datetime(2021, 1, 1),
-                    "end_date": datetime.datetime(2021, 1, 15),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 40.0,
-                            "longitude": -100.0,
-                            "bounding_box_degrees": 5.0,
-                        },
+        cases_list = [
+            {
+                "case_id_number": 1,
+                "title": "Lonely Case",
+                "start_date": datetime.datetime(2021, 1, 1),
+                "end_date": datetime.datetime(2021, 1, 15),
+                "location": {
+                    "type": "centered_region",
+                    "parameters": {
+                        "latitude": 40.0,
+                        "longitude": -100.0,
+                        "bounding_box_degrees": 5.0,
                     },
-                    "event_type": "lonely_event",
-                }
-            ]
-        }
+                },
+                "event_type": "lonely_event",
+            }
+        ]
 
-        operators = cases.build_case_operators(cases_dict, [])
+        operators = cases.build_case_operators(cases_list, [])
         assert len(operators) == 0
 
-    def test_build_case_operators_partial_event_matches(self):
+    def test_build_case_operators_partial_event_matches(self, individualcase_list):
         """Test case operators with complex event type matching."""
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Heat Wave",
-                    "start_date": datetime.datetime(2021, 6, 1),
-                    "end_date": datetime.datetime(2021, 6, 15),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 40.0,
-                            "longitude": -100.0,
-                            "bounding_box_degrees": 5.0,
-                        },
-                    },
-                    "event_type": "heat_wave",
-                },
-                {
-                    "case_id_number": 2,
-                    "title": "Drought",
-                    "start_date": datetime.datetime(2021, 7, 1),
-                    "end_date": datetime.datetime(2021, 7, 31),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 35.0,
-                            "longitude": -105.0,
-                            "bounding_box_degrees": 8.0,
-                        },
-                    },
-                    "event_type": "drought",
-                },
-                {
-                    "case_id_number": 3,
-                    "title": "Flood",
-                    "start_date": datetime.datetime(2021, 8, 1),
-                    "end_date": datetime.datetime(2021, 8, 10),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 45.0,
-                            "longitude": -90.0,
-                            "bounding_box_degrees": 3.0,
-                        },
-                    },
-                    "event_type": "flood",
-                },
-            ]
-        }
-
         # Evaluation object that only matches heat_wave and drought
         eval_obj = mock.Mock()
-        eval_obj.event_type = ["heat_wave", "drought"]
+        eval_obj.event_type = ["heat_wave", "cats_and_dogs"]
         eval_obj.metric = mock.Mock()
         eval_obj.target = mock.Mock()
         eval_obj.forecast = mock.Mock()
 
-        operators = cases.build_case_operators(cases_dict, [eval_obj])
+        operators = cases.build_case_operators(individualcase_list, [eval_obj])
 
         # Should create 2 operators (heat_wave and drought, but not flood)
-        assert len(operators) == 2
+        assert len(operators) == 1
 
         created_case_ids = [op.case_metadata.case_id_number for op in operators]
         assert 1 in created_case_ids  # heat_wave case
-        assert 2 in created_case_ids  # drought case
-        assert 3 not in created_case_ids  # flood case should be excluded
+        assert 2 not in created_case_ids  # cats_and_dogs case should be excluded
 
 
 class TestCasesIntegration:
     """Integration tests for the cases module."""
 
-    def test_full_workflow(self):
+    def test_full_workflow(self, cases_list_of_dicts):
         """Test the full workflow from dictionary to case operators."""
-        # Define cases dictionary as might come from YAML
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 100,
-                    "title": "California Heat Wave 2021",
-                    "start_date": datetime.datetime(2021, 6, 15),
-                    "end_date": datetime.datetime(2021, 6, 25),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 37.7749,
-                            "longitude": -122.4194,
-                            "bounding_box_degrees": 5.0,
-                        },
-                    },
-                    "event_type": "heat_wave",
-                },
-                {
-                    "case_id_number": 101,
-                    "title": "Texas Drought 2021",
-                    "start_date": datetime.datetime(2021, 7, 1),
-                    "end_date": datetime.datetime(2021, 8, 31),
-                    "location": {
-                        "type": "bounded_region",
-                        "parameters": {
-                            "latitude_min": 29.0,
-                            "latitude_max": 34.0,
-                            "longitude_min": -104.0,
-                            "longitude_max": -94.0,
-                        },
-                    },
-                    "event_type": "drought",
-                },
-            ]
-        }
 
         # Load individual cases
-        collection = cases.load_individual_cases(cases_dict)
-        assert len(collection.cases) == 2
+        case_list = cases.load_individual_cases(cases_list_of_dicts)
+        assert len(case_list) == 2
 
         # Create mock evaluation objects
         heat_wave_eval = mock.Mock()
@@ -1012,7 +630,7 @@ def test_full_workflow(self):
         evaluation_objects = [heat_wave_eval, multi_event_eval]
 
         # Build case operators
-        operators = cases.build_case_operators(cases_dict, evaluation_objects)
+        operators = cases.build_case_operators(case_list, evaluation_objects)
 
         # Should create 3 operators:
         # - Heat wave case with heat_wave_eval
@@ -1022,10 +640,10 @@ def test_full_workflow(self):
 
         # Verify the operators contain the correct cases
         heat_wave_operators = [
-            op for op in operators if op.case_metadata.case_id_number == 100
+            op for op in operators if op.case_metadata.event_type == "heat_wave"
         ]
         drought_operators = [
-            op for op in operators if op.case_metadata.case_id_number == 101
+            op for op in operators if op.case_metadata.event_type == "drought"
         ]
 
         assert len(heat_wave_operators) == 2  # Both eval objects match heat_wave
@@ -1045,415 +663,3 @@ def test_full_workflow(self):
                 assert isinstance(
                     operator.case_metadata.location, regions.BoundingBoxRegion
                 )
-
-
-class TestSelectCases:
-    """Test the select_cases method of IndividualCaseCollection."""
-
-    def create_test_collection(self):
-        """Helper to create a test case collection."""
-        region1 = regions.BoundingBoxRegion.create_region(
-            latitude_min=30.0,
-            latitude_max=40.0,
-            longitude_min=-110.0,
-            longitude_max=-100.0,
-        )
-        region2 = regions.BoundingBoxRegion.create_region(
-            latitude_min=35.0,
-            latitude_max=45.0,
-            longitude_min=-105.0,
-            longitude_max=-95.0,
-        )
-        region3 = regions.CenteredRegion.create_region(
-            latitude=50.0, longitude=-120.0, bounding_box_degrees=5.0
-        )
-
-        case1 = cases.IndividualCase(
-            case_id_number=1,
-            title="Southern Heat Wave",
-            start_date=datetime.datetime(2021, 6, 1),
-            end_date=datetime.datetime(2021, 6, 15),
-            location=region1,
-            event_type="heat_wave",
-        )
-
-        case2 = cases.IndividualCase(
-            case_id_number=2,
-            title="Central Drought",
-            start_date=datetime.datetime(2021, 7, 1),
-            end_date=datetime.datetime(2021, 7, 31),
-            location=region2,
-            event_type="drought",
-        )
-
-        case3 = cases.IndividualCase(
-            case_id_number=3,
-            title="Northern Storm",
-            start_date=datetime.datetime(2021, 8, 1),
-            end_date=datetime.datetime(2021, 8, 10),
-            location=region3,
-            event_type="storm",
-        )
-
-        return cases.IndividualCaseCollection(cases=[case1, case2, case3])
-
-    def test_select_by_event_type(self):
-        """Test selecting cases by event type."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="event_type", value="heat_wave")
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 1
-        assert selected.cases[0].case_id_number == 1
-        assert selected.cases[0].event_type == "heat_wave"
-
-    def test_select_by_case_id_number(self):
-        """Test selecting cases by case ID number."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="case_id_number", value=2)
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 1
-        assert selected.cases[0].case_id_number == 2
-        assert selected.cases[0].title == "Central Drought"
-
-    def test_select_by_multiple_case_id_numbers(self):
-        """Test selecting cases by list of case ID numbers."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="case_id_number", value=[1, 3])
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 2
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-        assert 3 in case_ids
-        assert 2 not in case_ids
-
-    def test_select_by_case_id_number_list_all_cases(self):
-        """Test selecting all cases by providing list of all IDs."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="case_id_number", value=[1, 2, 3])
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 3
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-        assert 2 in case_ids
-        assert 3 in case_ids
-
-    def test_select_by_case_id_number_list_single_id(self):
-        """Test selecting by list with single case ID number."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="case_id_number", value=[2])
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 1
-        assert selected.cases[0].case_id_number == 2
-
-    def test_select_by_case_id_number_list_no_matches(self):
-        """Test selecting by list with no matching case IDs."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="case_id_number", value=[99, 100])
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 0
-
-    def test_select_by_case_id_number_list_partial_matches(self):
-        """Test selecting by list with partial matches."""
-        collection = self.create_test_collection()
-
-        # List includes both existing (1, 2) and non-existing (99) IDs
-        selected = collection.select_cases(by="case_id_number", value=[1, 2, 99])
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 2
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-        assert 2 in case_ids
-        assert 99 not in case_ids
-
-    def test_select_by_case_id_number_list_empty(self):
-        """Test selecting by empty list of case IDs."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="case_id_number", value=[])
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 0
-
-    def test_select_by_case_id_number_list_with_duplicates(self):
-        """Test selecting by list with duplicate case IDs."""
-        collection = self.create_test_collection()
-
-        # List with duplicate ID 1
-        selected = collection.select_cases(by="case_id_number", value=[1, 1, 2])
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        # Should still return 2 unique cases
-        assert len(selected.cases) == 2
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-        assert 2 in case_ids
-
-    def test_select_by_case_id_number_list_inplace_false(self):
-        """Test list selection doesn't modify original collection."""
-        collection = self.create_test_collection()
-        original_length = len(collection.cases)
-
-        selected = collection.select_cases(
-            by="case_id_number", value=[1, 3], inplace=False
-        )
-
-        # Collection should be unchanged
-        assert len(collection.cases) == original_length
-        # Returned collection should be filtered
-        assert len(selected.cases) == 2
-
-    def test_select_by_case_id_number_list_inplace_true(self):
-        """Test list selection modifies collection in place."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(
-            by="case_id_number", value=[2, 3], inplace=True
-        )
-
-        # Collection should be modified
-        assert len(collection.cases) == 2
-        case_ids = [case.case_id_number for case in collection.cases]
-        assert 2 in case_ids
-        assert 3 in case_ids
-        assert 1 not in case_ids
-        # Returned collection should match
-        assert len(selected.cases) == 2
-
-    def test_select_by_title(self):
-        """Test selecting cases by title."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="title", value="Northern Storm")
-
-        assert isinstance(selected, cases.IndividualCaseCollection)
-        assert len(selected.cases) == 1
-        assert selected.cases[0].case_id_number == 3
-        assert selected.cases[0].title == "Northern Storm"
-
-    def test_select_by_region_object_overlapping(self):
-        """Test selecting cases by Region that overlaps with cases."""
-        collection = self.create_test_collection()
-
-        # Create a region that overlaps with case1 and case2
-        search_region = regions.BoundingBoxRegion.create_region(
-            latitude_min=32.0,
-            latitude_max=38.0,
-            longitude_min=-108.0,
-            longitude_max=-98.0,
-        )
-
-        selected = collection.select_cases(by="location", value=search_region)
-
-        # Should select cases 1 and 2 (both overlap with search_region)
-        assert len(selected.cases) == 2
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-        assert 2 in case_ids
-
-    def test_select_by_region_object_no_overlap(self):
-        """Test selecting cases by Region that doesn't overlap."""
-        collection = self.create_test_collection()
-
-        # Create a region that doesn't overlap with any cases
-        search_region = regions.BoundingBoxRegion.create_region(
-            latitude_min=10.0,
-            latitude_max=20.0,
-            longitude_min=-80.0,
-            longitude_max=-70.0,
-        )
-
-        selected = collection.select_cases(by="location", value=search_region)
-
-        assert len(selected.cases) == 0
-
-    def test_select_by_region_object_single_match(self):
-        """Test selecting cases by Region matching single case."""
-        collection = self.create_test_collection()
-
-        # Create a region that only overlaps with case3
-        search_region = regions.CenteredRegion.create_region(
-            latitude=50.0, longitude=-120.0, bounding_box_degrees=3.0
-        )
-
-        selected = collection.select_cases(by="location", value=search_region)
-
-        assert len(selected.cases) == 1
-        assert selected.cases[0].case_id_number == 3
-
-    def test_select_by_tuple_coordinates_overlapping(self):
-        """Test selecting cases by tuple of coordinates."""
-        collection = self.create_test_collection()
-
-        # Tuple format: longitude_min, lat_min, lon_max, lat_max
-        search_coords = (-108.0, 32.0, -98.0, 38.0)
-
-        selected = collection.select_cases(by="location", value=search_coords)
-
-        # Should select cases 1 and 2
-        assert len(selected.cases) == 2
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-        assert 2 in case_ids
-
-    def test_select_by_list_coordinates_overlapping(self):
-        """Test selecting cases by list of coordinates."""
-        collection = self.create_test_collection()
-
-        # List format: longitude_min, lat_min, lon_max, lat_max
-        search_coords = [-108.0, 32.0, -98.0, 38.0]
-
-        selected = collection.select_cases(by="location", value=search_coords)
-
-        # Should select cases 1 and 2
-        assert len(selected.cases) == 2
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-        assert 2 in case_ids
-
-    def test_select_by_coordinates_no_overlap(self):
-        """Test selecting cases by coordinates that don't overlap."""
-        collection = self.create_test_collection()
-
-        # Coordinates far from any cases
-        search_coords = (-80.0, 10.0, -70.0, 20.0)
-
-        selected = collection.select_cases(by="location", value=search_coords)
-
-        assert len(selected.cases) == 0
-
-    def test_select_by_coordinates_contains_case(self):
-        """Test selecting when search region contains a case."""
-        collection = self.create_test_collection()
-
-        # Large region that contains case1
-        search_coords = (-115.0, 25.0, -95.0, 45.0)
-
-        selected = collection.select_cases(by="location", value=search_coords)
-
-        # Should select cases 1 and 2 (both within or overlapping)
-        assert len(selected.cases) >= 1
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-
-    def test_select_by_coordinates_contained_by_case(self):
-        """Test selecting when search region is contained by a case."""
-        collection = self.create_test_collection()
-
-        # Small region inside case1's bounds
-        search_coords = (-107.0, 32.0, -103.0, 36.0)
-
-        selected = collection.select_cases(by="location", value=search_coords)
-
-        # Should select case1 (contains the search region)
-        assert len(selected.cases) >= 1
-        case_ids = [case.case_id_number for case in selected.cases]
-        assert 1 in case_ids
-
-    def test_select_inplace_false(self):
-        """Test select_cases without modifying original collection."""
-        collection = self.create_test_collection()
-        original_length = len(collection.cases)
-
-        selected = collection.select_cases(
-            by="event_type", value="heat_wave", inplace=False
-        )
-
-        # Collection should be unchanged
-        assert len(collection.cases) == original_length
-        # But returned list should be filtered
-        assert len(selected.cases) == 1
-
-    def test_select_inplace_true(self):
-        """Test select_cases modifying collection in place."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(
-            by="event_type", value="drought", inplace=True
-        )
-
-        # Collection should be modified
-        assert len(collection.cases) == 1
-        assert collection.cases[0].event_type == "drought"
-        # Returned list should match
-        assert len(selected.cases) == 1
-        assert selected.cases[0].event_type == "drought"
-
-    def test_select_invalid_field_raises_error(self):
-        """Test that selecting by invalid field raises ValueError."""
-        collection = self.create_test_collection()
-
-        with pytest.raises(ValueError, match="Invalid field to select"):
-            collection.select_cases(by="invalid_field", value="something")
-
-    def test_select_empty_result_set(self):
-        """Test selecting with criteria that match nothing."""
-        collection = self.create_test_collection()
-
-        selected = collection.select_cases(by="event_type", value="nonexistent_type")
-
-        assert len(selected.cases) == 0
-        assert isinstance(selected, cases.IndividualCaseCollection)
-
-    def test_select_all_cases_by_large_region(self):
-        """Test selecting all cases with very large region."""
-        collection = self.create_test_collection()
-
-        # Very large region that encompasses everything
-        large_region = regions.BoundingBoxRegion.create_region(
-            latitude_min=0.0,
-            latitude_max=90.0,
-            longitude_min=-180.0,
-            longitude_max=-50.0,
-        )
-
-        selected = collection.select_cases(by="location", value=large_region)
-
-        # Should select all 3 cases
-        assert len(selected.cases) == 3
-        assert isinstance(selected, cases.IndividualCaseCollection)
-
-    def test_select_by_region_edge_touching(self):
-        """Test selection when regions share an edge."""
-        region1 = regions.BoundingBoxRegion.create_region(
-            latitude_min=30.0,
-            latitude_max=40.0,
-            longitude_min=-110.0,
-            longitude_max=-100.0,
-        )
-
-        case1 = cases.IndividualCase(
-            case_id_number=1,
-            title="Edge Case",
-            start_date=datetime.datetime(2021, 6, 1),
-            end_date=datetime.datetime(2021, 6, 15),
-            location=region1,
-            event_type="test",
-        )
-
-        collection = cases.IndividualCaseCollection(cases=[case1])
-
-        # Region sharing an edge (touches at longitude=-100.0)
-        edge_region = regions.BoundingBoxRegion.create_region(
-            latitude_min=30.0,
-            latitude_max=40.0,
-            longitude_min=-100.0,
-            longitude_max=-90.0,
-        )
-
-        selected = collection.select_cases(by="location", value=edge_region)
-
-        # Should select the case (edges touching count as intersecting)
-        assert len(selected.cases) == 1
diff --git a/tests/test_evaluate.py b/tests/test_evaluate.py
index fe23d95c..9220093f 100644
--- a/tests/test_evaluate.py
+++ b/tests/test_evaluate.py
@@ -45,27 +45,25 @@ def sample_individual_case():
 
 
 @pytest.fixture
-def sample_cases_dict(sample_individual_case):
+def sample_cases_list(sample_individual_case):
     """Create a sample cases dictionary."""
-    return {
-        "cases": [
-            {
-                "case_id_number": 1,
-                "title": "Test Heat Wave",
-                "start_date": datetime.datetime(2021, 6, 20),
-                "end_date": datetime.datetime(2021, 6, 25),
-                "location": {
-                    "type": "centered_region",
-                    "parameters": {
-                        "latitude": 45.0,
-                        "longitude": -120.0,
-                        "bounding_box_degrees": 5.0,
-                    },
+    return [
+        {
+            "case_id_number": 1,
+            "title": "Test Heat Wave",
+            "start_date": datetime.datetime(2021, 6, 20),
+            "end_date": datetime.datetime(2021, 6, 25),
+            "location": {
+                "type": "centered_region",
+                "parameters": {
+                    "latitude": 45.0,
+                    "longitude": -120.0,
+                    "bounding_box_degrees": 5.0,
                 },
-                "event_type": "heat_wave",
-            }
-        ]
-    }
+            },
+            "event_type": "heat_wave",
+        }
+    ]
 
 
 @pytest.fixture
@@ -238,10 +236,10 @@ class TestExtremeWeatherBench:
     """Test the ExtremeWeatherBench class."""
 
     def assert_cases_equal(self, actual, expected):
-        """Assert that two IndividualCaseCollection instances are equal."""
-        assert len(actual.cases) == len(expected.cases)
+        """Assert that two list of IndividualCase objects are equal."""
+        assert len(actual) == len(expected)
 
-        for actual_case, expected_case in zip(actual.cases, expected.cases):
+        for actual_case, expected_case in zip(actual, expected):
             assert actual_case.case_id_number == expected_case.case_id_number
             assert actual_case.title == expected_case.title
             assert actual_case.start_date == expected_case.start_date
@@ -260,26 +258,26 @@ def assert_cases_equal(self, actual, expected):
                     == expected_case.location.bounding_box_degrees
                 )
 
-    def test_initialization(self, sample_cases_dict, sample_evaluation_object):
+    def test_initialization(self, sample_cases_list, sample_evaluation_object):
         """Test ExtremeWeatherBench initialization."""
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=sample_cases_dict,
+            case_metadata=sample_cases_list,
             evaluation_objects=[sample_evaluation_object],
         )
 
         self.assert_cases_equal(
-            ewb.case_metadata, cases.load_individual_cases(sample_cases_dict)
+            ewb.case_metadata, cases.load_individual_cases(sample_cases_list)
         )
         assert ewb.evaluation_objects == [sample_evaluation_object]
         assert ewb.cache_dir is None
 
     def test_initialization_with_cache_dir(
-        self, sample_cases_dict, sample_evaluation_object
+        self, sample_cases_list, sample_evaluation_object
     ):
         """Test ExtremeWeatherBench initialization with cache directory."""
         cache_dir = "/tmp/test_cache"
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=sample_cases_dict,
+            case_metadata=sample_cases_list,
             evaluation_objects=[sample_evaluation_object],
             cache_dir=cache_dir,
         )
@@ -288,12 +286,12 @@ def test_initialization_with_cache_dir(
         assert ewb.cache_dir == pathlib.Path(cache_dir)
 
     def test_initialization_with_path_cache_dir(
-        self, sample_cases_dict, sample_evaluation_object
+        self, sample_cases_list, sample_evaluation_object
     ):
         """Test ExtremeWeatherBench initialization with Path cache directory."""
         cache_dir = pathlib.Path("/tmp/test_cache")
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=sample_cases_dict,
+            case_metadata=sample_cases_list,
             evaluation_objects=[sample_evaluation_object],
             cache_dir=cache_dir,
         )
@@ -304,7 +302,7 @@ def test_initialization_with_path_cache_dir(
     def test_case_operators_property(
         self,
         mock_build_case_operators,
-        sample_cases_dict,
+        sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
     ):
@@ -312,7 +310,7 @@ def test_case_operators_property(
         mock_build_case_operators.return_value = [sample_case_operator]
 
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=sample_cases_dict,
+            case_metadata=sample_cases_list,
             evaluation_objects=[sample_evaluation_object],
         )
 
@@ -322,10 +320,10 @@ def test_case_operators_property(
         mock_build_case_operators.assert_called_once()
         call_args = mock_build_case_operators.call_args[0]
 
-        # Check that the first argument (case collection) has the right structure
-        passed_case_collection = call_args[0]
+        # Check that the first argument (case list) has the right structure
+        passed_case_list = call_args[0]
         self.assert_cases_equal(
-            passed_case_collection, cases.load_individual_cases(sample_cases_dict)
+            passed_case_list, cases.load_individual_cases(sample_cases_list)
         )
 
         # Check that the second argument (evaluation objects) is correct
@@ -338,7 +336,7 @@ def test_case_operators_property(
     def test_run_serial(
         self,
         mock_run_case_operators,
-        sample_cases_dict,
+        sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
     ):
@@ -360,7 +358,7 @@ def test_run_serial(
             mock_run_case_operators.return_value = mock_result
 
             ewb = evaluate.ExtremeWeatherBench(
-                case_metadata=sample_cases_dict,
+                case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
@@ -379,7 +377,7 @@ def test_run_serial(
     def test_run_parallel(
         self,
         mock_run_case_operators,
-        sample_cases_dict,
+        sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
     ):
@@ -399,7 +397,7 @@ def test_run_parallel(
             mock_run_case_operators.return_value = mock_result
 
             ewb = evaluate.ExtremeWeatherBench(
-                case_metadata=sample_cases_dict,
+                case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
@@ -417,7 +415,7 @@ def test_run_parallel(
     def test_run_with_kwargs(
         self,
         mock_run_case_operators,
-        sample_cases_dict,
+        sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
     ):
@@ -429,7 +427,7 @@ def test_run_with_kwargs(
             mock_run_case_operators.return_value = mock_result
 
             ewb = evaluate.ExtremeWeatherBench(
-                case_metadata=sample_cases_dict,
+                case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
@@ -444,7 +442,7 @@ def test_run_with_kwargs(
     def test_run_empty_results(
         self,
         mock_run_case_operators,
-        sample_cases_dict,
+        sample_cases_list,
         sample_evaluation_object,
     ):
         """Test the run method handles empty results."""
@@ -452,7 +450,7 @@ def test_run_empty_results(
             mock_run_case_operators.return_value = []
 
             ewb = evaluate.ExtremeWeatherBench(
-                case_metadata=sample_cases_dict,
+                case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
@@ -466,7 +464,7 @@ def test_run_empty_results(
     def test_run_with_caching(
         self,
         mock_compute_case_operator,
-        sample_cases_dict,
+        sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
     ):
@@ -501,7 +499,7 @@ def mock_compute_with_caching(case_operator, cache_dir_arg, **kwargs):
                 mock_compute_case_operator.side_effect = mock_compute_with_caching
 
                 ewb = evaluate.ExtremeWeatherBench(
-                    case_metadata=sample_cases_dict,
+                    case_metadata=sample_cases_list,
                     evaluation_objects=[sample_evaluation_object],
                     cache_dir=cache_dir,
                 )
@@ -517,7 +515,7 @@ def mock_compute_with_caching(case_operator, cache_dir_arg, **kwargs):
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
     def test_run_multiple_cases(
-        self, mock_compute_case_operator, sample_cases_dict, sample_evaluation_object
+        self, mock_compute_case_operator, sample_cases_list, sample_evaluation_object
     ):
         """Test the run method with multiple case operators."""
         # Create multiple case operators
@@ -536,7 +534,7 @@ def test_run_multiple_cases(
             ]
 
             ewb = evaluate.ExtremeWeatherBench(
-                case_metadata=sample_cases_dict,
+                case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
@@ -1591,22 +1589,20 @@ class TestErrorHandling:
     """Test error handling and edge cases."""
 
     def test_extremeweatherbench_empty_cases(self, sample_evaluation_object):
-        """Test ExtremeWeatherBench with empty cases."""
-        empty_cases = {"cases": []}
+        """Test ExtremeWeatherBench with empty cases.
+
+        Should return an empty DataFrame when no cases are provided.
+        """
+        empty_cases = []
 
         ewb = evaluate.ExtremeWeatherBench(
             case_metadata=empty_cases,
             evaluation_objects=[sample_evaluation_object],
         )
 
-        with mock.patch("extremeweatherbench.cases.build_case_operators") as mock_build:
-            mock_build.return_value = []
-
-            result = ewb.run()
-
-            # Should return empty DataFrame when no cases
-            assert isinstance(result, pd.DataFrame)
-            assert len(result) == 0
+        result = ewb.run()
+        assert isinstance(result, pd.DataFrame)
+        assert len(result) == 0
 
     def test_compute_case_operator_exception_handling(self, sample_case_operator):
         """Test exception handling in compute_case_operator."""
@@ -1735,13 +1731,13 @@ def consume_generator(generator):
 
     @mock.patch("extremeweatherbench.evaluate._run_case_operators")
     def test_run_method_exception_propagation(
-        self, mock_run_case_operators, sample_cases_dict, sample_evaluation_object
+        self, mock_run_case_operators, sample_cases_list, sample_evaluation_object
     ):
         """Test that ExtremeWeatherBench.run() propagates exceptions correctly."""
         mock_run_case_operators.side_effect = Exception("Execution failed")
 
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=sample_cases_dict,
+            case_metadata=sample_cases_list,
             evaluation_objects=[sample_evaluation_object],
         )
 
@@ -1803,7 +1799,7 @@ def test_end_to_end_workflow(
         self,
         mock_maybe_subset_variables,
         mock_derive_variables,
-        sample_cases_dict,
+        sample_cases_list,
         sample_evaluation_object,
         sample_forecast_dataset,
         sample_target_dataset,
@@ -1869,7 +1865,7 @@ def test_end_to_end_workflow(
 
             # Create and run the workflow
             ewb = evaluate.ExtremeWeatherBench(
-                case_metadata=sample_cases_dict,
+                case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
@@ -1887,7 +1883,7 @@ def test_end_to_end_workflow(
     def test_multiple_variables_and_metrics(
         self,
         mock_maybe_subset_variables,
-        sample_cases_dict,
+        sample_cases_list,
         sample_forecast_dataset,
         sample_target_dataset,
     ):
@@ -1973,7 +1969,7 @@ def test_multiple_variables_and_metrics(
                 mock_eval.return_value = mock_result_df
 
                 ewb = evaluate.ExtremeWeatherBench(
-                    case_metadata=sample_cases_dict,
+                    case_metadata=sample_cases_list,
                     evaluation_objects=[eval_obj],
                 )
 
@@ -1984,7 +1980,7 @@ def test_multiple_variables_and_metrics(
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
     def test_serial_vs_parallel_results_consistency(
-        self, mock_compute_case_operator, sample_cases_dict, sample_evaluation_object
+        self, mock_compute_case_operator, sample_cases_list, sample_evaluation_object
     ):
         """Test that serial and parallel execution produce identical results."""
         # Setup mock case operators
@@ -2011,7 +2007,7 @@ def test_serial_vs_parallel_results_consistency(
         )
 
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=sample_cases_dict,
+            case_metadata=sample_cases_list,
             evaluation_objects=[sample_evaluation_object],
         )
 
diff --git a/tests/test_evaluate_cli.py b/tests/test_evaluate_cli.py
index 42c586be..e5797712 100644
--- a/tests/test_evaluate_cli.py
+++ b/tests/test_evaluate_cli.py
@@ -2,6 +2,7 @@
 
 import pickle
 import tempfile
+import textwrap
 from pathlib import Path
 from unittest import mock
 
@@ -42,11 +43,11 @@ def temp_config_dir():
 @pytest.fixture
 def sample_config_py(temp_config_dir):
     """Create a sample Python config file."""
-    config_content = """
-# Simple test config that doesn't import complex modules
-evaluation_objects = []
-cases_dict = {"cases": []}
-"""
+    config_content = textwrap.dedent("""
+        # Simple test config that doesn't import complex modules
+        evaluation_objects = []
+        case_list = []
+        """)
     config_file = temp_config_dir / "test_config.py"
     config_file.write_text(config_content)
     return config_file
@@ -91,7 +92,7 @@ def test_default_mode_basic(
         mock_ewb_class.return_value = mock_ewb
 
         # Mock loading default cases
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         result = runner.invoke(
             evaluate_cli.cli_runner, ["--default", "--output-dir", str(temp_config_dir)]
@@ -120,7 +121,7 @@ def test_default_mode_with_cache_dir(
         mock_ewb.case_operators = []
         mock_ewb.run.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         cache_dir = temp_config_dir / "cache"
 
@@ -170,9 +171,9 @@ def test_config_file_nonexistent(self, runner):
 
     def test_config_file_missing_evaluation_objects(self, runner, temp_config_dir):
         """Test config file missing required evaluation_objects."""
-        config_content = """
-cases_dict = {"cases": []}
-        """
+        config_content = textwrap.dedent("""
+        cases_list = []
+        """)
         config_file = temp_config_dir / "bad_config.py"
         config_file.write_text(config_content)
 
@@ -183,11 +184,11 @@ def test_config_file_missing_evaluation_objects(self, runner, temp_config_dir):
         assert result.exit_code != 0
         # Output suppressed - only check exit code
 
-    def test_config_file_missing_cases_dict(self, runner, temp_config_dir):
-        """Test config file missing required cases_dict."""
-        config_content = """
-evaluation_objects = []
-        """
+    def test_config_file_missing_case_list(self, runner, temp_config_dir):
+        """Test config file missing required case_list."""
+        config_content = textwrap.dedent("""
+        evaluation_objects = []
+        """)
         config_file = temp_config_dir / "bad_config.py"
         config_file.write_text(config_content)
 
@@ -220,7 +221,7 @@ def test_parallel_execution(
         mock_ewb.case_operators = [mock.Mock(), mock.Mock(), mock.Mock()]
         mock_ewb.run.return_value = pd.DataFrame({"test": [1, 2, 3]})
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         result = runner.invoke(evaluate_cli.cli_runner, ["--default", "--n-jobs", "3"])
 
@@ -245,7 +246,7 @@ def test_serial_execution_default(
         mock_ewb.case_operators = []
         mock_ewb.run.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         result = runner.invoke(evaluate_cli.cli_runner, ["--default"])
 
@@ -279,7 +280,7 @@ def test_save_case_operators(
         mock_ewb.case_operators = [mock_case_op1, mock_case_op2]
         mock_ewb.run.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         # Use temp directory for pickle file to ensure cleanup
         save_path = temp_config_dir / "case_ops.pkl"
@@ -318,7 +319,7 @@ def test_save_case_operators_creates_directory(
         mock_ewb.case_operators = []
         mock_ewb.run.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         # Use nested path within temp directory for auto-cleanup
         nested_path = temp_config_dir / "nested" / "dirs" / "case_ops.pkl"
@@ -370,7 +371,7 @@ def test_output_directory_creation(
         mock_ewb.case_operators = []
         mock_ewb.run.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         output_dir = temp_config_dir / "new_output_dir"
         assert not output_dir.exists()
@@ -396,7 +397,7 @@ def test_default_output_directory(
         mock_ewb.case_operators = []
         mock_ewb.run.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         # Use isolated filesystem to avoid creating files in actual directories
         with runner.isolated_filesystem():
@@ -436,7 +437,7 @@ def test_results_saved_to_csv(
         mock_ewb.case_operators = []
         mock_ewb.run.return_value = mock_results
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         # Use temp directory for output to ensure cleanup
         result = runner.invoke(
@@ -457,7 +458,7 @@ def test_empty_results_handling(self, mock_ewb_class, mock_load_cases, runner):
         mock_ewb.case_operators = []
         mock_ewb.run.return_value = pd.DataFrame()  # Empty results
         mock_ewb_class.return_value = mock_ewb
-        mock_load_cases.return_value = {"cases": []}
+        mock_load_cases.return_value = []
 
         result = runner.invoke(evaluate_cli.cli_runner, ["--default"])
 
@@ -468,10 +469,10 @@ def test_empty_results_handling(self, mock_ewb_class, mock_load_cases, runner):
 class TestHelperFunctions:
     """Test helper function functionality."""
 
-    @mock.patch("extremeweatherbench.cases.load_ewb_events_yaml_into_case_collection")
+    @mock.patch("extremeweatherbench.cases.load_ewb_events_yaml_into_case_list")
     def test_load_default_cases(self, mock_load_yaml):
         """Test _load_default_cases function."""
-        mock_cases = {"cases": [{"id": 1}]}
+        mock_cases = [{"id": 1}]
         mock_load_yaml.return_value = mock_cases
 
         result = evaluate_cli._load_default_cases()
diff --git a/tests/test_integration.py b/tests/test_integration.py
index 1d269d5a..07fba348 100644
--- a/tests/test_integration.py
+++ b/tests/test_integration.py
@@ -576,29 +576,27 @@ def test_full_workflow_single_variable(
         )
 
         # Create cases dict
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Test Case",
-                    "start_date": datetime.datetime(2021, 6, 20),
-                    "end_date": datetime.datetime(2021, 6, 25),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 45.0,
-                            "longitude": -120.0,
-                            "bounding_box_degrees": 5.0,
-                        },
+        cases_list = [
+            {
+                "case_id_number": 1,
+                "title": "Test Case",
+                "start_date": datetime.datetime(2021, 6, 20),
+                "end_date": datetime.datetime(2021, 6, 25),
+                "location": {
+                    "type": "centered_region",
+                    "parameters": {
+                        "latitude": 45.0,
+                        "longitude": -120.0,
+                        "bounding_box_degrees": 5.0,
                     },
-                    "event_type": "test_event",
-                }
-            ]
-        }
+                },
+                "event_type": "test_event",
+            }
+        ]
 
         # Create and run ExtremeWeatherBench
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=cases_dict,
+            case_metadata=cases_list,
             evaluation_objects=[evaluation_obj],
         )
 
@@ -657,29 +655,27 @@ def test_full_workflow_multiple_variables(
         )
 
         # Create cases dict
-        cases_dict = {
-            "cases": [
-                {
-                    "case_id_number": 1,
-                    "title": "Test Case",
-                    "start_date": datetime.datetime(2021, 6, 20),
-                    "end_date": datetime.datetime(2021, 6, 25),
-                    "location": {
-                        "type": "centered_region",
-                        "parameters": {
-                            "latitude": 45.0,
-                            "longitude": -120.0,
-                            "bounding_box_degrees": 5.0,
-                        },
+        cases_list = [
+            {
+                "case_id_number": 1,
+                "title": "Test Case",
+                "start_date": datetime.datetime(2021, 6, 20),
+                "end_date": datetime.datetime(2021, 6, 25),
+                "location": {
+                    "type": "centered_region",
+                    "parameters": {
+                        "latitude": 45.0,
+                        "longitude": -120.0,
+                        "bounding_box_degrees": 5.0,
                     },
-                    "event_type": "test_event",
-                }
-            ]
-        }
+                },
+                "event_type": "test_event",
+            }
+        ]
 
         # Create and run ExtremeWeatherBench
         ewb = evaluate.ExtremeWeatherBench(
-            case_metadata=cases_dict,
+            case_metadata=cases_list,
             evaluation_objects=[evaluation_obj],
         )
 
diff --git a/tests/test_regions.py b/tests/test_regions.py
index dc580f4c..ffae7fc8 100644
--- a/tests/test_regions.py
+++ b/tests/test_regions.py
@@ -1418,9 +1418,7 @@ def sample_cases(self):
             event_type="snow_storm",
         )
 
-        return cases.IndividualCaseCollection(
-            cases=[intersecting_case, contained_case, outside_case]
-        )
+        return [intersecting_case, contained_case, outside_case]
 
     def test_subsetter_initialization_with_region(self, target_region):
         """Test regions.RegionSubsetter initialization with Region object."""
@@ -1449,64 +1447,58 @@ def test_subsetter_initialization_with_dict(self):
         assert subsetter.method == "percent"
         assert subsetter.percent_threshold == 0.75
 
-    def test_subset_case_collection_intersects(self, target_region, sample_cases):
+    def test_subset_case_list_intersects(self, target_region, sample_cases):
         """Test subsetting with intersects method."""
         subsetter = regions.RegionSubsetter(region=target_region, method="intersects")
 
-        subset_cases = subsetter.subset_case_collection(sample_cases)
+        subset_cases = subsetter.subset_case_list(sample_cases)
 
         # Should include intersecting and contained cases, but not outside
-        assert len(subset_cases.cases) == 2
-        case_ids = {case.case_id_number for case in subset_cases.cases}
-        assert case_ids == {1, 2}  # intersecting and contained
+        assert len(subset_cases) == 2
+        case_ids = [case.case_id_number for case in subset_cases]
+        assert case_ids == [1, 2]  # intersecting and contained
 
-    def test_subset_case_collection_all(self, target_region, sample_cases):
+    def test_subset_case_list_all(self, target_region, sample_cases):
         """Test subsetting with all method."""
         subsetter = regions.RegionSubsetter(region=target_region, method="all")
 
-        subset_cases = subsetter.subset_case_collection(sample_cases)
+        subset_cases = subsetter.subset_case_list(sample_cases)
 
         # Should only include contained case
-        assert len(subset_cases.cases) == 1
-        assert subset_cases.cases[0].case_id_number == 2  # contained case
+        assert len(subset_cases) == 1
+        assert subset_cases[0].case_id_number == 2  # contained case
 
-    def test_subset_case_collection_percent(self, target_region, sample_cases):
+    def test_subset_case_list_percent(self, target_region, sample_cases):
         """Test subsetting with percent method."""
         subsetter = regions.RegionSubsetter(
             region=target_region, method="percent", percent_threshold=0.5
         )
 
-        subset_cases = subsetter.subset_case_collection(sample_cases)
+        subset_cases = subsetter.subset_case_list(sample_cases)
 
         # This depends on the actual overlap, but should include at least the contained
         # case
-        case_ids = {case.case_id_number for case in subset_cases.cases}
+        case_ids = [case.case_id_number for case in subset_cases]
         assert 2 in case_ids  # contained case should always be included
 
-    def test_subset_case_collection_different_thresholds(
-        self, target_region, sample_cases
-    ):
+    def test_subset_case_list_different_thresholds(self, target_region, sample_cases):
         """Test subsetting with different percent thresholds."""
         # Low threshold - should include more cases
         low_threshold_subsetter = regions.RegionSubsetter(
             region=target_region, method="percent", percent_threshold=0.1
         )
 
-        low_threshold_cases = low_threshold_subsetter.subset_case_collection(
-            sample_cases
-        )
+        low_threshold_cases = low_threshold_subsetter.subset_case_list(sample_cases)
 
         # High threshold - should include fewer cases
         high_threshold_subsetter = regions.RegionSubsetter(
             region=target_region, method="percent", percent_threshold=0.9
         )
 
-        high_threshold_cases = high_threshold_subsetter.subset_case_collection(
-            sample_cases
-        )
+        high_threshold_cases = high_threshold_subsetter.subset_case_list(sample_cases)
 
         # Low threshold should include at least as many as high threshold
-        assert len(low_threshold_cases.cases) >= len(high_threshold_cases.cases)
+        assert len(low_threshold_cases) >= len(high_threshold_cases)
 
     def test_subset_results_to_region(self, target_region, sample_cases):
         """Test subsetting results DataFrame."""
@@ -1557,8 +1549,8 @@ class TestConvenienceFunctions:
     """Test convenience functions for region subsetting."""
 
     @pytest.fixture
-    def sample_case_collection(self):
-        """Create a sample case collection."""
+    def sample_case_list(self):
+        """Create a sample case list."""
         from extremeweatherbench import cases
 
         case1 = cases.IndividualCase(
@@ -1589,9 +1581,9 @@ def sample_case_collection(self):
             event_type="cold_wave",
         )
 
-        return cases.IndividualCaseCollection(cases=[case1, case2])
+        return [case1, case2]
 
-    def test_subset_cases_to_region_with_region_object(self, sample_case_collection):
+    def test_subset_cases_to_region_with_region_object(self, sample_case_list):
         """Test regions.subset_cases_to_region with Region object."""
         target_region = regions.BoundingBoxRegion.create_region(
             latitude_min=40.0,
@@ -1601,14 +1593,14 @@ def test_subset_cases_to_region_with_region_object(self, sample_case_collection)
         )
 
         subset_cases = regions.subset_cases_to_region(
-            sample_case_collection, target_region, method="intersects"
+            sample_case_list, target_region, method="intersects"
         )
 
         # Should include case 1 (intersects) but not case 2 (outside)
-        assert len(subset_cases.cases) == 1
-        assert subset_cases.cases[0].case_id_number == 1
+        assert len(subset_cases) == 1
+        assert subset_cases[0].case_id_number == 1
 
-    def test_subset_cases_to_region_with_dict(self, sample_case_collection):
+    def test_subset_cases_to_region_with_dict(self, sample_case_list):
         """Test regions.subset_cases_to_region with dictionary."""
         region_dict = {
             "latitude_min": 40.0,
@@ -1618,13 +1610,13 @@ def test_subset_cases_to_region_with_dict(self, sample_case_collection):
         }
 
         subset_cases = regions.subset_cases_to_region(
-            sample_case_collection, region_dict, method="intersects"
+            sample_case_list, region_dict, method="intersects"
         )
 
-        assert len(subset_cases.cases) == 1
-        assert subset_cases.cases[0].case_id_number == 1
+        assert len(subset_cases) == 1
+        assert subset_cases[0].case_id_number == 1
 
-    def test_subset_cases_to_region_with_percent_method(self, sample_case_collection):
+    def test_subset_cases_to_region_with_percent_method(self, sample_case_list):
         """Test regions.subset_cases_to_region with percent method."""
         target_region = regions.BoundingBoxRegion.create_region(
             latitude_min=40.0,
@@ -1634,16 +1626,16 @@ def test_subset_cases_to_region_with_percent_method(self, sample_case_collection
         )
 
         subset_cases = regions.subset_cases_to_region(
-            sample_case_collection,
+            sample_case_list,
             target_region,
             method="percent",
             percent_threshold=0.3,
         )
 
         # Results depend on actual area overlap calculations
-        assert isinstance(subset_cases, type(sample_case_collection))
+        assert isinstance(subset_cases, type(sample_case_list))
 
-    def test_subset_cases_to_region_all_method(self, sample_case_collection):
+    def test_subset_cases_to_region_all_method(self, sample_case_list):
         """Test regions.subset_cases_to_region with all method."""
         # Create a region that contains case 1 completely
         large_region = regions.BoundingBoxRegion.create_region(
@@ -1654,13 +1646,13 @@ def test_subset_cases_to_region_all_method(self, sample_case_collection):
         )
 
         subset_cases = regions.subset_cases_to_region(
-            sample_case_collection, large_region, method="all"
+            sample_case_list, large_region, method="all"
         )
 
         # Should include case 1 which is completely within the large region
-        assert len(subset_cases.cases) >= 1
+        assert len(subset_cases) >= 1
 
-    def test_subset_results_to_region_convenience(self, sample_case_collection):
+    def test_subset_results_to_region_convenience(self, sample_case_list):
         """Test regions.subset_results_to_region convenience function."""
         # Create mock results
         results_df = pd.DataFrame(
@@ -1681,7 +1673,7 @@ def test_subset_results_to_region_convenience(self, sample_case_collection):
         subsetter = regions.RegionSubsetter(region=target_region, method="intersects")
 
         subset_results = regions.subset_results_to_region(
-            subsetter, results_df, sample_case_collection
+            subsetter, results_df, sample_case_list
         )
 
         # Should only include results for case 1
@@ -1692,11 +1684,10 @@ def test_subset_results_to_region_convenience(self, sample_case_collection):
 class TestRegionSubsettingEdgeCases:
     """Test edge cases for region subsetting."""
 
-    def test_empty_case_collection(self):
-        """Test subsetting with empty case collection."""
-        from extremeweatherbench import cases
+    def test_empty_case_list(self):
+        """Test subsetting with empty case list."""
 
-        empty_collection = cases.IndividualCaseCollection(cases=[])
+        empty_list = []
         target_region = regions.BoundingBoxRegion.create_region(
             latitude_min=40.0,
             latitude_max=50.0,
@@ -1706,8 +1697,8 @@ def test_empty_case_collection(self):
 
         subsetter = regions.RegionSubsetter(region=target_region, method="intersects")
 
-        subset_cases = subsetter.subset_case_collection(empty_collection)
-        assert len(subset_cases.cases) == 0
+        subset_cases = subsetter.subset_case_list(empty_list)
+        assert len(subset_cases) == 0
 
     def test_very_small_regions(self):
         """Test subsetting with very small"""
@@ -1728,8 +1719,6 @@ def test_very_small_regions(self):
             event_type="test",
         )
 
-        case_collection = cases.IndividualCaseCollection(cases=[tiny_case])
-
         # Create a target region that should intersect
         target_region = regions.BoundingBoxRegion.create_region(
             latitude_min=44.9,
@@ -1740,8 +1729,8 @@ def test_very_small_regions(self):
 
         subsetter = regions.RegionSubsetter(region=target_region, method="intersects")
 
-        subset_cases = subsetter.subset_case_collection(case_collection)
-        assert len(subset_cases.cases) == 1
+        subset_cases = subsetter.subset_case_list([tiny_case])
+        assert len(subset_cases) == 1
 
     def test_antimeridian_crossing_regions(self):
         """Test subsetting with regions that cross the antimeridian."""
@@ -1761,8 +1750,6 @@ def test_antimeridian_crossing_regions(self):
             event_type="test",
         )
 
-        case_collection = cases.IndividualCaseCollection(cases=[dateline_case])
-
         # Create a target region that might cross antimeridian
         target_region = regions.CenteredRegion.create_region(
             latitude=45.0,
@@ -1773,8 +1760,8 @@ def test_antimeridian_crossing_regions(self):
         subsetter = regions.RegionSubsetter(region=target_region, method="intersects")
 
         # Should handle antimeridian crossing gracefully
-        subset_cases = subsetter.subset_case_collection(case_collection)
-        assert isinstance(subset_cases, cases.IndividualCaseCollection)
+        subset_cases = subsetter.subset_case_list([dateline_case])
+        assert len(subset_cases) == 1
 
     def test_polar_regions(self):
         """Test subsetting with polar"""
@@ -1792,8 +1779,6 @@ def test_polar_regions(self):
             event_type="test",
         )
 
-        case_collection = cases.IndividualCaseCollection(cases=[polar_case])
-
         # Create a target region at high latitudes
         target_region = regions.BoundingBoxRegion.create_region(
             latitude_min=80.0,
@@ -1805,8 +1790,8 @@ def test_polar_regions(self):
         subsetter = regions.RegionSubsetter(region=target_region, method="intersects")
 
         # Should handle polar coordinates gracefully
-        subset_cases = subsetter.subset_case_collection(case_collection)
-        assert isinstance(subset_cases, cases.IndividualCaseCollection)
+        subset_cases = subsetter.subset_case_list([polar_case])
+        assert len(subset_cases) == 1
 
 
 class TestAdjustBoundsToDatasetConvention:

From 63f8b13f3052ec01b224774262de993ff5b590ba Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 23 Jan 2026 21:25:26 -0500
Subject: [PATCH 15/72] Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes
---
 src/extremeweatherbench/calc.py         |   2 +-
 src/extremeweatherbench/cases.py        |  62 ++-
 src/extremeweatherbench/derived.py      |  96 ++--
 src/extremeweatherbench/evaluate.py     |  15 +-
 src/extremeweatherbench/evaluate_cli.py |  19 +-
 src/extremeweatherbench/inputs.py       |  94 +++-
 src/extremeweatherbench/metrics.py      | 581 +++++++++++++++---------
 src/extremeweatherbench/regions.py      |  96 ++--
 src/extremeweatherbench/sources/base.py |  12 +-
 9 files changed, 597 insertions(+), 380 deletions(-)

diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index ef314349..28fcf94f 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -259,7 +259,7 @@ def geopotential_thickness(
         pressure_dim: The name of the pressure dimension. Default is "level".
 
     Returns:
-        The geopotential thickness in metersas an xarray DataArray.
+        The geopotential thickness in meters as an xarray DataArray.
     """
     geopotential_heights = da.sel({pressure_dim: top_level})
     geopotential_height_bottom = da.sel({pressure_dim: bottom_level})
diff --git a/src/extremeweatherbench/cases.py b/src/extremeweatherbench/cases.py
index a7800dc5..3f2e858c 100644
--- a/src/extremeweatherbench/cases.py
+++ b/src/extremeweatherbench/cases.py
@@ -24,18 +24,15 @@
 
 @dataclasses.dataclass
 class IndividualCase:
-    """Container for metadata defining a single or individual case.
-
-    An IndividualCase defines the relevant metadata for a single case study for a
-    given extreme weather event; it is designed to be easily instantiable through a
-    simple YAML-based configuration file.
+    """Container for metadata defining a single case study.
 
     Attributes:
-        case_id_number: A unique numerical identifier for the event.
-        start_date: The start date of the case, for use in subsetting data for analysis.
-        end_date: The end date of the case, for use in subsetting data for analysis.
-        location: A Location dataclass representing the location of a case.
-        event_type: A string representing the type of extreme weather event.
+        case_id_number: Unique numerical identifier for the event.
+        title: Title of the case study.
+        start_date: Start date for subsetting data for analysis.
+        end_date: End date for subsetting data for analysis.
+        location: Region object representing the case location.
+        event_type: String representing the type of extreme weather event.
     """
 
     case_id_number: int
@@ -48,18 +45,13 @@ class IndividualCase:
 
 @dataclasses.dataclass
 class CaseOperator:
-    """A class which stores the graph to process an individual case.
-
-    This class is used to store the graph to process an individual case. The purpose of
-    this class is to be a one-stop-shop for the evaluation of a single case. Multiple
-    CaseOperators can be run in parallel to evaluate multiple cases, or run through the
-    ExtremeWeatherBench.run() method to evaluate all cases in an evaluation in serial.
+    """Operator dataclass for an evaluation of a single evaluation object.
 
     Attributes:
-        case_metadata: IndividualCase metadata
-        metric_list: A list of metrics that are to be evaluated for the case operator
-        target_config: A TargetConfig object
-        forecast_config: A ForecastConfig object
+        case_metadata: IndividualCase metadata for this operator.
+        metric_list: List of metrics to evaluate for this case.
+        target: TargetBase object for ground truth data.
+        forecast: ForecastBase object for forecast data.
     """
 
     case_metadata: IndividualCase
@@ -75,8 +67,7 @@ def build_case_operators(
     """Build a CaseOperator from the case metadata and metric evaluation objects.
 
     Args:
-        cases: The case metadata to use for the case operators as a dictionary of cases
-            or a list of IndividualCases.
+        case_list: List of IndividualCase objects defining cases to process.
         evaluation_objects: The evaluation objects to apply to the case operators.
 
     Returns:
@@ -108,7 +99,7 @@ def load_individual_cases(
     Will pass through existing IndividualCase objects and convert dictionaries to IndividualCase objects.
 
     Args:
-        cases: A dictionary of cases based on the IndividualCase dataclass.
+        cases: A list of cases as either dicts or IndividualCase objects.
 
     Returns:
         A list of IndividualCase objects.
@@ -146,19 +137,18 @@ def load_individual_cases_from_yaml(
     Example of a yaml file:
 
     ```yaml
-    cases:
-      - case_id_number: 1
-        title: Event 1
-        start_date: 2021-01-01 00:00:00
-        end_date: 2021-01-03 00:00:00
-        location:
-            type: bounded_region
-            parameters:
-                latitude_min: 10.0
-                latitude_max: 55.6
-                longitude_min: 265.0
-                longitude_max: 283.3
-        event_type: tropical_cyclone
+    - case_id_number: 1
+    title: Event 1
+    start_date: 2021-01-01 00:00:00
+    end_date: 2021-01-03 00:00:00
+    location:
+        type: bounded_region
+        parameters:
+            latitude_min: 10.0
+            latitude_max: 55.6
+            longitude_min: 265.0
+            longitude_max: 283.3
+    event_type: tropical_cyclone
     ```
 
     Args:
diff --git a/src/extremeweatherbench/derived.py b/src/extremeweatherbench/derived.py
index 94c97a8d..0e609fbe 100644
--- a/src/extremeweatherbench/derived.py
+++ b/src/extremeweatherbench/derived.py
@@ -16,21 +16,26 @@
 
 
 class DerivedVariable(abc.ABC):
-    """An abstract base class defining the interface for ExtremeWeatherBench
-    derived variables.
-
-    A DerivedVariable is any variable or transform that requires extra computation than
-    what is provided in analysis or forecast data. Some examples include the
-    practically perfect hindcast, MLCAPE, IVT, or atmospheric river masks.
-
-    Attributes:
-        variables: A list of variables that are used to build the
-            derived variable.
-        output_variables: Optional list of variable names that specify
-            which outputs to use from the derived computation.
-        compute: A method that generates the derived variable from the variables.
-        derive_variable: An abstract method that defines the computation to
-            derive the derived_variable from variables.
+    """Abstract base class for ExtremeWeatherBench derived variables.
+
+    A DerivedVariable is any variable or transform that requires extra
+    computation beyond what is provided in analysis or forecast data. Examples
+    include the practically perfect hindcast, MLCAPE, IVT, or atmospheric
+    river masks.
+
+    Class attributes:
+        variables: List of variables used to build the derived variable
+
+    Instance attributes:
+        name: The name of the derived variable
+        output_variables: Optional list of variable names specifying which
+            outputs to use from the derived computation
+
+    Public methods:
+        compute: Build the derived variable from input variables
+
+    Abstract methods:
+        derive_variable: Define the computation to derive the variable
     """
 
     variables: List[str]
@@ -81,33 +86,28 @@ def compute(self, data: xr.Dataset, *args, **kwargs) -> xr.DataArray:
 
 
 class TropicalCycloneTrackVariables(DerivedVariable):
-    """A derived variable abstract class for tropical cyclone (TC) variables.
-
-    This class serves as a parent for TC-related derived variables and provides
-    shared track computation with caching to avoid reprocessing the same data
-    multiple times across different child classes.
+    """Derived variable class for tropical cyclone track-based variables.
 
-    The track data is computed once and cached, then child classes can extract
-    specific variables (like sea level pressure, wind speed) from the cached
-    track dataset.
+    Extends DerivedVariable to provide shared track computation with caching
+    for TC-related derived variables, avoiding reprocessing across child
+    classes. Track data is computed once and cached, then child classes can
+    extract specific variables (sea level pressure, wind speed, etc.).
 
-    Deriving the track locations using default TempestExtremes criteria:
+    Uses default TempestExtremes criteria for track identification:
     https://doi.org/10.5194/gmd-14-5023-2021
 
-    For forecast data, when track data is provided, the valid candidates
-    approach is filtered to only include candidates within 5 great circle
-    degrees of track data points and within 48 hours of the valid_time.
+    For forecasts with track data, valid candidates are filtered to include
+    only those within 5 great circle degrees and 48 hours of track points.
 
-    Track data is automatically obtained from the target dataset when using
-    the evaluation pipeline (via `requires_target_dataset=True` flag).
+    Track data is automatically obtained from target dataset via
+    `requires_target_dataset=True` flag in evaluation pipeline.
 
-    Attributes:
-        output_variables: Optional list of variable names that specify
-            which outputs to use from the derived computation.
-        name: The name of the derived variable. Defaults to class-level
-            name attribute if present, otherwise the class name.
-        requires_target_dataset: If True, target dataset will be passed to
-            this derived variable via kwargs.
+    Class attributes:
+        requires_target_dataset: If True, target dataset passed via kwargs
+
+    Instance attributes:
+        output_variables: Optional list specifying which outputs to use
+        name: Name of the derived variable
     """
 
     # required variables for TC track identification
@@ -287,8 +287,10 @@ def derive_variable(self, data: xr.Dataset, *args, **kwargs) -> xr.DataArray:
 
 
 class CravenBrooksSignificantSevere(DerivedVariable):
-    """A derived variable that computes the Craven-Brooks significant severe
-    convection index.
+    """Derived variable for Craven-Brooks significant severe convection index.
+
+    Extends DerivedVariable to compute the Craven-Brooks index for assessing
+    significant severe convection potential.
     """
 
     variables = [
@@ -391,18 +393,18 @@ def derive_variable(
 
 
 class AtmosphericRiverVariables(DerivedVariable):
-    """A derived variable that computes atmospheric river related variables.
+    """Derived variable for atmospheric river detection and characterization.
 
-    Calculates the IVT (Integrated Vapor Transport), atmospheric river mask, and land
-    intersection. IVT is calculated using the method described in Newell et al. 1992 and
-    elsewhere (e.g. Mo 2024).
+    Extends DerivedVariable to compute IVT (Integrated Vapor Transport),
+    atmospheric river mask, and land intersection. IVT calculation follows
+    Newell et al. 1992 and elsewhere (e.g. Mo 2024).
 
-    Output variables are: integrated_vapor_transport, atmospheric_river_mask, and
-    atmospheric_river_land_intersection. Users must declare at least one of the output
-    variables they want when calling the derived variable.
+    Output variables: integrated_vapor_transport, atmospheric_river_mask,
+    atmospheric_river_land_intersection. Users must declare at least one
+    output variable when calling the derived variable.
 
-    The Laplacian of IVT is calculated using a Gaussian blurring kernel with a
-    sigma of 3 grid points, meant to smooth out 0.25 degree grid scale features.
+    The Laplacian of IVT uses a Gaussian blurring kernel with sigma of 3
+    grid points to smooth 0.25 degree grid scale features.
     """
 
     variables = [
diff --git a/src/extremeweatherbench/evaluate.py b/src/extremeweatherbench/evaluate.py
index ad7953eb..64c0ba96 100644
--- a/src/extremeweatherbench/evaluate.py
+++ b/src/extremeweatherbench/evaluate.py
@@ -45,7 +45,7 @@ class ExtremeWeatherBench:
     results.
 
     Attributes:
-        case_metadata: A dictionary of cases or a list of IndividualCase objects to run.
+        case_metadata: A list of case dicts or IndividualCase objects to run.
         evaluation_objects: A list of evaluation objects to run.
         cache_dir: An optional directory to cache the mid-flight outputs of the
             workflow for serial runs.
@@ -61,6 +61,16 @@ def __init__(
         cache_dir: Optional[Union[str, pathlib.Path]] = None,
         region_subsetter: Optional["regions.RegionSubsetter"] = None,
     ):
+        """Initialize the ExtremeWeatherBench workflow.
+
+        Args:
+            case_metadata: List of case dicts or IndividualCase objects.
+            evaluation_objects: List of evaluation objects to run.
+            cache_dir: Optional directory for caching mid-flight outputs in
+                serial runs.
+            region_subsetter: Optional RegionSubsetter to filter cases by
+                spatial region.
+        """
         # Load the case metadata from the input
         self.case_metadata = cases.load_individual_cases(case_metadata)
         self.evaluation_objects = evaluation_objects
@@ -138,7 +148,8 @@ def _run_case_operators(
     Args:
         case_operators: List of case operators to run.
         cache_dir: Optional directory for caching (serial mode only).
-        **kwargs: Additional arguments, may include 'parallel_config' dict.
+        parallel_config: Optional dict of joblib parallel configuration.
+        **kwargs: Additional keyword arguments passed to case operators.
 
     Returns:
         List of result DataFrames.
diff --git a/src/extremeweatherbench/evaluate_cli.py b/src/extremeweatherbench/evaluate_cli.py
index 3216a1ef..9b978269 100644
--- a/src/extremeweatherbench/evaluate_cli.py
+++ b/src/extremeweatherbench/evaluate_cli.py
@@ -79,18 +79,17 @@ def cli_runner(
     save CaseOperator objects for later use or inspection.
 
     Args:
-        default: Use default Brightband evaluation objects with current directory as
-            output
-        config_file: Path to a config.py file containing evaluation objects
-        output_dir: Directory for analysis outputs (default: current directory)
+        default: Use default Brightband evaluation objects with current directory
+            as output.
+        config_file: Path to a config.py file containing evaluation objects.
+        output_dir: Directory for analysis outputs (default: current directory).
         cache_dir: Optional directory for caching intermediate data. When set,
             datasets or dataarrays are computed and cached as zarrs.
-        parallel_config: Parallel configuration using joblib (default: {'backend':
-            'threading', 'n_jobs': 8})
-        save_case_operators: Save CaseOperator objects to a pickle file at this path
-        n_jobs: Number of parallel jobs to run (default: 1 for serial execution)
-        parallel_config: Advanced parallel configuration using joblib. Takes precedence
-            over --n-jobs if provided.
+        n_jobs: Number of parallel jobs to run (default: 1 for serial execution).
+        parallel_config: Advanced parallel configuration using joblib. Takes
+            precedence over n_jobs if provided.
+        save_case_operators: Save CaseOperator objects to a pickle file at this
+            path.
     Examples:
         # Use default evaluation objects
         $ ewb --default
diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index 86f99ff0..cd0f52cd 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -153,15 +153,29 @@ def _default_preprocess(input_data: IncomingDataInput) -> IncomingDataInput:
 
 @dataclasses.dataclass
 class InputBase(abc.ABC):
-    """An abstract base dataclass for target and forecast data.
+    """Abstract base dataclass for target and forecast data.
+
+    This class provides the foundational interface for loading and processing
+    forecast and target datasets in ExtremeWeatherBench.
 
     Attributes:
-        source: The source of the data, which can be a local path or a remote URL/URI.
+        source: The source of the data, which can be a local path or a
+            remote URL/URI.
         name: The name of the input data source.
         variables: A list of variables to select from the data.
         variable_mapping: A dictionary of variable names to map to the data.
         storage_options: Storage/access options for the data.
         preprocess: A function to preprocess the data.
+
+    Public methods:
+        open_and_maybe_preprocess_data_from_source: Open and preprocess data
+        maybe_convert_to_dataset: Convert input data to xarray Dataset
+        add_source_to_dataset_attrs: Add source name to dataset attributes
+        maybe_map_variable_names: Map variable names if mapping provided
+
+    Abstract methods:
+        _open_data_from_source: Open the input data from source
+        subset_data_to_case: Subset data to case metadata
     """
 
     source: str
@@ -304,7 +318,17 @@ def maybe_map_variable_names(self, data: IncomingDataInput) -> IncomingDataInput
 
 @dataclasses.dataclass
 class ForecastBase(InputBase):
-    """A class defining the interface for ExtremeWeatherBench forecast data."""
+    """Forecast data interface for ExtremeWeatherBench.
+
+    Extends InputBase to provide functionality for forecast datasets with
+    init_time and lead_time dimensions.
+
+    Attributes:
+        chunks: Chunking strategy for dask arrays. Defaults to "auto".
+
+    Public methods:
+        subset_data_to_case: Subset forecast data to case (overrides parent)
+    """
 
     chunks: Optional[Union[dict, str]] = "auto"
 
@@ -401,7 +425,11 @@ class EvaluationObject:
 
 @dataclasses.dataclass
 class KerchunkForecast(ForecastBase):
-    """Forecast class for kerchunked forecast data."""
+    """Forecast class for kerchunk-referenced forecast data.
+
+    Extends ForecastBase for forecast data accessed via kerchunk references,
+    enabling efficient access to cloud-optimized datasets.
+    """
 
     chunks: Optional[Union[dict, str]] = "auto"
     storage_options: dict = dataclasses.field(default_factory=dict)
@@ -416,7 +444,10 @@ def _open_data_from_source(self) -> IncomingDataInput:
 
 @dataclasses.dataclass
 class ZarrForecast(ForecastBase):
-    """Forecast class for zarr forecast data."""
+    """Forecast class for zarr-format forecast data.
+
+    Extends ForecastBase for forecast data stored in zarr format.
+    """
 
     chunks: Optional[Union[dict, str]] = "auto"
 
@@ -431,11 +462,11 @@ def _open_data_from_source(self) -> IncomingDataInput:
 
 @dataclasses.dataclass
 class XarrayForecast(ForecastBase):
-    """Forecast class for datasets that were previously constructed and opened using xarray.
+    """Forecast class for pre-opened xarray datasets.
 
-    This class is intended for situations where the user has to manually prepare a dataset to
-    use in their evaluation. This can happen when the user is manually constructed such a
-    dataset from a collection of NetCDF or Zarr archives which need to be assembled into a
+    Extends ForecastBase for datasets previously constructed and opened using
+    xarray. Intended for situations where users manually prepare datasets from
+    collections of NetCDF or Zarr archives that need assembly into a
     single, master dataset.
 
     Attributes:
@@ -480,12 +511,15 @@ def _open_data_from_source(self) -> xr.Dataset:
 
 @dataclasses.dataclass
 class TargetBase(InputBase):
-    """An abstract base class for target data.
+    """Target (truth) data interface for ExtremeWeatherBench.
+
+    Extends InputBase to provide functionality for target datasets that serve
+    as ground truth for evaluation. Target data can be gridded datasets, point
+    observations, or any reference dataset. Targets need not match forecast
+    variables but must share a compatible coordinate system for evaluation.
 
-    A TargetBase is data that acts as the "truth" for a case. It can be a gridded
-    dataset, a point observation dataset, or any other reference dataset. Targets in EWB
-    are not required to be the same variable as the forecast dataset, but they must be
-    in the same coordinate system for evaluation.
+    Public methods:
+        maybe_align_forecast_to_target: Align forecast to target coordinates
     """
 
     def maybe_align_forecast_to_target(
@@ -513,8 +547,10 @@ def maybe_align_forecast_to_target(
 
 @dataclasses.dataclass
 class ERA5(TargetBase):
-    """Target class for ERA5 gridded data, ideally using the ARCO ERA5 dataset provided
-    by Google. Otherwise, either a different zarr source for ERA5.
+    """Target class for ERA5 gridded reanalysis data.
+
+    Extends TargetBase for ERA5 data, optimized for the ARCO ERA5 dataset
+    provided by Google or other zarr-based ERA5 sources.
     """
 
     name: str = "ERA5"
@@ -569,10 +605,10 @@ def maybe_align_forecast_to_target(
 
 @dataclasses.dataclass
 class GHCN(TargetBase):
-    """Target class for GHCN tabular data.
+    """Target class for GHCN (Global Historical Climatology Network) data.
 
-    Data is processed using polars to maintain the lazy loading paradigm in
-    open_data_from_source and to separate the subsetting into subset_data_to_case.
+    Extends TargetBase for tabular GHCN station observation data. Uses polars
+    for lazy loading and efficient subsetting of large tabular datasets.
     """
 
     name: str = "GHCN"
@@ -643,10 +679,11 @@ def maybe_align_forecast_to_target(
 
 @dataclasses.dataclass
 class LSR(TargetBase):
-    """Target class for local storm report (LSR) tabular data.
+    """Target class for Local Storm Report (LSR) tabular data.
 
-    run_pipeline() returns a dataset with LSRs as mapped to numeric values (1=wind, 2=hail, 3=tor). IndividualCase date ranges for LSRs should be 12 UTC to
-    the next day at 12 UTC (exclusive) to match SPC's reporting window.
+    Extends TargetBase for SPC local storm reports. Returns dataset with LSRs
+    mapped to numeric values (1=wind, 2=hail, 3=tornado). IndividualCase date
+    ranges should be 12 UTC to next day 12 UTC to match SPC reporting window.
     """
 
     name: str = "local_storm_reports"
@@ -745,7 +782,10 @@ def maybe_align_forecast_to_target(
 # TODO: get PPH connector working properly
 @dataclasses.dataclass
 class PPH(TargetBase):
-    """Target class for practically perfect hindcast data."""
+    """Target class for Practically Perfect Hindcast (PPH) data.
+
+    Extends TargetBase for practically perfect hindcast datasets.
+    """
 
     name: str = "practically_perfect_hindcast"
     source: str = PPH_URI
@@ -877,7 +917,11 @@ def _ibtracs_preprocess(data: IncomingDataInput) -> IncomingDataInput:
 
 @dataclasses.dataclass
 class IBTrACS(TargetBase):
-    """Target class for IBTrACS data."""
+    """Target class for IBTrACS tropical cyclone best track data.
+
+    Extends TargetBase for International Best Track Archive for Climate
+    Stewardship (IBTrACS) tropical cyclone track and intensity data.
+    """
 
     name: str = "IBTrACS"
     preprocess: Callable = _ibtracs_preprocess
@@ -1062,6 +1106,7 @@ def open_icechunk_dataset_from_datatree(
         group: The group within the datatree to open.
         branch: The icechunk branch to open. Defaults to "main".
         chunks: The chunk pattern for the datatree. defaults to "auto".
+
     Returns:
         The dataset for the specified group.
     """
@@ -1086,6 +1131,7 @@ def zarr_target_subsetter(
         data: The dataset to subset.
         case_metadata: The case metadata to subset the dataset to.
         time_variable: The time variable to use; defaults to "valid_time".
+        drop: Whether to drop masked values. Defaults to False.
 
     Returns:
         The subset dataset.
diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index 21bed2b5..873009af 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -50,19 +50,22 @@ def _compute_metric_with_docstring(self, *args, **kwargs):
 
 
 class BaseMetric(abc.ABC, metaclass=ComputeDocstringMetaclass):
-    """A BaseMetric class is an abstract class that defines the foundational interface
-    for all metrics.
-
-    Metrics are general operations applied between a forecast and analysis xarray
-    DataArray. EWB metrics prioritize the use of any arbitrary sets of forecasts and
-    analyses, so long as the spatiotemporal dimensions are the same.
-
-    Args:
-        name: The name of the metric.
-        preserve_dims: The dimensions to preserve in the computation. Defaults to
-            "lead_time".
-        forecast_variable: The forecast variable to use in the computation.
-        target_variable: The target variable to use in the computation.
+    """Abstract base class defining the foundational interface for all metrics.
+
+    Metrics are general operations applied between forecast and analysis xarray
+    DataArrays. EWB metrics prioritize the use of any arbitrary sets of
+    forecasts and analyses, so long as the spatiotemporal dimensions are the
+    same.
+
+    Public methods:
+        compute_metric: Public interface to compute the metric
+        maybe_expand_composite: Expand composite metrics into individual metrics
+        is_composite: Check if this is a composite metric
+        __repr__: String representation of the metric
+        __eq__: Check equality with another metric
+
+    Abstract methods:
+        _compute_metric: Logic to compute the metric (must be implemented)
     """
 
     def __init__(
@@ -72,6 +75,16 @@ def __init__(
         forecast_variable: Optional[str | derived.DerivedVariable] = None,
         target_variable: Optional[str | derived.DerivedVariable] = None,
     ):
+        """Initialize the base metric.
+
+        Args:
+            name: The name of the metric.
+            preserve_dims: The dimensions to preserve in the computation.
+                Defaults to "lead_time".
+            forecast_variable: The forecast variable to use in the
+                computation.
+            target_variable: The target variable to use in the computation.
+        """
         # Store the original variables (str or DerivedVariable instances)
         # Do NOT convert to string to preserve output_variables info
         self.name = name
@@ -179,13 +192,27 @@ def maybe_prepare_composite_kwargs(
 
 
 class CompositeMetric(BaseMetric):
-    """Base class for composite metrics.
+    """Base class for composite metrics that can contain multiple sub-metrics.
+
+    Extends BaseMetric to provide functionality for composite metrics that
+    aggregate multiple individual metrics for efficient evaluation.
 
-    This class provides common functionality for composite metrics.
-    Accepts the same arguments as BaseMetric.
+    Public methods:
+        maybe_expand_composite: Expand into individual metrics (overrides base)
+        is_composite: Check if has sub-metrics (overrides base)
+
+    Abstract methods:
+        maybe_prepare_composite_kwargs: Prepare kwargs for composite evaluation
+        _compute_metric: Compute the metric (must be implemented by subclasses)
     """
 
     def __init__(self, *args, **kwargs):
+        """Initialize the composite metric.
+
+        Args:
+            *args: Positional arguments passed to BaseMetric.__init__
+            **kwargs: Keyword arguments passed to BaseMetric.__init__
+        """
         super().__init__(*args, **kwargs)
         self._metric_instances: list["BaseMetric"] = []
 
@@ -242,36 +269,31 @@ def _compute_metric(
 
 
 class ThresholdMetric(CompositeMetric):
-    """Base class for threshold-based metrics.
-
-    This class provides common functionality for metrics that require
-    forecast and target thresholds for binarization.
-
-    Args:
-        name: The name of the metric. Defaults to "threshold_metrics".
-        preserve_dims: The dimensions to preserve in the computation. Defaults to
-            "lead_time".
-        forecast_variable: The forecast variable to use in the computation.
-        target_variable: The target variable to use in the computation.
-        forecast_threshold: The threshold for binarizing the forecast. Defaults to 0.5.
-        target_threshold: The threshold for binarizing the target. Defaults to 0.5.
-        metrics: A list of metrics to use as a composite. Defaults to None.
-
-    Can be used in two ways:
-    1. As a base class for specific threshold metrics (CriticalSuccessIndex,
-    FalseAlarmRatio, etc.)
-    2. As a composite metric to compute multiple threshold metrics
-       efficiently by reusing the transformed contingency manager.
-
-    Example of composite usage:
+    """Base class for threshold-based metrics with binary classification.
+
+    Extends CompositeMetric to provide functionality for metrics that require
+    forecast and target thresholds for binarization. Can be used as a base
+    class for specific threshold metrics or as a composite metric.
+
+    Public methods:
+        transformed_contingency_manager: Create contingency manager
+        maybe_prepare_composite_kwargs: Prepare kwargs (overrides parent)
+        __call__: Make instances callable with configured thresholds
+
+    Abstract methods:
+        _compute_metric: Compute the metric (must be implemented by subclasses)
+
+    Usage patterns:
+        1. As a base class for specific metrics (CriticalSuccessIndex, etc.)
+        2. As a composite metric to compute multiple threshold metrics
+           efficiently by reusing the transformed contingency manager
+
+    Example:
         composite = ThresholdMetric(
             metrics=[CriticalSuccessIndex, FalseAlarmRatio, Accuracy],
             forecast_threshold=0.7,
             target_threshold=0.5
         )
-        results = composite.compute_metric(forecast, target)
-        # Returns: {"critical_success_index": ...,
-        #           "false_alarm_ratio": ..., "accuracy": ...}
     """
 
     def __init__(
@@ -285,6 +307,23 @@ def __init__(
         metrics: Optional[list[Type["ThresholdMetric"]]] = None,
         **kwargs,
     ):
+        """Initialize the threshold metric.
+
+        Args:
+            name: The name of the metric. Defaults to "threshold_metrics".
+            preserve_dims: The dimensions to preserve in the computation.
+                Defaults to "lead_time".
+            forecast_variable: The forecast variable to use in the
+                computation.
+            target_variable: The target variable to use in the computation.
+            forecast_threshold: The threshold for binarizing the forecast.
+                Defaults to 0.5.
+            target_threshold: The threshold for binarizing the target.
+                Defaults to 0.5.
+            metrics: A list of metrics to use as a composite. Defaults to
+                None.
+            **kwargs: Additional keyword arguments passed to parent.
+        """
         super().__init__(
             name,
             preserve_dims=preserve_dims,
@@ -430,16 +469,22 @@ def _compute_metric(
 
 
 class CriticalSuccessIndex(ThresholdMetric):
-    """Critical Success Index metric.
+    """Compute Critical Success Index (CSI) from binary classifications.
 
-    The Critical Success Index is computed between the forecast and target using the
-    preserve_dims dimensions.
-
-    Args:
-        name: The name of the metric. Defaults to "CriticalSuccessIndex".
+    Extends ThresholdMetric to compute CSI between forecast and target using
+    the preserve_dims dimensions. CSI measures the fraction of correctly
+    predicted events.
     """
 
     def __init__(self, name: str = "CriticalSuccessIndex", *args, **kwargs):
+        """Initialize the Critical Success Index metric.
+
+        Args:
+            name: The name of the metric. Defaults to
+                "CriticalSuccessIndex".
+            *args: Additional positional arguments passed to ThresholdMetric.
+            **kwargs: Additional keyword arguments passed to ThresholdMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -462,16 +507,21 @@ def _compute_metric(
 
 
 class FalseAlarmRatio(ThresholdMetric):
-    """False Alarm Ratio metric.
-
-    The False Alarm Ratio is computed between the forecast and target using the
-    preserve_dims dimensions. Note that this is not the same as the False Alarm Rate.
+    """Compute False Alarm Ratio (FAR) from binary classifications.
 
-    Args:
-        name: The name of the metric. Defaults to "FalseAlarmRatio".
+    Extends ThresholdMetric to compute FAR between forecast and target using
+    the preserve_dims dimensions. FAR measures the fraction of predicted
+    events that did not occur. Note: FAR is not the same as False Alarm Rate.
     """
 
     def __init__(self, name: str = "FalseAlarmRatio", *args, **kwargs):
+        """Initialize the False Alarm Ratio metric.
+
+        Args:
+            name: The name of the metric. Defaults to "FalseAlarmRatio".
+            *args: Additional positional arguments passed to ThresholdMetric.
+            **kwargs: Additional keyword arguments passed to ThresholdMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -494,16 +544,21 @@ def _compute_metric(
 
 
 class TruePositives(ThresholdMetric):
-    """True Positive ratio.
-
-    The True Positive is the number of times the forecast is a true positive (top right
-    cell in the contingency table) divided by the total number of observations.
+    """Compute True Positive ratio from binary classifications.
 
-    Args:
-        name: The name of the metric. Defaults to "TruePositives".
+    Extends ThresholdMetric to compute the ratio of true positives (correctly
+    predicted events) to the total number of observations. Corresponds to the
+    top right cell in the contingency table.
     """
 
     def __init__(self, name: str = "TruePositives", *args, **kwargs):
+        """Initialize the True Positives metric.
+
+        Args:
+            name: The name of the metric. Defaults to "TruePositives".
+            *args: Additional positional arguments passed to ThresholdMetric.
+            **kwargs: Additional keyword arguments passed to ThresholdMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -527,16 +582,20 @@ def _compute_metric(
 
 
 class FalsePositives(ThresholdMetric):
-    """False Positive ratio.
+    """Compute False Positive ratio from binary classifications.
 
-    The False Positive is the number of times the forecast is a false positive divided
-    by the total number of observations.
-
-    Args:
-        name: The name of the metric. Defaults to "FalsePositives".
+    Extends ThresholdMetric to compute the ratio of false positives
+    (incorrectly predicted events) to the total number of observations.
     """
 
     def __init__(self, name: str = "FalsePositives", *args, **kwargs):
+        """Initialize the False Positives metric.
+
+        Args:
+            name: The name of the metric. Defaults to "FalsePositives".
+            *args: Additional positional arguments passed to ThresholdMetric.
+            **kwargs: Additional keyword arguments passed to ThresholdMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -560,16 +619,20 @@ def _compute_metric(
 
 
 class TrueNegatives(ThresholdMetric):
-    """True Negative ratio.
-
-    The True Negative is the number of times the forecast is a true negative divided by
-    the total number of observations.
+    """Compute True Negative ratio from binary classifications.
 
-    Args:
-        name: The name of the metric. Defaults to "TrueNegatives".
+    Extends ThresholdMetric to compute the ratio of true negatives (correctly
+    predicted non-events) to the total number of observations.
     """
 
     def __init__(self, name: str = "TrueNegatives", *args, **kwargs):
+        """Initialize the True Negatives metric.
+
+        Args:
+            name: The name of the metric. Defaults to "TrueNegatives".
+            *args: Additional positional arguments passed to ThresholdMetric.
+            **kwargs: Additional keyword arguments passed to ThresholdMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -593,16 +656,21 @@ def _compute_metric(
 
 
 class FalseNegatives(ThresholdMetric):
-    """False Negative ratio.
-
-    The False Negative is the number of times the forecast is a false negative (top left
-    cell in the contingency table) divided by the total number of observations.
+    """Compute False Negative ratio from binary classifications.
 
-    Args:
-        name: The name of the metric. Defaults to "FalseNegatives".
+    Extends ThresholdMetric to compute the ratio of false negatives (missed
+    events) to the total number of observations. Corresponds to the top left
+    cell in the contingency table.
     """
 
     def __init__(self, name: str = "FalseNegatives", *args, **kwargs):
+        """Initialize the False Negatives metric.
+
+        Args:
+            name: The name of the metric. Defaults to "FalseNegatives".
+            *args: Additional positional arguments passed to ThresholdMetric.
+            **kwargs: Additional keyword arguments passed to ThresholdMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -626,17 +694,21 @@ def _compute_metric(
 
 
 class Accuracy(ThresholdMetric):
-    """Accuracy metric.
+    """Compute classification accuracy from binary classifications.
 
-    The Accuracy is the number of times the forecast is correct (top right or bottom
-    right cell in the contingency table) divided by the total number of observations, or
-    (true positives + true negatives) / (total number of samples).
-
-    Args:
-        name: The name of the metric. Defaults to "Accuracy".
+    Extends ThresholdMetric to compute the ratio of correct predictions (true
+    positives + true negatives) to the total number of observations. Measures
+    overall correctness of the forecast.
     """
 
     def __init__(self, name: str = "Accuracy", *args, **kwargs):
+        """Initialize the Accuracy metric.
+
+        Args:
+            name: The name of the metric. Defaults to "Accuracy".
+            *args: Additional positional arguments passed to ThresholdMetric.
+            **kwargs: Additional keyword arguments passed to ThresholdMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -659,19 +731,10 @@ def _compute_metric(
 
 
 class MeanSquaredError(BaseMetric):
-    """Mean Squared Error metric.
-
-    Args:
-        name: The name of the metric. Defaults to "MeanSquaredError".
-        interval_where_one: From scores, endpoints of the interval where the threshold
-            weights are 1. Must be increasing. Infinite endpoints are permissible. By
-            supplying a tuple of arrays, endpoints can vary with dimension.
-        interval_where_positive: From scores, endpoints of the interval where the
-            threshold weights are positive. Must be increasing. Infinite endpoints are
-            only permissible when the corresponding interval_where_one endpoint is
-            infinite. By supplying a tuple of arrays, endpoints can vary with dimension.
-        weights: From scores, an array of weights to apply to the score (e.g., weighting
-            a grid by latitude). If None, no weights are applied.
+    """Compute Mean Squared Error between forecast and target.
+
+    Extends BaseMetric to calculate MSE with optional interval-based
+    weighting and custom weights for spatial/temporal averaging.
     """
 
     def __init__(
@@ -687,6 +750,20 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Mean Squared Error metric.
+
+        Args:
+            name: The name of the metric. Defaults to "MeanSquaredError".
+            interval_where_one: Endpoints of the interval where threshold
+                weights are 1. Must be increasing. Infinite endpoints
+                permissible.
+            interval_where_positive: Endpoints of the interval where threshold
+                weights are positive. Must be increasing.
+            weights: Array of weights to apply to the score (e.g., latitude
+                weighting). If None, no weights are applied.
+            *args: Additional positional arguments passed to BaseMetric.
+            **kwargs: Additional keyword arguments passed to BaseMetric.
+        """
         super().__init__(name, *args, **kwargs)
         self.interval_where_one = interval_where_one
         self.interval_where_positive = interval_where_positive
@@ -711,19 +788,10 @@ def _compute_metric(
 
 
 class MeanAbsoluteError(BaseMetric):
-    """Mean Absolute Error metric.
-
-    Args:
-        name: The name of the metric. Defaults to "MeanAbsoluteError".
-        interval_where_one: From scores, endpoints of the interval where the threshold
-            weights are 1. Must be increasing. Infinite endpoints are permissible. By
-            supplying a tuple of arrays, endpoints can vary with dimension.
-        interval_where_positive: From scores, endpoints of the interval where the
-            threshold weights are positive. Must be increasing. Infinite endpoints are
-            only permissible when the corresponding interval_where_one endpoint is
-            infinite. By supplying a tuple of arrays, endpoints can vary with dimension.
-        weights: From scores, an array of weights to apply to the score (e.g., weighting
-            a grid by latitude). If None, no weights are applied.
+    """Compute Mean Absolute Error between forecast and target.
+
+    Extends BaseMetric to calculate MAE with optional interval-based
+    weighting and custom weights for spatial/temporal averaging.
     """
 
     def __init__(
@@ -739,6 +807,20 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Mean Absolute Error metric.
+
+        Args:
+            name: The name of the metric. Defaults to "MeanAbsoluteError".
+            interval_where_one: Endpoints of the interval where threshold
+                weights are 1. Must be increasing. Infinite endpoints
+                permissible.
+            interval_where_positive: Endpoints of the interval where threshold
+                weights are positive. Must be increasing.
+            weights: Array of weights to apply to the score (e.g., latitude
+                weighting). If None, no weights are applied.
+            *args: Additional positional arguments passed to BaseMetric.
+            **kwargs: Additional keyword arguments passed to BaseMetric.
+        """
         self.interval_where_one = interval_where_one
         self.interval_where_positive = interval_where_positive
         self.weights = weights
@@ -772,16 +854,20 @@ def _compute_metric(
 
 
 class MeanError(BaseMetric):
-    """Mean Error (bias) metric.
+    """Compute Mean Error (bias) between forecast and target.
 
-    The mean error (or mean bias error) is computed between the forecast and target
-    using the preserve_dims dimensions.
-
-    Args:
-        name: The name of the metric. Defaults to "MeanError".
+    Extends BaseMetric to calculate mean error (bias) using the preserve_dims
+    dimensions. Positive values indicate forecast exceeds target.
     """
 
     def __init__(self, name: str = "MeanError", *args, **kwargs):
+        """Initialize the Mean Error metric.
+
+        Args:
+            name: The name of the metric. Defaults to "MeanError".
+            *args: Additional positional arguments passed to BaseMetric.
+            **kwargs: Additional keyword arguments passed to BaseMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -805,16 +891,20 @@ def _compute_metric(
 
 
 class RootMeanSquaredError(BaseMetric):
-    """Root Mean Square Error metric.
-
-    The Root Mean Square Error is computed between the forecast and target using the
-    preserve_dims dimensions.
+    """Compute Root Mean Squared Error between forecast and target.
 
-    Args:
-        name: The name of the metric. Defaults to "RootMeanSquaredError".
+    Extends BaseMetric to calculate RMSE using the preserve_dims dimensions.
+    RMSE is the square root of the mean squared error.
     """
 
     def __init__(self, name: str = "RootMeanSquaredError", *args, **kwargs):
+        """Initialize the Root Mean Squared Error metric.
+
+        Args:
+            name: The name of the metric. Defaults to "RootMeanSquaredError".
+            *args: Additional positional arguments passed to BaseMetric.
+            **kwargs: Additional keyword arguments passed to BaseMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
@@ -838,20 +928,11 @@ def _compute_metric(
 
 
 class EarlySignal(BaseMetric):
-    """Early Signal detection metric.
-
-    This metric finds the first occurrence where a signal is detected based on
-    threshold criteria and returns the corresponding init_time, lead_time, and
-    valid_time information. The metric is designed to be flexible for different
-    signal detection criteria that can be specified in applied metrics downstream.
-
-    Args:
-        name: The name of the metric.
-        comparison_operator: The comparison operator to use for signal detection.
-        threshold: The threshold value for signal detection.
-        spatial_aggregation: The spatial aggregation method to use for signal detection.
-            Options are "any" (any gridpoint meets criteria), "all" (all gridpoints
-            meet criteria), or "half" (at least half of gridpoints meet criteria).
+    """Detect first occurrence of signal exceeding threshold criteria.
+
+    Extends BaseMetric to find the earliest time when a signal is detected
+    based on threshold criteria, returning init_time, lead_time, and
+    valid_time information. Flexible for different signal detection criteria.
     """
 
     def __init__(
@@ -864,6 +945,17 @@ def __init__(
         spatial_aggregation: Literal["any", "all", "half"] = "any",
         **kwargs,
     ):
+        """Initialize the Early Signal detection metric.
+
+        Args:
+            name: The name of the metric. Defaults to "EarlySignal".
+            comparison_operator: The comparison operator for signal detection.
+            threshold: The threshold value for signal detection.
+            spatial_aggregation: Spatial aggregation method. Options: "any"
+                (any gridpoint meets criteria), "all" (all gridpoints meet
+                criteria), or "half" (at least half meet criteria).
+            **kwargs: Additional keyword arguments passed to BaseMetric.
+        """
         # Extract threshold params before passing to super
         self.comparison_operator = utils.maybe_get_operator(comparison_operator)
         self.threshold = threshold
@@ -929,19 +1021,11 @@ def _compute_metric(
 
 
 class MaximumMeanAbsoluteError(MeanAbsoluteError):
-    """Computes the mean absolute error between the forecast and target maximum values.
-
-    The forecast is filtered to a time window around the target's maximum using
-    tolerance_range_hours (in the event of variation between the timing between the
-    target and forecast maximum values). The mean absolute error is computed between the
-    filtered forecast and target maximum value.
-
-    Args:
-        tolerance_range_hours: The time window (hours) around the target's maximum
-            value to search for forecast minimum. Defaults to 24 hours.
-        reduce_spatial_dims: The spatial dimensions to reduce. Defaults to
-            ["latitude", "longitude"].
-        name: The name of the metric. Defaults to "MaximumMeanAbsoluteError".
+    """Compute MAE between forecast and target maximum values.
+
+    Extends MeanAbsoluteError to filter forecast to a time window around the
+    target's maximum using tolerance_range_hours. Useful for evaluating peak
+    value timing and magnitude.
     """
 
     def __init__(
@@ -952,6 +1036,20 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Maximum Mean Absolute Error metric.
+
+        Args:
+            tolerance_range_hours: Time window (hours) around target's
+                maximum to search for forecast maximum. Defaults to 24.
+            reduce_spatial_dims: Spatial dimensions to reduce. Defaults to
+                ["latitude", "longitude"].
+            name: The name of the metric. Defaults to
+                "MaximumMeanAbsoluteError".
+            *args: Additional positional arguments passed to
+                MeanAbsoluteError.
+            **kwargs: Additional keyword arguments passed to
+                MeanAbsoluteError.
+        """
         self.tolerance_range_hours = tolerance_range_hours
         self.reduce_spatial_dims = reduce_spatial_dims
         super().__init__(name, *args, **kwargs)
@@ -1007,19 +1105,11 @@ def _compute_metric(
 
 
 class MinimumMeanAbsoluteError(MeanAbsoluteError):
-    """Computes the mean absolute error between the forecast and target minimum values.
-
-    The forecast is filtered to a time window around the target's minimum using
-    tolerance_range_hours (in the event of variation between the timing between the
-    target and forecast minimum values). The mean absolute error is computed between the
-    filtered forecast and target minimum value.
-
-    Args:
-        tolerance_range_hours: The time window (hours) around the target's minimum
-            value to search for forecast minimum. Defaults to 24 hours.
-        reduce_spatial_dims: The spatial dimensions to reduce. Defaults to
-            ["latitude", "longitude"].
-        name: The name of the metric. Defaults to "MinimumMeanAbsoluteError".
+    """Compute MAE between forecast and target minimum values.
+
+    Extends MeanAbsoluteError to filter forecast to a time window around the
+    target's minimum using tolerance_range_hours. Useful for evaluating
+    minimum value timing and magnitude.
     """
 
     def __init__(
@@ -1030,6 +1120,20 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Minimum Mean Absolute Error metric.
+
+        Args:
+            tolerance_range_hours: Time window (hours) around target's
+                minimum to search for forecast minimum. Defaults to 24.
+            reduce_spatial_dims: Spatial dimensions to reduce. Defaults to
+                ["latitude", "longitude"].
+            name: The name of the metric. Defaults to
+                "MinimumMeanAbsoluteError".
+            *args: Additional positional arguments passed to
+                MeanAbsoluteError.
+            **kwargs: Additional keyword arguments passed to
+                MeanAbsoluteError.
+        """
         self.tolerance_range_hours = tolerance_range_hours
         self.reduce_spatial_dims = reduce_spatial_dims
         super().__init__(name, *args, **kwargs)
@@ -1082,16 +1186,11 @@ def _compute_metric(
 
 
 class MaximumLowestMeanAbsoluteError(MeanAbsoluteError):
-    """Mean Absolute Error of the maximum of aggregated minimum values.
-
-    Meant for heatwave evaluation by aggregating the minimum values over a day and then
-    computing the MeanAbsoluteError between the warmest nighttime (daily minimum)
-    temperature in the target and forecast.
+    """Compute MAE of maximum aggregated minimum values for heatwaves.
 
-    Args:
-        tolerance_range_hours: The time window (hours) around the target's max-min
-            value to search for forecast max-min. Defaults to 24 hours.
-        name: The name of the metric. Defaults to "MaximumLowestMeanAbsoluteError".
+    Extends MeanAbsoluteError for heatwave evaluation by aggregating daily
+    minimum values and computing MAE between the warmest nighttime (daily
+    minimum) temperature in target and forecast.
     """
 
     def __init__(
@@ -1101,6 +1200,18 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Maximum Lowest Mean Absolute Error metric.
+
+        Args:
+            tolerance_range_hours: Time window (hours) around target's
+                max-min value to search for forecast max-min. Defaults to 24.
+            name: The name of the metric. Defaults to
+                "MaximumLowestMeanAbsoluteError".
+            *args: Additional positional arguments passed to
+                MeanAbsoluteError.
+            **kwargs: Additional keyword arguments passed to
+                MeanAbsoluteError.
+        """
         self.tolerance_range_hours = tolerance_range_hours
         super().__init__(name, *args, **kwargs)
 
@@ -1184,22 +1295,10 @@ def _compute_metric(
 
 
 class DurationMeanError(MeanError):
-    """Compute the duration of a case's event.
-
-    This metric computes the mean error between the forecast and target durations.
-
-    Args:
-        threshold_criteria: The criteria for event detection. Can be either a DataArray
-            of a climatology with dimensions (dayofyear, hour, latitude, longitude) or a
-            float value representing a fixed threshold.
-        reduce_spatial_dims: The spatial dimensions to reduce prior to applying threshold
-            criteria. Defaults to ["latitude", "longitude"].
-        op_func: Comparison operator or string (e.g., operator.ge for >=).
-        name: Name of the metric.
-        preserve_dims: Dimensions to preserve during aggregation. Defaults to
-            "init_time".
-        product_time_resolution_hours: Whether to product the duration by the time
-            resolution of the forecast (in hours). Defaults to False.
+    """Compute mean error of event duration between forecast and target.
+
+    Extends MeanError to compute the mean error between forecast and target
+    event durations based on threshold criteria and spatial aggregation.
     """
 
     def __init__(
@@ -1211,6 +1310,23 @@ def __init__(
         preserve_dims: str = "init_time",
         product_time_resolution_hours: bool = False,
     ):
+        """Initialize the Duration Mean Error metric.
+
+        Args:
+            threshold_criteria: Criteria for event detection. Either a
+                DataArray of climatology with dimensions (dayofyear, hour,
+                latitude, longitude) or a float fixed threshold.
+            reduce_spatial_dims: Spatial dimensions to reduce prior to
+                applying threshold criteria. Defaults to ["latitude",
+                "longitude"].
+            op_func: Comparison operator or string (e.g., operator.ge for
+                >=).
+            name: Name of the metric. Defaults to "DurationMeanError".
+            preserve_dims: Dimensions to preserve during aggregation.
+                Defaults to "init_time".
+            product_time_resolution_hours: Whether to multiply duration by
+                time resolution of forecast (in hours). Defaults to False.
+        """
         super().__init__(name=name, preserve_dims=preserve_dims)
         self.reduce_spatial_dims = reduce_spatial_dims
         self.threshold_criteria = threshold_criteria
@@ -1307,15 +1423,17 @@ def _compute_metric(
 
 
 class LandfallMetric(CompositeMetric):
-    """Base class for landfall metrics.
+    """Base class for tropical cyclone landfall metrics.
+
+    Extends CompositeMetric to compute landfalls using calc.find_landfalls,
+    which utilizes land geometry and line segments based on track data to
+    determine intersections.
 
-    Landfall metrics compute landfalls using the calc.find_landfalls function, which
-    utilizes a land geometry and line segments based on track data to determine
-    intersections.
+    Can be used as a base class for custom landfall metrics, as a mixin with
+    other metrics, or as a composite metric for multiple landfall metrics.
 
-    Can be used as a base class for custom landfall metrics, as a mixin with other
-    metrics, or as a composite metric for multiple landfall metrics (which utilize
-    identical landfalling locations).
+    Public methods:
+        maybe_prepare_composite_kwargs: Prepare kwargs for landfall composites
     """
 
     def __init__(
@@ -1521,13 +1639,11 @@ def _compute_metric(
 
 
 class SpatialDisplacement(BaseMetric):
-    """Spatial displacement error metric for atmospheric rivers and similar events.
-
-    Computes the great circle distance between the center of mass of forecast
-    and target spatial patterns.
+    """Compute spatial displacement between forecast and target patterns.
 
-    Args:
-        name: The name of the metric. Defaults to "spatial_displacement".
+    Extends BaseMetric to compute great circle distance between centers of
+    mass of forecast and target spatial patterns. Useful for atmospheric
+    rivers and similar spatial features.
     """
 
     def __init__(
@@ -1535,6 +1651,13 @@ def __init__(
         name: str = "spatial_displacement",
         **kwargs: Any,
     ):
+        """Initialize the Spatial Displacement metric.
+
+        Args:
+            name: The name of the metric. Defaults to
+                "spatial_displacement".
+            **kwargs: Additional keyword arguments passed to BaseMetric.
+        """
         super().__init__(name, **kwargs)
 
     def _compute_metric(
@@ -1621,13 +1744,10 @@ def center_of_mass_ufunc(data):
 
 
 class LandfallDisplacement(LandfallMetric):
-    """Calculate the distance between forecast and target landfall positions.
+    """Compute distance between forecast and target landfall positions.
 
-    This metric computes the distance between the forecast and target
-    landfall positions, defaulting to kilometers.
-
-    Args:
-        name: The name of the metric. Defaults to "landfall_displacement".
+    Extends LandfallMetric to calculate the spatial distance between forecast
+    and target landfall positions, defaulting to kilometers.
     """
 
     def __init__(
@@ -1636,6 +1756,14 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Landfall Displacement metric.
+
+        Args:
+            name: The name of the metric. Defaults to
+                "landfall_displacement".
+            *args: Additional positional arguments passed to LandfallMetric.
+            **kwargs: Additional keyword arguments passed to LandfallMetric.
+        """
         super().__init__(name, *args, **kwargs)
         self.units = kwargs.get("units", "km")
 
@@ -1717,15 +1845,11 @@ def _compute_metric(
 
 
 class LandfallTimeMeanError(LandfallMetric):
-    """Landfall time mean error.
-
-    This metric computes the mean error between the forecast and target landfall times.
-    A positive value indicates the forecast landfall time is later than the target
-    landfall time, a negative value indicates the forecast landfall time is earlier than
-    the target landfall time.
+    """Compute mean error between forecast and target landfall times.
 
-    Args:
-        name: The name of the metric. Defaults to "landfall_time_me".
+    Extends LandfallMetric to calculate timing difference. Positive values
+    indicate forecast landfall is later than target; negative values indicate
+    forecast landfall is earlier than target.
     """
 
     def __init__(
@@ -1734,6 +1858,13 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Landfall Time Mean Error metric.
+
+        Args:
+            name: The name of the metric. Defaults to "landfall_time_me".
+            *args: Additional positional arguments passed to LandfallMetric.
+            **kwargs: Additional keyword arguments passed to LandfallMetric.
+        """
         super().__init__(name, *args, **kwargs)
 
     def calculate_time_difference(
@@ -1791,18 +1922,14 @@ def _compute_metric(
 
 
 class LandfallIntensityMeanAbsoluteError(LandfallMetric, MeanAbsoluteError):
-    """Compute the MeanAbsoluteError between forecast and target.
+    """Compute MAE of forecast and target intensity at landfall.
 
-    This metric computes the mean absolute error between forecast and target
-    intensity at landfall.
+    Extends both LandfallMetric and MeanAbsoluteError to calculate mean
+    absolute error between forecast and target intensity at landfall time.
 
     The intensity variable is determined by forecast_variable and
-    target_variable. To evaluate multiple intensity variables (e.g.,
-    surface_wind_speed and air_pressure_at_mean_sea_level), create
-    separate metric instances for each variable.
-
-    Args:
-        name: The name of the metric. Defaults to "landfall_intensity_mae".
+    target_variable. For multiple intensity variables, create separate metric
+    instances for each variable.
     """
 
     def __init__(
@@ -1811,6 +1938,14 @@ def __init__(
         *args,
         **kwargs,
     ):
+        """Initialize the Landfall Intensity Mean Absolute Error metric.
+
+        Args:
+            name: The name of the metric. Defaults to
+                "landfall_intensity_mae".
+            *args: Additional positional arguments passed to parent classes.
+            **kwargs: Additional keyword arguments passed to parent classes.
+        """
         super().__init__(name, *args, **kwargs)
 
     def _compute_metric(
diff --git a/src/extremeweatherbench/regions.py b/src/extremeweatherbench/regions.py
index f2580ccb..5a36dc0e 100644
--- a/src/extremeweatherbench/regions.py
+++ b/src/extremeweatherbench/regions.py
@@ -22,7 +22,21 @@
 
 
 class Region(abc.ABC):
-    """Base class for different region representations."""
+    """Base class for different region representations.
+
+    This abstract class defines the interface for geographic regions used in
+    ExtremeWeatherBench. Regions can be centered, bounding boxes, or defined
+    by shapefiles.
+
+    Public methods:
+        create_region: Abstract factory method to create a region
+        as_geopandas: Convert region to GeoDataFrame representation
+        get_adjusted_bounds: Get region bounds adjusted to dataset convention
+        mask: Mask a dataset to this region
+        intersects: Check if this region intersects another region
+        contains: Check if this region contains another region
+        area_overlap_fraction: Calculate area overlap with another region
+    """
 
     @classmethod
     @abc.abstractmethod
@@ -159,17 +173,11 @@ def area_overlap_fraction(self, other: "Region") -> float:
 
 
 class CenteredRegion(Region):
-    """A region defined by a center point and a bounding box.
-
-    bounding_box_degrees is the width (length) of one or all sides, not half size;
-    e.g., bounding_box_degrees=10.0 means a 10 degree by 10 degree box around
-    the center point.
+    """Region defined by center point and bounding box.
 
-    Attributes:
-        latitude: Center latitude
-        longitude: Center longitude
-        bounding_box_degrees: Size of bounding box in degrees or tuple of
-            (lat_degrees, lon_degrees)
+    Extends Region to define a region using a center point and bounding box
+    dimensions. The bounding_box_degrees is the full width/height (not half
+    size); e.g., 10.0 means a 10x10 degree box around the center.
     """
 
     def __repr__(self):
@@ -182,6 +190,15 @@ def __repr__(self):
     def __init__(
         self, latitude: float, longitude: float, bounding_box_degrees: float | tuple
     ):
+        """Initialize the CenteredRegion.
+
+        Args:
+            latitude: Center latitude in degrees.
+            longitude: Center longitude in degrees.
+            bounding_box_degrees: Size of bounding box in degrees. Either a
+                single float (square box) or tuple of (lat_degrees,
+                lon_degrees).
+        """
         self.latitude = latitude
         self.longitude = longitude
         self.bounding_box_degrees = bounding_box_degrees
@@ -229,13 +246,9 @@ def as_geopandas(self) -> gpd.GeoDataFrame:
 
 
 class BoundingBoxRegion(Region):
-    """A region defined by explicit latitude and longitude bounds.
+    """Region defined by explicit latitude and longitude bounds.
 
-    Attributes:
-        latitude_min: Minimum latitude bound
-        latitude_max: Maximum latitude bound
-        longitude_min: Minimum longitude bound
-        longitude_max: Maximum longitude bound
+    Extends Region to define a region using explicit bounding box coordinates.
     """
 
     def __repr__(self):
@@ -253,6 +266,14 @@ def __init__(
         longitude_min: float,
         longitude_max: float,
     ):
+        """Initialize the BoundingBoxRegion.
+
+        Args:
+            latitude_min: Minimum latitude bound in degrees.
+            latitude_max: Maximum latitude bound in degrees.
+            longitude_min: Minimum longitude bound in degrees.
+            longitude_max: Maximum longitude bound in degrees.
+        """
         self.latitude_min = latitude_min
         self.latitude_max = latitude_max
         self.longitude_min = longitude_min
@@ -286,19 +307,21 @@ def as_geopandas(self) -> gpd.GeoDataFrame:
 
 
 class ShapefileRegion(Region):
-    """A region defined by a shapefile.
-
-    A geopandas object shapefile is read in and stored as an attribute
-    on instantiation.
+    """Region defined by a shapefile.
 
-    Attributes:
-        shapefile_path: Local or remote path to the .shp shapefile
+    Extends Region to define a region using a shapefile. The shapefile is read
+    using geopandas on instantiation.
     """
 
     def __repr__(self):
         return f"{self.__class__.__name__}(shapefile_path={self.shapefile_path})"
 
     def __init__(self, shapefile_path: str | pathlib.Path):
+        """Initialize the ShapefileRegion.
+
+        Args:
+            shapefile_path: Local or remote path to the .shp shapefile.
+        """
         self.shapefile_path = pathlib.Path(shapefile_path)
 
     @classmethod
@@ -465,16 +488,17 @@ def _create_geopandas_from_bounds(
 
 
 class RegionSubsetter:
-    """A utility class for subsetting ExtremeWeatherBench objects by region.
+    """Utility class for subsetting ExtremeWeatherBench objects by region.
 
-    Attributes:
-        region: The region to subset to. Can be a Region object or a
-            dictionary of bounds with keys "latitude_min", "latitude_max",
-            "longitude_min", and "longitude_max".
-        method: The method to use for subsetting. Options:
-            - "intersects": Include cases where ANY part of a case intersects region
-            - "percent": Include cases where percent of case area overlaps with region.
-            - "all": Only include cases where entirety of a case is within region
+    Provides methods for filtering case collections based on spatial overlap
+    with a specified region using various inclusion criteria.
+
+    Public methods:
+        subset: Subset a case collection based on region overlap
+
+    Instance attributes:
+        region: The region to subset to
+        method: The subsetting method used
         percent_threshold: Threshold for percent overlap (0.0 to 1.0)
     """
 
@@ -491,10 +515,10 @@ def __init__(
         """Initialize the RegionSubsetter.
 
         Args:
-            region: The region to subset to. Can be a Region object or a
-                dictionary of bounds with keys "latitude_min", "latitude_max",
-                "longitude_min", and "longitude_max".
-            method: The method to use for subsetting. Options:
+            region: The region to subset to. Can be a Region object or
+                dictionary with keys "latitude_min", "latitude_max",
+                "longitude_min", "longitude_max".
+            method: The subsetting method. Options:
                 - "intersects": Include cases where ANY part of a case intersects region
                 - "percent": Include cases where percent of case area overlaps with
                 region
diff --git a/src/extremeweatherbench/sources/base.py b/src/extremeweatherbench/sources/base.py
index 0abed12c..dd58641c 100644
--- a/src/extremeweatherbench/sources/base.py
+++ b/src/extremeweatherbench/sources/base.py
@@ -6,7 +6,17 @@
 
 @runtime_checkable
 class Source(Protocol):
-    """A protocol for input sources."""
+    """Protocol defining the interface for input data sources.
+
+    This protocol specifies the methods that input source implementations must
+    provide for variable extraction, temporal validation, and spatial data
+    checking.
+
+    Required methods:
+        safely_pull_variables: Extract specified variables from data
+        check_for_valid_times: Check if data has valid times in date range
+        check_for_spatial_data: Check if data has spatial coverage for region
+    """
 
     def safely_pull_variables(
         self,

From 5350563ee630b2cbe794c4ba9e02a9c68bf0eaac Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 23 Jan 2026 21:35:16 -0500
Subject: [PATCH 16/72] add explanation for dim reqs (#320)

---
 docs/usage.md | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/docs/usage.md b/docs/usage.md
index a5e67b8c..5190a704 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -8,11 +8,6 @@ To run the Brightband-based evaluation on an existing AIWP model (FCN v2), which
 includes the default 337 cases for heat waves, freezes, severe convective days, 
 tropical cyclones, and atmospheric rivers:
 
-```bash
-ewb --default
-```
-
-or:
 
 ```python
 from extremeweatherbench import evaluate, defaults, cases
@@ -28,12 +23,21 @@ outputs = ewb.run()
 outputs.to_csv('your_outputs.csv')
 ```
 
+or:
+
+```bash
+ewb --default
+```
 ## Running an Evaluation for a Single Event Type
 
 ExtremeWeatherBench has default event types and cases for heat waves, freezes, severe convection, tropical cyclones, and atmospheric rivers.
 
 To run an evaluation, there are three components required: a forecast, a target, and an evaluation object.
 
+ExtremeWeatherBench requires forecasts to have `init_time`, `lead_time`, `latitude`, and `longitude` dimensions at minimum. If not already in that naming convention, initializing a `ForecastBase` object with a `variable_mapping` to map to those names is required. Other dimensions such as pressure level (`level`) can be included.
+
+Targets require at least a `valid_time` with at least one spatial dimension. Examples include `location`, `station`, or (`latitude`, `longitude`). Forecasts are aligned to targets during the steps immediately prior to evaluating a metric.
+
 ```python
 from extremeweatherbench import inputs
 ```

From 71f3a0b8d5a4d1dba3e3097d325461ef9a651957 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 23 Jan 2026 23:07:22 -0500
Subject: [PATCH 17/72] Update `defaults` and `inputs` to include new CIRA
 icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos
---
 README.md                           |  49 +----
 docs/recipes/cira_forecast.md       |  39 ++--
 docs/usage.md                       |  28 ++-
 src/extremeweatherbench/defaults.py | 140 ++++++++-------
 src/extremeweatherbench/inputs.py   |  59 ++++++
 tests/test_defaults.py              |  95 ++++++++--
 tests/test_inputs.py                | 267 +++++++++++++++++++++++++++-
 7 files changed, 520 insertions(+), 157 deletions(-)

diff --git a/README.md b/README.md
index f0249f50..d90aff16 100644
--- a/README.md
+++ b/README.md
@@ -67,48 +67,11 @@ $ ewb --default
 ```python
 from extremeweatherbench import cases, inputs, metrics, evaluate, utils
 
-# Select model
-model = 'FOUR_v200_GFS'
-
-# Set up path to directory of file - zarr or kerchunk/virtualizarr json/parquet
-forecast_dir = f'gs://extremeweatherbench/{model}.parq'
-
-# Preprocessing function exclusive to handling the CIRA parquets
-def preprocess_bb_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
-    """Preprocess CIRA kerchunk (parquet) data in the ExtremeWeatherBench bucket.
-    A preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
-    Args:
-        ds: The forecast dataset to rename.
-    Returns:
-        The renamed forecast dataset.
-    """
-    ds = ds.rename({"time": "lead_time"})
-
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
-
-    return ds
-
-# Define a forecast object; in this case, a KerchunkForecast
-fcnv2_forecast = inputs.KerchunkForecast(
-    name="fcnv2_forecast", # identifier for this forecast in results
-    source=forecast_dir, # source path
-    variables=["surface_air_temperature"], # variables to use in the evaluation
-    variable_mapping=inputs.CIRA_metadata_variable_mapping, # mapping to use for variables in forecast dataset to EWB variable names
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}}, # storage options for access
-    preprocess=preprocess_bb_cira_forecast_dataset # required preprocessing function for CIRA references
-)
+# Load in a forecast; here, we load in GFS initialized FCNv2 from the CIRA MLWP archive with a default variable built-in for convenience
+fcnv2_heatwave_forecast = defaults.cira_fcnv2_heatwave_forecast
 
-# Load in ERA5; source defaults to the ARCO ERA5 dataset from Google and variable mapping is provided by default as well
-era5_heatwave_target = inputs.ERA5(
-    variables=["surface_air_temperature"], # variable to use in the evaluation
-    storage_options={"remote_options": {"anon": True}}, # storage options for access
-    chunks=None, # define chunks for the ERA5 data
-)
+# Load in ERA5 with another default convenience variable 
+era5_heatwave_target = defaults.era5_heatwave_target
 
 # EvaluationObjects are used to evaluate a single forecast source against a single target source with a defined event type. Event types are declared with each case. One or more metrics can be evaluated with each EvaluationObject.
 heatwave_evaluation_list = [
@@ -120,7 +83,7 @@ heatwave_evaluation_list = [
             metrics.MaximumLowestMeanAbsoluteError(),
         ],
         target=era5_heatwave_target,
-        forecast=fcnv2_forecast,
+        forecast=fcnv2_heatwave_forecast,
     ),
 ]
 # Load in the EWB default list of event cases
@@ -134,7 +97,7 @@ ewb_instance = evaluate.ExtremeWeatherBench(
 
 # Execute a parallel run and return the evaluation results as a pandas DataFrame
 heatwave_outputs = ewb_instance.run(
-    parallel_config={'backend':'loky','n_jobs':16} # Uses 16 jobs with the loky backend
+    parallel_config={'n_jobs':16} # Uses 16 jobs with the loky backend as default
 )
 
 # Save the results
diff --git a/docs/recipes/cira_forecast.md b/docs/recipes/cira_forecast.md
index 43a7a6ac..9cf57b90 100644
--- a/docs/recipes/cira_forecast.md
+++ b/docs/recipes/cira_forecast.md
@@ -2,22 +2,10 @@
 
 We have a dedicated virtual reference icechunk store for CIRA data **up to May 26th, 2025** available at `gs://extremeweatherbench/cira-icechunk`. Compared to using parquet virtual references, we have seen a speed improvements of around 2x with ~25% more memory usage.
 
-## Loading the store
-
-```python
-
-from extremeweatherbench import cases, inputs, metrics, evaluate, defaults
-import datetime
-import icechunk
-
-storage = icechunk.gcs_storage(
-    bucket="extremeweatherbench", prefix="cira-icechunk", anonymous=True
-)
-```
-
 ## Accessing a CIRA Model from the store
 
 ```python
+from extremeweatherbench import inputs
 
 group_list = inputs.list_groups_in_icechunk_datatree(storage)
 ```
@@ -39,22 +27,33 @@ group_list = inputs.list_groups_in_icechunk_datatree(storage)
 
 ```python
 
-# Find FCNv2's name in the group list
-fcnv2_group = [n for n in group_list if 'FOUR_v200_GFS' in n][0]
-
 # Helper function to access the virtual dataset
-fcnv2 = inputs.open_icechunk_dataset_from_datatree(
+fcnv2 = inputs.get_cira_icechunk(model_name='FOUR_v200_IFS')
+```
+
+`fcnv2` is a `ForecastBase` object ready to be used within EWB's evaluation framework. 
+
+> **Detailed Explanation**: `inputs.get_cira_icechunk` is syntactic sugar for this: 
+```python
+import icechunk
+
+storage = icechunk.gcs_storage(
+    bucket="extremeweatherbench", prefix="cira-icechunk", anonymous=True
+)
+
+fcnv2_icechunk_ds = inputs.open_icechunk_dataset_from_datatree(
     storage=storage, 
-    group=fcnv2_group, 
+    group="FOUR_v200_IFS", 
     authorize_virtual_chunk_access=inputs.CIRA_CREDENTIALS
     )
-fcnv2_icechunk_forecast_object = inputs.XarrayForecast(
+
+fcnv2 = inputs.XarrayForecast(
     ds=fcnv2,
     variable_mapping=inputs.CIRA_metadata_variable_mapping
     )
 ```
 
-`fcnv2_icechunk_forecast_object` is a `ForecastBase` object ready to be used within EWB's evaluation framework.
+Which is a three step process of accessing the icechunk storage, loading the dataset from the datatree/zarr group format, and finally applying that `Dataset` in a `ForecastBase` object.
 
 ## Set up metrics and target for evaluation
 
diff --git a/docs/usage.md b/docs/usage.md
index 5190a704..d5e37778 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -41,7 +41,7 @@ Targets require at least a `valid_time` with at least one spatial dimension. Exa
 ```python
 from extremeweatherbench import inputs
 ```
-There are two built-in `ForecastBase` classes to set up a forecast: `ZarrForecast` and `KerchunkForecast`. Here is an example of a `ZarrForecast`, using Weatherbench2's HRES zarr store:
+There are three built-in `ForecastBase` classes to set up a forecast: `ZarrForecast`, `XarrayForecast`, and `KerchunkForecast`. Here is an example of a `ZarrForecast`, using Weatherbench2's HRES zarr store:
 
 ```python
 hres_forecast = inputs.ZarrForecast(
@@ -60,9 +60,9 @@ There are required arguments, namely:
 - `variables`*
 - `variable_mapping`
 
-* `variables` can be defined within one or more metrics instead of in a `ForecastBase` object.
+* `variables` can alternatively be defined within one or more metrics, instead of in a `ForecastBase` object.
 
-A forecast needs a `source`, which is a link to the zarr store in this case. A `name` is required to identify the outputs. It also needs `variables` defined, which are based on CF Conventions. A list of variable namings exists in `defaults.py` as `DEFAULT_VARIABLE_NAMES`. Each forecast will likely have different names for their variables, so a `variable_mapping` dictionary is also essential to process the variables, as well as the coordinates and dimensions. EWB uses `lead_time`, `init_time`, and `valid_time` as time coordinates. The HRES data is mapped from `prediction_timedelta` to `lead_time`, as an example. `storage_options` define access patterns for the data if needed. These are passed to the opening function, e.g. `xarray.open_zarr`.
+> **Detailed Explanation**: A forecast needs a `source`, which is a link to the zarr store in this case. A `name` is required to identify the outputs. It also needs `variables` defined, which are based on CF Conventions. A list of variable namings exists in `defaults.py` as `DEFAULT_VARIABLE_NAMES`. Each forecast will likely have different names for their variables, so a `variable_mapping` dictionary is also essential to process the variables, as well as the coordinates and dimensions. EWB uses `lead_time`, `init_time`, and `valid_time` as time coordinates. The HRES data is mapped from `prediction_timedelta` to `lead_time`, as an example. `storage_options` define access patterns for the data if needed. These are passed to the opening function, e.g. `xarray.open_zarr`.
 
 Next, a target dataset must be defined as well to evaluate against. For this evaluation, we'll use ERA5:
 
@@ -75,7 +75,19 @@ era5_heatwave_target = inputs.ERA5(
 )
 ```
 
-Similarly to forecasts, we need to define the `source`, which here is the ARCO ERA5 provided by Google. `variables` are again required to be set for the `inputs.ERA5` class; `variable_mapping` defaults to `inputs.ERA5_metadata_variable_mapping` for many existing variables and likely is not required to be set unless your use case is for less common variables. Both forecasts and targets, if relevant, have an optional `chunks` parameter which defaults to what should be the most efficient value - usually `None` or `'auto'`, but can be changed as seen above.
+Note that EWB provides defaults for arguments, so most users will be able to instead write this (if defining variables with the intent of it applying to all metrics):
+
+```python
+era5_heatwave_target = inputs.ERA5(variables=['surface_air_temperature'])
+```
+
+Or (if defining variables as arguments to the metrics):
+
+```python
+era5_heatwave_target = inputs.ERA5()
+```
+
+> **Detailed Explanation**: Similarly to forecasts, we need to define the `source`, which here is the ARCO ERA5 provided by Google. `variables` are used to subset `inputs.ERA5` in an evaluation; `variable_mapping` defaults to `inputs.ERA5_metadata_variable_mapping` for many existing variables and likely is not required to be set unless your use case is for less common variables. Both forecasts and targets, if relevant, have an optional `chunks` parameter which defaults to what should be the most efficient value - usually `None` or `'auto'`, but can be changed as seen above. *If using the ARCO ERA5 and setting `chunks=None`, it is critical to order your subsetting by variables -> time -> `.sel` or `.isel` latitude & longitude -> rechunk. [See this Github comment](https://github.com/pydata/xarray/issues/8902#issuecomment-2036435045).
 
 We then set up an `EvaluationObject` list:
 
@@ -102,11 +114,11 @@ Plugging these all in:
 
 ```python
 from extremeweatherbench import cases, evaluate
-case_yaml = cases.load_ewb_events_yaml_into_case_list()
+case_list = cases.load_ewb_events_yaml_into_case_list()
 
 
 ewb_instance = evaluate.ExtremeWeatherBench(
-    cases=case_yaml,
+    cases=case_list,
     evaluation_objects=heatwave_evaluation_list,
 )
 
@@ -115,6 +127,8 @@ outputs = ewb_instance.run()
 outputs.to_csv('your_file_name.csv')
 ```
 
-Where the EWB default events YAML file is loaded in using a built-in utility helper function, then applied to an instance of `evaluate.ExtremeWeatherBench` along with the `EvaluationObject` list. Finally, we run the evaluation with the `.run()` method, where defaults are typically sufficient to run with a small to moderate-sized virtual machine. after subsetting and prior to metric calculation.
+Where the EWB default events YAML file is loaded in using a built-in utility helper function, then applied to an instance of `evaluate.ExtremeWeatherBench` along with the `EvaluationObject` list. Finally, we trigger the evaluation with the `.run()` method, where defaults are typically sufficient to run with a small to moderate-sized virtual machine. after subsetting and prior to metric calculation. 
+
+Running locally is feasible but is typically bottlenecked heavily by IO and network bandwidth. Even on a gigabit connection, the rate of data access is significantly slower compared to within a cloud provider VM.
 
 The outputs are returned as a pandas DataFrame and can be manipulated in the script, a notebook, or post-hoc after saving it.
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index a78cdcca..7dcc68d6 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -58,28 +58,36 @@
 ]
 
 
-def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_cira_forecast_dataset(
+    ds: xr.Dataset, kerchunk: bool = True
+) -> xr.Dataset:
     """A preprocess function for CIRA data that renames the time coordinate to
     lead_time, creates a valid_time coordinate, and sets the lead time range and
     resolution not present in the original dataset.
 
     Args:
         ds: The forecast dataset to preprocess.
-
+        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
     Returns:
         The preprocessed forecast dataset.
     """
-    ds = ds.rename({"time": "lead_time"})
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
+
+    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
+    # and set the lead time range and resolution. Otherwise, pass through the dataset.
+    if kerchunk:
+        ds = ds.rename({"time": "lead_time"})
+        # The evaluation configuration is used to set the lead time range and resolution.
+        ds["lead_time"] = np.array(
+            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+        ).astype("timedelta64[ns]")
     return ds
 
 
 # Preprocessing function for CIRA data that includes geopotential thickness calculation
 # required for tropical cyclone tracks
-def _preprocess_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_cira_tc_forecast_dataset(
+    ds: xr.Dataset, kerchunk: bool = True
+) -> xr.Dataset:
     """A preprocess function for CIRA data that includes geopotential thickness
     calculation required for tropical cyclone tracks.
 
@@ -89,16 +97,18 @@ def _preprocess_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
     Args:
         ds: The forecast dataset to rename.
-
+        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
     Returns:
         The renamed forecast dataset.
     """
-    ds = ds.rename({"time": "lead_time"})
-
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
+    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
+    # and set the lead time range and resolution. Otherwise, pass through the dataset.
+    if kerchunk:
+        ds = ds.rename({"time": "lead_time"})
+        # The evaluation configuration is used to set the lead time range and resolution.
+        ds["lead_time"] = np.array(
+            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+        ).astype("timedelta64[ns]")
 
     # Calculate the geopotential thickness required for tropical cyclone tracks
     ds["geopotential_thickness"] = (
@@ -133,23 +143,27 @@ def _preprocess_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
 
 # Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_ar_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_cira_ar_forecast_dataset(
+    ds: xr.Dataset, kerchunk: bool = True
+) -> xr.Dataset:
     """An example preprocess function that renames the time coordinate to lead_time,
     creates a valid_time coordinate, and sets the lead time range and resolution not
     present in the original dataset.
 
     Args:
         ds: The forecast dataset to rename.
-
+        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
     Returns:
         The renamed forecast dataset.
     """
-    ds = ds.rename({"time": "lead_time"})
-
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
+    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
+    # and set the lead time range and resolution. Otherwise, pass through the dataset.
+    if kerchunk:
+        ds = ds.rename({"time": "lead_time"})
+        # The evaluation configuration is used to set the lead time range and resolution.
+        ds["lead_time"] = np.array(
+            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+        ).astype("timedelta64[ns]")
     if "q" not in ds.variables:
         # Calculate specific humidity from relative humidity and air temperature
         ds["specific_humidity"] = calc.specific_humidity_from_relative_humidity(
@@ -161,23 +175,27 @@ def _preprocess_ar_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
 
 # Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_severe_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+def _preprocess_severe_cira_forecast_dataset(
+    ds: xr.Dataset, kerchunk: bool = True
+) -> xr.Dataset:
     """An example preprocess function that renames the time coordinate to lead_time,
     creates a valid_time coordinate, and sets the lead time range and resolution not
     present in the original dataset.
 
     Args:
         ds: The forecast dataset to rename.
-
+        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
     Returns:
         The renamed forecast dataset.
     """
-    ds = ds.rename({"time": "lead_time"})
-
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
+    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
+    # and set the lead time range and resolution. Otherwise, pass through the dataset.
+    if kerchunk:
+        ds = ds.rename({"time": "lead_time"})
+        # The evaluation configuration is used to set the lead time range and resolution.
+        ds["lead_time"] = np.array(
+            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+        ).astype("timedelta64[ns]")
     if "q" not in ds.variables:
         # Calculate specific humidity from relative humidity and air temperature
         ds["specific_humidity"] = calc.specific_humidity_from_relative_humidity(
@@ -243,51 +261,39 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
 ibtracs_target = inputs.IBTrACS()
 
 # Forecasts
-cira_heatwave_forecast = inputs.KerchunkForecast(
-    name="FourCastNetv2",
-    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
+cira_fcnv2_heatwave_forecast = inputs.get_cira_icechunk(
+    model_name="FOUR_v200_GFS",
     variables=["surface_air_temperature"],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_cira_forecast_dataset,
+    name="FourCastNetv2",
 )
 
-cira_freeze_forecast = inputs.KerchunkForecast(
-    name="FourCastNetv2",
-    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
+cira_fcnv2_freeze_forecast = inputs.get_cira_icechunk(
+    model_name="FOUR_v200_GFS",
     variables=["surface_air_temperature"],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_cira_forecast_dataset,
+    name="FourCastNetv2",
 )
 
-cira_tropical_cyclone_forecast = inputs.KerchunkForecast(
-    name="FourCastNetv2",
-    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
+cira_fcnv2_tropical_cyclone_forecast = inputs.get_cira_icechunk(
+    model_name="FOUR_v200_GFS",
     variables=[derived.TropicalCycloneTrackVariables()],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
+    name="FourCastNetv2",
     preprocess=_preprocess_cira_tc_forecast_dataset,
 )
-cira_atmospheric_river_forecast = inputs.KerchunkForecast(
-    name="FourCastNetv2",
-    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
+cira_fcnv2_atmospheric_river_forecast = inputs.get_cira_icechunk(
+    model_name="FOUR_v200_GFS",
     variables=[
         derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_ar_cira_forecast_dataset,
+    name="FourCastNetv2",
+    preprocess=_preprocess_cira_ar_forecast_dataset,
 )
 
-cira_severe_convection_forecast = inputs.KerchunkForecast(
-    name="FourCastNetv2",
-    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
+cira_fcnv2_severe_convection_forecast = inputs.get_cira_icechunk(
+    model_name="FOUR_v200_GFS",
     variables=[derived.CravenBrooksSignificantSevere()],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
+    name="FourCastNetv2",
     preprocess=_preprocess_severe_cira_forecast_dataset,
 )
 
@@ -363,37 +369,37 @@ def get_brightband_evaluation_objects() -> list[inputs.EvaluationObject]:
             event_type="heat_wave",
             metric_list=heatwave_metric_list,
             target=era5_heatwave_target,
-            forecast=cira_heatwave_forecast,
+            forecast=cira_fcnv2_heatwave_forecast,
         ),
         inputs.EvaluationObject(
             event_type="heat_wave",
             metric_list=heatwave_metric_list,
             target=ghcn_heatwave_target,
-            forecast=cira_heatwave_forecast,
+            forecast=cira_fcnv2_heatwave_forecast,
         ),
         inputs.EvaluationObject(
             event_type="freeze",
             metric_list=freeze_metric_list,
             target=era5_freeze_target,
-            forecast=cira_freeze_forecast,
+            forecast=cira_fcnv2_freeze_forecast,
         ),
         inputs.EvaluationObject(
             event_type="freeze",
             metric_list=freeze_metric_list,
             target=ghcn_freeze_target,
-            forecast=cira_freeze_forecast,
+            forecast=cira_fcnv2_freeze_forecast,
         ),
         inputs.EvaluationObject(
             event_type="severe_convection",
             metric_list=pph_metric_list,
             target=pph_target,
-            forecast=cira_severe_convection_forecast,
+            forecast=cira_fcnv2_severe_convection_forecast,
         ),
         inputs.EvaluationObject(
             event_type="severe_convection",
             metric_list=lsr_metric_list,
             target=lsr_target,
-            forecast=cira_severe_convection_forecast,
+            forecast=cira_fcnv2_severe_convection_forecast,
         ),
         inputs.EvaluationObject(
             event_type="atmospheric_river",
@@ -403,12 +409,12 @@ def get_brightband_evaluation_objects() -> list[inputs.EvaluationObject]:
                 metrics.EarlySignal(),
             ],
             target=era5_atmospheric_river_target,
-            forecast=cira_atmospheric_river_forecast,
+            forecast=cira_fcnv2_atmospheric_river_forecast,
         ),
         inputs.EvaluationObject(
             event_type="tropical_cyclone",
             metric_list=composite_landfall_metrics,
             target=ibtracs_target,
-            forecast=cira_tropical_cyclone_forecast,
+            forecast=cira_fcnv2_tropical_cyclone_forecast,
         ),
     ]
diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index cd0f52cd..8708dde9 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -145,6 +145,17 @@
     {"s3://noaa-oar-mlwp-data/": icechunk.s3_credentials(anonymous=True)}
 )
 
+CIRA_MODEL_NAMES = [
+    "AURO_v100_GFS",
+    "FOUR_v200_IFS",
+    "PANG_v100_IFS",
+    "FOUR_v200_GFS",
+    "GRAP_v100_GFS",
+    "AURO_v100_IFS",
+    "PANG_v100_GFS",
+    "GRAP_v100_IFS",
+]
+
 
 def _default_preprocess(input_data: IncomingDataInput) -> IncomingDataInput:
     """Default forecast preprocess function that does nothing."""
@@ -1268,3 +1279,51 @@ def check_for_missing_data(
         return False
     else:
         return True
+
+
+def get_cira_icechunk(
+    model_name: str,
+    variables: list[Union[str, derived.DerivedVariable]] = [],
+    preprocess: Callable = _default_preprocess,
+    name: Optional[str] = None,
+) -> XarrayForecast:
+    """Get a CIRA icechunk forecast object for a given model name.
+
+    Args:
+        model_name: The name of the model from CIRA to get the forecast object for. For
+            example, "FOUR_v200_GFS". For a list of available models, see
+            `extremeweatherbench.defaults.CIRA_MODEL_NAMES`.
+        variables: The variables to select from the model. Defaults to all variables.
+        preprocess: The preprocessing function to apply to the model. Defaults to the
+            default passthrough preprocess function.
+        name: The name of the forecast object. Defaults to model_name by default unless
+            `name` is provided.
+    Returns:
+        An XarrayForecast object for the given model.
+    """
+    # Check if the model name is valid
+    if model_name not in CIRA_MODEL_NAMES:
+        raise ValueError(
+            f"Model name {model_name} not found in CIRA_MODEL_NAMES. Model names must be one of: {CIRA_MODEL_NAMES}"
+        )
+
+    # Get the CIRA icechunkstorage
+    cira_storage = icechunk.gcs_storage(
+        bucket="extremeweatherbench", prefix="cira-icechunk", anonymous=True
+    )
+
+    # The models are distinct groups within the icechunk store; open the group
+    # corresponding to the model name
+    cira_model_ds = open_icechunk_dataset_from_datatree(
+        cira_storage, model_name, authorize_virtual_chunk_access=CIRA_CREDENTIALS
+    )
+
+    # Create the XarrayForecast object for the given model
+    cira_model_forecast = XarrayForecast(
+        ds=cira_model_ds,
+        variables=variables,
+        variable_mapping=CIRA_metadata_variable_mapping,
+        name=name if name else model_name,
+        preprocess=preprocess,
+    )
+    return cira_model_forecast
diff --git a/tests/test_defaults.py b/tests/test_defaults.py
index cbbea8bd..ff452c91 100644
--- a/tests/test_defaults.py
+++ b/tests/test_defaults.py
@@ -120,10 +120,22 @@ def test_target_objects_exist(self):
 
     def test_forecast_objects_exist(self):
         """Test that forecast objects are properly defined."""
-        assert hasattr(defaults, "cira_heatwave_forecast")
-        assert hasattr(defaults, "cira_freeze_forecast")
-        assert isinstance(defaults.cira_heatwave_forecast, inputs.KerchunkForecast)
-        assert isinstance(defaults.cira_freeze_forecast, inputs.KerchunkForecast)
+        assert hasattr(defaults, "cira_fcnv2_heatwave_forecast")
+        assert hasattr(defaults, "cira_fcnv2_freeze_forecast")
+        assert hasattr(defaults, "cira_fcnv2_tropical_cyclone_forecast")
+        assert hasattr(defaults, "cira_fcnv2_atmospheric_river_forecast")
+        assert hasattr(defaults, "cira_fcnv2_severe_convection_forecast")
+        assert isinstance(defaults.cira_fcnv2_heatwave_forecast, inputs.XarrayForecast)
+        assert isinstance(defaults.cira_fcnv2_freeze_forecast, inputs.XarrayForecast)
+        assert isinstance(
+            defaults.cira_fcnv2_tropical_cyclone_forecast, inputs.XarrayForecast
+        )
+        assert isinstance(
+            defaults.cira_fcnv2_atmospheric_river_forecast, inputs.XarrayForecast
+        )
+        assert isinstance(
+            defaults.cira_fcnv2_severe_convection_forecast, inputs.XarrayForecast
+        )
 
     def test_era5_heatwave_target_configuration(self):
         """Test ERA5 heatwave target configuration."""
@@ -149,21 +161,6 @@ def test_era5_freeze_target_configuration(self):
         for key, value in expected_mapping.items():
             assert target.variable_mapping[key] == value
 
-    def test_cira_forecasts_have_preprocess_function(self):
-        """Test that CIRA forecasts have the preprocess function set."""
-        assert defaults.cira_heatwave_forecast.preprocess is not None
-        assert defaults.cira_freeze_forecast.preprocess is not None
-
-        # Test that the preprocess function is the expected one
-        assert (
-            defaults.cira_heatwave_forecast.preprocess
-            == defaults._preprocess_cira_forecast_dataset
-        )
-        assert (
-            defaults.cira_freeze_forecast.preprocess
-            == defaults._preprocess_cira_forecast_dataset
-        )
-
     def test_get_brightband_evaluation_objects_no_exceptions(self):
         """Test that get_brightband_evaluation_objects runs without exceptions."""
         try:
@@ -173,3 +170,63 @@ def test_get_brightband_evaluation_objects_no_exceptions(self):
             assert len(result) > 0
         except Exception as e:
             pytest.fail(f"get_brightband_evaluation_objects raised an exception: {e}")
+
+
+class TestCiraFcnv2PreprocessFunctions:
+    """Tests that each cira_fcnv2 forecast has the correct preprocessing function."""
+
+    def test_heatwave_forecast_has_default_preprocess(self):
+        """Test that cira_fcnv2_heatwave_forecast uses default preprocess."""
+        forecast = defaults.cira_fcnv2_heatwave_forecast
+        assert forecast.preprocess == inputs._default_preprocess
+
+    def test_freeze_forecast_has_default_preprocess(self):
+        """Test that cira_fcnv2_freeze_forecast uses default preprocess."""
+        forecast = defaults.cira_fcnv2_freeze_forecast
+        assert forecast.preprocess == inputs._default_preprocess
+
+    def test_tropical_cyclone_forecast_has_tc_preprocess(self):
+        """Test that cira_fcnv2_tropical_cyclone_forecast uses TC preprocess."""
+        forecast = defaults.cira_fcnv2_tropical_cyclone_forecast
+        assert forecast.preprocess == defaults._preprocess_cira_tc_forecast_dataset
+
+    def test_atmospheric_river_forecast_has_ar_preprocess(self):
+        """Test that cira_fcnv2_atmospheric_river_forecast uses AR preprocess."""
+        forecast = defaults.cira_fcnv2_atmospheric_river_forecast
+        assert forecast.preprocess == defaults._preprocess_cira_ar_forecast_dataset
+
+    def test_severe_convection_forecast_has_severe_preprocess(self):
+        """Test that cira_fcnv2_severe_convection_forecast uses severe preprocess."""
+        forecast = defaults.cira_fcnv2_severe_convection_forecast
+        assert forecast.preprocess == defaults._preprocess_severe_cira_forecast_dataset
+
+    def test_all_forecasts_have_preprocess_attribute(self):
+        """Test that all cira_fcnv2 forecasts have a preprocess attribute set."""
+        forecasts = [
+            defaults.cira_fcnv2_heatwave_forecast,
+            defaults.cira_fcnv2_freeze_forecast,
+            defaults.cira_fcnv2_tropical_cyclone_forecast,
+            defaults.cira_fcnv2_atmospheric_river_forecast,
+            defaults.cira_fcnv2_severe_convection_forecast,
+        ]
+        for forecast in forecasts:
+            assert hasattr(forecast, "preprocess")
+            assert forecast.preprocess is not None
+            assert callable(forecast.preprocess)
+
+    def test_preprocess_functions_are_distinct_where_expected(self):
+        """Test that different event types use different preprocess functions."""
+        # TC, AR, and severe should have distinct preprocess functions
+        tc_preprocess = defaults.cira_fcnv2_tropical_cyclone_forecast.preprocess
+        ar_preprocess = defaults.cira_fcnv2_atmospheric_river_forecast.preprocess
+        severe_preprocess = defaults.cira_fcnv2_severe_convection_forecast.preprocess
+
+        assert tc_preprocess != ar_preprocess
+        assert tc_preprocess != severe_preprocess
+        # Note: AR and severe could be the same or different depending on impl
+
+    def test_heatwave_and_freeze_use_same_preprocess(self):
+        """Test that heatwave and freeze forecasts use the same preprocess."""
+        heatwave_preprocess = defaults.cira_fcnv2_heatwave_forecast.preprocess
+        freeze_preprocess = defaults.cira_fcnv2_freeze_forecast.preprocess
+        assert heatwave_preprocess == freeze_preprocess
diff --git a/tests/test_inputs.py b/tests/test_inputs.py
index 4531d150..0725afb0 100644
--- a/tests/test_inputs.py
+++ b/tests/test_inputs.py
@@ -2233,7 +2233,9 @@ def test_xarray_forecast_none_handling_for_optional_params(
         """Test that None values are properly converted to empty defaults."""
         # Explicitly pass None to test the None handling in __init__
         forecast = inputs.XarrayForecast(
-            ds=sample_forecast_with_valid_time, variables=None, variable_mapping=None
+            ds=sample_forecast_with_valid_time,
+            variables=None,
+            variable_mapping=None,  # type: ignore
         )
 
         # Should be converted to empty containers
@@ -2323,3 +2325,266 @@ def test_default_preprocess():
     df = pd.DataFrame({"a": [1, 2, 3]})
     result_df = inputs._default_preprocess(df)
     assert result_df is df
+
+
+class TestGetCIRAIcechunk:
+    """Tests for get_cira_icechunk function."""
+
+    def test_invalid_model_name_raises_value_error(self):
+        """Test that an invalid model name raises ValueError."""
+        with pytest.raises(ValueError) as exc_info:
+            inputs.get_cira_icechunk(model_name="INVALID_MODEL")
+
+        assert "INVALID_MODEL" in str(exc_info.value)
+        assert "CIRA_MODEL_NAMES" in str(exc_info.value)
+
+    def test_empty_model_name_raises_value_error(self):
+        """Test that an empty model name raises ValueError."""
+        with pytest.raises(ValueError):
+            inputs.get_cira_icechunk(model_name="")
+
+    def test_none_model_name_raises_error(self):
+        """Test that None as model name raises appropriate error."""
+        with pytest.raises((ValueError, TypeError)):
+            inputs.get_cira_icechunk(model_name=None)  # type: ignore
+
+    def test_case_sensitive_model_name(self):
+        """Test that model name matching is case-sensitive."""
+        # Lowercase version of a valid model name should fail
+        with pytest.raises(ValueError):
+            inputs.get_cira_icechunk(model_name="four_v200_gfs")
+
+        # Mixed case should fail
+        with pytest.raises(ValueError):
+            inputs.get_cira_icechunk(model_name="Four_V200_GFS")
+
+    def test_partial_model_name_raises_value_error(self):
+        """Test that partial model names are rejected."""
+        with pytest.raises(ValueError):
+            inputs.get_cira_icechunk(model_name="FOUR")
+
+        with pytest.raises(ValueError):
+            inputs.get_cira_icechunk(model_name="GFS")
+
+    def test_model_name_with_extra_chars_raises_value_error(self):
+        """Test that model names with extra characters are rejected."""
+        with pytest.raises(ValueError):
+            inputs.get_cira_icechunk(model_name="FOUR_v200_GFS_extra")
+
+        with pytest.raises(ValueError):
+            inputs.get_cira_icechunk(model_name=" FOUR_v200_GFS")
+
+    def test_error_message_lists_valid_model_names(self):
+        """Test that the error message includes the list of valid model names."""
+        with pytest.raises(ValueError) as exc_info:
+            inputs.get_cira_icechunk(model_name="BAD_MODEL")
+
+        error_msg = str(exc_info.value)
+        # Check that at least some valid model names are shown in the error
+        assert "FOUR_v200_GFS" in error_msg or "CIRA_MODEL_NAMES" in error_msg
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_valid_model_name_four_v200_gfs(
+        self, mock_forecast, mock_open, mock_storage
+    ):
+        """Test that FOUR_v200_GFS is a valid model name."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        result = inputs.get_cira_icechunk(model_name="FOUR_v200_GFS")
+
+        assert result is not None
+        mock_storage.assert_called_once()
+        mock_open.assert_called_once()
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_valid_model_name_auro_v100_gfs(
+        self, mock_forecast, mock_open, mock_storage
+    ):
+        """Test that AURO_v100_GFS is a valid model name."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        result = inputs.get_cira_icechunk(model_name="AURO_v100_GFS")
+
+        assert result is not None
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_all_cira_model_names_are_valid(
+        self, mock_forecast, mock_open, mock_storage
+    ):
+        """Test that all model names in CIRA_MODEL_NAMES are accepted."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        for model_name in inputs.CIRA_MODEL_NAMES:
+            result = inputs.get_cira_icechunk(model_name=model_name)
+            assert result is not None, f"Model {model_name} should be valid"
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_custom_name_parameter(self, mock_forecast, mock_open, mock_storage):
+        """Test that a custom name parameter is passed to XarrayForecast."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        inputs.get_cira_icechunk(model_name="FOUR_v200_GFS", name="CustomName")
+
+        # Check that XarrayForecast was called with the custom name
+        call_kwargs = mock_forecast.call_args[1]
+        assert call_kwargs["name"] == "CustomName"
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_default_name_uses_model_name(self, mock_forecast, mock_open, mock_storage):
+        """Test that name inputs to model_name when not provided."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        inputs.get_cira_icechunk(model_name="FOUR_v200_GFS")
+
+        call_kwargs = mock_forecast.call_args[1]
+        assert call_kwargs["name"] == "FOUR_v200_GFS"
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_empty_variables_list(self, mock_forecast, mock_open, mock_storage):
+        """Test that empty variables list is valid."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        result = inputs.get_cira_icechunk(model_name="FOUR_v200_GFS", variables=[])
+
+        assert result is not None
+        call_kwargs = mock_forecast.call_args[1]
+        assert call_kwargs["variables"] == []
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_custom_variables_list(self, mock_forecast, mock_open, mock_storage):
+        """Test that a custom variables list is passed through."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        variables = ["surface_air_temperature", "air_pressure"]
+        inputs.get_cira_icechunk(model_name="FOUR_v200_GFS", variables=variables)
+
+        call_kwargs = mock_forecast.call_args[1]
+        assert call_kwargs["variables"] == variables
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_custom_preprocess_function(self, mock_forecast, mock_open, mock_storage):
+        """Test that a custom preprocess function is passed through."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        def custom_preprocess(ds: xr.Dataset) -> xr.Dataset:
+            return ds
+
+        inputs.get_cira_icechunk(
+            model_name="FOUR_v200_GFS", preprocess=custom_preprocess
+        )
+
+        call_kwargs = mock_forecast.call_args[1]
+        assert call_kwargs["preprocess"] == custom_preprocess
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_returns_xarray_forecast_object(
+        self, mock_forecast, mock_open, mock_storage
+    ):
+        """Test that the function returns an XarrayForecast object."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        expected_forecast = mock.MagicMock()
+        mock_forecast.return_value = expected_forecast
+
+        result = inputs.get_cira_icechunk(model_name="FOUR_v200_GFS")
+
+        assert result is expected_forecast
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_gcs_storage_configuration(self, mock_forecast, mock_open, mock_storage):
+        """Test that GCS storage is configured with correct parameters."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        inputs.get_cira_icechunk(model_name="FOUR_v200_GFS")
+
+        mock_storage.assert_called_once_with(
+            bucket="extremeweatherbench", prefix="cira-icechunk", anonymous=True
+        )
+
+    @mock.patch("extremeweatherbench.inputs.icechunk.gcs_storage")
+    @mock.patch("extremeweatherbench.inputs.open_icechunk_dataset_from_datatree")
+    @mock.patch("extremeweatherbench.inputs.XarrayForecast")
+    def test_uses_cira_variable_mapping(self, mock_forecast, mock_open, mock_storage):
+        """Test that CIRA metadata variable mapping is used."""
+        mock_storage.return_value = mock.MagicMock()
+        mock_open.return_value = mock.MagicMock()
+        mock_forecast.return_value = mock.MagicMock()
+
+        inputs.get_cira_icechunk(model_name="FOUR_v200_GFS")
+
+        call_kwargs = mock_forecast.call_args[1]
+        assert call_kwargs["variable_mapping"] == inputs.CIRA_metadata_variable_mapping
+
+
+class TestCiraModelNames:
+    """Tests for CIRA_MODEL_NAMES constant."""
+
+    def test_cira_model_names_is_list(self):
+        """Test that CIRA_MODEL_NAMES is a list."""
+        assert isinstance(inputs.CIRA_MODEL_NAMES, list)
+
+    def test_cira_model_names_not_empty(self):
+        """Test that CIRA_MODEL_NAMES is not empty."""
+        assert len(inputs.CIRA_MODEL_NAMES) > 0
+
+    def test_cira_model_names_contains_expected_models(self):
+        """Test that CIRA_MODEL_NAMES contains expected model names."""
+        expected_models = [
+            "FOUR_v200_GFS",
+            "FOUR_v200_IFS",
+            "AURO_v100_GFS",
+            "AURO_v100_IFS",
+            "PANG_v100_GFS",
+            "PANG_v100_IFS",
+            "GRAP_v100_GFS",
+            "GRAP_v100_IFS",
+        ]
+        for model in expected_models:
+            assert model in inputs.CIRA_MODEL_NAMES
+
+    def test_cira_model_names_all_strings(self):
+        """Test that all entries in CIRA_MODEL_NAMES are strings."""
+        for model in inputs.CIRA_MODEL_NAMES:
+            assert isinstance(model, str)
+
+    def test_cira_model_names_no_duplicates(self):
+        """Test that CIRA_MODEL_NAMES has no duplicate entries."""
+        assert len(inputs.CIRA_MODEL_NAMES) == len(set(inputs.CIRA_MODEL_NAMES))

From 79f4b741f9e7b9b9d9fdb0c5b7eb7ae2a33c767d Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 26 Jan 2026 15:08:04 -0500
Subject: [PATCH 18/72] Bump version from 0.2.0 to 0.3.0 (#324)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 4994a179..5edb482d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "extremeweatherbench"
-version = "0.2.0"
+version = "0.3.0"
 description = "Benchmarking weather and weather AI models using extreme events"
 readme = "README.md"
 requires-python = ">=3.11,<3.14"

From b1fc1b626eec90cf8dbfb5567be92016f3f1d0ef Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 26 Jan 2026 15:18:36 -0500
Subject: [PATCH 19/72] Updated API (#321)

* move cache dir creation to init, rename funcs, add parallel/serial check function, update test names

* update naming

* add run method for backwards compatibility

* update tests

* add tests and cover if serial and parallel_config is not None

* feat: redesign public API with hierarchical namespace submodules

- Add ewb.evaluation() as main entry point (alias for ExtremeWeatherBench)
- Create namespace submodules: ewb.targets, ewb.forecasts, ewb.metrics,
  ewb.derived, ewb.regions, ewb.cases, ewb.defaults
- Expose all classes at top level for convenience (ewb.ERA5, etc.)
- Add ewb.load_cases() convenience alias
- Update all example files to use new import pattern
- Update usage.md documentation
- Maintain backward compatibility with existing imports

* ruff/linting. add utils to init

* add test coverage for module loading patterns

* ruff

* Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes

* add explanation for dim reqs (#320)

* Update `defaults` and `inputs` to include new CIRA icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos

* update defaults var refs
---
 data_prep/ar_bounds.py                        |   6 +-
 data_prep/ibtracs_bounds.py                   |  26 +-
 .../practically_perfect_hindcast_from_lsr.py  |   3 +-
 data_prep/severe_convection_bounds.py         |   3 +-
 data_prep/subset_heat_cold_events.py          |   3 +-
 docs/examples/applied_ar.py                   |  61 +--
 docs/examples/applied_freeze.py               |  32 +-
 docs/examples/applied_heatwave.py             |  32 +-
 docs/examples/applied_severe.py               |  40 +-
 docs/examples/applied_tc.py                   | 102 +++--
 docs/examples/example_config.py               |  21 +-
 docs/parallelism.md                           |   2 +-
 docs/usage.md                                 |  86 ++--
 scripts/brightband_evaluation.py              |   2 +-
 src/extremeweatherbench/__init__.py           | 339 ++++++++++++++++
 src/extremeweatherbench/cases.py              |   5 +-
 src/extremeweatherbench/defaults.py           |   2 +-
 src/extremeweatherbench/derived.py            |   2 +-
 src/extremeweatherbench/evaluate.py           | 188 +++++----
 src/extremeweatherbench/evaluate_cli.py       |   6 +-
 src/extremeweatherbench/inputs.py             |   7 +-
 src/extremeweatherbench/regions.py            |   2 +-
 src/extremeweatherbench/sources/base.py       |   2 +-
 .../sources/pandas_dataframe.py               |   4 +-
 .../sources/polars_lazyframe.py               |   4 +-
 .../sources/xarray_dataarray.py               |   3 +-
 .../sources/xarray_dataset.py                 |   2 +-
 tests/test_evaluate.py                        | 370 ++++++++++--------
 tests/test_evaluate_cli.py                    |  34 +-
 tests/test_init.py                            | 259 ++++++++++++
 tests/test_integration.py                     |   4 +-
 31 files changed, 1210 insertions(+), 442 deletions(-)
 create mode 100644 tests/test_init.py

diff --git a/data_prep/ar_bounds.py b/data_prep/ar_bounds.py
index b642d334..1c4cf5cf 100644
--- a/data_prep/ar_bounds.py
+++ b/data_prep/ar_bounds.py
@@ -17,7 +17,11 @@
 from dask.distributed import Client
 from matplotlib.patches import Rectangle
 
-from extremeweatherbench import cases, derived, inputs, regions, utils
+import extremeweatherbench.cases as cases
+import extremeweatherbench.derived as derived
+import extremeweatherbench.inputs as inputs
+import extremeweatherbench.regions as regions
+import extremeweatherbench.utils as utils
 from extremeweatherbench.events import atmospheric_river as ar
 
 logging.basicConfig()
diff --git a/data_prep/ibtracs_bounds.py b/data_prep/ibtracs_bounds.py
index 0a2ecc0a..0dc962d9 100644
--- a/data_prep/ibtracs_bounds.py
+++ b/data_prep/ibtracs_bounds.py
@@ -4,6 +4,7 @@
 import logging
 import re
 from importlib import resources
+from typing import TYPE_CHECKING
 
 import cartopy.crs as ccrs
 import cartopy.feature as cfeature
@@ -14,8 +15,11 @@
 import yaml
 from matplotlib.patches import Rectangle
 
+import extremeweatherbench as ewb
 import extremeweatherbench.data
-from extremeweatherbench import cases, inputs, regions, utils
+
+if TYPE_CHECKING:
+    from extremeweatherbench.regions import Region
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -67,7 +71,7 @@ def calculate_extent_bounds(
     bottom_lat: float,
     top_lat: float,
     extent_buffer: float = 250,
-) -> regions.Region:
+) -> Region:
     """Calculate extent bounds with buffer.
 
     Args:
@@ -94,9 +98,9 @@ def calculate_extent_bounds(
         calculate_end_point(bottom_lat, right_lon, 90, extent_buffer), 1
     )
 
-    new_left_lon = np.round(utils.convert_longitude_to_360(new_left_lon), 1)
-    new_right_lon = np.round(utils.convert_longitude_to_360(new_right_lon), 1)
-    new_box = regions.BoundingBoxRegion(
+    new_left_lon = np.round(ewb.utils.convert_longitude_to_360(new_left_lon), 1)
+    new_right_lon = np.round(ewb.utils.convert_longitude_to_360(new_right_lon), 1)
+    new_box = ewb.regions.BoundingBoxRegion(
         new_bottom_lat, new_top_lat, new_left_lon, new_right_lon
     )
     return new_box
@@ -164,10 +168,10 @@ def load_and_process_ibtracs_data():
     """
     logger.info("Loading IBTrACS data...")
 
-    IBTRACS = inputs.IBTrACS(
-        source=inputs.IBTRACS_URI,
+    IBTRACS = ewb.inputs.IBTrACS(
+        source=ewb.inputs.IBTRACS_URI,
         variables=["vmax", "slp"],
-        variable_mapping=inputs.IBTrACS_metadata_variable_mapping,
+        variable_mapping=ewb.inputs.IBTrACS_metadata_variable_mapping,
         storage_options={},
     )
 
@@ -177,7 +181,7 @@ def load_and_process_ibtracs_data():
     # Get all storms from 2020 - 2025 seasons
     all_storms_2020_2025_lf = IBTRACS_lf.filter(
         (pl.col("SEASON").cast(pl.Int32) >= 2020)
-    ).select(inputs.IBTrACS_metadata_variable_mapping.values())
+    ).select(ewb.inputs.IBTrACS_metadata_variable_mapping.values())
 
     schema = all_storms_2020_2025_lf.collect_schema()
     # Convert pressure and surface wind columns to float, replacing " " with null
@@ -464,7 +468,7 @@ def find_storm_bounds_for_case(storm_name, storm_bounds, all_storms_df):
             # If we found both, merge them by taking the bounding box that
             # encompasses both
             if bounds1 is not None and bounds2 is not None:
-                merged_bbox = regions.BoundingBoxRegion(
+                merged_bbox = ewb.regions.BoundingBoxRegion(
                     latitude_min=min(
                         bounds1.iloc[0].latitude_min, bounds2.iloc[0].latitude_min
                     ),
@@ -537,7 +541,7 @@ def update_cases_with_storm_bounds(storm_bounds, all_storms_df):
     """
     logger.info("Updating cases with storm bounds...")
 
-    cases_all = cases.load_ewb_events_yaml_into_case_list()
+    cases_all = ewb.cases.load_ewb_events_yaml_into_case_list()
     cases_new = cases_all.copy()
 
     # Update the yaml cases with storm bounds from IBTrACS data
diff --git a/data_prep/practically_perfect_hindcast_from_lsr.py b/data_prep/practically_perfect_hindcast_from_lsr.py
index db114242..1e96cbda 100644
--- a/data_prep/practically_perfect_hindcast_from_lsr.py
+++ b/data_prep/practically_perfect_hindcast_from_lsr.py
@@ -11,7 +11,8 @@
 from scipy.ndimage import gaussian_filter
 from tqdm.auto import tqdm
 
-from extremeweatherbench import inputs, utils
+import extremeweatherbench.inputs as inputs
+import extremeweatherbench.utils as utils
 
 
 def sparse_practically_perfect_hindcast(
diff --git a/data_prep/severe_convection_bounds.py b/data_prep/severe_convection_bounds.py
index 12e1bcf9..11985632 100644
--- a/data_prep/severe_convection_bounds.py
+++ b/data_prep/severe_convection_bounds.py
@@ -17,7 +17,8 @@
 import yaml
 from scipy.ndimage import label
 
-from extremeweatherbench import calc, cases
+import extremeweatherbench.calc as calc
+import extremeweatherbench.cases as cases
 
 # Radius of Earth in km (mean radius)
 EARTH_RADIUS_KM = 6371.0
diff --git a/data_prep/subset_heat_cold_events.py b/data_prep/subset_heat_cold_events.py
index e109889b..ee6b4add 100644
--- a/data_prep/subset_heat_cold_events.py
+++ b/data_prep/subset_heat_cold_events.py
@@ -13,7 +13,8 @@
 from matplotlib import dates as mdates
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 
-from extremeweatherbench import cases, utils
+import extremeweatherbench.cases as cases
+import extremeweatherbench.utils as utils
 
 sns.set_theme(style="whitegrid", context="talk")
 
diff --git a/docs/examples/applied_ar.py b/docs/examples/applied_ar.py
index 75239f47..a99e5a0e 100644
--- a/docs/examples/applied_ar.py
+++ b/docs/examples/applied_ar.py
@@ -4,7 +4,7 @@
 import numpy as np
 import xarray as xr
 
-from extremeweatherbench import cases, derived, evaluate, inputs, metrics
+import extremeweatherbench as ewb
 
 # %%
 
@@ -38,85 +38,86 @@ def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
 
 # Load case data from the default events.yaml
-# Users can also define their own cases
-case_yaml = cases.load_ewb_events_yaml_into_case_list()
-case_yaml = [n for n in case_yaml if n.case_id_number == 114]
-case_yaml[0].start_date = datetime.datetime(2022, 12, 27, 11, 0, 0)
-case_yaml[0].end_date = datetime.datetime(2022, 12, 27, 13, 0, 0)
+# Users can also define their own cases_dict structure
+case_yaml = ewb.load_cases()
+case_list = [n for n in case_yaml if n.case_id_number == 114]
+
+case_list[0].start_date = datetime.datetime(2022, 12, 27, 11, 0, 0)
+case_list[0].end_date = datetime.datetime(2022, 12, 27, 13, 0, 0)
 # Define ERA5 target
-era5_target = inputs.ERA5(
+era5_target = ewb.targets.ERA5(
     variables=[
-        derived.AtmosphericRiverVariables(
+        ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
 )
 
 # Define forecast (HRES)
-hres_forecast = inputs.ZarrForecast(
+hres_forecast = ewb.forecasts.ZarrForecast(
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     name="HRES",
     variables=[
-        derived.AtmosphericRiverVariables(
+        ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
-    variable_mapping=inputs.HRES_metadata_variable_mapping,
+    variable_mapping=ewb.HRES_metadata_variable_mapping,
 )
 
-grap_forecast = inputs.KerchunkForecast(
+grap_forecast = ewb.forecasts.KerchunkForecast(
     name="Graphcast",
     source="gs://extremeweatherbench/GRAP_v100_IFS.parq",
     variables=[
-        derived.AtmosphericRiverVariables(
+        ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
+    variable_mapping=ewb.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
     preprocess=_preprocess_cira_forecast_dataset,
 )
 
-pang_forecast = inputs.KerchunkForecast(
+pang_forecast = ewb.forecasts.KerchunkForecast(
     name="Pangu",
     source="gs://extremeweatherbench/PANG_v100_IFS.parq",
     variables=[
-        derived.AtmosphericRiverVariables(
+        ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
+    variable_mapping=ewb.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
     preprocess=_preprocess_cira_forecast_dataset,
 )
 # Create a list of evaluation objects for atmospheric river
 ar_evaluation_objects = [
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="atmospheric_river",
         metric_list=[
-            metrics.CriticalSuccessIndex(),
-            metrics.EarlySignal(),
-            metrics.SpatialDisplacement(),
+            ewb.metrics.CriticalSuccessIndex(),
+            ewb.metrics.EarlySignal(),
+            ewb.metrics.SpatialDisplacement(),
         ],
         target=era5_target,
         forecast=hres_forecast,
     ),
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="atmospheric_river",
         metric_list=[
-            metrics.CriticalSuccessIndex(),
-            metrics.EarlySignal(),
-            metrics.SpatialDisplacement(),
+            ewb.metrics.CriticalSuccessIndex(),
+            ewb.metrics.EarlySignal(),
+            ewb.metrics.SpatialDisplacement(),
         ],
         target=era5_target,
         forecast=grap_forecast,
     ),
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="atmospheric_river",
         metric_list=[
-            metrics.CriticalSuccessIndex(),
-            metrics.EarlySignal(),
-            metrics.SpatialDisplacement(),
+            ewb.metrics.CriticalSuccessIndex(),
+            ewb.metrics.EarlySignal(),
+            ewb.metrics.SpatialDisplacement(),
         ],
         target=era5_target,
         forecast=pang_forecast,
@@ -126,7 +127,7 @@ def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench; will only run on cases with event_type
     # atmospheric_river
-    ar_ewb = evaluate.ExtremeWeatherBench(
+    ar_ewb = ewb.evaluation(
         case_metadata=case_yaml,
         evaluation_objects=ar_evaluation_objects,
     )
diff --git a/docs/examples/applied_freeze.py b/docs/examples/applied_freeze.py
index 8b2325cd..864d76f7 100644
--- a/docs/examples/applied_freeze.py
+++ b/docs/examples/applied_freeze.py
@@ -1,55 +1,55 @@
 import logging
 import operator
 
-from extremeweatherbench import cases, defaults, evaluate, inputs, metrics
+import extremeweatherbench as ewb
 
 # Set the logger level to INFO
 logger = logging.getLogger("extremeweatherbench")
 logger.setLevel(logging.INFO)
 
 # Load case data from the default events.yaml
-# Users can also define their own cases
-case_yaml = cases.load_ewb_events_yaml_into_case_list()
+# Users can also define their own cases_dict structure
+case_yaml = ewb.load_cases()
 
 # Define targets
 # ERA5 target
-era5_freeze_target = inputs.ERA5(
+era5_freeze_target = ewb.targets.ERA5(
     variables=["surface_air_temperature"],
     chunks=None,
 )
 
 # GHCN target
-ghcn_freeze_target = inputs.GHCN(variables=["surface_air_temperature"])
+ghcn_freeze_target = ewb.targets.GHCN(variables=["surface_air_temperature"])
 
 # Define forecast (FCNv2 CIRA Virtualizarr)
-fcnv2_forecast = inputs.KerchunkForecast(
+fcnv2_forecast = ewb.forecasts.KerchunkForecast(
     name="fcnv2_forecast",
     source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
     variables=["surface_air_temperature"],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
+    variable_mapping=ewb.CIRA_metadata_variable_mapping,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=defaults._preprocess_cira_forecast_dataset,
+    preprocess=ewb.defaults._preprocess_bb_cira_forecast_dataset,
 )
 
 # Load the climatology for DurationMeanError
-climatology = defaults.get_climatology(quantile=0.85)
+climatology = ewb.get_climatology(quantile=0.85)
 
 # Define the metrics
 metrics_list = [
-    metrics.RootMeanSquaredError(),
-    metrics.MinimumMeanAbsoluteError(),
-    metrics.DurationMeanError(threshold_criteria=climatology, op_func=operator.le),
+    ewb.metrics.RootMeanSquaredError(),
+    ewb.metrics.MinimumMeanAbsoluteError(),
+    ewb.metrics.DurationMeanError(threshold_criteria=climatology, op_func=operator.le),
 ]
 
 # Create a list of evaluation objects for freeze
 freeze_evaluation_object = [
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="freeze",
         metric_list=metrics_list,
         target=ghcn_freeze_target,
         forecast=fcnv2_forecast,
     ),
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="freeze",
         metric_list=metrics_list,
         target=era5_freeze_target,
@@ -59,13 +59,13 @@
 
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench runner instance
-    ewb = evaluate.ExtremeWeatherBench(
+    freeze_ewb = ewb.evaluation(
         case_metadata=case_yaml,
         evaluation_objects=freeze_evaluation_object,
     )
 
     # Run the workflow
-    outputs = ewb.run(parallel_config={"backend": "loky", "n_jobs": 1})
+    outputs = freeze_ewb.run(parallel_config={"backend": "loky", "n_jobs": 1})
 
     # Print the outputs; can be saved if desired
     outputs.to_csv("freeze_outputs.csv")
diff --git a/docs/examples/applied_heatwave.py b/docs/examples/applied_heatwave.py
index 7f44b081..22c9b809 100644
--- a/docs/examples/applied_heatwave.py
+++ b/docs/examples/applied_heatwave.py
@@ -1,56 +1,56 @@
 import logging
 import operator
 
-from extremeweatherbench import cases, defaults, evaluate, inputs, metrics
+import extremeweatherbench as ewb
 
 # Set the logger level to INFO
 logger = logging.getLogger("extremeweatherbench")
 logger.setLevel(logging.INFO)
 
 # Load case data from the default events.yaml
-# Users can also define their own cases
-case_yaml = cases.load_ewb_events_yaml_into_case_list()
+# Users can also define their own cases_dict structure
+case_yaml = ewb.load_cases()
 
 # Define targets
 # ERA5 target
-era5_heatwave_target = inputs.ERA5(
+era5_heatwave_target = ewb.targets.ERA5(
     variables=["surface_air_temperature"],
     chunks=None,
 )
 
 # GHCN target
-ghcn_heatwave_target = inputs.GHCN(
+ghcn_heatwave_target = ewb.targets.GHCN(
     variables=["surface_air_temperature"],
 )
 
 # Define forecast (HRES)
-hres_forecast = inputs.ZarrForecast(
+hres_forecast = ewb.forecasts.ZarrForecast(
     name="hres_forecast",
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     variables=["surface_air_temperature"],
-    variable_mapping=inputs.HRES_metadata_variable_mapping,
+    variable_mapping=ewb.HRES_metadata_variable_mapping,
 )
 
 # Load the climatology for DurationMeanError
-climatology = defaults.get_climatology(quantile=0.85)
+climatology = ewb.get_climatology(quantile=0.85)
 
 # Define the metrics
 metrics_list = [
-    metrics.MaximumMeanAbsoluteError(),
-    metrics.RootMeanSquaredError(),
-    metrics.DurationMeanError(threshold_criteria=climatology, op_func=operator.ge),
-    metrics.MaximumLowestMeanAbsoluteError(),
+    ewb.metrics.MaximumMeanAbsoluteError(),
+    ewb.metrics.RootMeanSquaredError(),
+    ewb.metrics.DurationMeanError(threshold_criteria=climatology, op_func=operator.ge),
+    ewb.metrics.MaximumLowestMeanAbsoluteError(),
 ]
 
 # Create a list of evaluation objects for heatwave
 heatwave_evaluation_object = [
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="heat_wave",
         metric_list=metrics_list,
         target=ghcn_heatwave_target,
         forecast=hres_forecast,
     ),
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="heat_wave",
         metric_list=metrics_list,
         target=era5_heatwave_target,
@@ -59,11 +59,11 @@
 ]
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench
-    ewb = evaluate.ExtremeWeatherBench(
+    heatwave_ewb = ewb.evaluation(
         case_metadata=case_yaml,
         evaluation_objects=heatwave_evaluation_object,
     )
 
     # Run the workflow
-    outputs = ewb.run(parallel_config={"backend": "loky", "n_jobs": 2})
+    outputs = heatwave_ewb.run(parallel_config={"backend": "loky", "n_jobs": 2})
     outputs.to_csv("applied_heatwave_outputs.csv")
diff --git a/docs/examples/applied_severe.py b/docs/examples/applied_severe.py
index 6cdba3f9..f6a07003 100644
--- a/docs/examples/applied_severe.py
+++ b/docs/examples/applied_severe.py
@@ -1,6 +1,6 @@
 import logging
 
-from extremeweatherbench import cases, derived, evaluate, inputs, metrics
+import extremeweatherbench as ewb
 
 # Set the logger level to INFO
 logger = logging.getLogger("extremeweatherbench")
@@ -8,45 +8,45 @@
 
 
 # Load case data from the default events.yaml
-case_yaml = cases.load_ewb_events_yaml_into_case_list()
-case_yaml = [n for n in case_yaml if n.case_id_number == 305]
+case_yaml = ewb.load_cases()
+case_list = [n for n in case_yaml if n.case_id_number == 305]
 
 # Define PPH target
-pph_target = inputs.PPH(
+pph_target = ewb.targets.PPH(
     variables=["practically_perfect_hindcast"],
 )
 
 # Define LSR target
-lsr_target = inputs.LSR()
+lsr_target = ewb.targets.LSR()
 
 # Define HRES forecast
-hres_forecast = inputs.ZarrForecast(
+hres_forecast = ewb.forecasts.ZarrForecast(
     name="hres_forecast",
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
-    variables=[derived.CravenBrooksSignificantSevere(layer_depth=100)],
-    variable_mapping=inputs.HRES_metadata_variable_mapping,
+    variables=[ewb.derived.CravenBrooksSignificantSevere(layer_depth=100)],
+    variable_mapping=ewb.HRES_metadata_variable_mapping,
     storage_options={"remote_options": {"anon": True}},
 )
 
 # Define pph metrics as thresholdmetric to share scores contingency table
 pph_metrics = [
-    metrics.ThresholdMetric(
+    ewb.metrics.ThresholdMetric(
         metrics=[
-            metrics.CriticalSuccessIndex,
-            metrics.FalseAlarmRatio,
+            ewb.metrics.CriticalSuccessIndex,
+            ewb.metrics.FalseAlarmRatio,
         ],
         forecast_threshold=15000,
         target_threshold=0.3,
     ),
-    metrics.EarlySignal(threshold=15000),
+    ewb.metrics.EarlySignal(threshold=15000),
 ]
 
 # Define LSR metrics as thresholdmetric to share scores contingency table
 lsr_metrics = [
-    metrics.ThresholdMetric(
+    ewb.metrics.ThresholdMetric(
         metrics=[
-            metrics.TruePositives,
-            metrics.FalseNegatives,
+            ewb.metrics.TruePositives,
+            ewb.metrics.FalseNegatives,
         ],
         forecast_threshold=15000,
         target_threshold=0.5,
@@ -56,7 +56,7 @@
 # Define evaluation objects for severe convection:
 # One evaluation object for PPH
 pph_evaluation_objects = [
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="severe_convection",
         metric_list=pph_metrics,
         target=pph_target,
@@ -66,7 +66,7 @@
 
 # One evaluation object for LSR
 lsr_evaluation_objects = [
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="severe_convection",
         metric_list=lsr_metrics,
         target=lsr_target,
@@ -76,14 +76,14 @@
 
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench with both evaluation objects
-    ewb = evaluate.ExtremeWeatherBench(
-        case_metadata=case_yaml,
+    severe_ewb = ewb.evaluation(
+        case_metadata=case_list,
         evaluation_objects=lsr_evaluation_objects + pph_evaluation_objects,
     )
     logger.info("Starting EWB run")
 
     # Run the workflow with parllel_config backend set to dask
-    outputs = ewb.run(parallel_config={"backend": "loky", "n_jobs": 3})
+    outputs = severe_ewb.run(parallel_config={"backend": "loky", "n_jobs": 3})
 
     # Save the results to a CSV file
     outputs.to_csv("applied_severe_convection_results.csv")
diff --git a/docs/examples/applied_tc.py b/docs/examples/applied_tc.py
index 79d4d3b4..e0d17d3c 100644
--- a/docs/examples/applied_tc.py
+++ b/docs/examples/applied_tc.py
@@ -1,56 +1,102 @@
 import logging
 
-from extremeweatherbench import cases, defaults, derived, evaluate, inputs, metrics
+import numpy as np
+import xarray as xr
+
+import extremeweatherbench as ewb
 
 # Set the logger level to INFO
 logger = logging.getLogger("extremeweatherbench")
 logger.setLevel(logging.INFO)
 
 
-# Load the case list from the YAML file
-case_yaml = cases.load_ewb_events_yaml_into_case_list()
+# Preprocessing function for CIRA data that includes geopotential thickness calculation
+# required for tropical cyclone tracks
+def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """An example preprocess function that renames the time coordinate to lead_time,
+    creates a valid_time coordinate, and sets the lead time range and resolution not
+    present in the original dataset.
+
+    Args:
+        ds: The forecast dataset to rename.
+
+    Returns:
+        The renamed forecast dataset.
+    """
+    ds = ds.rename({"time": "lead_time"})
+    # The evaluation configuration is used to set the lead time range and resolution.
+    ds["lead_time"] = np.array(
+        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+    ).astype("timedelta64[ns]")
+    ds["geopotential_thickness"] = ewb.calc.geopotential_thickness(
+        ds["z"], top_level_value=300, bottom_level_value=500
+    )
+    return ds
+
+
+# Preprocessing function for HRES data that includes geopotential thickness calculation
+# required for tropical cyclone tracks
+def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """An example preprocess function that renames the time coordinate to lead_time,
+    creates a valid_time coordinate, and sets the lead time range and resolution not
+    present in the original dataset.
+
+    Args:
+        ds: The forecast dataset to rename.
+    """
+    ds["geopotential_thickness"] = ewb.calc.geopotential_thickness(
+        ds["geopotential"],
+        top_level_value=300,
+        bottom_level_value=500,
+        geopotential=True,
+    )
+    return ds
+
+
+# Load the case collection from the YAML file
+case_yaml = ewb.load_cases()
 
 # Select single case (TC Ida)
-case_yaml = [n for n in case_yaml if n.case_id_number == 220]
+case_list = [n for n in case_yaml if n.case_id_number == 220]
 
 # Define IBTrACS target, no arguments needed as defaults are sufficient
-ibtracs_target = inputs.IBTrACS()
+ibtracs_target = ewb.targets.IBTrACS()
 
 # Define HRES forecast
-hres_forecast = inputs.ZarrForecast(
+hres_forecast = ewb.forecasts.ZarrForecast(
     name="hres_forecast",
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     # Define tropical cyclone track derivedvariable to include in the forecast
-    variables=[derived.TropicalCycloneTrackVariables()],
+    variables=[ewb.derived.TropicalCycloneTrackVariables()],
     # Define metadata variable mapping for HRES forecast
-    variable_mapping=inputs.HRES_metadata_variable_mapping,
+    variable_mapping=ewb.HRES_metadata_variable_mapping,
     storage_options={"remote_options": {"anon": True}},
     # Preprocess the HRES forecast to include geopotential thickness calculation
-    preprocess=defaults._preprocess_hres_tc_forecast_dataset,
+    preprocess=ewb.defaults._preprocess_hres_tc_forecast_dataset,
 )
 
-# Define FCNv2 forecast
-fcnv2_forecast = inputs.KerchunkForecast(
+# Define FCNv2 forecast, this is the old version for reference only
+fcnv2_forecast = ewb.forecasts.KerchunkForecast(
     name="fcn_forecast",
     source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
-    variables=[derived.TropicalCycloneTrackVariables()],
+    variables=[ewb.derived.TropicalCycloneTrackVariables()],
     # Define metadata variable mapping for FCNv2 forecast
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
+    variable_mapping=ewb.CIRA_metadata_variable_mapping,
     # Preprocess the FCNv2 forecast to include geopotential thickness calculation
-    preprocess=defaults._preprocess_cira_tc_forecast_dataset,
+    preprocess=ewb.defaults._preprocess_cira_tc_forecast_dataset,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
 )
 
 # Define Pangu forecast
-pangu_forecast = inputs.KerchunkForecast(
+pangu_forecast = ewb.forecasts.KerchunkForecast(
     name="pangu_forecast",
     source="gs://extremeweatherbench/PANG_v100_GFS.parq",
-    variables=[derived.TropicalCycloneTrackVariables()],
+    variables=[ewb.derived.TropicalCycloneTrackVariables()],
     # Define metadata variable mapping for Pangu forecast
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
+    variable_mapping=ewb.CIRA_metadata_variable_mapping,
     # Preprocess the Pangu forecast to include geopotential thickness calculation
     # which uses the same preprocessing function as the FCNv2 forecast
-    preprocess=defaults._preprocess_cira_tc_forecast_dataset,
+    preprocess=ewb.defaults._preprocess_cira_tc_forecast_dataset,
     storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
 )
 
@@ -60,11 +106,11 @@
 # the evaluation to occur, in the case of multiple landfalls, for the next landfall in
 # time to be evaluated against
 composite_landfall_metrics = [
-    metrics.LandfallMetric(
+    ewb.metrics.LandfallMetric(
         metrics=[
-            metrics.LandfallIntensityMeanAbsoluteError,
-            metrics.LandfallTimeMeanError,
-            metrics.LandfallDisplacement,
+            ewb.metrics.LandfallIntensityMeanAbsoluteError,
+            ewb.metrics.LandfallTimeMeanError,
+            ewb.metrics.LandfallDisplacement,
         ],
         approach="next",
         # Set the intensity variable to use for the metric
@@ -77,21 +123,21 @@
 # the relevant cases inside the events YAML file
 tc_evaluation_object = [
     # HRES forecast
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="tropical_cyclone",
         metric_list=composite_landfall_metrics,
         target=ibtracs_target,
         forecast=hres_forecast,
     ),
     # Pangu forecast
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="tropical_cyclone",
         metric_list=composite_landfall_metrics,
         target=ibtracs_target,
         forecast=pangu_forecast,
     ),
     # FCNv2 forecast
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="tropical_cyclone",
         metric_list=composite_landfall_metrics,
         target=ibtracs_target,
@@ -101,13 +147,13 @@
 
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench
-    ewb = evaluate.ExtremeWeatherBench(
-        case_metadata=case_yaml,
+    tc_ewb = ewb.evaluation(
+        case_metadata=case_list,
         evaluation_objects=tc_evaluation_object,
     )
     logger.info("Starting EWB run")
     # Run the workflow with parallel_config backend set to dask
-    outputs = ewb.run(
+    outputs = tc_ewb.run(
         parallel_config={"backend": "loky", "n_jobs": 3},
     )
     outputs.to_csv("tc_metric_test_results.csv")
diff --git a/docs/examples/example_config.py b/docs/examples/example_config.py
index 3aff5967..f41d5e44 100644
--- a/docs/examples/example_config.py
+++ b/docs/examples/example_config.py
@@ -7,31 +7,29 @@
     ewb --config-file example_config.py
 """
 
-from extremeweatherbench import cases, inputs, metrics
+import extremeweatherbench as ewb
 
 # Define targets (observation data)
-era5_heatwave_target = inputs.ERA5(
+era5_heatwave_target = ewb.targets.ERA5(
     variables=["surface_air_temperature"],
     chunks=None,
 )
 
 # Define forecasts
-fcnv2_forecast = inputs.KerchunkForecast(
+fcnv2_forecast = ewb.forecasts.KerchunkForecast(
     name="fcnv2_forecast",
     source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
     variables=["surface_air_temperature"],
-    variable_mapping=inputs.CIRA_metadata_variable_mapping,
+    variable_mapping=ewb.CIRA_metadata_variable_mapping,
 )
 
 # Define evaluation objects
 evaluation_objects = [
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="heat_wave",
         metric_list=[
-            metrics.MaximumMeanAbsoluteError(),
-            metrics.RootMeanSquaredError(),
-            metrics.OnsetMeanError(),
-            metrics.DurationMeanError(),
+            ewb.metrics.MaximumMeanAbsoluteError(),
+            ewb.metrics.RootMeanSquaredError(),
         ],
         target=era5_heatwave_target,
         forecast=fcnv2_forecast,
@@ -39,8 +37,9 @@
 ]
 
 # Load case data from the default events.yaml
-# Users can also define their own cases
-cases_list = cases.load_ewb_events_yaml_into_case_list()
+# Users can also define their own cases_dict structure
+cases_list = ewb.load_cases()
+
 # Alternatively, users could define custom cases like this:
 # cases_list = [
 #         {
diff --git a/docs/parallelism.md b/docs/parallelism.md
index 98ceb4be..fae25fea 100644
--- a/docs/parallelism.md
+++ b/docs/parallelism.md
@@ -35,7 +35,7 @@ ewb = evaluate.ExtremeWeatherBench(
 # The larger the machine, the larger n_jobs can be (a bit of an oversimplification)
 parallel_config = {"backend":"loky","n_jobs":len(evaluation_objects)}
 
-outputs = ewb.run(parallel_config=parallel_config)
+outputs = ewb.run_evaluation(parallel_config=parallel_config)
 ```
 
 The _safest_ approach is to run EWB in serial, with `n_jobs` set to 1. `Dask` will still be invoked during each `CaseOperator` when the case executes and computes the directed acyclic graph, only one at a time. That said, for evaluations with more cases this approach would likely be too time-consuming. 
\ No newline at end of file
diff --git a/docs/usage.md b/docs/usage.md
index d5e37778..ddd372fa 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -10,16 +10,17 @@ tropical cyclones, and atmospheric rivers:
 
 
 ```python
-from extremeweatherbench import evaluate, defaults, cases
+import extremeweatherbench as ewb
 
-eval_objects = defaults.get_brightband_evaluation_objects()
+eval_objects = ewb.get_brightband_evaluation_objects()
+cases = ewb.load_cases()
 
-cases = cases.load_ewb_events_yaml_into_case_list()
-ewb = ExtremeWeatherBench(cases=cases, 
-evaluation_objects=eval_objects)
-
-outputs = ewb.run()
+runner = ewb.evaluation(
+    case_metadata=cases, 
+    evaluation_objects=eval_objects
+)
 
+outputs = runner.run()
 outputs.to_csv('your_outputs.csv')
 ```
 
@@ -28,6 +29,30 @@ or:
 ```bash
 ewb --default
 ```
+
+## API Overview
+
+ExtremeWeatherBench provides a hierarchical API for accessing its components:
+
+```python
+import extremeweatherbench as ewb
+
+# Main evaluation entry point
+ewb.evaluation(...)  # Alias for ExtremeWeatherBench class
+
+# Hierarchical access via namespaces
+ewb.targets.ERA5(...)           # Target classes
+ewb.forecasts.ZarrForecast(...) # Forecast classes
+ewb.metrics.MeanAbsoluteError() # Metric classes
+ewb.derived.AtmosphericRiverVariables() # Derived variables
+ewb.regions.BoundingBoxRegion(...) # Region classes
+ewb.cases.IndividualCase       # Case metadata classes
+
+# Also available at top level for convenience
+ewb.ERA5(...)
+ewb.ZarrForecast(...)
+ewb.load_cases()
+```
 ## Running an Evaluation for a Single Event Type
 
 ExtremeWeatherBench has default event types and cases for heat waves, freezes, severe convection, tropical cyclones, and atmospheric rivers.
@@ -39,20 +64,20 @@ ExtremeWeatherBench requires forecasts to have `init_time`, `lead_time`, `latitu
 Targets require at least a `valid_time` with at least one spatial dimension. Examples include `location`, `station`, or (`latitude`, `longitude`). Forecasts are aligned to targets during the steps immediately prior to evaluating a metric.
 
 ```python
-from extremeweatherbench import inputs
+import extremeweatherbench as ewb
 ```
 There are three built-in `ForecastBase` classes to set up a forecast: `ZarrForecast`, `XarrayForecast`, and `KerchunkForecast`. Here is an example of a `ZarrForecast`, using Weatherbench2's HRES zarr store:
 
 ```python
-hres_forecast = inputs.ZarrForecast(
+hres_forecast = ewb.forecasts.ZarrForecast(
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     name="HRES",
     variables=["surface_air_temperature"],
-    variable_mapping=inputs.HRES_metadata_variable_mapping, # built-in mapping available
+    variable_mapping=ewb.HRES_metadata_variable_mapping, # built-in mapping available
     storage_options={"remote_options": {"anon": True}},
-
 )
 ```
+
 There are required arguments, namely:
 
 - `source`
@@ -67,8 +92,8 @@ There are required arguments, namely:
 Next, a target dataset must be defined as well to evaluate against. For this evaluation, we'll use ERA5:
 
 ```python
-era5_heatwave_target = inputs.ERA5(
-    source=inputs.ARCO_ERA5_FULL_URI,
+era5_heatwave_target = ewb.targets.ERA5(
+    source=ewb.ARCO_ERA5_FULL_URI,
     variables=["surface_air_temperature"],
     storage_options={"remote_options": {"anon": True}},
     chunks=None,
@@ -87,48 +112,53 @@ Or (if defining variables as arguments to the metrics):
 era5_heatwave_target = inputs.ERA5()
 ```
 
-> **Detailed Explanation**: Similarly to forecasts, we need to define the `source`, which here is the ARCO ERA5 provided by Google. `variables` are used to subset `inputs.ERA5` in an evaluation; `variable_mapping` defaults to `inputs.ERA5_metadata_variable_mapping` for many existing variables and likely is not required to be set unless your use case is for less common variables. Both forecasts and targets, if relevant, have an optional `chunks` parameter which defaults to what should be the most efficient value - usually `None` or `'auto'`, but can be changed as seen above. *If using the ARCO ERA5 and setting `chunks=None`, it is critical to order your subsetting by variables -> time -> `.sel` or `.isel` latitude & longitude -> rechunk. [See this Github comment](https://github.com/pydata/xarray/issues/8902#issuecomment-2036435045).
+> **Detailed Explanation**: Similarly to forecasts, we need to define the `source`, which here is the ARCO ERA5 provided by Google. `variables` are used to subset `ewb.inputs.ERA5` in an evaluation; `variable_mapping` defaults to `ewb.inputs.ERA5_metadata_variable_mapping` for many existing variables and likely is not required to be set unless your use case is for less common variables. Both forecasts and targets, if relevant, have an optional `chunks` parameter which defaults to what should be the most efficient value - usually `None` or `'auto'`, but can be changed as seen above. *If using the ARCO ERA5 and setting `chunks=None`, it is critical to order your subsetting by variables -> time -> `.sel` or `.isel` latitude & longitude -> rechunk. [See this Github comment](https://github.com/pydata/xarray/issues/8902#issuecomment-2036435045).
 
 We then set up an `EvaluationObject` list:
 
 ```python
-from extremeweatherbench import metrics
-
 heatwave_evaluation_list = [
-    inputs.EvaluationObject(
+    ewb.EvaluationObject(
         event_type="heat_wave",
         metric_list=[
-            metrics.MaximumMeanAbsoluteError(),
-            metrics.RootMeanSquaredError(),
-            metrics.MaximumLowestMeanAbsoluteError()
+            ewb.metrics.MaximumMeanAbsoluteError(),
+            ewb.metrics.RootMeanSquaredError(),
+            ewb.metrics.MaximumLowestMeanAbsoluteError()
         ],
         target=era5_heatwave_target,
         forecast=hres_forecast,
     ),
 ]
 ```
+
 Which includes the event_type of interest (as defined in the case dictionary or YAML file used), the list of metrics to run, one target, and one forecast.
 There can be multiple `EvaluationObjects` which are used for an evaluation run.
 
 Plugging these all in:
 
 ```python
-from extremeweatherbench import cases, evaluate
-case_list = cases.load_ewb_events_yaml_into_case_list()
-
+case_yaml = ewb.load_cases()
 
-ewb_instance = evaluate.ExtremeWeatherBench(
-    cases=case_list,
+ewb_instance = ewb.evaluation(
+    case_metadata=case_yaml,
     evaluation_objects=heatwave_evaluation_list,
 )
 
 outputs = ewb_instance.run()
-
 outputs.to_csv('your_file_name.csv')
 ```
 
-Where the EWB default events YAML file is loaded in using a built-in utility helper function, then applied to an instance of `evaluate.ExtremeWeatherBench` along with the `EvaluationObject` list. Finally, we trigger the evaluation with the `.run()` method, where defaults are typically sufficient to run with a small to moderate-sized virtual machine. after subsetting and prior to metric calculation. 
+Where the EWB default events YAML file is loaded in using `ewb.load_cases()`, then applied to an instance of `ewb.evaluation` along with the `EvaluationObject` list. Finally, we run the evaluation with the `.run()` method, where defaults are typically sufficient to run with a small to moderate-sized virtual machine.
 
 Running locally is feasible but is typically bottlenecked heavily by IO and network bandwidth. Even on a gigabit connection, the rate of data access is significantly slower compared to within a cloud provider VM.
 
-The outputs are returned as a pandas DataFrame and can be manipulated in the script, a notebook, or post-hoc after saving it.
+The outputs are returned as a pandas DataFrame and can be manipulated in the script, a notebook, etc.
+
+## Backward Compatibility
+
+All existing import patterns remain functional:
+
+```python
+from extremeweatherbench import evaluate, inputs, cases, metrics  # Still works
+from extremeweatherbench.evaluate import ExtremeWeatherBench  # Still works
+```
diff --git a/scripts/brightband_evaluation.py b/scripts/brightband_evaluation.py
index 4086ef51..1dc8103b 100644
--- a/scripts/brightband_evaluation.py
+++ b/scripts/brightband_evaluation.py
@@ -39,5 +39,5 @@ def configure_logger(level=logging.INFO):
 
     # Set up parallel configuration
     parallel_config = {"backend": "loky", "n_jobs": n_processes}
-    results = ewb.run(parallel_config=parallel_config)
+    results = ewb.run_evaluation(parallel_config=parallel_config)
     results.to_csv("brightband_evaluation_results.csv", index=False)
diff --git a/src/extremeweatherbench/__init__.py b/src/extremeweatherbench/__init__.py
index e69de29b..af479c2f 100644
--- a/src/extremeweatherbench/__init__.py
+++ b/src/extremeweatherbench/__init__.py
@@ -0,0 +1,339 @@
+"""ExtremeWeatherBench: A benchmarking framework for extreme weather forecasts.
+
+This module provides the public API for ExtremeWeatherBench. Users can import
+the package and access all key functionality:
+
+    import extremeweatherbench as ewb
+
+    # Main entry point for evaluation
+    ewb.evaluation(case_metadata=..., evaluation_objects=...)
+
+    # Hierarchical access via namespace submodules
+    ewb.targets.ERA5(...)
+    ewb.forecasts.ZarrForecast(...)
+    ewb.metrics.MeanAbsoluteError(...)
+
+    # Also available at top level
+    ewb.ERA5(...)
+    ewb.load_cases()
+"""
+
+from types import SimpleNamespace
+
+# Import actual modules for backwards compatibility
+from extremeweatherbench import calc, cases, defaults, derived, metrics, regions, utils
+
+# Import specific items for top-level access
+from extremeweatherbench.calc import (
+    convert_from_cartesian_to_latlon,
+    geopotential_thickness,
+    great_circle_mask,
+    haversine_distance,
+    maybe_calculate_wind_speed,
+    mixing_ratio,
+    orography,
+    pressure_at_surface,
+    saturation_mixing_ratio,
+    saturation_vapor_pressure,
+    specific_humidity_from_relative_humidity,
+)
+from extremeweatherbench.cases import (
+    CaseOperator,
+    IndividualCase,
+    build_case_operators,
+    load_ewb_events_yaml_into_case_list,
+    load_individual_cases,
+    load_individual_cases_from_yaml,
+    read_incoming_yaml,
+)
+from extremeweatherbench.defaults import (
+    DEFAULT_COORDINATE_VARIABLES,
+    DEFAULT_VARIABLE_NAMES,
+    cira_fcnv2_atmospheric_river_forecast,
+    cira_fcnv2_freeze_forecast,
+    cira_fcnv2_heatwave_forecast,
+    cira_fcnv2_severe_convection_forecast,
+    cira_fcnv2_tropical_cyclone_forecast,
+    era5_atmospheric_river_target,
+    era5_freeze_target,
+    era5_heatwave_target,
+    get_brightband_evaluation_objects,
+    get_climatology,
+    ghcn_freeze_target,
+    ghcn_heatwave_target,
+    ibtracs_target,
+    lsr_target,
+    pph_target,
+)
+from extremeweatherbench.derived import (
+    AtmosphericRiverVariables,
+    CravenBrooksSignificantSevere,
+    DerivedVariable,
+    TropicalCycloneTrackVariables,
+    maybe_derive_variables,
+    maybe_include_variables_from_derived_input,
+)
+from extremeweatherbench.evaluate import ExtremeWeatherBench
+from extremeweatherbench.inputs import (
+    ARCO_ERA5_FULL_URI,
+    DEFAULT_GHCN_URI,
+    ERA5,
+    GHCN,
+    IBTRACS_URI,
+    LSR,
+    LSR_URI,
+    PPH,
+    PPH_URI,
+    CIRA_metadata_variable_mapping,
+    ERA5_metadata_variable_mapping,
+    EvaluationObject,
+    ForecastBase,
+    HRES_metadata_variable_mapping,
+    IBTrACS,
+    IBTrACS_metadata_variable_mapping,
+    InputBase,
+    KerchunkForecast,
+    TargetBase,
+    XarrayForecast,
+    ZarrForecast,
+    align_forecast_to_target,
+    check_for_missing_data,
+    maybe_subset_variables,
+    open_kerchunk_reference,
+    zarr_target_subsetter,
+)
+from extremeweatherbench.metrics import (
+    Accuracy,
+    BaseMetric,
+    CompositeMetric,
+    CriticalSuccessIndex,
+    DurationMeanError,
+    EarlySignal,
+    FalseAlarmRatio,
+    FalseNegatives,
+    FalsePositives,
+    LandfallDisplacement,
+    LandfallIntensityMeanAbsoluteError,
+    LandfallMetric,
+    LandfallTimeMeanError,
+    MaximumLowestMeanAbsoluteError,
+    MaximumMeanAbsoluteError,
+    MeanAbsoluteError,
+    MeanError,
+    MeanSquaredError,
+    MinimumMeanAbsoluteError,
+    RootMeanSquaredError,
+    SpatialDisplacement,
+    ThresholdMetric,
+    TrueNegatives,
+    TruePositives,
+)
+from extremeweatherbench.regions import (
+    REGION_TYPES,
+    BoundingBoxRegion,
+    CenteredRegion,
+    Region,
+    RegionSubsetter,
+    ShapefileRegion,
+    map_to_create_region,
+    subset_cases_to_region,
+    subset_results_to_region,
+)
+from extremeweatherbench.utils import (
+    check_for_vars,
+    convert_day_yearofday_to_time,
+    convert_init_time_to_valid_time,
+    convert_longitude_to_180,
+    convert_longitude_to_360,
+    convert_valid_time_to_init_time,
+    derive_indices_from_init_time_and_lead_time,
+    determine_temporal_resolution,
+    extract_tc_names,
+    filter_kwargs_for_callable,
+    find_common_init_times,
+    idx_to_coords,
+    interp_climatology_to_target,
+    is_valid_landfall,
+    load_land_geometry,
+    maybe_cache_and_compute,
+    maybe_densify_dataarray,
+    maybe_get_closest_timestamp_to_center_of_valid_times,
+    maybe_get_operator,
+    min_if_all_timesteps_present,
+    min_if_all_timesteps_present_forecast,
+    read_event_yaml,
+    remove_ocean_gridpoints,
+    stack_dataarray_from_dims,
+)
+
+# Aliases
+evaluation = ExtremeWeatherBench
+load_cases = load_ewb_events_yaml_into_case_list
+
+# Namespace submodules for convenient grouping (these don't shadow actual modules)
+targets = SimpleNamespace(
+    TargetBase=TargetBase,
+    ERA5=ERA5,
+    GHCN=GHCN,
+    IBTrACS=IBTrACS,
+    LSR=LSR,
+    PPH=PPH,
+)
+
+forecasts = SimpleNamespace(
+    ForecastBase=ForecastBase,
+    ZarrForecast=ZarrForecast,
+    KerchunkForecast=KerchunkForecast,
+    XarrayForecast=XarrayForecast,
+)
+
+__all__ = [
+    # Core evaluation
+    "evaluation",
+    "ExtremeWeatherBench",
+    # Modules
+    "calc",
+    "cases",
+    "defaults",
+    "derived",
+    "metrics",
+    "regions",
+    "utils",
+    # Namespace submodules
+    "targets",
+    "forecasts",
+    # Aliases
+    "load_cases",
+    # calc
+    "convert_from_cartesian_to_latlon",
+    "geopotential_thickness",
+    "great_circle_mask",
+    "haversine_distance",
+    "maybe_calculate_wind_speed",
+    "mixing_ratio",
+    "orography",
+    "pressure_at_surface",
+    "saturation_mixing_ratio",
+    "saturation_vapor_pressure",
+    "specific_humidity_from_relative_humidity",
+    # cases
+    "CaseOperator",
+    "IndividualCase",
+    "build_case_operators",
+    "load_ewb_events_yaml_into_case_list",
+    "load_individual_cases",
+    "load_individual_cases_from_yaml",
+    "read_incoming_yaml",
+    # defaults
+    "DEFAULT_COORDINATE_VARIABLES",
+    "DEFAULT_VARIABLE_NAMES",
+    "cira_fcnv2_atmospheric_river_forecast",
+    "cira_fcnv2_freeze_forecast",
+    "cira_fcnv2_heatwave_forecast",
+    "cira_fcnv2_severe_convection_forecast",
+    "cira_fcnv2_tropical_cyclone_forecast",
+    "era5_atmospheric_river_target",
+    "era5_freeze_target",
+    "era5_heatwave_target",
+    "get_brightband_evaluation_objects",
+    "get_climatology",
+    "ghcn_freeze_target",
+    "ghcn_heatwave_target",
+    "ibtracs_target",
+    "lsr_target",
+    "pph_target",
+    # derived
+    "AtmosphericRiverVariables",
+    "CravenBrooksSignificantSevere",
+    "DerivedVariable",
+    "TropicalCycloneTrackVariables",
+    "maybe_derive_variables",
+    "maybe_include_variables_from_derived_input",
+    # inputs
+    "ARCO_ERA5_FULL_URI",
+    "CIRA_metadata_variable_mapping",
+    "DEFAULT_GHCN_URI",
+    "ERA5",
+    "ERA5_metadata_variable_mapping",
+    "EvaluationObject",
+    "ForecastBase",
+    "GHCN",
+    "HRES_metadata_variable_mapping",
+    "IBTrACS",
+    "IBTrACS_metadata_variable_mapping",
+    "IBTRACS_URI",
+    "InputBase",
+    "KerchunkForecast",
+    "LSR",
+    "LSR_URI",
+    "PPH",
+    "PPH_URI",
+    "TargetBase",
+    "XarrayForecast",
+    "ZarrForecast",
+    "align_forecast_to_target",
+    "check_for_missing_data",
+    "maybe_subset_variables",
+    "open_kerchunk_reference",
+    "zarr_target_subsetter",
+    # metrics
+    "Accuracy",
+    "BaseMetric",
+    "CompositeMetric",
+    "CriticalSuccessIndex",
+    "DurationMeanError",
+    "EarlySignal",
+    "FalseAlarmRatio",
+    "FalseNegatives",
+    "FalsePositives",
+    "LandfallDisplacement",
+    "LandfallIntensityMeanAbsoluteError",
+    "LandfallMetric",
+    "LandfallTimeMeanError",
+    "MaximumLowestMeanAbsoluteError",
+    "MaximumMeanAbsoluteError",
+    "MeanAbsoluteError",
+    "MeanError",
+    "MeanSquaredError",
+    "MinimumMeanAbsoluteError",
+    "RootMeanSquaredError",
+    "SpatialDisplacement",
+    "ThresholdMetric",
+    "TrueNegatives",
+    "TruePositives",
+    # regions
+    "BoundingBoxRegion",
+    "CenteredRegion",
+    "REGION_TYPES",
+    "Region",
+    "RegionSubsetter",
+    "ShapefileRegion",
+    "map_to_create_region",
+    "subset_cases_to_region",
+    "subset_results_to_region",
+    # utils
+    "check_for_vars",
+    "convert_day_yearofday_to_time",
+    "convert_init_time_to_valid_time",
+    "convert_longitude_to_180",
+    "convert_longitude_to_360",
+    "convert_valid_time_to_init_time",
+    "derive_indices_from_init_time_and_lead_time",
+    "determine_temporal_resolution",
+    "extract_tc_names",
+    "filter_kwargs_for_callable",
+    "find_common_init_times",
+    "idx_to_coords",
+    "interp_climatology_to_target",
+    "is_valid_landfall",
+    "load_land_geometry",
+    "maybe_cache_and_compute",
+    "maybe_densify_dataarray",
+    "maybe_get_closest_timestamp_to_center_of_valid_times",
+    "maybe_get_operator",
+    "min_if_all_timesteps_present",
+    "min_if_all_timesteps_present_forecast",
+    "read_event_yaml",
+    "remove_ocean_gridpoints",
+    "stack_dataarray_from_dims",
+]
diff --git a/src/extremeweatherbench/cases.py b/src/extremeweatherbench/cases.py
index 3f2e858c..236104c9 100644
--- a/src/extremeweatherbench/cases.py
+++ b/src/extremeweatherbench/cases.py
@@ -14,10 +14,11 @@
 import dacite
 import yaml  # type: ignore[import]
 
-from extremeweatherbench import regions
+import extremeweatherbench.regions as regions
 
 if TYPE_CHECKING:
-    from extremeweatherbench import inputs, metrics
+    import extremeweatherbench.inputs as inputs
+    import extremeweatherbench.metrics as metrics
 
 logger = logging.getLogger(__name__)
 
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index 7dcc68d6..41d20e16 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -309,7 +309,7 @@ def get_brightband_evaluation_objects() -> list[inputs.EvaluationObject]:
         routine.
     """
     # Import metrics here to avoid circular import
-    from extremeweatherbench import metrics
+    import extremeweatherbench.metrics as metrics
 
     heatwave_metric_list: list[metrics.BaseMetric] = [
         metrics.MaximumMeanAbsoluteError(),
diff --git a/src/extremeweatherbench/derived.py b/src/extremeweatherbench/derived.py
index 0e609fbe..7517d232 100644
--- a/src/extremeweatherbench/derived.py
+++ b/src/extremeweatherbench/derived.py
@@ -10,7 +10,7 @@
 from extremeweatherbench.events import tropical_cyclone
 
 if TYPE_CHECKING:
-    from extremeweatherbench import cases
+    import extremeweatherbench.cases as cases
 
 logger = logging.getLogger(__name__)
 
diff --git a/src/extremeweatherbench/evaluate.py b/src/extremeweatherbench/evaluate.py
index 64c0ba96..504d7694 100644
--- a/src/extremeweatherbench/evaluate.py
+++ b/src/extremeweatherbench/evaluate.py
@@ -15,10 +15,15 @@
 from tqdm.contrib.logging import logging_redirect_tqdm
 from tqdm.dask import TqdmCallback
 
-from extremeweatherbench import cases, derived, inputs, metrics, sources, utils
+import extremeweatherbench.cases as cases
+import extremeweatherbench.derived as derived
+import extremeweatherbench.inputs as inputs
+import extremeweatherbench.metrics as metrics
+import extremeweatherbench.sources as sources
+import extremeweatherbench.utils as utils
 
 if TYPE_CHECKING:
-    from extremeweatherbench import regions
+    import extremeweatherbench.regions as regions
 
 logger = logging.getLogger(__name__)
 
@@ -101,11 +106,11 @@ def run(
         parallel_config: Optional[dict] = None,
         **kwargs,
     ) -> pd.DataFrame:
-        """Runs the ExtremeWeatherBench workflow.
+        """Runs the ExtremeWeatherBench evaluation workflow.
 
-        This method will run the workflow in the order of the case operators, optionally
-        caching the mid-flight outputs of the workflow if cache_dir was provided for
-        serial runs.
+        This method will run the evaluation workflow in the order of the case operators,
+        optionally caching the mid-flight outputs of the workflow if cache_dir was
+        provided for serial runs.
 
         Args:
             n_jobs: The number of jobs to run in parallel. If None, defaults to the
@@ -113,16 +118,60 @@ def run(
                 Ignored if parallel_config is provided.
             parallel_config: Optional dictionary of joblib parallel configuration.
                 If provided, this takes precedence over n_jobs. If not provided and
-                n_jobs is specified, a default config with loky backend is used.
+                n_jobs is specified, a default config with the loky backend is used.
+            **kwargs: Additional arguments to pass to compute_case_operator.
+        Returns:
+            A concatenated dataframe of the evaluation results.
+        """
+        logger.warning("The run method is deprecated. Use run_evaluation instead.")
+        logger.info("Running ExtremeWeatherBench evaluations...")
+
+        # Check for serial or parallel configuration
+        parallel_config = _parallel_serial_config_check(n_jobs, parallel_config)
+
+        run_results = _run_evaluation(
+            self.case_operators,
+            cache_dir=self.cache_dir,
+            parallel_config=parallel_config,
+            **kwargs,
+        )
+
+        # If there are results, concatenate them and return, else return an empty
+        # DataFrame with the expected columns
+        if run_results:
+            return _safe_concat(run_results, ignore_index=True)
+        else:
+            return pd.DataFrame(columns=OUTPUT_COLUMNS)
+
+    def run_evaluation(
+        self,
+        n_jobs: Optional[int] = None,
+        parallel_config: Optional[dict] = None,
+        **kwargs,
+    ) -> pd.DataFrame:
+        """Runs the ExtremeWeatherBench evaluation workflow.
+
+        This method will run the evaluation workflow in the order of the case operators,
+        optionally caching the mid-flight outputs of the workflow if cache_dir was
+        provided for serial runs.
 
+        Args:
+            n_jobs: The number of jobs to run in parallel. If None, defaults to the
+                joblib backend default value. If 1, the workflow will run serially.
+                Ignored if parallel_config is provided.
+            parallel_config: Optional dictionary of joblib parallel configuration.
+                If provided, this takes precedence over n_jobs. If not provided and
+                n_jobs is specified, a default config with the loky backend is used.
+            **kwargs: Additional arguments to pass to compute_case_operator.
         Returns:
             A concatenated dataframe of the evaluation results.
         """
-        logger.info("Running ExtremeWeatherBench workflow...")
+        logger.info("Running ExtremeWeatherBench evaluations...")
 
         # Check for serial or parallel configuration
         parallel_config = _parallel_serial_config_check(n_jobs, parallel_config)
-        run_results = _run_case_operators(
+
+        run_results = _run_evaluation(
             self.case_operators,
             cache_dir=self.cache_dir,
             parallel_config=parallel_config,
@@ -137,7 +186,48 @@ def run(
             return pd.DataFrame(columns=OUTPUT_COLUMNS)
 
 
-def _run_case_operators(
+def _parallel_serial_config_check(
+    n_jobs: Optional[int] = None,
+    parallel_config: Optional[dict] = None,
+) -> Optional[dict]:
+    """Check if running in serial or parallel mode.
+
+    Args:
+        n_jobs: The number of jobs to run in parallel. If None, defaults to the
+            joblib backend default value. If 1, the workflow will run serially.
+        parallel_config: Optional dictionary of joblib parallel configuration. If
+            provided, this takes precedence over n_jobs. If not provided and n_jobs is
+            specified, a default config with loky backend is used.
+    Returns:
+        None if running in serial mode, otherwise a dictionary of joblib parallel
+        configuration.
+    """
+    # Determine if running in serial or parallel mode
+    # Serial: n_jobs=1 or (parallel_config with n_jobs=1)
+    # Parallel: n_jobs>1 or (parallel_config with n_jobs>1)
+    is_serial = (
+        (n_jobs == 1)
+        or (parallel_config is not None and parallel_config.get("n_jobs") == 1)
+        or (n_jobs is None and parallel_config is None)
+    )
+    logger.debug("Running in %s mode.", "serial" if is_serial else "parallel")
+
+    if not is_serial:
+        # Build parallel_config if not provided
+        if parallel_config is None and n_jobs is not None:
+            logger.debug(
+                "No parallel_config provided, using loky backend and %s jobs.",
+                n_jobs,
+            )
+            parallel_config = {"backend": "loky", "n_jobs": n_jobs}
+    # If running in serial mode, set parallel_config to None if not already
+    else:
+        parallel_config = None
+    # Return the maybe updated kwargs
+    return parallel_config
+
+
+def _run_evaluation(
     case_operators: list["cases.CaseOperator"],
     cache_dir: Optional[pathlib.Path] = None,
     parallel_config: Optional[dict] = None,
@@ -154,37 +244,29 @@ def _run_case_operators(
     Returns:
         List of result DataFrames.
     """
-    with logging_redirect_tqdm():
-        # Run in parallel if parallel_config exists and n_jobs != 1
-        if parallel_config is not None:
+    if parallel_config is not None:
+        with logging_redirect_tqdm():
             logger.info("Running case operators in parallel...")
-            return _run_parallel(
+            run_results = _run_parallel_evaluation(
                 case_operators,
                 cache_dir=cache_dir,
                 parallel_config=parallel_config,
                 **kwargs,
             )
-        else:
-            logger.info("Running case operators in serial...")
-            return _run_serial(case_operators, cache_dir=cache_dir, **kwargs)
-
-
-def _run_serial(
-    case_operators: list["cases.CaseOperator"],
-    cache_dir: Optional[pathlib.Path] = None,
-    **kwargs,
-) -> list[pd.DataFrame]:
-    """Run the case operators in serial."""
-    run_results = []
+    else:
+        logger.info("Running case operators in serial...")
+        run_results = []
+        for case_operator in tqdm(case_operators):
+            run_results.append(
+                compute_case_operator(case_operator, cache_dir, **kwargs)
+            )
 
-    # Loop over the case operators
-    for case_operator in tqdm(case_operators):
-        run_results.append(compute_case_operator(case_operator, cache_dir, **kwargs))
     return run_results
 
 
-def _run_parallel(
+def _run_parallel_evaluation(
     case_operators: list["cases.CaseOperator"],
+    parallel_config: dict,
     cache_dir: Optional[pathlib.Path] = None,
     **kwargs,
 ) -> list[pd.DataFrame]:
@@ -197,11 +279,6 @@ def _run_parallel(
     Returns:
         List of result DataFrames.
     """
-    parallel_config = kwargs.pop("parallel_config", None)
-
-    if parallel_config is None:
-        raise ValueError("parallel_config must be provided to _run_parallel")
-
     if parallel_config.get("n_jobs") is None:
         logger.warning("No number of jobs provided, using joblib backend default.")
 
@@ -900,44 +977,3 @@ def _safe_concat(
         return pd.concat(valid_dfs, ignore_index=ignore_index)
     else:
         return pd.DataFrame(columns=OUTPUT_COLUMNS)
-
-
-def _parallel_serial_config_check(
-    n_jobs: Optional[int] = None,
-    parallel_config: Optional[dict] = None,
-) -> Optional[dict]:
-    """Check if running in serial or parallel mode.
-
-    Args:
-        n_jobs: The number of jobs to run in parallel. If None, defaults to the
-            joblib backend default value. If 1, the workflow will run serially.
-        parallel_config: Optional dictionary of joblib parallel configuration. If
-            provided, this takes precedence over n_jobs. If not provided and n_jobs is
-            specified, a default config with loky backend is used.
-    Returns:
-        None if running in serial mode, otherwise a dictionary of joblib parallel
-        configuration.
-    """
-    # Determine if running in serial or parallel mode
-    # Serial: n_jobs=1 or (parallel_config with n_jobs=1)
-    # Parallel: n_jobs>1 or (parallel_config with n_jobs>1)
-    is_serial = (
-        (n_jobs == 1)
-        or (parallel_config is not None and parallel_config.get("n_jobs") == 1)
-        or (n_jobs is None and parallel_config is None)
-    )
-    logger.debug("Running in %s mode.", "serial" if is_serial else "parallel")
-
-    if not is_serial:
-        # Build parallel_config if not provided
-        if parallel_config is None and n_jobs is not None:
-            logger.debug(
-                "No parallel_config provided, using loky backend and %s jobs.",
-                n_jobs,
-            )
-            parallel_config = {"backend": "loky", "n_jobs": n_jobs}
-    # If running in serial mode, set parallel_config to None if not already
-    else:
-        parallel_config = None
-    # Return the maybe updated kwargs
-    return parallel_config
diff --git a/src/extremeweatherbench/evaluate_cli.py b/src/extremeweatherbench/evaluate_cli.py
index 9b978269..1a96dfc1 100644
--- a/src/extremeweatherbench/evaluate_cli.py
+++ b/src/extremeweatherbench/evaluate_cli.py
@@ -7,7 +7,9 @@
 import click
 import pandas as pd
 
-from extremeweatherbench import cases, defaults, evaluate
+import extremeweatherbench.cases as cases
+import extremeweatherbench.defaults as defaults
+import extremeweatherbench.evaluate as evaluate
 
 
 @click.command()
@@ -152,7 +154,7 @@ def cli_runner(
 
     # Run evaluation
     click.echo("Running evaluation...")
-    results = ewb.run(
+    results = ewb.run_evaluation(
         n_jobs=n_jobs,
         parallel_config=parallel_config,
     )
diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index 8708dde9..37aba5a5 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -19,10 +19,13 @@
 import polars as pl
 import xarray as xr
 
-from extremeweatherbench import cases, derived, sources, utils
+import extremeweatherbench.cases as cases
+import extremeweatherbench.derived as derived
+import extremeweatherbench.sources as sources
+import extremeweatherbench.utils as utils
 
 if TYPE_CHECKING:
-    from extremeweatherbench import metrics
+    import extremeweatherbench.metrics as metrics
 
 logger = logging.getLogger(__name__)
 
diff --git a/src/extremeweatherbench/regions.py b/src/extremeweatherbench/regions.py
index 5a36dc0e..4e6c7730 100644
--- a/src/extremeweatherbench/regions.py
+++ b/src/extremeweatherbench/regions.py
@@ -16,7 +16,7 @@
 from extremeweatherbench import utils
 
 if TYPE_CHECKING:
-    from extremeweatherbench import cases
+    import extremeweatherbench.cases as cases
 
 logger = logging.getLogger(__name__)
 
diff --git a/src/extremeweatherbench/sources/base.py b/src/extremeweatherbench/sources/base.py
index dd58641c..e7dbda6e 100644
--- a/src/extremeweatherbench/sources/base.py
+++ b/src/extremeweatherbench/sources/base.py
@@ -1,7 +1,7 @@
 import datetime
 from typing import Any, Protocol, runtime_checkable
 
-from extremeweatherbench import regions
+import extremeweatherbench.regions as regions
 
 
 @runtime_checkable
diff --git a/src/extremeweatherbench/sources/pandas_dataframe.py b/src/extremeweatherbench/sources/pandas_dataframe.py
index 31bc4062..b6eb91a9 100644
--- a/src/extremeweatherbench/sources/pandas_dataframe.py
+++ b/src/extremeweatherbench/sources/pandas_dataframe.py
@@ -8,7 +8,7 @@
 from extremeweatherbench import utils
 
 if TYPE_CHECKING:
-    from extremeweatherbench import regions
+    import extremeweatherbench.regions as regions
 
 
 def safely_pull_variables(
@@ -43,7 +43,7 @@ def safely_pull_variables(
         >>> list(result.columns)
         ['temp']
     """
-    from extremeweatherbench import defaults
+    import extremeweatherbench.defaults as defaults
 
     # Get column names from DataFrame
     available_columns = list(data.columns)
diff --git a/src/extremeweatherbench/sources/polars_lazyframe.py b/src/extremeweatherbench/sources/polars_lazyframe.py
index e9e56cf4..f0caa41e 100644
--- a/src/extremeweatherbench/sources/polars_lazyframe.py
+++ b/src/extremeweatherbench/sources/polars_lazyframe.py
@@ -8,7 +8,7 @@
 from extremeweatherbench import utils
 
 if TYPE_CHECKING:
-    from extremeweatherbench import regions
+    import extremeweatherbench.regions as regions
 
 
 def safely_pull_variables(
@@ -47,7 +47,7 @@ def safely_pull_variables(
         >>> result.collect().columns
         ['temp']
     """
-    from extremeweatherbench import defaults
+    import extremeweatherbench.defaults as defaults
 
     # Get column names from LazyFrame
     available_columns = data.collect_schema().names()
diff --git a/src/extremeweatherbench/sources/xarray_dataarray.py b/src/extremeweatherbench/sources/xarray_dataarray.py
index e58d82d6..f3b4e734 100644
--- a/src/extremeweatherbench/sources/xarray_dataarray.py
+++ b/src/extremeweatherbench/sources/xarray_dataarray.py
@@ -5,7 +5,8 @@
 import pandas as pd
 import xarray as xr
 
-from extremeweatherbench import regions, utils
+import extremeweatherbench.regions as regions
+import extremeweatherbench.utils as utils
 
 
 def safely_pull_variables(
diff --git a/src/extremeweatherbench/sources/xarray_dataset.py b/src/extremeweatherbench/sources/xarray_dataset.py
index 56d52618..ae8e8b89 100644
--- a/src/extremeweatherbench/sources/xarray_dataset.py
+++ b/src/extremeweatherbench/sources/xarray_dataset.py
@@ -9,7 +9,7 @@
 from extremeweatherbench import utils
 
 if TYPE_CHECKING:
-    from extremeweatherbench import regions
+    import extremeweatherbench.regions as regions
 
 
 def safely_pull_variables(
diff --git a/tests/test_evaluate.py b/tests/test_evaluate.py
index 9220093f..18569e6b 100644
--- a/tests/test_evaluate.py
+++ b/tests/test_evaluate.py
@@ -332,10 +332,10 @@ def test_case_operators_property(
         # Check that the result is what the mock returned
         assert result == [sample_case_operator]
 
-    @mock.patch("extremeweatherbench.evaluate._run_case_operators")
-    def test_run_serial(
+    @mock.patch("extremeweatherbench.evaluate._run_evaluation")
+    def test_run_serial_evaluation(
         self,
-        mock_run_case_operators,
+        mock_run_evaluation,
         sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
@@ -345,7 +345,7 @@ def test_run_serial(
         with mock.patch.object(
             evaluate.ExtremeWeatherBench, "case_operators", new=[sample_case_operator]
         ):
-            # Mock _run_case_operators to return a list of DataFrames
+            # Mock _run_evaluation to return a list of DataFrames
             mock_result = [
                 pd.DataFrame(
                     {
@@ -355,17 +355,17 @@ def test_run_serial(
                     }
                 )
             ]
-            mock_run_case_operators.return_value = mock_result
+            mock_run_evaluation.return_value = mock_result
 
             ewb = evaluate.ExtremeWeatherBench(
                 case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
-            result = ewb.run(n_jobs=1)
+            result = ewb.run_evaluation(n_jobs=1)
 
-            # Serial mode passes parallel_config=None
-            mock_run_case_operators.assert_called_once_with(
+            # Serial mode should pass parallel_config=None
+            mock_run_evaluation.assert_called_once_with(
                 [sample_case_operator],
                 cache_dir=None,
                 parallel_config=None,
@@ -373,10 +373,10 @@ def test_run_serial(
             assert isinstance(result, pd.DataFrame)
             assert len(result) == 1
 
-    @mock.patch("extremeweatherbench.evaluate._run_case_operators")
-    def test_run_parallel(
+    @mock.patch("extremeweatherbench.evaluate._run_evaluation")
+    def test_run_parallel_evaluation(
         self,
-        mock_run_case_operators,
+        mock_run_evaluation,
         sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
@@ -394,16 +394,16 @@ def test_run_parallel(
                     }
                 )
             ]
-            mock_run_case_operators.return_value = mock_result
+            mock_run_evaluation.return_value = mock_result
 
             ewb = evaluate.ExtremeWeatherBench(
                 case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
-            result = ewb.run(n_jobs=2)
+            result = ewb.run_evaluation(n_jobs=2)
 
-            mock_run_case_operators.assert_called_once_with(
+            mock_run_evaluation.assert_called_once_with(
                 [sample_case_operator],
                 cache_dir=None,
                 parallel_config={"backend": "loky", "n_jobs": 2},
@@ -411,10 +411,10 @@ def test_run_parallel(
             assert isinstance(result, pd.DataFrame)
             assert len(result) == 1
 
-    @mock.patch("extremeweatherbench.evaluate._run_case_operators")
+    @mock.patch("extremeweatherbench.evaluate._run_evaluation")
     def test_run_with_kwargs(
         self,
-        mock_run_case_operators,
+        mock_run_evaluation,
         sample_cases_list,
         sample_evaluation_object,
         sample_case_operator,
@@ -424,37 +424,37 @@ def test_run_with_kwargs(
             evaluate.ExtremeWeatherBench, "case_operators", new=[sample_case_operator]
         ):
             mock_result = [pd.DataFrame({"value": [1.0]})]
-            mock_run_case_operators.return_value = mock_result
+            mock_run_evaluation.return_value = mock_result
 
             ewb = evaluate.ExtremeWeatherBench(
                 case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
-            result = ewb.run(n_jobs=1, threshold=0.5)
+            result = ewb.run_evaluation(n_jobs=1, threshold=0.5)
 
             # Check that kwargs were passed through
-            call_args = mock_run_case_operators.call_args
+            call_args = mock_run_evaluation.call_args
             assert call_args[1]["threshold"] == 0.5
             assert isinstance(result, pd.DataFrame)
 
-    @mock.patch("extremeweatherbench.evaluate._run_case_operators")
+    @mock.patch("extremeweatherbench.evaluate._run_evaluation")
     def test_run_empty_results(
         self,
-        mock_run_case_operators,
+        mock_run_evaluation,
         sample_cases_list,
         sample_evaluation_object,
     ):
         """Test the run method handles empty results."""
         with mock.patch.object(evaluate.ExtremeWeatherBench, "case_operators", new=[]):
-            mock_run_case_operators.return_value = []
+            mock_run_evaluation.return_value = []
 
             ewb = evaluate.ExtremeWeatherBench(
                 case_metadata=sample_cases_list,
                 evaluation_objects=[sample_evaluation_object],
             )
 
-            result = ewb.run()
+            result = ewb.run_evaluation()
 
             assert isinstance(result, pd.DataFrame)
             assert len(result) == 0
@@ -504,7 +504,7 @@ def mock_compute_with_caching(case_operator, cache_dir_arg, **kwargs):
                     cache_dir=cache_dir,
                 )
 
-                ewb.run(n_jobs=1)
+                ewb.run_evaluation(n_jobs=1)
 
                 # Check that cache directory was created
                 assert cache_dir.exists()
@@ -538,7 +538,7 @@ def test_run_multiple_cases(
                 evaluation_objects=[sample_evaluation_object],
             )
 
-            result = ewb.run()
+            result = ewb.run_evaluation()
 
             assert mock_compute_case_operator.call_count == 2
             assert len(result) == 2
@@ -546,107 +546,111 @@ def test_run_multiple_cases(
 
 
 class TestRunCaseOperators:
-    """Test the _run_case_operators function."""
+    """Test the _run_evaluation function."""
 
-    @mock.patch("extremeweatherbench.evaluate._run_serial")
-    def test_run_case_operators_serial(self, mock_run_serial, sample_case_operator):
-        """Test _run_case_operators routes to serial execution."""
-        mock_results = [pd.DataFrame({"value": [1.0]})]
-        mock_run_serial.return_value = mock_results
+    @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
+    @mock.patch("tqdm.auto.tqdm")
+    def test_run_evaluation_serial(
+        self, mock_tqdm, mock_compute_case_operator, sample_case_operator
+    ):
+        """Test _run_evaluation executes serially when parallel_config=None."""
+        mock_tqdm.return_value = [sample_case_operator]
+        mock_results = pd.DataFrame({"value": [1.0]})
+        mock_compute_case_operator.return_value = mock_results
 
         # Serial mode: don't pass parallel_config
-        result = evaluate._run_case_operators([sample_case_operator], cache_dir=None)
+        result = evaluate._run_evaluation([sample_case_operator], cache_dir=None)
 
-        mock_run_serial.assert_called_once_with([sample_case_operator], cache_dir=None)
-        assert result == mock_results
+        mock_compute_case_operator.assert_called_once_with(sample_case_operator, None)
+        assert len(result) == 1
+        assert result[0].equals(mock_results)
 
-    @mock.patch("extremeweatherbench.evaluate._run_parallel")
-    def test_run_case_operators_parallel(self, mock_run_parallel, sample_case_operator):
-        """Test _run_case_operators routes to parallel execution."""
+    @mock.patch("extremeweatherbench.evaluate._run_parallel_evaluation")
+    def test_run_evaluation_parallel(
+        self, mock_run_parallel_evaluation, sample_case_operator
+    ):
+        """Test _run_evaluation routes to parallel execution."""
         mock_results = [pd.DataFrame({"value": [1.0]})]
-        mock_run_parallel.return_value = mock_results
+        mock_run_parallel_evaluation.return_value = mock_results
 
-        result = evaluate._run_case_operators(
+        result = evaluate._run_evaluation(
             [sample_case_operator],
             cache_dir=None,
             parallel_config={"backend": "threading", "n_jobs": 4},
         )
 
-        mock_run_parallel.assert_called_once_with(
+        mock_run_parallel_evaluation.assert_called_once_with(
             [sample_case_operator],
             cache_dir=None,
             parallel_config={"backend": "threading", "n_jobs": 4},
         )
         assert result == mock_results
 
-    @mock.patch("extremeweatherbench.evaluate._run_serial")
-    def test_run_case_operators_with_kwargs(
-        self, mock_run_serial, sample_case_operator
+    @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
+    @mock.patch("tqdm.auto.tqdm")
+    def test_run_evaluation_with_kwargs(
+        self, mock_tqdm, mock_compute_case_operator, sample_case_operator
     ):
-        """Test _run_case_operators passes kwargs correctly."""
-        mock_results = [pd.DataFrame({"value": [1.0]})]
-        mock_run_serial.return_value = mock_results
+        """Test _run_evaluation passes kwargs correctly in serial mode."""
+        mock_tqdm.return_value = [sample_case_operator]
+        mock_results = pd.DataFrame({"value": [1.0]})
+        mock_compute_case_operator.return_value = mock_results
 
         # Serial mode: don't pass parallel_config
-        result = evaluate._run_case_operators(
+        result = evaluate._run_evaluation(
             [sample_case_operator],
             cache_dir=None,
             threshold=0.5,
         )
 
-        call_args = mock_run_serial.call_args
-        assert call_args[0][0] == [sample_case_operator]
-        assert call_args[1]["cache_dir"] is None
+        call_args = mock_compute_case_operator.call_args
+        assert call_args[0][0] == sample_case_operator
+        assert call_args[0][1] is None  # cache_dir
         assert call_args[1]["threshold"] == 0.5
         assert isinstance(result, list)
 
-    @mock.patch("extremeweatherbench.evaluate._run_parallel")
-    def test_run_case_operators_parallel_with_kwargs(
-        self, mock_run_parallel, sample_case_operator
+    @mock.patch("extremeweatherbench.evaluate._run_parallel_evaluation")
+    def test_run_evaluation_parallel_with_kwargs(
+        self, mock_run_parallel_evaluation, sample_case_operator
     ):
-        """Test _run_case_operators passes kwargs to parallel execution."""
+        """Test _run_evaluation passes kwargs to parallel execution."""
         mock_results = [pd.DataFrame({"value": [1.0]})]
-        mock_run_parallel.return_value = mock_results
+        mock_run_parallel_evaluation.return_value = mock_results
 
-        result = evaluate._run_case_operators(
+        result = evaluate._run_evaluation(
             [sample_case_operator],
             parallel_config={"backend": "threading", "n_jobs": 2},
             custom_param="test_value",
         )
 
-        call_args = mock_run_parallel.call_args
+        call_args = mock_run_parallel_evaluation.call_args
         assert call_args[0][0] == [sample_case_operator]
         assert call_args[1]["parallel_config"] == {"backend": "threading", "n_jobs": 2}
         assert call_args[1]["custom_param"] == "test_value"
         assert isinstance(result, list)
 
-    def test_run_case_operators_empty_list(self):
-        """Test _run_case_operators with empty case operator list."""
-        with mock.patch("extremeweatherbench.evaluate._run_serial") as mock_serial:
-            mock_serial.return_value = []
-
-            # Serial mode: don't pass parallel_config
-            result = evaluate._run_case_operators([], cache_dir=None)
-
-            mock_serial.assert_called_once_with([], cache_dir=None)
-            assert result == []
+    def test_run_evaluation_empty_list(self):
+        """Test _run_evaluation with empty case operator list."""
+        # Serial mode: don't pass parallel_config
+        result = evaluate._run_evaluation([], cache_dir=None)
+        assert result == []
 
 
 class TestRunSerial:
-    """Test the _run_serial function."""
+    """Test the serial execution path of _run_evaluation."""
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_serial_basic(
+    def test_run_serial_evaluation_basic(
         self, mock_tqdm, mock_compute_case_operator, sample_case_operator
     ):
-        """Test basic _run_serial functionality."""
+        """Test basic serial execution functionality."""
         # Setup mocks
         mock_tqdm.return_value = [sample_case_operator]  # tqdm returns iterable
         mock_result = pd.DataFrame({"value": [1.0], "case_id_number": [1]})
         mock_compute_case_operator.return_value = mock_result
 
-        result = evaluate._run_serial([sample_case_operator])
+        result = evaluate._run_evaluation([sample_case_operator], parallel_config=None)
 
         mock_compute_case_operator.assert_called_once_with(sample_case_operator, None)
         assert len(result) == 1
@@ -654,8 +658,10 @@ def test_run_serial_basic(
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_serial_multiple_cases(self, mock_tqdm, mock_compute_case_operator):
-        """Test _run_serial with multiple case operators."""
+    def test_run_serial_evaluation_multiple_cases(
+        self, mock_tqdm, mock_compute_case_operator
+    ):
+        """Test serial execution with multiple case operators."""
         case_op_1 = mock.Mock()
         case_op_2 = mock.Mock()
         case_operators = [case_op_1, case_op_2]
@@ -666,7 +672,7 @@ def test_run_serial_multiple_cases(self, mock_tqdm, mock_compute_case_operator):
             pd.DataFrame({"value": [2.0], "case_id_number": [2]}),
         ]
 
-        result = evaluate._run_serial(case_operators)
+        result = evaluate._run_evaluation(case_operators, parallel_config=None)
 
         assert mock_compute_case_operator.call_count == 2
         assert len(result) == 2
@@ -675,16 +681,19 @@ def test_run_serial_multiple_cases(self, mock_tqdm, mock_compute_case_operator):
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_serial_with_kwargs(
+    def test_run_serial_evaluation_with_kwargs(
         self, mock_tqdm, mock_compute_case_operator, sample_case_operator
     ):
-        """Test _run_serial passes kwargs to compute_case_operator."""
+        """Test serial execution passes kwargs to compute_case_operator."""
         mock_tqdm.return_value = [sample_case_operator]
         mock_result = pd.DataFrame({"value": [1.0]})
         mock_compute_case_operator.return_value = mock_result
 
-        result = evaluate._run_serial(
-            [sample_case_operator], threshold=0.7, custom_param="test"
+        result = evaluate._run_evaluation(
+            [sample_case_operator],
+            parallel_config=None,
+            threshold=0.7,
+            custom_param="test",
         )
 
         call_args = mock_compute_case_operator.call_args
@@ -693,22 +702,22 @@ def test_run_serial_with_kwargs(
         assert call_args[1]["custom_param"] == "test"
         assert isinstance(result, list)
 
-    def test_run_serial_empty_list(self):
-        """Test _run_serial with empty case operator list."""
-        result = evaluate._run_serial([])
+    def test_run_serial_evaluation_empty_list(self):
+        """Test serial execution with empty case operator list."""
+        result = evaluate._run_evaluation([], parallel_config=None)
         assert result == []
 
 
 class TestRunParallel:
-    """Test the _run_parallel function."""
+    """Test the _run_parallel_evaluation function."""
 
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
     @mock.patch("joblib.delayed")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_parallel_basic(
+    def test_run_parallel_evaluation_basic(
         self, mock_tqdm, mock_delayed, mock_parallel_class, sample_case_operator
     ):
-        """Test basic _run_parallel functionality."""
+        """Test basic _run_parallel_evaluation functionality."""
         # Setup mocks
         mock_tqdm.return_value = [sample_case_operator]
         mock_delayed_func = mock.Mock()
@@ -719,7 +728,7 @@ def test_run_parallel_basic(
         mock_result = [pd.DataFrame({"value": [1.0], "case_id_number": [1]})]
         mock_parallel_instance.return_value = mock_result
 
-        result = evaluate._run_parallel(
+        result = evaluate._run_parallel_evaluation(
             [sample_case_operator],
             parallel_config={"backend": "threading", "n_jobs": 2},
         )
@@ -735,10 +744,10 @@ def test_run_parallel_basic(
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
     @mock.patch("joblib.delayed")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_parallel_with_none_n_jobs(
+    def test_run_parallel_evaluation_with_none_n_jobs(
         self, mock_tqdm, mock_delayed, mock_parallel_class, sample_case_operator
     ):
-        """Test _run_parallel with n_jobs=None (should use all CPUs)."""
+        """Test _run_parallel_evaluation with n_jobs=None (should use all CPUs)."""
         mock_tqdm.return_value = [sample_case_operator]
         mock_delayed_func = mock.Mock()
         mock_delayed.return_value = mock_delayed_func
@@ -749,7 +758,7 @@ def test_run_parallel_with_none_n_jobs(
         mock_parallel_instance.return_value = mock_result
 
         with mock.patch("extremeweatherbench.evaluate.logger.warning") as mock_warning:
-            result = evaluate._run_parallel(
+            result = evaluate._run_parallel_evaluation(
                 [sample_case_operator],
                 parallel_config={"backend": "threading", "n_jobs": None},
             )
@@ -765,7 +774,7 @@ def test_run_parallel_with_none_n_jobs(
 
     @mock.patch("joblib.parallel_config")
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
-    def test_run_parallel_n_jobs_in_config(
+    def test_run_parallel_evaluation_n_jobs_in_config(
         self, mock_parallel_class, mock_parallel_config
     ):
         """Test that n_jobs is passed through parallel_config, not directly."""
@@ -782,7 +791,7 @@ def test_run_parallel_n_jobs_in_config(
         )
         mock_parallel_config.return_value.__exit__ = mock.Mock(return_value=False)
 
-        result = evaluate._run_parallel(
+        result = evaluate._run_parallel_evaluation(
             [sample_case_operator],
             parallel_config={"backend": "threading", "n_jobs": 4},
         )
@@ -800,10 +809,10 @@ def test_run_parallel_n_jobs_in_config(
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
     @mock.patch("joblib.delayed")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_parallel_multiple_cases(
+    def test_run_parallel_evaluation_multiple_cases(
         self, mock_tqdm, mock_delayed, mock_parallel_class
     ):
-        """Test _run_parallel with multiple case operators."""
+        """Test _run_parallel_evaluation with multiple case operators."""
         case_op_1 = mock.Mock()
         case_op_2 = mock.Mock()
         case_operators = [case_op_1, case_op_2]
@@ -820,7 +829,7 @@ def test_run_parallel_multiple_cases(
         ]
         mock_parallel_instance.return_value = mock_result
 
-        result = evaluate._run_parallel(
+        result = evaluate._run_parallel_evaluation(
             case_operators, parallel_config={"backend": "threading", "n_jobs": 4}
         )
 
@@ -831,10 +840,10 @@ def test_run_parallel_multiple_cases(
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
     @mock.patch("joblib.delayed")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_parallel_with_kwargs(
+    def test_run_parallel_evaluation_with_kwargs(
         self, mock_tqdm, mock_delayed, mock_parallel_class, sample_case_operator
     ):
-        """Test _run_parallel passes kwargs correctly."""
+        """Test _run_parallel_evaluation passes kwargs correctly."""
         mock_tqdm.return_value = [sample_case_operator]
         mock_delayed_func = mock.Mock()
         mock_delayed.return_value = mock_delayed_func
@@ -844,7 +853,7 @@ def test_run_parallel_with_kwargs(
         mock_result = [pd.DataFrame({"value": [1.0]})]
         mock_parallel_instance.return_value = mock_result
 
-        result = evaluate._run_parallel(
+        result = evaluate._run_parallel_evaluation(
             [sample_case_operator],
             parallel_config={"backend": "threading", "n_jobs": 2},
             threshold=0.8,
@@ -861,8 +870,8 @@ def test_run_parallel_with_kwargs(
         assert len(delayed_calls) == 1
         assert isinstance(result, list)
 
-    def test_run_parallel_empty_list(self):
-        """Test _run_parallel with empty case operator list."""
+    def test_run_parallel_evaluation_empty_list(self):
+        """Test _run_parallel_evaluation with empty case operator list."""
         with mock.patch(
             "extremeweatherbench.utils.ParallelTqdm"
         ) as mock_parallel_class:
@@ -872,7 +881,7 @@ def test_run_parallel_empty_list(self):
                 mock_parallel_class.return_value = mock_parallel_instance
                 mock_parallel_instance.return_value = []
 
-                result = evaluate._run_parallel(
+                result = evaluate._run_parallel_evaluation(
                     [], parallel_config={"backend": "threading", "n_jobs": 2}
                 )
 
@@ -883,10 +892,10 @@ def test_run_parallel_empty_list(self):
     )
     @mock.patch("dask.distributed.Client")
     @mock.patch("dask.distributed.LocalCluster")
-    def test_run_parallel_dask_backend_auto_client(
+    def test_run_parallel_evaluation_dask_backend_auto_client(
         self, mock_local_cluster, mock_client_class, sample_case_operator
     ):
-        """Test _run_parallel with dask backend automatically creates client."""
+        """Test _run_parallel_evaluation with dask backend automatically creates client."""
         # Mock Client.current() to raise ValueError (no existing client)
         mock_client_class.current.side_effect = ValueError("No client found")
 
@@ -905,7 +914,7 @@ def test_run_parallel_dask_backend_auto_client(
             mock_parallel_instance.return_value = [pd.DataFrame({"test": [1]})]
 
             with mock.patch("joblib.parallel_config"):
-                result = evaluate._run_parallel(
+                result = evaluate._run_parallel_evaluation(
                     [sample_case_operator],
                     parallel_config={"backend": "dask", "n_jobs": 2},
                 )
@@ -919,10 +928,10 @@ def test_run_parallel_dask_backend_auto_client(
         not HAS_DASK_DISTRIBUTED, reason="dask.distributed not installed"
     )
     @mock.patch("dask.distributed.Client")
-    def test_run_parallel_dask_backend_existing_client(
+    def test_run_parallel_evaluation_dask_backend_existing_client(
         self, mock_client_class, sample_case_operator
     ):
-        """Test _run_parallel with dask backend uses existing client."""
+        """Test _run_parallel_evaluation with dask backend uses existing client."""
         # Mock existing client
         mock_existing_client = mock.Mock()
         mock_client_class.current.return_value = mock_existing_client
@@ -934,7 +943,7 @@ def test_run_parallel_dask_backend_existing_client(
             mock_parallel_instance.return_value = [pd.DataFrame({"test": [1]})]
 
             with mock.patch("joblib.parallel_config"):
-                result = evaluate._run_parallel(
+                result = evaluate._run_parallel_evaluation(
                     [sample_case_operator],
                     parallel_config={"backend": "dask", "n_jobs": 2},
                 )
@@ -1385,7 +1394,7 @@ def test_run_pipeline_target(
     def test_run_pipeline_invalid_source(self, sample_case_operator):
         """Test run_pipeline function with invalid input source."""
         with pytest.raises(AttributeError, match="'str' object has no attribute"):
-            evaluate.run_pipeline(sample_case_operator.case_metadata, "invalid")
+            evaluate.run_pipeline(sample_case_operator.case_metadata, "invalid")  # type: ignore
 
     def test_maybe_cache_and_compute_with_cache_dir(
         self, sample_forecast_dataset, sample_target_dataset, sample_individual_case
@@ -1600,9 +1609,14 @@ def test_extremeweatherbench_empty_cases(self, sample_evaluation_object):
             evaluation_objects=[sample_evaluation_object],
         )
 
-        result = ewb.run()
-        assert isinstance(result, pd.DataFrame)
-        assert len(result) == 0
+        with mock.patch("extremeweatherbench.cases.build_case_operators") as mock_build:
+            mock_build.return_value = []
+
+            result = ewb.run_evaluation()
+
+            # Should return empty DataFrame when no cases
+            assert isinstance(result, pd.DataFrame)
+            assert len(result) == 0
 
     def test_compute_case_operator_exception_handling(self, sample_case_operator):
         """Test exception handling in compute_case_operator."""
@@ -1645,49 +1659,53 @@ def test_evaluate_metric_computation_failure(
                 case_operator=sample_case_operator,
             )
 
-    @mock.patch("extremeweatherbench.evaluate._run_serial")
-    def test_run_case_operators_serial_exception(
-        self, mock_run_serial, sample_case_operator
+    @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
+    @mock.patch("tqdm.auto.tqdm")
+    def test_run_evaluation_serial_exception(
+        self, mock_tqdm, mock_compute_case_operator, sample_case_operator
     ):
-        """Test _run_case_operators handles exceptions in serial execution."""
-        mock_run_serial.side_effect = Exception("Serial execution failed")
+        """Test _run_evaluation handles exceptions in serial execution."""
+        mock_tqdm.return_value = [sample_case_operator]
+        mock_compute_case_operator.side_effect = Exception("Serial execution failed")
 
         with pytest.raises(Exception, match="Serial execution failed"):
             # Serial mode: don't pass parallel_config
-            evaluate._run_case_operators([sample_case_operator], None)
+            evaluate._run_evaluation([sample_case_operator], parallel_config=None)
 
-    @mock.patch("extremeweatherbench.evaluate._run_parallel")
-    def test_run_case_operators_parallel_exception(
-        self, mock_run_parallel, sample_case_operator
+    @mock.patch("extremeweatherbench.evaluate._run_parallel_evaluation")
+    def test_run_evaluation_parallel_exception(
+        self, mock_run_parallel_evaluation, sample_case_operator
     ):
-        """Test _run_case_operators handles exceptions in parallel execution."""
-        mock_run_parallel.side_effect = Exception("Parallel execution failed")
+        """Test _run_evaluation handles exceptions in parallel execution."""
+        mock_run_parallel_evaluation.side_effect = Exception(
+            "Parallel execution failed"
+        )
 
         with pytest.raises(Exception, match="Parallel execution failed"):
-            evaluate._run_case_operators(
+            evaluate._run_evaluation(
                 [sample_case_operator],
                 parallel_config={"backend": "threading", "n_jobs": 2},
             )
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_serial_case_operator_exception(
+    def test_run_serial_evaluation_case_operator_exception(
         self, mock_tqdm, mock_compute_case_operator, sample_case_operator
     ):
-        """Test _run_serial handles exceptions from individual case operators."""
+        """Test serial execution handles exceptions from individual case operators."""
         mock_tqdm.return_value = [sample_case_operator]
         mock_compute_case_operator.side_effect = Exception("Case operator failed")
 
         with pytest.raises(Exception, match="Case operator failed"):
-            evaluate._run_serial([sample_case_operator])
+            evaluate._run_evaluation([sample_case_operator], parallel_config=None)
 
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
     @mock.patch("joblib.delayed")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_parallel_joblib_exception(
+    def test_run_parallel_evaluation_joblib_exception(
         self, mock_tqdm, mock_delayed, mock_parallel_class, sample_case_operator
     ):
-        """Test _run_parallel handles joblib Parallel exceptions."""
+        """Test _run_parallel_evaluation handles joblib Parallel exceptions."""
         mock_tqdm.return_value = [sample_case_operator]
         mock_delayed_func = mock.Mock()
         mock_delayed.return_value = mock_delayed_func
@@ -1697,7 +1715,7 @@ def test_run_parallel_joblib_exception(
         mock_parallel_instance.side_effect = Exception("Joblib parallel failed")
 
         with pytest.raises(Exception, match="Joblib parallel failed"):
-            evaluate._run_parallel(
+            evaluate._run_parallel_evaluation(
                 [sample_case_operator],
                 parallel_config={"backend": "threading", "n_jobs": 2},
             )
@@ -1705,10 +1723,10 @@ def test_run_parallel_joblib_exception(
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
     @mock.patch("joblib.delayed")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_parallel_delayed_function_exception(
+    def test_run_parallel_evaluation_delayed_function_exception(
         self, mock_tqdm, mock_delayed, mock_parallel_class, sample_case_operator
     ):
-        """Test _run_parallel handles exceptions in delayed functions."""
+        """Test _run_parallel_evaluation handles exceptions in delayed functions."""
         mock_tqdm.return_value = [sample_case_operator]
 
         # Mock delayed to raise an exception
@@ -1724,12 +1742,12 @@ def consume_generator(generator):
         mock_parallel_instance.side_effect = consume_generator
 
         with pytest.raises(Exception, match="Delayed function creation failed"):
-            evaluate._run_parallel(
+            evaluate._run_parallel_evaluation(
                 [sample_case_operator],
                 parallel_config={"backend": "threading", "n_jobs": 2},
             )
 
-    @mock.patch("extremeweatherbench.evaluate._run_case_operators")
+    @mock.patch("extremeweatherbench.evaluate._run_evaluation")
     def test_run_method_exception_propagation(
         self, mock_run_case_operators, sample_cases_list, sample_evaluation_object
     ):
@@ -1742,12 +1760,14 @@ def test_run_method_exception_propagation(
         )
 
         with pytest.raises(Exception, match="Execution failed"):
-            ewb.run()
+            ewb.run_evaluation()
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_serial_partial_failure(self, mock_tqdm, mock_compute_case_operator):
-        """Test _run_serial behavior when some case operators fail."""
+    def test_run_serial_evaluation_partial_failure(
+        self, mock_tqdm, mock_compute_case_operator
+    ):
+        """Test serial execution behavior when some case operators fail."""
         case_op_1 = mock.Mock()
         case_op_2 = mock.Mock()
         case_op_3 = mock.Mock()
@@ -1764,7 +1784,7 @@ def test_run_serial_partial_failure(self, mock_tqdm, mock_compute_case_operator)
 
         # Should fail on the second case operator
         with pytest.raises(Exception, match="Case operator 2 failed"):
-            evaluate._run_serial(case_operators)
+            evaluate._run_evaluation(case_operators, parallel_config=None)
 
         # Should have tried only the first two
         assert mock_compute_case_operator.call_count == 2
@@ -1772,10 +1792,10 @@ def test_run_serial_partial_failure(self, mock_tqdm, mock_compute_case_operator)
     @mock.patch("extremeweatherbench.utils.ParallelTqdm")
     @mock.patch("joblib.delayed")
     @mock.patch("tqdm.auto.tqdm")
-    def test_run_parallel_invalid_n_jobs(
+    def test_run_parallel_evaluation_invalid_n_jobs(
         self, mock_tqdm, mock_delayed, mock_parallel_class, sample_case_operator
     ):
-        """Test _run_parallel with invalid n_jobs parameter."""
+        """Test _run_parallel_evaluation with invalid n_jobs parameter."""
         mock_tqdm.return_value = [sample_case_operator]
         mock_delayed_func = mock.Mock()
         mock_delayed.return_value = mock_delayed_func
@@ -1784,7 +1804,7 @@ def test_run_parallel_invalid_n_jobs(
         mock_parallel_class.side_effect = ValueError("Invalid n_jobs parameter")
 
         with pytest.raises(ValueError, match="Invalid n_jobs parameter"):
-            evaluate._run_parallel(
+            evaluate._run_parallel_evaluation(
                 [sample_case_operator],
                 parallel_config={"backend": "threading", "n_jobs": -5},
             )
@@ -1869,7 +1889,7 @@ def test_end_to_end_workflow(
                 evaluation_objects=[sample_evaluation_object],
             )
 
-            result = ewb.run()
+            result = ewb.run_evaluation()
 
         # Verify the result structure
         assert isinstance(result, pd.DataFrame)
@@ -1973,7 +1993,7 @@ def test_multiple_variables_and_metrics(
                     evaluation_objects=[eval_obj],
                 )
 
-                result = ewb.run()
+                result = ewb.run_evaluation()
 
                 # Should have results for each metric combination
                 assert len(result) >= 2  # At least 2 metrics * 1 case
@@ -2016,12 +2036,12 @@ def test_serial_vs_parallel_results_consistency(
 
             # Test serial execution
             mock_compute_case_operator.side_effect = [result_1, result_2]
-            serial_result = ewb.run(n_jobs=1)
+            serial_result = ewb.run_evaluation(n_jobs=1)
 
             # Reset mock and test parallel execution
             mock_compute_case_operator.reset_mock()
             mock_compute_case_operator.side_effect = [result_1, result_2]
-            parallel_result = ewb.run(n_jobs=2)
+            parallel_result = ewb.run_evaluation(n_jobs=2)
 
             # Both should produce valid DataFrames with same structure
             assert isinstance(serial_result, pd.DataFrame)
@@ -2031,12 +2051,16 @@ def test_serial_vs_parallel_results_consistency(
             assert list(serial_result.columns) == list(parallel_result.columns)
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
-    def test_execution_method_performance_comparison(self, mock_compute_case_operator):
+    @mock.patch("tqdm.auto.tqdm")
+    def test_execution_method_performance_comparison(
+        self, mock_tqdm, mock_compute_case_operator
+    ):
         """Test that both execution methods handle the same workload."""
         import time
 
         # Create many case operators to simulate realistic workload
         case_operators = [mock.Mock() for _ in range(10)]
+        mock_tqdm.return_value = case_operators
 
         # Mock results
         mock_results = [
@@ -2051,13 +2075,13 @@ def test_execution_method_performance_comparison(self, mock_compute_case_operato
             for i in range(10)
         ]
 
-        # Test serial execution timing - call _run_serial directly
+        # Test serial execution timing - call _run_evaluation in serial mode
         mock_compute_case_operator.side_effect = mock_results
         start_time = time.time()
-        serial_result = evaluate._run_serial(case_operators)
+        serial_result = evaluate._run_evaluation(case_operators, parallel_config=None)
         serial_time = time.time() - start_time
 
-        # Test parallel execution timing - call _run_parallel directly with mocked
+        # Test parallel execution timing - call _run_parallel_evaluation directly with mocked
         # Parallel
         serial_call_count = mock_compute_case_operator.call_count
         mock_compute_case_operator.side_effect = mock_results
@@ -2070,7 +2094,7 @@ def test_execution_method_performance_comparison(self, mock_compute_case_operato
             mock_parallel_instance.return_value = mock_results
 
             start_time = time.time()
-            parallel_result = evaluate._run_parallel(
+            parallel_result = evaluate._run_parallel_evaluation(
                 case_operators, parallel_config={"backend": "threading", "n_jobs": 2}
             )
             parallel_time = time.time() - start_time
@@ -2087,9 +2111,11 @@ def test_execution_method_performance_comparison(self, mock_compute_case_operato
         assert parallel_time >= 0
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
-    def test_mixed_execution_parameters(self, mock_compute_case_operator):
+    @mock.patch("tqdm.auto.tqdm")
+    def test_mixed_execution_parameters(self, mock_tqdm, mock_compute_case_operator):
         """Test various parameter combinations for execution methods."""
         case_operators = [mock.Mock(), mock.Mock()]
+        mock_tqdm.return_value = case_operators
         mock_results = [
             pd.DataFrame({"value": [1.0], "case_id_number": [1]}),
             pd.DataFrame({"value": [2.0], "case_id_number": [2]}),
@@ -2112,7 +2138,7 @@ def test_mixed_execution_parameters(self, mock_compute_case_operator):
             mock_compute_case_operator.side_effect = mock_results
 
             if config["method"] == "serial":
-                result = evaluate._run_serial(*config["args"])
+                result = evaluate._run_evaluation(*config["args"], parallel_config=None)
                 # All configurations should produce valid results
                 assert isinstance(result, list)
                 assert len(result) == 2
@@ -2135,7 +2161,9 @@ def test_mixed_execution_parameters(self, mock_compute_case_operator):
                             "n_jobs": n_jobs,
                         }
 
-                    result = evaluate._run_parallel(*config["args"], **kwargs)
+                    result = evaluate._run_parallel_evaluation(
+                        *config["args"], **kwargs
+                    )
 
                     # All configurations should produce valid results
                     assert isinstance(result, list)
@@ -2156,13 +2184,19 @@ def mock_compute_with_kwargs(case_op, cache_dir, **kwargs):
 
         mock_compute_with_kwargs.captured_kwargs = {}
 
-        with mock.patch(
-            "extremeweatherbench.evaluate.compute_case_operator",
-            side_effect=mock_compute_with_kwargs,
+        with (
+            mock.patch(
+                "extremeweatherbench.evaluate.compute_case_operator",
+                side_effect=mock_compute_with_kwargs,
+            ),
+            mock.patch("tqdm.auto.tqdm", return_value=[case_operator]),
         ):
             # Test serial kwargs propagation
-            result = evaluate._run_serial(
-                [case_operator], custom_param="serial_test", threshold=0.9
+            result = evaluate._run_evaluation(
+                [case_operator],
+                parallel_config=None,
+                custom_param="serial_test",
+                threshold=0.9,
             )
 
             captured = mock_compute_with_kwargs.captured_kwargs
@@ -2185,7 +2219,7 @@ def mock_compute_with_kwargs(case_op, cache_dir, **kwargs):
                     # Reset captured kwargs
                     mock_compute_with_kwargs.captured_kwargs = {}
 
-                    result = evaluate._run_parallel(
+                    result = evaluate._run_parallel_evaluation(
                         [case_operator],
                         parallel_config={"backend": "threading", "n_jobs": 2},
                         custom_param="parallel_test",
@@ -2198,20 +2232,20 @@ def mock_compute_with_kwargs(case_op, cache_dir, **kwargs):
 
     def test_empty_case_operators_all_methods(self):
         """Test that all execution methods handle empty case operator lists."""
-        # Test _run_case_operators
-        result = evaluate._run_case_operators([], parallel_config={"n_jobs": 1})
+        # Test _run_evaluation with parallel config
+        result = evaluate._run_evaluation([], parallel_config={"n_jobs": 1})
         assert result == []
 
-        result = evaluate._run_case_operators(
+        result = evaluate._run_evaluation(
             [], parallel_config={"backend": "threading", "n_jobs": 2}
         )
         assert result == []
 
-        # Test _run_serial
-        result = evaluate._run_serial([])
+        # Test _run_evaluation in serial mode
+        result = evaluate._run_evaluation([], parallel_config=None)
         assert result == []
 
-        # Test _run_parallel
+        # Test _run_parallel_evaluation
         with mock.patch(
             "extremeweatherbench.utils.ParallelTqdm"
         ) as mock_parallel_class:
@@ -2219,17 +2253,21 @@ def test_empty_case_operators_all_methods(self):
             mock_parallel_class.return_value = mock_parallel_instance
             mock_parallel_instance.return_value = []
 
-            result = evaluate._run_parallel(
+            result = evaluate._run_parallel_evaluation(
                 [], parallel_config={"backend": "threading", "n_jobs": 2}
             )
             assert result == []
 
     @mock.patch("extremeweatherbench.evaluate.compute_case_operator")
-    def test_large_case_operator_list_handling(self, mock_compute_case_operator):
+    @mock.patch("tqdm.auto.tqdm")
+    def test_large_case_operator_list_handling(
+        self, mock_tqdm, mock_compute_case_operator
+    ):
         """Test handling of large numbers of case operators."""
         # Create a large list of case operators
         num_cases = 100
         case_operators = [mock.Mock() for _ in range(num_cases)]
+        mock_tqdm.return_value = case_operators
 
         # Create mock results
         mock_results = [
@@ -2241,7 +2279,7 @@ def test_large_case_operator_list_handling(self, mock_compute_case_operator):
 
         # Test serial execution
         mock_compute_case_operator.side_effect = mock_results
-        serial_results = evaluate._run_serial(case_operators)
+        serial_results = evaluate._run_evaluation(case_operators, parallel_config=None)
 
         assert len(serial_results) == num_cases
         assert mock_compute_case_operator.call_count == num_cases
@@ -2257,7 +2295,7 @@ def test_large_case_operator_list_handling(self, mock_compute_case_operator):
             mock_parallel_class.return_value = mock_parallel_instance
             mock_parallel_instance.return_value = mock_results
 
-            parallel_results = evaluate._run_parallel(
+            parallel_results = evaluate._run_parallel_evaluation(
                 case_operators, parallel_config={"backend": "threading", "n_jobs": 4}
             )
 
diff --git a/tests/test_evaluate_cli.py b/tests/test_evaluate_cli.py
index e5797712..2bda6879 100644
--- a/tests/test_evaluate_cli.py
+++ b/tests/test_evaluate_cli.py
@@ -88,7 +88,7 @@ def test_default_mode_basic(
         # Mock the ExtremeWeatherBench class and its methods
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = [mock.Mock(), mock.Mock()]  # Mock 2 case operators
-        mock_ewb.run.return_value = pd.DataFrame({"test": [1, 2]})
+        mock_ewb.run_evaluation.return_value = pd.DataFrame({"test": [1, 2]})
         mock_ewb_class.return_value = mock_ewb
 
         # Mock loading default cases
@@ -100,7 +100,7 @@ def test_default_mode_basic(
 
         assert result.exit_code == 0
         mock_ewb_class.assert_called_once()
-        mock_ewb.run.assert_called_once()
+        mock_ewb.run_evaluation.assert_called_once()
 
     @mock.patch(
         "extremeweatherbench.defaults.get_brightband_evaluation_objects",
@@ -119,7 +119,7 @@ def test_default_mode_with_cache_dir(
         """Test default mode with cache directory."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = []
-        mock_ewb.run.return_value = pd.DataFrame()
+        mock_ewb.run_evaluation.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -145,7 +145,7 @@ def test_config_file_mode_basic(
         """Test basic config file mode execution."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = [mock.Mock()]
-        mock_ewb.run.return_value = pd.DataFrame({"test": [1]})
+        mock_ewb.run_evaluation.return_value = pd.DataFrame({"test": [1]})
         mock_ewb_class.return_value = mock_ewb
 
         result = runner.invoke(
@@ -219,15 +219,15 @@ def test_parallel_execution(
         """Test parallel execution mode."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = [mock.Mock(), mock.Mock(), mock.Mock()]
-        mock_ewb.run.return_value = pd.DataFrame({"test": [1, 2, 3]})
+        mock_ewb.run_evaluation.return_value = pd.DataFrame({"test": [1, 2, 3]})
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
         result = runner.invoke(evaluate_cli.cli_runner, ["--default", "--n-jobs", "3"])
 
         assert result.exit_code == 0
-        # Verify ewb.run was called with parallel config
-        mock_ewb.run.assert_called_once_with(
+        # Verify ewb.run_evaluation was called with parallel config
+        mock_ewb.run_evaluation.assert_called_once_with(
             n_jobs=3,
             parallel_config=None,
         )
@@ -244,7 +244,7 @@ def test_serial_execution_default(
         """Test that serial execution is default (parallel=1)."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = []
-        mock_ewb.run.return_value = pd.DataFrame()
+        mock_ewb.run_evaluation.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -252,7 +252,7 @@ def test_serial_execution_default(
 
         assert result.exit_code == 0
         # Output suppressed - only check exit code
-        mock_ewb.run.assert_called_once()
+        mock_ewb.run_evaluation.assert_called_once()
 
 
 class TestCaseOperatorSaving:
@@ -278,7 +278,7 @@ def test_save_case_operators(
         mock_case_op2 = {"id": 2, "type": "test_case_op"}
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = [mock_case_op1, mock_case_op2]
-        mock_ewb.run.return_value = pd.DataFrame()
+        mock_ewb.run_evaluation.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -317,7 +317,7 @@ def test_save_case_operators_creates_directory(
         """Test that saving case operators creates parent directories."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = []
-        mock_ewb.run.return_value = pd.DataFrame()
+        mock_ewb.run_evaluation.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -369,7 +369,7 @@ def test_output_directory_creation(
         """Test that output directory is created if it doesn't exist."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = []
-        mock_ewb.run.return_value = pd.DataFrame()
+        mock_ewb.run_evaluation.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -395,7 +395,7 @@ def test_default_output_directory(
         """Test that default output directory is current working directory."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = []
-        mock_ewb.run.return_value = pd.DataFrame()
+        mock_ewb.run_evaluation.return_value = pd.DataFrame()
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -435,7 +435,7 @@ def test_results_saved_to_csv(
 
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = []
-        mock_ewb.run.return_value = mock_results
+        mock_ewb.run_evaluation.return_value = mock_results
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -456,7 +456,7 @@ def test_empty_results_handling(self, mock_ewb_class, mock_load_cases, runner):
         """Test handling when no results are returned."""
         mock_ewb = mock.Mock()
         mock_ewb.case_operators = []
-        mock_ewb.run.return_value = pd.DataFrame()  # Empty results
+        mock_ewb.run_evaluation.return_value = pd.DataFrame()  # Empty results
         mock_ewb_class.return_value = mock_ewb
         mock_load_cases.return_value = []
 
@@ -469,7 +469,9 @@ def test_empty_results_handling(self, mock_ewb_class, mock_load_cases, runner):
 class TestHelperFunctions:
     """Test helper function functionality."""
 
-    @mock.patch("extremeweatherbench.cases.load_ewb_events_yaml_into_case_list")
+    @mock.patch(
+        "extremeweatherbench.evaluate_cli.cases.load_ewb_events_yaml_into_case_list"
+    )
     def test_load_default_cases(self, mock_load_yaml):
         """Test _load_default_cases function."""
         mock_cases = [{"id": 1}]
diff --git a/tests/test_init.py b/tests/test_init.py
new file mode 100644
index 00000000..00517ae8
--- /dev/null
+++ b/tests/test_init.py
@@ -0,0 +1,259 @@
+"""Tests for the extremeweatherbench package __init__.py API."""
+
+import types
+
+
+class TestModuleImports:
+    """Test that submodules are importable and are actual modules."""
+
+    def test_calc_is_module(self):
+        """Test that calc is an actual module, not a SimpleNamespace."""
+        from extremeweatherbench import calc
+
+        assert isinstance(calc, types.ModuleType)
+
+    def test_utils_is_module(self):
+        """Test that utils is an actual module, not a SimpleNamespace."""
+        from extremeweatherbench import utils
+
+        assert isinstance(utils, types.ModuleType)
+
+    def test_metrics_is_module(self):
+        """Test that metrics is an actual module, not a SimpleNamespace."""
+        from extremeweatherbench import metrics
+
+        assert isinstance(metrics, types.ModuleType)
+
+    def test_regions_is_module(self):
+        """Test that regions is an actual module, not a SimpleNamespace."""
+        from extremeweatherbench import regions
+
+        assert isinstance(regions, types.ModuleType)
+
+    def test_derived_is_module(self):
+        """Test that derived is an actual module, not a SimpleNamespace."""
+        from extremeweatherbench import derived
+
+        assert isinstance(derived, types.ModuleType)
+
+    def test_defaults_is_module(self):
+        """Test that defaults is an actual module, not a SimpleNamespace."""
+        from extremeweatherbench import defaults
+
+        assert isinstance(defaults, types.ModuleType)
+
+    def test_cases_is_module(self):
+        """Test that cases is an actual module, not a SimpleNamespace."""
+        from extremeweatherbench import cases
+
+        assert isinstance(cases, types.ModuleType)
+
+
+class TestModuleAccessPatterns:
+    """Test both import patterns work identically."""
+
+    def test_ewb_dot_notation_equals_direct_import_calc(self):
+        """Test ewb.calc is the same object as direct import."""
+        import extremeweatherbench as ewb
+        from extremeweatherbench import calc
+
+        assert ewb.calc is calc
+
+    def test_ewb_dot_notation_equals_direct_import_metrics(self):
+        """Test ewb.metrics is the same object as direct import."""
+        import extremeweatherbench as ewb
+        from extremeweatherbench import metrics
+
+        assert ewb.metrics is metrics
+
+    def test_ewb_dot_notation_equals_direct_import_utils(self):
+        """Test ewb.utils is the same object as direct import."""
+        import extremeweatherbench as ewb
+        from extremeweatherbench import utils
+
+        assert ewb.utils is utils
+
+
+class TestModuleLevelConstants:
+    """Test that module-level constants are accessible."""
+
+    def test_calc_g0_accessible(self):
+        """Test that calc.g0 constant is accessible."""
+        from extremeweatherbench import calc
+
+        assert hasattr(calc, "g0")
+        assert calc.g0 == 9.80665
+
+    def test_calc_epsilon_accessible(self):
+        """Test that calc.epsilon constant is accessible."""
+        from extremeweatherbench import calc
+
+        assert hasattr(calc, "epsilon")
+        assert isinstance(calc.epsilon, float)
+
+
+class TestPrivateFunctionAccess:
+    """Test that private functions are accessible for testing purposes."""
+
+    def test_calc_private_functions_accessible(self):
+        """Test that private functions in calc are accessible."""
+        from extremeweatherbench import calc
+
+        assert hasattr(calc, "_is_true_landfall")
+        assert hasattr(calc, "_detect_landfalls_wrapper")
+        assert hasattr(calc, "_mask_init_time_boundaries")
+        assert hasattr(calc, "_interpolate_and_format_landfalls")
+
+    def test_utils_private_functions_accessible(self):
+        """Test that private functions in utils are accessible."""
+        from extremeweatherbench import utils
+
+        assert hasattr(utils, "_create_nan_dataarray")
+        assert hasattr(utils, "_cache_maybe_densify_helper")
+
+    def test_derived_private_functions_accessible(self):
+        """Test that private functions in derived are accessible."""
+        from extremeweatherbench import derived
+
+        assert hasattr(derived, "_maybe_convert_variable_to_string")
+
+    def test_defaults_private_functions_accessible(self):
+        """Test that private functions in defaults are accessible."""
+        from extremeweatherbench import defaults
+
+        assert hasattr(defaults, "_preprocess_cira_forecast_dataset")
+
+    def test_regions_private_functions_accessible(self):
+        """Test that private functions in regions are accessible."""
+        from extremeweatherbench import regions
+
+        assert hasattr(regions, "_adjust_bounds_to_dataset_convention")
+
+
+class TestPublicFunctionAccess:
+    """Test that all public functions are accessible via module."""
+
+    def test_calc_public_functions(self):
+        """Test public functions in calc are accessible."""
+        from extremeweatherbench import calc
+
+        assert hasattr(calc, "find_landfalls")
+        assert hasattr(calc, "nantrapezoid")
+        assert hasattr(calc, "dewpoint_from_specific_humidity")
+        assert hasattr(calc, "find_land_intersection")
+        assert hasattr(calc, "haversine_distance")
+
+    def test_utils_public_functions(self):
+        """Test public functions in utils are accessible."""
+        from extremeweatherbench import utils
+
+        assert hasattr(utils, "reduce_dataarray")
+        assert hasattr(utils, "stack_dataarray_from_dims")
+        assert hasattr(utils, "convert_longitude_to_360")
+
+
+class TestTopLevelImports:
+    """Test that top-level imports work for commonly used items."""
+
+    def test_top_level_metric_imports(self):
+        """Test that metrics can be imported at top level."""
+        from extremeweatherbench import (
+            MeanAbsoluteError,
+            MeanError,
+            MeanSquaredError,
+            RootMeanSquaredError,
+        )
+
+        assert MeanAbsoluteError is not None
+        assert MeanError is not None
+        assert MeanSquaredError is not None
+        assert RootMeanSquaredError is not None
+
+    def test_top_level_input_imports(self):
+        """Test that input classes can be imported at top level."""
+        from extremeweatherbench import ERA5, GHCN, IBTrACS, ZarrForecast
+
+        assert ERA5 is not None
+        assert GHCN is not None
+        assert IBTrACS is not None
+        assert ZarrForecast is not None
+
+    def test_top_level_region_imports(self):
+        """Test that region classes can be imported at top level."""
+        from extremeweatherbench import BoundingBoxRegion, CenteredRegion, Region
+
+        assert Region is not None
+        assert BoundingBoxRegion is not None
+        assert CenteredRegion is not None
+
+    def test_top_level_case_imports(self):
+        """Test that case classes can be imported at top level."""
+        from extremeweatherbench import CaseOperator, IndividualCase
+
+        assert IndividualCase is not None
+        assert CaseOperator is not None
+
+    def test_evaluation_alias(self):
+        """Test that evaluation alias works."""
+        from extremeweatherbench import ExtremeWeatherBench, evaluation
+
+        assert evaluation is ExtremeWeatherBench
+
+    def test_load_cases_alias(self):
+        """Test that load_cases alias works."""
+        from extremeweatherbench import (
+            load_cases,
+            load_ewb_events_yaml_into_case_list,
+        )
+
+        assert load_cases is load_ewb_events_yaml_into_case_list
+
+
+class TestNamespaceSubmodules:
+    """Test the convenience namespace submodules."""
+
+    def test_targets_namespace(self):
+        """Test targets SimpleNamespace contains expected items."""
+        from extremeweatherbench import targets
+
+        assert isinstance(targets, types.SimpleNamespace)
+        assert hasattr(targets, "ERA5")
+        assert hasattr(targets, "GHCN")
+        assert hasattr(targets, "IBTrACS")
+        assert hasattr(targets, "TargetBase")
+
+    def test_forecasts_namespace(self):
+        """Test forecasts SimpleNamespace contains expected items."""
+        from extremeweatherbench import forecasts
+
+        assert isinstance(forecasts, types.SimpleNamespace)
+        assert hasattr(forecasts, "ZarrForecast")
+        assert hasattr(forecasts, "KerchunkForecast")
+        assert hasattr(forecasts, "ForecastBase")
+
+
+class TestMockPatching:
+    """Test that mock.patch.object works with module imports."""
+
+    def test_mock_patch_object_on_calc(self):
+        """Test that mock.patch.object works on calc module."""
+        from unittest import mock
+
+        from extremeweatherbench import calc
+
+        with mock.patch.object(calc, "haversine_distance") as mock_func:
+            mock_func.return_value = 42.0
+            result = calc.haversine_distance([0, 0], [1, 1])
+            assert result == 42.0
+            mock_func.assert_called_once()
+
+    def test_mock_patch_string_on_calc(self):
+        """Test that mock.patch with string path works on calc module."""
+        from unittest import mock
+
+        with mock.patch("extremeweatherbench.calc.haversine_distance") as mock_func:
+            mock_func.return_value = 100.0
+            from extremeweatherbench import calc
+
+            result = calc.haversine_distance([0, 0], [1, 1])
+            assert result == 100.0
diff --git a/tests/test_integration.py b/tests/test_integration.py
index 07fba348..43ba0d30 100644
--- a/tests/test_integration.py
+++ b/tests/test_integration.py
@@ -600,7 +600,7 @@ def test_full_workflow_single_variable(
             evaluation_objects=[evaluation_obj],
         )
 
-        result = ewb.run()
+        result = ewb.run_evaluation()
 
         # Verify results
         assert isinstance(result, pd.DataFrame)
@@ -679,7 +679,7 @@ def test_full_workflow_multiple_variables(
             evaluation_objects=[evaluation_obj],
         )
 
-        result = ewb.run()
+        result = ewb.run_evaluation()
 
         # Verify results
         assert isinstance(result, pd.DataFrame)

From 34a39657b19db89621628c797c45d78017bbd161 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 26 Jan 2026 15:34:04 -0500
Subject: [PATCH 20/72] Golden tests (#323)

* first pass for gt test infra + yaml

* use shapefile for severe convection and catch latitude swap

* add ignore for golden test when running pytest by default

* ruff

* move pytest addopts and markers to pyproject.toml

* Remove `IndividualCaseCollection` (#317)

* update all references to IndividualCaseCollection and convert dicts/ "cases": keys to lists

* update template

* make questions bold

* add whitespace

* remove indent error and typo from evaluate_cli

* make load_individual_cases include passthrough for existing dataclasses

* ruff

* add comment for clarification on list comp

* ruff (again)

* remove all references to collection, replace with list

* ruff

* rename collection -> list

* ruff

* Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes

* add explanation for dim reqs (#320)

* Update `defaults` and `inputs` to include new CIRA icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos

* Bump version from 0.2.0 to 0.3.0 (#324)

* Updated API (#321)

* move cache dir creation to init, rename funcs, add parallel/serial check function, update test names

* update naming

* add run method for backwards compatibility

* update tests

* add tests and cover if serial and parallel_config is not None

* feat: redesign public API with hierarchical namespace submodules

- Add ewb.evaluation() as main entry point (alias for ExtremeWeatherBench)
- Create namespace submodules: ewb.targets, ewb.forecasts, ewb.metrics,
  ewb.derived, ewb.regions, ewb.cases, ewb.defaults
- Expose all classes at top level for convenience (ewb.ERA5, etc.)
- Add ewb.load_cases() convenience alias
- Update all example files to use new import pattern
- Update usage.md documentation
- Maintain backward compatibility with existing imports

* ruff/linting. add utils to init

* add test coverage for module loading patterns

* ruff

* Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes

* add explanation for dim reqs (#320)

* Update `defaults` and `inputs` to include new CIRA icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos

* update defaults var refs

* remove to_csv
---
 pyproject.toml                     |   7 +
 pytest.ini                         |   5 -
 src/extremeweatherbench/regions.py |   8 +-
 tests/data/golden_tests.yaml       |  57 ++++++
 tests/data/south_carolina_110m.dbf | Bin 0 -> 11881 bytes
 tests/data/south_carolina_110m.prj |   1 +
 tests/data/south_carolina_110m.shp | Bin 0 -> 700 bytes
 tests/data/south_carolina_110m.shx | Bin 0 -> 108 bytes
 tests/test_golden.py               | 282 +++++++++++++++++++++++++++++
 9 files changed, 354 insertions(+), 6 deletions(-)
 delete mode 100644 pytest.ini
 create mode 100644 tests/data/golden_tests.yaml
 create mode 100644 tests/data/south_carolina_110m.dbf
 create mode 100644 tests/data/south_carolina_110m.prj
 create mode 100644 tests/data/south_carolina_110m.shp
 create mode 100644 tests/data/south_carolina_110m.shx
 create mode 100644 tests/test_golden.py

diff --git a/pyproject.toml b/pyproject.toml
index 5edb482d..614ad657 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -125,3 +125,10 @@ docstring-code-line-length = "dynamic"
 
 [tool.ruff.lint.isort]
 case-sensitive = true
+
+[tool.pytest]
+addopts = ["--ignore=tests/test_golden.py", "--cov=extremeweatherbench"]
+markers = [
+    "integration: marks tests as integration tests (may be slow)",
+    "slow: marks tests as slow (may take longer to complete)",
+]
diff --git a/pytest.ini b/pytest.ini
deleted file mode 100644
index 29aab096..00000000
--- a/pytest.ini
+++ /dev/null
@@ -1,5 +0,0 @@
-[pytest]
-addopts = --cov=extremeweatherbench
-markers =
-    integration: marks tests as integration tests (may be slow)
-    slow: marks tests as slow (may take longer to complete)
\ No newline at end of file
diff --git a/src/extremeweatherbench/regions.py b/src/extremeweatherbench/regions.py
index 4e6c7730..7f86189e 100644
--- a/src/extremeweatherbench/regions.py
+++ b/src/extremeweatherbench/regions.py
@@ -361,8 +361,14 @@ def mask(self, dataset: xr.Dataset, drop: bool = False) -> xr.Dataset:
 
         # Note: ShapefileRegion.mask uses slice which doesn't support
         # prime/antimeridian crossing with OR logic, but regionmask handles it
+        # Check if latitude is ascending or descending to handle slice correctly
+        lat_ascending = dataset.latitude[0] < dataset.latitude[-1]
+        if lat_ascending:
+            lat_slice = slice(latitude_min, latitude_max)
+        else:
+            lat_slice = slice(latitude_max, latitude_min)
         dataset = dataset.sel(
-            latitude=slice(latitude_max, latitude_min),
+            latitude=lat_slice,
             longitude=slice(longitude_min, longitude_max),
             drop=drop,
         )
diff --git a/tests/data/golden_tests.yaml b/tests/data/golden_tests.yaml
new file mode 100644
index 00000000..35036e6b
--- /dev/null
+++ b/tests/data/golden_tests.yaml
@@ -0,0 +1,57 @@
+- case_id_number: 1
+  title: NYC Heat Wave
+  start_date: 2022-06-19 12:00:00
+  end_date: 2022-06-24 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.5
+      latitude_max: 41.5
+      longitude_min: -75
+      longitude_max: -73.5
+  event_type: heat_wave
+- case_id_number: 2
+  title: Europe Freeze
+  start_date: 2022-12-14 06:00:00
+  end_date: 2022-12-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 50
+      latitude_max: 55
+      longitude_min: -5
+      longitude_max: 5
+  event_type: freeze
+- case_id_number: 3
+  title: April 2022 South Carolina
+  start_date: 2022-04-05 12:00:00
+  end_date: 2022-04-06 12:00:00
+  location:
+    type: shapefile_region
+    parameters:
+      shapefile_path: tests/data/south_carolina_110m.shp
+  event_type: severe_convection
+- case_id_number: 4
+  title: Atmospheric River Alaska
+  start_date: 2021-06-24 00:00:00
+  end_date: 2021-06-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 50
+      latitude_max: 55
+      longitude_min: 185
+      longitude_max: 200
+  event_type: atmospheric_river
+- case_id_number: 5
+  title: Tropical Cyclone Max
+  start_date: 2023-10-06 00:00:00
+  end_date: 2023-10-12 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 11.6
+      latitude_max: 20.1
+      longitude_min: 256.1
+      longitude_max: 262.4
+  event_type: tropical_cyclone
diff --git a/tests/data/south_carolina_110m.dbf b/tests/data/south_carolina_110m.dbf
new file mode 100644
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diff --git a/tests/data/south_carolina_110m.prj b/tests/data/south_carolina_110m.prj
new file mode 100644
index 00000000..f45cbadf
--- /dev/null
+++ b/tests/data/south_carolina_110m.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/tests/data/south_carolina_110m.shp b/tests/data/south_carolina_110m.shp
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diff --git a/tests/test_golden.py b/tests/test_golden.py
new file mode 100644
index 00000000..8476f0ba
--- /dev/null
+++ b/tests/test_golden.py
@@ -0,0 +1,282 @@
+"""Tests which use the full end-to-end EWB workflow.
+
+These tests are likely incompatible with Github Actions and will be used on a VM
+or other virtual environment. These are intended to be fairly lightweight marquee
+examples of each event type and core metrics. If the values deviate from expected
+for a release, it will be flagged as a failure."""
+
+
+# Load case data from the default events.yaml
+
+import pathlib
+
+import pytest
+
+from extremeweatherbench import cases, defaults, derived, evaluate, inputs, metrics
+
+
+@pytest.fixture(scope="module")
+def reference_data_dir():
+    """Path to reference data directory."""
+    path = pathlib.Path(__file__).parent / "data"
+    if not path.exists():
+        pytest.skip(
+            "Reference data not found. Run 'uv run data/generate_cape_reference_data.py' first."
+        )
+    return path
+
+
+@pytest.fixture(scope="module")
+def golden_tests_event_data(reference_data_dir):
+    """Load golden tests event data."""
+    ref_file = reference_data_dir / "golden_tests.yaml"
+    if not ref_file.exists():
+        pytest.skip(f"Golden tests event data not found: {ref_file}")
+
+    return cases.load_individual_cases_from_yaml(ref_file)
+
+
+@pytest.mark.integration
+class TestGoldenTests:
+    """Golden tests."""
+
+    def test_heatwaves(self, golden_tests_event_data):
+        """Heatwave tests."""
+        # Define heatwave objects
+        era5_heatwave_target = inputs.ERA5()
+        ghcn_heatwave_target = inputs.GHCN()
+
+        heatwave_metrics = [
+            metrics.MaximumMeanAbsoluteError(
+                forecast_variable="surface_air_temperature",
+                target_variable="surface_air_temperature",
+            ),
+            metrics.RootMeanSquaredError(
+                forecast_variable="surface_air_temperature",
+                target_variable="surface_air_temperature",
+            ),
+            metrics.DurationMeanError(
+                threshold_criteria=defaults.get_climatology(quantile=0.85)
+            ),
+            metrics.MaximumLowestMeanAbsoluteError(
+                forecast_variable="surface_air_temperature",
+                target_variable="surface_air_temperature",
+            ),
+        ]
+
+        hres_heatwave_forecast = inputs.ZarrForecast(
+            name="hres_heatwave_forecast",
+            source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
+            variables=["surface_air_temperature"],
+            variable_mapping=inputs.HRES_metadata_variable_mapping,
+        )
+
+        heatwave_evaluation_objects = [
+            inputs.EvaluationObject(
+                event_type="heat_wave",
+                metric_list=heatwave_metrics,
+                target=era5_heatwave_target,
+                forecast=hres_heatwave_forecast,
+            ),
+            inputs.EvaluationObject(
+                event_type="heat_wave",
+                metric_list=heatwave_metrics,
+                target=ghcn_heatwave_target,
+                forecast=hres_heatwave_forecast,
+            ),
+        ]
+        # Run the evaluation
+        ewb = evaluate.ExtremeWeatherBench(
+            case_metadata=golden_tests_event_data,
+            evaluation_objects=heatwave_evaluation_objects,
+        )
+
+        ewb.run(
+            parallel_config={
+                "backend": "loky",
+                "n_jobs": len(heatwave_evaluation_objects) * len(heatwave_metrics),
+            },
+        )
+
+    def test_freezes(self, golden_tests_event_data):
+        """Freeze tests."""
+        era5_freeze_target = inputs.ERA5()
+        ghcn_freeze_target = inputs.GHCN()
+        hres_freeze_forecast = inputs.ZarrForecast(
+            name="hres_freeze_forecast",
+            source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
+            variables=["surface_air_temperature"],
+            variable_mapping=inputs.HRES_metadata_variable_mapping,
+        )
+        freeze_metrics = [
+            metrics.MinimumMeanAbsoluteError(
+                forecast_variable="surface_air_temperature",
+                target_variable="surface_air_temperature",
+            ),
+            metrics.RootMeanSquaredError(
+                forecast_variable="surface_air_temperature",
+                target_variable="surface_air_temperature",
+            ),
+            metrics.DurationMeanError(
+                threshold_criteria=defaults.get_climatology(quantile=0.15)
+            ),
+        ]
+        freeze_evaluation_objects = [
+            inputs.EvaluationObject(
+                event_type="freeze",
+                metric_list=freeze_metrics,
+                target=era5_freeze_target,
+                forecast=hres_freeze_forecast,
+            ),
+            inputs.EvaluationObject(
+                event_type="freeze",
+                metric_list=freeze_metrics,
+                target=ghcn_freeze_target,
+                forecast=hres_freeze_forecast,
+            ),
+        ]
+        # Run the evaluation
+        ewb = evaluate.ExtremeWeatherBench(
+            case_metadata=golden_tests_event_data,
+            evaluation_objects=freeze_evaluation_objects,
+        )
+        ewb.run(
+            parallel_config={
+                "backend": "loky",
+                "n_jobs": len(freeze_evaluation_objects) * len(freeze_metrics),
+            },
+        )
+
+    def test_severe_convection(self, golden_tests_event_data):
+        """Severe convection tests."""
+        lsr_severe_convection_target = inputs.LSR()
+        pph_severe_convection_target = inputs.PPH()
+        hres_severe_convection_forecast = inputs.ZarrForecast(
+            name="hres_severe_convection_forecast",
+            source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
+            variables=[derived.CravenBrooksSignificantSevere()],
+            variable_mapping=inputs.HRES_metadata_variable_mapping,
+        )
+        severe_convection_metrics = [
+            metrics.ThresholdMetric(
+                metrics=[metrics.CriticalSuccessIndex, metrics.FalseAlarmRatio],
+                forecast_threshold=15000,
+                target_threshold=0.3,
+            ),
+            metrics.EarlySignal(threshold=15000),
+        ]
+        severe_convection_evaluation_objects = [
+            inputs.EvaluationObject(
+                event_type="severe_convection",
+                metric_list=severe_convection_metrics,
+                target=lsr_severe_convection_target,
+                forecast=hres_severe_convection_forecast,
+            ),
+            inputs.EvaluationObject(
+                event_type="severe_convection",
+                metric_list=severe_convection_metrics,
+                target=pph_severe_convection_target,
+                forecast=hres_severe_convection_forecast,
+            ),
+        ]
+        # Run the evaluation
+        ewb = evaluate.ExtremeWeatherBench(
+            case_metadata=golden_tests_event_data,
+            evaluation_objects=severe_convection_evaluation_objects,
+        )
+        ewb.run(
+            parallel_config={
+                "backend": "loky",
+                "n_jobs": len(severe_convection_evaluation_objects)
+                * len(severe_convection_metrics),
+            },
+        )
+
+    def test_atmospheric_river(self, golden_tests_event_data):
+        """Atmospheric river tests."""
+        era5_atmospheric_river_target = inputs.ERA5()
+        hres_atmospheric_river_forecast = inputs.ZarrForecast(
+            name="hres_atmospheric_river_forecast",
+            source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
+            variables=[
+                derived.AtmosphericRiverVariables(
+                    output_variables=[
+                        "atmospheric_river_mask",
+                        "integrated_vapor_transport",
+                        "atmospheric_river_land_intersection",
+                    ]
+                )
+            ],
+            variable_mapping=inputs.HRES_metadata_variable_mapping,
+        )
+        atmospheric_river_metrics = [
+            metrics.CriticalSuccessIndex(),
+            metrics.EarlySignal(),
+            metrics.SpatialDisplacement(),
+        ]
+        atmospheric_river_evaluation_objects = [
+            inputs.EvaluationObject(
+                event_type="atmospheric_river",
+                metric_list=atmospheric_river_metrics,
+                target=era5_atmospheric_river_target,
+                forecast=hres_atmospheric_river_forecast,
+            ),
+        ]
+        # Run the evaluation
+        ewb = evaluate.ExtremeWeatherBench(
+            case_metadata=golden_tests_event_data,
+            evaluation_objects=atmospheric_river_evaluation_objects,
+        )
+        ewb.run(
+            parallel_config={
+                "backend": "loky",
+                "n_jobs": len(atmospheric_river_evaluation_objects)
+                * len(atmospheric_river_metrics),
+            },
+        )
+
+    def test_tropical_cyclone(self, golden_tests_event_data):
+        """Tropical cyclone tests."""
+        ibtracs_tropical_cyclone_target = inputs.IBTrACS()
+        hres_tropical_cyclone_forecast = inputs.ZarrForecast(
+            name="hres_tropical_cyclone_forecast",
+            source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
+            variables=[derived.TropicalCycloneTrackVariables()],
+            variable_mapping=inputs.HRES_metadata_variable_mapping,
+        )
+        tropical_cyclone_metrics = [
+            metrics.LandfallMetric(
+                metrics=[
+                    metrics.LandfallIntensityMeanAbsoluteError,
+                    metrics.LandfallTimeMeanError,
+                    metrics.LandfallDisplacement,
+                ],
+                approach="next",
+                forecast_variable="air_pressure_at_mean_sea_level",
+                target_variable="air_pressure_at_mean_sea_level",
+            ),
+        ]
+        tropical_cyclone_evaluation_objects = [
+            inputs.EvaluationObject(
+                event_type="tropical_cyclone",
+                metric_list=tropical_cyclone_metrics,
+                target=ibtracs_tropical_cyclone_target,
+                forecast=hres_tropical_cyclone_forecast,
+            ),
+        ]
+        # Run the evaluation
+        ewb = evaluate.ExtremeWeatherBench(
+            case_metadata=golden_tests_event_data,
+            evaluation_objects=tropical_cyclone_evaluation_objects,
+        )
+        ewb.run(
+            parallel_config={
+                "backend": "loky",
+                "n_jobs": len(tropical_cyclone_evaluation_objects)
+                * len(tropical_cyclone_metrics),
+            },
+        )
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

From 8979beddc60124259c5b97e26f8a73e3a1e7c69e Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 26 Jan 2026 18:24:30 -0500
Subject: [PATCH 21/72] PyPI Preparation (#315)

* update build-system and project

* update workflows, publish, and pyproject

* add justfile and twine

* update publish yaml

* change to python 3.10 as minimum requirement

* kerchunk needs 3.11, swapping pyproject and tests to remove 3.10

* change workflows to use version matrix

* align workflows

* Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes

* add explanation for dim reqs (#320)

* Update `defaults` and `inputs` to include new CIRA icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos

* Bump version from 0.2.0 to 0.3.0 (#324)

* Updated API (#321)

* move cache dir creation to init, rename funcs, add parallel/serial check function, update test names

* update naming

* add run method for backwards compatibility

* update tests

* add tests and cover if serial and parallel_config is not None

* feat: redesign public API with hierarchical namespace submodules

- Add ewb.evaluation() as main entry point (alias for ExtremeWeatherBench)
- Create namespace submodules: ewb.targets, ewb.forecasts, ewb.metrics,
  ewb.derived, ewb.regions, ewb.cases, ewb.defaults
- Expose all classes at top level for convenience (ewb.ERA5, etc.)
- Add ewb.load_cases() convenience alias
- Update all example files to use new import pattern
- Update usage.md documentation
- Maintain backward compatibility with existing imports

* ruff/linting. add utils to init

* add test coverage for module loading patterns

* ruff

* Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes

* add explanation for dim reqs (#320)

* Update `defaults` and `inputs` to include new CIRA icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos

* update defaults var refs

* Golden tests (#323)

* first pass for gt test infra + yaml

* use shapefile for severe convection and catch latitude swap

* add ignore for golden test when running pytest by default

* ruff

* move pytest addopts and markers to pyproject.toml

* Remove `IndividualCaseCollection` (#317)

* update all references to IndividualCaseCollection and convert dicts/ "cases": keys to lists

* update template

* make questions bold

* add whitespace

* remove indent error and typo from evaluate_cli

* make load_individual_cases include passthrough for existing dataclasses

* ruff

* add comment for clarification on list comp

* ruff (again)

* remove all references to collection, replace with list

* ruff

* rename collection -> list

* ruff

* Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes

* add explanation for dim reqs (#320)

* Update `defaults` and `inputs` to include new CIRA icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos

* Bump version from 0.2.0 to 0.3.0 (#324)

* Updated API (#321)

* move cache dir creation to init, rename funcs, add parallel/serial check function, update test names

* update naming

* add run method for backwards compatibility

* update tests

* add tests and cover if serial and parallel_config is not None

* feat: redesign public API with hierarchical namespace submodules

- Add ewb.evaluation() as main entry point (alias for ExtremeWeatherBench)
- Create namespace submodules: ewb.targets, ewb.forecasts, ewb.metrics,
  ewb.derived, ewb.regions, ewb.cases, ewb.defaults
- Expose all classes at top level for convenience (ewb.ERA5, etc.)
- Add ewb.load_cases() convenience alias
- Update all example files to use new import pattern
- Update usage.md documentation
- Maintain backward compatibility with existing imports

* ruff/linting. add utils to init

* add test coverage for module loading patterns

* ruff

* Cleanup docstrings in repo (#318)

* update these docstrings

* remove docstring changes markdown

* update docstrings

* update other docstrings

* remove individualcasecollection reference, update based on develop changes

* add explanation for dim reqs (#320)

* Update `defaults` and `inputs` to include new CIRA icechunk store (#319)

* more explicit naming, add func and model names var

* add test coverage, ruff, linting

* update readme for new cira approach

* move cira func and model ref to inputs

* update docs

* module wasnt called for moved func

* update tests for moving func and var

* ruff

* fix mock typos

* update defaults var refs

* remove to_csv

* swap pyproject tools to hatch; add if and packages-dir to publish
---
 .github/workflows/publish.yaml                | 86 +++++++++++++++++++
 .github/workflows/run-pre-commit.yaml         | 36 ++++++++
 .github/workflows/{ci.yaml => run-tests.yaml} | 32 ++-----
 Justfile                                      | 56 +++++++++++-
 pyproject.toml                                | 63 ++++++++++++--
 5 files changed, 239 insertions(+), 34 deletions(-)
 create mode 100644 .github/workflows/publish.yaml
 create mode 100644 .github/workflows/run-pre-commit.yaml
 rename .github/workflows/{ci.yaml => run-tests.yaml} (50%)

diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
new file mode 100644
index 00000000..c4167a05
--- /dev/null
+++ b/.github/workflows/publish.yaml
@@ -0,0 +1,86 @@
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  release-build:
+    name: Build release distribution
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Build release distributions
+        run: |
+          python -m pip install build
+          python -m build
+
+      - name: Upload distributions
+        uses: actions/upload-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+  pypi-publish:
+    name: Publish release distribution to PyPI
+    runs-on: ubuntu-latest
+    if: startsWith(github.ref, 'refs/tags/')
+    needs:
+      - release-build
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
+
+    # Dedicated environments with protections for publishing are strongly recommended.
+    # For more information, see: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#deployment-protection-rules
+    environment:
+      name: ewb-pypi-release
+      url: https://pypi.org/p/extremeweatherbench
+
+    steps:
+      - name: Retrieve release distributions
+        uses: actions/download-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+      - name: Publish release distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: dist/
+
+  publish-to-testpypi:
+    name: Publish release distribution to TestPyPI
+    runs-on: ubuntu-latest
+    if: startsWith(github.ref, 'refs/tags/')
+    needs:
+      - release-build
+
+    permissions:
+      id-token: write
+
+    environment:
+      name: ewb-testpypi-release
+      url: https://test.pypi.org/p/extremeweatherbench
+
+    steps:
+      - name: Retrieve release distributions
+        uses: actions/download-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+      - name: Publish release distributions to TestPyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository-url: https://test.pypi.org/legacy/
+          packages-dir: dist/
diff --git a/.github/workflows/run-pre-commit.yaml b/.github/workflows/run-pre-commit.yaml
new file mode 100644
index 00000000..42e6056f
--- /dev/null
+++ b/.github/workflows/run-pre-commit.yaml
@@ -0,0 +1,36 @@
+name: Run pre-commit
+
+on:
+  pull_request:
+    branches: [main, develop]
+  push:
+    branches: [main]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python env with uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "0.5.6"
+          enable-cache: true
+
+      - name: "Set up Python"
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install the project
+        run: uv sync --all-extras --all-groups
+
+      - name: Run pre-commit hooks
+        run: uv run pre-commit run --all-files
diff --git a/.github/workflows/ci.yaml b/.github/workflows/run-tests.yaml
similarity index 50%
rename from .github/workflows/ci.yaml
rename to .github/workflows/run-tests.yaml
index e042a833..99baeb36 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/run-tests.yaml
@@ -1,23 +1,20 @@
-name: ci
+name: Run tests
 
 on:
   pull_request:
+    branches: [main, develop]
   push:
     branches: [main]
 
-jobs:
-  pre-commit:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v3
-      - uses: pre-commit/action@v3.0.1
+permissions:
+  contents: read
 
-  test:
+jobs:
+  build:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
 
@@ -30,24 +27,13 @@ jobs:
       - name: "Set up Python"
         uses: actions/setup-python@v5
         with:
-          python-version-file: "pyproject.toml"
+          python-version: ${{ matrix.python-version }}
 
       - name: Install the project
-        run: uv sync --all-extras --dev
+        run: uv sync --all-extras --all-groups
 
       - name: Run tests
         run: uv run pytest
 
       - name: Generate Coverage Report
         run: uv run coverage report -m
-
-  golden-tests:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: extractions/setup-just@v3
-        with:
-          just-version: 1.43.1
-
-      - name: Run golden tests with just
-        run: just golden-tests
diff --git a/Justfile b/Justfile
index f0c45f50..cddbd2d1 100644
--- a/Justfile
+++ b/Justfile
@@ -1,7 +1,57 @@
+# NOTE: We automatically load a .env file containing the "GH_TOKEN" environment variable
+# for use with semantic-release. If this isn't present, then those commands will likely fail.
+set dotenv-load
+
 # List all available recipes
 default:
     @just --list
 
-# Placeholder for golden tests
-golden-tests:
-    @just --list 
\ No newline at end of file
+# Run the complete test suite
+test:
+    @echo "Running tests"
+    uv run pytest
+
+# Serve a local build of the project documentation at http://localhost:8000
+serve-docs:
+    @echo "Serving docs at http://localhost:8000"
+    uv run --extra docs mkdocs serve
+
+# Build the project documentation
+build-docs:
+    @echo "Building docs"
+    uv run --extra docs mkdocs build
+
+# Run the pre-commit hooks on all files in the repo
+pre-commit:
+    @echo "Running pre-commit hooks"
+    uv run pre-commit run --all-files
+
+# Run the coverage report
+coverage:
+    @echo "Running coverage report"
+    uv run coverage run -m pytest
+    uv run coverage report
+
+# Determine the next version number
+next-version:
+    @echo "Determining next version"
+    uv run semantic-release version --print
+
+# Create a minor release
+minor-release:
+    @echo "Creating minor release"
+    uv run semantic-release -vvv --noop version --minor --no-changelog
+
+# Create a patch release
+patch-release:
+    @echo "Creating patch release"
+    uv run semantic-release -vvv --noop version --patch --no-changelog
+
+# Upload a release to PyPI
+pypi-upload tag:
+    @echo "Uploading release {{tag}} to PyPI"
+    git checkout {{tag}}
+    rm -rf dist
+    uv run python -m build
+    uv run twine upload dist/*
+    git checkout -
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 614ad657..d7dea885 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,7 +2,25 @@
 name = "extremeweatherbench"
 version = "0.3.0"
 description = "Benchmarking weather and weather AI models using extreme events"
+keywords = [
+    "weather",
+    "extreme events",
+    "benchmarking",
+    "forecasting",
+    "climate",
+]
+license = { file = "LICENSE" }
 readme = "README.md"
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Scientific/Engineering :: Atmospheric Science",
+]
 requires-python = ">=3.11,<3.14"
 dependencies = [
     "dacite>=1.8.1",
@@ -63,6 +81,8 @@ dev = [
     "types-pytz>=2025.2.0.20250809",
     "types-pyyaml>=6.0.12.20241230",
     "types-tqdm>=4.67.0.20250809",
+    "python-semantic-release>=10.3.0",
+    "twine>=5.1.1",
 ]
 docs = [
     "mkdocs>=1.6.1",
@@ -72,17 +92,22 @@ docs = [
     "pymdown-extensions>=10.19.1",
 ]
 
+complete = ["extremeweatherbench[data-prep,multiprocessing]"]
+
 [build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
+requires = ["hatchling >= 1.26"]
+build-backend = "hatchling.build"
 
-[tool.setuptools]
-packages = ["extremeweatherbench"]
-package-dir = { "" = "src" }
-include-package-data = true
+[tool.hatch.build.targets.wheel]
+packages = ["src/extremeweatherbench"]
+
+[tool.hatch.build.targets.sdist]
+include = ["src/extremeweatherbench/**/*"]
+
+[project.urls]
+Documentation = "https://extremeweatherbench.readthedocs.io/"
+Repository = "https://github.com/brightbandtech/extremeweatherbench"
 
-[tool.setuptools.package-data]
-extremeweatherbench = ["data/**/*", "data/**/.*"]
 
 [project.scripts]
 ewb = "extremeweatherbench.evaluate_cli:cli_runner"
@@ -126,6 +151,28 @@ docstring-code-line-length = "dynamic"
 [tool.ruff.lint.isort]
 case-sensitive = true
 
+[tool.semantic_release]
+version_toml = ["pyproject.toml:project.version"]
+branch = "main"
+dist_path = "dist/"
+upload_to_pypi = false
+remote = { type = "github" }
+commit_author = "semantic-release <semantic-release@python>"
+commit_parser = "conventional"
+commit_parser_options = { parse_squash_commits = "false", parse_merge_commits = "true" }
+minor_tag = "[minor]"
+patch_tag = "[patch]"
+major_tag = "[major]"
+build_command = """
+  uv lock --offline
+  git add uv.lock
+  uv build
+"""
+# Only create GitHub releases for the current version, not historical ones
+github_release_mode = "latest"
+# Ensure assets are only uploaded for the current release, not past ones
+upload_assets_for_all_releases = false
+
 [tool.pytest]
 addopts = ["--ignore=tests/test_golden.py", "--cov=extremeweatherbench"]
 markers = [

From 54f5c354a3670d39380fb9b95aafa607dcb7b9da Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Mon, 26 Jan 2026 23:33:30 +0000
Subject: [PATCH 22/72] update pyproject version for release

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index d7dea885..887442c3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "extremeweatherbench"
-version = "0.3.0"
+version = "1.0.0"
 description = "Benchmarking weather and weather AI models using extreme events"
 keywords = [
     "weather",

From 43fafbff3ce28466112caed43fdf1856928b126a Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 26 Jan 2026 19:06:01 -0500
Subject: [PATCH 23/72] Remove duplicate function and fixtures (#326)

* chore: remove duplicate function and fixtures

- Remove duplicate _parallel_serial_config_check function from evaluate.py
  (was defined twice at lines 189 and 982 with identical implementation)
- Remove duplicate runner fixture from test_evaluate_cli.py
  (already defined in conftest.py)
- Remove duplicate temp_config_dir fixture from test_evaluate_cli.py
  (already defined in conftest.py)
- Remove unused tempfile import from test_evaluate_cli.py

* ruff
---
 src/extremeweatherbench/evaluate.py | 41 -----------------------------
 tests/test_evaluate_cli.py          | 20 --------------
 2 files changed, 61 deletions(-)

diff --git a/src/extremeweatherbench/evaluate.py b/src/extremeweatherbench/evaluate.py
index 7efafbf2..504d7694 100644
--- a/src/extremeweatherbench/evaluate.py
+++ b/src/extremeweatherbench/evaluate.py
@@ -977,44 +977,3 @@ def _safe_concat(
         return pd.concat(valid_dfs, ignore_index=ignore_index)
     else:
         return pd.DataFrame(columns=OUTPUT_COLUMNS)
-
-
-def _parallel_serial_config_check(
-    n_jobs: Optional[int] = None,
-    parallel_config: Optional[dict] = None,
-) -> Optional[dict]:
-    """Check if running in serial or parallel mode.
-
-    Args:
-        n_jobs: The number of jobs to run in parallel. If None, defaults to the
-            joblib backend default value. If 1, the workflow will run serially.
-        parallel_config: Optional dictionary of joblib parallel configuration. If
-            provided, this takes precedence over n_jobs. If not provided and n_jobs is
-            specified, a default config with loky backend is used.
-    Returns:
-        None if running in serial mode, otherwise a dictionary of joblib parallel
-        configuration.
-    """
-    # Determine if running in serial or parallel mode
-    # Serial: n_jobs=1 or (parallel_config with n_jobs=1)
-    # Parallel: n_jobs>1 or (parallel_config with n_jobs>1)
-    is_serial = (
-        (n_jobs == 1)
-        or (parallel_config is not None and parallel_config.get("n_jobs") == 1)
-        or (n_jobs is None and parallel_config is None)
-    )
-    logger.debug("Running in %s mode.", "serial" if is_serial else "parallel")
-
-    if not is_serial:
-        # Build parallel_config if not provided
-        if parallel_config is None and n_jobs is not None:
-            logger.debug(
-                "No parallel_config provided, using loky backend and %s jobs.",
-                n_jobs,
-            )
-            parallel_config = {"backend": "loky", "n_jobs": n_jobs}
-    # If running in serial mode, set parallel_config to None if not already
-    else:
-        parallel_config = None
-    # Return the maybe updated kwargs
-    return parallel_config
diff --git a/tests/test_evaluate_cli.py b/tests/test_evaluate_cli.py
index 2bda6879..ca71d408 100644
--- a/tests/test_evaluate_cli.py
+++ b/tests/test_evaluate_cli.py
@@ -1,12 +1,9 @@
 """Tests for the evaluate_cli interface."""
 
 import pickle
-import tempfile
 import textwrap
-from pathlib import Path
 from unittest import mock
 
-import click.testing
 import pandas as pd
 import pytest
 
@@ -23,23 +20,6 @@ def suppress_cli_output():
         yield
 
 
-@pytest.fixture
-def runner():
-    """Create a Click test runner with output suppression."""
-    return click.testing.CliRunner()
-
-
-@pytest.fixture
-def temp_config_dir():
-    """Create a temporary directory for config files and test outputs.
-
-    This ensures all test files are created in temporary directories and automatically
-    cleaned up after each test.
-    """
-    with tempfile.TemporaryDirectory() as temp_dir:
-        yield Path(temp_dir)
-
-
 @pytest.fixture
 def sample_config_py(temp_config_dir):
     """Create a sample Python config file."""

From 42c176ec5981ebb6c283f008c25b09a8c1fe3308 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 2 Mar 2026 11:53:43 -0500
Subject: [PATCH 24/72] update install docs for pypi and fix typo in link

---
 docs/installation.md | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 4d61c309..b54c9ae0 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -5,20 +5,18 @@
 You can install ExtremeWeatherBench using: 
 
 ```
-uv pip install git+https://github.com/brightband/ExtremeWeatherBench.git
-uv sync --all-extras
+uv add extremeweatherbench
 ```
 
 ## Using pip
 Alternatively, you can use pip in the same fashion (using a virtual environment is recommended):
 ```
-pip install git+https://github.com/brightband/ExtremeWeatherBench.git
+pip install extremeweatherbench
 ```
 
-Alternatively, for developers, you can install the package in development mode:
-
+For development, an easy approach is clone the repository and use `uv` to sync all required and optional dependencies:
 
 ``` 
-git clone https://github.com/brightband/ExtremeWeatherBench.git
+git clone https://github.com/brightbandtech/ExtremeWeatherBench.git
 uv sync --all-extras
 ```
\ No newline at end of file

From 9794aaf23ff99de8103407d1ba6abb4ab90fe64d Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 20 Mar 2026 16:30:41 -0400
Subject: [PATCH 25/72] Update ar calcs with improved parallelization (#329)

* update ar calcs with improved parallelization

* ruff formatting

* update attr name

* remove diagnostic logging; ruff formatting; update ar bounds code to work, add zarr 3 warning catch, add optional time dimension str arg

* more ar_bounds work to get it moving, looks good here

* mypy

* mypy

* update time to valid_time to follow ewb conventions
---
 data_prep/ar_bounds.py                        |  79 +++----
 src/extremeweatherbench/calc.py               | 129 +++++++----
 .../events/atmospheric_river.py               |  97 +++------
 src/extremeweatherbench/inputs.py             |   7 +
 tests/test_atmospheric_river.py               | 184 ++++++++--------
 tests/test_calc.py                            | 204 ++++++++----------
 tests/test_init.py                            |   2 +-
 7 files changed, 337 insertions(+), 365 deletions(-)

diff --git a/data_prep/ar_bounds.py b/data_prep/ar_bounds.py
index 1c4cf5cf..92e44306 100644
--- a/data_prep/ar_bounds.py
+++ b/data_prep/ar_bounds.py
@@ -17,11 +17,7 @@
 from dask.distributed import Client
 from matplotlib.patches import Rectangle
 
-import extremeweatherbench.cases as cases
-import extremeweatherbench.derived as derived
-import extremeweatherbench.inputs as inputs
-import extremeweatherbench.regions as regions
-import extremeweatherbench.utils as utils
+from extremeweatherbench import calc, cases, derived, inputs, regions, utils
 from extremeweatherbench.events import atmospheric_river as ar
 
 logging.basicConfig()
@@ -413,31 +409,6 @@ def find_timestamp_peak_field(
     return peak_time_idx, peak_ivt_value
 
 
-def create_composite_ar_mask(
-    ar_mask: xr.DataArray,
-    land_intersection: Optional[xr.DataArray] = None,
-) -> Tuple[xr.DataArray, Optional[xr.DataArray]]:
-    """Create composite AR masks by taking maximum over time.
-
-    Args:
-        ar_mask: Binary AR mask with time dimension.
-        land_intersection: Optional land intersection mask with time dim.
-
-    Returns:
-        Tuple of (composite_ar_mask, composite_land_intersection).
-    """
-    time_dim = "valid_time" if "valid_time" in ar_mask.dims else "time"
-
-    # Create composite by taking max over time dimension
-    composite_mask = ar_mask.max(dim=time_dim)
-
-    composite_land = None
-    if land_intersection is not None:
-        composite_land = land_intersection.max(dim=time_dim)
-
-    return composite_mask, composite_land
-
-
 def expand_bounds_to_contiguous_ar(
     ar_mask: xr.DataArray,
     land_intersection: xr.DataArray,
@@ -835,7 +806,23 @@ def process_ar_event(
     era5_data = era5_data.sel(
         valid_time=era5_data.valid_time.dt.hour.isin([0, 6, 12, 18])
     )
-    era5_data = inputs.maybe_subset_variables(era5_data, variables=era5_ar.variables)
+
+    # Rough check, if you're adding variables beyond AtmosphericRiverVariables or
+    # specific_humidity, u_component..., v_component..., this will fail
+    if isinstance(era5_ar.variables, list):
+        if len(era5_ar.variables) > 1 and isinstance(
+            era5_ar.variables[0], derived.DerivedVariable
+        ):
+            raise ValueError(
+                "Only accepted variables are derived.AtmosphericRiverVariables"
+                " or [specific_humidity, u_component_of_wind, v_component_of_wind]."
+            )
+        elif isinstance(era5_ar.variables[0], derived.DerivedVariable):
+            era5_ar_vars = era5_ar.variables[0].variables
+        else:
+            era5_ar_vars = era5_ar.variables
+
+    era5_data = inputs.maybe_subset_variables(era5_data, variables=era5_ar_vars)
     era5_subset = era5_ar.subset_data_to_case(era5_data, case)
     era5_subset = era5_subset.chunk()
     # Generate IVT
@@ -844,19 +831,17 @@ def process_ar_event(
         specific_humidity=era5_subset["specific_humidity"],
         eastward_wind=era5_subset["eastward_wind"],
         northward_wind=era5_subset["northward_wind"],
-        levels=era5_subset["adjusted_level"],
-    )
+    ).persist()
     ivt_da.name = "integrated_vapor_transport"
     # Compute IVT Laplacian
-    ivt_laplacian = ar.integrated_vapor_transport_laplacian(ivt_da)
+    ivt_laplacian = ar.integrated_vapor_transport_laplacian(ivt_da).compute()
     ivt_laplacian.name = "integrated_vapor_transport_laplacian"
-
     # Compute AR mask
     ar_mask = ar.atmospheric_river_mask(
         ivt=ivt_da,
         ivt_laplacian=ivt_laplacian,
         min_size_gridpoints=AR_OBJECT_CONFIG["min_area_gridpoints"],
-    )
+    ).compute()
 
     # Generate land mask for peak time finding
     logger.info("  Generating land mask...")
@@ -902,20 +887,14 @@ def process_ar_event(
 
     # Create composite AR mask over entire time range (max)
     logger.info("  Creating composite AR mask over time...")
-    composite_ar_mask, composite_land_intersection = create_composite_ar_mask(
-        ar_mask, land_intersection=land_mask
+    composite_ar_mask = ar_mask.max(
+        dim=["valid_time" if "valid_time" in ar_mask.dims else "time"]
     )
 
     logger.info(
         "  Composite AR mask has %s grid points", composite_ar_mask.sum().values
     )
-    logger.info(
-        "  Composite land intersection has %s grid points",
-        composite_land_intersection.sum().values
-        if composite_land_intersection is not None
-        else 0,
-    )
-
+    composite_land_intersection = calc.find_land_intersection(composite_ar_mask)
     # Find bounds using composite mask & expand to contiguous AR
     left_lon, right_lon, bottom_lat, top_lat, largest_obj_metadata = (
         find_ar_bounds_from_largest_object(
@@ -1052,11 +1031,11 @@ def main():
         # shapes
     }
     if parallel:
-        with joblib.parallel_backend("dask"):
-            ar_bounds_results_enhanced = joblib.Parallel(n_jobs=len(ar_events))(
-                joblib.delayed(process_ar_event)(single_case, era5_ar, AR_OBJECT_CONFIG)
-                for single_case in ar_events
-            )
+        # Fixing the n_jobs arbitrarily; change as desired
+        ar_bounds_results_enhanced = joblib.Parallel(n_jobs=8)(
+            joblib.delayed(process_ar_event)(single_case, era5_ar, AR_OBJECT_CONFIG)
+            for single_case in ar_events
+        )
     else:
         # Run in serial using a list comprehension
         ar_bounds_results_enhanced = [
diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index 28fcf94f..125eb51e 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -6,6 +6,9 @@
 import scores.categorical as categorical
 import shapely
 import xarray as xr
+from numba import float64, guvectorize
+from scipy import ndimage
+from skimage import filters
 
 from extremeweatherbench import utils
 
@@ -276,50 +279,60 @@ def geopotential_thickness(
     return geopotential_thickness
 
 
-def nantrapezoid(
-    y: np.ndarray,
-    x: np.ndarray | None = None,
-    dx: float = 1.0,
-    axis: int = -1,
-):
-    """Trapezoid rule for arrays with nans.
+@guvectorize(
+    [(float64[:], float64[:], float64[:])],
+    "(n),(n)->()",
+    nopython=True,
+    target="parallel",
+)
+def _nantrapezoid_kernel(y, x, out):
+    """1D nan-aware trapezoid integration kernel."""
+    total = 0.0
+    for i in range(len(y) - 1):
+        y0 = y[i]
+        y1 = y[i + 1]
+        dx = x[i + 1] - x[i]
+        # skip intervals where either endpoint is nan
+        if not (np.isnan(y0) or np.isnan(y1)):
+            total += dx * (y0 + y1) / 2.0
+    out[()] = total
+
+
+def nantrapezoid_4d(y, x):
+    """
+    Wrapper that moves the integration axis to last position,
+    calls the guvectorize kernel, then returns result.
 
-    Identical to np.trapezoid but with nans handled correctly in the summation.
+    y: (time, level, lat, lon)
+    x: (level,) or same shape as y
     """
-    y = np.asanyarray(y)
-    if x is None:
-        # Create an array of the step size
-        d = np.full(y.shape[axis] - 1, dx) if y.shape[axis] > 1 else np.array([dx])
-        # reshape to correct shape
-        shape = [1] * y.ndim
-        shape[axis] = d.shape[0]
-        d = d.reshape(shape)
-    else:
-        x = np.asanyarray(x)
-        if x.ndim == 1:
-            d = np.diff(x)
-            # reshape to correct shape
-            shape = [1] * y.ndim
-            shape[axis] = d.shape[0]
-            d = d.reshape(shape)
-        else:
-            d = np.diff(x, axis=axis)
-    if y.ndim != d.ndim:
-        d = np.expand_dims(d, axis=1)
-    nd = y.ndim
-    slice1 = [slice(None)] * nd
-    slice2 = [slice(None)] * nd
-    slice1[axis] = slice(1, None)
-    slice2[axis] = slice(None, -1)
-    try:
-        # This is the only location different from np.trapezoid
-        ret = np.nansum(d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0, axis=axis)
-    except ValueError:
-        # Operations didn't work, cast to ndarray
-        d = np.asarray(d)
-        y = np.asarray(y)
-        ret = np.add.reduce(d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0, axis)
-    return ret
+
+    return _nantrapezoid_kernel(y, x)
+
+
+def nantrapezoid_pressure_levels(da: xr.DataArray):
+    """Calculates the integral using the trapezoid rule for arrays with nans.
+
+    Args:
+        da: a DataArray with dimensions (time, latitude, longitude, level). Level units
+            are in hPa.
+
+    Returns a DataArray of the computed quantity integrated over the entire column.
+    """
+
+    # Convert levels to Pascals
+    levels_pa = da["level"] * 100
+
+    output = xr.apply_ufunc(
+        nantrapezoid_4d,
+        da,
+        levels_pa,
+        input_core_dims=[["level"], ["level"]],
+        output_core_dims=[[]],
+        dask="parallelized",
+        output_dtypes=[float],
+    )
+    return output
 
 
 def specific_humidity_from_relative_humidity(
@@ -871,3 +884,35 @@ def _is_true_landfall(
         # - AttributeError: invalid/None geometry
         # - ValueError/TypeError: invalid coordinate values
         return False
+
+
+def _binary_dilation_ufunc(data: xr.DataArray, dilation_radius: int) -> xr.DataArray:
+    """Apply binary dilation to a single 2D (lat, lon) slice.
+
+    Args:
+        data: 2D boolean array of shape (lat, lon)
+        dilation_radius: radius for the dilation in gridpoints
+
+    Returns:
+        Dilated boolean array of shape (lat, lon)
+    """
+    size = dilation_radius * 2 + 1
+    struct = np.ones((size, size))
+    return np.expand_dims(
+        ndimage.binary_dilation(data.squeeze(), structure=struct).astype(np.int8),
+        axis=0,
+    )
+
+
+def _compute_blurred_laplacian_ufunc(data: xr.DataArray, sigma: float) -> xr.DataArray:
+    """Compute blurred Laplacian using scipy filters.
+
+    Args:
+        data: IVT data to compute the blurred Laplacian of; data must be 2D
+        sigma: the standard deviation for the Gaussian filter
+
+    Returns:
+        The blurred Laplacian of IVT
+    """
+    laplace_data = filters.laplace(data)
+    return ndimage.gaussian_filter(laplace_data, sigma=sigma)
diff --git a/src/extremeweatherbench/events/atmospheric_river.py b/src/extremeweatherbench/events/atmospheric_river.py
index a382fda6..01da7f76 100644
--- a/src/extremeweatherbench/events/atmospheric_river.py
+++ b/src/extremeweatherbench/events/atmospheric_river.py
@@ -1,7 +1,7 @@
+import dask
 import numpy as np
 import xarray as xr
 from scipy import ndimage
-from skimage import filters
 
 from extremeweatherbench import calc
 
@@ -13,6 +13,7 @@ def atmospheric_river_mask(
     ivt_threshold: float = 400,
     dilation_radius: int = 8,
     min_size_gridpoints: int = 500,
+    time_dimension: str = "valid_time",
 ) -> xr.DataArray:
     """Calculate atmospheric river mask using IVT and Laplacian thresholds.
 
@@ -32,6 +33,8 @@ def atmospheric_river_mask(
             gridpoints
         min_size_gridpoints: the minimum size of the atmospheric river in
             gridpoints
+        time_dimension: name of time dimension. Defaults to the EWB standard
+            'valid_time'.
 
     Returns:
         The atmospheric river mask as a DataArray
@@ -39,31 +42,40 @@ def atmospheric_river_mask(
 
     # Get all coordinates except level for the intersection DataArray
     coords_dict = {dim: ivt.coords[dim] for dim in ivt.dims if dim != "level"}
-
     # Create boolean masks for each condition
-    has_high_laplacian = np.abs(ivt_laplacian) >= laplacian_threshold
-    has_high_ivt = ivt >= ivt_threshold
-
-    # For the Laplacian condition, we want to check if there's a value >=
-    # laplacian_threshold within 8 gridpoints (0.25 degrees)
-    dilation_radius = dilation_radius * 2 + 1
-    struct = np.ones((dilation_radius, dilation_radius))
-    dilated_laplacian = ndimage.binary_dilation(
-        has_high_laplacian, structure=struct, axes=(-2, -1)
+    has_high_laplacian, has_high_ivt = dask.compute(
+        np.abs(ivt_laplacian) >= laplacian_threshold,
+        ivt >= ivt_threshold,
     )
+    # For the Laplacian condition, we want to check if there's a value >=
+    # laplacian_threshold within 8 gridpoints (0.25 degrees).
+    # Apply binary dilation lazily via apply_ufunc before computing
+    # This keeps dilation in the Dask graph alongside ivt/laplacian tasks
+    dilated_laplacian = xr.apply_ufunc(
+        calc._binary_dilation_ufunc,
+        has_high_laplacian.chunk({time_dimension: 1, "latitude": -1, "longitude": -1}),
+        dilation_radius,
+        input_core_dims=[["latitude", "longitude"], []],
+        output_core_dims=[["latitude", "longitude"]],
+        dask="parallelized",
+        keep_attrs=True,
+        output_dtypes=[np.int8],
+    ).compute()
+    # dilation_radius = dilation_radius * 2 + 1
+    # struct = np.ones((dilation_radius, dilation_radius))
+    # dilated_laplacian = ndimage.binary_dilation(
+    #     has_high_laplacian, structure=struct, axes=(-2, -1)
+    # )
 
     # Combine conditions without tropical restriction initially
     initial_intersection = xr.where(dilated_laplacian & has_high_ivt, 1, 0)
-
     # Label connected components and get their sizes
     labeled_array, _ = ndimage.label(initial_intersection)
     unique_labels, label_counts = np.unique(labeled_array, return_counts=True)
-
     # Filter by size first (excluding background label 0)
     size_valid_labels = unique_labels[
         np.where((label_counts >= min_size_gridpoints) & (unique_labels != 0))
     ]
-
     # Collect all size-valid feature labels
     valid_features = []
     for label_num in size_valid_labels:
@@ -71,7 +83,6 @@ def atmospheric_river_mask(
 
     # Create final mask using valid features
     feature_mask = np.isin(labeled_array, valid_features)
-
     # Final result with size threshold applied
     ar_mask = xr.DataArray(
         xr.where(feature_mask, 1, 0), coords=coords_dict, dims=coords_dict.keys()
@@ -84,7 +95,6 @@ def integrated_vapor_transport(
     specific_humidity: xr.DataArray,
     eastward_wind: xr.DataArray,
     northward_wind: xr.DataArray,
-    levels: xr.DataArray,
 ) -> xr.DataArray:
     """Compute integrated vapor transport from humidity and winds.
 
@@ -92,44 +102,25 @@ def integrated_vapor_transport(
         specific_humidity: a DataArray containing specific humidity
         eastward_wind: a DataArray containing eastward wind (u-component)
         northward_wind: a DataArray containing northward wind (v-component)
-        levels: a DataArray containing pressure levels
 
     Returns:
         Integrated vapor transport as a DataArray
     """
 
-    # Get required coordinates excluding level dimension
-    coords_dict = {
-        dim: specific_humidity.coords[dim]
-        for dim in specific_humidity.dims
-        if dim != "level"
-    }
-    level_axis = list(specific_humidity.dims).index("level")
-
-    # Compute IVT components using nantrapezoid
-    eastward_ivt_arr = (
-        calc.nantrapezoid(
-            eastward_wind * specific_humidity,
-            x=levels * 100,  # convert to Pa
-            axis=level_axis,
+    # Compute IVT components using nantrapezoid_pressure_levels
+    eastward_ivt = (
+        calc.nantrapezoid_pressure_levels(
+            da=eastward_wind * specific_humidity,
         )
         / calc.g0
     )
-    eastward_ivt = xr.DataArray(
-        eastward_ivt_arr, coords=coords_dict, dims=coords_dict.keys()
-    )
 
-    northward_ivt_arr = (
-        calc.nantrapezoid(
-            northward_wind * specific_humidity,
-            x=levels * 100,  # convert to Pa
-            axis=level_axis,
+    northward_ivt = (
+        calc.nantrapezoid_pressure_levels(
+            da=northward_wind * specific_humidity,
         )
         / calc.g0
     )
-    northward_ivt = xr.DataArray(
-        northward_ivt_arr, coords=coords_dict, dims=coords_dict.keys()
-    )
 
     # Compute IVT using components
     ivt_magnitude = xr.ufuncs.hypot(eastward_ivt, northward_ivt)
@@ -137,20 +128,6 @@ def integrated_vapor_transport(
     return ivt_magnitude
 
 
-def _compute_blurred_laplacian_ufunc(data: xr.DataArray, sigma: float) -> xr.DataArray:
-    """Compute blurred Laplacian using scipy filters.
-
-    Args:
-        data: IVT data to compute the blurred Laplacian of; data must be 2D
-        sigma: the standard deviation for the Gaussian filter
-
-    Returns:
-        The blurred Laplacian of IVT
-    """
-    laplace_data = filters.laplace(data)
-    return ndimage.gaussian_filter(laplace_data, sigma=sigma)
-
-
 def integrated_vapor_transport_laplacian(
     ivt: xr.DataArray, sigma: float = 3
 ) -> xr.DataArray:
@@ -163,19 +140,16 @@ def integrated_vapor_transport_laplacian(
     Returns:
         The blurred Laplacian of IVT
     """
-    # TODO(189): determine if numba can be used to speed up this computation
     laplacian = xr.apply_ufunc(
-        _compute_blurred_laplacian_ufunc,
+        calc._compute_blurred_laplacian_ufunc,
         ivt,
         sigma,
         input_core_dims=[["latitude", "longitude"], []],
         output_core_dims=[["latitude", "longitude"]],
-        dask="allowed",
+        dask="parallelized",
         keep_attrs=True,
         output_dtypes=[float],
     )
-
-    # Add name to the dataarray
     laplacian.name = "integrated_vapor_transport_blurred_laplacian"
     return laplacian
 
@@ -195,7 +169,6 @@ def build_atmospheric_river_mask_and_land_intersection(data: xr.Dataset) -> xr.D
         specific_humidity=data["specific_humidity"],
         eastward_wind=data["eastward_wind"],
         northward_wind=data["northward_wind"],
-        levels=data["level"],
     )
 
     # Compute IVT Laplacian
diff --git a/src/extremeweatherbench/inputs.py b/src/extremeweatherbench/inputs.py
index 37aba5a5..96886177 100644
--- a/src/extremeweatherbench/inputs.py
+++ b/src/extremeweatherbench/inputs.py
@@ -1,6 +1,7 @@
 import abc
 import dataclasses
 import logging
+import warnings
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -27,6 +28,12 @@
 if TYPE_CHECKING:
     import extremeweatherbench.metrics as metrics
 
+warnings.filterwarnings(
+    "ignore",
+    message="Numcodecs codecs are not in the Zarr version 3 specification*",
+    category=UserWarning,
+)
+
 logger = logging.getLogger(__name__)
 
 #: Storage/access options for gridded target datasets.
diff --git a/tests/test_atmospheric_river.py b/tests/test_atmospheric_river.py
index 1b1e5c3f..98a15c0a 100644
--- a/tests/test_atmospheric_river.py
+++ b/tests/test_atmospheric_river.py
@@ -46,9 +46,9 @@ def sample_ar_dataset(self):
         specific_hum = calc.specific_humidity_from_relative_humidity(
             air_temperature=xr.DataArray(
                 air_temp,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -56,9 +56,9 @@ def sample_ar_dataset(self):
             ),
             relative_humidity=xr.DataArray(
                 rel_humidity,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -70,37 +70,37 @@ def sample_ar_dataset(self):
         dataset = xr.Dataset(
             {
                 "integrated_vapor_transport": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     ivt_data,
                 ),
                 "integrated_vapor_transport_laplacian": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     ivt_laplacian,
                 ),
                 "eastward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "northward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "air_temperature": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     air_temp,
                 ),
                 "relative_humidity": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rel_humidity,
                 ),
                 "specific_humidity": specific_hum,
                 "geopotential_at_surface": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     rng.uniform(0, 1000, data_shape_3d),
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
@@ -121,7 +121,7 @@ def test_atmospheric_river_mask_basic(self, sample_ar_dataset):
         assert isinstance(result, xr.DataArray)
 
         # Should have correct dimensions (no level dimension)
-        expected_dims = ["time", "latitude", "longitude"]
+        expected_dims = ["valid_time", "latitude", "longitude"]
         assert list(result.dims) == expected_dims
 
         # Should have correct shape
@@ -192,16 +192,16 @@ def test_atmospheric_river_mask_nan_handling(self):
         dataset = xr.Dataset(
             {
                 "integrated_vapor_transport": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     ivt_data,
                 ),
                 "integrated_vapor_transport_laplacian": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     ivt_laplacian,
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
             },
@@ -244,9 +244,9 @@ def sample_ivt_dataset(self):
         specific_hum = calc.specific_humidity_from_relative_humidity(
             air_temperature=xr.DataArray(
                 air_temp,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -254,9 +254,9 @@ def sample_ivt_dataset(self):
             ),
             relative_humidity=xr.DataArray(
                 rel_humidity,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -268,29 +268,29 @@ def sample_ivt_dataset(self):
         dataset = xr.Dataset(
             {
                 "eastward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "northward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "air_temperature": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     air_temp,
                 ),
                 "relative_humidity": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rel_humidity,
                 ),
                 "specific_humidity": specific_hum,
                 "geopotential_at_surface": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     rng.uniform(0, 1000, data_shape_3d),
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
@@ -306,14 +306,13 @@ def test_integrated_vapor_transport_basic(self, sample_ivt_dataset):
             specific_humidity=sample_ivt_dataset["specific_humidity"],
             eastward_wind=sample_ivt_dataset["eastward_wind"],
             northward_wind=sample_ivt_dataset["northward_wind"],
-            levels=sample_ivt_dataset["adjusted_level"],
         )
 
         # Should return a DataArray
         assert isinstance(result, xr.DataArray)
 
         # Should have correct dimensions (no level dimension)
-        expected_dims = ["time", "latitude", "longitude"]
+        expected_dims = ["valid_time", "latitude", "longitude"]
         assert list(result.dims) == expected_dims
 
         # Should have correct shape
@@ -322,6 +321,9 @@ def test_integrated_vapor_transport_basic(self, sample_ivt_dataset):
         # Values should be positive (IVT magnitude)
         assert (result >= 0).all()
 
+        # There needs to be many > 0
+        assert (result > 0).any()
+
         # Values should be reasonable for IVT (typically 0-3000 kg/m/s)
         # Some extreme values may exceed 1000 but should be under 3000
         assert (result < 3000).all()
@@ -351,9 +353,9 @@ def test_integrated_vapor_transport_nan_handling(self):
         specific_hum = calc.specific_humidity_from_relative_humidity(
             air_temperature=xr.DataArray(
                 air_temp,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -361,9 +363,9 @@ def test_integrated_vapor_transport_nan_handling(self):
             ),
             relative_humidity=xr.DataArray(
                 rel_humidity,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -375,29 +377,29 @@ def test_integrated_vapor_transport_nan_handling(self):
         dataset = xr.Dataset(
             {
                 "eastward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     eastward_wind,
                 ),
                 "northward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     northward_wind,
                 ),
                 "air_temperature": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     air_temp,
                 ),
                 "relative_humidity": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rel_humidity,
                 ),
                 "specific_humidity": specific_hum,
                 "geopotential_at_surface": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     rng.uniform(0, 1000, data_shape_3d),
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
@@ -408,7 +410,6 @@ def test_integrated_vapor_transport_nan_handling(self):
             specific_humidity=dataset["specific_humidity"],
             eastward_wind=dataset["eastward_wind"],
             northward_wind=dataset["northward_wind"],
-            levels=dataset["adjusted_level"],
         )
 
         # Should return a DataArray
@@ -428,24 +429,21 @@ def test_integrated_vapor_transport_missing_variables(self):
         # Create some valid DataArrays
         eastward_wind = xr.DataArray(
             rng.uniform(-20, 20, (1, 6, 5, 5)),
-            dims=["time", "level", "latitude", "longitude"],
+            dims=["valid_time", "level", "latitude", "longitude"],
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
             },
         )
 
-        levels = xr.DataArray(level, dims=["level"], coords={"level": level})
-
         # Should raise an error when required variables are missing (None values)
         with pytest.raises((TypeError, AttributeError)):
             atmospheric_river.integrated_vapor_transport(
                 specific_humidity=None,
                 eastward_wind=eastward_wind,
                 northward_wind=eastward_wind,
-                levels=levels,
             )
 
     def test_integrated_vapor_transport_low_pressure_levels(self):
@@ -468,9 +466,9 @@ def test_integrated_vapor_transport_low_pressure_levels(self):
         specific_hum = calc.specific_humidity_from_relative_humidity(
             air_temperature=xr.DataArray(
                 air_temp,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -478,9 +476,9 @@ def test_integrated_vapor_transport_low_pressure_levels(self):
             ),
             relative_humidity=xr.DataArray(
                 rel_humidity,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -496,29 +494,29 @@ def test_integrated_vapor_transport_low_pressure_levels(self):
         dataset = xr.Dataset(
             {
                 "eastward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "northward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "air_temperature": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     air_temp,
                 ),
                 "relative_humidity": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rel_humidity,
                 ),
                 "specific_humidity": specific_hum,
                 "geopotential_at_surface": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     rng.uniform(0, 1000, data_shape_3d),
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
@@ -533,14 +531,13 @@ def test_integrated_vapor_transport_low_pressure_levels(self):
             specific_humidity=dataset_filtered["specific_humidity"],
             eastward_wind=dataset_filtered["eastward_wind"],
             northward_wind=dataset_filtered["northward_wind"],
-            levels=dataset_filtered["adjusted_level"],
         )
 
         # Should return a DataArray
         assert isinstance(result, xr.DataArray)
 
         # Should have correct dimensions (no level dimension)
-        expected_dims = ["time", "latitude", "longitude"]
+        expected_dims = ["valid_time", "latitude", "longitude"]
         assert list(result.dims) == expected_dims
 
         # Should have correct shape (no level dimension)
@@ -549,6 +546,9 @@ def test_integrated_vapor_transport_low_pressure_levels(self):
         # Values should be positive (IVT magnitude)
         assert (result >= 0).all()
 
+        # There needs to be many > 0
+        assert (result > 0).any()
+
         # Values should be reasonable for IVT
         assert (result < 3000).all()
 
@@ -570,9 +570,9 @@ def sample_ivt_dataarray(self):
 
         ivt = xr.DataArray(
             ivt_data,
-            dims=["time", "latitude", "longitude"],
+            dims=["valid_time", "latitude", "longitude"],
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
             },
@@ -591,7 +591,7 @@ def test_integrated_vapor_transport_laplacian_basic(self, sample_ivt_dataarray):
         assert isinstance(result, xr.DataArray)
 
         # Should have correct dimensions
-        assert list(result.dims) == ["time", "latitude", "longitude"]
+        assert list(result.dims) == ["valid_time", "latitude", "longitude"]
 
         # Should have correct shape
         assert result.shape == (2, 10, 10)
@@ -632,9 +632,9 @@ def test_integrated_vapor_transport_laplacian_nan_handling(self):
 
         ivt = xr.DataArray(
             ivt_data,
-            dims=["time", "latitude", "longitude"],
+            dims=["valid_time", "latitude", "longitude"],
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
             },
@@ -666,9 +666,9 @@ def sample_ar_mask(self):
 
         ar_mask = xr.DataArray(
             ar_mask_data,
-            dims=["time", "latitude", "longitude"],
+            dims=["valid_time", "latitude", "longitude"],
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
             },
@@ -684,7 +684,7 @@ def test_find_land_intersection_basic(self, sample_ar_mask):
         assert isinstance(result, xr.DataArray)
 
         # Should have correct dimensions
-        expected_dims = ["time", "latitude", "longitude"]
+        expected_dims = ["valid_time", "latitude", "longitude"]
         assert list(result.dims) == expected_dims
 
         # Should have correct shape
@@ -704,9 +704,9 @@ def test_find_land_intersection_empty_mask(self):
         # Create empty AR mask
         ar_mask = xr.DataArray(
             np.zeros((1, 5, 5), dtype=int),
-            dims=["time", "latitude", "longitude"],
+            dims=["valid_time", "latitude", "longitude"],
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
             },
@@ -718,7 +718,7 @@ def test_find_land_intersection_empty_mask(self):
         assert isinstance(result, xr.DataArray)
 
         # Should have correct dimensions
-        expected_dims = ["time", "latitude", "longitude"]
+        expected_dims = ["valid_time", "latitude", "longitude"]
         assert list(result.dims) == expected_dims
 
         # Should have correct shape
@@ -752,9 +752,9 @@ def sample_full_dataset(self):
         specific_hum = calc.specific_humidity_from_relative_humidity(
             air_temperature=xr.DataArray(
                 air_temp,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -762,9 +762,9 @@ def sample_full_dataset(self):
             ),
             relative_humidity=xr.DataArray(
                 rel_humidity,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -776,29 +776,29 @@ def sample_full_dataset(self):
         dataset = xr.Dataset(
             {
                 "eastward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "northward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(-20, 20, data_shape_4d),
                 ),
                 "air_temperature": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     air_temp,
                 ),
                 "relative_humidity": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rel_humidity,
                 ),
                 "specific_humidity": specific_hum,
                 "geopotential_at_surface": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     rng.uniform(0, 1000, data_shape_3d),
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
@@ -824,14 +824,14 @@ def test_build_mask_and_land_intersection_basic(self, sample_full_dataset):
         # Atmospheric river mask should be a DataArray
         ar_mask = result["atmospheric_river_mask"]
         assert isinstance(ar_mask, xr.DataArray)
-        assert list(ar_mask.dims) == ["time", "latitude", "longitude"]
+        assert list(ar_mask.dims) == ["valid_time", "latitude", "longitude"]
 
         # Land intersection should be a DataArray with binary values
         land_intersection = result["atmospheric_river_land_intersection"]
 
         # The land intersection is stored as a DataArray with binary values
         assert isinstance(land_intersection, xr.DataArray)
-        assert list(land_intersection.dims) == ["time", "latitude", "longitude"]
+        assert list(land_intersection.dims) == ["valid_time", "latitude", "longitude"]
         # Values should be 0 or 1 (binary mask)
         assert set(land_intersection.values.flatten()).issubset({0, 1})
 
@@ -849,16 +849,16 @@ def test_build_mask_and_land_intersection_missing_variables(self):
         dataset = xr.Dataset(
             {
                 "air_temperature": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rng.uniform(250, 300, (1, 6, 5, 5)),
                 ),
                 "geopotential_at_surface": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     rng.uniform(0, 1000, data_shape_3d),
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
@@ -896,9 +896,9 @@ def test_build_mask_and_land_intersection_nan_handling(self):
         specific_hum = calc.specific_humidity_from_relative_humidity(
             air_temperature=xr.DataArray(
                 air_temp,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -906,9 +906,9 @@ def test_build_mask_and_land_intersection_nan_handling(self):
             ),
             relative_humidity=xr.DataArray(
                 rel_humidity,
-                dims=["time", "level", "latitude", "longitude"],
+                dims=["valid_time", "level", "latitude", "longitude"],
                 coords={
-                    "time": time,
+                    "valid_time": time,
                     "level": level,
                     "latitude": lat,
                     "longitude": lon,
@@ -920,29 +920,29 @@ def test_build_mask_and_land_intersection_nan_handling(self):
         dataset = xr.Dataset(
             {
                 "eastward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     eastward_wind,
                 ),
                 "northward_wind": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     northward_wind,
                 ),
                 "air_temperature": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     air_temp,
                 ),
                 "relative_humidity": (
-                    ["time", "level", "latitude", "longitude"],
+                    ["valid_time", "level", "latitude", "longitude"],
                     rel_humidity,
                 ),
                 "specific_humidity": specific_hum,
                 "geopotential_at_surface": (
-                    ["time", "latitude", "longitude"],
+                    ["valid_time", "latitude", "longitude"],
                     rng.uniform(0, 1000, data_shape_3d),
                 ),
             },
             coords={
-                "time": time,
+                "valid_time": time,
                 "latitude": lat,
                 "longitude": lon,
                 "level": level,
diff --git a/tests/test_calc.py b/tests/test_calc.py
index 7ff38eef..73294655 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -1002,141 +1002,109 @@ def test_find_land_intersection_with_custom_land_mask(self):
         assert result.shape == mask.shape
 
 
-class TestNantrapezoid:
-    """Taken from numpy testing code, with modification to include handling nans."""
+class TestNantrapezoidPressureLevels:
+    """Tests for nantrapezoid using guvectorize kernel over xarray DataArrays."""
+
+    def _make_da(self, y_values, levels_hpa):
+        """Helper to wrap a numpy array in an xarray DataArray with level coord."""
+        return xr.DataArray(
+            y_values,
+            dims=["level"],
+            coords={"level": levels_hpa},
+        )
+
+    def _make_da_nd(self, y_values, levels_hpa, extra_dims, extra_coords):
+        """Helper for multi-dimensional DataArrays with level coord."""
+        dims = list(extra_dims) + ["level"]
+        coords = {**extra_coords, "level": levels_hpa}
+        return xr.DataArray(y_values, dims=dims, coords=coords)
 
     def test_simple(self):
+        """Test integral of normal distribution equals 1."""
         x = np.arange(-10, 10, 0.1)
-        r = calc.nantrapezoid(np.exp(-0.5 * x**2) / np.sqrt(2 * np.pi), dx=0.1)
-        # check integral of normal equals 1
-        testing.assert_almost_equal(r, 1, 7)
-
-    def test_ndim(self):
-        x = np.linspace(0, 1, 3)
-        y = np.linspace(0, 2, 8)
-        z = np.linspace(0, 3, 13)
-
-        wx = np.ones_like(x) * (x[1] - x[0])
-        wx[0] /= 2
-        wx[-1] /= 2
-        wy = np.ones_like(y) * (y[1] - y[0])
-        wy[0] /= 2
-        wy[-1] /= 2
-        wz = np.ones_like(z) * (z[1] - z[0])
-        wz[0] /= 2
-        wz[-1] /= 2
-
-        q = x[:, None, None] + y[None, :, None] + z[None, None, :]
-
-        qx = (q * wx[:, None, None]).sum(axis=0)
-        qy = (q * wy[None, :, None]).sum(axis=1)
-        qz = (q * wz[None, None, :]).sum(axis=2)
-
-        # n-d `x`
-        r = calc.nantrapezoid(q, x=x[:, None, None], axis=0)
-        testing.assert_almost_equal(r, qx)
-        r = calc.nantrapezoid(q, x=y[None, :, None], axis=1)
-        testing.assert_almost_equal(r, qy)
-        r = calc.nantrapezoid(q, x=z[None, None, :], axis=2)
-        testing.assert_almost_equal(r, qz)
-
-        # 1-d `x`
-        r = calc.nantrapezoid(q, x=x, axis=0)
-        testing.assert_almost_equal(r, qx)
-        r = calc.nantrapezoid(q, x=y, axis=1)
-        testing.assert_almost_equal(r, qy)
-        r = calc.nantrapezoid(q, x=z, axis=2)
-        testing.assert_almost_equal(r, qz)
-
-    def test_masked(self):
-        # Testing that masked arrays behave as if the function is 0 where
-        # masked
-        x = np.arange(5)
-        y = x * x
-        mask = x == 2
-        ym = np.ma.array(y, mask=mask)
-        r = 13.0  # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16))
-        testing.assert_almost_equal(calc.nantrapezoid(ym, x), r)
-
-        xm = np.ma.array(x, mask=mask)
-        testing.assert_almost_equal(calc.nantrapezoid(ym, xm), r)
-
-        xm = np.ma.array(x, mask=mask)
-        testing.assert_almost_equal(calc.nantrapezoid(y, xm), r)
+        y = np.exp(-0.5 * x**2) / np.sqrt(2 * np.pi)
+        # Use x values as levels in hPa — kernel receives x*100 as Pascals
+        # so we divide by 100 to get back the original x spacing
+        levels_hpa = x / 100
+        da = self._make_da(y, levels_hpa)
+        result = calc.nantrapezoid_pressure_levels(da)
+        testing.assert_almost_equal(float(result), 1, 7)
 
     def test_nan_handling(self):
-        """Test that nantrapezoid properly handles NaN values."""
-        # Test with NaN values in y
-        x = np.array([0, 1, 2, 3, 4])
-        y = np.array([0, 1, np.nan, 9, 16])
-
-        # Should ignore NaN values in the integration
-        result = calc.nantrapezoid(y, x)
-
-        # Compare with manual calculation ignoring NaN
-        # Integration should be: 0.5*(0+1)*1 + 0.5*(9+16)*1 = 0.5 + 12.5 = 13.0
-        expected = 13.0
-        testing.assert_almost_equal(result, expected)
+        """Test that nantrapezoid properly skips intervals where either endpoint is NaN."""
+        # Kernel skips interval if y0 OR y1 is NaN
+        # intervals: (0,1), (1,nan)=skip, (nan,9)=skip, (9,16)
+        # result: 0.5*(0+1)*1 + 0.5*(9+16)*1 = 0.5 + 12.5 = 13.0
+        levels_hpa = (
+            np.array([0, 1, 2, 3, 4]) / 100
+        )  # convert to hPa so *100 = original
+        y = np.array([0.0, 1.0, np.nan, 9.0, 16.0])
+        da = self._make_da(y, levels_hpa)
+        result = calc.nantrapezoid_pressure_levels(da)
+        testing.assert_almost_equal(float(result), 13.0)
 
     def test_all_nan_values(self):
-        """Test nantrapezoid with all NaN values."""
-        x = np.array([0, 1, 2, 3])
+        """Test nantrapezoid with all NaN values returns 0."""
+        levels_hpa = np.array([0, 1, 2, 3]) / 100
         y = np.array([np.nan, np.nan, np.nan, np.nan])
-
-        result = calc.nantrapezoid(y, x)
-
-        # Should return 0 when all values are NaN (nansum behavior)
-        assert result == 0.0
+        da = self._make_da(y, levels_hpa)
+        result = calc.nantrapezoid_pressure_levels(da)
+        assert float(result) == 0.0
 
     def test_mixed_nan_and_finite(self):
-        """Test nantrapezoid with mixed NaN and finite values."""
-        # Test case with NaN at beginning
-        x = np.array([0, 1, 2, 3, 4])
-        y = np.array([np.nan, 1, 4, 9, 16])
-
-        result = calc.nantrapezoid(y, x)
-
-        # Should integrate only the finite portions
-        # Integration: 0.5*(1+4)*1 + 0.5*(4+9)*1 + 0.5*(9+16)*1 = 21.5
-        expected = 21.5
-        testing.assert_almost_equal(result, expected)
-
-    def test_nan_in_x_coordinates(self):
-        """Test nantrapezoid with NaN values in x coordinates."""
-        x = np.array([0, 1, np.nan, 3, 4])
-        y = np.array([0, 1, 4, 9, 16])
+        """Test nantrapezoid with NaN at beginning.
 
-        # Should handle NaN in x coordinates properly
-        result = calc.nantrapezoid(y, x)
-
-        # The function should still work, handling NaN appropriately
-        assert not np.isnan(result) or np.isnan(result)  # Either valid result or NaN
+        Kernel skips intervals where either endpoint is NaN.
+        intervals: (nan,1)=skip, (1,4), (4,9), (9,16)
+        result: 0.5*(1+4)*1 + 0.5*(4+9)*1 + 0.5*(9+16)*1 = 2.5 + 6.5 + 12.5 = 21.5
+        """
+        levels_hpa = np.array([0, 1, 2, 3, 4]) / 100
+        y = np.array([np.nan, 1.0, 4.0, 9.0, 16.0])
+        da = self._make_da(y, levels_hpa)
+        result = calc.nantrapezoid_pressure_levels(da)
+        testing.assert_almost_equal(float(result), 21.5)
 
     def test_multidimensional_with_nans(self):
-        """Test nantrapezoid with multidimensional arrays containing NaNs."""
-        x = np.array([0, 1, 2, 3])
-        y = np.array([[0, 1, np.nan, 9], [1, np.nan, 4, 16], [np.nan, 2, 8, 25]])
-
-        # Test integration along axis 1 (columns)
-        result = calc.nantrapezoid(y, x, axis=1)
-
-        # Should return array with proper NaN handling for each row
+        """Test nantrapezoid with multidimensional DataArray containing NaNs."""
+        levels_hpa = np.array([0, 1, 2, 3]) / 100
+        y = np.array(
+            [
+                [0.0, 1.0, np.nan, 9.0],
+                [1.0, np.nan, 4.0, 16.0],
+                [np.nan, 2.0, 8.0, 25.0],
+            ]
+        )
+        da = self._make_da_nd(
+            y,
+            levels_hpa,
+            extra_dims=["time"],
+            extra_coords={"time": [0, 1, 2]},
+        )
+        result = calc.nantrapezoid_pressure_levels(da)
         assert result.shape == (3,)
-        # At least some results should be finite (not all NaN)
-        assert not np.isnan(result).all()
+        assert not np.isnan(result.values).all()
 
-    def test_nantrapezoid_dimension_expansion(self):
-        """Test nantrapezoid with dimension mismatch (line 228 coverage)."""
-        # Create a case where y.ndim != d.ndim to trigger dimension expansion
-        x = np.array([0, 1, 2])
-        y = np.array([[1, 2, 3]])  # 2D array
+    def test_no_nans(self):
+        """Test nantrapezoid matches expected trapezoid result with no NaNs."""
+        levels_hpa = np.array([0, 1, 2, 3, 4]) / 100
+        y = np.array([0.0, 1.0, 4.0, 9.0, 16.0])
+        da = self._make_da(y, levels_hpa)
+        result = calc.nantrapezoid_pressure_levels(da)
+        # manual: 0.5*(0+1) + 0.5*(1+4) + 0.5*(4+9) + 0.5*(9+16) = 0.5+2.5+6.5+12.5 = 22.0
+        testing.assert_almost_equal(float(result), 22.0)
 
-        # This should trigger the dimension expansion on line 228
-        result = calc.nantrapezoid(y, x, axis=1)
+    def test_level_conversion_to_pascals(self):
+        """Test that level coordinates are correctly converted from hPa to Pascals.
 
-        # Should still work and return proper result
-        assert result.shape == (1,)
-        assert not np.isnan(result)
+        The kernel receives levels in Pascals (hPa * 100), so dx values
+        are 100x larger than the hPa spacing.
+        """
+        # Two levels: 900 and 1000 hPa → 90000 and 100000 Pa → dx = 10000 Pa
+        levels_hpa = np.array([900.0, 1000.0])
+        y = np.array([1.0, 1.0])  # constant 1, integral = 1 * dx = 10000
+        da = self._make_da(y, levels_hpa)
+        result = calc.nantrapezoid_pressure_levels(da)
+        testing.assert_almost_equal(float(result), 10000.0)
 
 
 class TestDewpointFromSpecificHumidity:
diff --git a/tests/test_init.py b/tests/test_init.py
index 00517ae8..49e8fcb8 100644
--- a/tests/test_init.py
+++ b/tests/test_init.py
@@ -138,7 +138,7 @@ def test_calc_public_functions(self):
         from extremeweatherbench import calc
 
         assert hasattr(calc, "find_landfalls")
-        assert hasattr(calc, "nantrapezoid")
+        assert hasattr(calc, "nantrapezoid_pressure_levels")
         assert hasattr(calc, "dewpoint_from_specific_humidity")
         assert hasattr(calc, "find_land_intersection")
         assert hasattr(calc, "haversine_distance")

From 50adbd9a327eb58269409c1c749faddc43f2f0c1 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 20 Mar 2026 17:24:47 -0400
Subject: [PATCH 26/72] bump pyproject version (#331)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 887442c3..da2061a2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "extremeweatherbench"
-version = "1.0.0"
+version = "1.0.1"
 description = "Benchmarking weather and weather AI models using extreme events"
 keywords = [
     "weather",

From e2cd113bdea2fcdce9608f1b15a99b02ff5ebd37 Mon Sep 17 00:00:00 2001
From: Rodrigo Almeida <rodrigo.almeida94@outlook.pt>
Date: Sat, 21 Mar 2026 03:52:02 +0100
Subject: [PATCH 27/72] Add ROCSS (#300)

* Add ROC skill metrics and tests

* format

---------

Co-authored-by: Rodrigo Almeida <rodrigo.almeida@hhi.fraunhofer.de>
---
 src/extremeweatherbench/metrics.py | 51 ++++++++++++++++
 tests/test_metrics.py              | 93 ++++++++++++++++++++++++++++++
 2 files changed, 144 insertions(+)

diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index 873009af..5e1034a6 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -730,6 +730,57 @@ def _compute_metric(
         return transformed.accuracy()
 
 
+class ReceiverOperatingCharacteristic(ThresholdMetric):
+    """Receiver Operating Characteristic metric."""
+
+    def __init__(self, name: str = "ReceiverOperatingCharacteristic", *args, **kwargs):
+        super().__init__(name, *args, **kwargs)
+
+    def _compute_metric(
+        self,
+        forecast: xr.DataArray,
+        target: xr.DataArray,
+        **kwargs: Any,
+    ) -> Any:
+        preserve_dims = kwargs.get("preserve_dims", self.preserve_dims)
+        op_func = utils.maybe_get_operator(kwargs.get("op_func", operator.ge))
+
+        # Binarize forecast and target using thresholds
+        binary_forecast = utils.maybe_densify_dataarray(
+            op_func(forecast, kwargs.get("forecast_threshold", self.forecast_threshold))
+        ).astype(float)
+        binary_target = utils.maybe_densify_dataarray(
+            op_func(target, kwargs.get("target_threshold", self.target_threshold))
+        ).astype(float)
+
+        return scores.probability.roc_curve_data(
+            binary_forecast,
+            binary_target,
+            thresholds="auto",
+            preserve_dims=preserve_dims,
+            weights=None,
+        )
+
+
+class ReceiverOperatingCharacteristicSkillScore(ReceiverOperatingCharacteristic):
+    """Receiver Operating Characteristic Skill Score metric."""
+
+    def __init__(
+        self, name: str = "ReceiverOperatingCharacteristicSkillScore", *args, **kwargs
+    ):
+        super().__init__(name, *args, **kwargs)
+
+    def _compute_metric(
+        self,
+        forecast: xr.DataArray,
+        target: xr.DataArray,
+        auc_reference: float = 0.5,
+        **kwargs: Any,
+    ) -> Any:
+        roc_curve_data = super()._compute_metric(forecast, target, **kwargs)
+        return (roc_curve_data["AUC"] - auc_reference) / (1 - auc_reference)
+
+
 class MeanSquaredError(BaseMetric):
     """Compute Mean Squared Error between forecast and target.
 
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index a7056806..ca1b63f8 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -250,6 +250,15 @@ def test_accuracy_threshold_metric(self):
         assert acc_metric.forecast_threshold == 15000
         assert acc_metric.target_threshold == 0.3
 
+    def test_roc_threshold_metric(self):
+        """Test ROC threshold metric instantiation and properties."""
+        roc_metric = metrics.ReceiverOperatingCharacteristic(
+            forecast_threshold=15000, target_threshold=0.3
+        )
+        assert isinstance(roc_metric, metrics.ThresholdMetric)
+        assert roc_metric.forecast_threshold == 15000
+        assert roc_metric.target_threshold == 0.3
+
     def test_threshold_metric_instance_interface(self):
         """Test that instance callable interface works."""
         # Create test data
@@ -1979,6 +1988,90 @@ def test_compute_metric(self):
         assert isinstance(result, xr.DataArray)
 
 
+class TestROCSS:
+    """Tests for the ROCSS metric."""
+
+    def test_instantiation(self):
+        """Test that ROCSS can be instantiated."""
+        metric = metrics.ReceiverOperatingCharacteristicSkillScore()
+        assert isinstance(metric, metrics.ReceiverOperatingCharacteristic)
+
+    def test_compute_metric(self):
+        """Test ROCSS computation."""
+        metric = metrics.ReceiverOperatingCharacteristicSkillScore(
+            forecast_threshold=0.5, target_threshold=0.5
+        )
+
+        forecast = xr.DataArray(
+            data=[0.8, 0.3, 0.7, 0.2],
+            dims=["lead_time"],
+            coords={"lead_time": [0, 1, 2, 3]},
+        )
+        target = xr.DataArray(
+            data=[0.9, 0.1, 0.8, 0.6],
+            dims=["lead_time"],
+            coords={"lead_time": [0, 1, 2, 3]},
+        )
+
+        result = metric._compute_metric(forecast, target)
+        assert isinstance(result, xr.DataArray)
+
+    def test_skill_score_zero_when_auc_matches_reference(self):
+        """ROCSS should be zero when AUC equals the reference value."""
+        metric = metrics.ReceiverOperatingCharacteristicSkillScore(
+            forecast_threshold=0.5, target_threshold=0.5, preserve_dims=None
+        )
+
+        forecast = xr.DataArray(
+            data=[0.8, 0.3, 0.7, 0.2],
+            dims=["sample"],
+            coords={"sample": [0, 1, 2, 3]},
+        )
+        target = xr.DataArray(
+            data=[0.9, 0.1, 0.8, 0.6],
+            dims=["sample"],
+            coords={"sample": [0, 1, 2, 3]},
+        )
+
+        roc_metric = metrics.ReceiverOperatingCharacteristic(
+            forecast_threshold=0.5, target_threshold=0.5, preserve_dims=None
+        )
+        roc_curve_data = roc_metric._compute_metric(forecast, target)
+        auc = roc_curve_data["AUC"]
+
+        auc_reference = float(auc)
+        result = metric._compute_metric(forecast, target, auc_reference=auc_reference)
+
+        xr.testing.assert_allclose(result, xr.zeros_like(auc))
+
+    def test_skill_score_scales_auc_above_reference(self):
+        """ROCSS scales the AUC improvement over the reference."""
+        forecast = xr.DataArray(
+            data=[0.9, 0.7, 0.6, 0.2],
+            dims=["sample"],
+            coords={"sample": [0, 1, 2, 3]},
+        )
+        target = xr.DataArray(
+            data=[0.8, 0.4, 0.9, 0.3],
+            dims=["sample"],
+            coords={"sample": [0, 1, 2, 3]},
+        )
+
+        roc_metric = metrics.ReceiverOperatingCharacteristic(
+            forecast_threshold=0.6, target_threshold=0.5, preserve_dims=None
+        )
+        roc_curve_data = roc_metric._compute_metric(forecast, target)
+        auc = roc_curve_data["AUC"]
+
+        metric = metrics.ReceiverOperatingCharacteristicSkillScore(
+            forecast_threshold=0.6, target_threshold=0.5, preserve_dims=None
+        )
+        result = metric._compute_metric(forecast, target, auc_reference=0.5)
+
+        expected = (auc - 0.5) / (1 - 0.5)
+        xr.testing.assert_allclose(result, expected)
+
+
 class TestMetricIntegration:
     """Integration tests for metric classes."""
 

From cf0e2966064a339452ae77aa38c0fe7a84c3f557 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 26 Mar 2026 12:00:31 -0400
Subject: [PATCH 28/72] update pyproject with icechunk (#335)

---
 pyproject.toml |    2 +-
 uv.lock        | 5345 ++++++++++++++++++++++++++----------------------
 2 files changed, 2933 insertions(+), 2414 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index da2061a2..b3b2f2ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,12 +48,12 @@ dependencies = [
     "cartopy==0.24.1",
     "numba>=0.63.1",
     "sparse>=0.17.0",
+    "icechunk>=1.1.14",
 ]
 
 [project.optional-dependencies]
 data-prep = [
     "fsspec>=2024.12.0",
-    "icechunk>=1.1.14",
     "matplotlib>=3.10.0",
     "obstore>=0.8.2",
     "scipy>=1.13",
diff --git a/uv.lock b/uv.lock
index 266bc78b..23db2e06 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 3
 requires-python = ">=3.11, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32'",
@@ -13,9 +14,9 @@ resolution-markers = [
 name = "affine"
 version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132 }
+sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662 },
+    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662, upload-time = "2023-01-19T23:44:28.833Z" },
 ]
 
 [[package]]
@@ -31,18 +32,18 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/bd/5c/f898e42fbdfc88780104881a05173f1d65a84437d99faf5771fcd09f38b1/aiobotocore-3.1.0.tar.gz", hash = "sha256:5897bde0a47b21e6f7bd00942527b099728ac5c6c6d1dcc34e999de24f3c8872", size = 122488 }
+sdist = { url = "https://files.pythonhosted.org/packages/bd/5c/f898e42fbdfc88780104881a05173f1d65a84437d99faf5771fcd09f38b1/aiobotocore-3.1.0.tar.gz", hash = "sha256:5897bde0a47b21e6f7bd00942527b099728ac5c6c6d1dcc34e999de24f3c8872", size = 122488, upload-time = "2026-01-02T20:28:30.328Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/e4/b3/923e0bc13669f06459c39a037ca7f5c1661ce252e141798a32803288c410/aiobotocore-3.1.0-py3-none-any.whl", hash = "sha256:4801f7b996f37a48faed017d23d8eae71e5fe0c3ec6bf0d9c5ce4f4d1853fce8", size = 87661, upload-time = "2026-01-02T20:28:28.817Z" },
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 [[package]]
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 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
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     { name = "yarl" },
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-version = "0.2.0"
+version = "1.0.1"
 source = { editable = "." }
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@@ -1027,6 +1125,7 @@ dependencies = [
     { name = "frozenlist" },
     { name = "gcsfs" },
     { name = "geopandas" },
+    { name = "icechunk" },
     { name = "joblib" },
     { name = "kerchunk", extra = ["dev"] },
     { name = "numba" },
@@ -1049,7 +1148,6 @@ dependencies = [
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     { name = "fsspec" },
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     { name = "matplotlib" },
     { name = "obstore" },
     { name = "scipy" },
@@ -1063,6 +1161,9 @@ multiprocessing = [
 ]
 
 [package.dev-dependencies]
+complete = [
+    { name = "extremeweatherbench", extra = ["data-prep", "multiprocessing"] },
+]
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@@ -1076,7 +1177,9 @@ dev = [
     { name = "pytest" },
     { name = "pytest-cov" },
     { name = "pytest-mock" },
+    { name = "python-semantic-release" },
     { name = "ruff" },
+    { name = "twine" },
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     { name = "types-pyyaml" },
     { name = "types-tqdm" },
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 ]
+provides-extras = ["data-prep", "multiprocessing"]
 
 [package.metadata.requires-dev]
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 dev = [
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     { name = "pytest-cov", specifier = ">=6.0.0" },
     { name = "pytest-mock", specifier = ">=3.14.0" },
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@@ -4351,35 +4798,35 @@ source = { registry = "https://pypi.org/simple" }
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From 3e0bff05cecbc1e96c22a91912500a7422b95def Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 26 Mar 2026 12:31:09 -0400
Subject: [PATCH 29/72] move dask and distributed to core deps (#336)

---
 pyproject.toml |  5 +++--
 uv.lock        | 16 +++++++---------
 2 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index b3b2f2ea..6b74fca2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,6 +49,8 @@ dependencies = [
     "numba>=0.63.1",
     "sparse>=0.17.0",
     "icechunk>=1.1.14",
+    "dask[complete]>=2025.1.0",
+    "distributed>=2025.1.0",
 ]
 
 [project.optional-dependencies]
@@ -61,7 +63,6 @@ data-prep = [
     "ujson>=5.10.0",
     "virtualizarr==2.1.2",
 ]
-multiprocessing = ["dask[complete]>=2025.1.0", "distributed>=2025.1.0"]
 
 [dependency-groups]
 dev = [
@@ -92,7 +93,7 @@ docs = [
     "pymdown-extensions>=10.19.1",
 ]
 
-complete = ["extremeweatherbench[data-prep,multiprocessing]"]
+complete = ["extremeweatherbench[data-prep]"]
 
 [build-system]
 requires = ["hatchling >= 1.26"]
diff --git a/uv.lock b/uv.lock
index 23db2e06..2a23e2e5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1120,6 +1120,8 @@ dependencies = [
     { name = "cartopy" },
     { name = "click" },
     { name = "dacite" },
+    { name = "dask", extra = ["complete"] },
+    { name = "distributed" },
     { name = "eccodes" },
     { name = "flox" },
     { name = "frozenlist" },
@@ -1155,14 +1157,10 @@ data-prep = [
     { name = "ujson" },
     { name = "virtualizarr" },
 ]
-multiprocessing = [
-    { name = "dask", extra = ["complete"] },
-    { name = "distributed" },
-]
 
 [package.dev-dependencies]
 complete = [
-    { name = "extremeweatherbench", extra = ["data-prep", "multiprocessing"] },
+    { name = "extremeweatherbench", extra = ["data-prep"] },
 ]
 dev = [
     { name = "deptry" },
@@ -1197,8 +1195,8 @@ requires-dist = [
     { name = "cartopy", specifier = "==0.24.1" },
     { name = "click", specifier = ">=8.1.8" },
     { name = "dacite", specifier = ">=1.8.1" },
-    { name = "dask", extras = ["complete"], marker = "extra == 'multiprocessing'", specifier = ">=2025.1.0" },
-    { name = "distributed", marker = "extra == 'multiprocessing'", specifier = ">=2025.1.0" },
+    { name = "dask", extras = ["complete"], specifier = ">=2025.1.0" },
+    { name = "distributed", specifier = ">=2025.1.0" },
     { name = "eccodes", specifier = "==2.42.0" },
     { name = "flox", specifier = ">=0.10.0" },
     { name = "frozenlist", specifier = "==1.5.0" },
@@ -1230,10 +1228,10 @@ requires-dist = [
     { name = "xarray", extras = ["io"], specifier = ">=2025.7.1" },
     { name = "zarr", specifier = ">=3.0.9,<4" },
 ]
-provides-extras = ["data-prep", "multiprocessing"]
+provides-extras = ["data-prep"]
 
 [package.metadata.requires-dev]
-complete = [{ name = "extremeweatherbench", extras = ["data-prep", "multiprocessing"], editable = "." }]
+complete = [{ name = "extremeweatherbench", extras = ["data-prep"], editable = "." }]
 dev = [
     { name = "deptry", specifier = ">=0.23.0" },
     { name = "ipykernel", specifier = ">=6.29.5" },

From a7d59d2875ee51d3c72b2899cd162a3cfc402ca0 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 26 Mar 2026 12:58:13 -0400
Subject: [PATCH 30/72] Make new AR parallelization work for forecasts (>1 time
 dim) (#334)

* allow for lead time as additional dim for binary dilation function

* update deps to include icechunk and dask

* update comment

* update comments, remove unneeded list loop
---
 pyproject.toml                                |  2 +
 src/extremeweatherbench/calc.py               | 14 +--
 .../events/atmospheric_river.py               | 25 +++---
 tests/test_atmospheric_river.py               | 46 ++++++++++
 tests/test_calc.py                            | 89 +++++++++++++++++++
 uv.lock                                       |  2 +
 6 files changed, 157 insertions(+), 21 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6b74fca2..504a76b8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,6 +62,8 @@ data-prep = [
     "seaborn>=0.13.2",
     "ujson>=5.10.0",
     "virtualizarr==2.1.2",
+    "dask[complete]>=2025.1.0",
+    "distributed>=2025.1.0",
 ]
 
 [dependency-groups]
diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index 125eb51e..c71de09a 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -887,20 +887,22 @@ def _is_true_landfall(
 
 
 def _binary_dilation_ufunc(data: xr.DataArray, dilation_radius: int) -> xr.DataArray:
-    """Apply binary dilation to a single 2D (lat, lon) slice.
+    """Apply binary dilation along the last two (lat, lon) axes.
+
+    Uses axes=(-2, -1) so the function works correctly for any number of
+    leading broadcast dimensions (e.g. valid_time, lead_time).
 
     Args:
-        data: 2D boolean array of shape (lat, lon)
+        data: Array of shape (..., lat, lon)
         dilation_radius: radius for the dilation in gridpoints
 
     Returns:
-        Dilated boolean array of shape (lat, lon)
+        Dilated array of the same shape as data
     """
     size = dilation_radius * 2 + 1
     struct = np.ones((size, size))
-    return np.expand_dims(
-        ndimage.binary_dilation(data.squeeze(), structure=struct).astype(np.int8),
-        axis=0,
+    return ndimage.binary_dilation(data, structure=struct, axes=(-2, -1)).astype(
+        np.int8
     )
 
 
diff --git a/src/extremeweatherbench/events/atmospheric_river.py b/src/extremeweatherbench/events/atmospheric_river.py
index 01da7f76..4346838d 100644
--- a/src/extremeweatherbench/events/atmospheric_river.py
+++ b/src/extremeweatherbench/events/atmospheric_river.py
@@ -1,4 +1,3 @@
-import dask
 import numpy as np
 import xarray as xr
 from scipy import ndimage
@@ -42,15 +41,17 @@ def atmospheric_river_mask(
 
     # Get all coordinates except level for the intersection DataArray
     coords_dict = {dim: ivt.coords[dim] for dim in ivt.dims if dim != "level"}
+
     # Create boolean masks for each condition
-    has_high_laplacian, has_high_ivt = dask.compute(
+    has_high_laplacian, has_high_ivt = (
         np.abs(ivt_laplacian) >= laplacian_threshold,
         ivt >= ivt_threshold,
     )
+
     # For the Laplacian condition, we want to check if there's a value >=
     # laplacian_threshold within 8 gridpoints (0.25 degrees).
-    # Apply binary dilation lazily via apply_ufunc before computing
-    # This keeps dilation in the Dask graph alongside ivt/laplacian tasks
+    # Apply binary dilation lazily via apply_ufunc; this keeps dilation in the Dask
+    # graph after ivt/laplacian tasks
     dilated_laplacian = xr.apply_ufunc(
         calc._binary_dilation_ufunc,
         has_high_laplacian.chunk({time_dimension: 1, "latitude": -1, "longitude": -1}),
@@ -60,29 +61,23 @@ def atmospheric_river_mask(
         dask="parallelized",
         keep_attrs=True,
         output_dtypes=[np.int8],
-    ).compute()
-    # dilation_radius = dilation_radius * 2 + 1
-    # struct = np.ones((dilation_radius, dilation_radius))
-    # dilated_laplacian = ndimage.binary_dilation(
-    #     has_high_laplacian, structure=struct, axes=(-2, -1)
-    # )
+    )
 
     # Combine conditions without tropical restriction initially
     initial_intersection = xr.where(dilated_laplacian & has_high_ivt, 1, 0)
+
     # Label connected components and get their sizes
     labeled_array, _ = ndimage.label(initial_intersection)
     unique_labels, label_counts = np.unique(labeled_array, return_counts=True)
+
     # Filter by size first (excluding background label 0)
     size_valid_labels = unique_labels[
         np.where((label_counts >= min_size_gridpoints) & (unique_labels != 0))
     ]
-    # Collect all size-valid feature labels
-    valid_features = []
-    for label_num in size_valid_labels:
-        valid_features.append(label_num)
 
     # Create final mask using valid features
-    feature_mask = np.isin(labeled_array, valid_features)
+    feature_mask = np.isin(labeled_array, size_valid_labels)
+
     # Final result with size threshold applied
     ar_mask = xr.DataArray(
         xr.where(feature_mask, 1, 0), coords=coords_dict, dims=coords_dict.keys()
diff --git a/tests/test_atmospheric_river.py b/tests/test_atmospheric_river.py
index 98a15c0a..72b22246 100644
--- a/tests/test_atmospheric_river.py
+++ b/tests/test_atmospheric_river.py
@@ -177,6 +177,52 @@ def test_atmospheric_river_mask_size_filtering(self, sample_ar_dataset):
         # Small minimum size should have more features
         assert result_small_min.sum() >= result_large_min.sum()
 
+    def test_atmospheric_river_mask_4d_input(self):
+        """Regression test: 4D (lead_time, valid_time, lat, lon) input must not
+        raise IndexError in _binary_dilation_ufunc."""
+        lead = [0, 6, 12]
+        time = pd.date_range("2023-01-01", periods=2, freq="6h")
+        lat = np.linspace(20, 50, 20)
+        lon = np.linspace(-130, -100, 20)
+
+        shape_4d = (len(lead), len(time), len(lat), len(lon))
+        ivt_data = rng.uniform(100, 300, shape_4d)
+        ivt_data[0, 0, 5:15, 5:15] = 500
+        ivt_laplacian = rng.uniform(-2, 2, shape_4d)
+        ivt_laplacian[0, 0, 5:15, 5:15] = 3.0
+
+        ivt = xr.DataArray(
+            ivt_data,
+            dims=["lead_time", "valid_time", "latitude", "longitude"],
+            coords={
+                "lead_time": lead,
+                "valid_time": time,
+                "latitude": lat,
+                "longitude": lon,
+            },
+        )
+        ivt_lap = xr.DataArray(
+            ivt_laplacian,
+            dims=["lead_time", "valid_time", "latitude", "longitude"],
+            coords={
+                "lead_time": lead,
+                "valid_time": time,
+                "latitude": lat,
+                "longitude": lon,
+            },
+        )
+
+        result = atmospheric_river.atmospheric_river_mask(
+            ivt=ivt,
+            ivt_laplacian=ivt_lap,
+        )
+
+        assert isinstance(result, xr.DataArray)
+        # All non-lat/lon dims should be preserved in the result
+        assert "latitude" in result.dims
+        assert "longitude" in result.dims
+        assert set(result.values.flatten()).issubset({0, 1})
+
     def test_atmospheric_river_mask_nan_handling(self):
         """Test atmospheric river mask with NaN values."""
         time = pd.date_range("2023-01-01", periods=2, freq="6h")
diff --git a/tests/test_calc.py b/tests/test_calc.py
index 73294655..9a8de50f 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -1107,6 +1107,95 @@ def test_level_conversion_to_pascals(self):
         testing.assert_almost_equal(float(result), 10000.0)
 
 
+class TestBinaryDilationUfunc:
+    """Tests for _binary_dilation_ufunc."""
+
+    def test_binary_dilation_ufunc_2d(self):
+        """Test that 2D (lat, lon) input is handled correctly."""
+        data = np.zeros((10, 10), dtype=bool)
+        data[5, 5] = True
+        result = calc._binary_dilation_ufunc(data, dilation_radius=1)
+        assert result.shape == (10, 10)
+        assert result.dtype == np.int8
+        assert result[5, 5] == 1
+        assert result[4, 5] == 1
+
+    def test_binary_dilation_ufunc_3d(self):
+        """Test that 3D (valid_time, lat, lon) input is handled correctly."""
+        data = np.zeros((3, 10, 10), dtype=bool)
+        data[0, 5, 5] = True
+        result = calc._binary_dilation_ufunc(data, dilation_radius=1)
+        assert result.shape == (3, 10, 10)
+        assert result.dtype == np.int8
+        assert result[0, 5, 5] == 1
+        assert result[0, 4, 5] == 1
+        # Other time steps should be zero
+        assert result[1].sum() == 0
+
+    def test_binary_dilation_ufunc_4d(self):
+        """Regression test: 4D (lead_time, valid_time, lat, lon) input must not
+        raise IndexError due to structure/ndim mismatch."""
+        data = np.zeros((4, 2, 10, 10), dtype=bool)
+        data[0, 0, 5, 5] = True
+        # Before the fix this raised IndexError: tuple index out of range
+        result = calc._binary_dilation_ufunc(data, dilation_radius=1)
+        assert result.shape == (4, 2, 10, 10)
+        assert result.dtype == np.int8
+        assert result[0, 0, 5, 5] == 1
+        assert result[0, 0, 4, 5] == 1
+        # Other lead/time slices untouched
+        assert result[1].sum() == 0
+
+    def test_binary_dilation_ufunc_dilates_correctly(self):
+        """Verify dilation expands a True region by the correct radius."""
+        radius = 2
+        data = np.zeros((20, 20), dtype=bool)
+        data[10, 10] = True
+        result = calc._binary_dilation_ufunc(data, dilation_radius=radius)
+        # The dilation structure covers a (2r+1) x (2r+1) square
+        size = radius * 2 + 1
+        assert (
+            result[10 - radius : 10 + radius + 1, 10 - radius : 10 + radius + 1].sum()
+            == size * size
+        )
+
+    def test_binary_dilation_ufunc_via_apply_ufunc_4d(self):
+        """Regression test: apply_ufunc path with 4D dask array must not crash."""
+        import xarray as xr
+
+        lead = [0, 6, 12]
+        time = pd.date_range("2023-01-01", periods=2, freq="6h")
+        lat = np.linspace(20, 50, 10)
+        lon = np.linspace(-130, -100, 10)
+
+        data = np.zeros((len(lead), len(time), len(lat), len(lon)), dtype=bool)
+        data[0, 0, 5, 5] = True
+
+        da = xr.DataArray(
+            data,
+            dims=["lead_time", "valid_time", "latitude", "longitude"],
+            coords={
+                "lead_time": lead,
+                "valid_time": time,
+                "latitude": lat,
+                "longitude": lon,
+            },
+        )
+
+        result = xr.apply_ufunc(
+            calc._binary_dilation_ufunc,
+            da.chunk({"valid_time": 1, "latitude": -1, "longitude": -1}),
+            1,
+            input_core_dims=[["latitude", "longitude"], []],
+            output_core_dims=[["latitude", "longitude"]],
+            dask="parallelized",
+            output_dtypes=[np.int8],
+        ).compute()
+
+        assert result.shape == da.shape
+        assert result.sel(lead_time=0, valid_time=time[0]).values[5, 5] == 1
+
+
 class TestDewpointFromSpecificHumidity:
     """Test the dewpoint_from_specific_humidity function."""
 
diff --git a/uv.lock b/uv.lock
index 2a23e2e5..71ba499e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1149,6 +1149,8 @@ dependencies = [
 
 [package.optional-dependencies]
 data-prep = [
+    { name = "dask", extra = ["complete"] },
+    { name = "distributed" },
     { name = "fsspec" },
     { name = "matplotlib" },
     { name = "obstore" },

From 349dbd877127faeb4aad6fbb50a73b43737c7932 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 26 Mar 2026 20:12:14 -0400
Subject: [PATCH 31/72] Update to `icechunk` for example scripts (#333)

* update preprocessers and refs to icechunk stores

* remove print statements

* swap freeze climatology to 0.15

* run -> run evaluation

* ruff
---
 docs/examples/applied_ar.py         |  60 ++-------
 docs/examples/applied_freeze.py     |  19 +--
 docs/examples/applied_heatwave.py   |   4 +-
 docs/examples/applied_severe.py     |   6 +-
 docs/examples/applied_tc.py         |  87 ++-----------
 src/extremeweatherbench/defaults.py | 194 ++++++++++++++++------------
 tests/test_defaults.py              |  19 ++-
 tests/test_init.py                  |   6 -
 8 files changed, 162 insertions(+), 233 deletions(-)

diff --git a/docs/examples/applied_ar.py b/docs/examples/applied_ar.py
index a99e5a0e..8bd82177 100644
--- a/docs/examples/applied_ar.py
+++ b/docs/examples/applied_ar.py
@@ -1,50 +1,18 @@
-import datetime
 import logging
 
-import numpy as np
-import xarray as xr
-
 import extremeweatherbench as ewb
 
-# %%
-
 # Set the logger level to INFO
 logger = logging.getLogger("extremeweatherbench")
 logger.setLevel(logging.INFO)
 
 
-# Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
-    """An example preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
-
-    Args:
-        ds: The forecast dataset to rename.
-
-    Returns:
-        The renamed forecast dataset.
-    """
-    ds = ds.rename({"time": "lead_time"})
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
-    ds["surface_wind_speed"] = np.hypot(ds["u10"], ds["v10"])
-    ds["surface_wind_from_direction"] = (
-        np.degrees(np.arctan2(ds["u10"], ds["v10"])) % 360
-    )
-    return ds
-
-
 # Load case data from the default events.yaml
 # Users can also define their own cases_dict structure
 case_yaml = ewb.load_cases()
-case_list = [n for n in case_yaml if n.case_id_number == 114]
 
-case_list[0].start_date = datetime.datetime(2022, 12, 27, 11, 0, 0)
-case_list[0].end_date = datetime.datetime(2022, 12, 27, 13, 0, 0)
-# Define ERA5 target
+
+# ERA5 target
 era5_target = ewb.targets.ERA5(
     variables=[
         ewb.derived.AtmosphericRiverVariables(
@@ -53,7 +21,7 @@ def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     ],
 )
 
-# Define forecast (HRES)
+# Forecast (HRES)
 hres_forecast = ewb.forecasts.ZarrForecast(
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     name="HRES",
@@ -65,30 +33,26 @@ def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     variable_mapping=ewb.HRES_metadata_variable_mapping,
 )
 
-grap_forecast = ewb.forecasts.KerchunkForecast(
-    name="Graphcast",
-    source="gs://extremeweatherbench/GRAP_v100_IFS.parq",
+grap_forecast = ewb.inputs.get_cira_icechunk(
+    model_name="GRAP_v100_GFS",
     variables=[
         ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
-    variable_mapping=ewb.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_cira_forecast_dataset,
+    name="Graphcast",
+    preprocess=ewb.defaults.preprocess_cira_icechunk_ar_forecast_dataset,
 )
 
-pang_forecast = ewb.forecasts.KerchunkForecast(
-    name="Pangu",
-    source="gs://extremeweatherbench/PANG_v100_IFS.parq",
+pang_forecast = ewb.inputs.get_cira_icechunk(
+    model_name="PANG_v100_GFS",
     variables=[
         ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
-    variable_mapping=ewb.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=_preprocess_cira_forecast_dataset,
+    name="Pangu",
+    preprocess=ewb.defaults.preprocess_cira_icechunk_ar_forecast_dataset,
 )
 # Create a list of evaluation objects for atmospheric river
 ar_evaluation_objects = [
@@ -133,7 +97,7 @@ def _preprocess_cira_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     )
 
     # Run the workflow using 3 jobs
-    outputs = ar_ewb.run(parallel_config={"backend": "loky", "n_jobs": 3})
+    outputs = ar_ewb.run_evaluation(parallel_config={"backend": "loky", "n_jobs": 3})
 
     # Save the evaluation outputs to a csv file
     outputs.to_csv("ar_signal_outputs.csv")
diff --git a/docs/examples/applied_freeze.py b/docs/examples/applied_freeze.py
index 864d76f7..99d75708 100644
--- a/docs/examples/applied_freeze.py
+++ b/docs/examples/applied_freeze.py
@@ -11,7 +11,7 @@
 # Users can also define their own cases_dict structure
 case_yaml = ewb.load_cases()
 
-# Define targets
+
 # ERA5 target
 era5_freeze_target = ewb.targets.ERA5(
     variables=["surface_air_temperature"],
@@ -21,18 +21,11 @@
 # GHCN target
 ghcn_freeze_target = ewb.targets.GHCN(variables=["surface_air_temperature"])
 
-# Define forecast (FCNv2 CIRA Virtualizarr)
-fcnv2_forecast = ewb.forecasts.KerchunkForecast(
-    name="fcnv2_forecast",
-    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
-    variables=["surface_air_temperature"],
-    variable_mapping=ewb.CIRA_metadata_variable_mapping,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-    preprocess=ewb.defaults._preprocess_bb_cira_forecast_dataset,
-)
+# Forecast (FCNv2) using helper in defaults
+fcnv2_forecast = ewb.defaults.cira_fcnv2_freeze_forecast
 
 # Load the climatology for DurationMeanError
-climatology = ewb.get_climatology(quantile=0.85)
+climatology = ewb.get_climatology(quantile=0.15)
 
 # Define the metrics
 metrics_list = [
@@ -65,7 +58,9 @@
     )
 
     # Run the workflow
-    outputs = freeze_ewb.run(parallel_config={"backend": "loky", "n_jobs": 1})
+    outputs = freeze_ewb.run_evaluation(
+        parallel_config={"backend": "loky", "n_jobs": 1}
+    )
 
     # Print the outputs; can be saved if desired
     outputs.to_csv("freeze_outputs.csv")
diff --git a/docs/examples/applied_heatwave.py b/docs/examples/applied_heatwave.py
index 22c9b809..60254d2c 100644
--- a/docs/examples/applied_heatwave.py
+++ b/docs/examples/applied_heatwave.py
@@ -65,5 +65,7 @@
     )
 
     # Run the workflow
-    outputs = heatwave_ewb.run(parallel_config={"backend": "loky", "n_jobs": 2})
+    outputs = heatwave_ewb.run_evaluation(
+        parallel_config={"backend": "loky", "n_jobs": 2}
+    )
     outputs.to_csv("applied_heatwave_outputs.csv")
diff --git a/docs/examples/applied_severe.py b/docs/examples/applied_severe.py
index f6a07003..c668b9af 100644
--- a/docs/examples/applied_severe.py
+++ b/docs/examples/applied_severe.py
@@ -9,6 +9,8 @@
 
 # Load case data from the default events.yaml
 case_yaml = ewb.load_cases()
+
+# Subset to one severe convection case for example
 case_list = [n for n in case_yaml if n.case_id_number == 305]
 
 # Define PPH target
@@ -83,7 +85,9 @@
     logger.info("Starting EWB run")
 
     # Run the workflow with parllel_config backend set to dask
-    outputs = severe_ewb.run(parallel_config={"backend": "loky", "n_jobs": 3})
+    outputs = severe_ewb.run_evaluation(
+        parallel_config={"backend": "loky", "n_jobs": 3}
+    )
 
     # Save the results to a CSV file
     outputs.to_csv("applied_severe_convection_results.csv")
diff --git a/docs/examples/applied_tc.py b/docs/examples/applied_tc.py
index e0d17d3c..a803630d 100644
--- a/docs/examples/applied_tc.py
+++ b/docs/examples/applied_tc.py
@@ -1,8 +1,5 @@
 import logging
 
-import numpy as np
-import xarray as xr
-
 import extremeweatherbench as ewb
 
 # Set the logger level to INFO
@@ -10,49 +7,6 @@
 logger.setLevel(logging.INFO)
 
 
-# Preprocessing function for CIRA data that includes geopotential thickness calculation
-# required for tropical cyclone tracks
-def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
-    """An example preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
-
-    Args:
-        ds: The forecast dataset to rename.
-
-    Returns:
-        The renamed forecast dataset.
-    """
-    ds = ds.rename({"time": "lead_time"})
-    # The evaluation configuration is used to set the lead time range and resolution.
-    ds["lead_time"] = np.array(
-        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-    ).astype("timedelta64[ns]")
-    ds["geopotential_thickness"] = ewb.calc.geopotential_thickness(
-        ds["z"], top_level_value=300, bottom_level_value=500
-    )
-    return ds
-
-
-# Preprocessing function for HRES data that includes geopotential thickness calculation
-# required for tropical cyclone tracks
-def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
-    """An example preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
-
-    Args:
-        ds: The forecast dataset to rename.
-    """
-    ds["geopotential_thickness"] = ewb.calc.geopotential_thickness(
-        ds["geopotential"],
-        top_level_value=300,
-        bottom_level_value=500,
-        geopotential=True,
-    )
-    return ds
-
-
 # Load the case collection from the YAML file
 case_yaml = ewb.load_cases()
 
@@ -62,45 +16,31 @@ def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 # Define IBTrACS target, no arguments needed as defaults are sufficient
 ibtracs_target = ewb.targets.IBTrACS()
 
-# Define HRES forecast
+# # Define HRES forecast
 hres_forecast = ewb.forecasts.ZarrForecast(
     name="hres_forecast",
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
-    # Define tropical cyclone track derivedvariable to include in the forecast
+    # Define tropical cyclone track derived variable to include in the forecast
     variables=[ewb.derived.TropicalCycloneTrackVariables()],
     # Define metadata variable mapping for HRES forecast
     variable_mapping=ewb.HRES_metadata_variable_mapping,
     storage_options={"remote_options": {"anon": True}},
     # Preprocess the HRES forecast to include geopotential thickness calculation
-    preprocess=ewb.defaults._preprocess_hres_tc_forecast_dataset,
+    preprocess=ewb.defaults.preprocess_hres_tc_forecast_dataset,
 )
 
-# Define FCNv2 forecast, this is the old version for reference only
-fcnv2_forecast = ewb.forecasts.KerchunkForecast(
-    name="fcn_forecast",
-    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
-    variables=[ewb.derived.TropicalCycloneTrackVariables()],
-    # Define metadata variable mapping for FCNv2 forecast
-    variable_mapping=ewb.CIRA_metadata_variable_mapping,
-    # Preprocess the FCNv2 forecast to include geopotential thickness calculation
-    preprocess=ewb.defaults._preprocess_cira_tc_forecast_dataset,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
-)
 
-# Define Pangu forecast
-pangu_forecast = ewb.forecasts.KerchunkForecast(
-    name="pangu_forecast",
-    source="gs://extremeweatherbench/PANG_v100_GFS.parq",
+# Define FCNv2 forecast, using provided defaults helper
+fcnv2_forecast = ewb.defaults.cira_fcnv2_tropical_cyclone_forecast
+
+# Define Pangu forecast, using icechunk helper. Preprocesser available in defaults
+pangu_forecast = ewb.inputs.get_cira_icechunk(
+    model_name="PANG_v100_GFS",
     variables=[ewb.derived.TropicalCycloneTrackVariables()],
-    # Define metadata variable mapping for Pangu forecast
-    variable_mapping=ewb.CIRA_metadata_variable_mapping,
-    # Preprocess the Pangu forecast to include geopotential thickness calculation
-    # which uses the same preprocessing function as the FCNv2 forecast
-    preprocess=ewb.defaults._preprocess_cira_tc_forecast_dataset,
-    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
+    name="Pangu",
+    preprocess=ewb.defaults.preprocess_cira_icechunk_tc_forecast_dataset,
 )
 
-
 # Define composite metric for tropical cyclone track metrics. Using a composite metric
 # prevents recomputation of landfalls, saving significant time. approach="next" sets
 # the evaluation to occur, in the case of multiple landfalls, for the next landfall in
@@ -152,8 +92,7 @@ def _preprocess_hres_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
         evaluation_objects=tc_evaluation_object,
     )
     logger.info("Starting EWB run")
-    # Run the workflow with parallel_config backend set to dask
-    outputs = tc_ewb.run(
-        parallel_config={"backend": "loky", "n_jobs": 3},
+    outputs = tc_ewb.run_evaluation(
+        parallel_config={"backend": "loky", "n_jobs": 1},
     )
     outputs.to_csv("tc_metric_test_results.csv")
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index 41d20e16..241d6c15 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -58,69 +58,83 @@
 ]
 
 
-def _preprocess_cira_forecast_dataset(
-    ds: xr.Dataset, kerchunk: bool = True
-) -> xr.Dataset:
-    """A preprocess function for CIRA data that renames the time coordinate to
-    lead_time, creates a valid_time coordinate, and sets the lead time range and
-    resolution not present in the original dataset.
+def preprocess_cira_icechunk_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """A preprocess function for CIRA icechunk data that includes geopotential thickness
+    calculation required for tropical cyclone tracks.
 
     Args:
-        ds: The forecast dataset to preprocess.
-        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
+        ds: The forecast dataset.
+
     Returns:
-        The preprocessed forecast dataset.
+        The forecast dataset with geopotential thickness.
     """
-
-    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
-    # and set the lead time range and resolution. Otherwise, pass through the dataset.
-    if kerchunk:
-        ds = ds.rename({"time": "lead_time"})
-        # The evaluation configuration is used to set the lead time range and resolution.
-        ds["lead_time"] = np.array(
-            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-        ).astype("timedelta64[ns]")
+    # Calculate the geopotential thickness required for tropical cyclone tracks
+    ds["geopotential_thickness"] = (
+        calc.geopotential_thickness(ds["z"], top_level=300, bottom_level=500) / 9.81
+    )
     return ds
 
 
-# Preprocessing function for CIRA data that includes geopotential thickness calculation
-# required for tropical cyclone tracks
-def _preprocess_cira_tc_forecast_dataset(
-    ds: xr.Dataset, kerchunk: bool = True
-) -> xr.Dataset:
-    """A preprocess function for CIRA data that includes geopotential thickness
-    calculation required for tropical cyclone tracks.
-
-    This function renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
+def preprocess_cira_icechunk_ar_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """Preprocess CIRA icechunk data for atmospheric rivers.
 
     Args:
         ds: The forecast dataset to rename.
-        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
+
     Returns:
         The renamed forecast dataset.
     """
-    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
-    # and set the lead time range and resolution. Otherwise, pass through the dataset.
-    if kerchunk:
-        ds = ds.rename({"time": "lead_time"})
-        # The evaluation configuration is used to set the lead time range and resolution.
-        ds["lead_time"] = np.array(
-            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-        ).astype("timedelta64[ns]")
+    if "q" not in ds.variables:
+        # Calculate specific humidity from relative humidity and air temperature
+        ds["specific_humidity"] = calc.specific_humidity_from_relative_humidity(
+            air_temperature=ds["t"],
+            relative_humidity=ds["r"] / 100,  # Convert relative humidity to percentage
+            levels=ds["level"],
+        )
+    return ds
 
-    # Calculate the geopotential thickness required for tropical cyclone tracks
-    ds["geopotential_thickness"] = (
-        calc.geopotential_thickness(ds["z"], top_level=300, bottom_level=500) / 9.81
-    )
+
+def preprocess_cira_icechunk_severe_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """Preprocess CIRA icechunk data for severe convection.
+
+    Args:
+        ds: The forecast dataset.
+
+    Returns:
+        The forecast dataset with specific humidity if not already calculated.
+    """
+    if "q" not in ds.variables:
+        # Calculate specific humidity from relative humidity and air temperature
+        ds["specific_humidity"] = calc.specific_humidity_from_relative_humidity(
+            air_temperature=ds["t"],
+            relative_humidity=ds["r"] / 100,  # Convert relative humidity to percentage
+            levels=ds["level"],
+        )
     return ds
 
 
-# Preprocessing function for HRES data that includes geopotential thickness calculation
-# required for tropical cyclone tracks
-def _preprocess_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
-    """A preprocess function for CIRA data that includes geopotential thickness
+def preprocess_cira_kerchunk_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """A preprocess function for CIRA kerchunk data that renames the time coordinate to
+    lead_time, creates a valid_time coordinate, and sets the lead time range and
+    resolution not present in the original dataset.
+
+    Args:
+        ds: The forecast dataset.
+
+    Returns:
+        The preprocessed forecast dataset.
+    """
+
+    ds = ds.rename({"time": "lead_time"})
+    # The evaluation configuration is used to set the lead time range and resolution.
+    ds["lead_time"] = np.array(
+        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+    ).astype("timedelta64[ns]")
+    return ds
+
+
+def preprocess_cira_kerchunk_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """A preprocess function for CIRA kerchunk data that includes geopotential thickness
     calculation required for tropical cyclone tracks.
 
     This function renames the time coordinate to lead_time,
@@ -133,37 +147,34 @@ def _preprocess_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     Returns:
         The renamed forecast dataset.
     """
+    ds = ds.rename({"time": "lead_time"})
+    # The evaluation configuration is used to set the lead time range and resolution.
+    ds["lead_time"] = np.array(
+        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+    ).astype("timedelta64[ns]")
 
     # Calculate the geopotential thickness required for tropical cyclone tracks
     ds["geopotential_thickness"] = (
-        calc.geopotential_thickness(ds["geopotential"], top_level=300, bottom_level=500)
-        / 9.81
+        calc.geopotential_thickness(ds["z"], top_level=300, bottom_level=500) / 9.81
     )
     return ds
 
 
 # Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_cira_ar_forecast_dataset(
-    ds: xr.Dataset, kerchunk: bool = True
-) -> xr.Dataset:
-    """An example preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
+def preprocess_cira_kerchunk_ar_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """Preprocess CIRA kerchunk data for atmospheric rivers.
 
     Args:
-        ds: The forecast dataset to rename.
-        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
+        ds: The forecast dataset.
+
     Returns:
-        The renamed forecast dataset.
+        The renamed forecast dataset with specific humidity if not already calculated.
     """
-    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
-    # and set the lead time range and resolution. Otherwise, pass through the dataset.
-    if kerchunk:
-        ds = ds.rename({"time": "lead_time"})
-        # The evaluation configuration is used to set the lead time range and resolution.
-        ds["lead_time"] = np.array(
-            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-        ).astype("timedelta64[ns]")
+    ds = ds.rename({"time": "lead_time"})
+    # The evaluation configuration is used to set the lead time range and resolution.
+    ds["lead_time"] = np.array(
+        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+    ).astype("timedelta64[ns]")
     if "q" not in ds.variables:
         # Calculate specific humidity from relative humidity and air temperature
         ds["specific_humidity"] = calc.specific_humidity_from_relative_humidity(
@@ -175,27 +186,20 @@ def _preprocess_cira_ar_forecast_dataset(
 
 
 # Preprocess function for CIRA data using Brightband kerchunk parquets
-def _preprocess_severe_cira_forecast_dataset(
-    ds: xr.Dataset, kerchunk: bool = True
-) -> xr.Dataset:
-    """An example preprocess function that renames the time coordinate to lead_time,
-    creates a valid_time coordinate, and sets the lead time range and resolution not
-    present in the original dataset.
+def preprocess_cira_kerchunk_severe_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """Preprocess CIRA kerchunk data for severe convection.
 
     Args:
-        ds: The forecast dataset to rename.
-        kerchunk: Whether the dataset is a kerchunk reference. Defaults to True.
+        ds: The forecast dataset.
+
     Returns:
-        The renamed forecast dataset.
+        The renamed forecast dataset with specific humidity if not already calculated.
     """
-    # If the dataset is a kerchunk, we need to rename the time coordinate to lead_time
-    # and set the lead time range and resolution. Otherwise, pass through the dataset.
-    if kerchunk:
-        ds = ds.rename({"time": "lead_time"})
-        # The evaluation configuration is used to set the lead time range and resolution.
-        ds["lead_time"] = np.array(
-            [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
-        ).astype("timedelta64[ns]")
+    ds = ds.rename({"time": "lead_time"})
+    # The evaluation configuration is used to set the lead time range and resolution.
+    ds["lead_time"] = np.array(
+        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
+    ).astype("timedelta64[ns]")
     if "q" not in ds.variables:
         # Calculate specific humidity from relative humidity and air temperature
         ds["specific_humidity"] = calc.specific_humidity_from_relative_humidity(
@@ -206,6 +210,26 @@ def _preprocess_severe_cira_forecast_dataset(
     return ds
 
 
+# Preprocessing function for HRES data that includes geopotential thickness calculation
+# required for tropical cyclone tracks
+def preprocess_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """Preprocess HRES data from the WeatherBench store for EWB tropical cyclones.
+
+    Args:
+        ds: The forecast dataset to rename.
+
+    Returns:
+        The forecast dataset with geopotential thickness.
+    """
+
+    # Calculate the geopotential thickness required for tropical cyclone tracks
+    ds["geopotential_thickness"] = (
+        calc.geopotential_thickness(ds["geopotential"], top_level=300, bottom_level=500)
+        / 9.81
+    )
+    return ds
+
+
 def get_climatology(quantile: float = 0.85) -> xr.DataArray:
     """Get the climatology dataset for the heatwave criteria."""
     if quantile not in [0.15, 0.85]:
@@ -277,7 +301,7 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
     model_name="FOUR_v200_GFS",
     variables=[derived.TropicalCycloneTrackVariables()],
     name="FourCastNetv2",
-    preprocess=_preprocess_cira_tc_forecast_dataset,
+    preprocess=preprocess_cira_icechunk_tc_forecast_dataset,
 )
 cira_fcnv2_atmospheric_river_forecast = inputs.get_cira_icechunk(
     model_name="FOUR_v200_GFS",
@@ -287,14 +311,14 @@ def get_climatology(quantile: float = 0.85) -> xr.DataArray:
         )
     ],
     name="FourCastNetv2",
-    preprocess=_preprocess_cira_ar_forecast_dataset,
+    preprocess=preprocess_cira_icechunk_ar_forecast_dataset,
 )
 
 cira_fcnv2_severe_convection_forecast = inputs.get_cira_icechunk(
     model_name="FOUR_v200_GFS",
     variables=[derived.CravenBrooksSignificantSevere()],
     name="FourCastNetv2",
-    preprocess=_preprocess_severe_cira_forecast_dataset,
+    preprocess=preprocess_cira_icechunk_severe_forecast_dataset,
 )
 
 
diff --git a/tests/test_defaults.py b/tests/test_defaults.py
index ff452c91..6a0e4a81 100644
--- a/tests/test_defaults.py
+++ b/tests/test_defaults.py
@@ -10,8 +10,8 @@
 class TestDefaults:
     """Test the defaults module."""
 
-    def test_preprocess_cira_forecast_dataset(self):
-        """Test the _preprocess_cira_forecast_dataset function."""
+    def test_preprocess_cira_kerchunk_forecast_dataset(self):
+        """Test the preprocess_cira_kerchunk_forecast_dataset function."""
 
         # Create a mock dataset with 'time' coordinate matching expected output size
         # The function creates lead_time with 41 values (0 to 240 by 6)
@@ -21,7 +21,7 @@ def test_preprocess_cira_forecast_dataset(self):
             {"temperature": (["time"], temp_data)}, coords={"time": time_data}
         )
 
-        result = defaults._preprocess_cira_forecast_dataset(mock_ds)
+        result = defaults.preprocess_cira_kerchunk_forecast_dataset(mock_ds)
 
         # Check that 'time' was renamed to 'lead_time'
         assert "lead_time" in result.coords
@@ -188,17 +188,24 @@ def test_freeze_forecast_has_default_preprocess(self):
     def test_tropical_cyclone_forecast_has_tc_preprocess(self):
         """Test that cira_fcnv2_tropical_cyclone_forecast uses TC preprocess."""
         forecast = defaults.cira_fcnv2_tropical_cyclone_forecast
-        assert forecast.preprocess == defaults._preprocess_cira_tc_forecast_dataset
+        assert (
+            forecast.preprocess == defaults.preprocess_cira_icechunk_tc_forecast_dataset
+        )
 
     def test_atmospheric_river_forecast_has_ar_preprocess(self):
         """Test that cira_fcnv2_atmospheric_river_forecast uses AR preprocess."""
         forecast = defaults.cira_fcnv2_atmospheric_river_forecast
-        assert forecast.preprocess == defaults._preprocess_cira_ar_forecast_dataset
+        assert (
+            forecast.preprocess == defaults.preprocess_cira_icechunk_ar_forecast_dataset
+        )
 
     def test_severe_convection_forecast_has_severe_preprocess(self):
         """Test that cira_fcnv2_severe_convection_forecast uses severe preprocess."""
         forecast = defaults.cira_fcnv2_severe_convection_forecast
-        assert forecast.preprocess == defaults._preprocess_severe_cira_forecast_dataset
+        assert (
+            forecast.preprocess
+            == defaults.preprocess_cira_icechunk_severe_forecast_dataset
+        )
 
     def test_all_forecasts_have_preprocess_attribute(self):
         """Test that all cira_fcnv2 forecasts have a preprocess attribute set."""
diff --git a/tests/test_init.py b/tests/test_init.py
index 49e8fcb8..c06d4f80 100644
--- a/tests/test_init.py
+++ b/tests/test_init.py
@@ -117,12 +117,6 @@ def test_derived_private_functions_accessible(self):
 
         assert hasattr(derived, "_maybe_convert_variable_to_string")
 
-    def test_defaults_private_functions_accessible(self):
-        """Test that private functions in defaults are accessible."""
-        from extremeweatherbench import defaults
-
-        assert hasattr(defaults, "_preprocess_cira_forecast_dataset")
-
     def test_regions_private_functions_accessible(self):
         """Test that private functions in regions are accessible."""
         from extremeweatherbench import regions

From d34ad17c49e080a32b25c1d18e808d612ef3607f Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 27 Mar 2026 13:47:45 -0400
Subject: [PATCH 32/72] Make numba (more) threadsafe working with dask (#337)

* swap target to cpu, 4d -> nd naming

* add coverage for numba funcs + check for thread safety

* ruff
---
 src/extremeweatherbench/calc.py |   8 +-
 tests/test_calc.py              | 156 ++++++++++++++++++++++++++++++++
 2 files changed, 160 insertions(+), 4 deletions(-)

diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index c71de09a..919d6b64 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -283,7 +283,7 @@ def geopotential_thickness(
     [(float64[:], float64[:], float64[:])],
     "(n),(n)->()",
     nopython=True,
-    target="parallel",
+    target="cpu",
 )
 def _nantrapezoid_kernel(y, x, out):
     """1D nan-aware trapezoid integration kernel."""
@@ -298,12 +298,12 @@ def _nantrapezoid_kernel(y, x, out):
     out[()] = total
 
 
-def nantrapezoid_4d(y, x):
+def nantrapezoid_nd(y, x):
     """
     Wrapper that moves the integration axis to last position,
     calls the guvectorize kernel, then returns result.
 
-    y: (time, level, lat, lon)
+    y: (..., level, lat, lon)
     x: (level,) or same shape as y
     """
 
@@ -324,7 +324,7 @@ def nantrapezoid_pressure_levels(da: xr.DataArray):
     levels_pa = da["level"] * 100
 
     output = xr.apply_ufunc(
-        nantrapezoid_4d,
+        nantrapezoid_nd,
         da,
         levels_pa,
         input_core_dims=[["level"], ["level"]],
diff --git a/tests/test_calc.py b/tests/test_calc.py
index 9a8de50f..3f4e41ff 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -1196,6 +1196,162 @@ def test_binary_dilation_ufunc_via_apply_ufunc_4d(self):
         assert result.sel(lead_time=0, valid_time=time[0]).values[5, 5] == 1
 
 
+class TestThreadSafety:
+    """Confirm target='cpu' kernel and binary dilation ufunc are thread-safe.
+
+    _nantrapezoid_kernel previously used target='parallel', which spawns
+    numba-internal threads. When multiple Dask workers called it
+    simultaneously, nested parallelism caused race conditions and incorrect
+    results. target='cpu' makes each invocation single-threaded so that
+    external thread pools (e.g. Dask's threaded scheduler) can call it
+    concurrently without corruption.
+    """
+
+    def test_nantrapezoid_kernel_concurrent_threads(self):
+        """_nantrapezoid_kernel must return the correct value from every
+        thread when invoked concurrently from many threads at once."""
+        import concurrent.futures
+
+        rng = np.random.default_rng(42)
+        n_levels = 200
+        x = np.sort(rng.uniform(10_000, 100_000, n_levels)).astype(np.float64)
+        y = rng.uniform(0.0, 1.0, n_levels).astype(np.float64)
+
+        ref = float(calc._nantrapezoid_kernel(y, x))
+
+        def call_kernel():
+            return float(calc._nantrapezoid_kernel(y, x))
+
+        n_workers = 16
+        n_calls = n_workers * 8
+        with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as ex:
+            futures = [ex.submit(call_kernel) for _ in range(n_calls)]
+            results = [f.result() for f in concurrent.futures.as_completed(futures)]
+
+        assert all(abs(r - ref) < 1e-6 for r in results), (
+            f"Thread-unsafe results detected; expected {ref}, got: {results}"
+        )
+
+    def test_nantrapezoid_pressure_levels_dask_threaded_matches_serial(self):
+        """nantrapezoid_pressure_levels computed with Dask's threaded
+        scheduler must match synchronous (single-threaded) computation.
+
+        This directly exercises the scenario where many Dask workers call
+        the cpu-target kernel concurrently across chunks.
+        """
+        rng = np.random.default_rng(0)
+        levels_hpa = np.array([1000, 925, 850, 700, 500, 300, 200], dtype=float)
+        n_time, n_lat, n_lon = 20, 8, 8
+
+        y = rng.uniform(0.0, 1e-3, (n_time, n_lat, n_lon, len(levels_hpa)))
+        y[::3, ::2, ::2, 0] = np.nan  # sprinkle NaNs to exercise nan path
+
+        da = xr.DataArray(
+            y,
+            dims=["time", "latitude", "longitude", "level"],
+            coords={"level": levels_hpa},
+        ).chunk({"time": 1})
+
+        serial = calc.nantrapezoid_pressure_levels(da).compute(scheduler="synchronous")
+        threaded = calc.nantrapezoid_pressure_levels(da).compute(
+            scheduler="threads", num_workers=8
+        )
+
+        xr.testing.assert_allclose(serial, threaded)
+
+    def test_nantrapezoid_nd_concurrent_threads(self):
+        """nantrapezoid_nd must produce identical results when called from
+        many threads at the same time (no shared mutable state)."""
+        import concurrent.futures
+
+        rng = np.random.default_rng(99)
+        levels = np.array([1000.0, 925, 850, 700, 500, 300, 200]) * 100  # Pa
+        n_lat, n_lon = 10, 10
+        y = rng.uniform(0.0, 1.0, (n_lat, n_lon, len(levels)))
+        x = levels.copy()
+
+        ref = calc.nantrapezoid_nd(y, x)
+
+        def call_nd():
+            return calc.nantrapezoid_nd(y, x)
+
+        n_workers = 12
+        n_calls = n_workers * 6
+        with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as ex:
+            futures = [ex.submit(call_nd) for _ in range(n_calls)]
+            results = [f.result() for f in concurrent.futures.as_completed(futures)]
+
+        for result in results:
+            np.testing.assert_allclose(result, ref, rtol=1e-6)
+
+    def test_binary_dilation_ufunc_concurrent_threads(self):
+        """_binary_dilation_ufunc must return correct results for every
+        thread when called concurrently from many threads at once."""
+        import concurrent.futures
+
+        rng = np.random.default_rng(7)
+        data = (rng.random((30, 30)) > 0.85).astype(bool)
+        radius = 2
+
+        ref = calc._binary_dilation_ufunc(data, radius)
+
+        def call_dilation():
+            return calc._binary_dilation_ufunc(data, radius)
+
+        n_workers = 16
+        n_calls = n_workers * 6
+        with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as ex:
+            futures = [ex.submit(call_dilation) for _ in range(n_calls)]
+            results = [f.result() for f in concurrent.futures.as_completed(futures)]
+
+        for result in results:
+            np.testing.assert_array_equal(result, ref)
+
+    def test_binary_dilation_via_apply_ufunc_threaded_dask(self):
+        """_binary_dilation_ufunc via apply_ufunc with dask='parallelized'
+        and a threaded scheduler must match the synchronous result.
+
+        This mirrors how atmospheric_river_mask calls the function in
+        production and validates that axes=(-2, -1) is safe under
+        concurrent execution across chunks.
+        """
+        rng = np.random.default_rng(13)
+        lead = [0, 6, 12, 18]
+        time = pd.date_range("2023-01-01", periods=6, freq="6h")
+        lat = np.linspace(20, 50, 16)
+        lon = np.linspace(-130, -100, 16)
+
+        data = (rng.random((len(lead), len(time), len(lat), len(lon))) > 0.9).astype(
+            bool
+        )
+        da = xr.DataArray(
+            data,
+            dims=["lead_time", "valid_time", "latitude", "longitude"],
+            coords={
+                "lead_time": lead,
+                "valid_time": time,
+                "latitude": lat,
+                "longitude": lon,
+            },
+        ).chunk({"lead_time": 1, "valid_time": 1, "latitude": -1, "longitude": -1})
+
+        def _apply(arr):
+            return xr.apply_ufunc(
+                calc._binary_dilation_ufunc,
+                arr,
+                1,
+                input_core_dims=[["latitude", "longitude"], []],
+                output_core_dims=[["latitude", "longitude"]],
+                dask="parallelized",
+                output_dtypes=[np.int8],
+            )
+
+        serial = _apply(da).compute(scheduler="synchronous")
+        threaded = _apply(da).compute(scheduler="threads", num_workers=8)
+
+        xr.testing.assert_equal(serial, threaded)
+
+
 class TestDewpointFromSpecificHumidity:
     """Test the dewpoint_from_specific_humidity function."""
 

From 72c6c13d800f75f1413fe65d18c08dd08de71eb3 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 30 Mar 2026 13:00:13 -0400
Subject: [PATCH 33/72] Landfall metric fixes (#338)

* ensure dimension exists when returning _interpolate_and_format_landfalls

* fix first landfall, make data flow schema more consistent

* shift landfall choice logic away from calc

* clean up landfall logic in metrics and calc

* update tests and formatting

* mypy typing catch (if a float managed to get in here i'd be impressed, but okay)
---
 src/extremeweatherbench/calc.py    | 305 +++++++++++++++--------------
 src/extremeweatherbench/metrics.py | 133 +++++--------
 src/extremeweatherbench/utils.py   |   2 -
 tests/test_calc.py                 |  77 ++++----
 tests/test_metrics.py              | 129 +++++++-----
 tests/test_utils.py                |  29 +--
 6 files changed, 323 insertions(+), 352 deletions(-)

diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index 919d6b64..36d8f0f9 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -1,3 +1,4 @@
+import logging
 from typing import Literal, Optional, Sequence, Union
 
 import numpy as np
@@ -16,6 +17,9 @@
 sat_press_0c: float = 6.112  # Saturation vapor pressure at 0°C (hPa)
 g0: float = 9.80665  # Standard gravity (m/s^2)
 
+logger = logging.getLogger("extremeweatherbench.calc")
+logger.setLevel(logging.INFO)
+
 
 def convert_from_cartesian_to_latlon(
     input_point: Union[np.ndarray, tuple[float, float]],
@@ -420,23 +424,24 @@ def dewpoint_from_specific_humidity(pressure: float, specific_humidity: float) -
 def find_landfalls(
     track_data: xr.DataArray,
     land_geom: Optional[shapely.geometry.Polygon] = None,
-    return_next_landfall: bool = False,
 ) -> xr.DataArray:
-    """Find landfall point(s) where tracked object intersects land.
+    """Find landfall point(s) where a tracked object intersects land.
 
-    Generalized landfall detection for any object (TC, AR, etc).
-    Expects DataArray with latitude, longitude, valid_time as coords.
+    Generalized landfall detection for any object (TC, AR, etc.).
 
     Args:
-        track_data: Track DataArray with latitude, longitude,
-            valid_time coords. Data values are interpolated at landfall.
-            Shape: (valid_time,) or (lead_time, valid_time)
-        return_next_landfall: If True, return next landfall; else only first landfall
-        land_geom: Shapely geometry for land intersection testing
+        track_data: Track DataArray with latitude, longitude, valid_time
+            coords. Data values are interpolated at the landfall point.
+            Shape: ``(valid_time,)`` for observations or
+            ``(lead_time, valid_time)`` for forecasts.
+        return_next_landfall: If True return all landfalls (used by the
+            "next" approach); if False return only the first landfall.
+        land_geom: Shapely geometry for land-intersection testing. Uses
+            the default NaturalEarth 10 m land polygon when None.
 
     Returns:
-        DataArray with landfall values and lat/lon/time as coords,
-        or None if no landfall found
+        DataArray with interpolated landfall values or empty if no landfalls are
+        detected.
     """
     # If no land geometry is provided, use default
     if land_geom is None:
@@ -453,42 +458,28 @@ def find_landfalls(
         # Convert to init_time coords for boundary detection
         track_data = utils.convert_valid_time_to_init_time(track_data)
 
-        # Flatten to single time dimension for vectorized processing
-        track_data_flat = track_data.stack(time=("init_time", "lead_time"))
+        # Flatten to single time dimension
+        track_data = track_data.stack(time=("init_time", "lead_time"))
 
         # Vectorized landfall detection
-        landfall_mask = _detect_landfalls_wrapper(track_data_flat, land_geom)
+        landfall_mask = _detect_landfalls_wrapper(track_data, land_geom)
 
         # Mask init_time boundaries
-        landfall_mask = _mask_init_time_boundaries(landfall_mask, track_data_flat)
-
-        # Interpolate at landfall points
-        result = _interpolate_and_format_landfalls(
-            track_data_flat,
-            landfall_mask,
-            land_geom,
-            return_next_landfall,
-            group_by="init_time",
-        )
+        landfall_mask = _mask_init_time_boundaries(landfall_mask, track_data)
 
-        return result
     else:
         # Process single track data
         landfall_mask = _detect_landfalls_wrapper(track_data, land_geom)
 
-        return _interpolate_and_format_landfalls(
-            track_data,
-            landfall_mask,
-            land_geom,
-            return_next_landfall,
-            group_by=None,
-        )
+    return _interpolate_and_format_landfalls(
+        track_data, landfall_mask, land_geom, is_forecast
+    )
 
 
 def find_next_landfall_for_init_time(
     forecast_landfalls: xr.DataArray,
     target_landfalls: xr.DataArray,
-) -> Optional[xr.DataArray]:
+) -> xr.DataArray:
     """Find unique next upcoming landfalls from target data.
 
     For each forecast initialization time, finds the next landfall
@@ -504,8 +495,8 @@ def find_next_landfall_for_init_time(
             with return_all=True (has landfall dimension)
 
     Returns:
-        DataArray with unique next landfalls indexed by init_time, or
-        None if no future landfalls exist
+        DataArray with unique next landfalls indexed by init_time. Returns
+        a zero-length DataArray if no future landfalls exist.
     """
     # Extract init_times from forecast landfalls
     if "init_time" in forecast_landfalls.dims:
@@ -528,22 +519,25 @@ def find_next_landfall_for_init_time(
     # Build result for all init_times, keeping only unique landfalls
     results = []
 
-    for i, init_t in enumerate(init_times):
+    for i, init_time in enumerate(init_times):
         if valid_mask[i]:
             # There is a future landfall for this init_time
             landfall_idx = next_indices[i]
             landfall = target_landfalls.isel(landfall=landfall_idx)
-            landfall = landfall.assign_coords(init_time=init_t)
+            landfall = landfall.expand_dims("init_time", axis=0)
+            landfall = landfall.assign_coords(init_time=("init_time", [init_time]))
 
             results.append(landfall)
 
-    # Combine landfalls indexed by init_time or return None if no results
-    return (
-        xr.concat(
-            results, dim="init_time", coords="different", compat="equals", join="outer"
+    # Combine landfalls indexed by init_time or return empty if no results
+    if not results:
+        return xr.DataArray(
+            np.array([], dtype=float),
+            dims=["init_time"],
+            coords={"init_time": np.array([], dtype="datetime64[ns]")},
         )
-        if results
-        else None
+    return xr.concat(
+        results, dim="init_time", coords="different", compat="equals", join="outer"
     )
 
 
@@ -596,7 +590,7 @@ def _check_landfall_scalar(lon1, lat1, lon2, lat2):
         lats,
         lons_180_next,
         lats_next,
-        vectorize=True,  # We already vectorized with np.vectorize
+        vectorize=True,
         dask="parallelized",
         output_dtypes=[bool],
     )
@@ -646,31 +640,43 @@ def _interpolate_and_format_landfalls(
     track_data: xr.DataArray,
     landfall_mask: xr.DataArray,
     land_geom: shapely.geometry.Polygon,
-    return_all_landfalls: bool,
-    group_by: Optional[str] = None,
-) -> Optional[xr.DataArray]:
+    is_forecast: bool,
+) -> xr.DataArray:
     """Interpolate landfall points and format output.
 
+    When no landfalls are detected the returned array is zero-length, but carries the
+    same dim and coordinate names as a populated result for the same arguments.
+
     Args:
         track_data: Original track data
         landfall_mask: Boolean mask of landfall locations
         land_geom: Land geometry for intersection
-        return_all_landfalls: Whether to return all or just first
         group_by: If not None, group by this coord (e.g., "init_time")
 
     Returns:
-        Formatted landfall DataArray or None if no landfalls
+        Formatted landfall DataArray
     """
+
+    landfall_da = xr.DataArray(
+        np.array([], dtype=float),
+        dims=["landfall"],
+        coords={
+            "landfall": np.array([], dtype=int),
+            "latitude": ("landfall", np.array([], dtype=float)),
+            "longitude": ("landfall", np.array([], dtype=float)),
+            "valid_time": ("landfall", np.array([], dtype="datetime64[ns]")),
+        },
+        name="value",
+    )
     # Determine time dimension
     time_dims = [d for d in track_data.dims if "time" in d.lower()]
-    if not time_dims:
-        return None
 
     # Get indices where landfalls occur
     landfall_indices = np.where(landfall_mask.values)[0]
 
-    if len(landfall_indices) == 0:
-        return None
+    # Return empty if no landfalls or no time dimension
+    if len(landfall_indices) == 0 or not time_dims:
+        return landfall_da
 
     # Extract coordinate arrays
     lats_vals = track_data.coords["latitude"].values
@@ -682,7 +688,6 @@ def _interpolate_and_format_landfalls(
     lons_180 = (lons_vals + 180) % 360 - 180
 
     # Get init_time if available
-    init_times = None
     if "init_time" in track_data.coords:
         init_times = track_data.coords["init_time"].values
 
@@ -729,109 +734,117 @@ def _interpolate_and_format_landfalls(
                 [(lons_180[i], lats_vals[i]), (landfall_lon, landfall_lat)]
             ).length
             frac = landfall_dist / full_dist
+            value = track_vals[i] + frac * (track_vals[i + 1] - track_vals[i])
+            if not np.isnan(track_vals[i]):
+                value = track_vals[i] + frac * (track_vals[i + 1] - track_vals[i])
+            else:
+                continue
 
-            landfall_point = {
-                "latitude": landfall_lat,
-                "longitude": utils.convert_longitude_to_360(landfall_lon),
-                "valid_time": times_vals[i]
-                + frac * (times_vals[i + 1] - times_vals[i]),
-                "value": track_vals[i] + frac * (track_vals[i + 1] - track_vals[i])
-                if not np.isnan(track_vals[i])
-                else np.nan,
-            }
+            # Create DataArray with landfall coordinate and value
+            landfall_point = xr.DataArray(
+                [value],
+                dims=["landfall"],
+                coords={
+                    "landfall": np.array([i], dtype=int),
+                    "latitude": (
+                        "landfall",
+                        np.array([landfall_lat], dtype=float),
+                    ),
+                    "longitude": (
+                        "landfall",
+                        np.array([landfall_lon], dtype=float),
+                    ),
+                    "valid_time": (
+                        "landfall",
+                        np.array(
+                            [
+                                times_vals[i]
+                                + frac * (times_vals[i + 1] - times_vals[i])
+                            ],
+                            dtype="datetime64[ns]",
+                        ),
+                    ),
+                },
+                name="value",
+            )
 
-            # Add init_time if available
-            if init_times is not None:
-                landfall_point["init_time"] = init_times[i]
+            # Add init_time if forecast data
+            if is_forecast:
+                landfall_point = landfall_point.expand_dims("init_time", axis=0)
+                landfall_point = landfall_point.assign_coords(
+                    init_time=("init_time", [init_times[i]])
+                )
 
             landfall_data.append(landfall_point)
 
-        except (IndexError, AttributeError, ValueError, TypeError, ZeroDivisionError):
+        except (
+            IndexError,
+            AttributeError,
+            ValueError,
+            TypeError,
+            ZeroDivisionError,
+        ) as e:
+            logger.warning(f"Error processing landfall at index {i}: {e}")
             continue
 
-    if not landfall_data:
-        return None
+    # Format output based on grouping
+    if landfall_data:
+        landfall_da = xr.concat(
+            landfall_data,
+            dim="landfall",
+            coords="different",
+            compat="equals",
+            join="outer",
+        )
 
-    # Check if all landfall values are NaN
-    all_nan = all(np.isnan(d.get("value", np.nan)) for d in landfall_data)
-    if all_nan:
-        return None
+    return landfall_da
 
-    # Format output based on grouping
-    if group_by == "init_time" and init_times is not None:
-        # Group by init_time and optionally keep first per group
-        results = []
-        unique_inits = np.unique([d["init_time"] for d in landfall_data])
-
-        for init_t in unique_inits:
-            init_landfalls = [d for d in landfall_data if d["init_time"] == init_t]
-
-            if not return_all_landfalls:
-                # Keep only first landfall for this init_time
-                init_landfalls = sorted(init_landfalls, key=lambda x: x["valid_time"])
-                init_landfalls = [init_landfalls[0]]
-
-            # Create DataArray with consistent landfall dimension
-            values = [d["value"] for d in init_landfalls]
-            coords = {
-                "latitude": (["landfall"], [d["latitude"] for d in init_landfalls]),
-                "longitude": (["landfall"], [d["longitude"] for d in init_landfalls]),
-                "valid_time": (["landfall"], [d["valid_time"] for d in init_landfalls]),
-                "landfall": np.arange(len(init_landfalls)),
-                "init_time": init_t,
-            }
-            da = xr.DataArray(
-                values,
-                dims=["landfall"],
-                coords=coords,
-                name=track_data.name or "landfall_value",
-            )
-            results.append(da)
 
-        return (
-            xr.concat(
-                results,
-                dim="init_time",
-                coords="different",
-                compat="equals",
-                join="outer",
-            )
-            if results
-            else None
-        )
+def broadcast_first_target_to_init_times(
+    init_times: xr.DataArray,
+    target: xr.DataArray,
+) -> xr.DataArray:
+    """Broadcast a single first-approach observation to ``(init_time,)`` shape.
 
-    else:
-        # No grouping - format as single or multiple landfalls
-        if return_all_landfalls:
-            # Return with landfall dimension
-            values = [d["value"] for d in landfall_data]
-            coords = {
-                "latitude": (["landfall"], [d["latitude"] for d in landfall_data]),
-                "longitude": (["landfall"], [d["longitude"] for d in landfall_data]),
-                "valid_time": (["landfall"], [d["valid_time"] for d in landfall_data]),
-                "landfall": np.arange(len(landfall_data)),
-            }
-            return xr.DataArray(
-                values,
-                dims=["landfall"],
-                coords=coords,
-                name=track_data.name or "landfall_value",
-            )
-        else:
-            # Return first landfall as scalar
-            d = landfall_data[0]
-            coords = {
-                "latitude": d["latitude"],
-                "longitude": d["longitude"],
-                "valid_time": d["valid_time"],
-            }
-            if "init_time" in d:
-                coords["init_time"] = d["init_time"]
-            return xr.DataArray(
-                d["value"],
-                coords=coords,
-                name=track_data.name or "landfall_value",
-            )
+    For the "first" landfall approach, ``find_landfalls`` returns the
+    observed target as ``(landfall,)`` with one element and no ``init_time``
+    dimension. This function converts it to ``(init_time,)`` so sub-metrics
+    always receive arrays with a consistent schema.
+
+    The caller is responsible for filtering ``forecast`` to only the
+    init_times that should be evaluated (e.g. those before the observed
+    landfall) before passing it here.
+
+    Args:
+        target: ``(landfall,)`` DataArray from ``find_landfalls`` for
+            observations. Must contain exactly one element.
+        forecast: ``(init_time,)`` DataArray from ``find_landfalls`` for
+            the forecast, used to determine which init_times the broadcast
+            target should span.
+
+    Returns:
+        ``(init_time,)`` DataArray with the target value, latitude,
+        longitude, and valid_time repeated across all forecast init_times.
+    """
+    t = target.isel(landfall=0)
+    n = len(init_times)
+    return xr.DataArray(
+        np.full(n, float(t.values)),
+        dims=["init_time"],
+        coords={
+            "init_time": init_times.values,
+            "latitude": ("init_time", np.full(n, float(t.coords["latitude"]))),
+            "longitude": (
+                "init_time",
+                np.full(n, float(t.coords["longitude"])),
+            ),
+            "valid_time": (
+                "init_time",
+                np.full(n, t.coords["valid_time"].values, dtype="datetime64[ns]"),
+            ),
+        },
+        name=target.name,
+    )
 
 
 # Keep existing _process_single_track_landfall for backward compatibility
diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index 5e1034a6..51df0d1e 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -1596,43 +1596,43 @@ def maybe_compute_landfalls(
         return_next_landfall = self.approach == "next"
 
         # Get first forecast landfall per init_time
-        forecast_landfalls = calc.find_landfalls(
-            forecast, return_next_landfall=return_next_landfall
-        )
+        forecast_landfalls = calc.find_landfalls(forecast)
 
-        # If no forecast landfalls, return NaN DataArrays for both forecast and target
-        if forecast_landfalls is None:
+        # find_landfalls never returns None; an empty array means no landfalls.
+        if len(forecast_landfalls) == 0:
             nan_landfalls = utils._create_nan_dataarray(self.preserve_dims)
             return (nan_landfalls, nan_landfalls.copy())
 
-        # Get only first forecast landfall per init_time
-        if "landfall" in forecast_landfalls.dims:
-            forecast_landfalls = forecast_landfalls.isel(landfall=0)
-
         # Get all target landfalls
-        target_landfalls_pre_init = calc.find_landfalls(
-            target, return_next_landfall=return_next_landfall
-        )
+        target_landfalls = calc.find_landfalls(target)
 
-        # If no target landfalls, return NaN DataArrays for both forecast and target
-        if target_landfalls_pre_init is None:
+        if len(target_landfalls) == 0:
             nan_landfalls = utils._create_nan_dataarray(self.preserve_dims)
             return (nan_landfalls, nan_landfalls.copy())
 
         if return_next_landfall:
             # Find next target landfall for each init_time
             target_landfalls = calc.find_next_landfall_for_init_time(
-                forecast_landfalls, target_landfalls_pre_init
+                forecast_landfalls, target_landfalls
             )
+            if len(target_landfalls) == 0:
+                nan_landfalls = utils._create_nan_dataarray(self.preserve_dims)
+                return (nan_landfalls, nan_landfalls.copy())
         else:
-            if "landfall" in target_landfalls_pre_init.dims:
-                target_landfalls = target_landfalls_pre_init.isel(landfall=0)
-            else:
-                target_landfalls = target_landfalls_pre_init
+            # First approach: target is (landfall,) with one observed event.
+            # Drop forecast init_times at or after the landfall to prevent
+            # data leakage, then broadcast the single target to (init_time,)
+            # so sub-metrics receive consistent arrays.
+            first_valid_time = (
+                target_landfalls.coords["valid_time"].isel(landfall=0).values
+            )
             forecast_landfalls = forecast_landfalls.where(
-                forecast_landfalls.init_time < target_landfalls.valid_time.values,
+                forecast_landfalls.init_time < first_valid_time,
                 drop=True,
             )
+            target_landfalls = calc.broadcast_first_target_to_init_times(
+                forecast_landfalls.init_time, target_landfalls
+            )
         return forecast_landfalls, target_landfalls
 
     def maybe_prepare_composite_kwargs(
@@ -1824,9 +1824,12 @@ def calculate_displacement(
         target_landfall: xr.DataArray,
         units: Literal["km", "kilometers", "deg", "degrees"] = "km",
     ) -> xr.DataArray:
-        """Calculate the distance between two landfall points in kilometers or degrees.
+        """Calculate the distance between two landfall points in km or degrees.
 
-        Handles both scalar and multi-dimensional (with init_time) DataArrays.
+        Handles targets indexed by init_time (next approach) or by landfall
+        (first approach). When the target has a landfall dim instead of
+        init_time, the single target coordinates are reused for every
+        forecast init_time.
 
         Args:
             forecast_landfall: Forecast landfall xarray DataArray
@@ -1835,47 +1838,19 @@ def calculate_displacement(
         Returns:
             Distance in the specified units as xarray DataArray
         """
-        # Find common init_times between forecast and target
-        common_init_times = utils.find_common_init_times(
-            forecast_landfall, target_landfall
+        forecast_landfall, target_landfall = xr.align(
+            forecast_landfall, target_landfall, join="inner"
         )
-
-        # If no common init_times, return NaN DataArray
-        if not common_init_times:
-            return utils._create_nan_dataarray(self.preserve_dims)
-
-        # Compute distance for each common init_time
-        distances = []
-        for init_time in common_init_times:
-            f_lat = forecast_landfall.sel(init_time=init_time).coords["latitude"].values
-            f_lon = (
-                forecast_landfall.sel(init_time=init_time).coords["longitude"].values
-            )
-            t_lat = target_landfall.sel(init_time=init_time).coords["latitude"].values
-            t_lon = target_landfall.sel(init_time=init_time).coords["longitude"].values
-
-            # Skip if any coordinates are NaN
-            if (
-                np.any(np.isnan(f_lat))
-                or np.any(np.isnan(f_lon))
-                or np.any(np.isnan(t_lat))
-                or np.any(np.isnan(t_lon))
-            ):
-                distances.append(np.nan)
-            else:
-                dist = calc.haversine_distance(
-                    [f_lat, f_lon], [t_lat, t_lon], units=units
-                )
-                # Ensure we append a scalar value
-                distances.append(
-                    float(dist.item()) if hasattr(dist, "item") else float(dist)
-                )
-
-        return xr.DataArray(
-            distances,
-            dims=["init_time"],
-            coords={"init_time": common_init_times},
+        if len(forecast_landfall) == 0:
+            return xr.DataArray(np.nan)
+        distances = calc.haversine_distance(
+            [forecast_landfall.latitude, forecast_landfall.longitude],
+            [target_landfall.latitude, target_landfall.longitude],
+            units=units,
         )
+        if not isinstance(distances, xr.DataArray):
+            raise TypeError("haversine_distance returned a scalar; expected DataArray")
+        return distances.where(forecast_landfall.notnull())
 
     def _compute_metric(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any
@@ -1925,6 +1900,11 @@ def calculate_time_difference(
     ) -> xr.DataArray:
         """Calculate the time difference between two landfall points in hours.
 
+        Handles targets indexed by init_time (next approach) or by landfall
+        (first approach). When the target has a landfall dim instead of
+        init_time, the single target valid_time is reused for every
+        forecast init_time.
+
         Args:
             forecast_landfall: Forecast landfall xarray DataArray.
             target_landfall: Target landfall xarray DataArray.
@@ -1933,30 +1913,16 @@ def calculate_time_difference(
             Time difference in hours (forecast_landfall - target_landfall)
             as xarray DataArray with init_time dimension.
         """
-        # Find common init_times between forecast and target
-        common_init_times = utils.find_common_init_times(
-            forecast_landfall, target_landfall
-        )
-
-        # If no common init_times, return NaN DataArray
-        if not common_init_times:
-            return utils._create_nan_dataarray(self.preserve_dims)
-
-        # Calculate time difference for each common init_time
-        time_diffs = []
-        for init_time in common_init_times:
-            time1 = forecast_landfall.sel(init_time=init_time).coords["valid_time"]
-            time2 = target_landfall.sel(init_time=init_time).coords["valid_time"]
-
-            # Calculate time difference in hours
-            time_diff = (time1 - time2) / np.timedelta64(1, "h")
-            time_diffs.append(float(time_diff.values))
 
-        return xr.DataArray(
-            time_diffs,
-            dims=self.preserve_dims,
-            coords={self.preserve_dims: common_init_times},
+        forecast_landfall, target_landfall = xr.align(
+            forecast_landfall, target_landfall, join="inner"
         )
+        if len(forecast_landfall) == 0:
+            return xr.DataArray(np.nan)
+        time_diffs = (
+            forecast_landfall.valid_time - target_landfall.valid_time
+        ) / np.timedelta64(1, "h")
+        return time_diffs.where(forecast_landfall.notnull())
 
     def _compute_metric(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any
@@ -2011,5 +1977,4 @@ def _compute_metric(
         ) or not utils.is_valid_landfall(target_landfall):
             return utils._create_nan_dataarray(self.preserve_dims)
 
-        # The complexity of the landfall outputs makes it easier just to use np.abs here
         return np.abs(forecast_landfall - target_landfall)
diff --git a/src/extremeweatherbench/utils.py b/src/extremeweatherbench/utils.py
index c459268d..65710234 100644
--- a/src/extremeweatherbench/utils.py
+++ b/src/extremeweatherbench/utils.py
@@ -61,7 +61,6 @@ def find_common_init_times(
     Returns:
         Sorted list of init_times present in both forecast and target.
     """
-    # Find common init_times between forecast and target
     forecast_init_times = set(forecast_landfall.coords["init_time"].values)
     target_init_times = set(target_landfall.coords["init_time"].values)
     common_init_times = sorted(forecast_init_times.intersection(target_init_times))
@@ -85,7 +84,6 @@ def is_valid_landfall(landfall: xr.DataArray | None) -> bool:
         return False
     if "init_time" not in landfall.coords:
         return False
-    # Check that we have actual data (not all NaN)
     if np.isnan(landfall.values).all():
         return False
     return True
diff --git a/tests/test_calc.py b/tests/test_calc.py
index 3f4e41ff..c510ea98 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -1539,9 +1539,7 @@ def test_find_landfalls_return_all(self):
         # Create land geometry with multiple potential crossings
         land_geom = shapely.geometry.box(-82, 24, -80, 28)
 
-        result = calc.find_landfalls(
-            track, land_geom=land_geom, return_next_landfall=True
-        )
+        result = calc.find_landfalls(track, land_geom=land_geom)
 
         # Should return DataArray with landfall dimension if found
         if result is not None:
@@ -1568,8 +1566,9 @@ def test_find_landfalls_single_point(self):
 
         result = calc.find_landfalls(track, land_geom=land_geom)
 
-        # Should return None for insufficient points
-        assert result is None
+        # Should return an empty DataArray (no landfalls detectable)
+        assert isinstance(result, xr.DataArray)
+        assert len(result) == 0
 
     def test_find_landfalls_with_track_dimension(self):
         """Test find_landfalls with singleton track dimension."""
@@ -1593,7 +1592,7 @@ def test_find_landfalls_with_track_dimension(self):
         # Should squeeze track dimension and process
         result = calc.find_landfalls(track, land_geom=land_geom)
 
-        assert result is None or isinstance(result, xr.DataArray)
+        assert isinstance(result, xr.DataArray)
 
     def test_find_next_landfall_for_init_time(self):
         """Test find_next_landfall_for_init_time function."""
@@ -1654,8 +1653,9 @@ def test_find_next_landfall_no_future_landfalls(self):
 
         result = calc.find_next_landfall_for_init_time(forecast, target_landfalls)
 
-        # Should return None when no future landfalls
-        assert result is None
+        # Should return empty DataArray when no future landfalls
+        assert isinstance(result, xr.DataArray)
+        assert len(result) == 0
 
     def test_find_next_landfall_without_init_time(self):
         """Test find_next_landfall with forecast without init_time."""
@@ -1780,7 +1780,7 @@ def test_find_landfalls_with_unnamed_dataarray(self):
         result = calc.find_landfalls(track, land_geom=land_geom)
 
         # Should handle unnamed DataArray
-        assert result is None or isinstance(result, xr.DataArray)
+        assert isinstance(result, xr.DataArray)
 
     def test_find_landfalls_all_nan_track(self):
         """Test find_landfalls with all NaN values in track."""
@@ -1802,8 +1802,9 @@ def test_find_landfalls_all_nan_track(self):
 
         result = calc.find_landfalls(track, land_geom=land_geom)
 
-        # Should return None for all NaN track
-        assert result is None
+        # Should return an empty DataArray when all track values are NaN
+        assert isinstance(result, xr.DataArray)
+        assert len(result) == 0
 
     def test_detect_landfalls_wrapper(self):
         """Test _detect_landfalls_wrapper function."""
@@ -1899,9 +1900,9 @@ def test_interpolate_and_format_landfalls(self):
 
         land_geom = shapely.geometry.box(-82, 24, -80, 26)
 
-        # Test with return_all_landfalls=False (first only)
+        # Test with is_forecast=False (non-forecast track)
         result = calc._interpolate_and_format_landfalls(
-            track, landfall_mask, land_geom, return_all_landfalls=False
+            track, landfall_mask, land_geom, is_forecast=False
         )
 
         # Should return DataArray or None
@@ -1911,7 +1912,7 @@ def test_interpolate_and_format_landfalls(self):
             assert "longitude" in result.coords
             assert "valid_time" in result.coords
 
-        # Test with return_all_landfalls=True
+        # Test with multiple landfalls
         landfall_mask_all = xr.DataArray(
             [False, True, True, False],
             dims=["valid_time"],
@@ -1919,7 +1920,7 @@ def test_interpolate_and_format_landfalls(self):
         )
 
         result_all = calc._interpolate_and_format_landfalls(
-            track, landfall_mask_all, land_geom, return_all_landfalls=True
+            track, landfall_mask_all, land_geom, is_forecast=False
         )
 
         if result_all is not None:
@@ -1957,30 +1958,21 @@ def test_no_duplicate_landfalls_single_init_time(self):
 
         land_geom = shapely.geometry.box(-82, 24, -80, 26)
 
-        result = calc.find_landfalls(
-            track, land_geom=land_geom, return_next_landfall=True
-        )
+        result = calc.find_landfalls(track, land_geom=land_geom)
 
-        if result is not None:
-            # Check that each init_time has no duplicate landfalls
-            for init_t in result.init_time.values:
-                init_result = result.sel(init_time=init_t)
-                if "landfall" in init_result.dims:
-                    # Get valid_times for this init_time
-                    times = init_result.coords["valid_time"].values
-                    lats = init_result.coords["latitude"].values
-                    lons = init_result.coords["longitude"].values
-
-                    # Check for exact duplicates
-                    for i in range(len(times)):
-                        for j in range(i + 1, len(times)):
-                            # No two landfalls should be identical
-                            same_time = times[i] == times[j]
-                            same_lat = np.isclose(lats[i], lats[j], atol=0.01)
-                            same_lon = np.isclose(lons[i], lons[j], atol=0.01)
-                            assert not (same_time and same_lat and same_lon), (
-                                f"Duplicate landfalls at init_time={init_t}"
-                            )
+        if result is not None and len(result) > 0:
+            # Check that there are no duplicate landfalls (same time+location)
+            times = result.coords["valid_time"].values
+            lats_vals = result.coords["latitude"].values
+            lons_vals = result.coords["longitude"].values
+            for i in range(len(times)):
+                for j in range(i + 1, len(times)):
+                    same_time = times[i] == times[j]
+                    same_lat = np.isclose(lats_vals[i], lats_vals[j], atol=0.01)
+                    same_lon = np.isclose(lons_vals[i], lons_vals[j], atol=0.01)
+                    assert not (same_time and same_lat and same_lon), (
+                        "Duplicate landfalls found in result"
+                    )
 
     def test_consistent_landfall_dimension(self):
         """Test that all init_times have landfall dimension (no scalars)."""
@@ -2013,9 +2005,7 @@ def test_consistent_landfall_dimension(self):
 
         land_geom = shapely.geometry.box(-82, 24, -80, 28)
 
-        result = calc.find_landfalls(
-            track, land_geom=land_geom, return_next_landfall=True
-        )
+        result = calc.find_landfalls(track, land_geom=land_geom)
 
         if result is not None and "init_time" in result.dims:
             # Check that all init_times have consistent structure
@@ -2147,7 +2137,10 @@ def test_find_next_landfall_no_future_landfalls(self):
         result = calc.find_next_landfall_for_init_time(
             forecast_landfalls, target_landfalls
         )
-        assert result is None
+
+        # Should return empty DataArray when no future landfalls
+        assert isinstance(result, xr.DataArray)
+        assert len(result) == 0
 
 
 class TestLandfallMetricAlignment:
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index ca1b63f8..3b81c1bb 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -2183,15 +2183,17 @@ def test_landfall_metrics_with_mocked_data(self):
             },
         )
 
-        # Mock landfall data (now DataArrays instead of Datasets)
+        # Mock find_landfalls for the observation target. It returns (landfall,)
+        # as find_landfalls does for observation data; maybe_compute_landfalls
+        # calls broadcast_first_target_to_init_times to convert to (init_time,).
         mock_target_landfall = xr.DataArray(
             [40.0],
-            dims=["init_time"],
+            dims=["landfall"],
             coords={
-                "init_time": [pd.Timestamp("2023-09-15")],
-                "latitude": (["init_time"], [25.0]),
-                "longitude": (["init_time"], [-80.0]),
-                "valid_time": (["init_time"], [pd.Timestamp("2023-09-15 06:00")]),
+                "landfall": [0],
+                "latitude": (["landfall"], [25.0]),
+                "longitude": (["landfall"], [-80.0]),
+                "valid_time": (["landfall"], [pd.Timestamp("2023-09-15 06:00")]),
             },
             name="surface_wind_speed",
         )
@@ -2286,15 +2288,17 @@ def test_landfall_displacement_with_known_values(self):
             },
         )
 
-        # Mock landfall data
+        # Mock find_landfalls for the observation target — (landfall,) schema.
+        # broadcast_first_target_to_init_times converts it to (init_time,)
+        # inside maybe_compute_landfalls before the sub-metric sees it.
         mock_target_landfall = xr.DataArray(
             [45.0],
-            dims=["init_time"],
+            dims=["landfall"],
             coords={
-                "init_time": [pd.Timestamp("2023-09-15")],
-                "latitude": (["init_time"], [25.7617]),
-                "longitude": (["init_time"], [-80.1918]),
-                "valid_time": (["init_time"], [pd.Timestamp("2023-09-15 12:00")]),
+                "landfall": [0],
+                "latitude": (["landfall"], [25.7617]),
+                "longitude": (["landfall"], [-80.1918]),
+                "valid_time": (["landfall"], [pd.Timestamp("2023-09-15 12:00")]),
             },
             name="surface_wind_speed",
         )
@@ -2367,24 +2371,17 @@ def test_landfall_intensity_mae_with_known_values(self):
             },
         )
 
-        # Mock landfall data - both need init_time dimension
+        # Mock find_landfalls for the observation target — (landfall,) schema.
+        # broadcast_first_target_to_init_times converts it to (init_time,)
+        # inside maybe_compute_landfalls before the sub-metric sees it.
         mock_target_landfall = xr.DataArray(
-            [50.0, 50.0],
-            dims=["init_time"],
+            [50.0],
+            dims=["landfall"],
             coords={
-                "init_time": [
-                    pd.Timestamp("2023-09-14 12:00"),
-                    pd.Timestamp("2023-09-14 12:00"),
-                ],
-                "latitude": (["init_time"], [25.0, 25.0]),
-                "longitude": (["init_time"], [-80.0, -80.0]),
-                "valid_time": (
-                    ["init_time"],
-                    [
-                        pd.Timestamp("2023-09-15 12:00"),
-                        pd.Timestamp("2023-09-15 12:00"),
-                    ],
-                ),
+                "landfall": [0],
+                "latitude": (["landfall"], [25.0]),
+                "longitude": (["landfall"], [-80.0]),
+                "valid_time": (["landfall"], [pd.Timestamp("2023-09-15 12:00")]),
             },
             name="surface_wind_speed",
         )
@@ -2471,28 +2468,22 @@ def test_landfall_time_me_with_timing_errors(self):
             },
         )
 
-        # Mock landfall data with different timing
-        # Use matching init_times so they can be compared
+        # Mock find_landfalls for the observation target — (landfall,) schema.
+        # broadcast_first_target_to_init_times converts it to (init_time,)
+        # inside maybe_compute_landfalls before calculate_time_difference runs.
         common_init_times = [
             pd.Timestamp("2023-09-14 09:00"),
             pd.Timestamp("2023-09-14 14:00"),
             pd.Timestamp("2023-09-14 12:00"),
         ]
         mock_target_landfall = xr.DataArray(
-            [50.0, 50.0, 50.0],
-            dims=["init_time"],
+            [50.0],
+            dims=["landfall"],
             coords={
-                "init_time": common_init_times,
-                "latitude": (["init_time"], [25.0, 25.0, 25.0]),
-                "longitude": (["init_time"], [-80.0, -80.0, -80.0]),
-                "valid_time": (
-                    ["init_time"],
-                    [
-                        pd.Timestamp("2023-09-15 12:00"),
-                        pd.Timestamp("2023-09-15 12:00"),
-                        pd.Timestamp("2023-09-15 12:00"),
-                    ],
-                ),
+                "landfall": [0],
+                "latitude": (["landfall"], [25.0]),
+                "longitude": (["landfall"], [-80.0]),
+                "valid_time": (["landfall"], [pd.Timestamp("2023-09-15 12:00")]),
             },
             name="surface_wind_speed",
         )
@@ -3264,14 +3255,15 @@ def test_computes_landfalls_when_not_in_kwargs(self):
                     "longitude": (["init_time"], [-80.0]),
                 },
             )
+            # Target uses (landfall,) dim, matching _interpolate_and_format_landfalls
             mock_target = xr.DataArray(
                 [2.0],
-                dims=["init_time"],
+                dims=["landfall"],
                 coords={
-                    "init_time": [pd.Timestamp("2023-09-14")],
-                    "valid_time": (["init_time"], [pd.Timestamp("2023-09-16")]),
-                    "latitude": (["init_time"], [25.0]),
-                    "longitude": (["init_time"], [-80.0]),
+                    "landfall": [0],
+                    "valid_time": (["landfall"], [pd.Timestamp("2023-09-16")]),
+                    "latitude": (["landfall"], [25.0]),
+                    "longitude": (["landfall"], [-80.0]),
                 },
             )
 
@@ -3296,8 +3288,15 @@ def test_returns_nan_dataarrays_when_no_landfalls_found(self):
         forecast = xr.DataArray([1.0], dims=["valid_time"])
         target = xr.DataArray([1.0], dims=["valid_time"])
 
+        # find_landfalls now returns an empty DataArray (never None).
+        _empty = xr.DataArray(
+            np.array([], dtype=float),
+            dims=["init_time"],
+            coords={"init_time": np.array([], dtype="datetime64[ns]")},
+        )
+
         with mock.patch.object(calc, "find_landfalls") as mock_find:
-            mock_find.return_value = None
+            mock_find.return_value = _empty
 
             result_forecast, result_target = metric.maybe_compute_landfalls(
                 forecast, target
@@ -3317,8 +3316,14 @@ def test_computes_when_only_forecast_landfall_in_kwargs(self):
         forecast = xr.DataArray([1.0], dims=["valid_time"])
         target = xr.DataArray([1.0], dims=["valid_time"])
 
+        _empty = xr.DataArray(
+            np.array([], dtype=float),
+            dims=["init_time"],
+            coords={"init_time": np.array([], dtype="datetime64[ns]")},
+        )
+
         with mock.patch.object(calc, "find_landfalls") as mock_find:
-            mock_find.return_value = None
+            mock_find.return_value = _empty
 
             # Only provide forecast_landfall, not target_landfall
             result_forecast, result_target = metric.maybe_compute_landfalls(
@@ -3336,8 +3341,14 @@ def test_computes_when_only_target_landfall_in_kwargs(self):
         forecast = xr.DataArray([1.0], dims=["valid_time"])
         target = xr.DataArray([1.0], dims=["valid_time"])
 
+        _empty = xr.DataArray(
+            np.array([], dtype=float),
+            dims=["init_time"],
+            coords={"init_time": np.array([], dtype="datetime64[ns]")},
+        )
+
         with mock.patch.object(calc, "find_landfalls") as mock_find:
-            mock_find.return_value = None
+            mock_find.return_value = _empty
 
             # Only provide target_landfall, not forecast_landfall
             result_forecast, result_target = metric.maybe_compute_landfalls(
@@ -3354,8 +3365,14 @@ def test_returns_tuple_type(self):
         forecast = xr.DataArray([1.0], dims=["valid_time"])
         target = xr.DataArray([1.0], dims=["valid_time"])
 
+        _empty = xr.DataArray(
+            np.array([], dtype=float),
+            dims=["init_time"],
+            coords={"init_time": np.array([], dtype="datetime64[ns]")},
+        )
+
         with mock.patch.object(calc, "find_landfalls") as mock_find:
-            mock_find.return_value = None
+            mock_find.return_value = _empty
 
             result = metric.maybe_compute_landfalls(forecast, target)
 
@@ -3407,9 +3424,15 @@ def test_compute_metric_handles_no_landfalls(self):
         forecast = xr.DataArray([1.0], dims=["valid_time"])
         target = xr.DataArray([1.0], dims=["valid_time"])
 
+        _empty = xr.DataArray(
+            np.array([], dtype=float),
+            dims=["init_time"],
+            coords={"init_time": np.array([], dtype="datetime64[ns]")},
+        )
+
         for metric in metrics_to_test:
             with mock.patch.object(calc, "find_landfalls") as mock_find:
-                mock_find.return_value = None
+                mock_find.return_value = _empty
 
                 result = metric._compute_metric(forecast, target)
 
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 85da431c..ad935f5e 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1562,43 +1562,22 @@ class TestNoCircularImports:
 
     def test_import_utils_then_evaluate(self):
         """Test importing utils then evaluate doesn't cause circular imports."""
-        import sys
-
-        # Remove modules if already imported
-        modules_to_remove = [
-            k for k in sys.modules.keys() if k.startswith("extremeweatherbench")
-        ]
-        for mod in modules_to_remove:
-            del sys.modules[mod]
-
-        # Import utils first
-        # Then import evaluate
+        # Circular imports are caught at first import time (which already
+        # happened when pytest started). Wiping sys.modules and reimporting
+        # creates duplicate module objects that break @patch in other tests,
+        # so we just assert both modules are accessible.
         from extremeweatherbench import evaluate as evaluate_module
         from extremeweatherbench import utils as utils_module
 
-        # Verify both imported successfully
         assert utils_module is not None
         assert evaluate_module is not None
         assert hasattr(utils_module, "maybe_cache_and_compute")
 
     def test_import_evaluate_then_utils(self):
         """Test importing evaluate then utils works fine."""
-        import sys
-
-        # Remove modules if already imported
-        modules_to_remove = [
-            k for k in sys.modules.keys() if k.startswith("extremeweatherbench")
-        ]
-        for mod in modules_to_remove:
-            del sys.modules[mod]
-
-        # Import evaluate first
         from extremeweatherbench import evaluate as evaluate_module
-
-        # Then import utils
         from extremeweatherbench import utils as utils_module
 
-        # Verify both imported successfully
         assert evaluate_module is not None
         assert utils_module is not None
 

From c20c2b1eb94a8e2f6576137164c25d4bb2b0477f Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Mon, 30 Mar 2026 17:04:21 +0000
Subject: [PATCH 34/72] update landfall intensity mae metric to remove nans

---
 src/extremeweatherbench/metrics.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index 51df0d1e..a7803b1b 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -1977,4 +1977,8 @@ def _compute_metric(
         ) or not utils.is_valid_landfall(target_landfall):
             return utils._create_nan_dataarray(self.preserve_dims)
 
-        return np.abs(forecast_landfall - target_landfall)
+        return (
+            np.abs(forecast_landfall - target_landfall)
+            .where(forecast_landfall.notnull())
+            .mean(dim="landfall")
+        )

From ccf14fb92fa30b5496648e0e88f68279f4d401d0 Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Mon, 30 Mar 2026 17:56:50 +0000
Subject: [PATCH 35/72] add landfall number aggregation; remove nans

---
 src/extremeweatherbench/metrics.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index a7803b1b..ce3aeec9 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -1850,7 +1850,7 @@ def calculate_displacement(
         )
         if not isinstance(distances, xr.DataArray):
             raise TypeError("haversine_distance returned a scalar; expected DataArray")
-        return distances.where(forecast_landfall.notnull())
+        return distances.where(forecast_landfall.notnull()).mean(dim="landfall")
 
     def _compute_metric(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any
@@ -1922,7 +1922,7 @@ def calculate_time_difference(
         time_diffs = (
             forecast_landfall.valid_time - target_landfall.valid_time
         ) / np.timedelta64(1, "h")
-        return time_diffs.where(forecast_landfall.notnull())
+        return time_diffs.where(forecast_landfall.notnull()).mean(dim="landfall")
 
     def _compute_metric(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any

From 3e33f399918bc059299e0e5742d56491547738a7 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Tue, 31 Mar 2026 14:27:15 -0400
Subject: [PATCH 36/72] Landfall test dim fix (#341)

* align dims on tests

* add dim fix to calc tests
---
 tests/test_calc.py    |  68 ++++++++++++++++----------
 tests/test_metrics.py | 109 ++++++++++++++++++++++++------------------
 2 files changed, 105 insertions(+), 72 deletions(-)

diff --git a/tests/test_calc.py b/tests/test_calc.py
index c510ea98..8b611a7e 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -2152,32 +2152,41 @@ def test_common_init_times_in_distance_calculation(self):
 
         # Create forecast landfalls (3 init_times)
         forecast_landfalls = xr.DataArray(
-            [35.0, 36.0, 37.0],
-            dims=["init_time"],
+            [[35.0], [36.0], [37.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": pd.to_datetime(["2023-09-14", "2023-09-15", "2023-09-16"]),
+                "landfall": [0],
                 "valid_time": (
-                    ["init_time"],
-                    pd.to_datetime(["2023-09-15", "2023-09-16", "2023-09-17"]),
+                    ["init_time", "landfall"],
+                    [
+                        [pd.Timestamp("2023-09-15")],
+                        [pd.Timestamp("2023-09-16")],
+                        [pd.Timestamp("2023-09-17")],
+                    ],
                 ),
-                "latitude": (["init_time"], [24.0, 25.0, 26.0]),
-                "longitude": (["init_time"], [280.0, 279.0, 278.0]),
+                "latitude": (["init_time", "landfall"], [[24.0], [25.0], [26.0]]),
+                "longitude": (["init_time", "landfall"], [[280.0], [279.0], [278.0]]),
             },
             name="surface_wind_speed",
         )
 
         # Create target landfalls (only 2 matching init_times)
         target_landfalls = xr.DataArray(
-            [40.0, 45.0],
-            dims=["init_time"],
+            [[40.0], [45.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": pd.to_datetime(["2023-09-14", "2023-09-15"]),
+                "landfall": [0],
                 "valid_time": (
-                    ["init_time"],
-                    pd.to_datetime(["2023-09-15 06:00", "2023-09-16 06:00"]),
+                    ["init_time", "landfall"],
+                    [
+                        [pd.Timestamp("2023-09-15 06:00")],
+                        [pd.Timestamp("2023-09-16 06:00")],
+                    ],
                 ),
-                "latitude": (["init_time"], [24.5, 25.5]),
-                "longitude": (["init_time"], [279.5, 278.5]),
+                "latitude": (["init_time", "landfall"], [[24.5], [25.5]]),
+                "longitude": (["init_time", "landfall"], [[279.5], [278.5]]),
             },
             name="surface_wind_speed",
         )
@@ -2199,35 +2208,42 @@ def test_time_difference_aligned_calculation(self):
 
         # Create forecast and target with different init_times
         forecast_landfalls = xr.DataArray(
-            [35.0, 36.0, 37.0],
-            dims=["init_time"],
+            [[35.0], [36.0], [37.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": pd.to_datetime(
                     ["2023-09-14 00:00", "2023-09-14 12:00", "2023-09-15 00:00"]
                 ),
+                "landfall": [0],
                 "valid_time": (
-                    ["init_time"],
-                    pd.to_datetime(
-                        ["2023-09-15 00:00", "2023-09-15 12:00", "2023-09-16 00:00"]
-                    ),
+                    ["init_time", "landfall"],
+                    [
+                        [pd.Timestamp("2023-09-15 00:00")],
+                        [pd.Timestamp("2023-09-15 12:00")],
+                        [pd.Timestamp("2023-09-16 00:00")],
+                    ],
                 ),
-                "latitude": (["init_time"], [24.0, 25.0, 26.0]),
-                "longitude": (["init_time"], [280.0, 279.0, 278.0]),
+                "latitude": (["init_time", "landfall"], [[24.0], [25.0], [26.0]]),
+                "longitude": (["init_time", "landfall"], [[280.0], [279.0], [278.0]]),
             },
             name="surface_wind_speed",
         )
 
         target_landfalls = xr.DataArray(
-            [40.0, 45.0],
-            dims=["init_time"],
+            [[40.0], [45.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": pd.to_datetime(["2023-09-14 00:00", "2023-09-14 12:00"]),
+                "landfall": [0],
                 "valid_time": (
-                    ["init_time"],
-                    pd.to_datetime(["2023-09-15 06:00", "2023-09-15 18:00"]),
+                    ["init_time", "landfall"],
+                    [
+                        [pd.Timestamp("2023-09-15 06:00")],
+                        [pd.Timestamp("2023-09-15 18:00")],
+                    ],
                 ),
-                "latitude": (["init_time"], [24.5, 25.5]),
-                "longitude": (["init_time"], [279.5, 278.5]),
+                "latitude": (["init_time", "landfall"], [[24.5], [25.5]]),
+                "longitude": (["init_time", "landfall"], [[279.5], [278.5]]),
             },
             name="surface_wind_speed",
         )
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 3b81c1bb..e6d95b3c 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -2199,13 +2199,17 @@ def test_landfall_metrics_with_mocked_data(self):
         )
 
         mock_forecast_landfall = xr.DataArray(
-            [38.0],
-            dims=["init_time"],
+            [[38.0]],
+            dims=["init_time", "landfall"],
             coords={
-                "latitude": (["init_time"], [25.1]),
-                "longitude": (["init_time"], [-80.1]),
-                "valid_time": (["init_time"], [pd.Timestamp("2023-09-15 06:00")]),
                 "init_time": [pd.Timestamp("2023-09-15")],
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[25.1]]),
+                "longitude": (["init_time", "landfall"], [[-80.1]]),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [[pd.Timestamp("2023-09-15 06:00")]],
+                ),
             },
             name="surface_wind_speed",
         )
@@ -2304,13 +2308,17 @@ def test_landfall_displacement_with_known_values(self):
         )
 
         mock_forecast_landfall = xr.DataArray(
-            [42.0],
-            dims=["init_time"],
+            [[42.0]],
+            dims=["init_time", "landfall"],
             coords={
-                "latitude": (["init_time"], [26.1224]),
-                "longitude": (["init_time"], [-80.1373]),
-                "valid_time": (["init_time"], [pd.Timestamp("2023-09-15 12:00")]),
                 "init_time": [pd.Timestamp("2023-09-15")],
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[26.1224]]),
+                "longitude": (["init_time", "landfall"], [[-80.1373]]),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [[pd.Timestamp("2023-09-15 12:00")]],
+                ),
             },
             name="surface_wind_speed",
         )
@@ -2387,20 +2395,21 @@ def test_landfall_intensity_mae_with_known_values(self):
         )
 
         mock_forecast_landfall = xr.DataArray(
-            [53.0, 48.0],
-            dims=["init_time"],
+            [[53.0], [48.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": [
                     pd.Timestamp("2023-09-14 12:00"),
-                    pd.Timestamp("2023-09-14 12:00"),
+                    pd.Timestamp("2023-09-14 14:00"),
                 ],
-                "latitude": (["init_time"], [25.1, 25.2]),
-                "longitude": (["init_time"], [-80.1, -80.2]),
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[25.1], [25.2]]),
+                "longitude": (["init_time", "landfall"], [[-80.1], [-80.2]]),
                 "valid_time": (
-                    ["init_time"],
+                    ["init_time", "landfall"],
                     [
-                        pd.Timestamp("2023-09-15 12:00"),
-                        pd.Timestamp("2023-09-15 12:00"),
+                        [pd.Timestamp("2023-09-15 12:00")],
+                        [pd.Timestamp("2023-09-15 12:00")],
                     ],
                 ),
             },
@@ -2490,20 +2499,21 @@ def test_landfall_time_me_with_timing_errors(self):
 
         # Forecasts with early, late, and correct timing
         mock_forecast_landfall = xr.DataArray(
-            [50.0, 50.0, 50.0],
-            dims=["init_time"],
+            [[50.0], [50.0], [50.0]],
+            dims=["init_time", "landfall"],
             coords={
-                "latitude": (["init_time"], [25.0, 25.0, 25.0]),
-                "longitude": (["init_time"], [-80.0, -80.0, -80.0]),
+                "init_time": common_init_times,
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[25.0], [25.0], [25.0]]),
+                "longitude": (["init_time", "landfall"], [[-80.0], [-80.0], [-80.0]]),
                 "valid_time": (
-                    ["init_time"],
+                    ["init_time", "landfall"],
                     [
-                        pd.Timestamp("2023-09-15 09:00"),  # 3 hours early
-                        pd.Timestamp("2023-09-15 14:00"),  # 2 hours late
-                        pd.Timestamp("2023-09-15 12:00"),  # Perfect
+                        [pd.Timestamp("2023-09-15 09:00")],  # 3 hours early
+                        [pd.Timestamp("2023-09-15 14:00")],  # 2 hours late
+                        [pd.Timestamp("2023-09-15 12:00")],  # Perfect
                     ],
                 ),
-                "init_time": common_init_times,
             },
             name="surface_wind_speed",
         )
@@ -2701,22 +2711,24 @@ def test_landfall_displacement_no_common_init_times(self):
         metric = metrics.LandfallDisplacement(approach="first")
 
         forecast_landfall = xr.DataArray(
-            [35.0],
-            dims=["init_time"],
+            [[35.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": [pd.Timestamp("2023-09-14")],
-                "latitude": (["init_time"], [25.0]),
-                "longitude": (["init_time"], [-80.0]),
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[25.0]]),
+                "longitude": (["init_time", "landfall"], [[-80.0]]),
             },
         )
 
         target_landfall = xr.DataArray(
-            [40.0],
-            dims=["init_time"],
+            [[40.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": [pd.Timestamp("2023-09-15")],
-                "latitude": (["init_time"], [25.5]),
-                "longitude": (["init_time"], [-80.5]),
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[25.5]]),
+                "longitude": (["init_time", "landfall"], [[-80.5]]),
             },
         )
 
@@ -2729,22 +2741,24 @@ def test_landfall_displacement_with_nan_coordinates(self):
         metric = metrics.LandfallDisplacement(approach="first")
 
         forecast_landfall = xr.DataArray(
-            [35.0],
-            dims=["init_time"],
+            [[35.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": [pd.Timestamp("2023-09-14")],
-                "latitude": (["init_time"], [np.nan]),
-                "longitude": (["init_time"], [-80.0]),
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[np.nan]]),
+                "longitude": (["init_time", "landfall"], [[-80.0]]),
             },
         )
 
         target_landfall = xr.DataArray(
-            [40.0],
-            dims=["init_time"],
+            [[40.0]],
+            dims=["init_time", "landfall"],
             coords={
                 "init_time": [pd.Timestamp("2023-09-14")],
-                "latitude": (["init_time"], [25.5]),
-                "longitude": (["init_time"], [-80.5]),
+                "landfall": [0],
+                "latitude": (["init_time", "landfall"], [[25.5]]),
+                "longitude": (["init_time", "landfall"], [[-80.5]]),
             },
         )
 
@@ -2783,9 +2797,12 @@ def test_landfall_intensity_mae_basic(self):
         metric = metrics.LandfallIntensityMeanAbsoluteError(approach="first")
 
         forecast_landfall = xr.DataArray(
-            [50.0],
-            dims=["init_time"],
-            coords={"init_time": [pd.Timestamp("2023-09-14")]},
+            [[50.0]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [pd.Timestamp("2023-09-14")],
+                "landfall": [0],
+            },
         )
 
         target_landfall = xr.DataArray(

From f38a759ecd6e68df643297e4e994481d227cd500 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Tue, 31 Mar 2026 15:05:26 -0400
Subject: [PATCH 37/72] `EarlySignal` fixes (#340)

* mid-flight using mean in detection mask

* update early signal to include more robust aggregations

* add tests
---
 src/extremeweatherbench/metrics.py | 125 +++++++++++++------
 tests/test_metrics.py              | 194 +++++++++++++++++++++++++++++
 2 files changed, 284 insertions(+), 35 deletions(-)

diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index ce3aeec9..b58534c5 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -984,6 +984,11 @@ class EarlySignal(BaseMetric):
     Extends BaseMetric to find the earliest time when a signal is detected
     based on threshold criteria, returning init_time, lead_time, and
     valid_time information. Flexible for different signal detection criteria.
+
+    For any case, the default behavior is to confirm any valid signal in space, then
+    time. This behavior can be changed by setting spatial_aggregation and
+    temporal_aggregation to "any", "all", or "half". Further, the order of aggregation
+    can be changed by setting order to ["temporal", "spatial"] if desired.
     """
 
     def __init__(
@@ -994,6 +999,10 @@ def __init__(
         ] = ">=",
         threshold: float = 0.5,
         spatial_aggregation: Literal["any", "all", "half"] = "any",
+        temporal_aggregation: Literal["any", "all", "half"] = "any",
+        aggregation_order: tuple[
+            Literal["spatial", "temporal"], Literal["temporal", "spatial"]
+        ] = ("spatial", "temporal"),
         **kwargs,
     ):
         """Initialize the Early Signal detection metric.
@@ -1005,13 +1014,48 @@ def __init__(
             spatial_aggregation: Spatial aggregation method. Options: "any"
                 (any gridpoint meets criteria), "all" (all gridpoints meet
                 criteria), or "half" (at least half meet criteria).
+            temporal_aggregation: Temporal aggregation method. Options: "any"
+                (any valid time meets criteria), "all" (all valid times meet
+                criteria), or "half" (at least half valid times meet criteria).
+            aggregation_order: The order of spatial and temporal aggregation. Defaults
+                to ("spatial", "temporal").
             **kwargs: Additional keyword arguments passed to BaseMetric.
         """
-        # Extract threshold params before passing to super
+        super().__init__(name=name, **kwargs)
         self.comparison_operator = utils.maybe_get_operator(comparison_operator)
         self.threshold = threshold
         self.spatial_aggregation = spatial_aggregation
-        super().__init__(name, **kwargs)
+        self.temporal_aggregation = temporal_aggregation
+        self.aggregation_order = aggregation_order
+
+    def _apply_aggregation(
+        self,
+        detection_mask: xr.DataArray,
+        aggregation: Literal["any", "all", "half"],
+        dims_to_reduce: list[str],
+    ) -> xr.DataArray:
+        """Apply aggregation to the detection mask.
+
+        Args:
+            detection_mask: The detection mask DataArray.
+            aggregation: The aggregation method. Options: "any"
+                (any gridpoint meets criteria), "all" (all gridpoints meet
+                criteria), or "half" (at least half meet criteria).
+            dims_to_reduce: The dimensions to reduce.
+        Returns:
+            The aggregated detection mask DataArray.
+        """
+        if aggregation == "any":
+            # fillna(False): NaN means no data, not a True detection
+            result = detection_mask.fillna(False).any(dims_to_reduce)
+        elif aggregation == "all":
+            # fillna(True): NaN means no data, don't penalize "all" condition
+            result = detection_mask.fillna(True).all(dims_to_reduce)
+        elif aggregation == "half":
+            result = detection_mask.mean(dim=dims_to_reduce) >= 0.5
+        else:
+            raise ValueError(f"Aggregation '{aggregation}' not supported")
+        return result
 
     def _compute_metric(
         self,
@@ -1029,46 +1073,57 @@ def _compute_metric(
             Boolean DataArray with dims [init_time, lead_time] indicating
             whether criteria are met for each init_time and lead_time pair.
         """
-        if self.threshold is None:
-            # Return False for all when no detection criteria specified
-            dims = ["init_time", "lead_time"]
-            coords = {
-                "init_time": forecast.valid_time - forecast.lead_time,
-                "lead_time": forecast.lead_time,
-            }
-            if "valid_time" in forecast.dims:
-                dims.append("valid_time")
-                coords["valid_time"] = forecast.valid_time
-            return xr.DataArray(
-                False,
-                dims=dims,
-                coords=coords,
-                name=self.name,
-            )
-        # Create detection mask
-        detection_mask = self.comparison_operator(forecast, self.threshold)
 
-        # Apply spatial aggregation
+        # Create detection masks with nans preserved
+        forecast_detection_mask = self.comparison_operator(
+            forecast, self.threshold
+        ).where(~forecast.isnull())
+        target_detection_mask = self.comparison_operator(target, self.threshold).where(
+            ~target.isnull()
+        )
+
+        # Only assess forecast where target detects the event (value == 1);
+        # locations where target is 0 or NaN are excluded
+        forecast_detection_mask = forecast_detection_mask.where(
+            target_detection_mask == 1, drop=True
+        )
+
+        # Create lists of dimensions to reduce
         spatial_dims = [
             dim
-            for dim in detection_mask.dims
+            for dim in forecast_detection_mask.dims
             if dim not in ["init_time", "lead_time", "valid_time"]
+            and dim not in self.preserve_dims
+        ]
+        temporal_dims = [
+            dim
+            for dim in forecast_detection_mask.dims
+            if dim in ["init_time", "lead_time", "valid_time"]
+            and dim not in self.preserve_dims
         ]
 
-        if spatial_dims:
-            if self.spatial_aggregation == "any":
-                detection_mask = detection_mask.any(spatial_dims)
-            elif self.spatial_aggregation == "all":
-                detection_mask = detection_mask.all(spatial_dims)
-            elif self.spatial_aggregation == "half":
-                detection_mask = operator.ge(detection_mask.mean(spatial_dims), 0.5)
-            else:
-                raise ValueError(
-                    f"Spatial aggregation '{self.spatial_aggregation}' not supported"
-                )
+        # Apply aggregation in the order specified
+        if self.aggregation_order[0] == "spatial":
+            forecast_detection_mask = self._apply_aggregation(
+                forecast_detection_mask, self.spatial_aggregation, spatial_dims
+            )
+            forecast_detection_mask = self._apply_aggregation(
+                forecast_detection_mask,
+                self.temporal_aggregation,
+                temporal_dims,
+            )
+        elif self.aggregation_order[0] == "temporal":
+            forecast_detection_mask = self._apply_aggregation(
+                forecast_detection_mask,
+                self.temporal_aggregation,
+                temporal_dims,
+            )
+            forecast_detection_mask = self._apply_aggregation(
+                forecast_detection_mask, self.spatial_aggregation, spatial_dims
+            )
 
-        detection_mask.name = self.name
-        return detection_mask
+        forecast_detection_mask.name = self.name
+        return forecast_detection_mask
 
 
 class MaximumMeanAbsoluteError(MeanAbsoluteError):
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index e6d95b3c..df0b8cf8 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -3485,3 +3485,197 @@ def test_no_variables_provided(self):
         metric = metrics.MeanAbsoluteError()
         assert metric.forecast_variable is None
         assert metric.target_variable is None
+
+
+class TestEarlySignal:
+    """Tests for EarlySignal metric and its aggregation helpers."""
+
+    def _make_metric(self, **kwargs):
+        return metrics.EarlySignal(**kwargs)
+
+    def test_default_init(self):
+        """Default EarlySignal has expected attribute values."""
+        m = self._make_metric()
+        assert m.threshold == 0.5
+        assert m.spatial_aggregation == "any"
+        assert m.temporal_aggregation == "any"
+        assert m.aggregation_order == ("spatial", "temporal")
+
+    def test_custom_init(self):
+        """Custom params are stored correctly."""
+        m = self._make_metric(
+            threshold=1.0,
+            spatial_aggregation="all",
+            temporal_aggregation="half",
+            aggregation_order=("temporal", "spatial"),
+        )
+        assert m.threshold == 1.0
+        assert m.spatial_aggregation == "all"
+        assert m.temporal_aggregation == "half"
+        assert m.aggregation_order == ("temporal", "spatial")
+
+    def test_apply_aggregation_any_all_nan_returns_false(self):
+        """any over all-NaN slice should return False (not True)."""
+        m = self._make_metric()
+        da = xr.DataArray([np.nan, np.nan, np.nan], dims=["space"])
+        result = m._apply_aggregation(da, "any", ["space"])
+        assert bool(result.values) is False
+
+    def test_apply_aggregation_any_nan_and_zero_returns_false(self):
+        """any over NaN+False values must return False, not True."""
+        m = self._make_metric()
+        da = xr.DataArray([np.nan, np.nan, 0.0], dims=["space"])
+        result = m._apply_aggregation(da, "any", ["space"])
+        assert bool(result.values) is False
+
+    def test_apply_aggregation_any_nan_and_one_returns_true(self):
+        """any returns True when at least one non-NaN value is True."""
+        m = self._make_metric()
+        da = xr.DataArray([np.nan, 0.0, 1.0], dims=["space"])
+        result = m._apply_aggregation(da, "any", ["space"])
+        assert bool(result.values) is True
+
+    def test_apply_aggregation_all_all_nan_returns_true(self):
+        """all over all-NaN: no data means nothing fails the condition."""
+        m = self._make_metric()
+        da = xr.DataArray([np.nan, np.nan], dims=["space"])
+        result = m._apply_aggregation(da, "all", ["space"])
+        assert bool(result.values) is True
+
+    def test_apply_aggregation_all_nan_and_zero_returns_false(self):
+        """all returns False when a non-NaN False is present."""
+        m = self._make_metric()
+        da = xr.DataArray([np.nan, 1.0, 0.0], dims=["space"])
+        result = m._apply_aggregation(da, "all", ["space"])
+        assert bool(result.values) is False
+
+    def test_apply_aggregation_half_nan_excluded_from_denominator(self):
+        """half uses skipna mean, so NaN positions don't dilute fraction."""
+        m = self._make_metric()
+        # 2 True, 2 NaN means mean of valid = 1.0 >= 0.5 is True
+        # (with fillna(0) first it would be 2/4=0.5; still True, but with
+        # uneven splits it matters)
+        da = xr.DataArray([1.0, 1.0, np.nan, np.nan], dims=["space"])
+        result = m._apply_aggregation(da, "half", ["space"])
+        assert bool(result.values) is True
+
+    def test_apply_aggregation_half_nan_excluded_unequal_groups(self):
+        """NaN exclusion changes result vs fillna(0) when groups are uneven."""
+        m = self._make_metric()
+        # 1 True, 3 NaN → skipna mean = 1/1 = 1.0 >= 0.5 → True
+        # fillna(0) mean = 1/4 = 0.25 → False
+        da = xr.DataArray([1.0, np.nan, np.nan, np.nan], dims=["space"])
+        result = m._apply_aggregation(da, "half", ["space"])
+        assert bool(result.values) is True
+
+    def test_apply_aggregation_half_all_nan_returns_false(self):
+        """half over all-NaN returns False (NaN >= 0.5 is False)."""
+        m = self._make_metric()
+        da = xr.DataArray([np.nan, np.nan], dims=["space"])
+        result = m._apply_aggregation(da, "half", ["space"])
+        assert bool(result.values) is False
+
+    def test_apply_aggregation_invalid_raises(self):
+        """Unsupported aggregation raises ValueError."""
+        m = self._make_metric()
+        da = xr.DataArray([1.0], dims=["space"])
+        with pytest.raises(ValueError, match="not supported"):
+            m._apply_aggregation(da, "median", ["space"])
+
+    def _make_forecast_target(self, forecast_vals, target_vals, spatial_size=3):
+        """Build minimal forecast/target DataArrays with space+valid_time dims.
+
+        spatial_size ignored when vals are already 2-D arrays.
+        """
+        vtime = pd.to_timedelta(np.arange(len(forecast_vals)), unit="h")
+        forecast = xr.DataArray(
+            np.array(forecast_vals, dtype=float),
+            dims=["valid_time", "space"],
+            coords={"valid_time": vtime},
+        )
+        target = xr.DataArray(
+            np.array(target_vals, dtype=float),
+            dims=["valid_time", "space"],
+            coords={"valid_time": vtime},
+        )
+        return forecast, target
+
+    def test_compute_metric_target_zero_excluded(self):
+        """Forecast not evaluated at locations where target is 0."""
+        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+        # target row 0: all 0 → excluded; row 1: all 1 → included
+        # forecast row 1: all above threshold → should detect
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.9], [0.9, 0.9]],
+            target_vals=[[0.0, 0.0], [1.0, 1.0]],
+        )
+        result = m._compute_metric(forecast, target)
+        # spatial any→ True because row1 forecast > 0.5 at target==1 locs
+        assert bool(result.any().values)
+
+    def test_compute_metric_target_nan_excluded(self):
+        """Forecast not evaluated where target is NaN."""
+        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+        # Only target==1 at space=1, t=0; forecast is below threshold there
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.1]],
+            target_vals=[[np.nan, 1.0]],
+        )
+        result = m._compute_metric(forecast, target)
+        # space=0 excluded (target NaN); space=1 forecast=0.1 < 0.5 → False
+        assert not bool(result.any().values)
+
+    def test_compute_metric_only_target_one_locations_matter(self):
+        """High forecast values at target==0 locations don't trigger True."""
+        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+        # target: space=0 → 0 (excluded), space=1 → 1 (included)
+        # forecast: space=0 → 0.9 (would fire if not masked), space=1 → 0.1
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.1]],
+            target_vals=[[0.0, 1.0]],
+        )
+        result = m._compute_metric(forecast, target)
+        assert not bool(result.any().values)
+
+    def test_aggregation_order_spatial_then_temporal(self):
+        """spatial to temporal order produces a scalar result."""
+        m = self._make_metric(
+            spatial_aggregation="any",
+            temporal_aggregation="any",
+            aggregation_order=("spatial", "temporal"),
+        )
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.9], [0.9, 0.9]],
+            target_vals=[[1.0, 1.0], [1.0, 1.0]],
+        )
+        result = m._compute_metric(forecast, target)
+        assert result.ndim == 0
+
+    def test_aggregation_order_temporal_then_spatial(self):
+        """temporal to spatial order produces a scalar result."""
+        m = self._make_metric(
+            spatial_aggregation="any",
+            temporal_aggregation="any",
+            aggregation_order=("temporal", "spatial"),
+        )
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.9], [0.9, 0.9]],
+            target_vals=[[1.0, 1.0], [1.0, 1.0]],
+        )
+        result = m._compute_metric(forecast, target)
+        assert result.ndim == 0
+
+    def test_aggregation_order_both_detect(self):
+        """Both orders give the same True result when forecast validates."""
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.9], [0.9, 0.9]],
+            target_vals=[[1.0, 1.0], [1.0, 1.0]],
+        )
+        for order in [("spatial", "temporal"), ("temporal", "spatial")]:
+            m = self._make_metric(
+                spatial_aggregation="any",
+                temporal_aggregation="any",
+                aggregation_order=order,
+            )
+            result = m._compute_metric(forecast, target)
+            assert bool(result.values), f"Expected True for order={order}"

From 64ec2f0c61792c2f965f9076e63c2b40029fa173 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Tue, 31 Mar 2026 22:08:26 -0400
Subject: [PATCH 38/72] Relax TC tracking criteria (#339)

* match TempestExtremes better w/ distance between peaks and peak wind radius

* relax default surface pressure threshold

* ruff

* match defaults

* update default in test
---
 src/extremeweatherbench/derived.py            |  36 +-
 .../events/tropical_cyclone.py                | 187 ++++++----
 tests/test_tropical_cyclone.py                | 346 ++++++++++++++++++
 3 files changed, 484 insertions(+), 85 deletions(-)

diff --git a/src/extremeweatherbench/derived.py b/src/extremeweatherbench/derived.py
index 7517d232..1a776583 100644
--- a/src/extremeweatherbench/derived.py
+++ b/src/extremeweatherbench/derived.py
@@ -129,17 +129,18 @@ def __init__(
         name: Optional[str] = None,
         slp_contour_magnitude: float = 200.0,
         dz_contour_magnitude: float = -6.0,
-        min_distance_between_peaks: int = 5,
+        min_distance_between_peaks_degrees: float = 1.0,
         max_spatial_distance_degrees: float = 5.0,
         max_temporal_hours: float = 48.0,
         use_contour_validation: bool = True,
-        min_track_timesteps: int = 10,
+        timestep_count_wind_minimum: int = 2,
         latitude_max_degrees: float = 50.0,
-        surface_pressure_threshold: float = 100500.0,
+        surface_pressure_threshold: float = 102000.0,
         orography: Optional[xr.DataArray] = None,
         max_gc_distance_slp_contour_degrees: float = 5.5,
         max_gc_distance_dz_contour_degrees: float = 6.5,
         orography_filter_threshold: float = 150.0,
+        wind_search_radius_degrees: float = 2.0,
     ):
         """Initialize the TropicalCycloneTrackVariables variable.
 
@@ -152,20 +153,24 @@ def __init__(
                 Defaults to 200.0.
             dz_contour_magnitude: Geopotential thickness contour threshold in m.
                 Defaults to -6.0.
-            min_distance_between_peaks: Minimum grid points between detected peaks.
-                Defaults to 5.
+            min_distance_between_peaks_degrees: Minimum GCD distance between
+                detected peaks in degrees. Converted to grid points at runtime
+                using the actual model resolution. Defaults to 1.0 degree.
             max_spatial_distance_degrees: Maximum distance in degrees for track matching.
                 Defaults to 5.0.
             max_temporal_hours: Maximum hours between detections for track continuity.
                 Defaults to 48.0.
             use_contour_validation: Whether to apply closed contour validation.
                 Defaults to True.
-            min_track_timesteps: Minimum number of timesteps required for a valid track.
-                Defaults to 10.
+            timestep_count_wind_minimum: Minimum number of lead times where the
+                neighbourhood peak wind (max within wind_search_radius_degrees)
+                is >= 10 m/s for a track to be retained. Defaults to 10.
             latitude_max_degrees: Maximum latitude in degrees for TC detection.
                 Defaults to 50.0.
-            surface_pressure_threshold: Surface pressure threshold in Pa.
-                Defaults to 100500.0.
+            surface_pressure_threshold: Maximum SLP (Pa) a candidate grid
+                point may have to be considered for peak detection. Defaults
+                to 101325.0 Pa (standard atmosphere), so only below-average
+                pressure cells are examined.
             orography: Optional orography DataArray for terrain filtering.
                 Defaults to None.
             max_gc_distance_slp_contour_degrees: Maximum great circle distance for
@@ -175,21 +180,25 @@ def __init__(
                 degrees.
             orography_filter_threshold: Orography filter threshold in meters.
                 Defaults to 150.0.
+            wind_search_radius_degrees: GCD radius in degrees for neighbourhood
+                wind sampling, per TempestExtremes 2.1. Converted to grid
+                points at runtime. Defaults to 2.0 degrees.
         """
         super().__init__(output_variables=output_variables, name=name)
         self.slp_contour_magnitude = slp_contour_magnitude
         self.dz_contour_magnitude = dz_contour_magnitude
-        self.min_distance_between_peaks = min_distance_between_peaks
+        self.min_distance_between_peaks_degrees = min_distance_between_peaks_degrees
         self.max_spatial_distance_degrees = max_spatial_distance_degrees
         self.max_temporal_hours = max_temporal_hours
         self.use_contour_validation = use_contour_validation
-        self.min_track_timesteps = min_track_timesteps
+        self.timestep_count_wind_minimum = timestep_count_wind_minimum
         self.latitude_max_degrees = latitude_max_degrees
         self.surface_pressure_threshold = surface_pressure_threshold
         self.orography = orography
         self.max_gc_distance_slp_contour_degrees = max_gc_distance_slp_contour_degrees
         self.max_gc_distance_dz_contour_degrees = max_gc_distance_dz_contour_degrees
         self.orography_filter_threshold = orography_filter_threshold
+        self.wind_search_radius_degrees = wind_search_radius_degrees
 
     def get_or_compute_tracks(self, data: xr.Dataset, *args, **kwargs) -> xr.Dataset:
         """Get cached track data or compute if not already cached.
@@ -253,17 +262,18 @@ def get_or_compute_tracks(self, data: xr.Dataset, *args, **kwargs) -> xr.Dataset
             geopotential_thickness=prepared_data.get("geopotential_thickness", None),
             slp_contour_magnitude=self.slp_contour_magnitude,
             dz_contour_magnitude=self.dz_contour_magnitude,
-            min_distance_between_peaks=self.min_distance_between_peaks,
+            min_distance_between_peaks_degrees=self.min_distance_between_peaks_degrees,
             max_spatial_distance_degrees=self.max_spatial_distance_degrees,
             max_temporal_hours=self.max_temporal_hours,
             use_contour_validation=self.use_contour_validation,
-            min_track_timesteps=self.min_track_timesteps,
+            timestep_count_wind_minimum=self.timestep_count_wind_minimum,
             latitude_max_degrees=self.latitude_max_degrees,
             surface_pressure_threshold=self.surface_pressure_threshold,
             orography=self.orography,
             max_gc_distance_slp_contour_degrees=self.max_gc_distance_slp_contour_degrees,
             max_gc_distance_dz_contour_degrees=self.max_gc_distance_dz_contour_degrees,
             orography_filter_threshold=self.orography_filter_threshold,
+            wind_search_radius_degrees=self.wind_search_radius_degrees,
         )
         return tctracks_ds
 
diff --git a/src/extremeweatherbench/events/tropical_cyclone.py b/src/extremeweatherbench/events/tropical_cyclone.py
index 755b11e5..21e3c1cc 100644
--- a/src/extremeweatherbench/events/tropical_cyclone.py
+++ b/src/extremeweatherbench/events/tropical_cyclone.py
@@ -20,6 +20,35 @@
 Location = namedtuple("Location", ["latitude", "longitude"])
 
 
+def _degrees_to_gridpoints(
+    degrees: float,
+    lat_coords: npt.NDArray,
+    lon_coords: npt.NDArray,
+) -> int:
+    """Convert a great-circle degree distance to an integer gridpoint count.
+
+    Uses the mean of the lat and lon grid spacings as the representative
+    resolution, then rounds up so the returned neighbourhood always spans
+    at least ``degrees`` in every direction.
+
+    Args:
+        degrees: Spatial radius in degrees GCD.
+        lat_coords: 1-D array of latitude coordinate values.
+        lon_coords: 1-D array of longitude coordinate values.
+
+    Returns:
+        Number of grid points corresponding to ``degrees``.
+    """
+    lat_spacing = (
+        float(np.mean(np.abs(np.diff(lat_coords)))) if len(lat_coords) > 1 else 1.0
+    )
+    lon_spacing = (
+        float(np.mean(np.abs(np.diff(lon_coords)))) if len(lon_coords) > 1 else 1.0
+    )
+    mean_spacing = (lat_spacing + lon_spacing) / 2.0
+    return max(1, int(np.ceil(degrees / mean_spacing)))
+
+
 def generate_tc_tracks_by_init_time(
     sea_level_pressure: xr.DataArray,
     wind_speed: xr.DataArray,
@@ -27,17 +56,18 @@ def generate_tc_tracks_by_init_time(
     tc_track_analysis_data: xr.DataArray,
     slp_contour_magnitude: float = 200.0,
     dz_contour_magnitude: float = -6.0,
-    min_distance_between_peaks: int = 5,
+    min_distance_between_peaks_degrees: float = 1.0,
     max_spatial_distance_degrees: float = 5.0,
     max_temporal_hours: float = 48.0,
     use_contour_validation: bool = True,
-    min_track_timesteps: int = 10,
+    timestep_count_wind_minimum: int = 2,
     latitude_max_degrees: float = 50.0,
-    surface_pressure_threshold: float = 100500.0,
+    surface_pressure_threshold: float = 102000.0,
     orography: Optional[xr.DataArray] = None,
     max_gc_distance_slp_contour_degrees: float = 5.5,
     max_gc_distance_dz_contour_degrees: float = 6.5,
     orography_filter_threshold: float = 150.0,
+    wind_search_radius_degrees: float = 2.0,
 ) -> xr.Dataset:
     """Process all init_times and detect tropical cyclones.
 
@@ -64,15 +94,17 @@ def generate_tc_tracks_by_init_time(
             200.0 Pa
         dz_contour_magnitude: DZ contour threshold for validation in m. Defaults to
             -6.0 m
-        min_distance_between_peaks: Minimum distance between detected peaks in grid
-            points. Defaults to 5
+        min_distance_between_peaks_degrees: Minimum GCD distance between detected
+            peaks in degrees. Converted to grid points at runtime using the
+            actual grid resolution. Defaults to 1.0 degree
         max_spatial_distance_degrees: Max spatial distance for TC track data filtering
             in degrees. Defaults to 5.0 degrees
         max_temporal_hours: Maximum temporal window from init_time in hours. Defaults
             to 48.0 hours
         use_contour_validation: Whether to use contour validation. Defaults to True
-        min_track_timesteps: Minimum consecutive lead times for valid tracks. Defaults
-            to 10
+        timestep_count_wind_minimum: Minimum number of lead times where the neighbourhood
+            peak wind speed (max within wind_search_radius_degrees) is >= 10 m/s
+            for a track to be considered valid. Defaults to 2
         latitude_max_degrees: Maximum latitude for valid tracks in degrees. Defaults
             to 50.0 degrees
         surface_pressure_threshold: Maximum surface pressure threshold for valid peaks
@@ -84,6 +116,10 @@ def generate_tc_tracks_by_init_time(
             in degrees. Defaults to 6.5 degrees
         orography_filter_threshold: Threshold for the orography filter's max height
             in m. Defaults to 150.0 m
+        wind_search_radius_degrees: GCD radius in degrees used to sample the
+            neighbourhood maximum wind speed around each detected peak, following
+            TempestExtremes 2.1. Converted to grid points at runtime. Defaults
+            to 2.0 degrees
 
     Returns:
         xarray Dataset with detected tropical cyclone tracks
@@ -146,19 +182,20 @@ def generate_tc_tracks_by_init_time(
             "latitude": latitude.values,
             "longitude": longitude.values,
             "tc_track_data_df": tc_track_data_df,
-            "min_distance_between_peaks": min_distance_between_peaks,
+            "min_distance_between_peaks_degrees": min_distance_between_peaks_degrees,
             "max_spatial_distance_degrees": max_spatial_distance_degrees,
             "max_temporal_hours": max_temporal_hours,
             "slp_contour_magnitude": slp_contour_magnitude,
             "dz_contour_magnitude": dz_contour_magnitude,
             "use_contour_validation": use_contour_validation,
-            "min_track_timesteps": min_track_timesteps,
+            "timestep_count_wind_minimum": timestep_count_wind_minimum,
             "latitude_max_degrees": latitude_max_degrees,
             "surface_pressure_threshold": surface_pressure_threshold,
             "orography": orography,
             "max_gc_distance_slp_contour_degrees": max_gc_distance_slp_contour_degrees,
             "max_gc_distance_dz_contour_degrees": max_gc_distance_dz_contour_degrees,
             "orography_filter_threshold": orography_filter_threshold,
+            "wind_search_radius_degrees": wind_search_radius_degrees,
         },
         input_core_dims=[
             ["lead_time", "latitude", "longitude"],
@@ -223,19 +260,20 @@ def _process_single_init_time(
     latitude: npt.NDArray,
     longitude: npt.NDArray,
     tc_track_data_df: pd.DataFrame,
-    min_distance_between_peaks: int,
+    min_distance_between_peaks_degrees: float,
     max_spatial_distance_degrees: float,
     max_temporal_hours: float,
     slp_contour_magnitude: float,
     dz_contour_magnitude: float,
     use_contour_validation: bool,
-    min_track_timesteps: int,
+    timestep_count_wind_minimum: int,
     latitude_max_degrees: float,
     surface_pressure_threshold: float,
     orography: xr.DataArray,
     max_gc_distance_slp_contour_degrees: float,
     max_gc_distance_dz_contour_degrees: float,
     orography_filter_threshold: float,
+    wind_search_radius_degrees: float = 2.0,
 ) -> dict:
     """Process tropical cyclone track detection for a single init_time.
 
@@ -252,13 +290,15 @@ def _process_single_init_time(
         latitude: Latitude coordinates in degrees
         longitude: Longitude coordinates in degrees
         tc_track_data_df: TC track data for validation
-        min_distance_between_peaks: Min distance between peaks in grid points
+        min_distance_between_peaks_degrees: Min GCD distance between peaks in degrees.
+            Converted to grid points using actual grid resolution
         max_spatial_distance_degrees: Max spatial distance in degrees
         max_temporal_hours: Max temporal buffer in hours
         slp_contour_magnitude: SLP contour threshold in Pa
         dz_contour_magnitude: DZ contour threshold in m
         use_contour_validation: Whether to use contour validation
-        min_track_timesteps: Minimum consecutive lead times for valid tracks
+        timestep_count_wind_minimum: Minimum lead times with neighbourhood peak wind
+            >= 10 m/s for a track to be retained
         latitude_max_degrees: Maximum latitude for valid tracks in degrees
         surface_pressure_threshold: Maximum surface pressure threshold for valid peaks
             in Pa
@@ -269,6 +309,8 @@ def _process_single_init_time(
             in degrees
         orography_filter_threshold: Threshold for the orography filter's max height
             in m
+        wind_search_radius_degrees: GCD radius in degrees for neighbourhood wind
+            sampling, per TempestExtremes 2.1. Converted to grid points at runtime
 
     Returns:
         Dict containing filtered list of detections for this init_time
@@ -276,6 +318,15 @@ def _process_single_init_time(
     # Get the init_time value for this index
     current_init_time = init_time_values[init_time_idx]
 
+    # Convert degree-based spatial parameters to grid points using the
+    # actual grid resolution so the code is resolution-agnostic.
+    min_distance_between_peaks_gridpts = _degrees_to_gridpoints(
+        min_distance_between_peaks_degrees, latitude, longitude
+    )
+    wind_search_radius_gridpts = _degrees_to_gridpoints(
+        wind_search_radius_degrees, latitude, longitude
+    )
+
     # Find all (lead_time, valid_time) pairs for this init_time
     mask = np.abs(init_time_coord - current_init_time) <= np.timedelta64(1, "s")
     lead_time_indices, valid_time_indices = np.where(mask)
@@ -303,9 +354,10 @@ def _process_single_init_time(
     # Find peaks for all timesteps at once
     all_peaks = []
     for timestep_idx in range(n_timesteps):
-        slp_slice = slp_data[timestep_idx]
-        wind_slice = wind_data[timestep_idx]
-        dz_slice = dz_data[timestep_idx]
+        _lt_idx = lead_time_indices_seq[timestep_idx]
+        slp_slice = slp_data[_lt_idx]
+        wind_slice = wind_data[_lt_idx]
+        dz_slice = dz_data[_lt_idx]
 
         peaks = _find_peaks_batch(
             slp_slice=slp_slice,
@@ -313,7 +365,7 @@ def _process_single_init_time(
             timestep_idx=timestep_idx,
             valid_times=valid_times_seq,
             tc_track_data_df=tc_track_data_df,
-            min_distance_between_peaks=min_distance_between_peaks,
+            min_distance_between_peaks=min_distance_between_peaks_gridpts,
             latitude=latitude,
             longitude=longitude,
             max_spatial_distance_degrees=max_spatial_distance_degrees,
@@ -351,7 +403,20 @@ def _process_single_init_time(
             peak_lats = latitude[peaks[:, 0]]
             peak_lons = longitude[peaks[:, 1]]
             peak_slps = slp_slice[peaks[:, 0], peaks[:, 1]]
-            peak_winds = wind_slice[peaks[:, 0], peaks[:, 1]]
+            # Sample max wind within wind_search_radius_degrees of each
+            # detected peak (per TempestExtremes 2.1) to capture the eyewall
+            # rather than the low-wind eye at the SLP minimum.
+            _nrows, _ncols = wind_slice.shape
+            _nb = wind_search_radius_gridpts
+            peak_winds = np.array(
+                [
+                    wind_slice[
+                        max(0, r - _nb) : min(_nrows, r + _nb + 1),
+                        max(0, c - _nb) : min(_ncols, c + _nb + 1),
+                    ].max()
+                    for r, c in peaks
+                ]
+            )
 
             unassigned_peaks = list(range(len(peaks)))
 
@@ -414,55 +479,37 @@ def _process_single_init_time(
                     "longitude": peak_lons[peak_idx],
                 }
 
-    # Filter tracks by min_track_length consecutive lead times
-    if detections and min_track_timesteps > 1:
-        # Group detections by track_id and collect lead time indices
-        track_lead_times: dict[int, set[int]] = {}
+    logger.debug(
+        "Init %d: %d raw detections, %d unique tracks",
+        init_time_idx,
+        len(detections),
+        len({d["track_id"] for d in detections}),
+    )
+    # Filter tracks: require >= timestep_count_wind_minimum detections with wind >= 10 m/s
+    if detections and timestep_count_wind_minimum > 1:
+        # Count per-track detections where peak wind speed >= 10 m/s
+        track_wind_counts: dict[int, int] = {}
         for detection in detections:
             track_id = detection["track_id"]
-            lead_time_index = detection["lead_time_index"]
-
-            if track_id not in track_lead_times:
-                track_lead_times[track_id] = set()
-            # Convert lead_time_index to int
-            lead_time_index_int: int
-            if hasattr(lead_time_index, "item"):
-                lead_time_index_int = int(lead_time_index.item())
-            else:
-                lead_time_index_int = int(lead_time_index)
-            track_lead_times[track_id].add(lead_time_index_int)
-
-        # Check for consecutive lead times for each track
-        valid_track_ids: set[int] = set()
-        for track_id, lead_time_indices in track_lead_times.items():
-            # Sort lead time indices to check for consecutive sequences
-            sorted_lead_time_indices = sorted(lead_time_indices)
-
-            # Find longest consecutive sequence
-            max_consecutive = 1
-            current_consecutive = 1
-
-            for i in range(1, len(sorted_lead_time_indices)):
-                if sorted_lead_time_indices[i] == sorted_lead_time_indices[i - 1] + 1:
-                    current_consecutive += 1
-                    max_consecutive = max(max_consecutive, current_consecutive)
-                else:
-                    current_consecutive = 1
-
-            # Only keep tracks with min_track_length consecutive lead times
-            if max_consecutive >= min_track_timesteps:
-                valid_track_ids.add(track_id)
+            if float(detection["wind"]) >= 10.0:
+                track_wind_counts[track_id] = track_wind_counts.get(track_id, 0) + 1
+
+        valid_track_ids: set[int] = {
+            tid
+            for tid, count in track_wind_counts.items()
+            if count >= timestep_count_wind_minimum
+        }
 
         # Filter detections to only include valid tracks
         filtered_detections = [
             d for d in detections if d["track_id"] in valid_track_ids
         ]
         logger.debug(
-            "Init %d: Filtered %d -> %d detections (min_track_timesteps=%d)",
+            "Init %d: Filtered %d -> %d detections (timestep_count_wind_minimum=%d)",
             init_time_idx,
             len(detections),
             len(filtered_detections),
-            min_track_timesteps,
+            timestep_count_wind_minimum,
         )
         detections = filtered_detections
 
@@ -688,18 +735,18 @@ def _find_peaks_for_time_slice(
     current_valid_time: pd.Timestamp,
     tc_track_data_df: pd.DataFrame,
     min_distance_between_peaks: int,
-    lat_coords: Optional[npt.NDArray] = None,
-    lon_coords: Optional[npt.NDArray] = None,
-    max_spatial_distance_degrees: float = 5.0,
-    max_temporal_hours: float = 48.0,
-    slp_contour_magnitude: float = 200.0,
-    dz_contour_magnitude: float = -6.0,
-    use_contour_validation: bool = True,
-    is_first_timestep: bool = False,
-    surface_pressure_threshold: float = 100500.0,
-    latitude_max_degrees: float = 50.0,
-    max_gc_distance_slp_contour_degrees: float = 5.5,
-    max_gc_distance_dz_contour_degrees: float = 6.5,
+    lat_coords: npt.NDArray,
+    lon_coords: npt.NDArray,
+    max_spatial_distance_degrees: float,
+    max_temporal_hours: float,
+    slp_contour_magnitude: float,
+    dz_contour_magnitude: float,
+    use_contour_validation: bool,
+    is_first_timestep: bool,
+    surface_pressure_threshold: float,
+    latitude_max_degrees: float,
+    max_gc_distance_slp_contour_degrees: float,
+    max_gc_distance_dz_contour_degrees: float,
 ) -> npt.NDArray:
     """Find peaks with tropical cyclone track data filtering.
 
@@ -763,10 +810,6 @@ def _find_peaks_for_time_slice(
     if not np.any(low_pressure_mask):
         return np.array([])
 
-    # OPTIMIZED: Vectorized spatial masking
-    if lat_coords is None or lon_coords is None:
-        return np.array([])
-
     spatial_mask = _create_spatial_mask(
         lat_coords, lon_coords, nearby_tc_track_data, max_spatial_distance_degrees
     )
diff --git a/tests/test_tropical_cyclone.py b/tests/test_tropical_cyclone.py
index 828019ab..dfeab47b 100644
--- a/tests/test_tropical_cyclone.py
+++ b/tests/test_tropical_cyclone.py
@@ -8,6 +8,7 @@
 """
 
 # flake8: noqa: E501
+import math
 from unittest.mock import patch
 
 import numpy as np
@@ -15,6 +16,7 @@
 import pytest
 import xarray as xr
 
+from extremeweatherbench import derived
 from extremeweatherbench.events import tropical_cyclone
 
 
@@ -616,3 +618,347 @@ def test_full_tc_pipeline_mock(self, sample_tc_dataset, sample_ibtracs_dataset):
             ]
             for var in expected_vars:
                 assert var in result.data_vars
+
+
+# ---------------------------------------------------------------------------
+# Shared fixture for end-to-end filter tests
+# ---------------------------------------------------------------------------
+
+
+def _make_single_init_tc_dataset(n_lead: int = 12, n_wind_strong: int = 10):
+    """Build a minimal synthetic TC dataset with one clear init_time.
+
+    Grid: 1° resolution, 21×21 points (lat 10–30°, lon -80 to -60°).
+    Storm: SLP=98 000 Pa at centre (lat=20, lon=-70) every (lead, valid) pair
+    on the diagonal (same init_time T0).
+    Wind: 20 m/s at one gridpoint east of centre for the first
+    ``n_wind_strong`` diagonal pairs; 2 m/s everywhere else.
+    Contour validation is OFF so only the wind filter is exercised.
+
+    With a 1° grid ``_degrees_to_gridpoints(2.0, ...)`` = 2 gridpoints, so
+    the ±2-pt neighbourhood around the centre includes the +1-pt east cell.
+
+    Expected: n_wind_strong detections have neighbourhood wind ≥ 10 m/s.
+    """
+    lat = np.arange(10.0, 31.0, 1.0)  # 21 pts, 1 ° spacing
+    lon = np.arange(-80.0, -59.0, 1.0)  # 21 pts, 1 ° spacing
+    n_lat, n_lon = len(lat), len(lon)
+    c_lat, c_lon = 10, 10  # centre indices → lat=20°, lon=-70°
+
+    T0 = pd.Timestamp("2023-09-10")
+    lead_h = np.arange(n_lead) * 6  # hours
+    lead_td = (lead_h * np.timedelta64(1, "h")).astype("timedelta64[ns]")
+    valid_times = pd.date_range(T0, periods=n_lead, freq="6h")
+
+    # init_time[lt, vt] = valid_time[vt] - lead_time[lt]
+    init_2d = np.array(
+        [
+            [valid_times[vt].to_datetime64() - lead_td[lt] for vt in range(n_lead)]
+            for lt in range(n_lead)
+        ]
+    )
+
+    # SLP: 98 000 Pa at centre for every (lt, vt) pair; 102 000 elsewhere
+    slp = np.full((n_lead, n_lead, n_lat, n_lon), 102000.0)
+    for k in range(n_lead):
+        slp[k, k, c_lat, c_lon] = 98000.0
+
+    # Wind: 20 m/s east of centre for the first n_wind_strong diagonal pairs
+    wind = np.full((n_lead, n_lead, n_lat, n_lon), 2.0)
+    for k in range(n_wind_strong):
+        wind[k, k, c_lat, c_lon + 1] = 20.0
+
+    # Geopotential thickness: zeros (contour validation disabled)
+    dz = np.zeros((n_lead, n_lead, n_lat, n_lon))
+
+    ds = xr.Dataset(
+        {
+            "air_pressure_at_mean_sea_level": (
+                ["lead_time", "valid_time", "latitude", "longitude"],
+                slp,
+            ),
+            "surface_wind_speed": (
+                ["lead_time", "valid_time", "latitude", "longitude"],
+                wind,
+            ),
+            "geopotential_thickness": (
+                ["lead_time", "valid_time", "latitude", "longitude"],
+                dz,
+            ),
+        },
+        coords={
+            "lead_time": lead_td,
+            "valid_time": valid_times,
+            "latitude": lat,
+            "longitude": lon,
+            "init_time": (["lead_time", "valid_time"], init_2d),
+        },
+    )
+    return ds
+
+
+def _make_ibt_for_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """IBTrACS stub matching the storm centre in ``_make_single_init_tc_dataset``."""
+    valid_times = ds.valid_time.values
+    return xr.Dataset(
+        {
+            "latitude": (["valid_time"], np.full(len(valid_times), 20.0)),
+            "longitude": (["valid_time"], np.full(len(valid_times), -70.0)),
+        },
+        coords={"valid_time": valid_times},
+    )
+
+
+# ---------------------------------------------------------------------------
+# Tests for _degrees_to_gridpoints
+# ---------------------------------------------------------------------------
+
+
+class TestDegreesToGridpoints:
+    """Unit tests for the _degrees_to_gridpoints helper.
+
+    Expected values are derived independently via:
+        ceil(degrees / mean_spacing)
+    where mean_spacing = (mean(|Δlat|) + mean(|Δlon|)) / 2.
+    """
+
+    def test_uniform_025_deg_grid_1deg(self):
+        """1° radius on 0.25° grid → ceil(1/0.25)=4."""
+        lat = np.arange(0.0, 10.0, 0.25)
+        lon = np.arange(0.0, 10.0, 0.25)
+        assert tropical_cyclone._degrees_to_gridpoints(1.0, lat, lon) == 4
+
+    def test_uniform_025_deg_grid_2deg(self):
+        """2° radius on 0.25° grid → ceil(2/0.25)=8."""
+        lat = np.arange(0.0, 20.0, 0.25)
+        lon = np.arange(0.0, 20.0, 0.25)
+        assert tropical_cyclone._degrees_to_gridpoints(2.0, lat, lon) == 8
+
+    def test_uniform_025_deg_grid_5deg(self):
+        """5° radius on 0.25° grid → ceil(5/0.25)=20."""
+        lat = np.arange(0.0, 90.0, 0.25)
+        lon = np.arange(0.0, 360.0, 0.25)
+        assert tropical_cyclone._degrees_to_gridpoints(5.0, lat, lon) == 20
+
+    def test_uniform_05_deg_grid_2deg(self):
+        """2° radius on 0.5° grid → ceil(2/0.5)=4."""
+        lat = np.arange(0.0, 20.0, 0.5)
+        lon = np.arange(0.0, 20.0, 0.5)
+        assert tropical_cyclone._degrees_to_gridpoints(2.0, lat, lon) == 4
+
+    def test_uniform_1_deg_grid_2deg(self):
+        """2° radius on 1° grid → ceil(2/1)=2."""
+        lat = np.arange(10.0, 31.0, 1.0)
+        lon = np.arange(-80.0, -59.0, 1.0)
+        assert tropical_cyclone._degrees_to_gridpoints(2.0, lat, lon) == 2
+
+    def test_non_square_grid(self):
+        """Non-square grid (0.25° lat, 0.5° lon): mean=0.375; 1.5°→ceil(4)=4."""
+        lat = np.arange(0.0, 20.0, 0.25)  # 0.25° spacing
+        lon = np.arange(0.0, 20.0, 0.5)  # 0.5° spacing
+        # mean_spacing = (0.25 + 0.5) / 2 = 0.375
+        # ceil(1.5 / 0.375) = ceil(4.0) = 4
+        assert tropical_cyclone._degrees_to_gridpoints(1.5, lat, lon) == 4
+
+    def test_non_square_grid_fractional(self):
+        """Non-square grid (0.25° lat, 0.5° lon): 2°→ceil(5.333)=6."""
+        lat = np.arange(0.0, 20.0, 0.25)
+        lon = np.arange(0.0, 20.0, 0.5)
+        # mean_spacing = 0.375; ceil(2/0.375) = ceil(5.333) = 6
+        expected = math.ceil(2.0 / 0.375)
+        assert tropical_cyclone._degrees_to_gridpoints(2.0, lat, lon) == expected
+
+    def test_always_at_least_one(self):
+        """Very small degree value never returns zero."""
+        lat = np.arange(0.0, 10.0, 0.25)
+        lon = np.arange(0.0, 10.0, 0.25)
+        assert tropical_cyclone._degrees_to_gridpoints(0.01, lat, lon) >= 1
+
+    def test_single_point_coords_fallback(self):
+        """Single-point coordinates fall back to 1.0° spacing."""
+        lat = np.array([20.0])
+        lon = np.array([-70.0])
+        # mean_spacing = (1.0 + 1.0) / 2 = 1.0; ceil(2/1) = 2
+        assert tropical_cyclone._degrees_to_gridpoints(2.0, lat, lon) == 2
+
+    def test_result_is_integer(self):
+        """Return type is always a plain Python int."""
+        lat = np.arange(0.0, 10.0, 0.25)
+        lon = np.arange(0.0, 10.0, 0.25)
+        result = tropical_cyclone._degrees_to_gridpoints(1.0, lat, lon)
+        assert isinstance(result, int)
+
+    def test_monotone_in_degrees(self):
+        """Larger degree radius never gives fewer gridpoints."""
+        lat = np.arange(0.0, 20.0, 0.25)
+        lon = np.arange(0.0, 20.0, 0.25)
+        prev = tropical_cyclone._degrees_to_gridpoints(0.5, lat, lon)
+        for deg in [1.0, 1.5, 2.0, 3.0, 5.0]:
+            cur = tropical_cyclone._degrees_to_gridpoints(deg, lat, lon)
+            assert cur >= prev, f"Expected monotone increase at {deg}°"
+            prev = cur
+
+
+# ---------------------------------------------------------------------------
+# Tests for TropicalCycloneTrackVariables parameter defaults
+# ---------------------------------------------------------------------------
+
+
+class TestTropicalCycloneTrackVariablesDefaults:
+    """Verify new parameter names and default values."""
+
+    def test_min_distance_between_peaks_degrees_default(self):
+        tc = derived.TropicalCycloneTrackVariables()
+        assert tc.min_distance_between_peaks_degrees == 1.0
+
+    def test_wind_search_radius_degrees_default(self):
+        tc = derived.TropicalCycloneTrackVariables()
+        assert tc.wind_search_radius_degrees == 2.0
+
+    def test_timestep_count_wind_minimum_default(self):
+        tc = derived.TropicalCycloneTrackVariables()
+        assert tc.timestep_count_wind_minimum == 2
+
+    def test_custom_min_distance_between_peaks_degrees(self):
+        tc = derived.TropicalCycloneTrackVariables(
+            min_distance_between_peaks_degrees=0.5
+        )
+        assert tc.min_distance_between_peaks_degrees == 0.5
+
+    def test_custom_wind_search_radius_degrees(self):
+        tc = derived.TropicalCycloneTrackVariables(wind_search_radius_degrees=3.0)
+        assert tc.wind_search_radius_degrees == 3.0
+
+    def test_old_min_distance_between_peaks_kwarg_raises(self):
+        """Renamed parameter: old name must raise TypeError."""
+        with pytest.raises(TypeError):
+            tropical_cyclone.generate_tc_tracks_by_init_time(
+                xr.DataArray(),
+                xr.DataArray(),
+                xr.DataArray(),
+                xr.Dataset(),
+                min_distance_between_peaks=5,  # old name
+            )
+
+
+# ---------------------------------------------------------------------------
+# End-to-end tests for neighbourhood wind sampling and the wind-count filter
+# ---------------------------------------------------------------------------
+
+
+class TestNeighbourhoodWindSampling:
+    """Verify that peak_winds uses max in the ±wind_search_radius neighbourhood.
+
+    On the 1° test grid, _degrees_to_gridpoints(2.0, …)=2. The high-wind cell
+    is 1 gridpoint east of the SLP minimum, so it falls inside the ±2-pt box.
+    The wind AT the centre is 2 m/s; the neighbourhood max is 20 m/s.
+    The output surface_wind_speed must reflect the neighbourhood max (≥10 m/s),
+    not the centre value (2 m/s).
+    """
+
+    def test_output_wind_exceeds_centre_wind(self):
+        """Detected surface_wind_speed > wind at SLP minimum (2 m/s)."""
+        ds = _make_single_init_tc_dataset(n_lead=12, n_wind_strong=12)
+        ibt = _make_ibt_for_dataset(ds)
+        result = tropical_cyclone.generate_tc_tracks_by_init_time(
+            sea_level_pressure=ds["air_pressure_at_mean_sea_level"],
+            wind_speed=ds["surface_wind_speed"],
+            geopotential_thickness=ds["geopotential_thickness"],
+            tc_track_analysis_data=ibt,
+            timestep_count_wind_minimum=1,
+            use_contour_validation=False,
+            surface_pressure_threshold=102000.0,
+            wind_search_radius_degrees=2.0,
+        )
+        assert result.sizes.get("valid_time", 0) > 0, "Expected detections"
+        ws = result["surface_wind_speed"].values
+        # Centre wind is 2 m/s; neighbourhood max is 20 m/s
+        assert np.nanmax(ws) > 2.0, "Neighbourhood max must exceed centre wind"
+        assert np.nanmax(ws) >= 10.0, "At least one detection should be ≥10 m/s"
+
+    def test_output_wind_close_to_neighbourhood_max(self):
+        """Neighbourhood max (20 m/s) is returned, not centre value (2 m/s)."""
+        ds = _make_single_init_tc_dataset(n_lead=5, n_wind_strong=5)
+        ibt = _make_ibt_for_dataset(ds)
+        result = tropical_cyclone.generate_tc_tracks_by_init_time(
+            sea_level_pressure=ds["air_pressure_at_mean_sea_level"],
+            wind_speed=ds["surface_wind_speed"],
+            geopotential_thickness=ds["geopotential_thickness"],
+            tc_track_analysis_data=ibt,
+            timestep_count_wind_minimum=1,
+            use_contour_validation=False,
+            surface_pressure_threshold=102000.0,
+            wind_search_radius_degrees=2.0,
+        )
+        assert result.sizes.get("valid_time", 0) > 0
+        ws = result["surface_wind_speed"].values
+        ws_detected = ws[~np.isnan(ws)]
+        assert len(ws_detected) > 0, "No non-NaN wind values in output"
+        # All detected timesteps should carry the neighbourhood max (20 m/s)
+        assert np.allclose(ws_detected, 20.0, atol=1.0), (
+            f"Expected neighbourhood max ≈20 m/s, got {ws_detected}"
+        )
+
+
+class TestMinTrackTimestepsWindFilter:
+    """Verify the wind-count filter: track survives iff it has >= timestep_count_wind_minimum
+    detections where the neighbourhood peak wind is >= 10 m/s.
+
+    Setup: n_lead=12, n_wind_strong=10.  The single long-lived track has exactly
+    10 detections with neighbourhood wind=20 m/s and 2 with wind=2 m/s.
+    Independent expectation:
+      min_ts ≤ 10 → track passes → result has valid_time > 0
+      min_ts = 11 → track fails  → result has valid_time == 0
+    """
+
+    N_LEAD = 12
+    N_WIND_STRONG = 10  # independently chosen threshold
+
+    @pytest.fixture(scope="class")
+    def tc_ds(self):
+        return _make_single_init_tc_dataset(self.N_LEAD, self.N_WIND_STRONG)
+
+    @pytest.fixture(scope="class")
+    def ibt(self, tc_ds):
+        return _make_ibt_for_dataset(tc_ds)
+
+    def _run(self, tc_ds, ibt, min_ts):
+        return tropical_cyclone.generate_tc_tracks_by_init_time(
+            sea_level_pressure=tc_ds["air_pressure_at_mean_sea_level"],
+            wind_speed=tc_ds["surface_wind_speed"],
+            geopotential_thickness=tc_ds["geopotential_thickness"],
+            tc_track_analysis_data=ibt,
+            timestep_count_wind_minimum=min_ts,
+            use_contour_validation=False,
+            surface_pressure_threshold=102000.0,
+            wind_search_radius_degrees=2.0,
+        )
+
+    def test_passes_at_exact_threshold(self, tc_ds, ibt):
+        """min_ts == n_wind_strong: track has exactly enough windy steps."""
+        result = self._run(tc_ds, ibt, self.N_WIND_STRONG)
+        assert result.sizes.get("valid_time", 0) > 0, (
+            f"Expected detections with min_ts={self.N_WIND_STRONG}"
+        )
+
+    def test_passes_below_threshold(self, tc_ds, ibt):
+        """min_ts < n_wind_strong: track comfortably passes."""
+        for min_ts in [1, 5, self.N_WIND_STRONG - 1]:
+            result = self._run(tc_ds, ibt, min_ts)
+            assert result.sizes.get("valid_time", 0) > 0, (
+                f"Expected detections with min_ts={min_ts}"
+            )
+
+    def test_fails_above_threshold(self, tc_ds, ibt):
+        """min_ts > n_wind_strong: track lacks sufficient windy steps."""
+        result = self._run(tc_ds, ibt, self.N_WIND_STRONG + 1)
+        assert result.sizes.get("valid_time", 0) == 0, (
+            f"Expected 0 detections with min_ts={self.N_WIND_STRONG + 1}"
+        )
+
+    def test_boundary_is_sharp(self, tc_ds, ibt):
+        """One step above the exact threshold flips pass→fail."""
+        result_pass = self._run(tc_ds, ibt, self.N_WIND_STRONG)
+        result_fail = self._run(tc_ds, ibt, self.N_WIND_STRONG + 1)
+        assert result_pass.sizes.get("valid_time", 0) > 0
+        assert result_fail.sizes.get("valid_time", 0) == 0

From 83bc8ec779398ceecab81d3291c01f0e6952e6b4 Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Wed, 1 Apr 2026 02:43:15 +0000
Subject: [PATCH 39/72] set minimum wind >= 10m/s to ten steps by default

---
 src/extremeweatherbench/derived.py                 | 2 +-
 src/extremeweatherbench/events/tropical_cyclone.py | 2 +-
 tests/test_tropical_cyclone.py                     | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/extremeweatherbench/derived.py b/src/extremeweatherbench/derived.py
index 1a776583..560f8d24 100644
--- a/src/extremeweatherbench/derived.py
+++ b/src/extremeweatherbench/derived.py
@@ -133,7 +133,7 @@ def __init__(
         max_spatial_distance_degrees: float = 5.0,
         max_temporal_hours: float = 48.0,
         use_contour_validation: bool = True,
-        timestep_count_wind_minimum: int = 2,
+        timestep_count_wind_minimum: int = 10,
         latitude_max_degrees: float = 50.0,
         surface_pressure_threshold: float = 102000.0,
         orography: Optional[xr.DataArray] = None,
diff --git a/src/extremeweatherbench/events/tropical_cyclone.py b/src/extremeweatherbench/events/tropical_cyclone.py
index 21e3c1cc..97f43be2 100644
--- a/src/extremeweatherbench/events/tropical_cyclone.py
+++ b/src/extremeweatherbench/events/tropical_cyclone.py
@@ -60,7 +60,7 @@ def generate_tc_tracks_by_init_time(
     max_spatial_distance_degrees: float = 5.0,
     max_temporal_hours: float = 48.0,
     use_contour_validation: bool = True,
-    timestep_count_wind_minimum: int = 2,
+    timestep_count_wind_minimum: int = 10,
     latitude_max_degrees: float = 50.0,
     surface_pressure_threshold: float = 102000.0,
     orography: Optional[xr.DataArray] = None,
diff --git a/tests/test_tropical_cyclone.py b/tests/test_tropical_cyclone.py
index dfeab47b..8de5ec15 100644
--- a/tests/test_tropical_cyclone.py
+++ b/tests/test_tropical_cyclone.py
@@ -817,7 +817,7 @@ def test_wind_search_radius_degrees_default(self):
 
     def test_timestep_count_wind_minimum_default(self):
         tc = derived.TropicalCycloneTrackVariables()
-        assert tc.timestep_count_wind_minimum == 2
+        assert tc.timestep_count_wind_minimum == 10
 
     def test_custom_min_distance_between_peaks_degrees(self):
         tc = derived.TropicalCycloneTrackVariables(

From 0f0931dbd077aa8679f09ee747b98e2b57793946 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 1 Apr 2026 12:44:33 -0400
Subject: [PATCH 40/72] Catch non-ocean landfalls (#342)

* match TE better w/ distance between peaks and peak wind radius

* relax default surface pressure threshold

* ruff

* match defaults

* update default in test

* add ocean filter to avoid erroneous landfalls

* add ocean geoms to tests

* swap to 50m to avoid spurrious reefs

* update comments and typing

* add dedupe method to prevent 50km consecutive landfalls
---
 src/extremeweatherbench/calc.py  | 99 ++++++++++++++++++++++++++------
 src/extremeweatherbench/utils.py | 43 ++++++++++++--
 tests/test_calc.py               | 68 ++++++++++++++++++++--
 3 files changed, 181 insertions(+), 29 deletions(-)

diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index 36d8f0f9..a88b8702 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -424,29 +424,38 @@ def dewpoint_from_specific_humidity(pressure: float, specific_humidity: float) -
 def find_landfalls(
     track_data: xr.DataArray,
     land_geom: Optional[shapely.geometry.Polygon] = None,
+    ocean_geom: Optional[shapely.geometry.Polygon] = None,
 ) -> xr.DataArray:
     """Find landfall point(s) where a tracked object intersects land.
 
     Generalized landfall detection for any object (TC, AR, etc.).
+    Only crossings that originate from the open ocean are counted;
+    transitions out of inland lakes, ponds, or isolated bays are
+    excluded via the explicit ocean geometry check.
 
     Args:
         track_data: Track DataArray with latitude, longitude, valid_time
             coords. Data values are interpolated at the landfall point.
             Shape: ``(valid_time,)`` for observations or
             ``(lead_time, valid_time)`` for forecasts.
-        return_next_landfall: If True return all landfalls (used by the
-            "next" approach); if False return only the first landfall.
         land_geom: Shapely geometry for land-intersection testing. Uses
             the default NaturalEarth 10 m land polygon when None.
+        ocean_geom: Shapely geometry representing the open ocean. Used
+            to confirm the starting position is truly oceanic before
+            counting a crossing as a landfall. Defaults to the
+            NaturalEarth 10 m ocean polygon when None.
 
     Returns:
-        DataArray with interpolated landfall values or empty if no landfalls are
-        detected.
+        DataArray with interpolated landfall values or empty if no
+        landfalls are detected.
     """
     # If no land geometry is provided, use default
     if land_geom is None:
         land_geom = utils.load_land_geometry()
 
+    if ocean_geom is None:
+        ocean_geom = utils.load_ocean_geometry()
+
     # Squeeze track dimension if needed
     if "track" in track_data.dims and track_data.sizes["track"] == 1:
         track_data = track_data.squeeze("track", drop=True)
@@ -462,18 +471,19 @@ def find_landfalls(
         track_data = track_data.stack(time=("init_time", "lead_time"))
 
         # Vectorized landfall detection
-        landfall_mask = _detect_landfalls_wrapper(track_data, land_geom)
+        landfall_mask = _detect_landfalls_wrapper(track_data, land_geom, ocean_geom)
 
         # Mask init_time boundaries
         landfall_mask = _mask_init_time_boundaries(landfall_mask, track_data)
 
     else:
         # Process single track data
-        landfall_mask = _detect_landfalls_wrapper(track_data, land_geom)
+        landfall_mask = _detect_landfalls_wrapper(track_data, land_geom, ocean_geom)
 
-    return _interpolate_and_format_landfalls(
+    landfalls = _interpolate_and_format_landfalls(
         track_data, landfall_mask, land_geom, is_forecast
     )
+    return _deduplicate_landfalls(landfalls)
 
 
 def find_next_landfall_for_init_time(
@@ -544,12 +554,15 @@ def find_next_landfall_for_init_time(
 def _detect_landfalls_wrapper(
     track_data: xr.DataArray,
     land_geom: shapely.geometry.Polygon,
+    ocean_geom: shapely.geometry.Polygon,
 ) -> xr.DataArray:
     """Landfall detection across consecutive point pairs.
 
     Args:
         track_data: Track data with latitude, longitude coords
         land_geom: Land geometry for intersection testing
+        ocean_geom: Ocean geometry used to require the start point is
+            in the open ocean before counting a crossing as a landfall
 
     Returns:
         Boolean DataArray where True indicates a landfall between
@@ -581,7 +594,7 @@ def _check_landfall_scalar(lon1, lat1, lon2, lat2):
         # Handle NaN values (from shift at boundaries)
         if np.isnan(lon1) or np.isnan(lat1) or np.isnan(lon2) or np.isnan(lat2):
             return False
-        return _is_true_landfall(lon1, lat1, lon2, lat2, land_geom)
+        return _is_true_landfall(lon1, lat1, lon2, lat2, land_geom, ocean_geom)
 
     # Apply to get landfall mask
     landfall_mask = xr.apply_ufunc(
@@ -800,6 +813,54 @@ def _interpolate_and_format_landfalls(
     return landfall_da
 
 
+def _deduplicate_landfalls(
+    landfalls: xr.DataArray,
+    min_distance_km: float = 50.0,
+) -> xr.DataArray:
+    """Remove landfalls within ``min_distance_km`` of a prior landfall.
+
+    Iterates through the landfall dimension in order, keeping the first
+    occurrence and dropping any subsequent landfall whose great-circle
+    distance to the most recent kept landfall is less than
+    ``min_distance_km``. This eliminates spurious detections that arise
+    when a storm crosses a barrier island, enters a coastal bay, and then
+    crosses onto the mainland — all part of the same physical event.
+
+    Args:
+        landfalls: DataArray with a ``landfall`` dimension and
+            ``latitude``/``longitude`` coordinates, as returned by
+            ``find_landfalls``.
+        min_distance_km: Minimum great-circle distance (km) between two
+            consecutive kept landfalls. Defaults to 50 km.
+
+    Returns:
+        Deduplicated DataArray with the same structure as the input.
+    """
+    if len(landfalls) <= 1:
+        return landfalls
+
+    lats = landfalls.coords["latitude"].values
+    lons = landfalls.coords["longitude"].values
+
+    keep = np.ones(len(landfalls), dtype=bool)
+    last_idx = 0
+
+    for i in range(1, len(landfalls)):
+        lat1, lon1 = np.radians(lats[last_idx]), np.radians(lons[last_idx])
+        lat2, lon2 = np.radians(lats[i]), np.radians(lons[i])
+        dlat = lat2 - lat1
+        dlon = lon2 - lon1
+        a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
+        dist_km = 2 * 6371.0 * np.arcsin(np.sqrt(a))
+        if dist_km < min_distance_km:
+            keep[i] = False
+        else:
+            last_idx = i
+
+    kept_indices = np.where(keep)[0]
+    return landfalls.isel(landfall=kept_indices)
+
+
 def broadcast_first_target_to_init_times(
     init_times: xr.DataArray,
     target: xr.DataArray,
@@ -857,17 +918,21 @@ def _is_true_landfall(
     lon2: float,
     lat2: float,
     land_geom: shapely.geometry.Polygon,
+    ocean_geom: shapely.geometry.Polygon,
 ) -> bool:
     """Detect true landfall (ocean to land movement).
 
-    This function is a way to detect if a track crosses land. There are a few scenarios,
-    being ocean to land, ocean to ocean, and land to ocean. Ocean to ocean can have a
-    landfall if there is land between the two points.
+    Only segments that originate from the open ocean (as defined by the
+    Natural Earth ocean polygon) are considered potential landfalls. This
+    prevents false detections from inland lakes, ponds, or enclosed bays
+    that are not connected to the open ocean.
 
     Args:
         lon1, lat1: Starting point coordinates
         lon2, lat2: Ending point coordinates
         land_geom: Land geometry for intersection testing
+        ocean_geom: Ocean geometry used to verify the start point is in
+            the open ocean (not a lake, pond, or isolated bay)
 
     Returns:
         True if this represents a landfall, False otherwise
@@ -876,20 +941,20 @@ def _is_true_landfall(
         start_point = shapely.geometry.Point(lon1, lat1)
         end_point = shapely.geometry.Point(lon2, lat2)
 
-        start_over_land = land_geom.contains(start_point)
+        start_in_ocean = ocean_geom.contains(start_point)
         end_over_land = land_geom.contains(end_point)
 
-        # Ocean -> Land = LANDFALL
-        if not start_over_land and end_over_land:
+        # True ocean -> Land = LANDFALL
+        if start_in_ocean and end_over_land:
             return True
 
-        # Ocean -> Ocean, check if land is between
-        if not start_over_land and not end_over_land:
+        # True ocean -> ocean, check if land is between
+        if start_in_ocean and not end_over_land:
             segment = shapely.geometry.LineString([(lon1, lat1), (lon2, lat2)])
             if segment.intersects(land_geom):
                 return True
 
-        # Land -> Ocean or Ocean -> Ocean (no intersection) = NOT LANDFALL
+        # Lake/pond/bay -> Land, or Land -> Ocean = NOT LANDFALL
         return False
 
     except (AttributeError, ValueError, TypeError):
diff --git a/src/extremeweatherbench/utils.py b/src/extremeweatherbench/utils.py
index 65710234..e193e8fc 100644
--- a/src/extremeweatherbench/utils.py
+++ b/src/extremeweatherbench/utils.py
@@ -746,11 +746,18 @@ def reduce_dataarray(
         raise TypeError(f"method must be str or callable, got {type(method)}")
 
 
-def load_land_geometry(resolution: str = "10m") -> shapely.geometry.Polygon:
-    """Load the land geometry, excluding lakes.
+def load_land_geometry(
+    resolution: Literal["10m", "50m", "110m"] = "50m",
+) -> shapely.geometry.Polygon:
+    """Load the land geometry, excluding lakes and non-ocean bodies of water.
+
+    Args:
+        resolution: Natural Earth resolution ('10m', '50m', or '110m').
+            Defaults to '50m'.
 
     Returns:
-        The land geometry as a shapely Polygon with lakes excluded.
+        The land geometry as a shapely Polygon with lakes and
+        ocean-connected water bodies (bays, estuaries, seas) excluded.
     """
     land = shpreader.natural_earth(
         category="physical", name="land", resolution=resolution
@@ -758,7 +765,7 @@ def load_land_geometry(resolution: str = "10m") -> shapely.geometry.Polygon:
     land_geoms = list(shpreader.Reader(land).geometries())
     land_union = shapely.ops.unary_union(land_geoms)
 
-    # Exclude lakes to avoid false landfall detections in lakes/bays
+    # Exclude lakes to avoid false landfall detections
     try:
         lakes = shpreader.natural_earth(
             category="physical", name="lakes", resolution=resolution
@@ -768,13 +775,37 @@ def load_land_geometry(resolution: str = "10m") -> shapely.geometry.Polygon:
             lakes_union = shapely.ops.unary_union(lake_geoms)
             land_union = land_union.difference(lakes_union)
     except (OSError, ValueError):
-        # If lakes dataset is not available, return land without lakes
-        # This can happen if the dataset doesn't exist for the resolution
+        pass
+
+    # Exclude ocean-connected water bodies (bays, estuaries, coastal seas)
+    try:
+        ocean_union = load_ocean_geometry(resolution=resolution)
+        land_union = land_union.difference(ocean_union)
+    except (OSError, ValueError):
         pass
 
     return land_union
 
 
+def load_ocean_geometry(
+    resolution: Literal["10m", "50m", "110m"] = "50m",
+) -> shapely.geometry.Polygon:
+    """Load the ocean geometry from Natural Earth.
+
+    Args:
+        resolution: Natural Earth resolution ('10m', '50m', or '110m').
+            Defaults to '50m'.
+
+    Returns:
+        The ocean geometry as a unified shapely Polygon.
+    """
+    ocean = shpreader.natural_earth(
+        category="physical", name="ocean", resolution=resolution
+    )
+    ocean_geoms = list(shpreader.Reader(ocean).geometries())
+    return shapely.ops.unary_union(ocean_geoms)
+
+
 def _cache_maybe_densify_helper(
     data: xr.Dataset | xr.DataArray,
 ) -> xr.Dataset | xr.DataArray:
diff --git a/tests/test_calc.py b/tests/test_calc.py
index 8b611a7e..38fd3795 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -1693,12 +1693,13 @@ def test_is_true_landfall_ocean_to_land(self):
 
         # Create simple land geometry (box)
         land_geom = shapely.geometry.box(-81, 24, -80, 26)
+        ocean_geom = shapely.geometry.box(-85, 23, -81.5, 27)
 
         # Ocean point to land point (landfall)
         lon1, lat1 = -82.0, 25.0  # Ocean
         lon2, lat2 = -80.5, 25.0  # Land
 
-        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom)
+        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom, ocean_geom)
 
         # Should detect landfall
         assert result is True
@@ -1709,12 +1710,13 @@ def test_is_true_landfall_ocean_crossing_land(self):
 
         # Create land geometry
         land_geom = shapely.geometry.box(-81, 24, -80, 26)
+        ocean_geom = shapely.geometry.box(-85, 23, -81.5, 27)
 
         # Ocean to ocean but crossing land
         lon1, lat1 = -82.0, 25.0  # Ocean
         lon2, lat2 = -79.0, 25.0  # Ocean (other side)
 
-        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom)
+        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom, ocean_geom)
 
         # Should detect landfall (crossing)
         assert result is True
@@ -1725,12 +1727,13 @@ def test_is_true_landfall_land_to_ocean(self):
 
         # Create land geometry
         land_geom = shapely.geometry.box(-81, 24, -80, 26)
+        ocean_geom = shapely.geometry.box(-85, 23, -81.5, 27)
 
         # Land to ocean (not landfall)
         lon1, lat1 = -80.5, 25.0  # Land
         lon2, lat2 = -82.0, 25.0  # Ocean
 
-        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom)
+        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom, ocean_geom)
 
         # Should not detect landfall (exit, not entry)
         assert result is False
@@ -1741,12 +1744,13 @@ def test_is_true_landfall_ocean_to_ocean_no_intersection(self):
 
         # Create land geometry
         land_geom = shapely.geometry.box(-81, 24, -80, 26)
+        ocean_geom = shapely.geometry.box(-87, 23, -81.5, 27)
 
         # Ocean to ocean without crossing land
         lon1, lat1 = -85.0, 25.0  # Ocean (far away)
         lon2, lat2 = -84.0, 25.0  # Ocean (still far)
 
-        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom)
+        result = calc._is_true_landfall(lon1, lat1, lon2, lat2, land_geom, ocean_geom)
 
         # Should not detect landfall
         assert result is False
@@ -1754,7 +1758,7 @@ def test_is_true_landfall_ocean_to_ocean_no_intersection(self):
     def test_is_true_landfall_error_handling(self):
         """Test _is_true_landfall handles errors gracefully."""
         # Invalid geometry should return False
-        result = calc._is_true_landfall(0, 0, 1, 1, None)
+        result = calc._is_true_landfall(0, 0, 1, 1, None, None)
 
         # Should return False on error
         assert result is False
@@ -1823,8 +1827,9 @@ def test_detect_landfalls_wrapper(self):
         )
 
         land_geom = shapely.geometry.box(-82, 24, -80, 26)
+        ocean_geom = shapely.geometry.box(-87, 23, -82.5, 27)
 
-        result = calc._detect_landfalls_wrapper(track, land_geom)
+        result = calc._detect_landfalls_wrapper(track, land_geom, ocean_geom)
 
         # Should return boolean DataArray
         assert isinstance(result, xr.DataArray)
@@ -1928,6 +1933,57 @@ def test_interpolate_and_format_landfalls(self):
             # Should have landfall dimension
             assert "landfall" in result_all.dims
 
+    def test_deduplicate_landfalls_suppresses_nearby(self):
+        """Landfalls within 50 km of each other keep only the first."""
+        lats = np.array([25.0, 25.1, 26.0])
+        lons = np.array([-81.5, -81.4, -81.5])
+
+        landfalls = xr.DataArray(
+            [95000.0, 94500.0, 94000.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0, 1, 2],
+                "latitude": ("landfall", lats),
+                "longitude": ("landfall", lons),
+                "valid_time": (
+                    "landfall",
+                    pd.date_range("2024-10-09 20:00", periods=3, freq="3h"),
+                ),
+            },
+        )
+
+        result = calc._deduplicate_landfalls(landfalls, min_distance_km=50.0)
+
+        # First two are ~12 km apart; only the first should survive.
+        # Third is ~111 km from the first and must be kept.
+        assert len(result) == 2
+        assert np.isclose(result.coords["latitude"].values[0], 25.0)
+        assert np.isclose(result.coords["latitude"].values[1], 26.0)
+
+    def test_deduplicate_landfalls_preserves_distant(self):
+        """Landfalls more than 50 km apart are both preserved."""
+        lats = np.array([25.0, 26.0])
+        lons = np.array([-81.5, -81.5])
+
+        landfalls = xr.DataArray(
+            [95000.0, 94000.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0, 1],
+                "latitude": ("landfall", lats),
+                "longitude": ("landfall", lons),
+                "valid_time": (
+                    "landfall",
+                    pd.date_range("2024-10-09 20:00", periods=2, freq="6h"),
+                ),
+            },
+        )
+
+        result = calc._deduplicate_landfalls(landfalls, min_distance_km=50.0)
+
+        # ~111 km apart — both must be kept.
+        assert len(result) == 2
+
 
 class TestLandfallDeduplication:
     """Test landfall deduplication functionality."""

From 30a7e07b056e84599227f45a55d1dda55dfb6855 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 1 Apr 2026 13:38:45 -0400
Subject: [PATCH 41/72] enforce level chunk for nantrapezoid_pressure_levels
 (#343)

---
 src/extremeweatherbench/calc.py |  2 +
 tests/test_calc.py              | 84 +++++++++++++++++++++++++++++++++
 2 files changed, 86 insertions(+)

diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index a88b8702..f5f35e61 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -324,6 +324,8 @@ def nantrapezoid_pressure_levels(da: xr.DataArray):
     Returns a DataArray of the computed quantity integrated over the entire column.
     """
 
+    # Chunk the level dimension to ensure successful dask parallelization
+    da = da.chunk({"level": -1})
     # Convert levels to Pascals
     levels_pa = da["level"] * 100
 
diff --git a/tests/test_calc.py b/tests/test_calc.py
index 38fd3795..a7af8f31 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -1106,6 +1106,90 @@ def test_level_conversion_to_pascals(self):
         result = calc.nantrapezoid_pressure_levels(da)
         testing.assert_almost_equal(float(result), 10000.0)
 
+    def test_rechunks_level_to_single_chunk(self):
+        """Verify level is rechunked to -1 regardless of input chunking.
+
+        The function must apply da.chunk({"level": -1}) so that the
+        guvectorize kernel receives the full level axis in each task.
+        """
+        levels_hpa = np.array([1000.0, 850.0, 700.0, 500.0, 300.0, 200.0])
+        y = np.ones((4, len(levels_hpa)))
+        da = xr.DataArray(
+            y,
+            dims=["time", "level"],
+            coords={"time": np.arange(4), "level": levels_hpa},
+        ).chunk({"time": 1, "level": 2})  # level chunked into multiple pieces
+
+        # After calling the function the level dim must be a single chunk
+        result_da = calc.nantrapezoid_pressure_levels(da)
+        result_da.__dask_graph__()  # forces graph creation
+        # Inspect the rechunked intermediate: da inside the function has
+        # level chunk size == len(levels_hpa).  We verify correctness by
+        # checking the computed result matches the serial numpy reference.
+        serial = float(
+            calc.nantrapezoid_pressure_levels(
+                xr.DataArray(
+                    y[0],
+                    dims=["level"],
+                    coords={"level": levels_hpa},
+                )
+            )
+        )
+        computed = result_da.compute()
+        assert computed.shape == (4,)
+        testing.assert_almost_equal(float(computed[0]), serial)
+
+    def test_chunked_level_matches_unchunked(self):
+        """Results with a chunked level dim must match an unchunked input.
+
+        Before the da.chunk({"level": -1}) fix, apply_ufunc with
+        dask="parallelized" and a chunked level core-dim would silently
+        produce wrong or failed results. This test guards against
+        regression.
+        """
+        rng = np.random.default_rng(1)
+        levels_hpa = np.array([1000.0, 925.0, 850.0, 700.0, 500.0, 300.0, 200.0])
+        n_time, n_lat, n_lon = 3, 4, 4
+        y = rng.uniform(0.0, 1e-3, (n_time, n_lat, n_lon, len(levels_hpa)))
+        y[0, 0, 0, 2] = np.nan  # sprinkle a NaN
+
+        da_base = xr.DataArray(
+            y,
+            dims=["time", "latitude", "longitude", "level"],
+            coords={"level": levels_hpa},
+        )
+        chunked = da_base.chunk({"time": 1, "level": 3})
+        unchunked = da_base.chunk({"time": 1})  # level unchunked
+
+        result_chunked = calc.nantrapezoid_pressure_levels(chunked).compute()
+        result_unchunked = calc.nantrapezoid_pressure_levels(unchunked).compute()
+
+        xr.testing.assert_allclose(result_chunked, result_unchunked)
+
+    def test_level_single_chunk_after_rechunk(self):
+        """The dask graph for the rechunked da must show level as one chunk.
+
+        Directly inspects chunk sizes on the DataArray produced by
+        da.chunk({"level": -1}) inside the function, by mimicking the
+        same operation externally and confirming the chunk count == 1.
+        """
+        levels_hpa = np.array([1000.0, 850.0, 700.0, 500.0])
+        y = np.ones((2, len(levels_hpa)))
+        da = xr.DataArray(
+            y,
+            dims=["time", "level"],
+            coords={"time": np.arange(2), "level": levels_hpa},
+        ).chunk({"time": 1, "level": 2})
+
+        rechunked = da.chunk({"level": -1})
+        # chunks is a tuple of tuples; level is the second dim here
+        level_dim_idx = rechunked.dims.index("level")
+        level_chunks = rechunked.chunks[level_dim_idx]
+        assert len(level_chunks) == 1, (
+            f"Expected 1 level chunk after rechunking, got {len(level_chunks)}"
+        )
+        assert level_chunks[0] == len(levels_hpa)
+
 
 class TestBinaryDilationUfunc:
     """Tests for _binary_dilation_ufunc."""

From 923e2f27c456388a43065358c355565e6b0d2e0b Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 1 Apr 2026 22:45:00 -0400
Subject: [PATCH 42/72] Update ARs with pressure ceiling and cleaner derived
 variable (#344)

* add max level arg and move code into derive_variable for more clarity

* ruff
---
 src/extremeweatherbench/derived.py | 36 ++++++++++++++++++++++++++++--
 1 file changed, 34 insertions(+), 2 deletions(-)

diff --git a/src/extremeweatherbench/derived.py b/src/extremeweatherbench/derived.py
index 560f8d24..263d7d65 100644
--- a/src/extremeweatherbench/derived.py
+++ b/src/extremeweatherbench/derived.py
@@ -425,26 +425,58 @@ class AtmosphericRiverVariables(DerivedVariable):
 
     def __init__(
         self,
+        name: str = "atmospheric_river",
+        top_pressure_level: int = 300,
         output_variables: Optional[List[str]] = [
             "atmospheric_river_mask",
             "integrated_vapor_transport",
             "atmospheric_river_land_intersection",
         ],
-        name: Optional[str] = "atmospheric_river",
     ):
         """Initialize the AtmosphericRiverVariables variable.
 
         Args:
+            top_pressure_level: The top pressure level to compute integrated variables
+                for. Defaults to 300 hPa.
             output_variables: Optional list of variable names that specify
                 which outputs to use from the derived computation.
             name: The name of the derived variable. Defaults to class-level
                 name attribute if present, otherwise the class name.
         """
         super().__init__(output_variables=output_variables, name=name)
+        self.top_pressure_level = top_pressure_level
 
     def derive_variable(self, data: xr.Dataset, *args, **kwargs) -> xr.Dataset:
         """Derive the atmospheric river mask and land intersection."""
-        return ar.build_atmospheric_river_mask_and_land_intersection(data)
+
+        # Subset the data to the top pressure level
+        data = data.sel(level=data.level[data.level >= self.top_pressure_level])
+
+        # Generate IVT
+        ivt_data = ar.integrated_vapor_transport(
+            specific_humidity=data["specific_humidity"],
+            eastward_wind=data["eastward_wind"],
+            northward_wind=data["northward_wind"],
+        )
+
+        # Compute IVT Laplacian
+        ivt_laplacian = ar.integrated_vapor_transport_laplacian(ivt=ivt_data, sigma=3)
+
+        # Compute AR mask with default parameters
+        ar_mask_result = ar.atmospheric_river_mask(
+            ivt=ivt_data, ivt_laplacian=ivt_laplacian
+        )
+
+        # Compute land intersection
+        land_intersection = calc.find_land_intersection(ar_mask_result)
+
+        return xr.Dataset(
+            {
+                "atmospheric_river_mask": ar_mask_result,
+                "atmospheric_river_land_intersection": land_intersection,
+                "integrated_vapor_transport": ivt_data,
+            }
+        )
 
 
 def maybe_derive_variables(

From df024eb53d4a2803805050b0b2677baa955f9b9a Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 2 Apr 2026 11:19:38 -0400
Subject: [PATCH 43/72] Update GHCNh filtering (#346)

* first pass on ghcnh filtering update + change GHCN-H to GHCNh

* add return url option to circumvent downloading

* add ghcnh remote input (in-flight, scan_parquet cast broken)

* update ghnch script for more permissive filtering

* ruff

* remove class now that typing is fixed in parq
---
 data_prep/generate_ghcnh.py | 60 +++++++++++++++++--------------------
 1 file changed, 27 insertions(+), 33 deletions(-)

diff --git a/data_prep/generate_ghcnh.py b/data_prep/generate_ghcnh.py
index 934f5389..a9100d7a 100644
--- a/data_prep/generate_ghcnh.py
+++ b/data_prep/generate_ghcnh.py
@@ -1,4 +1,4 @@
-"""Streaming GHCN-H data download script that processes files immediately
+"""Streaming GHCNh data download script that processes files immediately
 to save disk space and skip already processed data.
 """
 
@@ -11,9 +11,11 @@
 import polars as pl
 from tqdm.asyncio import tqdm
 
+REJECTED_QC_CODES = {"2", "3", "6", "7"}
+
 
 async def download_station_list():
-    """Download and parse the official GHCN-H station list."""
+    """Download and parse the official GHCNh station list."""
     station_list_url = "https://www.ncei.noaa.gov/oa/global-historical-climatology-network/hourly/doc/ghcnh-station-list.txt"  # noqa: E501
 
     async with aiohttp.ClientSession() as session:
@@ -26,7 +28,7 @@ async def download_station_list():
 
 
 def parse_station_list(content):
-    """Parse the GHCN-H station list text format."""
+    """Parse the GHCNh station list text format."""
     stations = []
     lines = content.strip().split("\n")
 
@@ -83,11 +85,11 @@ def check_station_already_processed(station_id, year, main_parquet_file):
         # Only read the parquet file once and build station-year set
         if _existing_station_years is None or _cache_file_path != main_parquet_file:
             print("Loading existing data for duplicate checking with Polars...")
-            df = pl.read_parquet(main_parquet_file, columns=["station", "time"])
+            df = pl.read_parquet(main_parquet_file, columns=["station", "valid_time"])
 
-            # Extract year from time and create station-year combinations
+            # Extract year from valid_time and create station-year combinations
             station_years = (
-                df.with_columns(pl.col("time").dt.year().alias("year"))
+                df.with_columns(pl.col("valid_time").dt.year().alias("year"))
                 .select(["station", "year"])
                 .unique()
                 .to_pandas()  # Convert to pandas for set creation
@@ -232,7 +234,7 @@ def flush_batch_to_parquet(main_parquet_file):
         # Append to main parquet file
         if Path(main_parquet_file).exists():
             existing_df = pl.read_parquet(main_parquet_file)
-            combined_df = pl.concat([existing_df, batch_polars])
+            combined_df = pl.concat([existing_df, batch_polars], how="diagonal")
             combined_df.write_parquet(main_parquet_file)
         else:
             batch_polars.write_parquet(main_parquet_file)
@@ -254,21 +256,13 @@ def process_psv_file(file_path):
     try:
         df = pd.read_csv(file_path, sep="|", low_memory=False)
 
-        # Drop rows where temperature_quality_check is not 1 or 5
-        if "temperature_Quality_Code" in df.columns:
-            if isinstance(df["temperature_Quality_Code"].iloc[0], dict):
-                df = df[
-                    df["temperature_Quality_Code"].apply(
-                        lambda x: x.get("member0") in [1, 5]
-                        if x is not None and isinstance(x, dict)
-                        else False
-                    )
-                ]
-            else:
-                df["temperature_Quality_Code"] = df["temperature_Quality_Code"].astype(
-                    str
-                )
-                df = df[df["temperature_Quality_Code"].isin(["1", "5"])]
+        for qc_col in (
+            "temperature_Quality_Code",
+            "wind_speed_Quality_Code",
+        ):
+            if qc_col in df.columns:
+                codes = df[qc_col].astype(str)
+                df = df[~codes.isin(REJECTED_QC_CODES)]
 
         # Select required columns
         required_cols = [
@@ -312,7 +306,7 @@ def aggregate_to_hourly(data):
             lambda df: df.bfill().iloc[0], include_groups=False
         )
     data = data.reset_index(drop=True)
-    data["time"] = data["DATE"].dt.round("h")
+    data["valid_time"] = data["DATE"].dt.round("h")
     return data
 
 
@@ -346,7 +340,7 @@ def apply_data_transformations(df):
     )
 
     # Drop unnecessary columns if they exist
-    columns_to_drop = ["DATE", "hour"]
+    columns_to_drop = ["DATE", "hour", "hour_dist"]
     columns_to_drop = [col for col in columns_to_drop if col in df.columns]
     if columns_to_drop:
         df = df.drop(columns_to_drop, axis=1)
@@ -365,10 +359,10 @@ async def download_and_process_station_file_with_semaphore(
 
 
 async def download_all_stations_streaming(
-    years, output_dir, overwrite=False, max_concurrent=10
+    years, output_dir, overwrite=False, max_concurrent=100
 ):
-    """Download and immediately process GHCN-H stations to save disk space."""
-    print("Downloading and processing GHCN-H stations with streaming approach...")
+    """Download and immediately process GHCNh stations."""
+    print("Downloading and processing GHCNh stations with streaming approach...")
     station_df = await download_station_list()
     print(f"Found {len(station_df)} stations in official list")
 
@@ -532,18 +526,18 @@ async def process_with_progress(coro):
                 print("\nFinal dataset summary:")
                 print(f"  Total rows: {len(final_df)}")
                 print(f"  Unique stations: {final_df['station'].nunique()}")
-                if "time" in final_df.columns:
+                if "valid_time" in final_df.columns:
                     print(
-                        f"  Date range: {final_df['time'].min()} to "
-                        f"{final_df['time'].max()}"
+                        f"  Date range: {final_df['valid_time'].min()} to "
+                        f"{final_df['valid_time'].max()}"
                     )
             except Exception as e:
                 print(f"Error reading final dataset: {e}")
 
 
 def main():
-    """Main function for streaming GHCN-H download."""
-    print("GHCN-H Streaming Download Script")
+    """Main function for streaming GHCNh download."""
+    print("GHCNh Streaming Download Script")
     print("===============================")
     print("This script downloads, processes, and deletes files immediately!")
     print("Files are checked for existing data and skipped if already processed.")
@@ -563,7 +557,7 @@ def main():
     print(f"Streaming data for years: {years}")
     asyncio.run(
         download_all_stations_streaming(
-            years, output_dir, overwrite=False, max_concurrent=10
+            years, output_dir, overwrite=False, max_concurrent=1000
         )
     )
 

From bde92bfb38f1bf18f1ce488b9009bfb800f9135f Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 2 Apr 2026 13:49:16 -0400
Subject: [PATCH 44/72] update ES to avoid failing on dask-backed arrays and
 add a check in tests (#347)

---
 src/extremeweatherbench/metrics.py |  6 ++++--
 tests/test_metrics.py              | 21 +++++++++++++++++++++
 2 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index b58534c5..4309cc06 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -1083,9 +1083,11 @@ def _compute_metric(
         )
 
         # Only assess forecast where target detects the event (value == 1);
-        # locations where target is 0 or NaN are excluded
+        # locations where target is 0 or NaN are set to NaN (not dropped).
+        # drop=True uses boolean dask indexing which is unsupported; NaN
+        # values are handled correctly by _apply_aggregation downstream.
         forecast_detection_mask = forecast_detection_mask.where(
-            target_detection_mask == 1, drop=True
+            target_detection_mask == 1
         )
 
         # Create lists of dimensions to reduce
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index df0b8cf8..a5253812 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -3679,3 +3679,24 @@ def test_aggregation_order_both_detect(self):
             )
             result = m._compute_metric(forecast, target)
             assert bool(result.values), f"Expected True for order={order}"
+
+    def test_compute_metric_works_with_dask_backed_arrays(self):
+        """_compute_metric must not raise on dask-backed inputs.
+
+        drop=True in xr.DataArray.where() uses boolean indexing which
+        is unsupported for dask arrays; drop=False avoids this.
+        """
+        pytest.importorskip("dask.array")
+
+        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.1]],
+            target_vals=[[0.0, 1.0]],
+        )
+        forecast_dask = forecast.chunk({"valid_time": 1, "space": 1})
+        target_dask = target.chunk({"valid_time": 1, "space": 1})
+
+        result = m._compute_metric(forecast_dask, target_dask)
+        result_computed = result.compute()
+        # space=0 excluded (target=0); space=1: forecast=0.1 < 0.5 → False
+        assert not bool(result_computed.any().values)

From ad3d0572683c029b1cc12f393e089dfd4d130676 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 2 Apr 2026 15:12:30 -0400
Subject: [PATCH 45/72] Add preprocess func for heatwaves to remove ocean
 gridpoints (#348)

* add preprocessors to defaults and include in applied_heatwave script

* add preprocess to remove ocean gridpoints

* don't need two functions doing the same thing
---
 docs/examples/applied_heatwave.py   |  1 +
 src/extremeweatherbench/defaults.py | 15 ++++++++++++++-
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/docs/examples/applied_heatwave.py b/docs/examples/applied_heatwave.py
index 60254d2c..c1ca31ca 100644
--- a/docs/examples/applied_heatwave.py
+++ b/docs/examples/applied_heatwave.py
@@ -29,6 +29,7 @@
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     variables=["surface_air_temperature"],
     variable_mapping=ewb.HRES_metadata_variable_mapping,
+    preprocess=ewb.defaults.preprocess_heatwave_forecast_dataset,
 )
 
 # Load the climatology for DurationMeanError
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index 241d6c15..7936af19 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -4,7 +4,7 @@
 import numpy as np
 import xarray as xr
 
-from extremeweatherbench import calc, derived, inputs
+from extremeweatherbench import calc, derived, inputs, utils
 
 # Suppress noisy log messages
 logging.getLogger("urllib3.connectionpool").setLevel(logging.CRITICAL)
@@ -58,6 +58,19 @@
 ]
 
 
+def preprocess_heatwave_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
+    """Preprocess forecast data that removes ocean gridpoints for heatwave events.
+    Args:
+        ds: The forecast dataset.
+
+    Returns:
+        The forecast dataset with ocean gridpoints removed for heatwave events.
+    """
+
+    ds = utils.remove_ocean_gridpoints(ds)
+    return ds
+
+
 def preprocess_cira_icechunk_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
     """A preprocess function for CIRA icechunk data that includes geopotential thickness
     calculation required for tropical cyclone tracks.

From 2a754b39dfde7e9cfeb2db84668609c18dd3b8cf Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 2 Apr 2026 15:19:35 -0400
Subject: [PATCH 46/72] Add temperature event finder scripts (#345)

* add in all files, plots, and tests

* add ignore for test

* change file names, add plotting scripts, improve parallelization

* freeze -> cold

* update imports

* add min_gridpoints arg and return empty df

* remove tests for data prep

* update file names in comments

* remove old heat and cold bounds script

* manage longitude conversions

* longitude shenanigans
---
 data_prep/heat_cold_bounds_case.py   | 422 +++++++++++++++++++++
 data_prep/heat_cold_bounds_global.py | 531 +++++++++++++++++++++++++++
 data_prep/plot_temperature_events.py | 497 +++++++++++++++++++++++++
 data_prep/subset_heat_cold_events.py | 258 -------------
 4 files changed, 1450 insertions(+), 258 deletions(-)
 create mode 100644 data_prep/heat_cold_bounds_case.py
 create mode 100644 data_prep/heat_cold_bounds_global.py
 create mode 100644 data_prep/plot_temperature_events.py
 delete mode 100644 data_prep/subset_heat_cold_events.py

diff --git a/data_prep/heat_cold_bounds_case.py b/data_prep/heat_cold_bounds_case.py
new file mode 100644
index 00000000..3f398331
--- /dev/null
+++ b/data_prep/heat_cold_bounds_case.py
@@ -0,0 +1,422 @@
+#!/usr/bin/env python3
+"""Validate and expand heat wave / cold snap bounding boxes from events.yaml.
+
+For each heat_wave or cold snap event in events.yaml, iteratively
+grows the bounding box by 2 degrees in each direction until fewer
+than 50% of grid points on an edge exceed the climatological
+threshold (or 10 iterations).
+
+Usage:
+    python heat_cold_bounds_case.py \\
+        --output heat_cold_yaml.csv --n-workers 4
+"""
+
+import argparse
+import logging
+import pathlib
+import time as time_module
+from typing import Dict, List, Optional
+
+import joblib
+import numpy as np
+import pandas as pd
+import regionmask
+import scipy.ndimage as ndimage
+import xarray as xr
+from dask.distributed import Client, LocalCluster
+from plot_temperature_events import (
+    detect_time_dim,
+    max_consecutive_days,
+    plot_consecutive_map,
+)
+
+from extremeweatherbench import cases, defaults, inputs
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(name)s %(levelname)s %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+MIN_CONSECUTIVE_DAYS = 3
+EXPANSION_DEGREES = 2.0
+MAX_ITERATIONS = 10
+EDGE_VALIDITY_THRESHOLD = 0.5
+MIN_GRIDPOINTS = 500
+
+
+def _apply_consecutive_filter(
+    mask: np.ndarray,
+    min_days: int = MIN_CONSECUTIVE_DAYS,
+) -> np.ndarray:
+    """Keep only runs of ``min_days``+ True days (axis 0)."""
+    struct = np.zeros((min_days, 1, 1), dtype=bool)
+    struct[:, 0, 0] = True
+    eroded = ndimage.binary_erosion(
+        mask,
+        structure=struct,
+        border_value=False,
+    )
+    dilated = ndimage.binary_dilation(
+        eroded,
+        structure=struct,
+        border_value=False,
+    )
+    return dilated & mask
+
+
+def _edge_valid_fraction(
+    filtered: np.ndarray,
+    edge: str,
+    band_pts: int,
+) -> float:
+    """Fraction of grid points on *edge* with any valid day."""
+    if edge == "north":
+        strip = filtered[:, -band_pts:, :]
+    elif edge == "south":
+        strip = filtered[:, :band_pts, :]
+    elif edge == "west":
+        strip = filtered[:, :, :band_pts]
+    elif edge == "east":
+        strip = filtered[:, :, -band_pts:]
+    else:
+        raise ValueError(f"Unknown edge: {edge}")
+
+    has_event = strip.any(axis=0)
+    if has_event.size == 0:
+        return 0.0
+    return float(has_event.mean())
+
+
+def process_event(
+    single_case: cases.IndividualCase,
+) -> Optional[Dict]:
+    """Process one event: compute mask and iteratively expand.
+
+    Opens ERA5 and climatology inside the worker to avoid
+    serialisation of lazy zarr handles across joblib processes.
+    """
+    is_heatwave = single_case.event_type == "heat_wave"
+    logger.info(
+        "Processing case %d: %s (%s)",
+        single_case.case_id_number,
+        single_case.title,
+        single_case.event_type,
+    )
+
+    bounds = single_case.location.as_geopandas().total_bounds
+    lon_min, lat_min, lon_max, lat_max = bounds
+
+    start_date = pd.Timestamp(single_case.start_date) - pd.Timedelta(days=3)
+    end_date = pd.Timestamp(single_case.end_date) + pd.Timedelta(days=3)
+
+    pot_lat_min = max(
+        -90,
+        lat_min - MAX_ITERATIONS * EXPANSION_DEGREES,
+    )
+    pot_lat_max = min(
+        90,
+        lat_max + MAX_ITERATIONS * EXPANSION_DEGREES,
+    )
+    pot_lon_min = lon_min - MAX_ITERATIONS * EXPANSION_DEGREES
+    pot_lon_max = lon_max + MAX_ITERATIONS * EXPANSION_DEGREES
+
+    ds = xr.open_zarr(
+        inputs.ARCO_ERA5_FULL_URI,
+        storage_options={"token": "anon"},
+        chunks={},
+    )
+    tdim = detect_time_dim(ds)
+    t2m = ds["2m_temperature"].sel(
+        {tdim: slice(str(start_date), str(end_date))},
+    )
+    six_hourly = t2m[tdim].dt.hour.isin([0, 6, 12, 18])
+    t2m = t2m.sel({tdim: six_hourly}).sortby("latitude")
+
+    # Normalise ERA5 longitudes from 0-360 to -180/180 so that the
+    # case bounding boxes (always in -180/180 from geopandas) can be
+    # used directly for slicing without wrap-around issues.
+    if float(t2m.longitude.max()) > 180:
+        t2m = t2m.assign_coords(
+            longitude=(t2m.longitude.values + 180) % 360 - 180,
+        ).sortby("longitude")
+
+    t2m = t2m.sel(
+        latitude=slice(pot_lat_min, pot_lat_max),
+        longitude=slice(pot_lon_min, pot_lon_max),
+    )
+
+    if t2m.latitude.size == 0 or t2m.longitude.size == 0:
+        logger.warning(
+            "  Case %d: empty spatial selection"
+            " (lon=[%.2f, %.2f], lat=[%.2f, %.2f]) — skipping",
+            single_case.case_id_number,
+            pot_lon_min,
+            pot_lon_max,
+            pot_lat_min,
+            pot_lat_max,
+        )
+        return None
+
+    if is_heatwave:
+        daily = t2m.resample({tdim: "1D"}).max()
+        clim = (
+            defaults.get_climatology(0.85)
+            .max(
+                dim="hour",
+            )
+            .sortby("latitude")
+        )
+    else:
+        daily = t2m.resample({tdim: "1D"}).min()
+        clim = (
+            defaults.get_climatology(0.15)
+            .min(
+                dim="hour",
+            )
+            .sortby("latitude")
+        )
+
+    # Match climatology longitude convention to the (possibly
+    # remapped) ERA5 data so reindex_like aligns correctly.
+    if float(clim.longitude.max()) > 180:
+        clim = clim.assign_coords(
+            longitude=(clim.longitude.values + 180) % 360 - 180,
+        ).sortby("longitude")
+
+    doy = daily[tdim].dt.dayofyear
+    max_clim_doy = int(clim.dayofyear.max())
+    doy_capped = doy.clip(max=max_clim_doy)
+    clim_aligned = clim.sel(
+        dayofyear=doy_capped,
+    ).reindex_like(daily, method="nearest")
+
+    land_reg = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+    land_mask = (
+        land_reg.mask(
+            daily.longitude,
+            daily.latitude,
+        )
+        == 0
+    )
+
+    if is_heatwave:
+        exc_mask = (daily > clim_aligned) & land_mask
+    else:
+        exc_mask = (daily < clim_aligned) & land_mask
+
+    logger.info("  Computing exceedance mask...")
+    mask_np = exc_mask.compute().values.astype(bool)
+    filtered = _apply_consecutive_filter(mask_np)
+
+    all_lats = daily.latitude.values
+    all_lons = daily.longitude.values
+    grid_res = np.abs(np.diff(all_lats[:2]))[0] if len(all_lats) > 1 else 0.25
+    band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
+
+    def _lat_idx(val: float) -> int:
+        return int(np.argmin(np.abs(all_lats - val)))
+
+    def _lon_idx(val: float) -> int:
+        return int(np.argmin(np.abs(all_lons - val)))
+
+    idx_s = _lat_idx(lat_min)
+    idx_n = _lat_idx(lat_max)
+    idx_w = _lon_idx(lon_min)
+    idx_e = _lon_idx(lon_max)
+
+    edges_active = {
+        "north": True,
+        "south": True,
+        "east": True,
+        "west": True,
+    }
+    n_iter = 0
+
+    for iteration in range(MAX_ITERATIONS):
+        n_iter = iteration + 1
+        region = filtered[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+
+        all_done = True
+        for edge in list(edges_active.keys()):
+            if not edges_active[edge]:
+                continue
+
+            frac = _edge_valid_fraction(
+                region,
+                edge,
+                band_pts,
+            )
+            if frac < EDGE_VALIDITY_THRESHOLD:
+                edges_active[edge] = False
+            else:
+                all_done = False
+                if edge == "north":
+                    idx_n = min(len(all_lats) - 1, idx_n + band_pts)
+                elif edge == "south":
+                    idx_s = max(0, idx_s - band_pts)
+                elif edge == "east":
+                    idx_e = min(len(all_lons) - 1, idx_e + band_pts)
+                elif edge == "west":
+                    idx_w = max(0, idx_w - band_pts)
+
+        if all_done:
+            logger.info(
+                "  Converged at iteration %d",
+                n_iter,
+            )
+            break
+    else:
+        logger.info(
+            "  Reached max iterations (%d)",
+            MAX_ITERATIONS,
+        )
+
+    fin_lats = all_lats[idx_s : idx_n + 1]
+    fin_lons = all_lons[idx_w : idx_e + 1]
+    fin_filtered = filtered[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+    consec = max_consecutive_days(fin_filtered)
+    peak_gridpoints = int(fin_filtered.any(axis=0).sum())
+
+    result = {
+        "case_id": single_case.case_id_number,
+        "title": single_case.title,
+        "event_type": single_case.event_type,
+        "start_date": str(single_case.start_date),
+        "end_date": str(single_case.end_date),
+        "latitude_min": float(all_lats[idx_s]),
+        "latitude_max": float(all_lats[idx_n]),
+        "longitude_min": float(all_lons[idx_w]),
+        "longitude_max": float(all_lons[idx_e]),
+        "n_iterations": n_iter,
+        "_consec": consec,
+        "_lats": fin_lats,
+        "_lons": fin_lons,
+        "_peak_gridpoints": peak_gridpoints,
+    }
+    logger.info(
+        "  Final bounds: lat [%.2f, %.2f], lon [%.2f, %.2f]",
+        result["latitude_min"],
+        result["latitude_max"],
+        result["longitude_min"],
+        result["longitude_max"],
+    )
+    return result
+
+
+def results_to_dataframe(results: List[Dict]) -> pd.DataFrame:
+    """Convert result dicts to a labelled DataFrame."""
+    columns = [
+        "label",
+        "event_type",
+        "start_date",
+        "end_date",
+        "latitude_min",
+        "latitude_max",
+        "longitude_min",
+        "longitude_max",
+    ]
+    if not results:
+        return pd.DataFrame(columns=columns)
+
+    valid = [r for r in results if r is not None]
+    n_before = len(valid)
+    valid = [r for r in valid if r["_peak_gridpoints"] >= MIN_GRIDPOINTS]
+    logger.info(
+        "  Filtered %d events below %d gridpoints; %d remain",
+        n_before - len(valid),
+        MIN_GRIDPOINTS,
+        len(valid),
+    )
+
+    rows = [
+        {
+            "event_type": r["event_type"],
+            "start_date": r["start_date"],
+            "end_date": r["end_date"],
+            "latitude_min": r["latitude_min"],
+            "latitude_max": r["latitude_max"],
+            "longitude_min": r["longitude_min"],
+            "longitude_max": r["longitude_max"],
+        }
+        for r in valid
+    ]
+    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
+    df.insert(0, "label", range(1, len(df) + 1))
+    return df
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Validate / expand heat wave and cold snap bounds from events.yaml."
+        ),
+    )
+    parser.add_argument(
+        "--output",
+        default="heat_cold_yaml.csv",
+        help="Output CSV path",
+    )
+    parser.add_argument(
+        "--n-workers",
+        type=int,
+        default=4,
+        help="Number of parallel workers (joblib)",
+    )
+    args = parser.parse_args()
+
+    wall_start = time_module.time()
+
+    client = Client(LocalCluster(n_workers=args.n_workers))
+    logger.info("Dask dashboard: %s", client.dashboard_link)
+
+    events_yaml = cases.load_ewb_events_yaml_into_case_list()
+    hw_fz = [e for e in events_yaml if e.event_type in ("heat_wave", "cold_snap")]
+    logger.info(
+        "Found %d heat_wave / cold snap events",
+        len(hw_fz),
+    )
+
+    results = joblib.Parallel(n_jobs=args.n_workers)(
+        joblib.delayed(process_event)(c) for c in hw_fz
+    )
+
+    df = results_to_dataframe(results)
+    df.to_csv(args.output, index=False)
+
+    out_dir = pathlib.Path(args.output).parent
+    for r in results:
+        if r is None:
+            continue
+        consec = r["_consec"]
+        lats = r["_lats"]
+        lons = r["_lons"]
+        case_id = r["case_id"]
+        event_type = r["event_type"]
+        kind = "heatwave" if event_type == "heat_wave" else "cold_snap"
+        start = r["start_date"][:10]
+        end = r["end_date"][:10]
+        out_png = str(out_dir / f"case_{case_id}_consecutive_{kind}_days.png")
+        plot_consecutive_map(
+            consec,
+            lats,
+            lons,
+            event_type,
+            title=(
+                f"Consecutive {kind.capitalize()} Days — {r['title']}\n{start} to {end}"
+            ),
+            output_path=out_png,
+        )
+
+    elapsed = time_module.time() - wall_start
+    logger.info(
+        "Done: %d events -> %s (%.1f s)",
+        len(df),
+        args.output,
+        elapsed,
+    )
+    client.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data_prep/heat_cold_bounds_global.py b/data_prep/heat_cold_bounds_global.py
new file mode 100644
index 00000000..1e81845a
--- /dev/null
+++ b/data_prep/heat_cold_bounds_global.py
@@ -0,0 +1,531 @@
+#!/usr/bin/env python3
+"""Detect heat waves and cold snaps globally from ERA5 reanalysis.
+
+Scans ERA5 2m temperature over an input date range and identifies
+heat wave (daily max > 85th percentile for 3+ consecutive days)
+and cold snap (daily min < 15th percentile for 3+ consecutive days)
+events globally over land.
+
+Bounding boxes represent the maximum spatial extent of each event.
+Events terminate when area drops below 50% of their peak area.
+
+Usage:
+    python heat_cold_bounds_global.py \\
+        --start-date 2023-06-01 --end-date 2023-09-01 \\
+        --output heat_cold_global.csv --n-workers 4
+"""
+
+import argparse
+import logging
+import pathlib
+import time as time_module
+from typing import Dict, List, Optional, Tuple
+
+import numba as nb
+import numpy as np
+import pandas as pd
+import regionmask
+import scipy.ndimage as ndimage
+import xarray as xr
+from dask.distributed import Client, LocalCluster
+from plot_temperature_events import (
+    _align_climatology,
+    detect_time_dim,
+    max_consecutive_days,
+    open_era5_t2m,
+    plot_consecutive_map,
+)
+
+from extremeweatherbench import defaults
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(name)s %(levelname)s %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+MIN_CONSECUTIVE_DAYS = 3
+AREA_DECLINE_FRACTION = 0.5
+MIN_GRIDPOINTS = 500
+
+
+def get_daily_climatology_thresholds() -> Tuple[xr.DataArray, xr.DataArray]:
+    """Derive daily thresholds from 6-hourly percentile climatology.
+
+    Returns the max-over-hours of the 85th percentile (heat wave
+    threshold) and min-over-hours of the 15th percentile (cold snap
+    threshold), each indexed by dayofyear.
+    """
+    clim_85 = defaults.get_climatology(0.85)
+    clim_15 = defaults.get_climatology(0.15)
+    return (
+        clim_85.max(dim="hour").sortby("latitude"),
+        clim_15.min(dim="hour").sortby("latitude"),
+    )
+
+
+def build_land_mask(
+    lons: xr.DataArray,
+    lats: xr.DataArray,
+) -> xr.DataArray:
+    """Boolean land mask (True = land) for the given grid."""
+    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+    mask = land.mask(lons, lats)
+    return mask == 0
+
+
+def build_exceedance_masks(
+    t2m: xr.DataArray,
+    clim_daily_max: xr.DataArray,
+    clim_daily_min: xr.DataArray,
+    land_mask: xr.DataArray,
+) -> Tuple[xr.DataArray, xr.DataArray]:
+    """Build lazy boolean masks for heat wave / cold snap exceedance.
+
+    Daily max/min aggregation stays in the dask graph. The
+    climatology is loaded into memory once (small) and then
+    broadcast against the lazy daily arrays to avoid chunk
+    multiplication warnings.
+    """
+    tdim = detect_time_dim(t2m)
+
+    daily_max = t2m.resample({tdim: "1D"}).max()
+    daily_min = t2m.resample({tdim: "1D"}).min()
+
+    clim_max_aligned = _align_climatology(
+        clim_daily_max,
+        daily_max,
+        tdim,
+    )
+    clim_min_aligned = _align_climatology(
+        clim_daily_min,
+        daily_min,
+        tdim,
+    )
+
+    hw = (daily_max > clim_max_aligned) & land_mask
+    fz = (daily_min < clim_min_aligned) & land_mask
+    return hw, fz
+
+
+def apply_consecutive_filter(
+    mask: np.ndarray,
+    min_days: int = MIN_CONSECUTIVE_DAYS,
+) -> np.ndarray:
+    """Keep only grid points in runs of ``min_days``+ True days.
+
+    Binary erosion removes runs shorter than ``min_days``;
+    dilation restores surviving runs to their original extent.
+    Operates only along axis 0 (time).
+    """
+    struct = np.zeros((min_days, 1, 1), dtype=bool)
+    struct[:, 0, 0] = True
+    eroded = ndimage.binary_erosion(
+        mask,
+        structure=struct,
+        border_value=False,
+    )
+    dilated = ndimage.binary_dilation(
+        eroded,
+        structure=struct,
+        border_value=False,
+    )
+    return dilated & mask
+
+
+@nb.njit(cache=True)
+def _count_overlaps_nb(
+    labels_a: np.ndarray,
+    labels_b: np.ndarray,
+    n_a: int,
+    n_b: int,
+) -> np.ndarray:
+    """Pixel-count overlap matrix between two consecutive-day label grids.
+
+    Returns int32 (n_a, n_b) where result[i, j] is the number of
+    pixels where labels_a == i+1 and labels_b == j+1. Single-pass
+    JIT loop avoids the per-blob numpy scan in the Python tracker.
+    """
+    mat = np.zeros((n_a, n_b), dtype=np.int32)
+    rows, cols = labels_a.shape
+    for r in range(rows):
+        for c in range(cols):
+            a = labels_a[r, c]
+            b = labels_b[r, c]
+            if a > 0 and b > 0:
+                mat[a - 1, b - 1] += 1
+    return mat
+
+
+def _resolve_event(
+    oid: int,
+    overlap_mat: Optional[np.ndarray],
+    prev_map: Dict[int, int],
+    events: Dict[int, Dict],
+    cur_map: Dict[int, int],
+) -> Optional[int]:
+    """Find and merge events from the previous day for blob oid.
+
+    Uses a precomputed overlap matrix row instead of scanning the
+    full prev_labels array per blob.
+    """
+    if overlap_mat is None:
+        return None
+    col = overlap_mat[:, oid - 1]
+    prev_blob_ids = np.where(col > 0)[0] + 1
+    eids = {prev_map[p] for p in prev_blob_ids if p in prev_map}
+    alive = {e for e in eids if e in events and not events[e]["done"]}
+    if not alive:
+        return None
+
+    eid = min(alive)
+    for other in alive - {eid}:
+        merged = events.pop(other)
+        tgt = events[eid]
+        tgt["lat_min"] = min(tgt["lat_min"], merged["lat_min"])
+        tgt["lat_max"] = max(tgt["lat_max"], merged["lat_max"])
+        tgt["lon_min"] = min(tgt["lon_min"], merged["lon_min"])
+        tgt["lon_max"] = max(tgt["lon_max"], merged["lon_max"])
+        tgt["peak"] = max(tgt["peak"], merged["peak"])
+        tgt["start"] = min(tgt["start"], merged["start"])
+        for k, v in list(cur_map.items()):
+            if v == other:
+                cur_map[k] = eid
+    return eid
+
+
+def _terminate_declined_events(
+    events: Dict[int, Dict],
+    cur_map: Dict[int, int],
+) -> None:
+    """Terminate events absent today or below 50% of peak."""
+    active = set(cur_map.values())
+    for eid, ev in events.items():
+        if ev["done"]:
+            continue
+        if eid not in active:
+            ev["done"] = True
+        elif ev["area"] < AREA_DECLINE_FRACTION * ev["peak"]:
+            ev["done"] = True
+
+
+def detect_events(
+    filtered_mask: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    event_type: str,
+) -> List[Dict]:
+    """Track spatiotemporal events from a filtered boolean mask.
+
+    Labels one day at a time (low peak memory) but uses a numba-JIT
+    overlap matrix to match blobs across days. A single O(nlat*nlon)
+    pass replaces one O(nlat*nlon) scan per blob, which dominates
+    when many blobs are active simultaneously.
+
+    An event's bounding box is the union of all its daily extents.
+    Events terminate when their active area drops below 50% of peak.
+    """
+    n_days = filtered_mask.shape[0]
+    events: Dict[int, Dict] = {}
+    next_id = 1
+    prev_labels: Optional[np.ndarray] = None
+    prev_n_obj: int = 0
+    prev_map: Dict[int, int] = {}
+
+    for di in range(n_days):
+        day = filtered_mask[di]
+
+        if not day.any():
+            for ev in events.values():
+                if not ev["done"]:
+                    ev["done"] = True
+            prev_labels = None
+            prev_n_obj = 0
+            prev_map = {}
+            continue
+
+        labels, n_obj = ndimage.label(day)
+        labels = labels.astype(np.int32)
+        cur_map: Dict[int, int] = {}
+
+        overlap_mat: Optional[np.ndarray] = None
+        if prev_labels is not None and n_obj > 0 and prev_n_obj > 0:
+            overlap_mat = _count_overlaps_nb(
+                prev_labels,
+                labels,
+                prev_n_obj,
+                n_obj,
+            )
+
+        for oid in range(1, n_obj + 1):
+            om = labels == oid
+            area = int(om.sum())
+            li, lo = np.where(om)
+
+            eid = _resolve_event(
+                oid,
+                overlap_mat,
+                prev_map,
+                events,
+                cur_map,
+            )
+
+            if eid is None:
+                eid = next_id
+                next_id += 1
+                events[eid] = {
+                    "type": event_type,
+                    "start": dates[di],
+                    "end": dates[di],
+                    "lat_min": float(lats[li].min()),
+                    "lat_max": float(lats[li].max()),
+                    "lon_min": float(lons[lo].min()),
+                    "lon_max": float(lons[lo].max()),
+                    "peak": area,
+                    "area": area,
+                    "done": False,
+                }
+            else:
+                ev = events[eid]
+                ev["end"] = dates[di]
+                ev["lat_min"] = min(
+                    ev["lat_min"],
+                    float(lats[li].min()),
+                )
+                ev["lat_max"] = max(
+                    ev["lat_max"],
+                    float(lats[li].max()),
+                )
+                ev["lon_min"] = min(
+                    ev["lon_min"],
+                    float(lons[lo].min()),
+                )
+                ev["lon_max"] = max(
+                    ev["lon_max"],
+                    float(lons[lo].max()),
+                )
+                ev["peak"] = max(ev["peak"], area)
+                ev["area"] = area
+
+            cur_map[oid] = eid
+
+        _terminate_declined_events(events, cur_map)
+        prev_labels = labels
+        prev_n_obj = n_obj
+        prev_map = cur_map
+
+    for ev in events.values():
+        ev["done"] = True
+
+    return list(events.values())
+
+
+def events_to_dataframe(
+    events: List[Dict],
+    min_gridpoints: int = MIN_GRIDPOINTS,
+) -> pd.DataFrame:
+    """Convert event dicts to a labelled DataFrame.
+
+    Args:
+        events: Raw event dicts from ``detect_events``.
+        min_gridpoints: Drop events whose peak spatial extent
+            (in grid points) is below this threshold. Defaults
+            to the module-level ``MIN_GRIDPOINTS`` constant.
+    """
+    columns = [
+        "label",
+        "event_type",
+        "start_date",
+        "end_date",
+        "latitude_min",
+        "latitude_max",
+        "longitude_min",
+        "longitude_max",
+    ]
+    if not events:
+        return pd.DataFrame(columns=columns)
+
+    n_before = len(events)
+    events = [e for e in events if e["peak"] >= min_gridpoints]
+    logger.info(
+        "  Filtered %d events below %d gridpoints; %d remain",
+        n_before - len(events),
+        min_gridpoints,
+        len(events),
+    )
+
+    if not events:
+        return pd.DataFrame(columns=columns)
+
+    rows = [
+        {
+            "event_type": e["type"],
+            "start_date": str(e["start"]),
+            "end_date": str(e["end"]),
+            "latitude_min": e["lat_min"],
+            "latitude_max": e["lat_max"],
+            "longitude_min": e["lon_min"],
+            "longitude_max": e["lon_max"],
+        }
+        for e in events
+    ]
+    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
+    df.insert(0, "label", range(1, len(df) + 1))
+    return df
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=("Detect heat waves and cold snaps globally from ERA5."),
+    )
+    parser.add_argument(
+        "--start-date",
+        required=True,
+        help="Start date YYYY-MM-DD",
+    )
+    parser.add_argument(
+        "--end-date",
+        required=True,
+        help="End date YYYY-MM-DD",
+    )
+    parser.add_argument(
+        "--output",
+        default="heat_cold_global.csv",
+        help="Output CSV path",
+    )
+    parser.add_argument(
+        "--n-workers",
+        type=int,
+        default=4,
+        help="Number of dask workers",
+    )
+    args = parser.parse_args()
+
+    wall_start = time_module.time()
+    client = Client(
+        LocalCluster(n_workers=args.n_workers),
+    )
+    logger.info("Dask dashboard: %s", client.dashboard_link)
+
+    logger.info("Pre-warming numba JIT...")
+    _tiny = np.zeros((2, 2), dtype=np.int32)
+    _tiny[0, 0] = 1
+    _tiny[1, 1] = 1
+    _count_overlaps_nb(_tiny, _tiny, 1, 1)
+    logger.info("  numba ready")
+
+    logger.info("Opening ERA5 data...")
+    t2m = open_era5_t2m(args.start_date, args.end_date)
+    logger.info("  sizes=%s", dict(t2m.sizes))
+
+    logger.info("Loading climatology thresholds...")
+    clim_max, clim_min = get_daily_climatology_thresholds()
+
+    logger.info("Building land mask...")
+    land_mask = build_land_mask(t2m.longitude, t2m.latitude)
+
+    logger.info("Building exceedance masks (lazy)...")
+    hw_lazy, fz_lazy = build_exceedance_masks(
+        t2m,
+        clim_max,
+        clim_min,
+        land_mask,
+    )
+
+    tdim = detect_time_dim(hw_lazy)
+
+    logger.info("Computing heat wave mask...")
+    t0 = time_module.time()
+    hw_da = hw_lazy.compute()
+    logger.info("  done in %.1f s", time_module.time() - t0)
+
+    logger.info("Computing cold snap mask...")
+    t0 = time_module.time()
+    fz_da = fz_lazy.compute()
+    logger.info("  done in %.1f s", time_module.time() - t0)
+
+    dates = hw_da[tdim].values
+    lats = hw_da.latitude.values
+    lons = hw_da.longitude.values
+    hw_np = hw_da.values.astype(bool)
+    fz_np = fz_da.values.astype(bool)
+    del hw_da, fz_da
+
+    logger.info(
+        "Applying %d-day consecutive filter...",
+        MIN_CONSECUTIVE_DAYS,
+    )
+    hw_filt = apply_consecutive_filter(hw_np)
+    fz_filt = apply_consecutive_filter(fz_np)
+    logger.info(
+        "  HW: %d -> %d True cells",
+        hw_np.sum(),
+        hw_filt.sum(),
+    )
+    logger.info(
+        "  FZ: %d -> %d True cells",
+        fz_np.sum(),
+        fz_filt.sum(),
+    )
+    del hw_np, fz_np
+
+    logger.info("Detecting heat wave events...")
+    hw_ev = detect_events(
+        hw_filt,
+        dates,
+        lats,
+        lons,
+        "heat_wave",
+    )
+    logger.info("  %d events", len(hw_ev))
+
+    logger.info("Detecting cold snap events...")
+    fz_ev = detect_events(
+        fz_filt,
+        dates,
+        lats,
+        lons,
+        "cold_snap",
+    )
+    logger.info("  %d events", len(fz_ev))
+
+    logger.info("Computing max-consecutive-days fields for plots...")
+    stem = str(pathlib.Path(args.output).with_suffix(""))
+    hw_consec = max_consecutive_days(hw_filt)
+    plot_consecutive_map(
+        hw_consec,
+        lats,
+        lons,
+        "heat_wave",
+        title=(f"Consecutive Heatwave Days\n{args.start_date} to {args.end_date}"),
+        output_path=f"{stem}_heatwave.png",
+    )
+    del hw_consec, hw_filt
+
+    fz_consec = max_consecutive_days(fz_filt)
+    plot_consecutive_map(
+        fz_consec,
+        lats,
+        lons,
+        "cold_snap",
+        title=(f"Consecutive Cold Snap Days\n{args.start_date} to {args.end_date}"),
+        output_path=f"{stem}_cold_snap.png",
+    )
+    del fz_consec, fz_filt
+
+    df = events_to_dataframe(hw_ev + fz_ev)
+    df.to_csv(args.output, index=False)
+
+    elapsed = time_module.time() - wall_start
+    logger.info(
+        "Done: %d events -> %s (%.1f s)",
+        len(df),
+        args.output,
+        elapsed,
+    )
+    client.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data_prep/plot_temperature_events.py b/data_prep/plot_temperature_events.py
new file mode 100644
index 00000000..6517fdd9
--- /dev/null
+++ b/data_prep/plot_temperature_events.py
@@ -0,0 +1,497 @@
+#!/usr/bin/env python3
+"""Plotting utilities and CLI for heat wave and cold snap events.
+
+Handles both event types from a single entry point. The event type
+is auto-detected from events.yaml; no flag required.
+
+Exported functions used by heat_cold_bounds_global.py and
+heat_cold_bounds_case.py:
+    max_consecutive_days  -- compute field from boolean mask
+    plot_consecutive_map  -- pcolormesh map (Reds/Blues, discrete)
+
+CLI usage:
+    python plot_temperature_events.py --case-id-number 2
+    python plot_temperature_events.py --case-id-number 30 --output my_plot.png
+"""
+
+import argparse
+import logging
+import pathlib
+import time as time_module
+from typing import Literal, cast
+
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.colors as mcolors
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import regionmask
+import xarray as xr
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+from extremeweatherbench import cases, defaults, inputs
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(name)s %(levelname)s %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+MIN_CONSECUTIVE_DAYS = 3
+
+# ── shared ERA5 utilities ─────────────────────────────────────────────
+
+
+def detect_time_dim(obj: xr.Dataset | xr.DataArray) -> str:
+    """Return the name of the time dimension."""
+    for name in ("valid_time", "time"):
+        if name in obj.dims:
+            return name
+    raise ValueError(f"No time dimension found. Available: {list(obj.dims)}")
+
+
+def open_era5_t2m(start_date: str, end_date: str) -> xr.DataArray:
+    """Open ERA5 2m temperature lazily for a date range.
+
+    Selects 6-hourly timesteps (0/6/12/18 UTC) to match the
+    climatology base and sorts latitude to ascending order.
+    """
+    ds = xr.open_zarr(
+        inputs.ARCO_ERA5_FULL_URI,
+        storage_options={"token": "anon"},
+        chunks=None,
+    )
+    tdim = detect_time_dim(ds)
+    t2m = ds["2m_temperature"].sel({tdim: slice(start_date, end_date)})
+    six_hourly = t2m[tdim].dt.hour.isin([0, 6, 12, 18])
+    t2m = t2m.sel({tdim: six_hourly})
+    t2m = t2m.chunk({"latitude": -1, "longitude": -1})
+    return t2m.sortby("latitude")
+
+
+def _align_climatology(
+    clim: xr.DataArray,
+    daily: xr.DataArray,
+    tdim: str,
+) -> xr.DataArray:
+    """Align a dayofyear-indexed climatology to daily ERA5 data.
+
+    Computes the climatology into memory (366 x lat x lon) to avoid
+    dask chunk multiplication, then indexes by dayofyear via numpy.
+    """
+    clim_vals = clim.compute().values
+    clim_doy = clim.dayofyear.values
+    doy = daily[tdim].dt.dayofyear.values
+    max_doy = int(clim_doy.max())
+    doy_idx = np.clip(doy, 1, max_doy) - int(clim_doy.min())
+
+    clim_lats = clim.latitude.values
+    clim_lons = clim.longitude.values
+    daily_lats = daily.latitude.values
+    daily_lons = daily.longitude.values
+
+    if (
+        clim_lats.shape == daily_lats.shape
+        and np.allclose(clim_lats, daily_lats)
+        and clim_lons.shape == daily_lons.shape
+        and np.allclose(clim_lons, daily_lons)
+    ):
+        selected = clim_vals[doy_idx]
+    else:
+        lat_idx = np.array([np.argmin(np.abs(clim_lats - dl)) for dl in daily_lats])
+        lon_idx = np.array([np.argmin(np.abs(clim_lons - dl)) for dl in daily_lons])
+        selected = clim_vals[np.ix_(doy_idx, lat_idx, lon_idx)]
+
+    return xr.DataArray(selected, dims=daily.dims, coords=daily.coords)
+
+
+# ── constants for bounding-box overview ──────────────────────────────
+
+_HW_COLOR = "#d73027"
+_FZ_COLOR = "#4575b4"
+_BOX_ALPHA = 0.35
+_BOX_EDGE_ALPHA = 0.85
+
+
+# ── shared helpers ───────────────────────────────────────────────────
+
+
+def _to_plot_lon(lon: float) -> float:
+    """Wrap 0-360 longitude to -180..180 for PlateCarree."""
+    return lon - 360.0 if lon > 180.0 else lon
+
+
+def max_consecutive_days(mask_3d: np.ndarray) -> np.ndarray:
+    """Max consecutive True days per grid point along axis 0."""
+    nt, nlat, nlon = mask_3d.shape
+    flat = mask_3d.reshape(nt, -1).astype(np.int8)
+    result = np.zeros(flat.shape[1], dtype=np.int32)
+    for k in range(flat.shape[1]):
+        col = flat[:, k]
+        if not col.any():
+            continue
+        d = np.diff(np.concatenate(([0], col, [0])))
+        starts = np.where(d == 1)[0]
+        ends = np.where(d == -1)[0]
+        result[k] = int((ends - starts).max())
+    return result.reshape(nlat, nlon)
+
+
+def _add_map_features(ax) -> None:
+    """Add standard cartopy features to an axis."""
+    ax.coastlines(linewidth=0.5, zorder=10)
+    ax.add_feature(
+        cfeature.BORDERS,
+        linewidth=0.6,
+        edgecolor="grey",
+    )
+    ax.add_feature(
+        cfeature.STATES,
+        linewidth=0.4,
+        edgecolor="lightgrey",
+    )
+    ax.add_feature(
+        cfeature.LAKES,
+        linewidth=0.3,
+        edgecolor="none",
+        facecolor="none",
+    )
+    ax.add_feature(
+        cfeature.RIVERS,
+        linewidth=0.2,
+        edgecolor="none",
+    )
+
+
+# ── consecutive-days map ─────────────────────────────────────────────
+
+
+def plot_consecutive_map(
+    consec: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    event_type: Literal["heat_wave", "cold_snap"],
+    title: str,
+    output_path: str,
+    extent: tuple[float, float, float, float] | None = None,
+) -> None:
+    """Plot a max-consecutive-days field using the standard style.
+
+    Args:
+        consec: int32 2-D array (nlat, nlon) of max consecutive days.
+        lats: 1-D latitude array matching consec rows.
+        lons: 1-D longitude array (0-360) matching consec columns.
+        event_type: ``'heat_wave'`` or ``'cold_snap'``.
+        title: Two-line figure title (left-aligned).
+        output_path: Destination PNG file path.
+        extent: (lon_min, lon_max, lat_min, lat_max) in -180..180.
+            Derived from lats/lons when None.
+    """
+    plot_data = consec.astype(float)
+    plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
+
+    valid = plot_data[~np.isnan(plot_data)]
+    vmax = int(valid.max()) if valid.size > 0 else MIN_CONSECUTIVE_DAYS
+
+    cmap_name = "Reds" if event_type == "heat_wave" else "Blues"
+    n_levels = vmax - MIN_CONSECUTIVE_DAYS + 1
+    cmap = plt.colormaps.get_cmap(cmap_name).resampled(n_levels)
+    bin_edges = np.arange(MIN_CONSECUTIVE_DAYS - 0.5, vmax + 1.5, 1)
+    norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
+
+    plot_lons = np.array([_to_plot_lon(x) for x in lons])
+
+    if extent is None:
+        lon_min, lon_max = float(plot_lons.min()), float(plot_lons.max())
+        lat_min, lat_max = float(lats.min()), float(lats.max())
+    else:
+        lon_min, lon_max, lat_min, lat_max = extent
+
+    fig, ax = plt.subplots(
+        subplot_kw={"projection": ccrs.PlateCarree()},
+        figsize=(10, 8),
+    )
+    ax.set_extent(
+        [lon_min, lon_max, lat_min, lat_max],
+        crs=ccrs.PlateCarree(),
+    )
+    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
+
+    im = ax.pcolormesh(
+        plot_lons,
+        lats,
+        plot_data,
+        cmap=cmap,
+        norm=norm,
+        transform=ccrs.PlateCarree(),
+        shading="auto",
+    )
+    _add_map_features(ax)
+
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes(
+        "right",
+        size="5%",
+        pad=0.1,
+        axes_class=plt.Axes,
+    )
+    cbar = fig.colorbar(im, cax=cax)
+    cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
+    cbar.set_label("Consecutive Days", fontsize=12)
+
+    ax.set_title(title, loc="left", fontsize=13)
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("Saved consecutive-days plot to %s", output_path)
+
+
+# ── bounding-box overview ─────────────────────────────────────────────
+
+
+def plot_event_bounds(
+    df: pd.DataFrame,
+    csv_path: str,
+    title: str = "Detected Heat Wave and Cold Snap Events",
+) -> None:
+    """Draw bounding boxes for all events on a global Robinson map.
+
+    Saves a PNG alongside the CSV with the same stem.
+    """
+    if df.empty:
+        logger.warning("No events to plot -- skipping bounds plot.")
+        return
+
+    out_path = str(pathlib.Path(csv_path).with_suffix(".png"))
+    hw_count = int((df["event_type"] == "heat_wave").sum())
+    fz_count = int((df["event_type"] == "cold_snap").sum())
+
+    fig, ax = plt.subplots(
+        subplot_kw={"projection": ccrs.Robinson()},
+        figsize=(14, 7),
+    )
+    ax.set_global()
+    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
+    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=0)
+    ax.coastlines(linewidth=0.5, zorder=2)
+    ax.add_feature(
+        cfeature.BORDERS,
+        linewidth=0.3,
+        edgecolor="grey",
+        zorder=2,
+    )
+
+    for _, row in df.iterrows():
+        lon0 = _to_plot_lon(float(row["longitude_min"]))
+        lon1 = _to_plot_lon(float(row["longitude_max"]))
+        lat0, lat1 = float(row["latitude_min"]), float(row["latitude_max"])
+        if lon1 < lon0:
+            lon1 += 360.0
+        color = _HW_COLOR if row["event_type"] == "heat_wave" else _FZ_COLOR
+        kw = dict(
+            width=lon1 - lon0,
+            height=lat1 - lat0,
+            transform=ccrs.PlateCarree(),
+        )
+        ax.add_patch(
+            mpatches.Rectangle(
+                xy=(lon0, lat0),
+                facecolor=color,
+                edgecolor=color,
+                alpha=_BOX_ALPHA,
+                linewidth=1.0,
+                zorder=3,
+                **kw,
+            )
+        )
+        ax.add_patch(
+            mpatches.Rectangle(
+                xy=(lon0, lat0),
+                facecolor="none",
+                edgecolor=color,
+                alpha=_BOX_EDGE_ALPHA,
+                linewidth=1.0,
+                zorder=4,
+                **kw,
+            )
+        )
+
+    legend_handles = []
+    if hw_count:
+        legend_handles.append(
+            mpatches.Patch(
+                facecolor=_HW_COLOR,
+                alpha=0.7,
+                label=f"Heat wave ({hw_count})",
+            )
+        )
+    if fz_count:
+        legend_handles.append(
+            mpatches.Patch(
+                facecolor=_FZ_COLOR,
+                alpha=0.7,
+                label=f"Cold Snap ({fz_count})",
+            )
+        )
+    if legend_handles:
+        ax.legend(
+            handles=legend_handles,
+            loc="lower left",
+            fontsize=10,
+            framealpha=0.85,
+        )
+
+    ax.set_title(title, loc="left", fontsize=13)
+    fig.savefig(out_path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("Saved bounds plot to %s", out_path)
+
+
+# ── case-level data loading ───────────────────────────────────────────
+
+
+def load_case(case_id_number: int) -> cases.IndividualCase:
+    """Load a single case from events.yaml by case_id_number."""
+    all_cases = cases.load_ewb_events_yaml_into_case_list()
+    for c in all_cases:
+        if c.case_id_number == case_id_number:
+            return c
+    hw_ids = ", ".join(
+        str(c.case_id_number) for c in all_cases if c.event_type == "heat_wave"
+    )
+    cs_ids = ", ".join(
+        str(c.case_id_number) for c in all_cases if c.event_type == "cold_snap"
+    )
+    raise ValueError(
+        f"Case {case_id_number} not found.\n"
+        f"  heat_wave ids: {hw_ids}\n"
+        f"  cold_snap ids:    {cs_ids}"
+    )
+
+
+def compute_consecutive_field(
+    single_case: cases.IndividualCase,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Return (consecutive_days, lats, lons) for a case.
+
+    Works for both heat_wave (daily max > 85th pct) and
+    cold_snap (daily min < 15th pct) event types.
+    """
+    is_heatwave = single_case.event_type == "heat_wave"
+    start = str(single_case.start_date.date())
+    end = str(single_case.end_date.date())
+    bounds = single_case.location.as_geopandas().total_bounds
+    lon_min, lat_min, lon_max, lat_max = bounds
+
+    if lon_min < 0:
+        lon_min += 360
+    if lon_max < 0:
+        lon_max += 360
+
+    logger.info("Opening ERA5 for %s to %s ...", start, end)
+    t2m = open_era5_t2m(start, end)
+    t2m = t2m.sel(
+        latitude=slice(lat_min, lat_max),
+        longitude=slice(lon_min, lon_max),
+    )
+    logger.info("  Subset: %s", dict(t2m.sizes))
+
+    tdim = detect_time_dim(t2m)
+
+    if is_heatwave:
+        logger.info("Loading 85th-percentile climatology...")
+        daily = t2m.resample({tdim: "1D"}).max()
+        clim = defaults.get_climatology(0.85).max(dim="hour").sortby("latitude")
+    else:
+        logger.info("Loading 15th-percentile climatology...")
+        daily = t2m.resample({tdim: "1D"}).min()
+        clim = defaults.get_climatology(0.15).min(dim="hour").sortby("latitude")
+
+    clim = clim.sel(
+        latitude=daily.latitude,
+        longitude=daily.longitude,
+        method="nearest",
+    )
+    clim_aligned = _align_climatology(clim, daily, tdim)
+
+    logger.info("Building land mask...")
+    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+    land_mask = land.mask(daily.longitude, daily.latitude) == 0
+
+    logger.info("Computing exceedance mask...")
+    if is_heatwave:
+        exc = (daily > clim_aligned) & land_mask
+    else:
+        exc = (daily < clim_aligned) & land_mask
+    mask_np = exc.compute().values.astype(bool)
+
+    logger.info("Computing max consecutive days...")
+    consec = max_consecutive_days(mask_np)
+    return consec, daily.latitude.values, daily.longitude.values
+
+
+# ── CLI ───────────────────────────────────────────────────────────────
+
+
+def _default_output(case_id_number: int, event_type: str) -> str:
+    kind = "heatwave" if event_type == "heat_wave" else "cold_snap"
+    return f"case_{case_id_number}_consecutive_{kind}_days.png"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Plot consecutive heatwave or cold_snap days for a case from events.yaml."
+        ),
+    )
+    parser.add_argument(
+        "--case-id-number",
+        type=int,
+        required=True,
+        help="case_id_number from events.yaml",
+    )
+    parser.add_argument(
+        "--output",
+        default=None,
+        help=(
+            "Output PNG path "
+            "(default: case_N_consecutive_{heatwave|cold_snap}_days.png)"
+        ),
+    )
+    args = parser.parse_args()
+
+    t0 = time_module.time()
+    single_case = load_case(args.case_id_number)
+    logger.info(
+        "Case %d: %s (%s)",
+        single_case.case_id_number,
+        single_case.title,
+        single_case.event_type,
+    )
+
+    if args.output is None:
+        args.output = _default_output(
+            args.case_id_number,
+            single_case.event_type,
+        )
+
+    consec, lats, lons = compute_consecutive_field(single_case)
+
+    kind = "Heat Wave" if single_case.event_type == "heat_wave" else "Cold Snap"
+    start_str = str(single_case.start_date.date())
+    end_str = str(single_case.end_date.date())
+
+    plot_consecutive_map(
+        consec,
+        lats,
+        lons,
+        cast(Literal["heat_wave", "cold_snap"], single_case.event_type),
+        title=f"Consecutive {kind} Days\n{start_str} to {end_str}",
+        output_path=args.output,
+    )
+    logger.info("Done in %.1f s", time_module.time() - t0)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data_prep/subset_heat_cold_events.py b/data_prep/subset_heat_cold_events.py
deleted file mode 100644
index ee6b4add..00000000
--- a/data_prep/subset_heat_cold_events.py
+++ /dev/null
@@ -1,258 +0,0 @@
-"""Helper functions to identify the date ranges of heat waves and freeze events."""
-
-import datetime
-
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import xarray as xr
-from cartopy.mpl.gridliner import LatitudeFormatter, LongitudeFormatter
-from matplotlib import dates as mdates
-from mpl_toolkits.axes_grid1 import make_axes_locatable
-
-import extremeweatherbench.cases as cases
-import extremeweatherbench.utils as utils
-
-sns.set_theme(style="whitegrid", context="talk")
-
-
-def subset_event_and_mask_climatology(
-    era5: xr.Dataset,
-    climatology: xr.Dataset,
-    actual_start_date: datetime.datetime,
-    actual_end_date: datetime.datetime,
-    single_case: cases.IndividualCase,
-):
-    """Calculate the times where regional average of temperature exceeds the
-    climatology."""
-    era5_event = era5[["2m_temperature"]].sel(
-        time=slice(actual_start_date, actual_end_date)
-    )
-    era5_event = era5_event.sel(time=era5_event["time.hour"].isin([0, 6, 12, 18]))
-    subset_climatology = convert_day_yearofday_to_time(
-        climatology, np.unique(era5_event.time.dt.year.values)[0]
-    ).rename_vars({"2m_temperature": "2m_temperature_85th_percentile"})
-    location = single_case.location.as_geopandas().total_bounds
-    merged_dataset = xr.merge([subset_climatology, era5_event], join="inner")
-    merged_dataset = merged_dataset.sel(
-        latitude=slice(location[1], location[3]),
-        longitude=slice(location[0], location[2]),
-    )
-    merged_dataset = utils.remove_ocean_gridpoints(merged_dataset)
-    time_averaged_merged_dataset = merged_dataset.mean(["latitude", "longitude"])
-
-    mask = (
-        time_averaged_merged_dataset["2m_temperature"]
-        > time_averaged_merged_dataset["2m_temperature_85th_percentile"]
-    )
-    return mask.compute(), merged_dataset
-
-
-def find_heatwave_events(
-    era5: xr.Dataset,
-    climatology: xr.Dataset,
-    single_case: cases.IndividualCase,
-    plot: bool = True,
-):
-    """Find the start and end dates of heatwave events, stepping +- 6 hours until <
-    climatology timesteps are located."""
-    start_date = pd.to_datetime(single_case.start_date)
-    end_date = pd.to_datetime(single_case.end_date)
-    location_center = single_case.location
-    era5_event = era5[["2m_temperature"]].sel(time=slice(start_date, end_date))
-    era5_event = era5_event.sel(time=era5_event["time.hour"].isin([0, 6, 12, 18]))
-    subset_climatology = convert_day_yearofday_to_time(
-        climatology, np.unique(era5_event.time.dt.year.values)[0]
-    ).rename_vars({"2m_temperature": "2m_temperature_85th_percentile"})
-
-    mask, merged_dataset = subset_event_and_mask_climatology(
-        era5, subset_climatology, start_date, end_date, single_case
-    )
-    before = True
-    after = True
-    while before or after:
-        # Check if there are 48 hours before and after the event
-        try:
-            last_true_time = mask.where(mask, drop=True).time[-1].values
-            event_end_duration = (mask.time[-1] - last_true_time).values.astype(
-                "timedelta64[h]"
-            )
-            first_true_time = mask.where(mask, drop=True).time[0].values
-            event_start_duration = (mask.time[0] - first_true_time).values.astype(
-                "timedelta64[h]"
-            )
-            if np.datetime64(last_true_time, "D") == np.datetime64("2022-12-31"):
-                after = False
-            if abs(event_start_duration) >= np.timedelta64(6, "h"):
-                before = False
-            else:
-                start_date -= pd.DateOffset(hours=6)
-                mask, merged_dataset, time_based_merged_dataset = (
-                    subset_event_and_mask_climatology(
-                        era5, climatology, start_date, end_date, single_case
-                    )
-                )
-            if abs(event_end_duration) >= np.timedelta64(6, "h"):
-                after = False
-            else:
-                end_date += pd.DateOffset(hours=6)
-                mask, merged_dataset, time_based_merged_dataset = (
-                    subset_event_and_mask_climatology(
-                        era5, climatology, start_date, end_date, single_case
-                    )
-                )
-        except IndexError:
-            print(f"No dates valid for {location_center}, {start_date}, {end_date}")
-            before = False
-            after = False
-    start_date -= pd.DateOffset(hours=42)
-    end_date += pd.DateOffset(hours=42)
-
-    mask, merged_dataset, time_based_merged_dataset = subset_event_and_mask_climatology(
-        era5, climatology, start_date, end_date, single_case
-    )
-    if plot:
-        case_plot(merged_dataset, time_based_merged_dataset, single_case)
-    return (
-        mask,
-        time_based_merged_dataset.time.min().values,
-        time_based_merged_dataset.time.max().values,
-    )
-
-
-def case_plot(
-    merged_dataset: xr.Dataset,
-    time_based_merged_dataset: xr.Dataset,
-    single_case: cases.IndividualCase,
-):
-    """Plot the max timestep of the heatwave event, the average regional temperature
-    time series, and the associated climatology."""
-    fig, (ax1, ax2) = plt.subplots(
-        2, 1, figsize=(6, 10), gridspec_kw={"height_ratios": [1, 1]}
-    )
-    plt.subplots_adjust(hspace=0.3)
-    ax1.remove()
-    ax1 = plt.subplot(2, 1, 1, projection=ccrs.PlateCarree())
-    subset_timestep = (
-        merged_dataset["2m_temperature"].mean(["latitude", "longitude"])
-        == merged_dataset["2m_temperature"].mean(["latitude", "longitude"]).max()
-    )
-    im = (
-        (
-            merged_dataset["2m_temperature"]
-            - merged_dataset["2m_temperature_85th_percentile"]
-        )
-        .sel(time=subset_timestep)
-        .plot(
-            ax=ax1,
-            transform=ccrs.PlateCarree(),
-            cmap="inferno",
-            add_colorbar=False,
-            vmin=0,
-            vmax=20,
-        )
-    )
-    # Add coastlines and gridlines
-    ax1.coastlines()
-    ax1.add_feature(cfeature.BORDERS, linestyle=":")
-    ax1.add_feature(cfeature.LAND, edgecolor="black")
-    ax1.add_feature(cfeature.LAKES, edgecolor="black")
-    ax1.add_feature(cfeature.RIVERS, edgecolor="black")
-    ax1.add_feature(cfeature.STATES, edgecolor="grey")
-    # Add gridlines
-    gl = ax1.gridlines(draw_labels=True)
-    gl.top_labels = False
-    gl.right_labels = False
-    gl.xformatter = LongitudeFormatter()
-    gl.yformatter = LatitudeFormatter()
-    gl.xlabel_style = {"size": 12, "color": "k"}
-    gl.ylabel_style = {"size": 12, "color": "k"}
-    time_subset = merged_dataset["time"].sel(time=subset_timestep)
-    ax1.set_title(
-        f"Event ID {single_case.case_id_number}: 2m Temperature, "
-        f"{time_subset.dt.strftime('%Y-%m-%d %Hz').values[0]}",
-        fontsize=12,
-    )
-    # Add the location coordinate as a dot on the map
-    # Get center coordinates from the region bounds
-    bounds = single_case.location.as_geopandas().total_bounds
-    center_lon = (bounds[0] + bounds[2]) / 2  # (lon_min + lon_max) / 2
-    center_lat = (bounds[1] + bounds[3]) / 2  # (lat_min + lat_max) / 2
-
-    ax1.plot(
-        center_lon,
-        center_lat,
-        "ko",
-        markersize=10,
-        transform=ccrs.PlateCarree(),
-    )
-    # Create a colorbar with the same height as the plot
-    divider = make_axes_locatable(ax1)
-    cax = divider.append_axes("right", size="5%", pad=0.1, axes_class=plt.Axes)
-    cbar = fig.colorbar(im, cax=cax, label="Temp > 85th Percentile (C)")
-    cbar.set_label("Temp > 85th Percentile (C)", size=14)
-    lss = ["-.", "-"]
-    lc = ["k", "tab:red"]
-    lws = [0.75, 1.5]
-    for i, variable in enumerate(time_based_merged_dataset):
-        (time_based_merged_dataset[variable] - 273.15).plot(
-            ax=ax2, label=variable, lw=lws[i], ls=lss[i], c=lc[i]
-        )
-    ax2.legend(fontsize=12)
-    mask = (
-        time_based_merged_dataset["2m_temperature"]
-        > time_based_merged_dataset["2m_temperature_85th_percentile"]
-    )
-    start = None
-    for i, val in enumerate(mask.values):
-        if val and start is None:
-            start = time_based_merged_dataset.time[i].values
-        elif not val and start is not None:
-            ax2.axvspan(
-                start,
-                time_based_merged_dataset.time[i].values,
-                color="red",
-                alpha=0.1,
-            )
-            start = None
-    if start is not None:
-        ax2.axvspan(
-            start, time_based_merged_dataset.time[-1].values, color="red", alpha=0.1
-        )
-    ax2.set_title("Heatwave Event vs 85th Percentile Climatology", fontsize=14)
-    ax2.set_ylabel("Temperature (C)", fontsize=12)
-    ax2.set_xlabel("Time", fontsize=12)
-    ax2.tick_params(axis="x", labelsize=12)
-    # ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
-    ax2.xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))
-    ax2.xaxis.set_tick_params(rotation=45, labelsize=10, pad=0.0001)
-    ax2.tick_params(axis="y", labelsize=12)
-    ax2.legend(["2m Temperature, 85th Percentile", "2m Temperature"], fontsize=12)
-    plt.show()
-
-
-def convert_day_yearofday_to_time(dataset: xr.Dataset, year: int) -> xr.Dataset:
-    """Convert dayofyear and hour coordinates in an xarray Dataset to a new time
-    coordinate.
-
-    Args:
-        dataset: The input xarray dataset.
-        year: The base year to use for the time coordinate.
-
-    Returns:
-        The dataset with a new time coordinate.
-    """
-    # Create a new time coordinate by combining dayofyear and hour
-    time_dim = pd.date_range(
-        start=f"{year}-01-01",
-        periods=len(dataset["dayofyear"]) * len(dataset["hour"]),
-        freq="6h",
-    )
-    dataset = dataset.stack(time=("dayofyear", "hour")).drop(["dayofyear", "hour"])
-    # Assign the new time coordinate to the dataset
-    dataset = dataset.assign_coords(time=time_dim)
-
-    return dataset

From 318785d61aa95fc707d70fa9e599aa595dac86c7 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Sun, 5 Apr 2026 10:11:22 -0400
Subject: [PATCH 47/72] Add `overlap_target_threshold` to `EarlySignal` (#350)

* add optional arg and conditional

* update with tests to confirm functionality

* update tests
---
 docs/examples/applied_severe.py     |   2 +-
 src/extremeweatherbench/defaults.py |   2 +-
 src/extremeweatherbench/metrics.py  |  35 ++--
 tests/test_golden.py                |   2 +-
 tests/test_metrics.py               | 242 +++++++++++++++++++++++++---
 5 files changed, 246 insertions(+), 37 deletions(-)

diff --git a/docs/examples/applied_severe.py b/docs/examples/applied_severe.py
index c668b9af..4f4b1f66 100644
--- a/docs/examples/applied_severe.py
+++ b/docs/examples/applied_severe.py
@@ -40,7 +40,7 @@
         forecast_threshold=15000,
         target_threshold=0.3,
     ),
-    ewb.metrics.EarlySignal(threshold=15000),
+    ewb.metrics.EarlySignal(forecast_threshold=15000),
 ]
 
 # Define LSR metrics as thresholdmetric to share scores contingency table
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index 7936af19..c947632b 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -373,7 +373,7 @@ def get_brightband_evaluation_objects() -> list[inputs.EvaluationObject]:
             forecast_threshold=15000,
             target_threshold=0.3,
         ),
-        metrics.EarlySignal(threshold=15000),
+        metrics.EarlySignal(forecast_threshold=15000),
     ]
 
     # Define LSR metrics as thresholdmetric to share scores contingency table
diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index 4309cc06..33304f16 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -997,7 +997,8 @@ def __init__(
         comparison_operator: Union[
             Callable, Literal[">", ">=", "<", "<=", "==", "!="]
         ] = ">=",
-        threshold: float = 0.5,
+        forecast_threshold: float = 0.5,
+        overlap_target_threshold: float | None = None,
         spatial_aggregation: Literal["any", "all", "half"] = "any",
         temporal_aggregation: Literal["any", "all", "half"] = "any",
         aggregation_order: tuple[
@@ -1010,7 +1011,9 @@ def __init__(
         Args:
             name: The name of the metric. Defaults to "EarlySignal".
             comparison_operator: The comparison operator for signal detection.
-            threshold: The threshold value for signal detection.
+            forecast_threshold: The threshold value for signal detection.
+            overlap_target_threshold: The target threshold for signal detection if
+                target is meant to overlap. Defaults to None for no overlap applied.
             spatial_aggregation: Spatial aggregation method. Options: "any"
                 (any gridpoint meets criteria), "all" (all gridpoints meet
                 criteria), or "half" (at least half meet criteria).
@@ -1023,7 +1026,8 @@ def __init__(
         """
         super().__init__(name=name, **kwargs)
         self.comparison_operator = utils.maybe_get_operator(comparison_operator)
-        self.threshold = threshold
+        self.forecast_threshold = forecast_threshold
+        self.overlap_target_threshold = overlap_target_threshold
         self.spatial_aggregation = spatial_aggregation
         self.temporal_aggregation = temporal_aggregation
         self.aggregation_order = aggregation_order
@@ -1076,19 +1080,20 @@ def _compute_metric(
 
         # Create detection masks with nans preserved
         forecast_detection_mask = self.comparison_operator(
-            forecast, self.threshold
+            forecast, self.forecast_threshold
         ).where(~forecast.isnull())
-        target_detection_mask = self.comparison_operator(target, self.threshold).where(
-            ~target.isnull()
-        )
-
-        # Only assess forecast where target detects the event (value == 1);
-        # locations where target is 0 or NaN are set to NaN (not dropped).
-        # drop=True uses boolean dask indexing which is unsupported; NaN
-        # values are handled correctly by _apply_aggregation downstream.
-        forecast_detection_mask = forecast_detection_mask.where(
-            target_detection_mask == 1
-        )
+        if self.overlap_target_threshold:
+            target_detection_mask = self.comparison_operator(
+                target, self.overlap_target_threshold
+            ).where(~target.isnull())
+
+            # Only assess forecast where target detects the event (value == 1);
+            # locations where target is 0 or NaN are set to NaN (not dropped).
+            # drop=True uses boolean dask indexing which is unsupported; NaN
+            # values are handled correctly by _apply_aggregation downstream.
+            forecast_detection_mask = forecast_detection_mask.where(
+                target_detection_mask == 1
+            )
 
         # Create lists of dimensions to reduce
         spatial_dims = [
diff --git a/tests/test_golden.py b/tests/test_golden.py
index 8476f0ba..0093ec2b 100644
--- a/tests/test_golden.py
+++ b/tests/test_golden.py
@@ -163,7 +163,7 @@ def test_severe_convection(self, golden_tests_event_data):
                 forecast_threshold=15000,
                 target_threshold=0.3,
             ),
-            metrics.EarlySignal(threshold=15000),
+            metrics.EarlySignal(forecast_threshold=15000),
         ]
         severe_convection_evaluation_objects = [
             inputs.EvaluationObject(
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index a5253812..9f08aca4 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -3496,7 +3496,8 @@ def _make_metric(self, **kwargs):
     def test_default_init(self):
         """Default EarlySignal has expected attribute values."""
         m = self._make_metric()
-        assert m.threshold == 0.5
+        assert m.forecast_threshold == 0.5
+        assert m.overlap_target_threshold is None
         assert m.spatial_aggregation == "any"
         assert m.temporal_aggregation == "any"
         assert m.aggregation_order == ("spatial", "temporal")
@@ -3504,12 +3505,14 @@ def test_default_init(self):
     def test_custom_init(self):
         """Custom params are stored correctly."""
         m = self._make_metric(
-            threshold=1.0,
+            forecast_threshold=1.0,
+            overlap_target_threshold=0.8,
             spatial_aggregation="all",
             temporal_aggregation="half",
             aggregation_order=("temporal", "spatial"),
         )
-        assert m.threshold == 1.0
+        assert m.forecast_threshold == 1.0
+        assert m.overlap_target_threshold == 0.8
         assert m.spatial_aggregation == "all"
         assert m.temporal_aggregation == "half"
         assert m.aggregation_order == ("temporal", "spatial")
@@ -3602,7 +3605,11 @@ def _make_forecast_target(self, forecast_vals, target_vals, spatial_size=3):
 
     def test_compute_metric_target_zero_excluded(self):
         """Forecast not evaluated at locations where target is 0."""
-        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
         # target row 0: all 0 → excluded; row 1: all 1 → included
         # forecast row 1: all above threshold → should detect
         forecast, target = self._make_forecast_target(
@@ -3615,7 +3622,11 @@ def test_compute_metric_target_zero_excluded(self):
 
     def test_compute_metric_target_nan_excluded(self):
         """Forecast not evaluated where target is NaN."""
-        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
         # Only target==1 at space=1, t=0; forecast is below threshold there
         forecast, target = self._make_forecast_target(
             forecast_vals=[[0.9, 0.1]],
@@ -3627,7 +3638,11 @@ def test_compute_metric_target_nan_excluded(self):
 
     def test_compute_metric_only_target_one_locations_matter(self):
         """High forecast values at target==0 locations don't trigger True."""
-        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
         # target: space=0 → 0 (excluded), space=1 → 1 (included)
         # forecast: space=0 → 0.9 (would fire if not masked), space=1 → 0.1
         forecast, target = self._make_forecast_target(
@@ -3676,27 +3691,216 @@ def test_aggregation_order_both_detect(self):
                 spatial_aggregation="any",
                 temporal_aggregation="any",
                 aggregation_order=order,
+                overlap_target_threshold=0.5,
             )
             result = m._compute_metric(forecast, target)
             assert bool(result.values), f"Expected True for order={order}"
 
-    def test_compute_metric_works_with_dask_backed_arrays(self):
-        """_compute_metric must not raise on dask-backed inputs.
+    def test_no_target_masking_when_overlap_target_threshold_is_none(self):
+        """Without overlap_target_threshold, all forecast locations count."""
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=None,
+            spatial_aggregation="any",
+        )
+        # target is all 0, but with no overlap threshold
+        # the target is ignored and forecast is evaluated everywhere
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.9]],
+            target_vals=[[0.0, 0.0]],
+        )
+        result = m._compute_metric(forecast, target)
+        assert bool(result.any().values)
 
-        drop=True in xr.DataArray.where() uses boolean indexing which
-        is unsupported for dask arrays; drop=False avoids this.
-        """
-        pytest.importorskip("dask.array")
+    def test_target_masking_applied_when_overlap_target_threshold_set(self):
+        """With overlap_target_threshold, target==0 locations are excluded."""
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
+        # Same data as above but now overlap is enforced;
+        # target is all 0 so all forecast locations are masked → False
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.9]],
+            target_vals=[[0.0, 0.0]],
+        )
+        result = m._compute_metric(forecast, target)
+        assert not bool(result.any().values)
 
-        m = self._make_metric(threshold=0.5, spatial_aggregation="any")
+    def test_overlap_target_threshold_differs_from_forecast_threshold(self):
+        """overlap_target_threshold can be different from forecast_threshold."""
+        m = self._make_metric(
+            forecast_threshold=0.3,
+            overlap_target_threshold=0.8,
+            spatial_aggregation="any",
+        )
+        # target value 0.7 is below overlap_target_threshold 0.8 → masked
+        # target value 0.9 is above → included; forecast 0.5 >= 0.3 → True
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.5, 0.5]],
+            target_vals=[[0.7, 0.9]],
+        )
+        result = m._compute_metric(forecast, target)
+        assert bool(result.any().values)
+
+    def test_overlap_target_threshold_high_excludes_low_target(self):
+        """High overlap_target_threshold excludes moderate target values."""
+        m = self._make_metric(
+            forecast_threshold=0.3,
+            overlap_target_threshold=0.8,
+            spatial_aggregation="all",
+        )
+        # target 0.7 fails overlap threshold (0.8) → masked to NaN
+        # target 0.9 passes → included; forecast 0.5 >= 0.3 → True
+        # With "all" aggregation, NaN is filled True so result is True
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.5, 0.5]],
+            target_vals=[[0.7, 0.9]],
+        )
+        result = m._compute_metric(forecast, target)
+        assert bool(result.any().values)
+
+        # Now both targets below overlap threshold → all masked, no signal
+        # "all" over all-NaN fillna(True) → True (vacuous truth)
+        forecast2, target2 = self._make_forecast_target(
+            forecast_vals=[[0.5, 0.5]],
+            target_vals=[[0.1, 0.2]],
+        )
+        result2 = m._compute_metric(forecast2, target2)
+        assert bool(result2.any().values)
+
+    def test_no_overlap_ignores_target_values_entirely(self):
+        """With no overlap, forecast detection is independent of target."""
+        m_no_overlap = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=None,
+            spatial_aggregation="any",
+        )
+        m_overlap = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
+        # forecast high at space=0, low at space=1
+        # target 0 at space=0, 1 at space=1
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.1]],
+            target_vals=[[0.0, 1.0]],
+        )
+        # No overlap: forecast=0.9 at space=0 fires → True
+        result_no = m_no_overlap._compute_metric(forecast, target)
+        assert bool(result_no.any().values)
+
+        # With overlap: space=0 masked (target=0), space=1 forecast=0.1 → False
+        result_yes = m_overlap._compute_metric(forecast, target)
+        assert not bool(result_yes.any().values)
+
+    @staticmethod
+    def _to_backend(forecast, target, backend):
+        """Convert numpy-backed DataArrays to the requested backend."""
+        if backend == "numpy":
+            return forecast, target
+        elif backend == "dask":
+            return (
+                forecast.chunk({"valid_time": 1, "space": 1}),
+                target.chunk({"valid_time": 1, "space": 1}),
+            )
+        elif backend == "sparse":
+            return (
+                forecast.copy(data=sparse.COO.from_numpy(forecast.values)),
+                target.copy(data=sparse.COO.from_numpy(target.values)),
+            )
+        raise ValueError(f"Unknown backend: {backend}")
+
+    @staticmethod
+    def _resolve(result):
+        """Compute dask results; pass through numpy/sparse."""
+        if hasattr(result, "compute"):
+            return result.compute()
+        return result
+
+    @pytest.mark.parametrize("backend", ["numpy", "dask", "sparse"])
+    def test_no_overlap_across_backends(self, backend):
+        """Without overlap, forecast fires at all locations."""
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=None,
+            spatial_aggregation="any",
+        )
         forecast, target = self._make_forecast_target(
             forecast_vals=[[0.9, 0.1]],
             target_vals=[[0.0, 1.0]],
         )
-        forecast_dask = forecast.chunk({"valid_time": 1, "space": 1})
-        target_dask = target.chunk({"valid_time": 1, "space": 1})
+        f, t = self._to_backend(forecast, target, backend)
+        result = self._resolve(m._compute_metric(f, t))
+        # No overlap: space=0 forecast=0.9 >= 0.5 → True
+        assert bool(result.any().values)
 
-        result = m._compute_metric(forecast_dask, target_dask)
-        result_computed = result.compute()
-        # space=0 excluded (target=0); space=1: forecast=0.1 < 0.5 → False
-        assert not bool(result_computed.any().values)
+    @pytest.mark.parametrize("backend", ["numpy", "dask", "sparse"])
+    def test_overlap_masking_across_backends(self, backend):
+        """With overlap, target==0 locations are excluded."""
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.1]],
+            target_vals=[[0.0, 1.0]],
+        )
+        f, t = self._to_backend(forecast, target, backend)
+        result = self._resolve(m._compute_metric(f, t))
+        # space=0 excluded (target=0); space=1: f=0.1 < 0.5 → False
+        assert not bool(result.any().values)
+
+    @pytest.mark.parametrize("backend", ["numpy", "dask", "sparse"])
+    def test_overlap_detection_across_backends(self, backend):
+        """With overlap enabled, high forecast at target==1 fires."""
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.9]],
+            target_vals=[[1.0, 1.0]],
+        )
+        f, t = self._to_backend(forecast, target, backend)
+        result = self._resolve(m._compute_metric(f, t))
+        assert bool(result.any().values)
+
+    @pytest.mark.parametrize("backend", ["numpy", "dask", "sparse"])
+    def test_different_thresholds_across_backends(self, backend):
+        """Separate forecast and target thresholds work on all backends."""
+        m = self._make_metric(
+            forecast_threshold=0.3,
+            overlap_target_threshold=0.8,
+            spatial_aggregation="any",
+        )
+        # target 0.7 < 0.8 → masked; target 0.9 >= 0.8 → kept
+        # forecast 0.5 >= 0.3 at kept location → True
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.5, 0.5]],
+            target_vals=[[0.7, 0.9]],
+        )
+        f, t = self._to_backend(forecast, target, backend)
+        result = self._resolve(m._compute_metric(f, t))
+        assert bool(result.any().values)
+
+    @pytest.mark.parametrize("backend", ["numpy", "dask", "sparse"])
+    def test_nan_target_across_backends(self, backend):
+        """NaN in target is handled correctly on all backends."""
+        m = self._make_metric(
+            forecast_threshold=0.5,
+            overlap_target_threshold=0.5,
+            spatial_aggregation="any",
+        )
+        forecast, target = self._make_forecast_target(
+            forecast_vals=[[0.9, 0.1]],
+            target_vals=[[np.nan, 1.0]],
+        )
+        f, t = self._to_backend(forecast, target, backend)
+        result = self._resolve(m._compute_metric(f, t))
+        # space=0 excluded (NaN); space=1: f=0.1 < 0.5 → False
+        assert not bool(result.any().values)

From c97b2282d227dd196af851a57b6c95903ec4db44 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Sun, 5 Apr 2026 10:11:39 -0400
Subject: [PATCH 48/72] Climatology scripting (#349)

* add scripts

* rename script, combine into one

* ruff
---
 data_prep/climatology_to_icechunk.py | 268 +++++++++++
 data_prep/era5_climatology.py        | 648 +++++++++++++++++++++++++++
 2 files changed, 916 insertions(+)
 create mode 100644 data_prep/climatology_to_icechunk.py
 create mode 100644 data_prep/era5_climatology.py

diff --git a/data_prep/climatology_to_icechunk.py b/data_prep/climatology_to_icechunk.py
new file mode 100644
index 00000000..c8a243ae
--- /dev/null
+++ b/data_prep/climatology_to_icechunk.py
@@ -0,0 +1,268 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "icechunk>=0.1.0",
+#     "xarray>=2023.1",
+#     "zarr>=3.1.0",
+# ]
+# ///
+"""Write a percentile climatology netCDF into a GCS-backed icechunk store.
+
+The commit message is generated automatically from the dataset metadata
+written by generate_percentile_climatology.py (percentile, variable,
+coordinate ranges, etc.), so every snapshot in the store is
+self-describing.
+
+If the target store does not yet exist it is created; if it already
+exists the climatology is appended as a new snapshot on the requested
+branch.
+
+Usage:
+    # Authenticate first (write access to the EWB bucket):
+    gcloud auth application-default login
+
+    # Write / update the store:
+    uv run climatology_to_icechunk.py \\
+        --input era5_heatwave_climatology.nc \\
+        --bucket extremeweatherbench \\
+        --prefix climatology-icechunk \\
+        --branch main
+
+    # Dry-run: print the commit message without touching the store:
+    uv run climatology_to_icechunk.py \\
+        --input era5_heatwave_climatology.nc \\
+        --dry-run
+
+To read the store back:
+    import icechunk, xarray as xr
+    storage = icechunk.gcs_storage(
+        bucket="extremeweatherbench",
+        prefix="climatology-icechunk",
+        anonymous=True,
+    )
+    repo   = icechunk.Repository.open(storage)
+    session = repo.readonly_session(branch="main")
+    ds = xr.open_zarr(session.store)
+"""
+
+import argparse
+import logging
+import pathlib
+
+import icechunk
+import xarray as xr
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(message)s",
+    datefmt="%H:%M:%S",
+)
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Commit message
+# ---------------------------------------------------------------------------
+
+
+def build_commit_message(ds: xr.Dataset) -> str:
+    """Build a descriptive commit message from dataset metadata.
+
+    Pulls from dataset attributes set by generate_percentile_climatology.py
+    and from the coordinate ranges present in the dataset.
+    """
+    attrs = ds.attrs
+
+    # --- required metadata written by the generation script ---
+    percentile_label = attrs.get("percentile_label", "pXX")
+    percentile = attrs.get("percentile", "unknown")
+    source = attrs.get("source", "unknown source")
+
+    # --- variable name: the first non-coordinate data variable ---
+    data_vars = list(ds.data_vars)
+    variable = data_vars[0] if data_vars else "unknown_variable"
+
+    # --- coordinate ranges ---
+    parts = []
+
+    if "dayofyear" in ds.coords:
+        doys = ds.coords["dayofyear"].values
+        doy_str = (
+            f"doy {int(doys.min())}–{int(doys.max())}"
+            if len(doys) > 1
+            else f"doy {int(doys[0])}"
+        )
+        parts.append(doy_str)
+
+    if "hour" in ds.coords:
+        hours = sorted(int(h) for h in ds.coords["hour"].values)
+        parts.append(f"hours {hours}")
+
+    if "latitude" in ds.coords:
+        lats = ds.coords["latitude"].values
+        parts.append(f"lat {float(lats.min()):.1f}°–{float(lats.max()):.1f}°")
+
+    if "longitude" in ds.coords:
+        lons = ds.coords["longitude"].values
+        parts.append(f"lon {float(lons.min()):.1f}°–{float(lons.max()):.1f}°")
+
+    coord_summary = ", ".join(parts) if parts else "full domain"
+
+    # --- training period, if recorded ---
+    period_str = ""
+    if "start_year" in attrs and "end_year" in attrs:
+        period_str = f", period {attrs['start_year']}–{attrs['end_year']}"
+    elif "training_period" in attrs:
+        period_str = f", period {attrs['training_period']}"
+
+    msg = (
+        f"Add {percentile_label} ({float(percentile):.0%}) climatology "
+        f"of {variable} from {source}{period_str} "
+        f"[{coord_summary}]"
+    )
+    return msg
+
+
+# ---------------------------------------------------------------------------
+# Store helpers
+# ---------------------------------------------------------------------------
+
+
+def _open_or_create_repo(
+    storage: icechunk.Storage,
+    branch: str,
+) -> icechunk.Repository:
+    """Open an existing icechunk repo, or create one if it doesn't exist."""
+    try:
+        repo = icechunk.Repository.open(storage)
+        logger.info("Opened existing icechunk repository.")
+    except Exception:
+        logger.info("No existing repository found – creating a new one.")
+        repo = icechunk.Repository.create(storage, icechunk.RepositoryConfig.default())
+
+    if branch != "main" and branch not in repo.list_branches():
+        snapshot_id = repo.lookup_branch("main")
+        repo.create_branch(branch, snapshot_id=snapshot_id)
+        logger.info("Created branch '%s'.", branch)
+
+    return repo
+
+
+# ---------------------------------------------------------------------------
+# Main logic
+# ---------------------------------------------------------------------------
+
+
+def write_climatology_to_icechunk(
+    input_path: pathlib.Path,
+    bucket: str,
+    prefix: str,
+    branch: str,
+    application_credentials: str,
+    dry_run: bool,
+) -> None:
+    """Load a climatology netCDF and commit it to a GCS icechunk store."""
+    logger.info("Loading climatology from %s", input_path)
+    ds = xr.open_dataset(input_path)
+
+    commit_message = build_commit_message(ds)
+    logger.info("Commit message: %s", commit_message)
+
+    if dry_run:
+        logger.info("Dry run – skipping store write.")
+        return
+
+    # Build the GCS-backed storage. Pass an empty string for
+    # application_credentials to let the SDK pick up ADC automatically,
+    # or provide an explicit path to a service-account JSON file.
+    storage = icechunk.gcs_storage(
+        bucket=bucket,
+        prefix=prefix,
+        application_credentials=application_credentials,
+    )
+
+    repo = _open_or_create_repo(storage, branch)
+    session = repo.writable_session(branch=branch)
+
+    logger.info(
+        "Writing dataset to icechunk store (bucket=%s, prefix=%s, branch=%s)…",
+        bucket,
+        prefix,
+        branch,
+    )
+    ds.to_zarr(session.store, mode="w")
+
+    snapshot_id = session.commit(commit_message)
+    logger.info("Committed snapshot %s on branch '%s'.", snapshot_id, branch)
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Write a percentile climatology netCDF into a GCS-backed icechunk store."
+        ),
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--input",
+        type=pathlib.Path,
+        required=True,
+        help="Path to the climatology netCDF produced by "
+        "generate_percentile_climatology.py.",
+    )
+    parser.add_argument(
+        "--bucket",
+        default="extremeweatherbench",
+        help="GCS bucket name.",
+    )
+    parser.add_argument(
+        "--prefix",
+        default="climatology-icechunk",
+        help="Prefix (subdirectory) within the bucket for the store.",
+    )
+    parser.add_argument(
+        "--branch",
+        default="main",
+        help="Icechunk branch to commit to. Created if it does not exist.",
+    )
+    parser.add_argument(
+        "--application-credentials",
+        default="",
+        help=(
+            "Path to a GCP service-account JSON file. Leave empty to use "
+            "Application Default Credentials (gcloud auth application-default "
+            "login)."
+        ),
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print the commit message and exit without writing to the store.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not args.dry_run and not args.input.exists():
+        raise FileNotFoundError(f"Input file not found: {args.input}")
+
+    write_climatology_to_icechunk(
+        input_path=args.input,
+        bucket=args.bucket,
+        prefix=args.prefix,
+        branch=args.branch,
+        application_credentials=args.application_credentials,
+        dry_run=args.dry_run,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data_prep/era5_climatology.py b/data_prep/era5_climatology.py
new file mode 100644
index 00000000..a8c1ca47
--- /dev/null
+++ b/data_prep/era5_climatology.py
@@ -0,0 +1,648 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "gcsfs>=2024.12.0",
+#     "joblib>=1.3",
+#     "numpy>=1.24",
+#     "xarray>=2023.1",
+#     "zarr>=3.1.0",
+#     "flox>=0.9",
+# ]
+# ///
+"""ERA5 percentile climatology toolkit.
+
+Three subcommands, each selectable via the first positional argument:
+
+  generate       Compute a single-percentile climatology zarr from ERA5.
+  all-quantiles  Compute multiple quantiles in one pass, keeping the
+                 per-year rolling means in RAM across all quantiles.
+  combine        Merge per-quantile zarr stores (local and/or GCS) into
+                 a single zarr with a leading 'quantile' dimension.
+
+All ERA5 computation is done entirely in RAM due to large memory requirements; this
+could be optimized in the future.
+
+Variable names are the ARCO ERA5 variable names, available at 
+https://github.com/google-research/arco-era5
+
+Usage examples:
+--------------
+# One percentile:
+uv run data_prep/climatology.py generate \\
+    --variable 2m_temperature --percentile 0.85 \\
+    --output /home/taylor/data/climatology_zarr/2m_temperature_p85.zarr
+
+# Multiple quantiles in one pass:
+uv run data_prep/climatology.py all-quantiles \\
+    --variable 2m_temperature \\
+    --quantiles 0.10 0.25 0.50 0.75 0.90 \\
+    --output-dir /home/taylor/data/climatology_zarr \\
+    --n-workers 16
+
+# Combine local stores (+ optionally pull p15/p85 from GCS):
+uv run data_prep/climatology.py combine \\
+    --input-dir /home/taylor/data/climatology_zarr \\
+    --output /home/taylor/data/climatology_zarr/2m_temperature_combined.zarr \\
+    --gcs-quantiles 0.15 0.85
+"""
+
+import argparse
+import gc
+import logging
+import os
+import pathlib
+import signal
+import sys
+
+import joblib
+import numpy as np
+import xarray as xr
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(message)s",
+    datefmt="%H:%M:%S",
+)
+logger = logging.getLogger(__name__)
+
+ERA5_ARCO_URL = "gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3"
+GCS_CLIMATOLOGY_URL = (
+    "gs://extremeweatherbench/datasets/"
+    "surface_air_temperature_1990_2019_climatology.zarr"
+)
+SYNOPTIC_HOURS = [0, 6, 12, 18]
+
+# Chunk shape matches the existing EWB climatology store:
+# (dayofyear=46, hour=1, latitude=91, longitude=180)
+ZARR_CHUNKS = (46, 1, 91, 180)
+
+# Combined store: quantile dim is tiny, keep all quantiles in one chunk
+COMBINED_ZARR_CHUNKS = {
+    "quantile": -1,
+    "dayofyear": 46,
+    "hour": 1,
+    "latitude": 91,
+    "longitude": 180,
+}
+
+
+def _setup_worker_cleanup() -> None:
+    """Kill the process group on SIGINT/SIGTERM.
+
+    Only registers signal handlers, not atexit, so zarr's async cleanup
+    can finish normally on a clean exit.  On KeyboardInterrupt or SIGTERM
+    we SIGKILL the whole group to stop all threads immediately.
+
+    The parent shell is NOT in this group (os.setpgrp creates a new one),
+    so it is unaffected.
+    """
+    os.setpgrp()
+    pgid = os.getpgrp()
+
+    def _kill_group(signum: int, frame: object = None) -> None:
+        logger.info("Killing all worker processes (pgid=%d)...", pgid)
+        try:
+            os.killpg(pgid, signal.SIGKILL)
+        except ProcessLookupError:
+            pass
+        sys.exit(128 + signum)
+
+    signal.signal(signal.SIGINT, _kill_group)
+    signal.signal(signal.SIGTERM, _kill_group)
+
+
+def _rss_gb() -> float:
+    """Return current process resident memory in GB via /proc/self/status."""
+    try:
+        with open("/proc/self/status") as fh:
+            for line in fh:
+                if line.startswith("VmRSS:"):
+                    return int(line.split()[1]) / 1_000_000  # kB → GB
+    except Exception:
+        pass
+    return float("nan")
+
+
+def _build_weights(half_window_days: int) -> xr.DataArray:
+    """Build a normalised triangular weight DataArray."""
+    ramp = np.linspace(0, 1, half_window_days + 1)
+    weights = np.concatenate([ramp, ramp[::-1][1:]])
+    weights /= weights.sum()
+    return xr.DataArray(weights, dims=["window"])
+
+
+def _process_single_year(
+    year: int,
+    variable: str,
+    half_window_days: int,
+) -> xr.DataArray:
+    """Fetch and compute the rolling weighted mean for one year.
+
+    Opens its own ERA5 connection so this is safe to call from multiple
+    threads concurrently.
+    """
+    era5 = xr.open_zarr(
+        ERA5_ARCO_URL,
+        chunks=None,
+        storage_options=dict(token="anon"),
+    )
+
+    weight_da = _build_weights(half_window_days)
+    n_weights = len(weight_da)
+
+    # Buffer on both sides so the rolling window is valid at year edges.
+    start_date = f"{year - 1}-12-{31 - half_window_days}"
+    end_date = f"{year + 1}-01-{half_window_days}"
+
+    era5_window = era5.sel(time=slice(start_date, end_date))
+    era5_window = era5_window.sel(time=era5_window.time.dt.hour.isin(SYNOPTIC_HOURS))
+
+    year_data: xr.DataArray = era5_window[variable].compute()
+    logger.info(
+        "  %d: downloaded, shape=%s  RAM=%.1f GB",
+        year,
+        year_data.shape,
+        _rss_gb(),
+    )
+
+    hour_arrays = []
+    for hour in SYNOPTIC_HOURS:
+        hour_data = year_data.sel(time=year_data.time.dt.hour == hour)
+        rolled = hour_data.rolling(time=n_weights, center=True)
+        weighted = rolled.construct(
+            "window",
+            sliding_window_view_kwargs={"automatic_rechunk": True},
+        ).dot(weight_da)
+        weighted = weighted.compute()
+        hour_arrays.append(weighted)
+
+    combined = xr.concat(hour_arrays, dim="time").sortby("time")
+    del year_data, hour_arrays
+    combined = combined.sel(time=combined.time.dt.year == year)
+    # Restore the variable name lost through rolling/dot operations.
+    combined = combined.rename(variable)
+    logger.info(
+        "  %d: rolling done, shape=%s  RAM=%.1f GB",
+        year,
+        combined.shape,
+        _rss_gb(),
+    )
+    return combined
+
+
+def compute_year_rolling_means(
+    variable: str,
+    start_year: int,
+    end_year: int,
+    half_window_days: int,
+    n_workers: int,
+) -> list[xr.DataArray]:
+    """Compute rolling weighted means for all years.
+
+    Returns a list of per-year DataArrays (~6 GB each, ~180 GB total).
+    Uses joblib threading so arrays stay in shared memory — no pickling.
+    Recommended n_workers: 8-16 to avoid GCS rate-limiting.
+    """
+    years = list(range(start_year, end_year + 1))
+    logger.info(
+        "Per-year rolling means: fetching %d years with %d threads...  RAM=%.1f GB",
+        len(years),
+        n_workers,
+        _rss_gb(),
+    )
+
+    year_arrays: list[xr.DataArray] = joblib.Parallel(
+        n_jobs=n_workers, backend="threading"
+    )(
+        joblib.delayed(_process_single_year)(year, variable, half_window_days)
+        for year in years
+    )
+    gc.collect()
+
+    logger.info(
+        "Per-year rolling means complete: %d year arrays in memory.  RAM=%.1f GB",
+        len(year_arrays),
+        _rss_gb(),
+    )
+    return year_arrays
+
+
+def _compute_dayofyear_quantile(
+    dayofyear: int,
+    year_arrays: list[xr.DataArray],
+    percentile: float,
+) -> xr.DataArray | None:
+    """Compute the per-hour quantile for one day-of-year.
+
+    Slices only the dayofyear's timesteps from each year array (~500 MB peak
+    per call), then groups by hour and computes the quantile.  Safe to
+    call from multiple threads — isel returns new views.
+    """
+    year_slices = []
+    for ya in year_arrays:
+        mask = (ya.time.dt.dayofyear == dayofyear).values
+        if mask.any():
+            year_slices.append(ya.isel(time=mask))
+
+    if not year_slices:
+        return None
+
+    dayofyear_data = xr.concat(year_slices, dim="time")
+    dayofyear_data = dayofyear_data.sel(
+        time=dayofyear_data.time.dt.hour.isin(SYNOPTIC_HOURS)
+    )
+
+    return (
+        dayofyear_data.groupby("time.hour")
+        .quantile(percentile)
+        .drop_vars("quantile", errors="ignore")
+        .expand_dims({"dayofyear": [dayofyear]})
+    )
+
+
+def compute_dayofyear_quantile_climatology(
+    year_arrays: list[xr.DataArray],
+    variable: str,
+    percentile: float,
+    output_path: pathlib.Path,
+    start_year: int,
+    end_year: int,
+    n_workers: int,
+) -> None:
+    """Compute the quantile for all dayofyears and write to a zarr store.
+
+    Workers share year_arrays via threading (no memory copies).
+    Peak memory per worker: ~500 MB.
+    """
+    logger.info(
+        "Per-dayofyear quantile: computing %.0f%% climatology for %s  RAM=%.1f GB",
+        percentile * 100,
+        variable,
+        _rss_gb(),
+    )
+
+    dayofyear_results: list[xr.DataArray | None] = joblib.Parallel(
+        n_jobs=n_workers, backend="threading"
+    )(
+        joblib.delayed(_compute_dayofyear_quantile)(dayofyear, year_arrays, percentile)
+        for dayofyear in range(1, 367)
+    )
+
+    logger.info("Per-dayofyear quantile loop done.  RAM=%.1f GB", _rss_gb())
+
+    valid = [r for r in dayofyear_results if r is not None]
+    del dayofyear_results
+    logger.info("Combining %d dayofyear slices...  RAM=%.1f GB", len(valid), _rss_gb())
+    ds_out = xr.combine_by_coords(valid)
+    del valid
+    logger.info("Combined shape: %s  RAM=%.1f GB", dict(ds_out.sizes), _rss_gb())
+    ds_out.attrs["percentile"] = percentile
+    ds_out.attrs["percentile_label"] = f"p{int(round(percentile * 100)):02d}"
+    ds_out.attrs["source"] = "ERA5 ARCO"
+    ds_out.attrs["start_year"] = start_year
+    ds_out.attrs["end_year"] = end_year
+
+    encoding = {variable: {"chunks": list(ZARR_CHUNKS)}}
+
+    output_path = pathlib.Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    logger.info("Writing zarr store to %s...  RAM=%.1f GB", output_path, _rss_gb())
+    ds_out.to_zarr(output_path, mode="w", zarr_format=2, encoding=encoding)
+    del ds_out
+    gc.collect()
+    logger.info("Zarr store written: %s  RAM=%.1f GB", output_path, _rss_gb())
+
+
+def _load_local_store(zarr_path: pathlib.Path, variable: str) -> xr.DataArray:
+    """Open one local zarr and tag it with its quantile coordinate."""
+    ds = xr.open_zarr(zarr_path, chunks=None)
+    quantile_val = float(ds.attrs["percentile"])
+    da = ds[variable].expand_dims({"quantile": [quantile_val]})
+    logger.info(
+        "  Loaded local q=%.2f from %s  shape=%s",
+        quantile_val,
+        zarr_path.name,
+        dict(da.sizes),
+    )
+    return da
+
+
+def _load_gcs_store(quantile: float, variable: str) -> xr.DataArray:
+    """Load one quantile from the existing GCS multi-quantile store."""
+    da = xr.open_zarr(
+        GCS_CLIMATOLOGY_URL,
+        storage_options={"anon": True},
+        chunks=None,
+    )[variable].sel(quantile=quantile)
+    da = da.expand_dims({"quantile": [quantile]})
+    logger.info("  Loaded GCS q=%.2f  shape=%s", quantile, dict(da.sizes))
+    return da
+
+
+def combine_stores(
+    input_dir: pathlib.Path,
+    output: pathlib.Path,
+    variable: str,
+    gcs_quantiles: list[float],
+) -> None:
+    """Concatenate per-quantile stores into one zarr with a quantile dim."""
+    slices: list[xr.DataArray] = []
+
+    local_stores = sorted(input_dir.glob(f"{variable}_p*_climatology.zarr"))
+    if not local_stores:
+        logger.warning("No local zarr stores found in %s", input_dir)
+    for store in local_stores:
+        slices.append(_load_local_store(store, variable))
+
+    for q in gcs_quantiles:
+        slices.append(_load_gcs_store(q, variable))
+
+    if not slices:
+        raise RuntimeError("No data found — check --input-dir and --gcs-quantiles.")
+
+    output_variable = "surface_air_temperature"
+
+    logger.info(
+        "Concatenating %d quantile slices along 'quantile' dim...",
+        len(slices),
+    )
+    combined_da = xr.concat(slices, dim="quantile").sortby("quantile")
+    ds_out = combined_da.to_dataset(name=output_variable)
+    ds_out.attrs.update(
+        {
+            "source": "ERA5 ARCO",
+            "quantiles": sorted(combined_da.coords["quantile"].values.tolist()),
+        }
+    )
+    logger.info("Combined shape: %s", dict(ds_out.sizes))
+
+    n_quantiles = ds_out.sizes["quantile"]
+    encoding = {
+        output_variable: {
+            "chunks": [
+                n_quantiles,  # keep all quantiles in one chunk
+                COMBINED_ZARR_CHUNKS["dayofyear"],
+                COMBINED_ZARR_CHUNKS["hour"],
+                COMBINED_ZARR_CHUNKS["latitude"],
+                COMBINED_ZARR_CHUNKS["longitude"],
+            ]
+        }
+    }
+
+    output.parent.mkdir(parents=True, exist_ok=True)
+    logger.info("Writing combined zarr to %s...", output)
+    ds_out.to_zarr(output, mode="w", zarr_format=2, encoding=encoding)
+    logger.info("Done: %s", output)
+
+
+def _zarr_store_path(
+    output_dir: pathlib.Path, variable: str, quantile: float
+) -> pathlib.Path:
+    label = f"p{int(round(quantile * 100)):02d}"
+    return output_dir / f"{variable.replace(' ', '_')}_{label}_climatology.zarr"
+
+
+def _zarr_complete(zarr_path: pathlib.Path) -> bool:
+    """Return True if the zarr store exists and appears complete."""
+    return (zarr_path / "zarr.json").exists() or (zarr_path / ".zmetadata").exists()
+
+
+def cmd_generate(args: argparse.Namespace) -> None:
+    logger.info("Variable   : %s", args.variable)
+    logger.info("Percentile : %.0f%%", args.percentile * 100)
+    logger.info("Period     : %d-%d", args.start_year, args.end_year)
+    logger.info("Window     : +/-%d days", args.half_window_days)
+    logger.info("Workers    : %d", args.n_workers)
+    logger.info("Output     : %s", args.output)
+
+    year_arrays = compute_year_rolling_means(
+        variable=args.variable,
+        start_year=args.start_year,
+        end_year=args.end_year,
+        half_window_days=args.half_window_days,
+        n_workers=args.n_workers,
+    )
+    compute_dayofyear_quantile_climatology(
+        year_arrays=year_arrays,
+        variable=args.variable,
+        percentile=args.percentile,
+        output_path=args.output,
+        start_year=args.start_year,
+        end_year=args.end_year,
+        n_workers=args.n_workers,
+    )
+
+
+def cmd_all_quantiles(args: argparse.Namespace) -> None:
+    logger.info("Variable   : %s", args.variable)
+    logger.info("Quantiles  : %s", args.quantiles)
+    logger.info("Period     : %d–%d", args.start_year, args.end_year)
+    logger.info("Window     : ±%d days", args.half_window_days)
+    logger.info("Workers    : %d", args.n_workers)
+    logger.info("Output dir : %s", args.output_dir)
+
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    remaining = [
+        q
+        for q in args.quantiles
+        if not _zarr_complete(_zarr_store_path(args.output_dir, args.variable, q))
+    ]
+    if not remaining:
+        logger.info("All zarr stores already complete. Nothing to do.")
+        return
+
+    logger.info(
+        "Per-year rolling means: computing for %d-%d"
+        " (~180 GB of year arrays stored in RAM)...",
+        args.start_year,
+        args.end_year,
+    )
+    year_arrays = compute_year_rolling_means(
+        variable=args.variable,
+        start_year=args.start_year,
+        end_year=args.end_year,
+        half_window_days=args.half_window_days,
+        n_workers=args.n_workers,
+    )
+
+    for i, quantile in enumerate(remaining):
+        label = f"p{int(round(quantile * 100)):02d}"
+        zarr_out = _zarr_store_path(args.output_dir, args.variable, quantile)
+        logger.info("---- Quantile %s (%d/%d) ----", label, i + 1, len(remaining))
+        compute_dayofyear_quantile_climatology(
+            year_arrays=year_arrays,
+            variable=args.variable,
+            percentile=quantile,
+            output_path=zarr_out,
+            start_year=args.start_year,
+            end_year=args.end_year,
+            n_workers=args.n_workers,
+        )
+        logger.info("Quantile %s complete: %s", label, zarr_out)
+
+    logger.info("All %d quantiles complete.", len(remaining))
+
+
+def cmd_combine(args: argparse.Namespace) -> None:
+    logger.info("Input dir     : %s", args.input_dir)
+    logger.info("Output        : %s", args.output)
+    logger.info("Variable      : %s", args.variable)
+    if args.gcs_quantiles:
+        logger.info("GCS quantiles : %s", args.gcs_quantiles)
+    combine_stores(
+        input_dir=args.input_dir,
+        output=args.output,
+        variable=args.variable,
+        gcs_quantiles=args.gcs_quantiles,
+    )
+
+
+_DEFAULT_OUTPUT_DIR = pathlib.Path("/home/taylor/data/climatology_zarr")
+_DEFAULT_COMBINED = _DEFAULT_OUTPUT_DIR / "2m_temperature_combined.zarr"
+
+
+def _add_era5_args(p: argparse.ArgumentParser) -> None:
+    """Attach shared ERA5/period arguments to a subparser."""
+    p.add_argument(
+        "--variable",
+        default="2m_temperature",
+        help="ERA5 variable name.",
+    )
+    p.add_argument(
+        "--start-year",
+        type=int,
+        default=1990,
+        help="First year of the training period (inclusive).",
+    )
+    p.add_argument(
+        "--end-year",
+        type=int,
+        default=2019,
+        help="Last year of the training period (inclusive).",
+    )
+    p.add_argument(
+        "--half-window-days",
+        type=int,
+        default=10,
+        help="Half-width of the triangular rolling window in days.",
+    )
+    p.add_argument(
+        "--n-workers",
+        type=int,
+        default=16,
+        help=(
+            "Parallel threads. 8-16 avoids GCS rate-limiting during "
+            "per-year download; more threads speeds up per-dayofyear quantile."
+        ),
+    )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        prog="climatology.py",
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    p_gen = sub.add_parser(
+        "generate",
+        help="Compute a single-percentile climatology zarr from ERA5.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    _add_era5_args(p_gen)
+    p_gen.add_argument(
+        "--percentile",
+        type=float,
+        required=True,
+        help="Percentile as a fraction in (0, 1).",
+    )
+    p_gen.add_argument(
+        "--output",
+        type=pathlib.Path,
+        required=True,
+        help="Output zarr store path.",
+    )
+
+    p_all = sub.add_parser(
+        "all-quantiles",
+        help="Compute multiple quantiles in one pass (year arrays kept in RAM).",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    _add_era5_args(p_all)
+    p_all.add_argument(
+        "--quantiles",
+        nargs="+",
+        type=float,
+        default=[0.10, 0.25, 0.50, 0.75, 0.90],
+        help="Quantiles to compute, each in (0, 1).",
+    )
+    p_all.add_argument(
+        "--output-dir",
+        type=pathlib.Path,
+        default=_DEFAULT_OUTPUT_DIR,
+        help="Directory where per-quantile zarr stores are written.",
+    )
+
+    p_comb = sub.add_parser(
+        "combine",
+        help="Merge per-quantile zarr stores into one zarr with a quantile dim.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    p_comb.add_argument(
+        "--input-dir",
+        type=pathlib.Path,
+        default=_DEFAULT_OUTPUT_DIR,
+        help="Directory containing per-quantile zarr stores.",
+    )
+    p_comb.add_argument(
+        "--output",
+        type=pathlib.Path,
+        default=_DEFAULT_COMBINED,
+        help="Output path for the combined zarr store.",
+    )
+    p_comb.add_argument(
+        "--variable",
+        default="2m_temperature",
+        help="Variable name inside each source zarr store.",
+    )
+    p_comb.add_argument(
+        "--gcs-quantiles",
+        nargs="*",
+        type=float,
+        default=[],
+        metavar="Q",
+        help=(
+            "Additional quantiles to pull from the existing GCS climatology "
+            "store (e.g. 0.15 0.85).  Only 0.15 and 0.85 are available."
+        ),
+    )
+
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    if args.command in ("generate", "all-quantiles"):
+        _setup_worker_cleanup()
+
+    if args.command == "generate":
+        if not 0.0 < args.percentile < 1.0:
+            raise ValueError(f"--percentile must be in (0, 1), got {args.percentile}")
+        cmd_generate(args)
+
+    elif args.command == "all-quantiles":
+        bad = [q for q in args.quantiles if not 0.0 < q < 1.0]
+        if bad:
+            raise ValueError(f"Quantiles must be in (0, 1); got {bad}")
+        cmd_all_quantiles(args)
+
+    elif args.command == "combine":
+        cmd_combine(args)
+
+
+if __name__ == "__main__":
+    main()

From 4d4cea22f371dd62eca7b411456b22553fd207ac Mon Sep 17 00:00:00 2001
From: Amy McGovern <amcgovern@ou.edu>
Date: Tue, 7 Apr 2026 14:16:27 -0500
Subject: [PATCH 49/72] added marginal severe cases (#351)

---
 .../marginal-severe-convection-cases.yaml     | 1812 +++++++++++++++++
 1 file changed, 1812 insertions(+)
 create mode 100644 src/extremeweatherbench/data/marginal-severe-convection-cases.yaml

diff --git a/src/extremeweatherbench/data/marginal-severe-convection-cases.yaml b/src/extremeweatherbench/data/marginal-severe-convection-cases.yaml
new file mode 100644
index 00000000..f71a3f4c
--- /dev/null
+++ b/src/extremeweatherbench/data/marginal-severe-convection-cases.yaml
@@ -0,0 +1,1812 @@
+- case_id_number: 8000
+  end_date: 2022-04-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-04-16 12:00:00
+  title: 2022-04-16 Severe Convection Non-Event
+- case_id_number: 8001
+  end_date: 2022-09-03 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-09-02 12:00:00
+  title: 2022-09-02 Severe Convection Non-Event
+- case_id_number: 8002
+  end_date: 2022-10-11 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-10-10 12:00:00
+  title: 2022-10-10 Severe Convection Non-Event
+- case_id_number: 8003
+  end_date: 2022-09-06 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-09-05 12:00:00
+  title: 2022-09-05 Severe Convection Non-Event
+- case_id_number: 8004
+  end_date: 2023-11-07 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-11-06 12:00:00
+  title: 2023-11-06 Severe Convection Non-Event
+- case_id_number: 8005
+  end_date: 2024-02-20 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-02-19 12:00:00
+  title: 2024-02-19 Severe Convection Non-Event
+- case_id_number: 8006
+  end_date: 2022-04-20 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-04-19 12:00:00
+  title: 2022-04-19 Severe Convection Non-Event
+- case_id_number: 8007
+  end_date: 2022-04-19 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-04-18 12:00:00
+  title: 2022-04-18 Severe Convection Non-Event
+- case_id_number: 8008
+  end_date: 2021-02-11 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-02-10 12:00:00
+  title: 2021-02-10 Severe Convection Non-Event
+- case_id_number: 8009
+  end_date: 2023-02-23 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-02-22 12:00:00
+  title: 2023-02-22 Severe Convection Non-Event
+- case_id_number: 8010
+  end_date: 2021-03-07 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-03-06 12:00:00
+  title: 2021-03-06 Severe Convection Non-Event
+- case_id_number: 8011
+  end_date: 2021-10-24 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-10-23 12:00:00
+  title: 2021-10-23 Severe Convection Non-Event
+- case_id_number: 8012
+  end_date: 2020-03-15 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-03-14 12:00:00
+  title: 2020-03-14 Severe Convection Non-Event
+- case_id_number: 8013
+  end_date: 2022-07-25 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-07-24 12:00:00
+  title: 2022-07-24 Severe Convection Non-Event
+- case_id_number: 8014
+  end_date: 2023-09-19 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-09-18 12:00:00
+  title: 2023-09-18 Severe Convection Non-Event
+- case_id_number: 8015
+  end_date: 2021-10-01 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-30 12:00:00
+  title: 2021-09-30 Severe Convection Non-Event
+- case_id_number: 8016
+  end_date: 2021-09-07 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-06 12:00:00
+  title: 2021-09-06 Severe Convection Non-Event
+- case_id_number: 8017
+  end_date: 2022-09-08 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-09-07 12:00:00
+  title: 2022-09-07 Severe Convection Non-Event
+- case_id_number: 8018
+  end_date: 2022-08-08 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-08-07 12:00:00
+  title: 2022-08-07 Severe Convection Non-Event
+- case_id_number: 8019
+  end_date: 2024-04-23 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-04-22 12:00:00
+  title: 2024-04-22 Severe Convection Non-Event
+- case_id_number: 8020
+  end_date: 2021-09-11 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-10 12:00:00
+  title: 2021-09-10 Severe Convection Non-Event
+- case_id_number: 8021
+  end_date: 2022-11-26 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-11-25 12:00:00
+  title: 2022-11-25 Severe Convection Non-Event
+- case_id_number: 8022
+  end_date: 2023-08-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-08-28 12:00:00
+  title: 2023-08-28 Severe Convection Non-Event
+- case_id_number: 8023
+  end_date: 2024-08-25 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-08-24 12:00:00
+  title: 2024-08-24 Severe Convection Non-Event
+- case_id_number: 8024
+  end_date: 2023-04-25 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-04-24 12:00:00
+  title: 2023-04-24 Severe Convection Non-Event
+- case_id_number: 8025
+  end_date: 2024-01-27 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-01-26 12:00:00
+  title: 2024-01-26 Severe Convection Non-Event
+- case_id_number: 8026
+  end_date: 2021-03-12 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-03-11 12:00:00
+  title: 2021-03-11 Severe Convection Non-Event
+- case_id_number: 8027
+  end_date: 2023-03-09 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-03-08 12:00:00
+  title: 2023-03-08 Severe Convection Non-Event
+- case_id_number: 8028
+  end_date: 2020-06-16 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-06-15 12:00:00
+  title: 2020-06-15 Severe Convection Non-Event
+- case_id_number: 8029
+  end_date: 2024-04-20 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-04-19 12:00:00
+  title: 2024-04-19 Severe Convection Non-Event
+- case_id_number: 8030
+  end_date: 2022-10-24 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-10-23 12:00:00
+  title: 2022-10-23 Severe Convection Non-Event
+- case_id_number: 8031
+  end_date: 2023-03-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-03-28 12:00:00
+  title: 2023-03-28 Severe Convection Non-Event
+- case_id_number: 8032
+  end_date: 2020-11-15 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-11-14 12:00:00
+  title: 2020-11-14 Severe Convection Non-Event
+- case_id_number: 8033
+  end_date: 2021-09-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-03 12:00:00
+  title: 2021-09-03 Severe Convection Non-Event
+- case_id_number: 8034
+  end_date: 2024-03-18 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-03-17 12:00:00
+  title: 2024-03-17 Severe Convection Non-Event
+- case_id_number: 8035
+  end_date: 2024-03-07 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-03-06 12:00:00
+  title: 2024-03-06 Severe Convection Non-Event
+- case_id_number: 8036
+  end_date: 2022-10-02 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-10-01 12:00:00
+  title: 2022-10-01 Severe Convection Non-Event
+- case_id_number: 8037
+  end_date: 2021-04-27 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-04-26 12:00:00
+  title: 2021-04-26 Severe Convection Non-Event
+- case_id_number: 8038
+  end_date: 2020-03-23 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-03-22 12:00:00
+  title: 2020-03-22 Severe Convection Non-Event
+- case_id_number: 8039
+  end_date: 2022-08-14 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-08-13 12:00:00
+  title: 2022-08-13 Severe Convection Non-Event
+- case_id_number: 8040
+  end_date: 2023-04-24 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-04-23 12:00:00
+  title: 2023-04-23 Severe Convection Non-Event
+- case_id_number: 8041
+  end_date: 2021-08-07 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-08-06 12:00:00
+  title: 2021-08-06 Severe Convection Non-Event
+- case_id_number: 8042
+  end_date: 2023-05-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-05-03 12:00:00
+  title: 2023-05-03 Severe Convection Non-Event
+- case_id_number: 8043
+  end_date: 2023-08-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-08-21 12:00:00
+  title: 2023-08-21 Severe Convection Non-Event
+- case_id_number: 8044
+  end_date: 2021-09-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-21 12:00:00
+  title: 2021-09-21 Severe Convection Non-Event
+- case_id_number: 8045
+  end_date: 2020-06-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-06-16 12:00:00
+  title: 2020-06-16 Severe Convection Non-Event
+- case_id_number: 8046
+  end_date: 2022-10-12 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-10-11 12:00:00
+  title: 2022-10-11 Severe Convection Non-Event
+- case_id_number: 8047
+  end_date: 2020-03-02 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-03-01 12:00:00
+  title: 2020-03-01 Severe Convection Non-Event
+- case_id_number: 8048
+  end_date: 2023-02-08 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-02-07 12:00:00
+  title: 2023-02-07 Severe Convection Non-Event
+- case_id_number: 8049
+  end_date: 2024-08-26 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-08-25 12:00:00
+  title: 2024-08-25 Severe Convection Non-Event
+- case_id_number: 8050
+  end_date: 2022-08-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-08-16 12:00:00
+  title: 2022-08-16 Severe Convection Non-Event
+- case_id_number: 8051
+  end_date: 2023-04-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-04-16 12:00:00
+  title: 2023-04-16 Severe Convection Non-Event
+- case_id_number: 8052
+  end_date: 2021-04-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-04-16 12:00:00
+  title: 2021-04-16 Severe Convection Non-Event
+- case_id_number: 8053
+  end_date: 2023-08-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-08-16 12:00:00
+  title: 2023-08-16 Severe Convection Non-Event
+- case_id_number: 8054
+  end_date: 2024-02-13 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-02-12 12:00:00
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+  title: 2020-03-29 Severe Convection Non-Event
+- case_id_number: 8112
+  end_date: 2024-09-14 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-09-13 12:00:00
+  title: 2024-09-13 Severe Convection Non-Event
+- case_id_number: 8113
+  end_date: 2023-08-03 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-08-02 12:00:00
+  title: 2023-08-02 Severe Convection Non-Event
+- case_id_number: 8114
+  end_date: 2020-04-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-04-03 12:00:00
+  title: 2020-04-03 Severe Convection Non-Event
+- case_id_number: 8115
+  end_date: 2024-11-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-11-28 12:00:00
+  title: 2024-11-28 Severe Convection Non-Event
+- case_id_number: 8116
+  end_date: 2021-10-08 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-10-07 12:00:00
+  title: 2021-10-07 Severe Convection Non-Event
+- case_id_number: 8117
+  end_date: 2020-11-10 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-11-09 12:00:00
+  title: 2020-11-09 Severe Convection Non-Event
+- case_id_number: 8118
+  end_date: 2021-04-20 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-04-19 12:00:00
+  title: 2021-04-19 Severe Convection Non-Event
+- case_id_number: 8119
+  end_date: 2022-09-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-09-21 12:00:00
+  title: 2022-09-21 Severe Convection Non-Event
+- case_id_number: 8120
+  end_date: 2021-06-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-06-28 12:00:00
+  title: 2021-06-28 Severe Convection Non-Event
+- case_id_number: 8121
+  end_date: 2023-03-14 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-03-13 12:00:00
+  title: 2023-03-13 Severe Convection Non-Event
+- case_id_number: 8122
+  end_date: 2024-11-09 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-11-08 12:00:00
+  title: 2024-11-08 Severe Convection Non-Event
+- case_id_number: 8123
+  end_date: 2023-09-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-09-20 12:00:00
+  title: 2023-09-20 Severe Convection Non-Event
+- case_id_number: 8124
+  end_date: 2021-01-25 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-01-24 12:00:00
+  title: 2021-01-24 Severe Convection Non-Event
+- case_id_number: 8125
+  end_date: 2024-03-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-03-03 12:00:00
+  title: 2024-03-03 Severe Convection Non-Event
+- case_id_number: 8126
+  end_date: 2024-12-19 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-12-18 12:00:00
+  title: 2024-12-18 Severe Convection Non-Event
+- case_id_number: 8127
+  end_date: 2021-07-03 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-07-02 12:00:00
+  title: 2021-07-02 Severe Convection Non-Event
+- case_id_number: 8128
+  end_date: 2024-10-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-10-20 12:00:00
+  title: 2024-10-20 Severe Convection Non-Event
+- case_id_number: 8129
+  end_date: 2021-09-18 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-17 12:00:00
+  title: 2021-09-17 Severe Convection Non-Event
+- case_id_number: 8130
+  end_date: 2024-09-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-09-20 12:00:00
+  title: 2024-09-20 Severe Convection Non-Event
+- case_id_number: 8131
+  end_date: 2021-09-05 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-04 12:00:00
+  title: 2021-09-04 Severe Convection Non-Event
+- case_id_number: 8132
+  end_date: 2023-09-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-09-28 12:00:00
+  title: 2023-09-28 Severe Convection Non-Event
+- case_id_number: 8133
+  end_date: 2023-09-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-09-03 12:00:00
+  title: 2023-09-03 Severe Convection Non-Event
+- case_id_number: 8134
+  end_date: 2020-05-11 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-05-10 12:00:00
+  title: 2020-05-10 Severe Convection Non-Event
+- case_id_number: 8135
+  end_date: 2023-05-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-05-21 12:00:00
+  title: 2023-05-21 Severe Convection Non-Event
+- case_id_number: 8136
+  end_date: 2025-01-01 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-12-31 12:00:00
+  title: 2024-12-31 Severe Convection Non-Event
+- case_id_number: 8137
+  end_date: 2021-07-06 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-07-05 12:00:00
+  title: 2021-07-05 Severe Convection Non-Event
+- case_id_number: 8138
+  end_date: 2024-02-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-02-21 12:00:00
+  title: 2024-02-21 Severe Convection Non-Event
+- case_id_number: 8139
+  end_date: 2022-03-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-03-20 12:00:00
+  title: 2022-03-20 Severe Convection Non-Event
+- case_id_number: 8140
+  end_date: 2023-01-08 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-01-07 12:00:00
+  title: 2023-01-07 Severe Convection Non-Event
+- case_id_number: 8141
+  end_date: 2024-10-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-10-03 12:00:00
+  title: 2024-10-03 Severe Convection Non-Event
+- case_id_number: 8142
+  end_date: 2023-09-01 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-08-31 12:00:00
+  title: 2023-08-31 Severe Convection Non-Event
+- case_id_number: 8143
+  end_date: 2022-08-10 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-08-09 12:00:00
+  title: 2022-08-09 Severe Convection Non-Event
+- case_id_number: 8144
+  end_date: 2024-10-20 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-10-19 12:00:00
+  title: 2024-10-19 Severe Convection Non-Event
+- case_id_number: 8145
+  end_date: 2020-07-23 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-07-22 12:00:00
+  title: 2020-07-22 Severe Convection Non-Event
+- case_id_number: 8146
+  end_date: 2022-09-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-09-16 12:00:00
+  title: 2022-09-16 Severe Convection Non-Event
+- case_id_number: 8147
+  end_date: 2022-08-18 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-08-17 12:00:00
+  title: 2022-08-17 Severe Convection Non-Event
+- case_id_number: 8148
+  end_date: 2020-04-05 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-04-04 12:00:00
+  title: 2020-04-04 Severe Convection Non-Event
+- case_id_number: 8149
+  end_date: 2022-10-28 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-10-27 12:00:00
+  title: 2022-10-27 Severe Convection Non-Event
+- case_id_number: 8150
+  end_date: 2020-09-28 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-09-27 12:00:00
+  title: 2020-09-27 Severe Convection Non-Event

From d1c80eefaa15d801a11307e9801e8e7fa6fabf33 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 8 Apr 2026 21:02:59 -0400
Subject: [PATCH 50/72] add updated 2m temp climatology quantiles (#354)

---
 data_prep/era5_climatology.py       | 79 ++++++++++++++++++++---------
 src/extremeweatherbench/defaults.py | 25 +++++++--
 2 files changed, 74 insertions(+), 30 deletions(-)

diff --git a/data_prep/era5_climatology.py b/data_prep/era5_climatology.py
index a8c1ca47..aab19619 100644
--- a/data_prep/era5_climatology.py
+++ b/data_prep/era5_climatology.py
@@ -29,21 +29,17 @@
 Usage examples:
 --------------
 # One percentile:
-uv run data_prep/climatology.py generate \\
+uv run data_prep/era5_climatology.py generate \\
     --variable 2m_temperature --percentile 0.85 \\
-    --output /home/taylor/data/climatology_zarr/2m_temperature_p85.zarr
+    --output ~/climatology_zarr/2m_temperature_p85.zarr
 
 # Multiple quantiles in one pass:
-uv run data_prep/climatology.py all-quantiles \\
-    --variable 2m_temperature \\
-    --quantiles 0.10 0.25 0.50 0.75 0.90 \\
-    --output-dir /home/taylor/data/climatology_zarr \\
-    --n-workers 16
+uv run data_prep/era5_climatology.py all-quantiles  --variable 2m_temperature  --quantiles 0.10 0.25 0.50 0.75 0.90  --output-dir ~/climatology_zarr --n-workers 16 --gcs-quantiles 0.15 0.85
 
 # Combine local stores (+ optionally pull p15/p85 from GCS):
-uv run data_prep/climatology.py combine \\
-    --input-dir /home/taylor/data/climatology_zarr \\
-    --output /home/taylor/data/climatology_zarr/2m_temperature_combined.zarr \\
+uv run data_prep/era5_climatology.py combine \\
+    --input-dir ~/climatology_zarr \\
+    --output ~/climatology_zarr/2m_temperature_combined.zarr \\
     --gcs-quantiles 0.15 0.85
 """
 
@@ -342,11 +338,17 @@ def _load_gcs_store(quantile: float, variable: str) -> xr.DataArray:
 
 def combine_stores(
     input_dir: pathlib.Path,
-    output: pathlib.Path,
+    output: pathlib.Path | str,
     variable: str,
     gcs_quantiles: list[float],
+    application_credentials: str = "",
 ) -> None:
-    """Concatenate per-quantile stores into one zarr with a quantile dim."""
+    """Concatenate per-quantile stores into one zarr with a quantile dim.
+
+    ``output`` may be a local path or a GCS URL (``gs://bucket/prefix``).
+    When writing to GCS, ADC is used unless ``application_credentials``
+    points to a service-account JSON file.
+    """
     slices: list[xr.DataArray] = []
 
     local_stores = sorted(input_dir.glob(f"{variable}_p*_climatology.zarr"))
@@ -361,14 +363,12 @@ def combine_stores(
     if not slices:
         raise RuntimeError("No data found — check --input-dir and --gcs-quantiles.")
 
-    output_variable = "surface_air_temperature"
-
     logger.info(
         "Concatenating %d quantile slices along 'quantile' dim...",
         len(slices),
     )
     combined_da = xr.concat(slices, dim="quantile").sortby("quantile")
-    ds_out = combined_da.to_dataset(name=output_variable)
+    ds_out = combined_da.to_dataset(name=variable)
     ds_out.attrs.update(
         {
             "source": "ERA5 ARCO",
@@ -379,7 +379,7 @@ def combine_stores(
 
     n_quantiles = ds_out.sizes["quantile"]
     encoding = {
-        output_variable: {
+        variable: {
             "chunks": [
                 n_quantiles,  # keep all quantiles in one chunk
                 COMBINED_ZARR_CHUNKS["dayofyear"],
@@ -390,10 +390,25 @@ def combine_stores(
         }
     }
 
-    output.parent.mkdir(parents=True, exist_ok=True)
-    logger.info("Writing combined zarr to %s...", output)
-    ds_out.to_zarr(output, mode="w", zarr_format=2, encoding=encoding)
-    logger.info("Done: %s", output)
+    output_str = str(output)
+    if output_str.startswith("gs://"):
+        token = application_credentials if application_credentials else "google_default"
+        storage_options = {"token": token}
+        logger.info("Writing combined zarr to GCS: %s ...", output_str)
+        ds_out.to_zarr(
+            output_str,
+            mode="w",
+            zarr_format=2,
+            encoding=encoding,
+            storage_options=storage_options,
+        )
+    else:
+        local_out = pathlib.Path(output_str)
+        local_out.parent.mkdir(parents=True, exist_ok=True)
+        logger.info("Writing combined zarr to %s...", local_out)
+        ds_out.to_zarr(local_out, mode="w", zarr_format=2, encoding=encoding)
+
+    logger.info("Done: %s", output_str)
 
 
 def _zarr_store_path(
@@ -496,11 +511,15 @@ def cmd_combine(args: argparse.Namespace) -> None:
         output=args.output,
         variable=args.variable,
         gcs_quantiles=args.gcs_quantiles,
+        application_credentials=getattr(args, "application_credentials", ""),
     )
 
 
-_DEFAULT_OUTPUT_DIR = pathlib.Path("/home/taylor/data/climatology_zarr")
-_DEFAULT_COMBINED = _DEFAULT_OUTPUT_DIR / "2m_temperature_combined.zarr"
+_DEFAULT_OUTPUT_DIR = pathlib.Path("~/climatology_zarr")
+_DEFAULT_COMBINED = (
+    "gs://extremeweatherbench/datasets/"
+    "surface_air_temperature_1990_2019_climatology.zarr"
+)
 
 
 def _add_era5_args(p: argparse.ArgumentParser) -> None:
@@ -541,7 +560,7 @@ def _add_era5_args(p: argparse.ArgumentParser) -> None:
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
-        prog="climatology.py",
+        prog="era5_climatology.py",
         description=__doc__,
         formatter_class=argparse.RawDescriptionHelpFormatter,
     )
@@ -599,9 +618,11 @@ def parse_args() -> argparse.Namespace:
     )
     p_comb.add_argument(
         "--output",
-        type=pathlib.Path,
         default=_DEFAULT_COMBINED,
-        help="Output path for the combined zarr store.",
+        help=(
+            "Output path for the combined zarr store. "
+            "May be a local path or a GCS URL (gs://bucket/prefix)."
+        ),
     )
     p_comb.add_argument(
         "--variable",
@@ -619,6 +640,14 @@ def parse_args() -> argparse.Namespace:
             "store (e.g. 0.15 0.85).  Only 0.15 and 0.85 are available."
         ),
     )
+    p_comb.add_argument(
+        "--application-credentials",
+        default="",
+        help=(
+            "Path to a GCP service-account JSON file for GCS output. "
+            "Leave empty to use Application Default Credentials."
+        ),
+    )
 
     return parser.parse_args()
 
diff --git a/src/extremeweatherbench/defaults.py b/src/extremeweatherbench/defaults.py
index c947632b..50e670b6 100644
--- a/src/extremeweatherbench/defaults.py
+++ b/src/extremeweatherbench/defaults.py
@@ -244,14 +244,29 @@ def preprocess_hres_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
 
 
 def get_climatology(quantile: float = 0.85) -> xr.DataArray:
-    """Get the climatology dataset for the heatwave criteria."""
-    if quantile not in [0.15, 0.85]:
-        raise ValueError("Quantile must be 0.15 or 0.85")
+    """Open the surface temperature climatology for the given quantile.
+
+    Args:
+        quantile: Climatology quantile to retrieve. Must be one of
+            0.10, 0.15, 0.25, 0.50, 0.75, 0.85, 0.90. Default is
+            0.85.
+
+    Returns:
+        DataArray of 2m temperature climatology indexed by
+        (dayofyear, hour, latitude, longitude).
+
+    Raises:
+        ValueError: If quantile is not in the supported list.
+    """
+    _QUANTILE_LIST = [0.10, 0.15, 0.25, 0.50, 0.75, 0.85, 0.90]
+    if quantile not in _QUANTILE_LIST:
+        raise ValueError(f"Quantile must be one of {_QUANTILE_LIST}")
+    idx = _QUANTILE_LIST.index(quantile)
     return xr.open_zarr(
         "gs://extremeweatherbench/datasets/surface_air_temperature_1990_2019_climatology.zarr",  # noqa: E501
         storage_options={"anon": True},
-        chunks="auto",
-    )["2m_temperature"].sel(quantile=quantile)
+        chunks={},
+    )["2m_temperature"].isel(quantile=idx)
 
 
 # ERA5 targets

From 0a6b5898e6ed168b39724a84acfe9acad04afbda Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 8 Apr 2026 21:14:07 -0400
Subject: [PATCH 51/72] Temperature event bounds update (#353)

* update heat and cold bounds, update docstrings and methodology. add base_temp_events.yaml for reproducibility

* add defaults; remove top python line

* add lat min and max args

* ruff + mypy

* add restriction criteria and method

* add animation code

* simplify to include climatology bound(s), fix func names

* update animate

* ruff

* remove animate script

* rename

* update with rename
---
 data_prep/heat_cold_bounds_case.py            | 788 +++++++++++---
 data_prep/heat_cold_bounds_global.py          | 531 ----------
 data_prep/plot_temperature_events.py          | 201 +++-
 data_prep/temperature_bounds_global.py        | 968 ++++++++++++++++++
 .../data/base_temp_events.yaml                | 661 ++++++++++++
 src/extremeweatherbench/data/events.yaml      | 456 ++++-----
 6 files changed, 2689 insertions(+), 916 deletions(-)
 delete mode 100644 data_prep/heat_cold_bounds_global.py
 create mode 100644 data_prep/temperature_bounds_global.py
 create mode 100644 src/extremeweatherbench/data/base_temp_events.yaml

diff --git a/data_prep/heat_cold_bounds_case.py b/data_prep/heat_cold_bounds_case.py
index 3f398331..dba02627 100644
--- a/data_prep/heat_cold_bounds_case.py
+++ b/data_prep/heat_cold_bounds_case.py
@@ -1,10 +1,17 @@
-#!/usr/bin/env python3
-"""Validate and expand heat wave / cold snap bounding boxes from events.yaml.
-
-For each heat_wave or cold snap event in events.yaml, iteratively
-grows the bounding box by 2 degrees in each direction until fewer
-than 50% of grid points on an edge exceed the climatological
-threshold (or 10 iterations).
+"""Validate and expand heat wave / freeze bounding boxes.
+
+Reads heat_wave and freeze cases from base_temp_events.yaml (which uses
+centered_region),
+then:
+  1. Uses the centered_region center as the initial box.
+  2. Grows the time window forward from start_date-3 one day at a
+     time until the single-day exceedance fraction drops below 50 %
+     of its peak.
+  3. Iteratively grows the spatial box by 2 degrees in each direction
+     until fewer than 50 % of grid points on each edge exceed the
+     climatological threshold (or 10 iterations).
+  4. Writes the final bounds as bounded_region entries into
+     events.yaml.
 
 Usage:
     python heat_cold_bounds_case.py \\
@@ -12,12 +19,18 @@
 """
 
 import argparse
+import importlib
 import logging
 import pathlib
 import time as time_module
-from typing import Dict, List, Optional
+from typing import Dict, List, Literal, Optional, cast
 
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
 import joblib
+import matplotlib.colors as mcolors
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import regionmask
@@ -25,12 +38,15 @@
 import xarray as xr
 from dask.distributed import Client, LocalCluster
 from plot_temperature_events import (
+    VALID_QUANTILES,
     detect_time_dim,
     max_consecutive_days,
     plot_consecutive_map,
+    resolve_op,
 )
+from ruamel.yaml import YAML
 
-from extremeweatherbench import cases, defaults, inputs
+from extremeweatherbench import cases, defaults, inputs, regions
 
 logging.basicConfig(
     level=logging.INFO,
@@ -39,62 +55,256 @@
 logger = logging.getLogger(__name__)
 
 MIN_CONSECUTIVE_DAYS = 3
-EXPANSION_DEGREES = 2.0
-MAX_ITERATIONS = 10
+EXPANSION_DEGREES = 1
+MAX_ITERATIONS = 20
 EDGE_VALIDITY_THRESHOLD = 0.5
 MIN_GRIDPOINTS = 500
+TEMPORAL_LOAD_BUFFER_DAYS = 14
+MAX_TEMPORAL_DAYS = 21
 
 
 def _apply_consecutive_filter(
     mask: np.ndarray,
     min_days: int = MIN_CONSECUTIVE_DAYS,
+    max_grace_days: int = 1,
 ) -> np.ndarray:
-    """Keep only runs of ``min_days``+ True days (axis 0)."""
+    """Keep runs of ``min_days``+ True days along axis 0.
+
+    After ``min_days`` strict consecutive True days are established,
+    gaps of up to ``max_grace_days`` are bridged so the event can
+    continue. Runs that never reach ``min_days`` strict consecutive
+    True days are discarded.
+
+    Args:
+        mask: Boolean array of shape (time, lat, lon).
+        min_days: Minimum run length required to qualify as an
+            event. Default is 3 (MIN_CONSECUTIVE_DAYS).
+        max_grace_days: Maximum gap length to bridge after the
+            minimum run is established. Default is 1.
+
+    Returns:
+        Boolean array of the same shape with only qualifying runs
+        retained.
+    """
     struct = np.zeros((min_days, 1, 1), dtype=bool)
     struct[:, 0, 0] = True
-    eroded = ndimage.binary_erosion(
+
+    # Strict 3-day runs (no grace)
+    strict = (
+        ndimage.binary_dilation(
+            ndimage.binary_erosion(
+                mask,
+                structure=struct,
+                border_value=False,
+            ),
+            structure=struct,
+            border_value=False,
+        )
+        & mask
+    )
+
+    if max_grace_days <= 0:
+        return strict
+
+    # Fill gaps of ≤ max_grace_days
+    close_k = np.zeros((2 * max_grace_days + 1, 1, 1), dtype=bool)
+    close_k[:, 0, 0] = True
+    filled = ndimage.binary_closing(
         mask,
-        structure=struct,
+        structure=close_k,
         border_value=False,
     )
-    dilated = ndimage.binary_dilation(
-        eroded,
-        structure=struct,
-        border_value=False,
+
+    # Runs of min_days+ in the gap-filled mask
+    filled_runs = (
+        ndimage.binary_dilation(
+            ndimage.binary_erosion(
+                filled,
+                structure=struct,
+                border_value=False,
+            ),
+            structure=struct,
+            border_value=False,
+        )
+        & filled
     )
-    return dilated & mask
+
+    # Label each temporal run independently per grid point
+    lbl_struct = np.zeros((3, 3, 3), dtype=int)
+    lbl_struct[0, 1, 1] = 1
+    lbl_struct[1, 1, 1] = 1
+    lbl_struct[2, 1, 1] = 1
+    labels, _ = ndimage.label(filled_runs, structure=lbl_struct)
+
+    # Keep only runs that contain a strict 3-day block
+    valid = np.unique(labels[strict & (labels > 0)])
+    return np.isin(labels, valid) & filled_runs
 
 
 def _edge_valid_fraction(
-    filtered: np.ndarray,
+    mask_2d: np.ndarray,
+    land_2d: np.ndarray,
     edge: str,
     band_pts: int,
 ) -> float:
-    """Fraction of grid points on *edge* with any valid day."""
+    """Return the fraction of land grid points on an edge that are active.
+
+    Ocean/masked points are excluded from both numerator and denominator
+    so coastal edges aren't penalised.
+
+    Args:
+        mask_2d: 2-D boolean activity array (lat, lon).
+        land_2d: 2-D boolean land mask (lat, lon); True = land.
+        edge: One of "north", "south", "east", "west".
+        band_pts: Width of the edge strip in grid points.
+
+    Returns:
+        Fraction in [0, 1] of land points in the strip that are active,
+        or 0.0 if the strip contains no land points.
+
+    Raises:
+        ValueError: If edge is not one of the recognised values.
+    """
     if edge == "north":
-        strip = filtered[:, -band_pts:, :]
+        strip = mask_2d[-band_pts:, :]
+        land_strip = land_2d[-band_pts:, :]
     elif edge == "south":
-        strip = filtered[:, :band_pts, :]
+        strip = mask_2d[:band_pts, :]
+        land_strip = land_2d[:band_pts, :]
     elif edge == "west":
-        strip = filtered[:, :, :band_pts]
+        strip = mask_2d[:, :band_pts]
+        land_strip = land_2d[:, :band_pts]
     elif edge == "east":
-        strip = filtered[:, :, -band_pts:]
+        strip = mask_2d[:, -band_pts:]
+        land_strip = land_2d[:, -band_pts:]
     else:
         raise ValueError(f"Unknown edge: {edge}")
 
-    has_event = strip.any(axis=0)
-    if has_event.size == 0:
+    n_land = int(land_strip.sum())
+    if n_land == 0:
         return 0.0
-    return float(has_event.mean())
+    return float((strip & land_strip).sum()) / n_land
+
+
+def _to_plot_lon(lon: float) -> float:
+    """Convert 0-360 longitude to -180..180 for plotting.
+
+    Args:
+        lon: Longitude in 0-360 degrees.
+
+    Returns:
+        Longitude in -180..180 degrees.
+    """
+    return lon - 360 if lon > 180 else lon
+
+
+def plot_peak_day_with_bounds(
+    peak_mask: np.ndarray,
+    all_lats: np.ndarray,
+    all_lons: np.ndarray,
+    initial_box: tuple,
+    final_box: tuple,
+    event_type: str,
+    title: str,
+    output_path: str,
+) -> None:
+    """Plot the peak-footprint day with initial and final bounding boxes.
+
+    Args:
+        peak_mask: 2-D boolean array (lat, lon) for the peak day.
+        all_lats: 1-D latitude array matching peak_mask rows.
+        all_lons: 1-D longitude array (0-360) matching peak_mask cols.
+        initial_box: (lat_min, lat_max, lon_min, lon_max) of the
+            initial centered region.
+        final_box: (lat_min, lat_max, lon_min, lon_max) after spatial
+            expansion.
+        event_type: ``"heat_wave"`` or ``"freeze"``.
+        title: Figure title.
+        output_path: Destination PNG file path.
+    """
+    plot_lons = np.array([_to_plot_lon(x) for x in all_lons])
+    lon_min = float(plot_lons.min()) - 1
+    lon_max = float(plot_lons.max()) + 1
+    lat_min = float(all_lats.min()) - 1
+    lat_max = float(all_lats.max()) + 1
+
+    cmap = mcolors.ListedColormap(
+        ["white", "firebrick" if event_type == "heat_wave" else "steelblue"]
+    )
+    norm = mcolors.BoundaryNorm([0, 0.5, 1], cmap.N)
+
+    fig, ax = plt.subplots(
+        subplot_kw={"projection": ccrs.PlateCarree()},
+        figsize=(10, 8),
+    )
+    ax.set_extent(
+        [lon_min, lon_max, lat_min, lat_max],
+        crs=ccrs.PlateCarree(),
+    )
+    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
+
+    ax.pcolormesh(
+        plot_lons,
+        all_lats,
+        peak_mask.astype(float),
+        cmap=cmap,
+        norm=norm,
+        transform=ccrs.PlateCarree(),
+        shading="auto",
+    )
+    ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
+    ax.add_feature(cfeature.BORDERS, linewidth=0.3)
+
+    def _add_rect(box, color, label, ls="-"):
+        blat_min, blat_max, blon_min, blon_max = box
+        blon_min = _to_plot_lon(blon_min)
+        blon_max = _to_plot_lon(blon_max)
+        rect = mpatches.Rectangle(
+            (blon_min, blat_min),
+            blon_max - blon_min,
+            blat_max - blat_min,
+            linewidth=2,
+            edgecolor=color,
+            facecolor="none",
+            linestyle=ls,
+            transform=ccrs.PlateCarree(),
+            label=label,
+        )
+        ax.add_patch(rect)
+
+    _add_rect(initial_box, "blue", "Initial box", ls="--")
+    _add_rect(final_box, "green", "Final bounds", ls="-")
+
+    ax.legend(loc="lower left", fontsize=9)
+    ax.set_title(title, loc="left", fontsize=12)
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("  Saved peak-day plot: %s", output_path)
 
 
 def process_event(
     single_case: cases.IndividualCase,
+    out_dir: pathlib.Path = pathlib.Path("."),
+    quantile: float | None = None,
+    op_str: str | None = None,
 ) -> Optional[Dict]:
-    """Process one event: compute mask and iteratively expand.
-
-    Opens ERA5 and climatology inside the worker to avoid
-    serialisation of lazy zarr handles across joblib processes.
+    """Process one event: find the time window then expand bounds.
+
+    Args:
+        single_case: The individual case to process; must have a
+            CenteredRegion location.
+        out_dir: Directory in which plots are saved. Default is
+            pathlib.Path(".").
+        quantile: Climatology quantile. Default is None, which
+            resolves to 0.85 for heat_wave or 0.15 for freeze.
+        op_str: Comparison operator string (e.g. ">", ">=", "<",
+            "<="). Default is None, which resolves to ">" for
+            heat_wave or "<" for freeze.
+
+    Returns:
+        A dict with keys event_type, start_date, end_date,
+        latitude_min/max, longitude_min/max, case_id, title, and
+        _peak_gridpoints, or None if the case was skipped.
     """
     is_heatwave = single_case.event_type == "heat_wave"
     logger.info(
@@ -104,22 +314,59 @@ def process_event(
         single_case.event_type,
     )
 
-    bounds = single_case.location.as_geopandas().total_bounds
-    lon_min, lat_min, lon_max, lat_max = bounds
+    loc = single_case.location
+    if not isinstance(loc, regions.CenteredRegion):
+        logger.warning(
+            "  Case %d location is %s, not CenteredRegion — skipping",
+            single_case.case_id_number,
+            type(loc).__name__,
+        )
+        return None
+
+    center_lat = loc.latitude
+    center_lon = loc.longitude
+    if isinstance(loc.bounding_box_degrees, tuple):
+        half_lat = loc.bounding_box_degrees[0] / 2.0
+        half_lon = loc.bounding_box_degrees[1] / 2.0
+    else:
+        half_lat = loc.bounding_box_degrees / 2.0
+        half_lon = loc.bounding_box_degrees / 2.0
+
+    box_lat_min = center_lat - half_lat
+    box_lat_max = center_lat + half_lat
+    box_lon_min = center_lon - half_lon
+    box_lon_max = center_lon + half_lon
 
+    logger.info(
+        "  Initial box (%.1f°): center (%.2f, %.2f)"
+        "  lat [%.2f, %.2f], lon [%.2f, %.2f]",
+        loc.bounding_box_degrees
+        if not isinstance(loc.bounding_box_degrees, tuple)
+        else loc.bounding_box_degrees[0],
+        center_lat,
+        center_lon,
+        box_lat_min,
+        box_lat_max,
+        box_lon_min,
+        box_lon_max,
+    )
+
+    # Time range and spatial pre-fetch extent
     start_date = pd.Timestamp(single_case.start_date) - pd.Timedelta(days=3)
-    end_date = pd.Timestamp(single_case.end_date) + pd.Timedelta(days=3)
+    end_date = pd.Timestamp(single_case.end_date) + pd.Timedelta(
+        days=TEMPORAL_LOAD_BUFFER_DAYS
+    )
 
     pot_lat_min = max(
         -90,
-        lat_min - MAX_ITERATIONS * EXPANSION_DEGREES,
+        box_lat_min - MAX_ITERATIONS * EXPANSION_DEGREES,
     )
     pot_lat_max = min(
         90,
-        lat_max + MAX_ITERATIONS * EXPANSION_DEGREES,
+        box_lat_max + MAX_ITERATIONS * EXPANSION_DEGREES,
     )
-    pot_lon_min = lon_min - MAX_ITERATIONS * EXPANSION_DEGREES
-    pot_lon_max = lon_max + MAX_ITERATIONS * EXPANSION_DEGREES
+    pot_lon_min = box_lon_min - MAX_ITERATIONS * EXPANSION_DEGREES
+    pot_lon_max = box_lon_max + MAX_ITERATIONS * EXPANSION_DEGREES
 
     ds = xr.open_zarr(
         inputs.ARCO_ERA5_FULL_URI,
@@ -133,84 +380,78 @@ def process_event(
     six_hourly = t2m[tdim].dt.hour.isin([0, 6, 12, 18])
     t2m = t2m.sel({tdim: six_hourly}).sortby("latitude")
 
-    # Normalise ERA5 longitudes from 0-360 to -180/180 so that the
-    # case bounding boxes (always in -180/180 from geopandas) can be
-    # used directly for slicing without wrap-around issues.
     if float(t2m.longitude.max()) > 180:
         t2m = t2m.assign_coords(
             longitude=(t2m.longitude.values + 180) % 360 - 180,
         ).sortby("longitude")
 
+    # Convert center lon to match ERA5 convention for slicing
+    pot_lon_min_era = pot_lon_min
+    pot_lon_max_era = pot_lon_max
+    if pot_lon_min_era > 180:
+        pot_lon_min_era -= 360
+    if pot_lon_max_era > 180:
+        pot_lon_max_era -= 360
+
     t2m = t2m.sel(
         latitude=slice(pot_lat_min, pot_lat_max),
-        longitude=slice(pot_lon_min, pot_lon_max),
+        longitude=slice(
+            min(pot_lon_min_era, pot_lon_max_era),
+            max(pot_lon_min_era, pot_lon_max_era),
+        ),
     )
 
     if t2m.latitude.size == 0 or t2m.longitude.size == 0:
         logger.warning(
-            "  Case %d: empty spatial selection"
-            " (lon=[%.2f, %.2f], lat=[%.2f, %.2f]) — skipping",
+            "  Case %d: empty spatial selection — skipping",
             single_case.case_id_number,
-            pot_lon_min,
-            pot_lon_max,
-            pot_lat_min,
-            pot_lat_max,
         )
         return None
 
-    if is_heatwave:
-        daily = t2m.resample({tdim: "1D"}).max()
-        clim = (
-            defaults.get_climatology(0.85)
-            .max(
-                dim="hour",
-            )
-            .sortby("latitude")
-        )
-    else:
-        daily = t2m.resample({tdim: "1D"}).min()
-        clim = (
-            defaults.get_climatology(0.15)
-            .min(
-                dim="hour",
-            )
-            .sortby("latitude")
-        )
+    # 6-hourly exceedance then daily all-pass
+    if quantile is None:
+        quantile = 0.85 if is_heatwave else 0.15
+    if op_str is None:
+        op_str = ">" if is_heatwave else "<"
+    cmp = resolve_op(op_str)
+
+    clim = defaults.get_climatology(quantile).sortby("latitude")
 
-    # Match climatology longitude convention to the (possibly
-    # remapped) ERA5 data so reindex_like aligns correctly.
     if float(clim.longitude.max()) > 180:
         clim = clim.assign_coords(
             longitude=(clim.longitude.values + 180) % 360 - 180,
         ).sortby("longitude")
 
-    doy = daily[tdim].dt.dayofyear
+    doy = t2m[tdim].dt.dayofyear
+    hour = t2m[tdim].dt.hour
     max_clim_doy = int(clim.dayofyear.max())
     doy_capped = doy.clip(max=max_clim_doy)
+
     clim_aligned = clim.sel(
         dayofyear=doy_capped,
-    ).reindex_like(daily, method="nearest")
+        hour=hour,
+    ).reindex_like(t2m, method="nearest")
+
+    exc_6h = cmp(t2m, clim_aligned)
+
+    daily_all_pass = exc_6h.resample({tdim: "1D"}).min().astype(bool)
 
     land_reg = regionmask.defined_regions.natural_earth_v5_0_0.land_110
     land_mask = (
         land_reg.mask(
-            daily.longitude,
-            daily.latitude,
+            daily_all_pass.longitude,
+            daily_all_pass.latitude,
         )
         == 0
     )
 
-    if is_heatwave:
-        exc_mask = (daily > clim_aligned) & land_mask
-    else:
-        exc_mask = (daily < clim_aligned) & land_mask
+    exc_mask = daily_all_pass & land_mask
 
     logger.info("  Computing exceedance mask...")
     mask_np = exc_mask.compute().values.astype(bool)
-    filtered = _apply_consecutive_filter(mask_np)
 
-    all_lats = daily.latitude.values
-    all_lons = daily.longitude.values
+    all_lats = daily_all_pass.latitude.values
+    all_lons = daily_all_pass.longitude.values
     grid_res = np.abs(np.diff(all_lats[:2]))[0] if len(all_lats) > 1 else 0.25
     band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
 
@@ -220,22 +461,147 @@ def _lat_idx(val: float) -> int:
     def _lon_idx(val: float) -> int:
         return int(np.argmin(np.abs(all_lons - val)))
 
-    idx_s = _lat_idx(lat_min)
-    idx_n = _lat_idx(lat_max)
-    idx_w = _lon_idx(lon_min)
-    idx_e = _lon_idx(lon_max)
+    # Convert box bounds to ERA5 lon convention for index lookup
+    box_lon_min_era = box_lon_min
+    box_lon_max_era = box_lon_max
+    if box_lon_min_era > 180:
+        box_lon_min_era -= 360
+    if box_lon_max_era > 180:
+        box_lon_max_era -= 360
+
+    idx_s0 = _lat_idx(box_lat_min)
+    idx_n0 = _lat_idx(box_lat_max)
+    idx_w0 = _lon_idx(box_lon_min_era)
+    idx_e0 = _lon_idx(box_lon_max_era)
+
+    # Temporal iteration
+    # Land mask within the initial box (exclude ocean from denominator)
+    land_mask_np = land_mask.compute().values.astype(bool)
+    box_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
+    n_land_pts = int(box_land.sum())
+    if n_land_pts == 0:
+        logger.warning(
+            "  Case %d: no land points in initial box — skipping",
+            single_case.case_id_number,
+        )
+        return None
 
-    edges_active = {
-        "north": True,
-        "south": True,
-        "east": True,
-        "west": True,
-    }
+    n_days_total = min(mask_np.shape[0], MAX_TEMPORAL_DAYS)
+    final_t = n_days_total
+    established = False
+
+    for t in range(n_days_total):
+        if t < 2:
+            continue
+
+        # "Currently active": last 3 days all exceed threshold
+        box_sl = (
+            slice(idx_s0, idx_n0 + 1),
+            slice(idx_w0, idx_e0 + 1),
+        )
+        currently_active = (
+            mask_np[t][box_sl] & mask_np[t - 1][box_sl] & mask_np[t - 2][box_sl]
+        )
+        active_land = int((currently_active & box_land).sum())
+        frac = active_land / n_land_pts
+
+        if t == 2 and frac < EDGE_VALIDITY_THRESHOLD:
+            logger.warning(
+                "  Day 3: only %.1f%% of land points have 3 consecutive days (< 50%%)",
+                frac * 100,
+            )
+
+        if not established:
+            if frac >= EDGE_VALIDITY_THRESHOLD:
+                established = True
+                logger.info(
+                    "  Event established at day %d (%.1f%% of land points active)",
+                    t,
+                    frac * 100,
+                )
+        else:
+            if frac < EDGE_VALIDITY_THRESHOLD:
+                final_t = t + 1
+                logger.info(
+                    "  Temporal stop at day %d (%.1f%% < 50%% of land points)",
+                    t,
+                    frac * 100,
+                )
+                break
+
+        if t == 9:
+            logger.warning(
+                "  Exceeded 10 days (frac %.1f%%), continuing...",
+                frac * 100,
+            )
+
+    if not established:
+        logger.warning(
+            "  Case %d: event never reached 50%% of land points — using all %d days",
+            single_case.case_id_number,
+            final_t,
+        )
+
+    logger.info(
+        "  Using %d of %d available days",
+        final_t,
+        mask_np.shape[0],
+    )
+
+    mask_np = mask_np[:final_t]
+    filtered = _apply_consecutive_filter(mask_np)
+
+    # Spatial expansion on peak-footprint day
+    # Find the timestep(s) with the most active grid points.
+    # If tied: 2nd of 2, middle of odd count, first-middle of even.
+    daily_counts = filtered.sum(axis=(1, 2))
+    max_count = daily_counts.max()
+    (tied_days,) = np.where(daily_counts == max_count)
+    n_tied = len(tied_days)
+    if n_tied <= 2:
+        peak_day = int(tied_days[-1]) if n_tied == 2 else int(tied_days[0])
+    else:
+        peak_day = int(tied_days[(n_tied - 1) // 2])
+    peak_mask = filtered[peak_day]
+    logger.info(
+        "  Peak footprint on day %d (%d active grid points, %d tied days)",
+        peak_day,
+        int(max_count),
+        n_tied,
+    )
+
+    idx_s = idx_s0
+    idx_n = idx_n0
+    idx_w = idx_w0
+    idx_e = idx_e0
+
+    # Pre-check: disable edges that are >= 95% ocean
+    # in the initial box (prevents expansion through water).
+    init_region_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
+    edges_active = {}
+    for edge in ("north", "south", "east", "west"):
+        if edge == "north":
+            strip = init_region_land[-band_pts:, :]
+        elif edge == "south":
+            strip = init_region_land[:band_pts, :]
+        elif edge == "west":
+            strip = init_region_land[:, :band_pts]
+        else:
+            strip = init_region_land[:, -band_pts:]
+        land_frac = strip.sum() / max(strip.size, 1)
+        edges_active[edge] = land_frac >= 0.25
+        if not edges_active[edge]:
+            logger.info(
+                "  %s edge disabled (%.1f%% land in initial box)",
+                edge.capitalize(),
+                land_frac * 100,
+            )
     n_iter = 0
 
     for iteration in range(MAX_ITERATIONS):
         n_iter = iteration + 1
-        region = filtered[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+        region = peak_mask[idx_s : idx_n + 1, idx_w : idx_e + 1]
+        land_region = land_mask_np[idx_s : idx_n + 1, idx_w : idx_e + 1]
 
         all_done = True
         for edge in list(edges_active.keys()):
@@ -244,6 +610,7 @@ def _lon_idx(val: float) -> int:
 
             frac = _edge_valid_fraction(
                 region,
+                land_region,
                 edge,
                 band_pts,
             )
@@ -252,23 +619,29 @@ def _lon_idx(val: float) -> int:
             else:
                 all_done = False
                 if edge == "north":
-                    idx_n = min(len(all_lats) - 1, idx_n + band_pts)
+                    idx_n = min(
+                        len(all_lats) - 1,
+                        idx_n + band_pts,
+                    )
                 elif edge == "south":
                     idx_s = max(0, idx_s - band_pts)
                 elif edge == "east":
-                    idx_e = min(len(all_lons) - 1, idx_e + band_pts)
+                    idx_e = min(
+                        len(all_lons) - 1,
+                        idx_e + band_pts,
+                    )
                 elif edge == "west":
                     idx_w = max(0, idx_w - band_pts)
 
         if all_done:
             logger.info(
-                "  Converged at iteration %d",
+                "  Spatial expansion converged at iteration %d",
                 n_iter,
             )
             break
     else:
         logger.info(
-            "  Reached max iterations (%d)",
+            "  Reached max spatial iterations (%d)",
             MAX_ITERATIONS,
         )
 
@@ -276,7 +649,7 @@ def _lon_idx(val: float) -> int:
     fin_lons = all_lons[idx_w : idx_e + 1]
     fin_filtered = filtered[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
     consec = max_consecutive_days(fin_filtered)
-    peak_gridpoints = int(fin_filtered.any(axis=0).sum())
+    peak_gridpoints = int((consec >= MIN_CONSECUTIVE_DAYS).sum())
 
     result = {
         "case_id": single_case.case_id_number,
@@ -289,25 +662,83 @@ def _lon_idx(val: float) -> int:
         "longitude_min": float(all_lons[idx_w]),
         "longitude_max": float(all_lons[idx_e]),
         "n_iterations": n_iter,
+        "final_days": final_t,
         "_consec": consec,
         "_lats": fin_lats,
         "_lons": fin_lons,
         "_peak_gridpoints": peak_gridpoints,
     }
     logger.info(
-        "  Final bounds: lat [%.2f, %.2f], lon [%.2f, %.2f]",
+        "  Final bounds: lat [%.2f, %.2f], lon [%.2f, %.2f] (%d days, %d iterations)",
+        result["latitude_min"],
+        result["latitude_max"],
+        result["longitude_min"],
+        result["longitude_max"],
+        final_t,
+        n_iter,
+    )
+
+    kind = "heatwave" if is_heatwave else "freeze"
+    start = result["start_date"][:10]
+    end = result["end_date"][:10]
+    out_png = str(out_dir / f"case_{result['case_id']}_consecutive_{kind}_days.png")
+    plot_consecutive_map(
+        consec,
+        fin_lats,
+        fin_lons,
+        cast(Literal["heat_wave", "cold_snap"], single_case.event_type),
+        title=(
+            f"Consecutive {kind.capitalize()} Days"
+            f" — {result['title']}\n{start} to {end}"
+        ),
+        output_path=out_png,
+    )
+    logger.info("  Saved plot: %s", out_png)
+
+    initial_box = (box_lat_min, box_lat_max, box_lon_min, box_lon_max)
+    final_box = (
         result["latitude_min"],
         result["latitude_max"],
         result["longitude_min"],
         result["longitude_max"],
     )
+    peak_png = str(out_dir / f"case_{result['case_id']}_peak_day_{kind}.png")
+    plot_peak_day_with_bounds(
+        peak_mask,
+        all_lats,
+        all_lons,
+        initial_box,
+        final_box,
+        single_case.event_type,
+        title=(
+            f"Peak Footprint (day {peak_day}) — {result['title']}\n{start} to {end}"
+        ),
+        output_path=peak_png,
+    )
+
     return result
 
 
 def results_to_dataframe(results: List[Dict]) -> pd.DataFrame:
-    """Convert result dicts to a labelled DataFrame."""
+    """Convert result dicts to a labelled DataFrame.
+
+    Args:
+        results: List of dicts returned by process_event (may
+            contain None entries which are ignored). Each dict must
+            have keys event_type, start_date, end_date,
+            latitude_min/max, longitude_min/max, case_id, title, and
+            _peak_gridpoints.
+
+    Returns:
+        DataFrame with columns label, case_id, title, event_type,
+        start_date, end_date, latitude_min, latitude_max,
+        longitude_min, longitude_max, sorted by start_date.
+        Events below MIN_GRIDPOINTS are excluded.
+    """
     columns = [
         "label",
+        "case_id",
+        "title",
         "event_type",
         "start_date",
         "end_date",
@@ -346,10 +777,77 @@ def results_to_dataframe(results: List[Dict]) -> pd.DataFrame:
     return df
 
 
+def write_bounds_to_yaml(
+    results: List[Dict],
+    yaml_path: pathlib.Path,
+) -> None:
+    """Update events.yaml with computed bounded_region bounds.
+
+    Uses ruamel.yaml round-trip mode so indentation, quoting style,
+    key order, blank lines, and comments are preserved exactly.
+    Only heat_wave / freeze cases with a valid result are modified.
+
+    Args:
+        results: List of dicts returned by process_event (None
+            entries are ignored).
+        yaml_path: Path to the events.yaml file to update in-place.
+    """
+    result_map = {r["case_id"]: r for r in results if r is not None}
+    if not result_map:
+        logger.warning("write_bounds_to_yaml: no valid results, skipping.")
+        return
+
+    yaml = YAML(typ="rt")
+    yaml.preserve_quotes = True
+
+    with yaml_path.open("r") as fh:
+        data = yaml.load(fh)
+
+    updated = 0
+    for entry in data:
+        cid = entry.get("case_id_number")
+        if cid not in result_map:
+            continue
+        r = result_map[cid]
+        params = entry["location"]["parameters"]
+        params["latitude_min"] = round(r["latitude_min"], 2)
+        params["latitude_max"] = round(r["latitude_max"], 2)
+        params["longitude_min"] = round(r["longitude_min"], 2)
+        params["longitude_max"] = round(r["longitude_max"], 2)
+        updated += 1
+
+    with yaml_path.open("w") as fh:
+        yaml.dump(data, fh)
+
+    logger.info(
+        "write_bounds_to_yaml: updated %d cases in %s",
+        updated,
+        yaml_path,
+    )
+
+
+def _load_base_temp_events() -> list[cases.IndividualCase]:
+    """Load all cases from base_temp_events.yaml.
+
+    Returns:
+        List of IndividualCase objects parsed from the bundled
+        base_temp_events.yaml resource file.
+    """
+    import extremeweatherbench.data
+
+    old_yaml = importlib.resources.files(
+        extremeweatherbench.data,
+    ).joinpath("base_temp_events.yaml")
+    with importlib.resources.as_file(old_yaml) as f:
+        raw = cases.read_incoming_yaml(f)
+    return cases.load_individual_cases(raw)
+
+
 def main():
     parser = argparse.ArgumentParser(
         description=(
-            "Validate / expand heat wave and cold snap bounds from events.yaml."
+            "Validate / expand heat wave and freeze bounds"
+            " using base_temp_events.yaml centered_region data."
         ),
     )
     parser.add_argument(
@@ -363,6 +861,37 @@ def main():
         default=4,
         help="Number of parallel workers (joblib)",
     )
+    parser.add_argument(
+        "--case-min",
+        type=int,
+        default=None,
+        help="Minimum case_id_number to process (inclusive)",
+    )
+    parser.add_argument(
+        "--case-max",
+        type=int,
+        default=None,
+        help="Maximum case_id_number to process (inclusive)",
+    )
+    parser.add_argument(
+        "--quantile",
+        type=float,
+        default=None,
+        help=(
+            "Climatology quantile "
+            f"({VALID_QUANTILES}; "
+            "default: 0.85 for heat_wave, 0.15 for freeze)"
+        ),
+    )
+    parser.add_argument(
+        "--operator",
+        default=None,
+        help=(
+            "Comparison operator "
+            "(>, >=, <, <=, ==; "
+            "default: > for heat_wave, < for freeze)"
+        ),
+    )
     args = parser.parse_args()
 
     wall_start = time_module.time()
@@ -370,43 +899,46 @@ def main():
     client = Client(LocalCluster(n_workers=args.n_workers))
     logger.info("Dask dashboard: %s", client.dashboard_link)
 
-    events_yaml = cases.load_ewb_events_yaml_into_case_list()
-    hw_fz = [e for e in events_yaml if e.event_type in ("heat_wave", "cold_snap")]
+    old_cases = _load_base_temp_events()
+    hw_fz = [
+        e
+        for e in old_cases
+        if e.event_type in ("heat_wave", "freeze")
+        and (args.case_min is None or e.case_id_number >= args.case_min)
+        and (args.case_max is None or e.case_id_number <= args.case_max)
+    ]
     logger.info(
-        "Found %d heat_wave / cold snap events",
+        "Found %d heat_wave / freeze events in base_temp_events.yaml",
         len(hw_fz),
     )
 
+    out_dir = pathlib.Path(args.output).parent
+
     results = joblib.Parallel(n_jobs=args.n_workers)(
-        joblib.delayed(process_event)(c) for c in hw_fz
+        joblib.delayed(process_event)(
+            c,
+            out_dir,
+            quantile=args.quantile,
+            op_str=args.operator,
+        )
+        for c in hw_fz
     )
 
     df = results_to_dataframe(results)
-    df.to_csv(args.output, index=False)
-
-    out_dir = pathlib.Path(args.output).parent
-    for r in results:
-        if r is None:
-            continue
-        consec = r["_consec"]
-        lats = r["_lats"]
-        lons = r["_lons"]
-        case_id = r["case_id"]
-        event_type = r["event_type"]
-        kind = "heatwave" if event_type == "heat_wave" else "cold_snap"
-        start = r["start_date"][:10]
-        end = r["end_date"][:10]
-        out_png = str(out_dir / f"case_{case_id}_consecutive_{kind}_days.png")
-        plot_consecutive_map(
-            consec,
-            lats,
-            lons,
-            event_type,
-            title=(
-                f"Consecutive {kind.capitalize()} Days — {r['title']}\n{start} to {end}"
-            ),
-            output_path=out_png,
-        )
+    out_path = pathlib.Path(args.output)
+    if out_path.exists():
+        df.to_csv(out_path, index=False, mode="a", header=False)
+    else:
+        df.to_csv(out_path, index=False)
+
+    yaml_path = (
+        pathlib.Path(__file__).parent.parent
+        / "src"
+        / "extremeweatherbench"
+        / "data"
+        / "events.yaml"
+    )
+    write_bounds_to_yaml(results, yaml_path)
 
     elapsed = time_module.time() - wall_start
     logger.info(
diff --git a/data_prep/heat_cold_bounds_global.py b/data_prep/heat_cold_bounds_global.py
deleted file mode 100644
index 1e81845a..00000000
--- a/data_prep/heat_cold_bounds_global.py
+++ /dev/null
@@ -1,531 +0,0 @@
-#!/usr/bin/env python3
-"""Detect heat waves and cold snaps globally from ERA5 reanalysis.
-
-Scans ERA5 2m temperature over an input date range and identifies
-heat wave (daily max > 85th percentile for 3+ consecutive days)
-and cold snap (daily min < 15th percentile for 3+ consecutive days)
-events globally over land.
-
-Bounding boxes represent the maximum spatial extent of each event.
-Events terminate when area drops below 50% of their peak area.
-
-Usage:
-    python heat_cold_bounds_global.py \\
-        --start-date 2023-06-01 --end-date 2023-09-01 \\
-        --output heat_cold_global.csv --n-workers 4
-"""
-
-import argparse
-import logging
-import pathlib
-import time as time_module
-from typing import Dict, List, Optional, Tuple
-
-import numba as nb
-import numpy as np
-import pandas as pd
-import regionmask
-import scipy.ndimage as ndimage
-import xarray as xr
-from dask.distributed import Client, LocalCluster
-from plot_temperature_events import (
-    _align_climatology,
-    detect_time_dim,
-    max_consecutive_days,
-    open_era5_t2m,
-    plot_consecutive_map,
-)
-
-from extremeweatherbench import defaults
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(name)s %(levelname)s %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-MIN_CONSECUTIVE_DAYS = 3
-AREA_DECLINE_FRACTION = 0.5
-MIN_GRIDPOINTS = 500
-
-
-def get_daily_climatology_thresholds() -> Tuple[xr.DataArray, xr.DataArray]:
-    """Derive daily thresholds from 6-hourly percentile climatology.
-
-    Returns the max-over-hours of the 85th percentile (heat wave
-    threshold) and min-over-hours of the 15th percentile (cold snap
-    threshold), each indexed by dayofyear.
-    """
-    clim_85 = defaults.get_climatology(0.85)
-    clim_15 = defaults.get_climatology(0.15)
-    return (
-        clim_85.max(dim="hour").sortby("latitude"),
-        clim_15.min(dim="hour").sortby("latitude"),
-    )
-
-
-def build_land_mask(
-    lons: xr.DataArray,
-    lats: xr.DataArray,
-) -> xr.DataArray:
-    """Boolean land mask (True = land) for the given grid."""
-    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
-    mask = land.mask(lons, lats)
-    return mask == 0
-
-
-def build_exceedance_masks(
-    t2m: xr.DataArray,
-    clim_daily_max: xr.DataArray,
-    clim_daily_min: xr.DataArray,
-    land_mask: xr.DataArray,
-) -> Tuple[xr.DataArray, xr.DataArray]:
-    """Build lazy boolean masks for heat wave / cold snap exceedance.
-
-    Daily max/min aggregation stays in the dask graph. The
-    climatology is loaded into memory once (small) and then
-    broadcast against the lazy daily arrays to avoid chunk
-    multiplication warnings.
-    """
-    tdim = detect_time_dim(t2m)
-
-    daily_max = t2m.resample({tdim: "1D"}).max()
-    daily_min = t2m.resample({tdim: "1D"}).min()
-
-    clim_max_aligned = _align_climatology(
-        clim_daily_max,
-        daily_max,
-        tdim,
-    )
-    clim_min_aligned = _align_climatology(
-        clim_daily_min,
-        daily_min,
-        tdim,
-    )
-
-    hw = (daily_max > clim_max_aligned) & land_mask
-    fz = (daily_min < clim_min_aligned) & land_mask
-    return hw, fz
-
-
-def apply_consecutive_filter(
-    mask: np.ndarray,
-    min_days: int = MIN_CONSECUTIVE_DAYS,
-) -> np.ndarray:
-    """Keep only grid points in runs of ``min_days``+ True days.
-
-    Binary erosion removes runs shorter than ``min_days``;
-    dilation restores surviving runs to their original extent.
-    Operates only along axis 0 (time).
-    """
-    struct = np.zeros((min_days, 1, 1), dtype=bool)
-    struct[:, 0, 0] = True
-    eroded = ndimage.binary_erosion(
-        mask,
-        structure=struct,
-        border_value=False,
-    )
-    dilated = ndimage.binary_dilation(
-        eroded,
-        structure=struct,
-        border_value=False,
-    )
-    return dilated & mask
-
-
-@nb.njit(cache=True)
-def _count_overlaps_nb(
-    labels_a: np.ndarray,
-    labels_b: np.ndarray,
-    n_a: int,
-    n_b: int,
-) -> np.ndarray:
-    """Pixel-count overlap matrix between two consecutive-day label grids.
-
-    Returns int32 (n_a, n_b) where result[i, j] is the number of
-    pixels where labels_a == i+1 and labels_b == j+1. Single-pass
-    JIT loop avoids the per-blob numpy scan in the Python tracker.
-    """
-    mat = np.zeros((n_a, n_b), dtype=np.int32)
-    rows, cols = labels_a.shape
-    for r in range(rows):
-        for c in range(cols):
-            a = labels_a[r, c]
-            b = labels_b[r, c]
-            if a > 0 and b > 0:
-                mat[a - 1, b - 1] += 1
-    return mat
-
-
-def _resolve_event(
-    oid: int,
-    overlap_mat: Optional[np.ndarray],
-    prev_map: Dict[int, int],
-    events: Dict[int, Dict],
-    cur_map: Dict[int, int],
-) -> Optional[int]:
-    """Find and merge events from the previous day for blob oid.
-
-    Uses a precomputed overlap matrix row instead of scanning the
-    full prev_labels array per blob.
-    """
-    if overlap_mat is None:
-        return None
-    col = overlap_mat[:, oid - 1]
-    prev_blob_ids = np.where(col > 0)[0] + 1
-    eids = {prev_map[p] for p in prev_blob_ids if p in prev_map}
-    alive = {e for e in eids if e in events and not events[e]["done"]}
-    if not alive:
-        return None
-
-    eid = min(alive)
-    for other in alive - {eid}:
-        merged = events.pop(other)
-        tgt = events[eid]
-        tgt["lat_min"] = min(tgt["lat_min"], merged["lat_min"])
-        tgt["lat_max"] = max(tgt["lat_max"], merged["lat_max"])
-        tgt["lon_min"] = min(tgt["lon_min"], merged["lon_min"])
-        tgt["lon_max"] = max(tgt["lon_max"], merged["lon_max"])
-        tgt["peak"] = max(tgt["peak"], merged["peak"])
-        tgt["start"] = min(tgt["start"], merged["start"])
-        for k, v in list(cur_map.items()):
-            if v == other:
-                cur_map[k] = eid
-    return eid
-
-
-def _terminate_declined_events(
-    events: Dict[int, Dict],
-    cur_map: Dict[int, int],
-) -> None:
-    """Terminate events absent today or below 50% of peak."""
-    active = set(cur_map.values())
-    for eid, ev in events.items():
-        if ev["done"]:
-            continue
-        if eid not in active:
-            ev["done"] = True
-        elif ev["area"] < AREA_DECLINE_FRACTION * ev["peak"]:
-            ev["done"] = True
-
-
-def detect_events(
-    filtered_mask: np.ndarray,
-    dates: np.ndarray,
-    lats: np.ndarray,
-    lons: np.ndarray,
-    event_type: str,
-) -> List[Dict]:
-    """Track spatiotemporal events from a filtered boolean mask.
-
-    Labels one day at a time (low peak memory) but uses a numba-JIT
-    overlap matrix to match blobs across days. A single O(nlat*nlon)
-    pass replaces one O(nlat*nlon) scan per blob, which dominates
-    when many blobs are active simultaneously.
-
-    An event's bounding box is the union of all its daily extents.
-    Events terminate when their active area drops below 50% of peak.
-    """
-    n_days = filtered_mask.shape[0]
-    events: Dict[int, Dict] = {}
-    next_id = 1
-    prev_labels: Optional[np.ndarray] = None
-    prev_n_obj: int = 0
-    prev_map: Dict[int, int] = {}
-
-    for di in range(n_days):
-        day = filtered_mask[di]
-
-        if not day.any():
-            for ev in events.values():
-                if not ev["done"]:
-                    ev["done"] = True
-            prev_labels = None
-            prev_n_obj = 0
-            prev_map = {}
-            continue
-
-        labels, n_obj = ndimage.label(day)
-        labels = labels.astype(np.int32)
-        cur_map: Dict[int, int] = {}
-
-        overlap_mat: Optional[np.ndarray] = None
-        if prev_labels is not None and n_obj > 0 and prev_n_obj > 0:
-            overlap_mat = _count_overlaps_nb(
-                prev_labels,
-                labels,
-                prev_n_obj,
-                n_obj,
-            )
-
-        for oid in range(1, n_obj + 1):
-            om = labels == oid
-            area = int(om.sum())
-            li, lo = np.where(om)
-
-            eid = _resolve_event(
-                oid,
-                overlap_mat,
-                prev_map,
-                events,
-                cur_map,
-            )
-
-            if eid is None:
-                eid = next_id
-                next_id += 1
-                events[eid] = {
-                    "type": event_type,
-                    "start": dates[di],
-                    "end": dates[di],
-                    "lat_min": float(lats[li].min()),
-                    "lat_max": float(lats[li].max()),
-                    "lon_min": float(lons[lo].min()),
-                    "lon_max": float(lons[lo].max()),
-                    "peak": area,
-                    "area": area,
-                    "done": False,
-                }
-            else:
-                ev = events[eid]
-                ev["end"] = dates[di]
-                ev["lat_min"] = min(
-                    ev["lat_min"],
-                    float(lats[li].min()),
-                )
-                ev["lat_max"] = max(
-                    ev["lat_max"],
-                    float(lats[li].max()),
-                )
-                ev["lon_min"] = min(
-                    ev["lon_min"],
-                    float(lons[lo].min()),
-                )
-                ev["lon_max"] = max(
-                    ev["lon_max"],
-                    float(lons[lo].max()),
-                )
-                ev["peak"] = max(ev["peak"], area)
-                ev["area"] = area
-
-            cur_map[oid] = eid
-
-        _terminate_declined_events(events, cur_map)
-        prev_labels = labels
-        prev_n_obj = n_obj
-        prev_map = cur_map
-
-    for ev in events.values():
-        ev["done"] = True
-
-    return list(events.values())
-
-
-def events_to_dataframe(
-    events: List[Dict],
-    min_gridpoints: int = MIN_GRIDPOINTS,
-) -> pd.DataFrame:
-    """Convert event dicts to a labelled DataFrame.
-
-    Args:
-        events: Raw event dicts from ``detect_events``.
-        min_gridpoints: Drop events whose peak spatial extent
-            (in grid points) is below this threshold. Defaults
-            to the module-level ``MIN_GRIDPOINTS`` constant.
-    """
-    columns = [
-        "label",
-        "event_type",
-        "start_date",
-        "end_date",
-        "latitude_min",
-        "latitude_max",
-        "longitude_min",
-        "longitude_max",
-    ]
-    if not events:
-        return pd.DataFrame(columns=columns)
-
-    n_before = len(events)
-    events = [e for e in events if e["peak"] >= min_gridpoints]
-    logger.info(
-        "  Filtered %d events below %d gridpoints; %d remain",
-        n_before - len(events),
-        min_gridpoints,
-        len(events),
-    )
-
-    if not events:
-        return pd.DataFrame(columns=columns)
-
-    rows = [
-        {
-            "event_type": e["type"],
-            "start_date": str(e["start"]),
-            "end_date": str(e["end"]),
-            "latitude_min": e["lat_min"],
-            "latitude_max": e["lat_max"],
-            "longitude_min": e["lon_min"],
-            "longitude_max": e["lon_max"],
-        }
-        for e in events
-    ]
-    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
-    df.insert(0, "label", range(1, len(df) + 1))
-    return df
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description=("Detect heat waves and cold snaps globally from ERA5."),
-    )
-    parser.add_argument(
-        "--start-date",
-        required=True,
-        help="Start date YYYY-MM-DD",
-    )
-    parser.add_argument(
-        "--end-date",
-        required=True,
-        help="End date YYYY-MM-DD",
-    )
-    parser.add_argument(
-        "--output",
-        default="heat_cold_global.csv",
-        help="Output CSV path",
-    )
-    parser.add_argument(
-        "--n-workers",
-        type=int,
-        default=4,
-        help="Number of dask workers",
-    )
-    args = parser.parse_args()
-
-    wall_start = time_module.time()
-    client = Client(
-        LocalCluster(n_workers=args.n_workers),
-    )
-    logger.info("Dask dashboard: %s", client.dashboard_link)
-
-    logger.info("Pre-warming numba JIT...")
-    _tiny = np.zeros((2, 2), dtype=np.int32)
-    _tiny[0, 0] = 1
-    _tiny[1, 1] = 1
-    _count_overlaps_nb(_tiny, _tiny, 1, 1)
-    logger.info("  numba ready")
-
-    logger.info("Opening ERA5 data...")
-    t2m = open_era5_t2m(args.start_date, args.end_date)
-    logger.info("  sizes=%s", dict(t2m.sizes))
-
-    logger.info("Loading climatology thresholds...")
-    clim_max, clim_min = get_daily_climatology_thresholds()
-
-    logger.info("Building land mask...")
-    land_mask = build_land_mask(t2m.longitude, t2m.latitude)
-
-    logger.info("Building exceedance masks (lazy)...")
-    hw_lazy, fz_lazy = build_exceedance_masks(
-        t2m,
-        clim_max,
-        clim_min,
-        land_mask,
-    )
-
-    tdim = detect_time_dim(hw_lazy)
-
-    logger.info("Computing heat wave mask...")
-    t0 = time_module.time()
-    hw_da = hw_lazy.compute()
-    logger.info("  done in %.1f s", time_module.time() - t0)
-
-    logger.info("Computing cold snap mask...")
-    t0 = time_module.time()
-    fz_da = fz_lazy.compute()
-    logger.info("  done in %.1f s", time_module.time() - t0)
-
-    dates = hw_da[tdim].values
-    lats = hw_da.latitude.values
-    lons = hw_da.longitude.values
-    hw_np = hw_da.values.astype(bool)
-    fz_np = fz_da.values.astype(bool)
-    del hw_da, fz_da
-
-    logger.info(
-        "Applying %d-day consecutive filter...",
-        MIN_CONSECUTIVE_DAYS,
-    )
-    hw_filt = apply_consecutive_filter(hw_np)
-    fz_filt = apply_consecutive_filter(fz_np)
-    logger.info(
-        "  HW: %d -> %d True cells",
-        hw_np.sum(),
-        hw_filt.sum(),
-    )
-    logger.info(
-        "  FZ: %d -> %d True cells",
-        fz_np.sum(),
-        fz_filt.sum(),
-    )
-    del hw_np, fz_np
-
-    logger.info("Detecting heat wave events...")
-    hw_ev = detect_events(
-        hw_filt,
-        dates,
-        lats,
-        lons,
-        "heat_wave",
-    )
-    logger.info("  %d events", len(hw_ev))
-
-    logger.info("Detecting cold snap events...")
-    fz_ev = detect_events(
-        fz_filt,
-        dates,
-        lats,
-        lons,
-        "cold_snap",
-    )
-    logger.info("  %d events", len(fz_ev))
-
-    logger.info("Computing max-consecutive-days fields for plots...")
-    stem = str(pathlib.Path(args.output).with_suffix(""))
-    hw_consec = max_consecutive_days(hw_filt)
-    plot_consecutive_map(
-        hw_consec,
-        lats,
-        lons,
-        "heat_wave",
-        title=(f"Consecutive Heatwave Days\n{args.start_date} to {args.end_date}"),
-        output_path=f"{stem}_heatwave.png",
-    )
-    del hw_consec, hw_filt
-
-    fz_consec = max_consecutive_days(fz_filt)
-    plot_consecutive_map(
-        fz_consec,
-        lats,
-        lons,
-        "cold_snap",
-        title=(f"Consecutive Cold Snap Days\n{args.start_date} to {args.end_date}"),
-        output_path=f"{stem}_cold_snap.png",
-    )
-    del fz_consec, fz_filt
-
-    df = events_to_dataframe(hw_ev + fz_ev)
-    df.to_csv(args.output, index=False)
-
-    elapsed = time_module.time() - wall_start
-    logger.info(
-        "Done: %d events -> %s (%.1f s)",
-        len(df),
-        args.output,
-        elapsed,
-    )
-    client.close()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/data_prep/plot_temperature_events.py b/data_prep/plot_temperature_events.py
index 6517fdd9..e8c8708a 100644
--- a/data_prep/plot_temperature_events.py
+++ b/data_prep/plot_temperature_events.py
@@ -4,7 +4,7 @@
 Handles both event types from a single entry point. The event type
 is auto-detected from events.yaml; no flag required.
 
-Exported functions used by heat_cold_bounds_global.py and
+Exported functions used by temperature_bounds_global.py and
 heat_cold_bounds_case.py:
     max_consecutive_days  -- compute field from boolean mask
     plot_consecutive_map  -- pcolormesh map (Reds/Blues, discrete)
@@ -16,9 +16,10 @@
 
 import argparse
 import logging
+import operator as op_module
 import pathlib
 import time as time_module
-from typing import Literal, cast
+from typing import Callable, Literal, cast
 
 import cartopy.crs as ccrs
 import cartopy.feature as cfeature
@@ -41,11 +42,49 @@
 
 MIN_CONSECUTIVE_DAYS = 3
 
+_OP_MAP: dict[str, Callable] = {
+    ">": op_module.gt,
+    ">=": op_module.ge,
+    "<": op_module.lt,
+    "<=": op_module.le,
+    "==": op_module.eq,
+}
+
+VALID_QUANTILES = [0.10, 0.15, 0.25, 0.50, 0.75, 0.85, 0.90]
+
+
+def resolve_op(op_str: str) -> Callable:
+    """Convert a string operator to a callable.
+
+    Args:
+        op_str: One of ">", ">=", "<", "<=", "==".
+
+    Returns:
+        The corresponding operator callable.
+
+    Raises:
+        ValueError: If op_str is not a recognised operator.
+    """
+    if op_str not in _OP_MAP:
+        raise ValueError(f"Unknown operator {op_str!r}; choose from {list(_OP_MAP)}")
+    return _OP_MAP[op_str]
+
+
 # ── shared ERA5 utilities ─────────────────────────────────────────────
 
 
 def detect_time_dim(obj: xr.Dataset | xr.DataArray) -> str:
-    """Return the name of the time dimension."""
+    """Return the name of the time dimension.
+
+    Args:
+        obj: An xarray Dataset or DataArray.
+
+    Returns:
+        The name of the first matching time dimension.
+
+    Raises:
+        ValueError: If no recognised time dimension is found.
+    """
     for name in ("valid_time", "time"):
         if name in obj.dims:
             return name
@@ -57,6 +96,13 @@ def open_era5_t2m(start_date: str, end_date: str) -> xr.DataArray:
 
     Selects 6-hourly timesteps (0/6/12/18 UTC) to match the
     climatology base and sorts latitude to ascending order.
+
+    Args:
+        start_date: Inclusive start date string (YYYY-MM-DD).
+        end_date: Inclusive end date string (YYYY-MM-DD).
+
+    Returns:
+        Lazy DataArray of 2m temperature with ascending latitude.
     """
     ds = xr.open_zarr(
         inputs.ARCO_ERA5_FULL_URI,
@@ -80,6 +126,15 @@ def _align_climatology(
 
     Computes the climatology into memory (366 x lat x lon) to avoid
     dask chunk multiplication, then indexes by dayofyear via numpy.
+
+    Args:
+        clim: Climatology DataArray indexed by dayofyear.
+        daily: Daily ERA5 DataArray to align against.
+        tdim: Name of the time dimension in daily.
+
+    Returns:
+        DataArray with the same shape as daily, containing the
+        climatology value for each timestep's day-of-year.
     """
     clim_vals = clim.compute().values
     clim_doy = clim.dayofyear.values
@@ -119,12 +174,27 @@ def _align_climatology(
 
 
 def _to_plot_lon(lon: float) -> float:
-    """Wrap 0-360 longitude to -180..180 for PlateCarree."""
+    """Wrap 0-360 longitude to -180..180 for PlateCarree.
+
+    Args:
+        lon: Longitude in 0-360 degrees.
+
+    Returns:
+        Longitude in -180..180 degrees.
+    """
     return lon - 360.0 if lon > 180.0 else lon
 
 
 def max_consecutive_days(mask_3d: np.ndarray) -> np.ndarray:
-    """Max consecutive True days per grid point along axis 0."""
+    """Compute max consecutive True days per grid point.
+
+    Args:
+        mask_3d: Boolean array of shape (time, lat, lon).
+
+    Returns:
+        Int32 array of shape (lat, lon) with the maximum number of
+        consecutive True values along axis 0 for each grid point.
+    """
     nt, nlat, nlon = mask_3d.shape
     flat = mask_3d.reshape(nt, -1).astype(np.int8)
     result = np.zeros(flat.shape[1], dtype=np.int32)
@@ -140,7 +210,11 @@ def max_consecutive_days(mask_3d: np.ndarray) -> np.ndarray:
 
 
 def _add_map_features(ax) -> None:
-    """Add standard cartopy features to an axis."""
+    """Add standard cartopy features to an axis.
+
+    Args:
+        ax: A cartopy GeoAxes instance.
+    """
     ax.coastlines(linewidth=0.5, zorder=10)
     ax.add_feature(
         cfeature.BORDERS,
@@ -187,7 +261,7 @@ def plot_consecutive_map(
         title: Two-line figure title (left-aligned).
         output_path: Destination PNG file path.
         extent: (lon_min, lon_max, lat_min, lat_max) in -180..180.
-            Derived from lats/lons when None.
+            Derived from lats/lons when None. Default is None.
     """
     plot_data = consec.astype(float)
     plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
@@ -258,6 +332,14 @@ def plot_event_bounds(
     """Draw bounding boxes for all events on a global Robinson map.
 
     Saves a PNG alongside the CSV with the same stem.
+
+    Args:
+        df: DataFrame with columns event_type, longitude_min,
+            longitude_max, latitude_min, latitude_max.
+        csv_path: Path to the output CSV; the PNG is saved with the
+            same stem.
+        title: Figure title. Default is "Detected Heat Wave and
+            Cold Snap Events".
     """
     if df.empty:
         logger.warning("No events to plot -- skipping bounds plot.")
@@ -352,7 +434,17 @@ def plot_event_bounds(
 
 
 def load_case(case_id_number: int) -> cases.IndividualCase:
-    """Load a single case from events.yaml by case_id_number."""
+    """Load a single case from events.yaml by case_id_number.
+
+    Args:
+        case_id_number: Integer identifier for the case.
+
+    Returns:
+        The matching IndividualCase object.
+
+    Raises:
+        ValueError: If no case with the given ID exists.
+    """
     all_cases = cases.load_ewb_events_yaml_into_case_list()
     for c in all_cases:
         if c.case_id_number == case_id_number:
@@ -372,13 +464,33 @@ def load_case(case_id_number: int) -> cases.IndividualCase:
 
 def compute_consecutive_field(
     single_case: cases.IndividualCase,
+    quantile: float | None = None,
+    op_str: str | None = None,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """Return (consecutive_days, lats, lons) for a case.
 
-    Works for both heat_wave (daily max > 85th pct) and
-    cold_snap (daily min < 15th pct) event types.
+    Args:
+        single_case: The case to compute the consecutive field for.
+        quantile: Climatology quantile. Default is None, which
+            resolves to 0.85 for heat_wave or 0.15 for
+            freeze/cold_snap.
+        op_str: Comparison operator string (e.g. ">", ">=",
+            "<", "<="). Default is None, which resolves to ">"
+            for heat_wave or "<" for freeze.
+
+    Returns:
+        A tuple of (consec, lats, lons) where consec is an int32
+        array of shape (lat, lon) containing max consecutive event
+        days, lats is the 1-D latitude array, and lons is the 1-D
+        longitude array (0-360).
     """
     is_heatwave = single_case.event_type == "heat_wave"
+    if quantile is None:
+        quantile = 0.85 if is_heatwave else 0.15
+    if op_str is None:
+        op_str = ">" if is_heatwave else "<"
+    cmp = resolve_op(op_str)
+
     start = str(single_case.start_date.date())
     end = str(single_case.end_date.date())
     bounds = single_case.location.as_geopandas().total_bounds
@@ -399,36 +511,44 @@ def compute_consecutive_field(
 
     tdim = detect_time_dim(t2m)
 
-    if is_heatwave:
-        logger.info("Loading 85th-percentile climatology...")
-        daily = t2m.resample({tdim: "1D"}).max()
-        clim = defaults.get_climatology(0.85).max(dim="hour").sortby("latitude")
-    else:
-        logger.info("Loading 15th-percentile climatology...")
-        daily = t2m.resample({tdim: "1D"}).min()
-        clim = defaults.get_climatology(0.15).min(dim="hour").sortby("latitude")
+    logger.info(
+        "Loading q=%.2f climatology (op=%s)...",
+        quantile,
+        op_str,
+    )
+    clim = defaults.get_climatology(quantile).sortby("latitude")
 
     clim = clim.sel(
-        latitude=daily.latitude,
-        longitude=daily.longitude,
+        latitude=t2m.latitude,
+        longitude=t2m.longitude,
         method="nearest",
     )
-    clim_aligned = _align_climatology(clim, daily, tdim)
+
+    doy = t2m[tdim].dt.dayofyear
+    hour = t2m[tdim].dt.hour
+    max_clim_doy = int(clim.dayofyear.max())
+    doy_capped = doy.clip(max=max_clim_doy)
+
+    clim_aligned = clim.sel(
+        dayofyear=doy_capped,
+        hour=hour,
+    )
+
+    logger.info("Computing 6-hourly exceedance...")
+    exc_6h = cmp(t2m, clim_aligned)
+
+    daily_all_pass = exc_6h.resample({tdim: "1D"}).min().astype(bool)
 
     logger.info("Building land mask...")
     land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
-    land_mask = land.mask(daily.longitude, daily.latitude) == 0
+    land_mask = land.mask(daily_all_pass.longitude, daily_all_pass.latitude) == 0
 
-    logger.info("Computing exceedance mask...")
-    if is_heatwave:
-        exc = (daily > clim_aligned) & land_mask
-    else:
-        exc = (daily < clim_aligned) & land_mask
+    exc = daily_all_pass & land_mask
     mask_np = exc.compute().values.astype(bool)
 
     logger.info("Computing max consecutive days...")
     consec = max_consecutive_days(mask_np)
-    return consec, daily.latitude.values, daily.longitude.values
+    return consec, daily_all_pass.latitude.values, daily_all_pass.longitude.values
 
 
 # ── CLI ───────────────────────────────────────────────────────────────
@@ -459,6 +579,25 @@ def main() -> None:
             "(default: case_N_consecutive_{heatwave|cold_snap}_days.png)"
         ),
     )
+    parser.add_argument(
+        "--quantile",
+        type=float,
+        default=None,
+        help=(
+            "Climatology quantile "
+            f"(one of {VALID_QUANTILES}; "
+            "default: 0.85 for heat_wave, 0.15 for freeze)"
+        ),
+    )
+    parser.add_argument(
+        "--operator",
+        default=None,
+        help=(
+            "Comparison operator string "
+            "(>, >=, <, <=, ==; "
+            "default: > for heat_wave, < for freeze)"
+        ),
+    )
     args = parser.parse_args()
 
     t0 = time_module.time()
@@ -476,7 +615,11 @@ def main() -> None:
             single_case.event_type,
         )
 
-    consec, lats, lons = compute_consecutive_field(single_case)
+    consec, lats, lons = compute_consecutive_field(
+        single_case,
+        quantile=args.quantile,
+        op_str=args.operator,
+    )
 
     kind = "Heat Wave" if single_case.event_type == "heat_wave" else "Cold Snap"
     start_str = str(single_case.start_date.date())
diff --git a/data_prep/temperature_bounds_global.py b/data_prep/temperature_bounds_global.py
new file mode 100644
index 00000000..ce7e91d6
--- /dev/null
+++ b/data_prep/temperature_bounds_global.py
@@ -0,0 +1,968 @@
+"""Detect temperature exceedance events globally from ERA5 reanalysis.
+
+Scans ERA5 2m temperature over an input date range and identifies
+events where the daily temperature falls within a user-specified
+climatology quantile band for 3+ consecutive days, over land.
+
+At least one of --quantile-lower or --quantile-upper must be given.
+Both can be combined to define a band (e.g. 50th–85th percentile).
+
+Bounding boxes are first derived from blob tracking, then expanded
+using the same edge-validity logic as heat_cold_bounds_case.py:
+each edge grows by 1 degree while >= 50% of its land points are
+active on the peak-footprint day.  Events terminate when their
+active area drops below 50% of peak.
+
+Usage:
+    # Anything above the 85th percentile (heat wave)
+    python temperature_bounds_global.py \\
+        --start-date 2023-06-01 --end-date 2023-09-01 \\
+        --quantile-lower 0.85 --operator-lower ">=" \\
+        --event-type heat_wave --output heat_cold_global.csv
+
+    # Band between 50th and 85th (moderate heat)
+    python temperature_bounds_global.py \\
+        --start-date 2023-06-01 --end-date 2023-09-01 \\
+        --quantile-lower 0.50 --quantile-upper 0.85 \\
+        --event-type heat_wave --output heat_cold_global.csv
+"""
+
+import argparse
+import logging
+import pathlib
+import time as time_module
+from typing import Dict, List, Optional, Tuple
+
+import joblib
+import numba as nb
+import numpy as np
+import pandas as pd
+import regionmask
+import scipy.ndimage as ndimage
+import xarray as xr
+from dask.distributed import Client, LocalCluster
+from plot_temperature_events import (
+    VALID_QUANTILES,
+    detect_time_dim,
+    max_consecutive_days,
+    open_era5_t2m,
+    plot_consecutive_map,
+    resolve_op,
+)
+
+from extremeweatherbench import defaults
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(name)s %(levelname)s %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+MIN_CONSECUTIVE_DAYS = 3
+AREA_DECLINE_FRACTION = 0.5
+MIN_GRIDPOINTS = 500
+MIN_AREA_KM2 = 200000.0
+EXPANSION_DEGREES = 1
+MAX_SPATIAL_ITERATIONS = 20
+EDGE_VALIDITY_THRESHOLD = 0.5
+
+
+def compute_grid_cell_area(
+    lats: np.ndarray,
+    lons: np.ndarray,
+) -> np.ndarray:
+    """Return a 2-D (lat, lon) array of grid-cell areas in km².
+
+    Uses the spherical-Earth approximation:
+        area = R² × Δlat_rad × Δlon_rad × cos(lat)
+
+    Args:
+        lats: 1-D latitude array in degrees.
+        lons: 1-D longitude array in degrees; used only for shape.
+
+    Returns:
+        Float64 array of shape (len(lats), len(lons)) with each
+        cell's surface area in km².
+    """
+    R_KM = 6371.0
+    dlat = float(np.abs(np.diff(lats[:2]))[0]) if len(lats) > 1 else 0.25
+    dlon = float(np.abs(np.diff(lons[:2]))[0]) if len(lons) > 1 else 0.25
+    cell_km2 = R_KM**2 * np.deg2rad(dlat) * np.deg2rad(dlon) * np.cos(np.deg2rad(lats))
+    return np.outer(cell_km2, np.ones(len(lons)))
+
+
+def get_climatology_bounds(
+    q_lower: Optional[float] = None,
+    q_upper: Optional[float] = None,
+) -> Tuple[Optional[xr.DataArray], Optional[xr.DataArray]]:
+    """Return climatology DataArrays for the lower and/or upper bound.
+
+    At least one of q_lower or q_upper must be provided. Each returned
+    DataArray is indexed by (dayofyear, hour) and sorted by latitude.
+
+    Args:
+        q_lower: Quantile for the lower bound (e.g. 0.50 means temp
+            must exceed the 50th-percentile climatology). None skips
+            the lower-bound check.
+        q_upper: Quantile for the upper bound (e.g. 0.85 means temp
+            must not exceed the 85th-percentile climatology). None
+            skips the upper-bound check.
+
+    Returns:
+        Tuple (clim_lower, clim_upper); either element may be None
+        when the corresponding quantile argument is not supplied.
+
+    Raises:
+        ValueError: If both q_lower and q_upper are None.
+    """
+    if q_lower is None and q_upper is None:
+        raise ValueError("At least one of q_lower or q_upper must be set.")
+    clim_lower = (
+        defaults.get_climatology(q_lower).sortby("latitude")
+        if q_lower is not None
+        else None
+    )
+    clim_upper = (
+        defaults.get_climatology(q_upper).sortby("latitude")
+        if q_upper is not None
+        else None
+    )
+    return clim_lower, clim_upper
+
+
+def build_land_mask(
+    lons: xr.DataArray,
+    lats: xr.DataArray,
+) -> xr.DataArray:
+    """Build a boolean land mask (True = land) for the given grid.
+
+    Args:
+        lons: 1-D DataArray of longitudes.
+        lats: 1-D DataArray of latitudes.
+
+    Returns:
+        Boolean DataArray of the same shape as the meshgrid of lons
+        and lats, where True indicates a land grid point.
+    """
+    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+    mask = land.mask(lons, lats)
+    return mask == 0
+
+
+def _edge_valid_fraction(
+    mask_2d: np.ndarray,
+    land_2d: np.ndarray,
+    edge: str,
+    band_pts: int,
+) -> float:
+    """Return the fraction of land grid points on an edge that are active.
+
+    Ocean/masked points are excluded from both numerator and denominator
+    so coastal edges are not penalised.
+
+    Args:
+        mask_2d: 2-D boolean activity array (lat, lon).
+        land_2d: 2-D boolean land mask (lat, lon); True = land.
+        edge: One of "north", "south", "east", "west".
+        band_pts: Width of the edge strip in grid points.
+
+    Returns:
+        Fraction in [0, 1] of land points in the strip that are active,
+        or 0.0 if the strip contains no land points.
+
+    Raises:
+        ValueError: If edge is not one of the recognised values.
+    """
+    if edge == "north":
+        strip = mask_2d[-band_pts:, :]
+        land_strip = land_2d[-band_pts:, :]
+    elif edge == "south":
+        strip = mask_2d[:band_pts, :]
+        land_strip = land_2d[:band_pts, :]
+    elif edge == "west":
+        strip = mask_2d[:, :band_pts]
+        land_strip = land_2d[:, :band_pts]
+    elif edge == "east":
+        strip = mask_2d[:, -band_pts:]
+        land_strip = land_2d[:, -band_pts:]
+    else:
+        raise ValueError(f"Unknown edge: {edge}")
+    n_land = int(land_strip.sum())
+    if n_land == 0:
+        return 0.0
+    return float((strip & land_strip).sum()) / n_land
+
+
+def expand_event_bounds(
+    event: Dict,
+    filt_mask: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    land_mask_np: np.ndarray,
+) -> Dict:
+    """Expand a detected event's bounding box using case-script logic.
+
+    Starting from the blob-tracked bounding box, expands each edge
+    outward while >= EDGE_VALIDITY_THRESHOLD of land points on that
+    edge are active on the peak-footprint day. Also computes and stores
+    max_consecutive_days within the expanded region.
+
+    The expansion matches the spatial-growth algorithm used in
+    heat_cold_bounds_case.py: 1-degree steps, ocean-heavy edges
+    disabled upfront, convergence when all active edges drop below 50%.
+
+    Args:
+        event: Event dict from detect_events (keys: start, end,
+            lat_min, lat_max, lon_min, lon_max).
+        filt_mask: Boolean array (time, lat, lon) with consecutive-day
+            filtering already applied, aligned with dates/lats/lons.
+        dates: 1-D datetime64 array of daily timestamps (axis 0).
+        lats: 1-D latitude array (axis 1).
+        lons: 1-D longitude array (axis 2).
+        land_mask_np: 2-D boolean array (lat, lon); True = land.
+
+    Returns:
+        The same event dict with updated lat_min/lat_max/lon_min/
+        lon_max and a new max_consecutive_days key.
+    """
+    grid_res = float(np.abs(np.diff(lats[:2]))[0]) if len(lats) > 1 else 0.25
+    band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
+
+    start_date = np.datetime64(event["start"])
+    end_date = np.datetime64(event["end"])
+    t_mask = (dates >= start_date) & (dates <= end_date)
+    ev_filt = filt_mask[t_mask]
+
+    if ev_filt.shape[0] == 0:
+        event["max_consecutive_days"] = 0
+        return event
+
+    def _lat_idx(val: float) -> int:
+        return int(np.argmin(np.abs(lats - val)))
+
+    def _lon_idx(val: float) -> int:
+        return int(np.argmin(np.abs(lons - val)))
+
+    idx_s0 = _lat_idx(event["lat_min"])
+    idx_n0 = _lat_idx(event["lat_max"])
+    idx_w0 = _lon_idx(event["lon_min"])
+    idx_e0 = _lon_idx(event["lon_max"])
+
+    daily_counts = ev_filt.sum(axis=(1, 2))
+    max_count = daily_counts.max()
+    tied_days = np.where(daily_counts == max_count)[0]
+    n_tied = len(tied_days)
+    if n_tied <= 2:
+        peak_day = int(tied_days[-1]) if n_tied == 2 else int(tied_days[0])
+    else:
+        peak_day = int(tied_days[(n_tied - 1) // 2])
+    peak_mask = ev_filt[peak_day]
+
+    idx_s, idx_n, idx_w, idx_e = idx_s0, idx_n0, idx_w0, idx_e0
+
+    init_region_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
+    edges_active: Dict[str, bool] = {}
+    for edge in ("north", "south", "east", "west"):
+        if edge == "north":
+            strip = init_region_land[-band_pts:, :]
+        elif edge == "south":
+            strip = init_region_land[:band_pts, :]
+        elif edge == "west":
+            strip = init_region_land[:, :band_pts]
+        else:
+            strip = init_region_land[:, -band_pts:]
+        land_frac = strip.sum() / max(strip.size, 1)
+        edges_active[edge] = bool(land_frac >= 0.25)
+
+    for _ in range(MAX_SPATIAL_ITERATIONS):
+        region = peak_mask[idx_s : idx_n + 1, idx_w : idx_e + 1]
+        land_region = land_mask_np[idx_s : idx_n + 1, idx_w : idx_e + 1]
+
+        all_done = True
+        for edge in list(edges_active.keys()):
+            if not edges_active[edge]:
+                continue
+            frac = _edge_valid_fraction(region, land_region, edge, band_pts)
+            if frac < EDGE_VALIDITY_THRESHOLD:
+                edges_active[edge] = False
+            else:
+                all_done = False
+                if edge == "north":
+                    idx_n = min(len(lats) - 1, idx_n + band_pts)
+                elif edge == "south":
+                    idx_s = max(0, idx_s - band_pts)
+                elif edge == "east":
+                    idx_e = min(len(lons) - 1, idx_e + band_pts)
+                elif edge == "west":
+                    idx_w = max(0, idx_w - band_pts)
+
+        if all_done:
+            break
+
+    fin_filt = ev_filt[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+    consec = max_consecutive_days(fin_filt)
+    event["lat_min"] = float(lats[idx_s])
+    event["lat_max"] = float(lats[idx_n])
+    event["lon_min"] = float(lons[idx_w])
+    event["lon_max"] = float(lons[idx_e])
+    event["max_consecutive_days"] = int(consec.max()) if consec.size > 0 else 0
+    return event
+
+
+def build_exceedance_mask(
+    t2m: xr.DataArray,
+    clim_lower: Optional[xr.DataArray],
+    clim_upper: Optional[xr.DataArray],
+    land_mask: xr.DataArray,
+    op_lower: str = ">",
+    op_upper: str = "<",
+) -> xr.DataArray:
+    """Build a daily exceedance mask from 6-hourly temperature data.
+
+    Each 6-hourly timestep must satisfy all provided bounds. A day
+    passes only when every 6-hourly step satisfies every active bound.
+    The result is further masked to land points.
+
+    At least one of clim_lower or clim_upper must be provided.
+
+    Args:
+        t2m: 6-hourly 2m temperature DataArray.
+        clim_lower: Climatology for the lower bound, indexed by
+            (dayofyear, hour). None skips this bound.
+        clim_upper: Climatology for the upper bound, indexed by
+            (dayofyear, hour). None skips this bound.
+        land_mask: Boolean DataArray (True = land) matching t2m grid.
+        op_lower: Comparison operator for the lower bound.
+            Default is ">".
+        op_upper: Comparison operator for the upper bound.
+            Default is "<".
+
+    Returns:
+        Daily boolean DataArray masked to land where True indicates
+        every 6-hourly step satisfied all active bounds.
+    """
+    tdim = detect_time_dim(t2m)
+    doy = t2m[tdim].dt.dayofyear
+    hour = t2m[tdim].dt.hour
+    ref = clim_lower if clim_lower is not None else clim_upper
+    assert ref is not None
+    max_clim_doy = int(ref.dayofyear.max())
+    doy_capped = doy.clip(max=max_clim_doy)
+
+    mask_6h = xr.ones_like(t2m, dtype=bool)
+
+    if clim_lower is not None:
+        cmp = resolve_op(op_lower)
+        aligned = clim_lower.sel(dayofyear=doy_capped, hour=hour).reindex_like(
+            t2m, method="nearest"
+        )
+        mask_6h = mask_6h & cmp(t2m, aligned)
+
+    if clim_upper is not None:
+        cmp = resolve_op(op_upper)
+        aligned = clim_upper.sel(dayofyear=doy_capped, hour=hour).reindex_like(
+            t2m, method="nearest"
+        )
+        mask_6h = mask_6h & cmp(t2m, aligned)
+
+    return mask_6h.resample({tdim: "1D"}).min().astype(bool) & land_mask
+
+
+def apply_consecutive_filter(
+    mask: np.ndarray,
+    min_days: int = MIN_CONSECUTIVE_DAYS,
+    max_grace_days: int = 1,
+) -> np.ndarray:
+    """Keep runs of ``min_days``+ True days along axis 0.
+
+    After ``min_days`` strict consecutive True days are established,
+    gaps of up to ``max_grace_days`` are bridged so the event can
+    continue. Runs that never reach ``min_days`` strict consecutive
+    True days are discarded.
+
+    Args:
+        mask: Boolean array of shape (time, lat, lon).
+        min_days: Minimum run length required to qualify as an
+            event. Default is 3 (MIN_CONSECUTIVE_DAYS).
+        max_grace_days: Maximum gap length to bridge after the
+            minimum run is established. Default is 1.
+
+    Returns:
+        Boolean array of the same shape with only qualifying runs
+        retained.
+    """
+    struct = np.zeros((min_days, 1, 1), dtype=bool)
+    struct[:, 0, 0] = True
+
+    strict = (
+        ndimage.binary_dilation(
+            ndimage.binary_erosion(
+                mask,
+                structure=struct,
+                border_value=False,
+            ),
+            structure=struct,
+            border_value=False,
+        )
+        & mask
+    )
+
+    if max_grace_days <= 0:
+        return strict
+
+    close_k = np.zeros((2 * max_grace_days + 1, 1, 1), dtype=bool)
+    close_k[:, 0, 0] = True
+    filled = ndimage.binary_closing(
+        mask,
+        structure=close_k,
+        border_value=False,
+    )
+
+    filled_runs = (
+        ndimage.binary_dilation(
+            ndimage.binary_erosion(
+                filled,
+                structure=struct,
+                border_value=False,
+            ),
+            structure=struct,
+            border_value=False,
+        )
+        & filled
+    )
+
+    lbl_struct = np.zeros((3, 3, 3), dtype=int)
+    lbl_struct[0, 1, 1] = 1
+    lbl_struct[1, 1, 1] = 1
+    lbl_struct[2, 1, 1] = 1
+    labels, _ = ndimage.label(filled_runs, structure=lbl_struct)
+
+    valid = np.unique(labels[strict & (labels > 0)])
+    return np.isin(labels, valid) & filled_runs
+
+
+@nb.njit(cache=True)
+def _count_overlaps_nb(
+    labels_a: np.ndarray,
+    labels_b: np.ndarray,
+    n_a: int,
+    n_b: int,
+) -> np.ndarray:
+    """Compute a pixel-count overlap matrix between two label grids.
+
+    A single-pass JIT loop avoids the per-blob numpy scan in the
+    Python tracker.
+
+    Args:
+        labels_a: Integer label array for day t (shape lat x lon).
+        labels_b: Integer label array for day t+1 (shape lat x lon).
+        n_a: Number of blobs in labels_a.
+        n_b: Number of blobs in labels_b.
+
+    Returns:
+        Int32 array of shape (n_a, n_b) where element [i, j] is the
+        number of pixels where labels_a == i+1 and labels_b == j+1.
+    """
+    mat = np.zeros((n_a, n_b), dtype=np.int32)
+    rows, cols = labels_a.shape
+    for r in range(rows):
+        for c in range(cols):
+            a = labels_a[r, c]
+            b = labels_b[r, c]
+            if a > 0 and b > 0:
+                mat[a - 1, b - 1] += 1
+    return mat
+
+
+def _resolve_event(
+    oid: int,
+    overlap_mat: Optional[np.ndarray],
+    prev_map: Dict[int, int],
+    events: Dict[int, Dict],
+    cur_map: Dict[int, int],
+) -> Optional[int]:
+    """Find and merge prior-day events overlapping with blob oid.
+
+    Uses a precomputed overlap matrix column instead of scanning the
+    full prev_labels array per blob.
+
+    Args:
+        oid: Current-day blob label (1-indexed).
+        overlap_mat: Pixel-count overlap matrix from
+            _count_overlaps_nb, or None if no previous day.
+        prev_map: Mapping from previous-day blob label to event ID.
+        events: Mutable dict of all live events keyed by event ID.
+        cur_map: Mutable mapping from current-day blob label to event
+            ID; updated in-place when events are merged.
+
+    Returns:
+        The surviving event ID after merging, or None if no overlap
+        with a prior-day event was found.
+    """
+    if overlap_mat is None:
+        return None
+    col = overlap_mat[:, oid - 1]
+    prev_blob_ids = np.where(col > 0)[0] + 1
+    eids = {prev_map[p] for p in prev_blob_ids if p in prev_map}
+    alive = {e for e in eids if e in events and not events[e]["done"]}
+    if not alive:
+        return None
+
+    eid = min(alive)
+    for other in alive - {eid}:
+        merged = events.pop(other)
+        tgt = events[eid]
+        tgt["lat_min"] = min(tgt["lat_min"], merged["lat_min"])
+        tgt["lat_max"] = max(tgt["lat_max"], merged["lat_max"])
+        tgt["lon_min"] = min(tgt["lon_min"], merged["lon_min"])
+        tgt["lon_max"] = max(tgt["lon_max"], merged["lon_max"])
+        tgt["peak"] = max(tgt["peak"], merged["peak"])
+        tgt["peak_area_km2"] = max(tgt["peak_area_km2"], merged["peak_area_km2"])
+        tgt["start"] = min(tgt["start"], merged["start"])
+        for k, v in list(cur_map.items()):
+            if v == other:
+                cur_map[k] = eid
+    return eid
+
+
+def _terminate_declined_events(
+    events: Dict[int, Dict],
+    cur_map: Dict[int, int],
+) -> None:
+    """Mark events as done if absent today or below 50% of peak area.
+
+    Args:
+        events: Mutable dict of all live events keyed by event ID.
+        cur_map: Mapping from current-day blob label to event ID;
+            used to determine which events are still active today.
+    """
+    active = set(cur_map.values())
+    for eid, ev in events.items():
+        if ev["done"]:
+            continue
+        if eid not in active:
+            ev["done"] = True
+        elif ev["area"] < AREA_DECLINE_FRACTION * ev["peak"]:
+            ev["done"] = True
+
+
+def detect_events(
+    filtered_mask: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    event_type: str,
+    area_grid: Optional[np.ndarray] = None,
+) -> List[Dict]:
+    """Track spatiotemporal events from a filtered boolean mask.
+
+    Labels one day at a time (low peak memory) but uses a numba-JIT
+    overlap matrix to match blobs across days. A single O(nlat*nlon)
+    pass replaces one O(nlat*nlon) scan per blob, which dominates
+    when many blobs are active simultaneously.
+
+    An event's bounding box is the union of all its daily extents.
+    Events terminate when their active area drops below 50% of peak.
+
+    Args:
+        filtered_mask: Boolean array of shape (time, lat, lon) with
+            consecutive-day filtering already applied.
+        dates: 1-D array of date labels aligned with axis 0.
+        lats: 1-D latitude array aligned with axis 1.
+        lons: 1-D longitude array aligned with axis 2.
+        event_type: Label string stored in each returned event dict
+            (e.g. "heat_wave" or "cold_snap").
+        area_grid: Optional 2-D array (lat, lon) of grid-cell areas
+            in km². When provided, peak_area_km2 is tracked per event.
+
+    Returns:
+        List of event dicts with keys type, start, end, lat_min,
+        lat_max, lon_min, lon_max, peak, peak_area_km2, area, done.
+    """
+    n_days = filtered_mask.shape[0]
+    events: Dict[int, Dict] = {}
+    next_id = 1
+    prev_labels: Optional[np.ndarray] = None
+    prev_n_obj: int = 0
+    prev_map: Dict[int, int] = {}
+
+    for di in range(n_days):
+        day = filtered_mask[di]
+
+        if not day.any():
+            for ev in events.values():
+                if not ev["done"]:
+                    ev["done"] = True
+            prev_labels = None
+            prev_n_obj = 0
+            prev_map = {}
+            continue
+
+        labels, n_obj = ndimage.label(day)
+        labels = labels.astype(np.int32)
+        cur_map: Dict[int, int] = {}
+
+        overlap_mat: Optional[np.ndarray] = None
+        if prev_labels is not None and n_obj > 0 and prev_n_obj > 0:
+            overlap_mat = _count_overlaps_nb(
+                prev_labels,
+                labels,
+                prev_n_obj,
+                n_obj,
+            )
+
+        for oid in range(1, n_obj + 1):
+            om = labels == oid
+            area = int(om.sum())
+            li, lo = np.where(om)
+            blob_area_km2 = (
+                float(area_grid[li, lo].sum()) if area_grid is not None else 0.0
+            )
+
+            eid = _resolve_event(
+                oid,
+                overlap_mat,
+                prev_map,
+                events,
+                cur_map,
+            )
+
+            if eid is None:
+                eid = next_id
+                next_id += 1
+                events[eid] = {
+                    "type": event_type,
+                    "start": dates[di],
+                    "end": dates[di],
+                    "lat_min": float(lats[li].min()),
+                    "lat_max": float(lats[li].max()),
+                    "lon_min": float(lons[lo].min()),
+                    "lon_max": float(lons[lo].max()),
+                    "peak": area,
+                    "peak_area_km2": blob_area_km2,
+                    "area": area,
+                    "done": False,
+                }
+            else:
+                ev = events[eid]
+                ev["end"] = dates[di]
+                ev["peak_area_km2"] = max(ev["peak_area_km2"], blob_area_km2)
+                ev["lat_min"] = min(
+                    ev["lat_min"],
+                    float(lats[li].min()),
+                )
+                ev["lat_max"] = max(
+                    ev["lat_max"],
+                    float(lats[li].max()),
+                )
+                ev["lon_min"] = min(
+                    ev["lon_min"],
+                    float(lons[lo].min()),
+                )
+                ev["lon_max"] = max(
+                    ev["lon_max"],
+                    float(lons[lo].max()),
+                )
+                ev["peak"] = max(ev["peak"], area)
+                ev["area"] = area
+
+            cur_map[oid] = eid
+
+        _terminate_declined_events(events, cur_map)
+        prev_labels = labels
+        prev_n_obj = n_obj
+        prev_map = cur_map
+
+    for ev in events.values():
+        ev["done"] = True
+
+    return list(events.values())
+
+
+def events_to_dataframe(
+    events: List[Dict],
+    min_gridpoints: int = MIN_GRIDPOINTS,
+    min_area_km2: float = MIN_AREA_KM2,
+) -> pd.DataFrame:
+    """Convert event dicts to a labelled DataFrame.
+
+    Args:
+        events: Raw event dicts from ``detect_events``.
+        min_gridpoints: Drop events whose peak grid-point count is
+            below this threshold. Default is 500 (MIN_GRIDPOINTS).
+        min_area_km2: Drop events whose peak area (km²) is below
+            this threshold. Default is 200 000 (MIN_AREA_KM2).
+
+    Returns:
+        DataFrame with columns label, event_type, start_date,
+        end_date, latitude_min, latitude_max, longitude_min,
+        longitude_max, max_consecutive_days, sorted by start_date.
+        Events below either threshold are excluded.
+    """
+    columns = [
+        "label",
+        "event_type",
+        "start_date",
+        "end_date",
+        "latitude_min",
+        "latitude_max",
+        "longitude_min",
+        "longitude_max",
+        "max_consecutive_days",
+    ]
+    if not events:
+        return pd.DataFrame(columns=columns)
+
+    n_before = len(events)
+    events = [
+        e
+        for e in events
+        if e["peak"] >= min_gridpoints and e.get("peak_area_km2", 0.0) >= min_area_km2
+    ]
+    logger.info(
+        "  Filtered %d events (< %d pts or < %.0f km²); %d remain",
+        n_before - len(events),
+        min_gridpoints,
+        min_area_km2,
+        len(events),
+    )
+
+    if not events:
+        return pd.DataFrame(columns=columns)
+
+    rows = [
+        {
+            "event_type": e["type"],
+            "start_date": str(e["start"]),
+            "end_date": str(e["end"]),
+            "latitude_min": e["lat_min"],
+            "latitude_max": e["lat_max"],
+            "longitude_min": e["lon_min"],
+            "longitude_max": e["lon_max"],
+            "max_consecutive_days": e.get("max_consecutive_days", 0),
+        }
+        for e in events
+    ]
+    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
+    df.insert(0, "label", range(1, len(df) + 1))
+    return df
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=("Detect temperature exceedance events globally from ERA5."),
+    )
+    parser.add_argument(
+        "--start-date",
+        required=True,
+        help="Start date YYYY-MM-DD",
+    )
+    parser.add_argument(
+        "--end-date",
+        required=True,
+        help="End date YYYY-MM-DD",
+    )
+    parser.add_argument(
+        "--output",
+        default="events_global.csv",
+        help="Output CSV path",
+    )
+    parser.add_argument(
+        "--n-workers",
+        type=int,
+        default=4,
+        help="Number of dask workers",
+    )
+    parser.add_argument(
+        "--quantile-lower",
+        type=float,
+        default=None,
+        help=(
+            f"Lower-bound climatology quantile ({VALID_QUANTILES}). "
+            "Days where temp op_lower clim_lower are candidates. "
+            "At least one of --quantile-lower or --quantile-upper "
+            "is required."
+        ),
+    )
+    parser.add_argument(
+        "--operator-lower",
+        default=">",
+        help=("Comparison operator applied to the lower bound (default: >)"),
+    )
+    parser.add_argument(
+        "--quantile-upper",
+        type=float,
+        default=None,
+        help=(
+            f"Upper-bound climatology quantile ({VALID_QUANTILES}). "
+            "Days where temp op_upper clim_upper are candidates. "
+            "Combine with --quantile-lower to define a band."
+        ),
+    )
+    parser.add_argument(
+        "--operator-upper",
+        default="<",
+        help=("Comparison operator applied to the upper bound (default: <)"),
+    )
+    parser.add_argument(
+        "--lat-min",
+        type=float,
+        default=-90.0,
+        help="Minimum latitude to include in detection. Default -90.0",
+    )
+    parser.add_argument(
+        "--lat-max",
+        type=float,
+        default=90.0,
+        help="Maximum latitude to include in detection. Default 90.0",
+    )
+    args = parser.parse_args()
+
+    if args.quantile_lower is None and args.quantile_upper is None:
+        parser.error(
+            "At least one of --quantile-lower or --quantile-upper must be provided."
+        )
+
+    # Derive event_type label and plot colour from the quantiles.
+    # Format: "q{lower}+" / "q{upper}-" / "q{lower}-q{upper}"
+    ql, qu = args.quantile_lower, args.quantile_upper
+    if ql is not None and qu is not None:
+        event_type = f"q{ql:.2f}-q{qu:.2f}"
+    elif ql is not None:
+        event_type = f"q{ql:.2f}+"
+    else:
+        event_type = f"q{qu:.2f}-"
+    # Infer warm vs cold for the plot colourmap:
+    # warm when the lower bound is above the median (high percentile),
+    # cold when the upper bound is below the median (low percentile).
+    if ql is not None and ql >= 0.5:
+        plot_event_type = "heat_wave"
+    elif qu is not None and qu <= 0.5:
+        plot_event_type = "cold_snap"
+    else:
+        plot_event_type = "heat_wave"
+
+    wall_start = time_module.time()
+    client = Client(
+        LocalCluster(n_workers=args.n_workers),
+    )
+    logger.info("Dask dashboard: %s", client.dashboard_link)
+
+    logger.info("Pre-warming numba JIT...")
+    _tiny = np.zeros((2, 2), dtype=np.int32)
+    _tiny[0, 0] = 1
+    _tiny[1, 1] = 1
+    _count_overlaps_nb(_tiny, _tiny, 1, 1)
+    logger.info("  numba ready")
+
+    logger.info("Opening ERA5 data...")
+    t2m = open_era5_t2m(args.start_date, args.end_date)
+    if args.lat_min != -90.0 or args.lat_max != 90.0:
+        t2m = t2m.sel(latitude=slice(args.lat_min, args.lat_max))
+        logger.info(
+            "  Latitude filtered to [%.1f, %.1f]",
+            args.lat_min,
+            args.lat_max,
+        )
+    logger.info("  sizes=%s", dict(t2m.sizes))
+
+    logger.info(
+        "Loading climatology bounds (lower=%s, upper=%s)...",
+        args.quantile_lower,
+        args.quantile_upper,
+    )
+    clim_lower, clim_upper = get_climatology_bounds(
+        q_lower=args.quantile_lower,
+        q_upper=args.quantile_upper,
+    )
+
+    logger.info("Building land mask and grid-cell area array...")
+    land_mask = build_land_mask(t2m.longitude, t2m.latitude)
+    land_mask_np = land_mask.values.astype(bool)
+    area_grid = compute_grid_cell_area(t2m.latitude.values, t2m.longitude.values)
+
+    logger.info("Building exceedance mask (lazy)...")
+    exc_lazy = build_exceedance_mask(
+        t2m,
+        clim_lower,
+        clim_upper,
+        land_mask,
+        op_lower=args.operator_lower,
+        op_upper=args.operator_upper,
+    )
+
+    tdim = detect_time_dim(exc_lazy)
+
+    logger.info("Computing exceedance mask...")
+    t0 = time_module.time()
+    exc_da = exc_lazy.compute()
+    logger.info("  done in %.1f s", time_module.time() - t0)
+
+    dates = exc_da[tdim].values
+    lats = exc_da.latitude.values
+    lons = exc_da.longitude.values
+    exc_np = exc_da.values.astype(bool)
+    del exc_da
+
+    logger.info(
+        "Applying %d-day consecutive filter...",
+        MIN_CONSECUTIVE_DAYS,
+    )
+    exc_filt = apply_consecutive_filter(exc_np)
+    logger.info(
+        "  %d -> %d True cells",
+        exc_np.sum(),
+        exc_filt.sum(),
+    )
+    del exc_np
+
+    logger.info("Detecting events...")
+    t0 = time_module.time()
+    events = detect_events(exc_filt, dates, lats, lons, event_type, area_grid=area_grid)
+    logger.info("  %d events (%.1f s)", len(events), time_module.time() - t0)
+
+    logger.info(
+        "Expanding event bounds (%d-deg steps)...",
+        EXPANSION_DEGREES,
+    )
+    t0 = time_module.time()
+    events = joblib.Parallel(n_jobs=-1, prefer="threads")(
+        joblib.delayed(expand_event_bounds)(
+            ev, exc_filt, dates, lats, lons, land_mask_np
+        )
+        for ev in events
+    )
+    logger.info("  done in %.1f s", time_module.time() - t0)
+
+    logger.info("Computing max-consecutive-days plot...")
+    stem = str(pathlib.Path(args.output).with_suffix(""))
+    consec = max_consecutive_days(exc_filt)
+    plot_consecutive_map(
+        consec,
+        lats,
+        lons,
+        plot_event_type,
+        title=(
+            f"Consecutive Exceedance Days ({event_type})"
+            f"\n{args.start_date} to {args.end_date}"
+        ),
+        output_path=f"{stem}_consec.png",
+    )
+    del consec, exc_filt
+
+    df = events_to_dataframe(events)
+    df.to_csv(args.output, index=False)
+
+    elapsed = time_module.time() - wall_start
+    logger.info(
+        "Done: %d events -> %s (%.1f s)",
+        len(df),
+        args.output,
+        elapsed,
+    )
+    client.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/extremeweatherbench/data/base_temp_events.yaml b/src/extremeweatherbench/data/base_temp_events.yaml
new file mode 100644
index 00000000..5cf4ff1f
--- /dev/null
+++ b/src/extremeweatherbench/data/base_temp_events.yaml
@@ -0,0 +1,661 @@
+- case_id_number: 1
+  title: 2021 Pacific Northwest
+  start_date: 2021-06-20 00:00:00
+  end_date: 2021-07-03 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 47.6062
+      longitude: 237.6679
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 2
+  title: 2022 Upper Midwest
+  start_date: 2022-05-07 00:00:00
+  end_date: 2022-05-17 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 41.8781
+      longitude: 272.3702
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 3
+  title: 2022 California
+  start_date: 2022-06-07 00:00:00
+  end_date: 2022-06-15 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 34.0522
+      longitude: 241.7563
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 4
+  title: 2022 Texas
+  start_date: 2022-06-30 00:00:00
+  end_date: 2022-07-18 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 32.7767
+      longitude: 263.203
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 5
+  title: 2023 Pacific Northwest
+  start_date: 2023-05-10 00:00:00
+  end_date: 2023-05-23 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 47.6062
+      longitude: 237.6679
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 6
+  title: 2022 Mid-Atlantic
+  start_date: 2022-05-17 00:00:00
+  end_date: 2022-05-24 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 39.2904
+      longitude: 283.38779999999997
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 7
+  title: 2023 Australia
+  start_date: 2023-11-18 00:00:00
+  end_date: 2023-11-28 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -31.9505
+      longitude: 115.8605
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 8
+  title: 2023 Ireland
+  start_date: 2023-09-02 00:00:00
+  end_date: 2023-09-13 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 53.1424
+      longitude: 352.3079
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 9
+  title: 2023 Italy
+  start_date: 2023-07-07 00:00:00
+  end_date: 2023-07-27 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 41.9028
+      longitude: 12.4964
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 10
+  title: 2023 SW Europe
+  start_date: 2023-08-17 00:00:00
+  end_date: 2023-08-28 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 40.4637
+      longitude: 356.2508
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 11
+  title: 2023 South America
+  start_date: 2023-07-29 00:00:00
+  end_date: 2023-08-04 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -27.0
+      longitude: 294.5
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 12
+  title: 2023 China (Shanghai)
+  start_date: 2023-05-24 00:00:00
+  end_date: 2023-06-01 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 31.2304
+      longitude: 121.4737
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 13
+  title: 2023 China (Yunnan Province)
+  start_date: 2023-04-14 00:00:00
+  end_date: 2023-04-23 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 25.0458
+      longitude: 102.71
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 14
+  title: 2023 Algeria
+  start_date: 2023-07-05 00:00:00
+  end_date: 2023-07-27 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 36.7372
+      longitude: 3.0869
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 15
+  title: 2023 Iberian Peninsula
+  start_date: 2023-04-22 00:00:00
+  end_date: 2023-05-01 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 41.1496
+      longitude: 352.389
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 16
+  title: 2023 Southeast Asia
+  start_date: 2023-04-16 00:00:00
+  end_date: 2023-04-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 9.0
+      latitude_max: 31.5
+      longitude_min: 61.5
+      longitude_max: 113.75
+  event_type: heat_wave
+- case_id_number: 17
+  title: 2023 India
+  start_date: 2023-02-15 00:00:00
+  end_date: 2023-03-01 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 28.6139
+      longitude: 77.209
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 18
+  title: 2021 Western Russia
+  start_date: 2021-06-18 00:00:00
+  end_date: 2021-06-30 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 55.7558
+      longitude: 37.6173
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 19
+  title: 2022 Germany
+  start_date: 2022-12-23 00:00:00
+  end_date: 2022-12-31 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 52.52
+      longitude: 13.405
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 20
+  title: 2022 UK (August)
+  start_date: 2022-08-08 00:00:00
+  end_date: 2022-08-16 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 51.5074
+      longitude: 359.8722
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 21
+  title: 2022 UK (July)
+  start_date: 2022-07-15 00:00:00
+  end_date: 2022-07-23 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 51.5074
+      longitude: 359.8722
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 22
+  title: 2022 France
+  start_date: 2022-06-09 00:00:00
+  end_date: 2022-06-21 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 43.4832
+      longitude: 358.4414
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 23
+  title: 2022 Japan
+  start_date: 2022-06-20 00:00:00
+  end_date: 2022-07-05 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 35.6895
+      longitude: 139.6917
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 24
+  title: 2022 India
+  start_date: 2022-04-24 00:00:00
+  end_date: 2022-05-04 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 28.2769
+      longitude: 68.4376
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 25
+  title: 2022 East Antarctica
+  start_date: 2022-03-12 00:00:00
+  end_date: 2022-03-26 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -75.1
+      longitude: 123.35
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 26
+  title: 2022 West Australia
+  start_date: 2022-01-15 00:00:00
+  end_date: 2022-01-25 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -31.9505
+      longitude: 115.8605
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 27
+  title: 2021 Canada Plains
+  start_date: 2021-05-30 00:00:00
+  end_date: 2021-06-09 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 49.0
+      longitude: 262.43330000000003
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 28
+  title: 2021 New Zealand
+  start_date: 2021-01-12 18:00:00
+  end_date: 2021-01-18 18:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -43.8983
+      longitude: 171.731
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 29
+  title: 2020 Australia
+  start_date: 2020-11-25 00:00:00
+  end_date: 2020-12-01 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -33.8245
+      longitude: 150.9448
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 30
+  title: 2021 Texas
+  start_date: 2021-02-10 12:00:00
+  end_date: 2021-02-22 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 30.2672
+      longitude: 262.2569
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 31
+  title: 2022 Arkansas
+  start_date: 2022-02-17 18:00:00
+  end_date: 2022-03-01 06:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 34.7445
+      longitude: 267.7104
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 32
+  title: 2023 Germany
+  start_date: 2023-12-02 06:00:00
+  end_date: 2023-12-08 06:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 48.1351
+      longitude: 11.582
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 33
+  title: 2023 China
+  start_date: 2023-12-15 06:00:00
+  end_date: 2023-12-26 18:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 31.2304
+      longitude: 121.4737
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 34
+  title: 2023 Afghanistan
+  start_date: 2023-01-11 06:00:00
+  end_date: 2023-01-28 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 34.5553
+      longitude: 69.2075
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 35
+  title: 2022 Colorado
+  start_date: 2022-04-06 00:00:00
+  end_date: 2022-04-16 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 39.7392
+      longitude: 255.0097
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 72
+  title: May 2024 Texas
+  start_date: 2024-05-25 00:00:00
+  end_date: 2024-05-31 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 25.9017
+      longitude: 262.50260000000003
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 73
+  title: June 2024 Northeast US
+  start_date: 2024-06-17 00:00:00
+  end_date: 2024-06-23 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 41.8781
+      longitude: 286.771
+      bounding_box_degrees: 6
+  event_type: heat_wave
+- case_id_number: 74
+  title: July 2024 Southwest US
+  start_date: 2024-07-04 00:00:00
+  end_date: 2024-07-10 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 33.7701
+      longitude: 243.78539999999998
+      bounding_box_degrees: 6
+  event_type: heat_wave
+- case_id_number: 75
+  title: July 2024 Northeast US
+  start_date: 2024-07-07 00:00:00
+  end_date: 2024-07-13 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 40.7128
+      longitude: 285.994
+      bounding_box_degrees: 6
+  event_type: heat_wave
+- case_id_number: 76
+  title: July 2024 Mid-Atlantic US
+  start_date: 2024-07-15 00:00:00
+  end_date: 2024-07-21 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 39.9526
+      longitude: 284.8348
+      bounding_box_degrees: 6
+  event_type: heat_wave
+- case_id_number: 77
+  title: August 2024 Midwest US
+  start_date: 2024-08-25 00:00:00
+  end_date: 2024-08-31 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 40.1106
+      longitude: 271.79269999999997
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 78
+  title: July 2024 Antarctica
+  start_date: 2024-07-01 00:00:00
+  end_date: 2024-07-31 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -75.0
+      longitude: 15.0
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 79
+  title: August 2024 Canada
+  start_date: 2024-08-09 00:00:00
+  end_date: 2024-08-15 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 67.0
+      longitude: 248.0
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 80
+  title: August 2024 Australia
+  start_date: 2024-08-22 00:00:00
+  end_date: 2024-08-30 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -20.0
+      longitude: 120.0
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 81
+  title: July/August 2024 Japan
+  start_date: 2024-07-25 00:00:00
+  end_date: 2024-08-05 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 36.0
+      longitude: 138.0
+      bounding_box_degrees: 6
+  event_type: heat_wave
+- case_id_number: 82
+  title: June 2024 Saudi Arabia
+  start_date: 2024-06-16 00:00:00
+  end_date: 2024-06-22 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 24.0
+      longitude: 45.0
+      bounding_box_degrees: 6
+  event_type: heat_wave
+- case_id_number: 83
+  title: August 2024 Europe
+  start_date: 2024-08-10 00:00:00
+  end_date: 2024-08-16 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 48.3794
+      longitude: 10.8978
+      bounding_box_degrees: 10
+  event_type: heat_wave
+- case_id_number: 84
+  title: July 2024 Ukraine
+  start_date: 2024-07-12 00:00:00
+  end_date: 2024-07-18 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 50.4501
+      longitude: 30.5234
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 85
+  title: June 2024 Europe
+  start_date: 2024-06-20 00:00:00
+  end_date: 2024-06-30 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 52.3676
+      longitude: 4.9041
+      bounding_box_degrees: 6
+  event_type: heat_wave
+- case_id_number: 86
+  title: May 2024 Central Mexico
+  start_date: 2024-05-23 00:00:00
+  end_date: 2024-05-31 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 19.4326
+      longitude: 260.8668
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 87
+  title: May 2024 Pakistan/India
+  start_date: 2024-05-23 00:00:00
+  end_date: 2024-05-31 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 34.0
+      longitude: 76.0
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 88
+  title: August 2023 Chile
+  start_date: 2023-08-01 00:00:00
+  end_date: 2023-08-07 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: -33.4489
+      longitude: 289.3307
+      bounding_box_degrees: 5
+  event_type: heat_wave
+- case_id_number: 89
+  title: January 2024 Pacific Northwest
+  start_date: 2024-01-11 12:00:00
+  end_date: 2024-01-20 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 47.6062
+      longitude: 237.6679
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 90
+  title: April 2022 Nevada
+  start_date: 2022-04-11 18:00:00
+  end_date: 2022-04-17 18:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 40.8324
+      longitude: 244.2369
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 91
+  title: February/March 2021 New England
+  start_date: 2021-03-04 18:00:00
+  end_date: 2021-03-10 18:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 42.8509
+      longitude: 287.4421
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 92
+  title: January 2024 Northern Europe
+  start_date: 2024-01-01 06:00:00
+  end_date: 2024-01-09 12:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 59.3293
+      longitude: 18.0686
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 93
+  title: December 2022 Europe
+  start_date: 2022-12-10 06:00:00
+  end_date: 2022-12-20 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 52.52
+      longitude: 13.405
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 94
+  title: April 2024 Europe
+  start_date: 2024-04-16 12:00:00
+  end_date: 2024-04-27 12:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 48.8566
+      longitude: 2.3522
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 95
+  title: January 2023 North China
+  start_date: 2024-01-20 00:00:00
+  end_date: 2024-02-04 18:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 42.5246
+      longitude: 122.3853
+      bounding_box_degrees: 5
+  event_type: freeze
+- case_id_number: 96
+  title: April 2024 Sweden
+  start_date: 2024-04-02 00:00:00
+  end_date: 2024-04-08 00:00:00
+  location:
+    type: centered_region
+    parameters:
+      latitude: 60.1282
+      longitude: 18.6435
+      bounding_box_degrees: 5
+  event_type: freeze
diff --git a/src/extremeweatherbench/data/events.yaml b/src/extremeweatherbench/data/events.yaml
index e1486e95..97a753b6 100644
--- a/src/extremeweatherbench/data/events.yaml
+++ b/src/extremeweatherbench/data/events.yaml
@@ -5,10 +5,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 33.25
-      latitude_max: 68.0
-      longitude_min: 215.25
-      longitude_max: 256.0
+      latitude_min: 39.0
+      latitude_max: 65.0
+      longitude_min: -136.75
+      longitude_max: -105.75
   event_type: heat_wave
 - case_id_number: 2
   title: 2022 Upper Midwest
@@ -17,10 +17,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 19.5
-      latitude_max: 48.25
-      longitude_min: 252.0
-      longitude_max: 276.75
+      latitude_min: 31.5
+      latitude_max: 47.5
+      longitude_min: -102.25
+      longitude_max: -82.25
   event_type: heat_wave
 - case_id_number: 3
   title: 2022 California
@@ -29,10 +29,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 23.75
-      latitude_max: 44.5
-      longitude_min: 235.75
-      longitude_max: 266.25
+      latitude_min: 31.5
+      latitude_max: 42.5
+      longitude_min: -123.75
+      longitude_max: -104.75
   event_type: heat_wave
 - case_id_number: 4
   title: 2022 Texas
@@ -41,10 +41,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 20.5
-      latitude_max: 43.25
-      longitude_min: 254.75
-      longitude_max: 275.5
+      latitude_min: 25.25
+      latitude_max: 37.25
+      longitude_min: -103.25
+      longitude_max: -90.25
   event_type: heat_wave
 - case_id_number: 5
   title: 2023 Pacific Northwest
@@ -53,10 +53,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 35.25
-      latitude_max: 72.0
-      longitude_min: 219.25
-      longitude_max: 262.0
+      latitude_min: 42.0
+      latitude_max: 70.0
+      longitude_min: -130.75
+      longitude_max: -111.75
   event_type: heat_wave
 - case_id_number: 6
   title: 2022 Mid-Atlantic
@@ -65,10 +65,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 31.0
-      latitude_max: 42.25
-      longitude_min: 277.0
-      longitude_max: 287.75
+      latitude_min: 33.75
+      latitude_max: 41.75
+      longitude_min: -83.0
+      longitude_max: -74.0
   event_type: heat_wave
 - case_id_number: 7
   title: 2023 Australia
@@ -77,9 +77,9 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -35.0
-      latitude_max: -20.0
-      longitude_min: 113.5
+      latitude_min: -34.5
+      latitude_max: -23.5
+      longitude_min: 113.25
       longitude_max: 120.25
   event_type: heat_wave
 - case_id_number: 8
@@ -89,10 +89,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 40.75
-      latitude_max: 67.5
-      longitude_min: -9.75
-      longitude_max: 16.75
+      latitude_min: 50.75
+      latitude_max: 55.75
+      longitude_min: -10.25
+      longitude_max: -5.25
   event_type: heat_wave
 - case_id_number: 9
   title: 2023 Italy
@@ -101,10 +101,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 17.5
-      latitude_max: 52.25
-      longitude_min: -12.0
-      longitude_max: 32.75
+      latitude_min: 39.5
+      latitude_max: 52.5
+      longitude_min: 3.0
+      longitude_max: 17.0
   event_type: heat_wave
 - case_id_number: 10
   title: 2023 SW Europe
@@ -113,10 +113,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 36.0
-      latitude_max: 56.75
-      longitude_min: -9.25
-      longitude_max: 20.75
+      latitude_min: 35.0
+      latitude_max: 50.0
+      longitude_min: -10.25
+      longitude_max: 18.75
   event_type: heat_wave
 - case_id_number: 11
   title: 2023 South America
@@ -125,10 +125,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -44.25
-      latitude_max: -20.25
-      longitude_min: 285.75
-      longitude_max: 308.0
+      latitude_min: -34.5
+      latitude_max: -18.5
+      longitude_min: -73.0
+      longitude_max: -55.0
   event_type: heat_wave
 - case_id_number: 12
   title: 2023 China (Shanghai)
@@ -137,10 +137,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 16.75
-      latitude_max: 35.25
-      longitude_min: 99.0
-      longitude_max: 122.0
+      latitude_min: 23.75
+      latitude_max: 33.75
+      longitude_min: 119.0
+      longitude_max: 124.0
   event_type: heat_wave
 - case_id_number: 13
   title: 2023 China (Yunnan Province)
@@ -149,10 +149,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 12.75
-      latitude_max: 43.5
-      longitude_min: 92.25
-      longitude_max: 121.0
+      latitude_min: 14.5
+      latitude_max: 40.5
+      longitude_min: 96.25
+      longitude_max: 118.25
   event_type: heat_wave
 - case_id_number: 14
   title: 2023 Algeria
@@ -161,10 +161,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 16.25
-      latitude_max: 51.0
-      longitude_min: -17.0
-      longitude_max: 27.5
+      latitude_min: 29.25
+      latitude_max: 39.25
+      longitude_min: -11.5
+      longitude_max: 11.5
   event_type: heat_wave
 - case_id_number: 15
   title: 2023 Iberian Peninsula
@@ -174,9 +174,9 @@
     type: bounded_region
     parameters:
       latitude_min: 22.75
-      latitude_max: 46.75
-      longitude_min: -15.75
-      longitude_max: 8.75
+      latitude_max: 44.75
+      longitude_min: -10.0
+      longitude_max: 9.0
   event_type: heat_wave
 - case_id_number: 16
   title: 2023 Southeast Asia
@@ -197,10 +197,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 22.25
-      latitude_max: 53.0
-      longitude_min: 56.75
-      longitude_max: 101.5
+      latitude_min: 25.0
+      latitude_max: 37.0
+      longitude_min: 63.75
+      longitude_max: 93.75
   event_type: heat_wave
 - case_id_number: 18
   title: 2021 Western Russia
@@ -209,10 +209,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 35.5
-      latitude_max: 65.75
-      longitude_min: 13.25
-      longitude_max: 60.0
+      latitude_min: 40.25
+      latitude_max: 65.25
+      longitude_min: 15.25
+      longitude_max: 56.0
   event_type: heat_wave
 - case_id_number: 19
   title: 2022 Germany
@@ -221,10 +221,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 36.25
-      latitude_max: 56.25
-      longitude_min: -3.0
-      longitude_max: 21.75
+      latitude_min: 46.0
+      latitude_max: 55.0
+      longitude_min: -2.0
+      longitude_max: 17.0
   event_type: heat_wave
 - case_id_number: 20
   title: 2022 UK (August)
@@ -233,9 +233,9 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 39.25
+      latitude_min: 41.0
       latitude_max: 56.0
-      longitude_min: -9.75
+      longitude_min: -6.75
       longitude_max: 8.25
   event_type: heat_wave
 - case_id_number: 21
@@ -245,10 +245,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 37.25
-      latitude_max: 58.0
-      longitude_min: -9.75
-      longitude_max: 8.25
+      latitude_min: 37.0
+      latitude_max: 54.0
+      longitude_min: -7.75
+      longitude_max: 2.25
   event_type: heat_wave
 - case_id_number: 22
   title: 2022 France
@@ -257,10 +257,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 29.0
-      latitude_max: 51.75
-      longitude_min: -9.75
-      longitude_max: 12.75
+      latitude_min: 31.0
+      latitude_max: 51.0
+      longitude_min: -9.0
+      longitude_max: 10.0
   event_type: heat_wave
 - case_id_number: 23
   title: 2022 Japan
@@ -269,10 +269,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 31.25
-      latitude_max: 46.0
-      longitude_min: 125.25
-      longitude_max: 145.25
+      latitude_min: 33.25
+      latitude_max: 42.25
+      longitude_min: 126.25
+      longitude_max: 142.25
   event_type: heat_wave
 - case_id_number: 24
   title: 2022 India
@@ -281,10 +281,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 18.0
-      latitude_max: 52.75
+      latitude_min: 20.75
+      latitude_max: 41.75
       longitude_min: 58.0
-      longitude_max: 78.75
+      longitude_max: 76.0
   event_type: heat_wave
 - case_id_number: 25
   title: 2022 East Antarctica
@@ -294,9 +294,9 @@
     type: bounded_region
     parameters:
       latitude_min: -85.5
-      latitude_max: -65.5
-      longitude_min: 99.0
-      longitude_max: 147.75
+      latitude_max: -64.5
+      longitude_min: 101.0
+      longitude_max: 145.75
   event_type: heat_wave
 - case_id_number: 26
   title: 2022 West Australia
@@ -305,10 +305,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -35.0
-      latitude_max: -27.5
-      longitude_min: 114.25
-      longitude_max: 119.75
+      latitude_min: -35.5
+      latitude_max: -28.5
+      longitude_min: 113.25
+      longitude_max: 119.25
   event_type: heat_wave
 - case_id_number: 27
   title: 2021 Canada Plains
@@ -317,10 +317,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 38.5
-      latitude_max: 59.5
-      longitude_min: 238.0
-      longitude_max: 272.75
+      latitude_min: 39.5
+      latitude_max: 53.5
+      longitude_min: -120.0
+      longitude_max: -87.0
   event_type: heat_wave
 - case_id_number: 28
   title: 2021 New Zealand
@@ -330,9 +330,9 @@
     type: bounded_region
     parameters:
       latitude_min: -46.5
-      latitude_max: -40.75
-      longitude_min: 167.5
-      longitude_max: 175.25
+      latitude_max: -41.5
+      longitude_min: 149.0
+      longitude_max: 149.0
   event_type: heat_wave
 - case_id_number: 29
   title: 2020 Australia
@@ -341,10 +341,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -36.75
-      latitude_max: -17.5
-      longitude_min: 126.5
-      longitude_max: 152.5
+      latitude_min: -36.25
+      latitude_max: -22.25
+      longitude_min: 128.5
+      longitude_max: 153.5
   event_type: heat_wave
 - case_id_number: 30
   title: 2021 Texas
@@ -353,10 +353,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 24.0
-      latitude_max: 54.75
-      longitude_min: 250.0
-      longitude_max: 278.75
+      latitude_min: 23.75
+      latitude_max: 52.75
+      longitude_min: -107.25
+      longitude_max: -82.25
   event_type: freeze
 - case_id_number: 31
   title: 2022 Arkansas
@@ -365,10 +365,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 24.25
-      latitude_max: 59.0
-      longitude_min: 243.25
-      longitude_max: 272.0
+      latitude_min: 30.25
+      latitude_max: 57.0
+      longitude_min: -114.75
+      longitude_max: -88.75
   event_type: freeze
 - case_id_number: 32
   title: 2023 Germany
@@ -377,10 +377,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 44.25
-      latitude_max: 68.5
-      longitude_min: 7.25
-      longitude_max: 22.0
+      latitude_min: 45.75
+      latitude_max: 68.75
+      longitude_min: 9.0
+      longitude_max: 14.0
   event_type: freeze
 - case_id_number: 33
   title: 2023 China
@@ -389,10 +389,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 14.75
-      latitude_max: 55.5
-      longitude_min: 101.0
-      longitude_max: 141.75
+      latitude_min: 26.75
+      latitude_max: 53.5
+      longitude_min: 100.0
+      longitude_max: 124.0
   event_type: freeze
 - case_id_number: 34
   title: 2023 Afghanistan
@@ -401,10 +401,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 22.25
-      latitude_max: 49.0
+      latitude_min: 32.0
+      latitude_max: 47.0
       longitude_min: 50.75
-      longitude_max: 85.5
+      longitude_max: 71.75
   event_type: freeze
 - case_id_number: 35
   title: 2022 Colorado
@@ -413,10 +413,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 35.25
-      latitude_max: 64.0
-      longitude_min: 236.75
-      longitude_max: 261.5
+      latitude_min: 37.25
+      latitude_max: 42.25
+      longitude_min: -107.5
+      longitude_max: -102.5
   event_type: freeze
 - case_id_number: 36
   title: July 2024 South Dakota
@@ -857,10 +857,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 30.0
-      latitude_max: 42.75
-      longitude_min: 235.5
-      longitude_max: 248.75
+      latitude_min: 14.5
+      latitude_max: 31.5
+      longitude_min: -105.0
+      longitude_max: -95.0
   event_type: heat_wave
 - case_id_number: 73
   title: July 2024 Northeast US
@@ -869,10 +869,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 35.75
-      latitude_max: 47.5
-      longitude_min: 281.0
-      longitude_max: 300.0
+      latitude_min: 39.0
+      latitude_max: 48.0
+      longitude_min: -82.25
+      longitude_max: -69.25
   event_type: heat_wave
 - case_id_number: 74
   title: July 2024 Mid-Atlantic US
@@ -881,10 +881,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 35.25
-      latitude_max: 44.75
-      longitude_min: 280.0
-      longitude_max: 292.75
+      latitude_min: 30.75
+      latitude_max: 37.75
+      longitude_min: -124.25
+      longitude_max: -113.25
   event_type: heat_wave
 - case_id_number: 75
   title: August 2024 Midwest US
@@ -893,10 +893,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 36.0
-      latitude_max: 46.25
-      longitude_min: 265.5
-      longitude_max: 278.25
+      latitude_min: 37.75
+      latitude_max: 52.75
+      longitude_min: -78.0
+      longitude_max: -61.0
   event_type: heat_wave
 - case_id_number: 76
   title: July 2024 Antarctica
@@ -905,10 +905,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -90.0
-      latitude_max: -68.75
-      longitude_min: -9.5
-      longitude_max: 39.5
+      latitude_min: 37.0
+      latitude_max: 43.0
+      longitude_min: -81.25
+      longitude_max: -68.25
   event_type: heat_wave
 - case_id_number: 77
   title: August 2024 Canada
@@ -917,10 +917,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 54.5
-      latitude_max: 73.5
-      longitude_min: 231.5
-      longitude_max: 272.5
+      latitude_min: 37.5
+      latitude_max: 44.5
+      longitude_min: -90.75
+      longitude_max: -85.75
   event_type: heat_wave
 - case_id_number: 78
   title: August 2024 Australia
@@ -929,10 +929,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -32.5
-      latitude_max: -9.5
-      longitude_min: 116.25
-      longitude_max: 144.5
+      latitude_min: -83.5
+      latitude_max: -68.5
+      longitude_min: 4.5
+      longitude_max: 37.5
   event_type: heat_wave
 - case_id_number: 79
   title: July/August 2024 Japan
@@ -941,10 +941,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 31.25
-      latitude_max: 43.0
-      longitude_min: 123.0
-      longitude_max: 142.75
+      latitude_min: 58.5
+      latitude_max: 76.5
+      longitude_min: -133.5
+      longitude_max: -89.5
   event_type: heat_wave
 - case_id_number: 80
   title: June 2024 Saudi Arabia
@@ -953,10 +953,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 19.0
-      latitude_max: 35.0
-      longitude_min: 30.0
-      longitude_max: 54.0
+      latitude_min: -27.5
+      latitude_max: -17.5
+      longitude_min: 114.5
+      longitude_max: 142.5
   event_type: heat_wave
 - case_id_number: 81
   title: August 2024 Europe
@@ -965,10 +965,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 35.5
-      latitude_max: 55.25
-      longitude_min: 0.0
-      longitude_max: 25.75
+      latitude_min: 33.0
+      latitude_max: 39.0
+      longitude_min: 132.0
+      longitude_max: 141.0
   event_type: heat_wave
 - case_id_number: 82
   title: July 2024 Ukraine
@@ -977,10 +977,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 40.0
-      latitude_max: 56.75
-      longitude_min: 14.25
-      longitude_max: 45.0
+      latitude_min: 18.0
+      latitude_max: 36.0
+      longitude_min: 22.0
+      longitude_max: 55.0
   event_type: heat_wave
 - case_id_number: 83
   title: June 2024 Europe
@@ -989,10 +989,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 45.5
-      latitude_max: 63.25
-      longitude_min: -4.0
-      longitude_max: 25.75
+      latitude_min: 34.5
+      latitude_max: 53.5
+      longitude_min: 3.0
+      longitude_max: 22.0
   event_type: heat_wave
 - case_id_number: 84
   title: May 2024 Central Mexico
@@ -1001,10 +1001,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 7.25
-      latitude_max: 29.75
-      longitude_min: 252.5
-      longitude_max: 283.25
+      latitude_min: 39.0
+      latitude_max: 55.0
+      longitude_min: 15.0
+      longitude_max: 39.0
   event_type: heat_wave
 - case_id_number: 85
   title: May 2024 Pakistan/India
@@ -1013,10 +1013,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 23.5
-      latitude_max: 42.5
-      longitude_min: 71.5
-      longitude_max: 98.5
+      latitude_min: 48.25
+      latitude_max: 55.25
+      longitude_min: 2.0
+      longitude_max: 9.0
   event_type: heat_wave
 - case_id_number: 86
   title: August 2023 Chile
@@ -1025,10 +1025,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -37.25
-      latitude_max: -19.0
-      longitude_min: 288.5
-      longitude_max: 305.75
+      latitude_min: 14.0
+      latitude_max: 31.0
+      longitude_min: -104.75
+      longitude_max: -82.75
   event_type: heat_wave
 - case_id_number: 87
   title: January 2024 Pacific Northwest
@@ -1037,10 +1037,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 41.25
-      latitude_max: 66.0
-      longitude_min: 221.25
-      longitude_max: 262.0
+      latitude_min: 31.5
+      latitude_max: 36.5
+      longitude_min: 73.5
+      longitude_max: 78.5
   event_type: freeze
 - case_id_number: 88
   title: April 2022 Nevada
@@ -1049,10 +1049,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 36.5
-      latitude_max: 65.25
-      longitude_min: 225.75
-      longitude_max: 268.5
+      latitude_min: -42.0
+      latitude_max: -20.0
+      longitude_min: -73.25
+      longitude_max: -62.25
   event_type: freeze
 - case_id_number: 89
   title: February/March 2021 New England
@@ -1061,10 +1061,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 38.5
-      latitude_max: 47.0
-      longitude_min: 283.25
-      longitude_max: 291.5
+      latitude_min: 45.0
+      latitude_max: 69.0
+      longitude_min: -133.75
+      longitude_max: -100.0
   event_type: freeze
 - case_id_number: 90
   title: January 2024 Northern Europe
@@ -1073,10 +1073,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 51.25
-      latitude_max: 70.5
-      longitude_min: 5.25
-      longitude_max: 42.5
+      latitude_min: 38.25
+      latitude_max: 63.25
+      longitude_min: -118.25
+      longitude_max: -113.25
   event_type: freeze
 - case_id_number: 91
   title: December 2022 Europe
@@ -1085,10 +1085,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 46.25
-      latitude_max: 70.75
-      longitude_min: -9.75
-      longitude_max: 25.75
+      latitude_min: 40.25
+      latitude_max: 46.25
+      longitude_min: -75.0
+      longitude_max: -68.0
   event_type: freeze
 - case_id_number: 92
   title: April 2024 Europe
@@ -1097,10 +1097,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 40.5
-      latitude_max: 71.0
-      longitude_min: -4.0
-      longitude_max: 26.75
+      latitude_min: 56.75
+      latitude_max: 71.75
+      longitude_min: 3.5
+      longitude_max: 20.5
   event_type: freeze
 - case_id_number: 93
   title: January 2023 North China
@@ -1109,10 +1109,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 38.25
-      latitude_max: 47.0
-      longitude_min: 118.0
-      longitude_max: 130.75
+      latitude_min: 48.0
+      latitude_max: 55.0
+      longitude_min: -9.0
+      longitude_max: 16.0
   event_type: freeze
 - case_id_number: 94
   title: April 2024 Sweden
@@ -1121,10 +1121,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 58.25
-      latitude_max: 71.0
-      longitude_min: 8.25
-      longitude_max: 31.0
+      latitude_min: 42.25
+      latitude_max: 54.25
+      longitude_min: -3.25
+      longitude_max: 23.75
   event_type: freeze
 - case_id_number: 95
   title: November 2024 West Coast US
@@ -1133,10 +1133,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 27.8
-      latitude_max: 54.2
-      longitude_min: 222.4
-      longitude_max: 245.3
+      latitude_min: 40.0
+      latitude_max: 45.0
+      longitude_min: 120.0
+      longitude_max: 125.0
   event_type: atmospheric_river
 - case_id_number: 96
   title: October 2024 British Columbia
@@ -1145,10 +1145,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 32.8
-      latitude_max: 56.0
-      longitude_min: 217.3
-      longitude_max: 247.0
+      latitude_min: 57.75
+      latitude_max: 71.75
+      longitude_min: 10.25
+      longitude_max: 21.25
   event_type: atmospheric_river
 - case_id_number: 97
   title: September 2024 SW Alaska and British Columbia

From 9ad26d5ecf0e40e96f8d7116882cf6fbc515bde1 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 9 Apr 2026 19:48:20 -0400
Subject: [PATCH 52/72] Adjust some TC case bounds (#355)

* first pass bounds updates

* second pass to expand bounds
---
 src/extremeweatherbench/data/events.yaml | 144 +++++++++++------------
 1 file changed, 72 insertions(+), 72 deletions(-)

diff --git a/src/extremeweatherbench/data/events.yaml b/src/extremeweatherbench/data/events.yaml
index 97a753b6..d1165ea7 100644
--- a/src/extremeweatherbench/data/events.yaml
+++ b/src/extremeweatherbench/data/events.yaml
@@ -1872,15 +1872,15 @@
   event_type: tropical_cyclone
 - case_id_number: 157
   title: Hone
-  start_date: 2024-08-17 12:00:00
-  end_date: 2024-09-03 18:00:00
+  start_date: 2024-08-20 00:00:00
+  end_date: 2024-09-03 06:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 10.5
-      latitude_max: 28.3
-      longitude_min: 177.9
-      longitude_max: 231.5
+      latitude_min: 13.2
+      latitude_max: 32.0
+      longitude_min: 175.8
+      longitude_max: 224.8
   event_type: tropical_cyclone
 - case_id_number: 158
   title: Trami
@@ -1889,22 +1889,22 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 10.9
-      latitude_max: 19.9
-      longitude_min: 105.0
-      longitude_max: 139.8
+      latitude_min: 8.8
+      latitude_max: 22.0
+      longitude_min: 102.7
+      longitude_max: 143.3
   event_type: tropical_cyclone
 - case_id_number: 159
   title: Prapiroon (Butchoy)
-  start_date: 2024-07-17 06:00:00
+  start_date: 2024-07-13 12:00:00
   end_date: 2024-07-27 06:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 12.1
+      latitude_min: 5.5
       latitude_max: 24.0
       longitude_min: 104.3
-      longitude_max: 120.2
+      longitude_max: 132.0
   event_type: tropical_cyclone
 - case_id_number: 160
   title: Gaemi (Carina)
@@ -1913,46 +1913,46 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 9.4
-      latitude_max: 32.0
-      longitude_min: 110.6
-      longitude_max: 135.5
+      latitude_min: 7.2
+      latitude_max: 34.2
+      longitude_min: 106.4
+      longitude_max: 137.8
   event_type: tropical_cyclone
 - case_id_number: 161
   title: Shanshan
-  start_date: 2024-08-19 00:00:00
+  start_date: 2024-08-18 18:00:00
   end_date: 2024-09-03 18:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 13.8
-      latitude_max: 37.8
-      longitude_min: 127.7
-      longitude_max: 146.3
+      latitude_min: 11.1
+      latitude_max: 40.1
+      longitude_min: 125.4
+      longitude_max: 150.0
   event_type: tropical_cyclone
 - case_id_number: 162
   title: Yagi (Enteng)
-  start_date: 2024-08-30 00:00:00
+  start_date: 2024-08-28 12:00:00
   end_date: 2024-09-10 12:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 9.9
-      latitude_max: 23.8
-      longitude_min: 101.8
-      longitude_max: 128.7
+      latitude_min: 4.4
+      latitude_max: 26.0
+      longitude_min: 98.4
+      longitude_max: 141.4
   event_type: tropical_cyclone
 - case_id_number: 163
   title: Bebinca (Ferdie)
-  start_date: 2024-09-07 12:00:00
+  start_date: 2024-09-06 18:00:00
   end_date: 2024-09-20 12:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 7.9
+      latitude_min: 8.1
       latitude_max: 35.7
       longitude_min: 112.4
-      longitude_max: 149.9
+      longitude_max: 150.7
   event_type: tropical_cyclone
 - case_id_number: 164
   title: Soulik (Gener)
@@ -1968,27 +1968,27 @@
   event_type: tropical_cyclone
 - case_id_number: 165
   title: Krathon
-  start_date: 2024-09-24 12:00:00
+  start_date: 2024-09-24 00:00:00
   end_date: 2024-10-05 18:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 16.2
-      latitude_max: 25.0
+      latitude_min: 16.3
+      latitude_max: 26.3
       longitude_min: 116.8
-      longitude_max: 129.9
+      longitude_max: 130.6
   event_type: tropical_cyclone
 - case_id_number: 166
   title: Asna
-  start_date: 2024-08-28 00:00:00
-  end_date: 2024-09-04 00:00:00
+  start_date: 2024-08-23 00:00:00
+  end_date: 2024-09-05 06:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 18.8
-      latitude_max: 26.1
-      longitude_min: 59.0
-      longitude_max: 70.7
+      latitude_min: 17.5
+      latitude_max: 26.8
+      longitude_min: 58.3
+      longitude_max: 79.7
   event_type: tropical_cyclone
 - case_id_number: 167
   title: Kirrily
@@ -1998,33 +1998,33 @@
     type: bounded_region
     parameters:
       latitude_min: -31.8
-      latitude_max: -11.3
+      latitude_max: -11.5
       longitude_min: 135.3
-      longitude_max: 159.0
+      longitude_max: 158.9
   event_type: tropical_cyclone
 - case_id_number: 168
   title: Belal
-  start_date: 2024-01-10 12:00:00
-  end_date: 2024-01-21 12:00:00
+  start_date: 2024-01-09 18:00:00
+  end_date: 2024-01-25 06:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -26.5
-      latitude_max: -11.1
-      longitude_min: 50.8
-      longitude_max: 68.1
+      latitude_min: -35.7
+      latitude_max: -10.2
+      longitude_min: 49.9
+      longitude_max: 68.5
   event_type: tropical_cyclone
 - case_id_number: 169
   title: Alvaro
-  start_date: 2023-12-30 00:00:00
-  end_date: 2024-01-06 12:00:00
+  start_date: 2023-12-27 12:00:00
+  end_date: 2024-01-08 18:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -31.3
-      latitude_max: -18.5
-      longitude_min: 38.2
-      longitude_max: 59.9
+      latitude_min: -33.1
+      latitude_max: -17.0
+      longitude_min: 32.3
+      longitude_max: 70.5
   event_type: tropical_cyclone
 - case_id_number: 170
   title: Franklin
@@ -2213,10 +2213,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 21.1
-      latitude_max: 50.0
-      longitude_min: 132.3
-      longitude_max: 152.0
+      latitude_min: 18.9
+      latitude_max: 52.2
+      longitude_min: 129.9
+      longitude_max: 156.7
   event_type: tropical_cyclone
 - case_id_number: 186
   title: Saola (Goring)
@@ -2293,14 +2293,14 @@
 - case_id_number: 192
   title: Freddy
   start_date: 2023-02-02 12:00:00
-  end_date: 2023-03-15 18:00:00
+  end_date: 2023-03-16 12:00:00
   location:
     type: bounded_region
     parameters:
       latitude_min: -26.3
-      latitude_max: -8.9
-      longitude_min: 29.0
-      longitude_max: 121.6
+      latitude_max: -8.7
+      longitude_min: 28.8
+      longitude_max: 121.4
   event_type: tropical_cyclone
 - case_id_number: 193
   title: Jasper
@@ -2777,10 +2777,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -24.7
-      latitude_max: -9.3
-      longitude_min: 28.8
-      longitude_max: 88.1
+      latitude_min: -31.2
+      latitude_max: -7.1
+      longitude_min: 19.8
+      longitude_max: 90.7
   event_type: tropical_cyclone
 - case_id_number: 233
   title: Amanda and Cristobal
@@ -3024,15 +3024,15 @@
   event_type: tropical_cyclone
 - case_id_number: 253
   title: Remal
-  start_date: 2024-05-23 12:00:00
-  end_date: 2024-05-29 18:00:00
+  start_date: 2024-05-21 12:00:00
+  end_date: 2024-05-30 06:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 16.6
-      latitude_max: 26.4
-      longitude_min: 86.0
-      longitude_max: 92.8
+      latitude_min: 11.4
+      latitude_max: 27.3
+      longitude_min: 84.3
+      longitude_max: 94.1
   event_type: tropical_cyclone
 - case_id_number: 254
   title: Amphan

From 95802946821edf93caf99a3f0f656e3bbffcd267 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 10 Apr 2026 14:35:45 -0400
Subject: [PATCH 53/72] Tune duration logic (#357)

* add new duration method

* update duration logic with consecutive pair logic

* adjust test to match new duration logic
---
 src/extremeweatherbench/evaluate.py |   1 +
 src/extremeweatherbench/metrics.py  | 112 +++++++++++++++++++++++++---
 tests/test_metrics.py               |  79 ++++++++++----------
 3 files changed, 144 insertions(+), 48 deletions(-)

diff --git a/src/extremeweatherbench/evaluate.py b/src/extremeweatherbench/evaluate.py
index 504d7694..bcb87b3e 100644
--- a/src/extremeweatherbench/evaluate.py
+++ b/src/extremeweatherbench/evaluate.py
@@ -632,6 +632,7 @@ def _evaluate_metric_and_return_df(
     metric_result = metric.compute_metric(
         forecast_data,
         target_data,
+        case_metadata=case_operator.case_metadata,
         **kwargs,
     )
     # If data is sparse, densify it
diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index 33304f16..1a1fe673 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -1407,6 +1407,77 @@ def _compute_metric(
         )
 
 
+def _calculate_event_duration(
+    mask: xr.DataArray,
+    time_resolution_hours: int | float,
+    preserve_dims: str = "init_time",
+) -> xr.DataArray:
+    """Count total consecutive-run duration in hours along valid_time.
+
+    A run of N adjacent True timesteps each separated by exactly
+    time_resolution_hours counts as (N - 1) * time_resolution_hours
+    hours. An isolated True timestep contributes 0 hours.
+    Discontinuous True values (gap > time_resolution_hours between
+    any two True points) are not counted.
+
+    NaN values in mask are treated as False.
+
+    When preserve_dims is a regular array dimension (the simple
+    init_time case), consecutive hours are summed over valid_time,
+    returning total duration per preserve_dims slice.  When
+    preserve_dims is only a coordinate (the lead_time case where
+    init_time is a 2-D coord), a groupby aggregation is used
+    instead so that total hours are accumulated per unique
+    preserve_dims value.
+
+    For the init_time case the predecessor lookup shifts both
+    valid_time and lead_time by the same temporal distance
+    (expected_gap) so that the predecessor cell belongs to the
+    same forecast run.  The number of lead_time index steps is
+    expected_gap / lead_time_step (e.g. 4 for a daily target
+    on a 6 h lead_time grid).
+
+    Args:
+        mask: Boolean (or NaN-filled bool) DataArray with a
+            valid_time dimension.
+        time_resolution_hours: Expected temporal spacing in hours.
+        preserve_dims: Dimension or coordinate to aggregate over.
+            Defaults to "init_time".
+
+    Returns:
+        DataArray with valid_time reduced out, values in hours.
+    """
+    expected_gap = np.timedelta64(int(time_resolution_hours), "h")
+    mask = mask.fillna(False).astype(bool)
+    vt = mask.valid_time.values
+    gaps = np.concatenate([[False], (vt[1:] - vt[:-1]) == expected_gap])
+    is_expected_gap = xr.DataArray(gaps, dims=["valid_time"], coords={"valid_time": vt})
+    # When init_time is only a coordinate (not a dim) and lead_time is a dim,
+    # consecutive timesteps within one forecast run follow the diagonal of the
+    # (valid_time, lead_time) grid.  Shifting along valid_time alone always
+    # lands on NaN-filled cells (sparse outer-join grid), so shift both axes.
+    # The number of lead_time index steps must equal expected_gap in time so
+    # that the predecessor cell has the same init_time (vt - lead = const).
+    # For daily targets aligned to a 6 h lead_time grid this is 24h/6h = 4.
+    if "lead_time" in mask.dims and preserve_dims not in mask.dims:
+        lt_step = np.unique(np.diff(mask.lead_time.values))
+        n_lead_steps = (
+            max(1, int(expected_gap / lt_step[0]))
+            if len(lt_step) == 1 and lt_step[0] > np.timedelta64(0)
+            else 1
+        )
+        mask_prev = mask.shift(
+            {"valid_time": 1, "lead_time": n_lead_steps}, fill_value=False
+        )
+    else:
+        mask_prev = mask.shift(valid_time=1, fill_value=False)
+    consecutive_pairs = mask & mask_prev & is_expected_gap
+    hours = consecutive_pairs * time_resolution_hours
+    if preserve_dims in hours.dims:
+        return hours.sum("valid_time")
+    return hours.groupby(preserve_dims).sum()
+
+
 class DurationMeanError(MeanError):
     """Compute mean error of event duration between forecast and target.
 
@@ -1457,10 +1528,27 @@ def _compute_metric(
         Args:
             forecast: the forecast DataArray.
             target: the target DataArray.
+            **kwargs: Accepts case_metadata (IndividualCase) to apply
+                a ±48 h buffer around the case start/end before any
+                reduction. If absent, the buffer step is skipped.
 
         Returns:
-            The mean error between forecast and target event durations.
+            The mean error between forecast and target event durations
+            in hours.
         """
+        # Apply ±48 h buffer around case start/end before any reduction.
+        case_metadata = kwargs.get("case_metadata", None)
+        if case_metadata is not None:
+            buffer = np.timedelta64(48, "h")
+            case_start = np.datetime64(case_metadata.start_date) - buffer
+            case_end = np.datetime64(case_metadata.end_date) + buffer
+            forecast = forecast.sel(valid_time=slice(case_start, case_end))
+            target = target.sel(valid_time=slice(case_start, case_end))
+        else:
+            logger.debug(
+                "case_metadata not in kwargs; skipping ±48 h valid_time buffer"
+            )
+
         # Handle criteria - either climatology (xr.DataArray) or float threshold
         # Use local variable to avoid mutating self.threshold_criteria
         threshold_criteria = self.threshold_criteria
@@ -1517,16 +1605,20 @@ def _compute_metric(
                 dim={"lead_time": target.lead_time.size}
             ).where(forecast_valid_mask)
 
-        # Sum to get durations (NaN values are excluded by default)
-        forecast_duration = forecast_mask_final.groupby(self.preserve_dims).sum(
-            skipna=True
+        time_resolution_hours = utils.determine_temporal_resolution(forecast)
+        if time_resolution_hours is None:
+            logger.warning(
+                "Could not determine temporal resolution; duration defaults to 0"
+            )
+            time_resolution_hours = 0
+        # product_time_resolution_hours is deprecated; duration is now
+        # always returned in hours by _calculate_event_duration
+        forecast_duration = _calculate_event_duration(
+            forecast_mask_final, time_resolution_hours, self.preserve_dims
+        )
+        target_duration = _calculate_event_duration(
+            target_mask_final, time_resolution_hours, self.preserve_dims
         )
-        target_duration = target_mask_final.groupby(self.preserve_dims).sum(skipna=True)
-
-        if self.product_time_resolution_hours:
-            time_resolution_hours = utils.determine_temporal_resolution(forecast)
-            forecast_duration = forecast_duration * time_resolution_hours
-            target_duration = target_duration * time_resolution_hours
 
         return super()._compute_metric(
             forecast=forecast_duration,
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 9f08aca4..24b1e97a 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -1062,8 +1062,8 @@ def test_me_1_0_all_forecast_exceeds(self):
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         result = metric.compute_metric(forecast=forecast, target=target)
 
-        # Should be 1.0: forecast mask all 1s, target mask all 0s
-        assert np.isclose(result.values[0], 1.0)
+        # 9 consecutive 6 h intervals → 54 h; target = 0 h → ME = 54 h
+        assert np.isclose(result.values[0], 54.0)
 
     def test_me_0_5_half_forecast_exceeds(self):
         """Test MeanError= 0.5 when half forecast exceeds, all target below."""
@@ -1079,8 +1079,8 @@ def test_me_0_5_half_forecast_exceeds(self):
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         result = metric.compute_metric(forecast=forecast, target=target)
 
-        # Should be 0.5: forecast mask: 5 ones, 5 zeros; target: all zeros
-        assert np.isclose(result.values[0], 0.5)
+        # 4 consecutive 6 h intervals → 24 h; target = 0 h → ME = 24 h
+        assert np.isclose(result.values[0], 24.0)
 
     def test_me_neg_1_0_all_target_exceeds(self):
         """Test MeanError= -1.0 when all forecast below, all target exceeds."""
@@ -1095,8 +1095,8 @@ def test_me_neg_1_0_all_target_exceeds(self):
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         result = metric.compute_metric(forecast=forecast, target=target)
 
-        # Should be -1.0: forecast mask all 0s, target mask all 1s
-        assert np.isclose(result.values[0], -1.0)
+        # forecast = 0 h; target = 9 intervals × 6 h = 54 h → ME = -54 h
+        assert np.isclose(result.values[0], -54.0)
 
     def test_me_0_0_forecast_equals_target(self):
         """Test MeanError= 0.0 when forecast equals target."""
@@ -1128,8 +1128,8 @@ def test_me_0_3_three_timesteps_differ(self):
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         result = metric.compute_metric(forecast=forecast, target=target)
 
-        # Should be 0.3: forecast mask: 3 ones, 7 zeros; target: all zeros
-        assert np.isclose(result.values[0], 0.3)
+        # 2 consecutive 6 h intervals → 12 h; target = 0 h → ME = 12 h
+        assert np.isclose(result.values[0], 12.0)
 
     def test_me_with_lead_time_dimension(self):
         """Test MeanErrorwith forecast having lead_time dimension.
@@ -1201,16 +1201,22 @@ def test_me_with_lead_time_dimension(self):
         # Expected pattern: [1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 4, 3, 2, 1]
         # This reflects the number of (lead_time, valid_time) combos per init
 
-        # All values should be positive (forecast exceeds, target doesn't)
-        assert np.all(result.values > 0)
+        # First init_time covers only the first valid_time (no previous
+        # timestep), so its consecutive duration is 0 h; all others >= 0
+        assert np.all(result.values >= 0)
+        assert np.any(result.values > 0)
 
-        # Middle init_times should have the maximum value (n_lead_times)
+        # Middle init_times have n_lead_times timesteps → n_lead_times - 1
+        # consecutive intervals each spanning 6 h → max = 4 × 6 = 24 h
         max_value = np.max(result.values)
-        assert max_value == n_lead_times
+        assert max_value == (n_lead_times - 1) * 6
 
-        # Edge init_times should have value 1 (only one combination)
-        assert result.values[0] == 1
-        assert result.values[-1] == 1
+        # First init_time: only (lt=max, vt=t0) — no prior timestep → 0 h
+        assert result.values[0] == 0
+        # Last init_time: only (lt=0, vt=t_max) — 1 cell, no pairs → 0 h
+        assert result.values[-1] == 0
+        # Second-to-last: 2 cells → 1 consecutive pair → 6 h
+        assert result.values[-2] == 6
 
     def test_me_with_lead_time_partial_target_exceedance(self):
         """Test MeanErrorwith lead_time dims where target partially exceeds.
@@ -1320,13 +1326,11 @@ def test_me_with_nans_no_target_exceedance(self):
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         result = metric.compute_metric(forecast=forecast_with_nans, target=target)
 
-        # Should still be positive (forecast exceeds where not NaN)
-        # Result is reduced to scalar per init_time
+        # NaN at vt 2-3 (all locations) breaks the run into two segments:
+        # [0,1] → 1 interval and [4..9] → 5 intervals → (1+5)×6 = 36 h
         mean_result = float(result.values[0])
         assert mean_result > 0
-        # 8 out of 10 timesteps exceed (2 are NaN), target never exceeds
-        # So result should be 0.8 (8 timesteps where forecast=1, target=0)
-        assert np.isclose(mean_result, 0.8)
+        assert np.isclose(mean_result, 36.0)
 
     def test_me_with_nans_one_target_exceedance(self):
         """Test MeanErrorwith NaNs when one target value exceeds threshold.
@@ -1353,12 +1357,11 @@ def test_me_with_nans_one_target_exceedance(self):
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         result = metric.compute_metric(forecast=forecast_with_nans, target=target)
 
-        # Should be less than 1.0 because:
-        # - timestep 0: both exceed (diff=0)
-        # - timesteps 1-4, 7-9: forecast exceeds, target doesn't (diff=1)
-        # - timesteps 5-6: NaN positions excluded
-        assert result.values[0] < 1.0
+        # Partial NaN (one grid cell) does not affect the spatial mean;
+        # all 10 forecast timesteps still exceed → 9 intervals × 6 h = 54 h
+        # Target: only vt=0 is True; no consecutive pair → 0 h → ME = 54 h
         assert result.values[0] > 0
+        assert np.isclose(result.values[0], 54.0)
 
     def test_me_with_nans_all_but_nan_exceed(self):
         """Test MeanErrorwhen all non-NaN forecast/target values exceed threshold.
@@ -1416,10 +1419,11 @@ def test_me_with_nans_mixed_pattern(self):
         metric = metrics.DurationMeanError(threshold_criteria=climatology)
         result = metric.compute_metric(forecast=forecast_with_nans, target=target)
 
-        # Result should be positive but less than previous tests
-        # because some positions where forecast>target are NaN
+        # Partial NaN does not affect spatial mean; forecast [0..5] True
+        # → 5 intervals × 6 h = 30 h; target [0..2] True → 2 intervals
+        # × 6 h = 12 h; ME = 30 − 12 = 18 h
         assert result.values[0] > 0
-        assert result.values[0] < 1.0
+        assert np.isclose(result.values[0], 18.0)
 
         # Verify that the computation completed without errors
         assert not np.isnan(result.values[0])
@@ -1445,8 +1449,8 @@ def test_me_with_float_threshold_all_forecast_exceeds(self):
         metric = metrics.DurationMeanError(threshold_criteria=300.0)
         result = metric.compute_metric(forecast=forecast, target=target)
 
-        # Should be 1.0: forecast mask all 1s, target mask all 0s
-        assert np.isclose(result.values[0], 1.0)
+        # 9 consecutive 6 h intervals → 54 h; target = 0 h → ME = 54 h
+        assert np.isclose(result.values[0], 54.0)
 
     def test_me_with_float_threshold_mixed(self):
         """Test MeanErrorwith float threshold and mixed exceedance."""
@@ -1463,9 +1467,9 @@ def test_me_with_float_threshold_mixed(self):
         metric = metrics.DurationMeanError(threshold_criteria=300.0)
         result = metric.compute_metric(forecast=forecast, target=target)
 
-        # Forecast: 6 timesteps exceed, Target: 3 timesteps exceed
-        # MeanError= (6 - 3) / 10 = 0.3
-        assert np.isclose(result.values[0], 0.3)
+        # Forecast: 5 consecutive intervals × 6 h = 30 h
+        # Target: 2 consecutive intervals × 6 h = 12 h → ME = 18 h
+        assert np.isclose(result.values[0], 18.0)
 
     def test_float_and_climatology_produce_same_result(self):
         """Test that float threshold and equivalent climatology give same result."""
@@ -1515,9 +1519,8 @@ def test_sparse_array_handling(self):
 
         # Result should be valid (not NaN) and correct
         assert not np.isnan(result.values[0])
-        assert np.isclose(
-            result.values[0], 1.0
-        )  # All forecast exceeds, all target below
+        # 9 consecutive 6 h intervals → 54 h; target = 0 h → ME = 54 h
+        assert np.isclose(result.values[0], 54.0)
 
     def test_sparse_array_with_climatology(self):
         """Test sparse arrays with climatology threshold criteria."""
@@ -1544,8 +1547,8 @@ def test_sparse_array_with_climatology(self):
 
         # Result should be valid
         assert not np.isnan(result.values[0])
-        # Forecast: 6 timesteps exceed, Target: 3 timesteps exceed
-        assert np.isclose(result.values[0], 0.3)
+        # Forecast: 5 intervals × 6 h = 30 h; Target: 2 × 6 h = 12 h
+        assert np.isclose(result.values[0], 18.0)
 
 
 class TestThresholdMetric:

From aefbfa02300818aa63825385cfcb53395076fdef Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 10 Apr 2026 14:38:57 -0400
Subject: [PATCH 54/72] v1.0.2

---
 pyproject.toml |    2 +-
 uv.lock        | 5162 ++++++++++++++++++++++++------------------------
 2 files changed, 2582 insertions(+), 2582 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 504a76b8..d17b3da0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "extremeweatherbench"
-version = "1.0.1"
+version = "1.0.2"
 description = "Benchmarking weather and weather AI models using extreme events"
 keywords = [
     "weather",
diff --git a/uv.lock b/uv.lock
index 71ba499e..713cc0ac 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 3
 requires-python = ">=3.11, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32'",
@@ -14,9 +13,9 @@ resolution-markers = [
 name = "affine"
 version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662, upload-time = "2023-01-19T23:44:28.833Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662 },
 ]
 
 [[package]]
@@ -32,18 +31,18 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/bd/5c/f898e42fbdfc88780104881a05173f1d65a84437d99faf5771fcd09f38b1/aiobotocore-3.1.0.tar.gz", hash = "sha256:5897bde0a47b21e6f7bd00942527b099728ac5c6c6d1dcc34e999de24f3c8872", size = 122488, upload-time = "2026-01-02T20:28:30.328Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/bd/5c/f898e42fbdfc88780104881a05173f1d65a84437d99faf5771fcd09f38b1/aiobotocore-3.1.0.tar.gz", hash = "sha256:5897bde0a47b21e6f7bd00942527b099728ac5c6c6d1dcc34e999de24f3c8872", size = 122488 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e4/b3/923e0bc13669f06459c39a037ca7f5c1661ce252e141798a32803288c410/aiobotocore-3.1.0-py3-none-any.whl", hash = "sha256:4801f7b996f37a48faed017d23d8eae71e5fe0c3ec6bf0d9c5ce4f4d1853fce8", size = 87661, upload-time = "2026-01-02T20:28:28.817Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/b3/923e0bc13669f06459c39a037ca7f5c1661ce252e141798a32803288c410/aiobotocore-3.1.0-py3-none-any.whl", hash = "sha256:4801f7b996f37a48faed017d23d8eae71e5fe0c3ec6bf0d9c5ce4f4d1853fce8", size = 87661 },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
 ]
 
 [[package]]
@@ -59,68 +58,68 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556, upload-time = "2026-01-03T17:33:05.204Z" }
-wheels = [
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-    { url = "https://files.pythonhosted.org/packages/82/71/d5c31390d18d4f58115037c432b7e0348c60f6f53b727cad33172144a112/aiohttp-3.13.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cb93e166e6c28716c8c6aeb5f99dfb6d5ccf482d29fe9bf9a794110e6d0ab64", size = 499234, upload-time = "2026-01-03T17:29:44.822Z" },
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From 214a3a2bb9bf965a92cbdc57e7c9245e99d14eba Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Fri, 10 Apr 2026 20:26:34 -0400
Subject: [PATCH 55/72] Update global temp bound method; fix event case number
 mismatch (#359)

* add some parallelism and stricter event criteria

* smaller batching

* update default batch

* update event bounds; case shift issue

* rename

* ruff
---
 data_prep/temperature_bounds_global.py        | 847 ++++++++++++++----
 .../data/base_temp_events.yaml                |  76 +-
 src/extremeweatherbench/data/events.yaml      | 232 ++---
 3 files changed, 850 insertions(+), 305 deletions(-)

diff --git a/data_prep/temperature_bounds_global.py b/data_prep/temperature_bounds_global.py
index ce7e91d6..b3bc6038 100644
--- a/data_prep/temperature_bounds_global.py
+++ b/data_prep/temperature_bounds_global.py
@@ -5,7 +5,7 @@
 climatology quantile band for 3+ consecutive days, over land.
 
 At least one of --quantile-lower or --quantile-upper must be given.
-Both can be combined to define a band (e.g. 50th–85th percentile).
+Both can be combined to define a band (e.g. 50th-85th percentile).
 
 Bounding boxes are first derived from blob tracking, then expanded
 using the same edge-validity logic as heat_cold_bounds_case.py:
@@ -14,26 +14,32 @@
 active area drops below 50% of peak.
 
 Usage:
-    # Anything above the 85th percentile (heat wave)
+    # Anything below the 15th percentile (cold snap)
     python temperature_bounds_global.py \\
-        --start-date 2023-06-01 --end-date 2023-09-01 \\
-        --quantile-lower 0.85 --operator-lower ">=" \\
-        --event-type heat_wave --output heat_cold_global.csv
+        --start-date 2020-01-01 --end-date 2020-12-31 \\
+        --quantile-upper 0.15 \\
+        --output cold_q15_2020.csv
 
-    # Band between 50th and 85th (moderate heat)
+    # Anything above the 85th percentile (heat wave)
     python temperature_bounds_global.py \\
         --start-date 2023-06-01 --end-date 2023-09-01 \\
-        --quantile-lower 0.50 --quantile-upper 0.85 \\
-        --event-type heat_wave --output heat_cold_global.csv
+        --quantile-lower 0.85 --operator-lower ">=" \\
+        --output heat_q85_2023.csv
 """
 
 import argparse
 import logging
 import pathlib
 import time as time_module
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Literal, Optional, Tuple, cast
 
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import dask
 import joblib
+import matplotlib.colors as mcolors
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
 import numba as nb
 import numpy as np
 import pandas as pd
@@ -74,7 +80,7 @@ def compute_grid_cell_area(
     """Return a 2-D (lat, lon) array of grid-cell areas in km².
 
     Uses the spherical-Earth approximation:
-        area = R² × Δlat_rad × Δlon_rad × cos(lat)
+        area = R^2 * dlat_rad * dlon_rad * cos(lat)
 
     Args:
         lats: 1-D latitude array in degrees.
@@ -97,20 +103,12 @@ def get_climatology_bounds(
 ) -> Tuple[Optional[xr.DataArray], Optional[xr.DataArray]]:
     """Return climatology DataArrays for the lower and/or upper bound.
 
-    At least one of q_lower or q_upper must be provided. Each returned
-    DataArray is indexed by (dayofyear, hour) and sorted by latitude.
-
     Args:
-        q_lower: Quantile for the lower bound (e.g. 0.50 means temp
-            must exceed the 50th-percentile climatology). None skips
-            the lower-bound check.
-        q_upper: Quantile for the upper bound (e.g. 0.85 means temp
-            must not exceed the 85th-percentile climatology). None
-            skips the upper-bound check.
+        q_lower: Quantile for the lower bound. None skips it.
+        q_upper: Quantile for the upper bound. None skips it.
 
     Returns:
-        Tuple (clim_lower, clim_upper); either element may be None
-        when the corresponding quantile argument is not supplied.
+        Tuple (clim_lower, clim_upper); either may be None.
 
     Raises:
         ValueError: If both q_lower and q_upper are None.
@@ -141,8 +139,7 @@ def build_land_mask(
         lats: 1-D DataArray of latitudes.
 
     Returns:
-        Boolean DataArray of the same shape as the meshgrid of lons
-        and lats, where True indicates a land grid point.
+        Boolean DataArray where True indicates a land grid point.
     """
     land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
     mask = land.mask(lons, lats)
@@ -157,9 +154,6 @@ def _edge_valid_fraction(
 ) -> float:
     """Return the fraction of land grid points on an edge that are active.
 
-    Ocean/masked points are excluded from both numerator and denominator
-    so coastal edges are not penalised.
-
     Args:
         mask_2d: 2-D boolean activity array (lat, lon).
         land_2d: 2-D boolean land mask (lat, lon); True = land.
@@ -206,17 +200,13 @@ def expand_event_bounds(
     Starting from the blob-tracked bounding box, expands each edge
     outward while >= EDGE_VALIDITY_THRESHOLD of land points on that
     edge are active on the peak-footprint day. Also computes and stores
-    max_consecutive_days within the expanded region.
-
-    The expansion matches the spatial-growth algorithm used in
-    heat_cold_bounds_case.py: 1-degree steps, ocean-heavy edges
-    disabled upfront, convergence when all active edges drop below 50%.
+    max_consecutive_days and mean_consecutive_days within the expanded
+    region.
 
     Args:
-        event: Event dict from detect_events (keys: start, end,
-            lat_min, lat_max, lon_min, lon_max).
+        event: Event dict from detect_events.
         filt_mask: Boolean array (time, lat, lon) with consecutive-day
-            filtering already applied, aligned with dates/lats/lons.
+            filtering already applied.
         dates: 1-D datetime64 array of daily timestamps (axis 0).
         lats: 1-D latitude array (axis 1).
         lons: 1-D longitude array (axis 2).
@@ -224,7 +214,7 @@ def expand_event_bounds(
 
     Returns:
         The same event dict with updated lat_min/lat_max/lon_min/
-        lon_max and a new max_consecutive_days key.
+        lon_max, max_consecutive_days, and mean_consecutive_days.
     """
     grid_res = float(np.abs(np.diff(lats[:2]))[0]) if len(lats) > 1 else 0.25
     band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
@@ -236,6 +226,7 @@ def expand_event_bounds(
 
     if ev_filt.shape[0] == 0:
         event["max_consecutive_days"] = 0
+        event["mean_consecutive_days"] = 0.0
         return event
 
     def _lat_idx(val: float) -> int:
@@ -307,6 +298,10 @@ def _lon_idx(val: float) -> int:
     event["lon_min"] = float(lons[idx_w])
     event["lon_max"] = float(lons[idx_e])
     event["max_consecutive_days"] = int(consec.max()) if consec.size > 0 else 0
+    active = consec[consec >= MIN_CONSECUTIVE_DAYS]
+    event["mean_consecutive_days"] = (
+        round(float(active.mean()), 2) if active.size > 0 else 0.0
+    )
     return event
 
 
@@ -324,23 +319,16 @@ def build_exceedance_mask(
     passes only when every 6-hourly step satisfies every active bound.
     The result is further masked to land points.
 
-    At least one of clim_lower or clim_upper must be provided.
-
     Args:
         t2m: 6-hourly 2m temperature DataArray.
-        clim_lower: Climatology for the lower bound, indexed by
-            (dayofyear, hour). None skips this bound.
-        clim_upper: Climatology for the upper bound, indexed by
-            (dayofyear, hour). None skips this bound.
+        clim_lower: Climatology for the lower bound, or None.
+        clim_upper: Climatology for the upper bound, or None.
         land_mask: Boolean DataArray (True = land) matching t2m grid.
-        op_lower: Comparison operator for the lower bound.
-            Default is ">".
-        op_upper: Comparison operator for the upper bound.
-            Default is "<".
+        op_lower: Comparison operator for the lower bound. Default ">".
+        op_upper: Comparison operator for the upper bound. Default "<".
 
     Returns:
-        Daily boolean DataArray masked to land where True indicates
-        every 6-hourly step satisfied all active bounds.
+        Daily boolean DataArray masked to land.
     """
     tdim = detect_time_dim(t2m)
     doy = t2m[tdim].dt.dayofyear
@@ -383,14 +371,11 @@ def apply_consecutive_filter(
 
     Args:
         mask: Boolean array of shape (time, lat, lon).
-        min_days: Minimum run length required to qualify as an
-            event. Default is 3 (MIN_CONSECUTIVE_DAYS).
-        max_grace_days: Maximum gap length to bridge after the
-            minimum run is established. Default is 1.
+        min_days: Minimum run length. Default is 3.
+        max_grace_days: Maximum gap length to bridge. Default is 1.
 
     Returns:
-        Boolean array of the same shape with only qualifying runs
-        retained.
+        Boolean array of the same shape with qualifying runs only.
     """
     struct = np.zeros((min_days, 1, 1), dtype=bool)
     struct[:, 0, 0] = True
@@ -451,9 +436,6 @@ def _count_overlaps_nb(
 ) -> np.ndarray:
     """Compute a pixel-count overlap matrix between two label grids.
 
-    A single-pass JIT loop avoids the per-blob numpy scan in the
-    Python tracker.
-
     Args:
         labels_a: Integer label array for day t (shape lat x lon).
         labels_b: Integer label array for day t+1 (shape lat x lon).
@@ -484,21 +466,15 @@ def _resolve_event(
 ) -> Optional[int]:
     """Find and merge prior-day events overlapping with blob oid.
 
-    Uses a precomputed overlap matrix column instead of scanning the
-    full prev_labels array per blob.
-
     Args:
         oid: Current-day blob label (1-indexed).
-        overlap_mat: Pixel-count overlap matrix from
-            _count_overlaps_nb, or None if no previous day.
+        overlap_mat: Pixel-count overlap matrix, or None.
         prev_map: Mapping from previous-day blob label to event ID.
         events: Mutable dict of all live events keyed by event ID.
-        cur_map: Mutable mapping from current-day blob label to event
-            ID; updated in-place when events are merged.
+        cur_map: Mutable mapping from current-day blob label to event ID.
 
     Returns:
-        The surviving event ID after merging, or None if no overlap
-        with a prior-day event was found.
+        The surviving event ID after merging, or None if no overlap.
     """
     if overlap_mat is None:
         return None
@@ -534,8 +510,7 @@ def _terminate_declined_events(
 
     Args:
         events: Mutable dict of all live events keyed by event ID.
-        cur_map: Mapping from current-day blob label to event ID;
-            used to determine which events are still active today.
+        cur_map: Mapping from current-day blob label to event ID.
     """
     active = set(cur_map.values())
     for eid, ev in events.items():
@@ -554,16 +529,18 @@ def detect_events(
     lons: np.ndarray,
     event_type: str,
     area_grid: Optional[np.ndarray] = None,
+    min_seed_area_km2: float = 0.0,
 ) -> List[Dict]:
     """Track spatiotemporal events from a filtered boolean mask.
 
-    Labels one day at a time (low peak memory) but uses a numba-JIT
-    overlap matrix to match blobs across days. A single O(nlat*nlon)
-    pass replaces one O(nlat*nlon) scan per blob, which dominates
-    when many blobs are active simultaneously.
-
-    An event's bounding box is the union of all its daily extents.
-    Events terminate when their active area drops below 50% of peak.
+    New events are only seeded for blobs whose area meets or exceeds
+    ``min_seed_area_km2`` on the current day AND on each of the prior
+    ``MIN_CONSECUTIVE_DAYS - 1`` days (strict joint-area check). The
+    overlap of the current-day blob footprint with each previous day's
+    filtered mask must individually satisfy the threshold, ensuring the
+    contiguous region was jointly >= ``min_seed_area_km2`` for all
+    MIN_CONSECUTIVE_DAYS days. Blobs that fail but overlap an already-
+    established event still extend it.
 
     Args:
         filtered_mask: Boolean array of shape (time, lat, lon) with
@@ -571,14 +548,17 @@ def detect_events(
         dates: 1-D array of date labels aligned with axis 0.
         lats: 1-D latitude array aligned with axis 1.
         lons: 1-D longitude array aligned with axis 2.
-        event_type: Label string stored in each returned event dict
-            (e.g. "heat_wave" or "cold_snap").
+        event_type: Label string stored in each returned event dict.
         area_grid: Optional 2-D array (lat, lon) of grid-cell areas
-            in km². When provided, peak_area_km2 is tracked per event.
+            in km².
+        min_seed_area_km2: Minimum contiguous area in km² that the
+            blob footprint must satisfy on each of MIN_CONSECUTIVE_DAYS
+            days to seed a new event. Default is 0.0 (all blobs seed).
 
     Returns:
         List of event dicts with keys type, start, end, lat_min,
-        lat_max, lon_min, lon_max, peak, peak_area_km2, area, done.
+        lat_max, lon_min, lon_max, peak, peak_area_km2, area, done,
+        initial_bbox.
     """
     n_days = filtered_mask.shape[0]
     events: Dict[int, Dict] = {}
@@ -629,41 +609,61 @@ def detect_events(
             )
 
             if eid is None:
+                if blob_area_km2 < min_seed_area_km2:
+                    continue
+                # Strict joint-area check: the blob's footprint must
+                # also have been >= min_seed_area_km2 on each of the
+                # prior MIN_CONSECUTIVE_DAYS-1 days. Skip seeding when
+                # insufficient history is available.
+                if min_seed_area_km2 > 0:
+                    if di < MIN_CONSECUTIVE_DAYS - 1:
+                        continue
+                    joint_ok = True
+                    for k in range(1, MIN_CONSECUTIVE_DAYS):
+                        overlap = om & filtered_mask[di - k]
+                        prev_area = (
+                            float(area_grid[overlap].sum())
+                            if area_grid is not None
+                            else float(overlap.sum())
+                        )
+                        if prev_area < min_seed_area_km2:
+                            joint_ok = False
+                            break
+                    if not joint_ok:
+                        continue
                 eid = next_id
                 next_id += 1
+                _lat_min = float(lats[li].min())
+                _lat_max = float(lats[li].max())
+                _lon_min = float(lons[lo].min())
+                _lon_max = float(lons[lo].max())
                 events[eid] = {
                     "type": event_type,
                     "start": dates[di],
                     "end": dates[di],
-                    "lat_min": float(lats[li].min()),
-                    "lat_max": float(lats[li].max()),
-                    "lon_min": float(lons[lo].min()),
-                    "lon_max": float(lons[lo].max()),
+                    "lat_min": _lat_min,
+                    "lat_max": _lat_max,
+                    "lon_min": _lon_min,
+                    "lon_max": _lon_max,
                     "peak": area,
                     "peak_area_km2": blob_area_km2,
                     "area": area,
                     "done": False,
+                    "initial_bbox": (
+                        _lat_min,
+                        _lat_max,
+                        _lon_min,
+                        _lon_max,
+                    ),
                 }
             else:
                 ev = events[eid]
                 ev["end"] = dates[di]
                 ev["peak_area_km2"] = max(ev["peak_area_km2"], blob_area_km2)
-                ev["lat_min"] = min(
-                    ev["lat_min"],
-                    float(lats[li].min()),
-                )
-                ev["lat_max"] = max(
-                    ev["lat_max"],
-                    float(lats[li].max()),
-                )
-                ev["lon_min"] = min(
-                    ev["lon_min"],
-                    float(lons[lo].min()),
-                )
-                ev["lon_max"] = max(
-                    ev["lon_max"],
-                    float(lons[lo].max()),
-                )
+                ev["lat_min"] = min(ev["lat_min"], float(lats[li].min()))
+                ev["lat_max"] = max(ev["lat_max"], float(lats[li].max()))
+                ev["lon_min"] = min(ev["lon_min"], float(lons[lo].min()))
+                ev["lon_max"] = max(ev["lon_max"], float(lons[lo].max()))
                 ev["peak"] = max(ev["peak"], area)
                 ev["area"] = area
 
@@ -680,6 +680,65 @@ def detect_events(
     return list(events.values())
 
 
+def include_temps_with_events(
+    events: List[Dict],
+    t2m_daily_np: np.ndarray,
+    exc_filt: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+) -> List[Dict]:
+    """Add mean and minimum temperature (C) to each event dict.
+
+    Uses a pre-computed global daily-mean temperature array so that
+    all events share a single ERA5 fetch rather than making one Dask
+    compute call per event. Statistics are computed over exceedant
+    (active) grid point-days within each event's expanded bounding
+    box and time window.
+
+    Args:
+        events: Event dicts with lat_min/max, lon_min/max, start, end.
+        t2m_daily_np: Float32 array (days, lat, lon) of daily-mean
+            2 m temperature in Celsius, aligned with dates/lats/lons.
+        exc_filt: Boolean array (time, lat, lon) from
+            apply_consecutive_filter, aligned with dates/lats/lons.
+        dates: 1-D datetime64 array aligned with axis 0.
+        lats: 1-D latitude array aligned with axis 1.
+        lons: 1-D longitude array aligned with axis 2.
+
+    Returns:
+        The same event list with mean_temp_c and min_temp_c added
+        (NaN when no active points exist).
+    """
+
+    def _idx(arr: np.ndarray, val: float) -> int:
+        return int(np.argmin(np.abs(arr - val)))
+
+    for ev in events:
+        start_date = np.datetime64(ev["start"])
+        end_date = np.datetime64(ev["end"])
+        t_indices = np.where((dates >= start_date) & (dates <= end_date))[0]
+
+        idx_s = _idx(lats, ev["lat_min"])
+        idx_n = _idx(lats, ev["lat_max"])
+        idx_w = _idx(lons, ev["lon_min"])
+        idx_e = _idx(lons, ev["lon_max"])
+
+        exc_ev = exc_filt[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+        t2m_ev = t2m_daily_np[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+
+        n_days = min(t2m_ev.shape[0], exc_ev.shape[0])
+        active_temps = t2m_ev[:n_days][exc_ev[:n_days]]
+        if active_temps.size > 0:
+            ev["mean_temp_c"] = round(float(active_temps.mean()), 2)
+            ev["min_temp_c"] = round(float(active_temps.min()), 2)
+        else:
+            ev["mean_temp_c"] = float("nan")
+            ev["min_temp_c"] = float("nan")
+
+    return events
+
+
 def events_to_dataframe(
     events: List[Dict],
     min_gridpoints: int = MIN_GRIDPOINTS,
@@ -688,17 +747,16 @@ def events_to_dataframe(
     """Convert event dicts to a labelled DataFrame.
 
     Args:
-        events: Raw event dicts from ``detect_events``.
-        min_gridpoints: Drop events whose peak grid-point count is
-            below this threshold. Default is 500 (MIN_GRIDPOINTS).
-        min_area_km2: Drop events whose peak area (km²) is below
-            this threshold. Default is 200 000 (MIN_AREA_KM2).
+        events: Raw event dicts from detect_events (after enrichment).
+        min_gridpoints: Drop events below this peak grid-point count.
+        min_area_km2: Drop events below this peak area (km²).
 
     Returns:
         DataFrame with columns label, event_type, start_date,
-        end_date, latitude_min, latitude_max, longitude_min,
-        longitude_max, max_consecutive_days, sorted by start_date.
-        Events below either threshold are excluded.
+        end_date, latitude_min/max, longitude_min/max,
+        max_consecutive_days, mean_consecutive_days, mean_temp_c,
+        min_temp_c, sorted by start_date. Events with fewer than
+        MIN_CONSECUTIVE_DAYS consecutive days are also excluded.
     """
     columns = [
         "label",
@@ -710,6 +768,9 @@ def events_to_dataframe(
         "longitude_min",
         "longitude_max",
         "max_consecutive_days",
+        "mean_consecutive_days",
+        "mean_temp_c",
+        "min_temp_c",
     ]
     if not events:
         return pd.DataFrame(columns=columns)
@@ -718,13 +779,16 @@ def events_to_dataframe(
     events = [
         e
         for e in events
-        if e["peak"] >= min_gridpoints and e.get("peak_area_km2", 0.0) >= min_area_km2
+        if e["peak"] >= min_gridpoints
+        and e.get("peak_area_km2", 0.0) >= min_area_km2
+        and e.get("max_consecutive_days", 0) >= MIN_CONSECUTIVE_DAYS
     ]
     logger.info(
-        "  Filtered %d events (< %d pts or < %.0f km²); %d remain",
+        "  Filtered %d events (< %d pts, < %.0f km², or < %d consec days); %d remain",
         n_before - len(events),
         min_gridpoints,
         min_area_km2,
+        MIN_CONSECUTIVE_DAYS,
         len(events),
     )
 
@@ -741,6 +805,9 @@ def events_to_dataframe(
             "longitude_min": e["lon_min"],
             "longitude_max": e["lon_max"],
             "max_consecutive_days": e.get("max_consecutive_days", 0),
+            "mean_consecutive_days": e.get("mean_consecutive_days", 0.0),
+            "mean_temp_c": e.get("mean_temp_c", float("nan")),
+            "min_temp_c": e.get("min_temp_c", float("nan")),
         }
         for e in events
     ]
@@ -749,6 +816,310 @@ def events_to_dataframe(
     return df
 
 
+def _add_lon_rect(
+    ax: plt.Axes,
+    lon_min_0360: float,
+    lon_max_0360: float,
+    lat_min: float,
+    lat_max: float,
+    color: tuple,
+    linewidth: float = 0.8,
+    zorder: int = 4,
+) -> None:
+    """Add a lat/lon rectangle to ax, splitting at the antimeridian if needed.
+
+    Inputs use 0-360 longitude. When the event wraps around the 0°/360°
+    boundary the bounding box is split into two rectangles so that
+    matplotlib does not draw an inverted or near-zero-width patch.
+    """
+
+    def _to_180(lon: float) -> float:
+        return lon if lon <= 180 else lon - 360
+
+    lmin = _to_180(lon_min_0360)
+    lmax = _to_180(lon_max_0360)
+
+    def _rect(x0: float, x1: float) -> mpatches.Rectangle:
+        return mpatches.Rectangle(
+            (x0, lat_min),
+            x1 - x0,
+            lat_max - lat_min,
+            linewidth=linewidth,
+            edgecolor=color,
+            facecolor=(*color[:3], 0.25),
+            transform=ccrs.PlateCarree(),
+            zorder=zorder,
+        )
+
+    if lmin <= lmax:
+        ax.add_patch(_rect(lmin, lmax))
+    else:
+        # Event wraps across the antimeridian – draw two segments.
+        ax.add_patch(_rect(lmin, 180.0))
+        ax.add_patch(_rect(-180.0, lmax))
+
+
+def plot_events_global(
+    df: pd.DataFrame,
+    event_type: str,
+    title: str,
+    output_path: str,
+) -> None:
+    """Plot detected events as bounding boxes on a global Robinson map.
+
+    Each event is drawn as a rectangle coloured by its start date.
+
+    Args:
+        df: DataFrame with latitude_min/max, longitude_min/max,
+            start_date columns.
+        event_type: 'heat_wave' or 'cold_snap' for colormap.
+        title: Figure title.
+        output_path: Destination PNG file path.
+    """
+    cmap_name = "Reds" if event_type == "heat_wave" else "Blues"
+    cmap = plt.colormaps.get_cmap(cmap_name)
+
+    fig = plt.figure(figsize=(18, 9))
+    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
+    ax.set_global()
+    ax.add_feature(cfeature.OCEAN, facecolor="lightcyan", zorder=0)
+    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=1)
+    ax.coastlines(linewidth=0.5, zorder=3)
+    ax.add_feature(cfeature.BORDERS, linewidth=0.4, edgecolor="grey", zorder=3)
+    ax.gridlines(draw_labels=False, linewidth=0.3, color="grey", alpha=0.5, zorder=2)
+
+    if df.empty:
+        ax.set_title(f"{title}\n(no events)", fontsize=11, loc="left")
+        fig.savefig(output_path, dpi=150, bbox_inches="tight")
+        plt.close(fig)
+        logger.info("Events plot saved to %s (no events)", output_path)
+        return
+
+    start_dates = pd.to_datetime(df["start_date"])
+    date_min = start_dates.min()
+    date_max = start_dates.max()
+    span = max((date_max - date_min).days, 1)
+    norm = mcolors.Normalize(vmin=0, vmax=span)
+
+    for _, row in df.iterrows():
+        days = (pd.to_datetime(row["start_date"]) - date_min).days
+        color = cmap(norm(days))
+        _add_lon_rect(
+            ax,
+            row["longitude_min"],
+            row["longitude_max"],
+            row["latitude_min"],
+            row["latitude_max"],
+            color,
+        )
+
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])
+    cbar = fig.colorbar(sm, ax=ax, orientation="vertical", fraction=0.02, pad=0.02)
+    tick_days = np.linspace(0, span, min(7, span + 1))
+    cbar.set_ticks(tick_days)
+    cbar.set_ticklabels(
+        [(date_min + pd.Timedelta(days=int(d))).strftime("%Y-%m-%d") for d in tick_days]
+    )
+    cbar.set_label("Event start date", fontsize=9)
+    ax.set_title(f"{title}\n{len(df)} events", fontsize=11, loc="left")
+    fig.savefig(output_path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("Events plot saved to %s", output_path)
+
+
+def plot_events_high_consec(
+    df: pd.DataFrame,
+    title: str,
+    output_path: str,
+    min_consec: int = 6,
+) -> None:
+    """Plot events with >= min_consec days, coloured by consecutive days.
+
+    Uses the plasma colormap (neutral, not event-type specific).
+
+    Args:
+        df: DataFrame from events_to_dataframe with columns
+            latitude_min/max, longitude_min/max, max_consecutive_days.
+        title: Figure title.
+        output_path: Destination PNG file path.
+        min_consec: Minimum max_consecutive_days to include. Default 6.
+    """
+    sub = df[df["max_consecutive_days"] >= min_consec].copy()
+
+    fig = plt.figure(figsize=(18, 9))
+    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
+    ax.set_global()
+    ax.add_feature(cfeature.OCEAN, facecolor="lightcyan", zorder=0)
+    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=1)
+    ax.coastlines(linewidth=0.5, zorder=3)
+    ax.add_feature(cfeature.BORDERS, linewidth=0.4, edgecolor="grey", zorder=3)
+    ax.gridlines(draw_labels=False, linewidth=0.3, color="grey", alpha=0.5, zorder=2)
+
+    if sub.empty:
+        ax.set_title(
+            f"{title}\n(no events with >= {min_consec} consecutive days)",
+            fontsize=11,
+            loc="left",
+        )
+        fig.savefig(output_path, dpi=150, bbox_inches="tight")
+        plt.close(fig)
+        logger.info(
+            "High-consec plot saved to %s (no qualifying events)",
+            output_path,
+        )
+        return
+
+    vmin = min_consec
+    vmax = int(sub["max_consecutive_days"].max())
+    n_levels = vmax - vmin + 1
+    cmap = plt.colormaps.get_cmap("plasma_r").resampled(n_levels)
+    bin_edges = np.arange(vmin - 0.5, vmax + 1.5, 1)
+    norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
+
+    for _, row in sub.iterrows():
+        consec_val = int(row["max_consecutive_days"])
+        color = cmap(norm(consec_val))
+        _add_lon_rect(
+            ax,
+            row["longitude_min"],
+            row["longitude_max"],
+            row["latitude_min"],
+            row["latitude_max"],
+            color,
+        )
+
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])
+    cbar = fig.colorbar(sm, ax=ax, orientation="vertical", fraction=0.02, pad=0.02)
+    cbar.set_ticks(range(vmin, vmax + 1))
+    cbar.set_label("Max Consecutive Days", fontsize=9)
+    ax.set_title(f"{title}\n{len(sub)} events", fontsize=11, loc="left")
+    fig.savefig(output_path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("High-consec plot saved to %s", output_path)
+
+
+def plot_event_consec_maps(
+    df: pd.DataFrame,
+    exc_filt: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    stem: str,
+    min_consec: int = 6,
+) -> None:
+    """Plot a per-event consecutive-days map for each qualifying event.
+
+    For each row in df with max_consecutive_days >= min_consec, slices
+    exc_filt to the event's bounding box and time window, computes
+    per-grid-point max consecutive days, and saves a pcolormesh plot
+    using the plasma colormap.
+
+    Args:
+        df: DataFrame from events_to_dataframe with columns label,
+            start_date, end_date, latitude_min/max, longitude_min/max,
+            max_consecutive_days.
+        exc_filt: Boolean array (time, lat, lon) from
+            apply_consecutive_filter, aligned with dates/lats/lons.
+        dates: 1-D datetime64 array aligned with exc_filt axis 0.
+        lats: 1-D latitude array aligned with exc_filt axis 1.
+        lons: 1-D longitude array (0-360) aligned with exc_filt axis 2.
+        stem: Output path stem; each plot saved as
+            {stem}_event_{label:04d}_consec.png.
+        min_consec: Minimum max_consecutive_days to plot. Default 6.
+    """
+    qualifying = df[df["max_consecutive_days"] >= min_consec]
+    if qualifying.empty:
+        logger.info(
+            "No events with >= %d consecutive days to plot individually.",
+            min_consec,
+        )
+        return
+
+    logger.info(
+        "Plotting %d per-event consecutive-days maps...",
+        len(qualifying),
+    )
+
+    def _idx(arr: np.ndarray, val: float) -> int:
+        return int(np.argmin(np.abs(arr - val)))
+
+    plot_lons = np.where(lons > 180, lons - 360.0, lons)
+
+    for _, row in qualifying.iterrows():
+        label = int(row["label"])
+        start_date = np.datetime64(row["start_date"])
+        end_date = np.datetime64(row["end_date"])
+        t_indices = np.where((dates >= start_date) & (dates <= end_date))[0]
+
+        idx_s = _idx(lats, row["latitude_min"])
+        idx_n = _idx(lats, row["latitude_max"])
+        idx_w = _idx(lons, row["longitude_min"])
+        idx_e = _idx(lons, row["longitude_max"])
+
+        ev_filt = exc_filt[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+        consec = max_consecutive_days(ev_filt)
+
+        ev_lats = lats[idx_s : idx_n + 1]
+        ev_lons = plot_lons[idx_w : idx_e + 1]
+
+        plot_data = consec.astype(float)
+        plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
+        valid = plot_data[~np.isnan(plot_data)]
+        if valid.size == 0:
+            continue
+
+        vmax = int(valid.max())
+        n_levels = vmax - MIN_CONSECUTIVE_DAYS + 1
+        cmap = plt.colormaps.get_cmap("plasma_r").resampled(n_levels)
+        bin_edges = np.arange(MIN_CONSECUTIVE_DAYS - 0.5, vmax + 1.5, 1)
+        norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
+
+        fig, ax = plt.subplots(
+            subplot_kw={"projection": ccrs.PlateCarree()},
+            figsize=(10, 8),
+        )
+        ax.set_extent(
+            [
+                float(ev_lons.min()) - 1,
+                float(ev_lons.max()) + 1,
+                float(ev_lats.min()) - 1,
+                float(ev_lats.max()) + 1,
+            ],
+            crs=ccrs.PlateCarree(),
+        )
+        ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
+        im = ax.pcolormesh(
+            ev_lons,
+            ev_lats,
+            plot_data,
+            cmap=cmap,
+            norm=norm,
+            transform=ccrs.PlateCarree(),
+            shading="auto",
+        )
+        ax.coastlines(linewidth=0.5, zorder=10)
+        ax.add_feature(cfeature.BORDERS, linewidth=0.6, edgecolor="grey")
+        ax.add_feature(cfeature.STATES, linewidth=0.4, edgecolor="lightgrey")
+
+        cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+        cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
+        cbar.set_label("Consecutive Days", fontsize=12)
+
+        start_str = str(row["start_date"])[:10]
+        end_str = str(row["end_date"])[:10]
+        ax.set_title(
+            f"Event {label}: Consecutive Exceedance Days\n{start_str} to {end_str}",
+            loc="left",
+            fontsize=13,
+        )
+        out_path = f"{stem}_event_{label:04d}_consec.png"
+        fig.savefig(out_path, dpi=200, bbox_inches="tight")
+        plt.close(fig)
+        logger.info("  Event %04d saved to %s", label, out_path)
+
+
 def main():
     parser = argparse.ArgumentParser(
         description=("Detect temperature exceedance events globally from ERA5."),
@@ -766,7 +1137,7 @@ def main():
     parser.add_argument(
         "--output",
         default="events_global.csv",
-        help="Output CSV path",
+        help="Output CSV path stem (actual file will be {stem}_events.csv)",
     )
     parser.add_argument(
         "--n-workers",
@@ -774,21 +1145,32 @@ def main():
         default=4,
         help="Number of dask workers",
     )
+    parser.add_argument(
+        "--dask-batch",
+        type=int,
+        default=1,
+        help=(
+            "Number of monthly chunks to submit to Dask at once. "
+            "Default 1 (one month per batch). Dask still parallelizes "
+            "daily time steps within each month across workers. Larger "
+            "values increase cross-month parallelism but cause "
+            "rechunk-merge bottlenecks when months span many zarr "
+            "chunks."
+        ),
+    )
     parser.add_argument(
         "--quantile-lower",
         type=float,
         default=None,
         help=(
             f"Lower-bound climatology quantile ({VALID_QUANTILES}). "
-            "Days where temp op_lower clim_lower are candidates. "
-            "At least one of --quantile-lower or --quantile-upper "
-            "is required."
+            "At least one of --quantile-lower or --quantile-upper required."
         ),
     )
     parser.add_argument(
         "--operator-lower",
         default=">",
-        help=("Comparison operator applied to the lower bound (default: >)"),
+        help="Comparison operator for the lower bound (default: >)",
     )
     parser.add_argument(
         "--quantile-upper",
@@ -796,14 +1178,13 @@ def main():
         default=None,
         help=(
             f"Upper-bound climatology quantile ({VALID_QUANTILES}). "
-            "Days where temp op_upper clim_upper are candidates. "
             "Combine with --quantile-lower to define a band."
         ),
     )
     parser.add_argument(
         "--operator-upper",
         default="<",
-        help=("Comparison operator applied to the upper bound (default: <)"),
+        help="Comparison operator for the upper bound (default: <)",
     )
     parser.add_argument(
         "--lat-min",
@@ -817,6 +1198,41 @@ def main():
         default=90.0,
         help="Maximum latitude to include in detection. Default 90.0",
     )
+    parser.add_argument(
+        "--chunk-months",
+        type=int,
+        default=1,
+        help=(
+            "Process the exceedance mask this many months at a time "
+            "to stay within Dask worker memory limits. Set to 0 to "
+            "compute the full date range at once. Default is 1."
+        ),
+    )
+    parser.add_argument(
+        "--min-area-km2",
+        type=float,
+        default=200_000.0,
+        help=(
+            "Minimum contiguous blob area in km² required to seed a "
+            "new event AND on each of the prior MIN_CONSECUTIVE_DAYS-1 "
+            "days (joint-area check). Always applied, including in "
+            "sensitivity mode. Default 200 000."
+        ),
+    )
+    parser.add_argument(
+        "--sensitivity-thresholds",
+        type=float,
+        nargs="+",
+        default=None,
+        metavar="KM2",
+        help=(
+            "Count surviving events at each supplied area threshold "
+            "(km²) and write a summary CSV. Events are seeded at "
+            "--min-area-km2 first; thresholds below that value will "
+            "equal the seed-filter count. Example: "
+            "--sensitivity-thresholds 200000 300000 500000 1000000"
+        ),
+    )
     args = parser.parse_args()
 
     if args.quantile_lower is None and args.quantile_upper is None:
@@ -824,8 +1240,6 @@ def main():
             "At least one of --quantile-lower or --quantile-upper must be provided."
         )
 
-    # Derive event_type label and plot colour from the quantiles.
-    # Format: "q{lower}+" / "q{upper}-" / "q{lower}-q{upper}"
     ql, qu = args.quantile_lower, args.quantile_upper
     if ql is not None and qu is not None:
         event_type = f"q{ql:.2f}-q{qu:.2f}"
@@ -833,9 +1247,7 @@ def main():
         event_type = f"q{ql:.2f}+"
     else:
         event_type = f"q{qu:.2f}-"
-    # Infer warm vs cold for the plot colourmap:
-    # warm when the lower bound is above the median (high percentile),
-    # cold when the upper bound is below the median (low percentile).
+
     if ql is not None and ql >= 0.5:
         plot_event_type = "heat_wave"
     elif qu is not None and qu <= 0.5:
@@ -843,10 +1255,19 @@ def main():
     else:
         plot_event_type = "heat_wave"
 
+    is_sensitivity = args.sensitivity_thresholds is not None
+    if is_sensitivity:
+        below = [t for t in args.sensitivity_thresholds if t < args.min_area_km2]
+        if below:
+            logger.warning(
+                "Sensitivity thresholds %s are below the seed filter "
+                "(%.0f km²); those counts will equal the seed-filter total.",
+                below,
+                args.min_area_km2,
+            )
+
     wall_start = time_module.time()
-    client = Client(
-        LocalCluster(n_workers=args.n_workers),
-    )
+    client = Client(LocalCluster(n_workers=args.n_workers))
     logger.info("Dask dashboard: %s", client.dashboard_link)
 
     logger.info("Pre-warming numba JIT...")
@@ -882,22 +1303,84 @@ def main():
     land_mask_np = land_mask.values.astype(bool)
     area_grid = compute_grid_cell_area(t2m.latitude.values, t2m.longitude.values)
 
-    logger.info("Building exceedance mask (lazy)...")
-    exc_lazy = build_exceedance_mask(
-        t2m,
-        clim_lower,
-        clim_upper,
-        land_mask,
-        op_lower=args.operator_lower,
-        op_upper=args.operator_upper,
-    )
-
-    tdim = detect_time_dim(exc_lazy)
+    tdim = detect_time_dim(t2m)
 
-    logger.info("Computing exceedance mask...")
+    # ── Exceedance mask + daily temperature (combined, parallel) ──────
+    # Both passes read the same ERA5 data month-by-month. Combining them
+    # into one loop and submitting all lazy chunks to dask.compute() at
+    # once lets the Dask workers process months in parallel, halving ERA5
+    # reads and eliminating sequential chunk overhead.
     t0 = time_module.time()
-    exc_da = exc_lazy.compute()
-    logger.info("  done in %.1f s", time_module.time() - t0)
+    if args.chunk_months > 0:
+        logger.info(
+            "Building lazy graphs for exceedance mask + temperature "
+            "(%d-month chunks, parallel)...",
+            args.chunk_months,
+        )
+        month_starts = pd.date_range(args.start_date, args.end_date, freq="MS")
+        chunk_labels = []
+        exc_lazy_list = []
+        t2m_lazy_list = []
+        for i in range(0, len(month_starts), args.chunk_months):
+            ms = month_starts[i]
+            me = month_starts[min(i + args.chunk_months, len(month_starts)) - 1]
+            me = me + pd.offsets.MonthEnd(1)
+            chunk_labels.append(f"{ms.strftime('%Y-%m')} - {me.strftime('%Y-%m')}")
+            t2m_chunk = t2m.sel({tdim: slice(str(ms.date()), str(me.date()))})
+            exc_lazy_list.append(
+                build_exceedance_mask(
+                    t2m_chunk,
+                    clim_lower,
+                    clim_upper,
+                    land_mask,
+                    op_lower=args.operator_lower,
+                    op_upper=args.operator_upper,
+                )
+            )
+            t2m_lazy_list.append(t2m_chunk.resample({tdim: "1D"}).mean())
+
+        n_chunks = len(exc_lazy_list)
+        batch = args.dask_batch
+        logger.info(
+            "  Computing %d chunks, %d per batch, via Dask...",
+            n_chunks,
+            batch,
+        )
+        exc_parts: list = []
+        t2m_parts: list = []
+        for b0 in range(0, n_chunks, batch):
+            b1 = min(b0 + batch, n_chunks)
+            b_exc = exc_lazy_list[b0:b1]
+            b_t2m = t2m_lazy_list[b0:b1]
+            b_sz = b1 - b0
+            b_results = dask.compute(*b_exc, *b_t2m)
+            exc_parts.extend(b_results[:b_sz])
+            t2m_parts.extend(b_results[b_sz:])
+            for label in chunk_labels[b0:b1]:
+                logger.info("  chunk %s done", label)
+
+        exc_da = xr.concat(exc_parts, dim=tdim)
+        t2m_daily_da = xr.concat(t2m_parts, dim=tdim)
+        del exc_parts, t2m_parts
+    else:
+        logger.info(
+            "Building lazy graphs (all-at-once) for exceedance mask + temperature..."
+        )
+        exc_lazy = build_exceedance_mask(
+            t2m,
+            clim_lower,
+            clim_upper,
+            land_mask,
+            op_lower=args.operator_lower,
+            op_upper=args.operator_upper,
+        )
+        t2m_lazy = t2m.resample({tdim: "1D"}).mean()
+        exc_da, t2m_daily_da = dask.compute(exc_lazy, t2m_lazy)
+
+    logger.info(
+        "  exceedance mask + temperature done in %.1f s",
+        time_module.time() - t0,
+    )
 
     dates = exc_da[tdim].values
     lats = exc_da.latitude.values
@@ -905,6 +1388,9 @@ def main():
     exc_np = exc_da.values.astype(bool)
     del exc_da
 
+    t2m_daily_np = (t2m_daily_da.values - 273.15).astype(np.float32)
+    del t2m_daily_da
+
     logger.info(
         "Applying %d-day consecutive filter...",
         MIN_CONSECUTIVE_DAYS,
@@ -917,10 +1403,21 @@ def main():
     )
     del exc_np
 
-    logger.info("Detecting events...")
+    logger.info(
+        "Detecting events (min_seed_area_km2=%.0f)...",
+        args.min_area_km2,
+    )
     t0 = time_module.time()
-    events = detect_events(exc_filt, dates, lats, lons, event_type, area_grid=area_grid)
-    logger.info("  %d events (%.1f s)", len(events), time_module.time() - t0)
+    events = detect_events(
+        exc_filt,
+        dates,
+        lats,
+        lons,
+        event_type,
+        area_grid=area_grid,
+        min_seed_area_km2=args.min_area_km2,
+    )
+    logger.info("  %d raw events (%.1f s)", len(events), time_module.time() - t0)
 
     logger.info(
         "Expanding event bounds (%d-deg steps)...",
@@ -935,32 +1432,80 @@ def main():
     )
     logger.info("  done in %.1f s", time_module.time() - t0)
 
-    logger.info("Computing max-consecutive-days plot...")
+    logger.info("Computing per-event temperature statistics...")
+    t0 = time_module.time()
+    events = include_temps_with_events(
+        events, t2m_daily_np, exc_filt, dates, lats, lons
+    )
+    del t2m_daily_np
+    logger.info("  done in %.1f s", time_module.time() - t0)
+
     stem = str(pathlib.Path(args.output).with_suffix(""))
+
+    df = events_to_dataframe(events, min_area_km2=args.min_area_km2)
+    events_csv = f"{stem}_events.csv"
+    df.to_csv(events_csv, index=False)
+    logger.info("%d events written to %s", len(df), events_csv)
+
+    # ── Global consecutive-days map ───────────────────────────────────
+    logger.info("Computing max-consecutive-days plot...")
     consec = max_consecutive_days(exc_filt)
     plot_consecutive_map(
         consec,
         lats,
         lons,
-        plot_event_type,
+        cast(Literal["heat_wave", "cold_snap"], plot_event_type),
         title=(
             f"Consecutive Exceedance Days ({event_type})"
             f"\n{args.start_date} to {args.end_date}"
         ),
         output_path=f"{stem}_consec.png",
     )
-    del consec, exc_filt
+    del consec
 
-    df = events_to_dataframe(events)
-    df.to_csv(args.output, index=False)
+    # ── Overview and high-consec bounding-box plots ───────────────────
+    plot_events_global(
+        df,
+        event_type=plot_event_type,
+        title=(
+            f"Detected events ({event_type.replace('_', ' ')})"
+            f"\n{args.start_date} to {args.end_date}"
+        ),
+        output_path=f"{stem}_events.png",
+    )
+    plot_events_high_consec(
+        df,
+        title=(
+            f"Events \u22656 consecutive days ({event_type.replace('_', ' ')})"
+            f"\n{args.start_date} to {args.end_date}"
+        ),
+        output_path=f"{stem}_high_consec.png",
+    )
+
+    # ── Per-event consecutive-days maps (>= 6 consec days) ───────────
+    plot_event_consec_maps(df, exc_filt, dates, lats, lons, stem)
+
+    del exc_filt
+
+    # ── Sensitivity table ─────────────────────────────────────────────
+    if is_sensitivity:
+        thresholds = sorted(args.sensitivity_thresholds)
+        logger.info(
+            "Sensitivity test: counting events at %d thresholds...",
+            len(thresholds),
+        )
+        rows = []
+        for thresh in thresholds:
+            df_thresh = events_to_dataframe(events, min_area_km2=thresh)
+            rows.append({"area_threshold_km2": thresh, "n_events": len(df_thresh)})
+        sens_df = pd.DataFrame(rows)
+        print(sens_df.to_string(index=False))
+        sens_path = f"{stem}_sensitivity.csv"
+        sens_df.to_csv(sens_path, index=False)
+        logger.info("Sensitivity table saved to %s", sens_path)
 
     elapsed = time_module.time() - wall_start
-    logger.info(
-        "Done: %d events -> %s (%.1f s)",
-        len(df),
-        args.output,
-        elapsed,
-    )
+    logger.info("Done in %.1f s", elapsed)
     client.close()
 
 
diff --git a/src/extremeweatherbench/data/base_temp_events.yaml b/src/extremeweatherbench/data/base_temp_events.yaml
index 5cf4ff1f..2c670121 100644
--- a/src/extremeweatherbench/data/base_temp_events.yaml
+++ b/src/extremeweatherbench/data/base_temp_events.yaml
@@ -347,9 +347,9 @@
   location:
     type: centered_region
     parameters:
-      latitude: 48.1351
+      latitude: 53.1351
       longitude: 11.582
-      bounding_box_degrees: 5
+      bounding_box_degrees: 10
   event_type: freeze
 - case_id_number: 33
   title: 2023 China
@@ -374,17 +374,17 @@
       bounding_box_degrees: 5
   event_type: freeze
 - case_id_number: 35
-  title: 2022 Colorado
+  title: 2022 West US
   start_date: 2022-04-06 00:00:00
   end_date: 2022-04-16 00:00:00
   location:
     type: centered_region
     parameters:
-      latitude: 39.7392
+      latitude: 43.7392
       longitude: 255.0097
-      bounding_box_degrees: 5
+      bounding_box_degrees: 10
   event_type: freeze
-- case_id_number: 72
+- case_id_number: 70
   title: May 2024 Texas
   start_date: 2024-05-25 00:00:00
   end_date: 2024-05-31 00:00:00
@@ -395,7 +395,7 @@
       longitude: 262.50260000000003
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 73
+- case_id_number: 71
   title: June 2024 Northeast US
   start_date: 2024-06-17 00:00:00
   end_date: 2024-06-23 00:00:00
@@ -403,21 +403,21 @@
     type: centered_region
     parameters:
       latitude: 41.8781
-      longitude: 286.771
+      longitude: 280.771
       bounding_box_degrees: 6
   event_type: heat_wave
-- case_id_number: 74
+- case_id_number: 72
   title: July 2024 Southwest US
   start_date: 2024-07-04 00:00:00
   end_date: 2024-07-10 00:00:00
   location:
     type: centered_region
     parameters:
-      latitude: 33.7701
+      latitude: 36.7701
       longitude: 243.78539999999998
       bounding_box_degrees: 6
   event_type: heat_wave
-- case_id_number: 75
+- case_id_number: 73
   title: July 2024 Northeast US
   start_date: 2024-07-07 00:00:00
   end_date: 2024-07-13 00:00:00
@@ -428,7 +428,7 @@
       longitude: 285.994
       bounding_box_degrees: 6
   event_type: heat_wave
-- case_id_number: 76
+- case_id_number: 74
   title: July 2024 Mid-Atlantic US
   start_date: 2024-07-15 00:00:00
   end_date: 2024-07-21 00:00:00
@@ -439,7 +439,7 @@
       longitude: 284.8348
       bounding_box_degrees: 6
   event_type: heat_wave
-- case_id_number: 77
+- case_id_number: 75
   title: August 2024 Midwest US
   start_date: 2024-08-25 00:00:00
   end_date: 2024-08-31 00:00:00
@@ -447,10 +447,10 @@
     type: centered_region
     parameters:
       latitude: 40.1106
-      longitude: 271.79269999999997
+      longitude: 275.79269999999997
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 78
+- case_id_number: 76
   title: July 2024 Antarctica
   start_date: 2024-07-01 00:00:00
   end_date: 2024-07-31 00:00:00
@@ -461,7 +461,7 @@
       longitude: 15.0
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 79
+- case_id_number: 77
   title: August 2024 Canada
   start_date: 2024-08-09 00:00:00
   end_date: 2024-08-15 00:00:00
@@ -472,7 +472,7 @@
       longitude: 248.0
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 80
+- case_id_number: 78
   title: August 2024 Australia
   start_date: 2024-08-22 00:00:00
   end_date: 2024-08-30 00:00:00
@@ -483,7 +483,7 @@
       longitude: 120.0
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 81
+- case_id_number: 79
   title: July/August 2024 Japan
   start_date: 2024-07-25 00:00:00
   end_date: 2024-08-05 00:00:00
@@ -494,7 +494,7 @@
       longitude: 138.0
       bounding_box_degrees: 6
   event_type: heat_wave
-- case_id_number: 82
+- case_id_number: 80
   title: June 2024 Saudi Arabia
   start_date: 2024-06-16 00:00:00
   end_date: 2024-06-22 00:00:00
@@ -505,7 +505,7 @@
       longitude: 45.0
       bounding_box_degrees: 6
   event_type: heat_wave
-- case_id_number: 83
+- case_id_number: 81
   title: August 2024 Europe
   start_date: 2024-08-10 00:00:00
   end_date: 2024-08-16 00:00:00
@@ -516,7 +516,7 @@
       longitude: 10.8978
       bounding_box_degrees: 10
   event_type: heat_wave
-- case_id_number: 84
+- case_id_number: 82
   title: July 2024 Ukraine
   start_date: 2024-07-12 00:00:00
   end_date: 2024-07-18 00:00:00
@@ -527,7 +527,7 @@
       longitude: 30.5234
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 85
+- case_id_number: 83
   title: June 2024 Europe
   start_date: 2024-06-20 00:00:00
   end_date: 2024-06-30 00:00:00
@@ -538,7 +538,7 @@
       longitude: 4.9041
       bounding_box_degrees: 6
   event_type: heat_wave
-- case_id_number: 86
+- case_id_number: 84
   title: May 2024 Central Mexico
   start_date: 2024-05-23 00:00:00
   end_date: 2024-05-31 00:00:00
@@ -549,7 +549,7 @@
       longitude: 260.8668
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 87
+- case_id_number: 85
   title: May 2024 Pakistan/India
   start_date: 2024-05-23 00:00:00
   end_date: 2024-05-31 00:00:00
@@ -558,9 +558,9 @@
     parameters:
       latitude: 34.0
       longitude: 76.0
-      bounding_box_degrees: 5
+      bounding_box_degrees: 8
   event_type: heat_wave
-- case_id_number: 88
+- case_id_number: 86
   title: August 2023 Chile
   start_date: 2023-08-01 00:00:00
   end_date: 2023-08-07 00:00:00
@@ -571,7 +571,7 @@
       longitude: 289.3307
       bounding_box_degrees: 5
   event_type: heat_wave
-- case_id_number: 89
+- case_id_number: 87
   title: January 2024 Pacific Northwest
   start_date: 2024-01-11 12:00:00
   end_date: 2024-01-20 00:00:00
@@ -582,18 +582,18 @@
       longitude: 237.6679
       bounding_box_degrees: 5
   event_type: freeze
-- case_id_number: 90
-  title: April 2022 Nevada
+- case_id_number: 88
+  title: April 2022 Western North America
   start_date: 2022-04-11 18:00:00
   end_date: 2022-04-17 18:00:00
   location:
     type: centered_region
     parameters:
-      latitude: 40.8324
+      latitude: 47.8324
       longitude: 244.2369
       bounding_box_degrees: 5
   event_type: freeze
-- case_id_number: 91
+- case_id_number: 89
   title: February/March 2021 New England
   start_date: 2021-03-04 18:00:00
   end_date: 2021-03-10 18:00:00
@@ -604,7 +604,7 @@
       longitude: 287.4421
       bounding_box_degrees: 5
   event_type: freeze
-- case_id_number: 92
+- case_id_number: 90
   title: January 2024 Northern Europe
   start_date: 2024-01-01 06:00:00
   end_date: 2024-01-09 12:00:00
@@ -615,7 +615,7 @@
       longitude: 18.0686
       bounding_box_degrees: 5
   event_type: freeze
-- case_id_number: 93
+- case_id_number: 91
   title: December 2022 Europe
   start_date: 2022-12-10 06:00:00
   end_date: 2022-12-20 00:00:00
@@ -626,7 +626,7 @@
       longitude: 13.405
       bounding_box_degrees: 5
   event_type: freeze
-- case_id_number: 94
+- case_id_number: 92
   title: April 2024 Europe
   start_date: 2024-04-16 12:00:00
   end_date: 2024-04-27 12:00:00
@@ -637,7 +637,7 @@
       longitude: 2.3522
       bounding_box_degrees: 5
   event_type: freeze
-- case_id_number: 95
+- case_id_number: 93
   title: January 2023 North China
   start_date: 2024-01-20 00:00:00
   end_date: 2024-02-04 18:00:00
@@ -645,10 +645,10 @@
     type: centered_region
     parameters:
       latitude: 42.5246
-      longitude: 122.3853
-      bounding_box_degrees: 5
+      longitude: 120.3853
+      bounding_box_degrees: 8
   event_type: freeze
-- case_id_number: 96
+- case_id_number: 94
   title: April 2024 Sweden
   start_date: 2024-04-02 00:00:00
   end_date: 2024-04-08 00:00:00
diff --git a/src/extremeweatherbench/data/events.yaml b/src/extremeweatherbench/data/events.yaml
index d1165ea7..cdd7659e 100644
--- a/src/extremeweatherbench/data/events.yaml
+++ b/src/extremeweatherbench/data/events.yaml
@@ -377,10 +377,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 45.75
-      latitude_max: 68.75
-      longitude_min: 9.0
-      longitude_max: 14.0
+      latitude_min: 48.25
+      latitude_max: 68.25
+      longitude_min: 1.5
+      longitude_max: 21.5
   event_type: freeze
 - case_id_number: 33
   title: 2023 China
@@ -407,16 +407,16 @@
       longitude_max: 71.75
   event_type: freeze
 - case_id_number: 35
-  title: 2022 Colorado
+  title: 2022 West US
   start_date: 2022-04-06 00:00:00
   end_date: 2022-04-16 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 37.25
-      latitude_max: 42.25
-      longitude_min: -107.5
-      longitude_max: -102.5
+      latitude_min: 38.75
+      latitude_max: 64.75
+      longitude_min: -129.75
+      longitude_max: -91.0
   event_type: freeze
 - case_id_number: 36
   title: July 2024 South Dakota
@@ -833,10 +833,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 9.75
-      latitude_max: 32.25
-      longitude_min: 254.25
-      longitude_max: 277.0
+      latitude_min: 14.5
+      latitude_max: 31.5
+      longitude_min: -105.0
+      longitude_max: -95.0
   event_type: heat_wave
 - case_id_number: 71
   title: June 2024 Northeast US
@@ -846,9 +846,9 @@
     type: bounded_region
     parameters:
       latitude_min: 37.0
-      latitude_max: 50.75
-      longitude_min: 272.0
-      longitude_max: 307.25
+      latitude_max: 46.0
+      longitude_min: -87.25
+      longitude_max: -69.25
   event_type: heat_wave
 - case_id_number: 72
   title: July 2024 Southwest US
@@ -857,10 +857,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 14.5
-      latitude_max: 31.5
-      longitude_min: -105.0
-      longitude_max: -95.0
+      latitude_min: 28.75
+      latitude_max: 59.75
+      longitude_min: -125.25
+      longitude_max: -110.25
   event_type: heat_wave
 - case_id_number: 73
   title: July 2024 Northeast US
@@ -869,10 +869,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 39.0
-      latitude_max: 48.0
-      longitude_min: -82.25
-      longitude_max: -69.25
+      latitude_min: 37.75
+      latitude_max: 52.75
+      longitude_min: -78.0
+      longitude_max: -61.0
   event_type: heat_wave
 - case_id_number: 74
   title: July 2024 Mid-Atlantic US
@@ -881,10 +881,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 30.75
-      latitude_max: 37.75
-      longitude_min: -124.25
-      longitude_max: -113.25
+      latitude_min: 37.0
+      latitude_max: 43.0
+      longitude_min: -81.25
+      longitude_max: -68.25
   event_type: heat_wave
 - case_id_number: 75
   title: August 2024 Midwest US
@@ -893,10 +893,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 37.75
-      latitude_max: 52.75
-      longitude_min: -78.0
-      longitude_max: -61.0
+      latitude_min: 37.5
+      latitude_max: 43.5
+      longitude_min: -86.75
+      longitude_max: -81.75
   event_type: heat_wave
 - case_id_number: 76
   title: July 2024 Antarctica
@@ -905,10 +905,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 37.0
-      latitude_max: 43.0
-      longitude_min: -81.25
-      longitude_max: -68.25
+      latitude_min: -83.5
+      latitude_max: -68.5
+      longitude_min: 4.5
+      longitude_max: 37.5
   event_type: heat_wave
 - case_id_number: 77
   title: August 2024 Canada
@@ -917,10 +917,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 37.5
-      latitude_max: 44.5
-      longitude_min: -90.75
-      longitude_max: -85.75
+      latitude_min: 58.5
+      latitude_max: 76.5
+      longitude_min: -133.5
+      longitude_max: -89.5
   event_type: heat_wave
 - case_id_number: 78
   title: August 2024 Australia
@@ -929,10 +929,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -83.5
-      latitude_max: -68.5
-      longitude_min: 4.5
-      longitude_max: 37.5
+      latitude_min: -27.5
+      latitude_max: -17.5
+      longitude_min: 114.5
+      longitude_max: 142.5
   event_type: heat_wave
 - case_id_number: 79
   title: July/August 2024 Japan
@@ -941,10 +941,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 58.5
-      latitude_max: 76.5
-      longitude_min: -133.5
-      longitude_max: -89.5
+      latitude_min: 33.0
+      latitude_max: 39.0
+      longitude_min: 132.0
+      longitude_max: 141.0
   event_type: heat_wave
 - case_id_number: 80
   title: June 2024 Saudi Arabia
@@ -953,10 +953,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -27.5
-      latitude_max: -17.5
-      longitude_min: 114.5
-      longitude_max: 142.5
+      latitude_min: 18.0
+      latitude_max: 36.0
+      longitude_min: 22.0
+      longitude_max: 55.0
   event_type: heat_wave
 - case_id_number: 81
   title: August 2024 Europe
@@ -965,10 +965,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 33.0
-      latitude_max: 39.0
-      longitude_min: 132.0
-      longitude_max: 141.0
+      latitude_min: 34.5
+      latitude_max: 53.5
+      longitude_min: 3.0
+      longitude_max: 22.0
   event_type: heat_wave
 - case_id_number: 82
   title: July 2024 Ukraine
@@ -977,10 +977,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 18.0
-      latitude_max: 36.0
-      longitude_min: 22.0
-      longitude_max: 55.0
+      latitude_min: 39.0
+      latitude_max: 55.0
+      longitude_min: 15.0
+      longitude_max: 39.0
   event_type: heat_wave
 - case_id_number: 83
   title: June 2024 Europe
@@ -989,10 +989,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 34.5
-      latitude_max: 53.5
-      longitude_min: 3.0
-      longitude_max: 22.0
+      latitude_min: 48.25
+      latitude_max: 55.25
+      longitude_min: 2.0
+      longitude_max: 9.0
   event_type: heat_wave
 - case_id_number: 84
   title: May 2024 Central Mexico
@@ -1001,10 +1001,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 39.0
-      latitude_max: 55.0
-      longitude_min: 15.0
-      longitude_max: 39.0
+      latitude_min: 14.0
+      latitude_max: 31.0
+      longitude_min: -104.75
+      longitude_max: -82.75
   event_type: heat_wave
 - case_id_number: 85
   title: May 2024 Pakistan/India
@@ -1013,10 +1013,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 48.25
-      latitude_max: 55.25
-      longitude_min: 2.0
-      longitude_max: 9.0
+      latitude_min: 30.0
+      latitude_max: 43.0
+      longitude_min: 72.0
+      longitude_max: 86.0
   event_type: heat_wave
 - case_id_number: 86
   title: August 2023 Chile
@@ -1025,10 +1025,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 14.0
-      latitude_max: 31.0
-      longitude_min: -104.75
-      longitude_max: -82.75
+      latitude_min: -42.0
+      latitude_max: -20.0
+      longitude_min: -73.25
+      longitude_max: -62.25
   event_type: heat_wave
 - case_id_number: 87
   title: January 2024 Pacific Northwest
@@ -1037,10 +1037,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 31.5
-      latitude_max: 36.5
-      longitude_min: 73.5
-      longitude_max: 78.5
+      latitude_min: 45.0
+      latitude_max: 69.0
+      longitude_min: -133.75
+      longitude_max: -100.0
   event_type: freeze
 - case_id_number: 88
   title: April 2022 Nevada
@@ -1049,10 +1049,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -42.0
-      latitude_max: -20.0
-      longitude_min: -73.25
-      longitude_max: -62.25
+      latitude_min: 42.25
+      latitude_max: 67.25
+      longitude_min: -138.25
+      longitude_max: -93.5
   event_type: freeze
 - case_id_number: 89
   title: February/March 2021 New England
@@ -1061,10 +1061,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 45.0
-      latitude_max: 69.0
-      longitude_min: -133.75
-      longitude_max: -100.0
+      latitude_min: 40.25
+      latitude_max: 46.25
+      longitude_min: -75.0
+      longitude_max: -68.0
   event_type: freeze
 - case_id_number: 90
   title: January 2024 Northern Europe
@@ -1073,10 +1073,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 38.25
-      latitude_max: 63.25
-      longitude_min: -118.25
-      longitude_max: -113.25
+      latitude_min: 56.75
+      latitude_max: 71.75
+      longitude_min: 3.5
+      longitude_max: 20.5
   event_type: freeze
 - case_id_number: 91
   title: December 2022 Europe
@@ -1085,10 +1085,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 40.25
-      latitude_max: 46.25
-      longitude_min: -75.0
-      longitude_max: -68.0
+      latitude_min: 48.0
+      latitude_max: 55.0
+      longitude_min: -9.0
+      longitude_max: 16.0
   event_type: freeze
 - case_id_number: 92
   title: April 2024 Europe
@@ -1097,10 +1097,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 56.75
-      latitude_max: 71.75
-      longitude_min: 3.5
-      longitude_max: 20.5
+      latitude_min: 42.25
+      latitude_max: 54.25
+      longitude_min: -3.25
+      longitude_max: 23.75
   event_type: freeze
 - case_id_number: 93
   title: January 2023 North China
@@ -1109,10 +1109,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 48.0
-      latitude_max: 55.0
-      longitude_min: -9.0
-      longitude_max: 16.0
+      latitude_min: 38.5
+      latitude_max: 46.5
+      longitude_min: 116.5
+      longitude_max: 124.5
   event_type: freeze
 - case_id_number: 94
   title: April 2024 Sweden
@@ -1121,10 +1121,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 42.25
-      latitude_max: 54.25
-      longitude_min: -3.25
-      longitude_max: 23.75
+      latitude_min: 57.75
+      latitude_max: 71.75
+      longitude_min: 10.25
+      longitude_max: 21.25
   event_type: freeze
 - case_id_number: 95
   title: November 2024 West Coast US
@@ -1133,10 +1133,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 40.0
-      latitude_max: 45.0
-      longitude_min: 120.0
-      longitude_max: 125.0
+      latitude_min: 27.8
+      latitude_max: 54.2
+      longitude_min: 222.4
+      longitude_max: 245.3
   event_type: atmospheric_river
 - case_id_number: 96
   title: October 2024 British Columbia
@@ -1145,10 +1145,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: 57.75
-      latitude_max: 71.75
-      longitude_min: 10.25
-      longitude_max: 21.25
+      latitude_min: 32.8
+      latitude_max: 56.0
+      longitude_min: 217.3
+      longitude_max: 247.0
   event_type: atmospheric_river
 - case_id_number: 97
   title: September 2024 SW Alaska and British Columbia

From 3f663bc2b1f2fd185db2231b99515e99e3dcd729 Mon Sep 17 00:00:00 2001
From: taylor <mandelbaum.taylor@gmail.com>
Date: Sat, 11 Apr 2026 13:58:01 +0000
Subject: [PATCH 56/72] enable plot to be used in an axes

---
 data_prep/plot_temperature_events.py | 41 ++++++++++++++++++++--------
 1 file changed, 30 insertions(+), 11 deletions(-)

diff --git a/data_prep/plot_temperature_events.py b/data_prep/plot_temperature_events.py
index e8c8708a..745bfb6d 100644
--- a/data_prep/plot_temperature_events.py
+++ b/data_prep/plot_temperature_events.py
@@ -248,20 +248,29 @@ def plot_consecutive_map(
     lons: np.ndarray,
     event_type: Literal["heat_wave", "cold_snap"],
     title: str,
-    output_path: str,
+    output_path: str | None = None,
     extent: tuple[float, float, float, float] | None = None,
+    ax=None,
 ) -> None:
     """Plot a max-consecutive-days field using the standard style.
 
+    When ``ax`` is provided the plot is drawn into that axis and
+    nothing is saved to disk; ``output_path`` is ignored.  When
+    ``ax`` is None a new figure is created and saved to
+    ``output_path``.
+
     Args:
         consec: int32 2-D array (nlat, nlon) of max consecutive days.
         lats: 1-D latitude array matching consec rows.
         lons: 1-D longitude array (0-360) matching consec columns.
         event_type: ``'heat_wave'`` or ``'cold_snap'``.
         title: Two-line figure title (left-aligned).
-        output_path: Destination PNG file path.
+        output_path: Destination PNG file path. Required when ax is
+            None; ignored when ax is provided.
         extent: (lon_min, lon_max, lat_min, lat_max) in -180..180.
             Derived from lats/lons when None. Default is None.
+        ax: Optional existing cartopy GeoAxes to draw into. When
+            provided, no figure is created or saved. Default is None.
     """
     plot_data = consec.astype(float)
     plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
@@ -283,10 +292,15 @@ def plot_consecutive_map(
     else:
         lon_min, lon_max, lat_min, lat_max = extent
 
-    fig, ax = plt.subplots(
-        subplot_kw={"projection": ccrs.PlateCarree()},
-        figsize=(10, 8),
-    )
+    own_fig = ax is None
+    if own_fig:
+        fig, ax = plt.subplots(
+            subplot_kw={"projection": ccrs.PlateCarree()},
+            figsize=(10, 8),
+        )
+    else:
+        fig = ax.get_figure()
+
     ax.set_extent(
         [lon_min, lon_max, lat_min, lat_max],
         crs=ccrs.PlateCarree(),
@@ -307,18 +321,23 @@ def plot_consecutive_map(
     divider = make_axes_locatable(ax)
     cax = divider.append_axes(
         "right",
-        size="5%",
+        size="3.75%",
         pad=0.1,
         axes_class=plt.Axes,
     )
     cbar = fig.colorbar(im, cax=cax)
     cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
-    cbar.set_label("Consecutive Days", fontsize=12)
+    cbar.set_label("Consecutive Days", fontsize=12, labelpad=6)
+    cbar.ax.yaxis.set_label_position("left")
 
     ax.set_title(title, loc="left", fontsize=13)
-    fig.savefig(output_path, dpi=200, bbox_inches="tight")
-    plt.close(fig)
-    logger.info("Saved consecutive-days plot to %s", output_path)
+
+    if own_fig:
+        if output_path is None:
+            raise ValueError("output_path is required when ax is not provided.")
+        fig.savefig(output_path, dpi=200, bbox_inches="tight")
+        plt.close(fig)
+        logger.info("Saved consecutive-days plot to %s", output_path)
 
 
 # ── bounding-box overview ─────────────────────────────────────────────

From 1e779704e285528c16f2558df7d5ad1c52871b38 Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Sun, 12 Apr 2026 01:39:50 +0000
Subject: [PATCH 57/72] add p15-p85 events yaml

---
 .../data/marginal_temperature_events.yaml     | 912 ++++++++++++++++++
 1 file changed, 912 insertions(+)
 create mode 100644 src/extremeweatherbench/data/marginal_temperature_events.yaml

diff --git a/src/extremeweatherbench/data/marginal_temperature_events.yaml b/src/extremeweatherbench/data/marginal_temperature_events.yaml
new file mode 100644
index 00000000..a50d4781
--- /dev/null
+++ b/src/extremeweatherbench/data/marginal_temperature_events.yaml
@@ -0,0 +1,912 @@
+- case_id_number: 1
+  title: February 2020 Event 0036
+  start_date: 2020-02-09 00:00:00
+  end_date: 2020-02-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 49.0
+      latitude_max: 60.5
+      longitude_min: 28.25
+      longitude_max: 58.25
+  event_type: marginal_temperature
+- case_id_number: 2
+  title: February 2020 Event 0043
+  start_date: 2020-02-17 00:00:00
+  end_date: 2020-02-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: -68.0
+      longitude_min: 0.0
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 3
+  title: April 2020 Event 0074
+  start_date: 2020-04-06 00:00:00
+  end_date: 2020-04-11 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 58.5
+      latitude_max: 70.25
+      longitude_min: 228.0
+      longitude_max: 267.0
+  event_type: marginal_temperature
+- case_id_number: 4
+  title: May 2020 Event 0088
+  start_date: 2020-05-09 00:00:00
+  end_date: 2020-05-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -27.0
+      latitude_max: -14.75
+      longitude_min: 118.25
+      longitude_max: 140.0
+  event_type: marginal_temperature
+- case_id_number: 5
+  title: May 2020 Event 0096
+  start_date: 2020-05-21 00:00:00
+  end_date: 2020-05-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -84.5
+      latitude_max: -75.25
+      longitude_min: 201.75
+      longitude_max: 237.0
+  event_type: marginal_temperature
+- case_id_number: 6
+  title: May 2020 Event 0099
+  start_date: 2020-05-28 00:00:00
+  end_date: 2020-06-02 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -82.25
+      latitude_max: -74.5
+      longitude_min: 201.75
+      longitude_max: 237.5
+  event_type: marginal_temperature
+- case_id_number: 7
+  title: May 2020 Event 0103
+  start_date: 2020-05-30 00:00:00
+  end_date: 2020-06-07 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 67.75
+      latitude_max: 78.5
+      longitude_min: 236.0
+      longitude_max: 279.0
+  event_type: marginal_temperature
+- case_id_number: 8
+  title: June 2020 Event 0104
+  start_date: 2020-06-02 00:00:00
+  end_date: 2020-06-07 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -28.0
+      latitude_max: -15.5
+      longitude_min: 119.5
+      longitude_max: 140.0
+  event_type: marginal_temperature
+- case_id_number: 9
+  title: July 2020 Event 0115
+  start_date: 2020-07-13 00:00:00
+  end_date: 2020-07-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -78.25
+      latitude_max: -66.0
+      longitude_min: 93.5
+      longitude_max: 162.0
+  event_type: marginal_temperature
+- case_id_number: 10
+  title: July 2020 Event 0116
+  start_date: 2020-07-13 00:00:00
+  end_date: 2020-07-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 63.25
+      latitude_max: 75.5
+      longitude_min: 72.25
+      longitude_max: 97.25
+  event_type: marginal_temperature
+- case_id_number: 11
+  title: August 2020 Event 0126
+  start_date: 2020-08-05 00:00:00
+  end_date: 2020-08-12 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -87.75
+      latitude_max: -67.0
+      longitude_min: 90.75
+      longitude_max: 240.0
+  event_type: marginal_temperature
+- case_id_number: 12
+  title: September 2020 Event 0153
+  start_date: 2020-09-26 00:00:00
+  end_date: 2020-10-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: -70.0
+      longitude_min: 0.0
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 13
+  title: September 2020 Event 0154
+  start_date: 2020-09-26 00:00:00
+  end_date: 2020-10-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -85.75
+      latitude_max: -74.5
+      longitude_min: 324.25
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 14
+  title: October 2020 Event 0160
+  start_date: 2020-10-05 00:00:00
+  end_date: 2020-10-10 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 56.0
+      latitude_max: 71.5
+      longitude_min: 62.25
+      longitude_max: 99.0
+  event_type: marginal_temperature
+- case_id_number: 15
+  title: October 2020 Event 0166
+  start_date: 2020-10-12 00:00:00
+  end_date: 2020-10-17 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -84.5
+      latitude_max: -75.25
+      longitude_min: 201.75
+      longitude_max: 228.0
+  event_type: marginal_temperature
+- case_id_number: 16
+  title: October 2020 Event 0171
+  start_date: 2020-10-19 00:00:00
+  end_date: 2020-10-24 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 52.75
+      latitude_max: 67.5
+      longitude_min: 281.75
+      longitude_max: 299.5
+  event_type: marginal_temperature
+- case_id_number: 17
+  title: October 2020 Event 0172
+  start_date: 2020-10-21 00:00:00
+  end_date: 2020-10-26 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -87.0
+      latitude_max: -68.0
+      longitude_min: 0.0
+      longitude_max: 128.0
+  event_type: marginal_temperature
+- case_id_number: 18
+  title: December 2020 Event 0215
+  start_date: 2020-12-05 00:00:00
+  end_date: 2020-12-10 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 60.5
+      latitude_max: 78.5
+      longitude_min: 306.5
+      longitude_max: 341.5
+  event_type: marginal_temperature
+- case_id_number: 19
+  title: December 2020 Event 0236
+  start_date: 2020-12-27 00:00:00
+  end_date: 2021-01-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 43.5
+      latitude_max: 60.0
+      longitude_min: 0.0
+      longitude_max: 27.5
+  event_type: marginal_temperature
+- case_id_number: 20
+  title: February 2021 Event 0273
+  start_date: 2021-02-09 00:00:00
+  end_date: 2021-02-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -81.0
+      latitude_max: -69.5
+      longitude_min: 46.25
+      longitude_max: 76.75
+  event_type: marginal_temperature
+- case_id_number: 21
+  title: March 2021 Event 0315
+  start_date: 2021-03-26 00:00:00
+  end_date: 2021-04-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -78.75
+      latitude_max: -66.5
+      longitude_min: 88.0
+      longitude_max: 159.75
+  event_type: marginal_temperature
+- case_id_number: 22
+  title: May 2021 Event 0342
+  start_date: 2021-05-08 00:00:00
+  end_date: 2021-05-17 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -89.75
+      latitude_max: -75.5
+      longitude_min: 0.0
+      longitude_max: 265.25
+  event_type: marginal_temperature
+- case_id_number: 23
+  title: May 2021 Event 0356
+  start_date: 2021-05-29 00:00:00
+  end_date: 2021-06-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -86.5
+      latitude_max: -65.75
+      longitude_min: 91.25
+      longitude_max: 147.75
+  event_type: marginal_temperature
+- case_id_number: 24
+  title: June 2021 Event 0364
+  start_date: 2021-06-10 00:00:00
+  end_date: 2021-06-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -80.5
+      latitude_max: -72.0
+      longitude_min: 56.0
+      longitude_max: 108.0
+  event_type: marginal_temperature
+- case_id_number: 25
+  title: June 2021 Event 0372
+  start_date: 2021-06-20 00:00:00
+  end_date: 2021-06-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 64.25
+      latitude_max: 74.0
+      longitude_min: 70.75
+      longitude_max: 94.5
+  event_type: marginal_temperature
+- case_id_number: 26
+  title: June 2021 Event 0376
+  start_date: 2021-06-28 00:00:00
+  end_date: 2021-07-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -84.75
+      latitude_max: -72.75
+      longitude_min: 201.75
+      longitude_max: 292.75
+  event_type: marginal_temperature
+- case_id_number: 27
+  title: July 2021 Event 0378
+  start_date: 2021-07-03 00:00:00
+  end_date: 2021-07-09 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -89.5
+      latitude_max: -68.25
+      longitude_min: 0.0
+      longitude_max: 164.5
+  event_type: marginal_temperature
+- case_id_number: 28
+  title: October 2021 Event 0421
+  start_date: 2021-10-03 00:00:00
+  end_date: 2021-10-09 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: -71.5
+      longitude_min: 0.0
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 29
+  title: November 2021 Event 0450
+  start_date: 2021-11-09 00:00:00
+  end_date: 2021-11-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 44.75
+      latitude_max: 74.0
+      longitude_min: 27.0
+      longitude_max: 122.5
+  event_type: marginal_temperature
+- case_id_number: 30
+  title: December 2021 Event 0483
+  start_date: 2021-12-19 00:00:00
+  end_date: 2021-12-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 15.5
+      latitude_max: 23.75
+      longitude_min: 0.0
+      longitude_max: 11.0
+  event_type: marginal_temperature
+- case_id_number: 31
+  title: December 2021 Event 0485
+  start_date: 2021-12-21 00:00:00
+  end_date: 2021-12-26 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -80.5
+      latitude_max: -66.75
+      longitude_min: 51.25
+      longitude_max: 159.75
+  event_type: marginal_temperature
+- case_id_number: 32
+  title: January 2022 Event 0500
+  start_date: 2022-01-02 00:00:00
+  end_date: 2022-01-07 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 53.25
+      latitude_max: 62.5
+      longitude_min: 280.5
+      longitude_max: 298.5
+  event_type: marginal_temperature
+- case_id_number: 33
+  title: January 2022 Event 0505
+  start_date: 2022-01-08 00:00:00
+  end_date: 2022-01-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: -71.25
+      longitude_min: 0.0
+      longitude_max: 231.5
+  event_type: marginal_temperature
+- case_id_number: 34
+  title: January 2022 Event 0507
+  start_date: 2022-01-08 00:00:00
+  end_date: 2022-01-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 54.5
+      latitude_max: 63.5
+      longitude_min: 281.75
+      longitude_max: 293.0
+  event_type: marginal_temperature
+- case_id_number: 35
+  title: January 2022 Event 0520
+  start_date: 2022-01-18 00:00:00
+  end_date: 2022-01-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 57.75
+      latitude_max: 69.5
+      longitude_min: 247.5
+      longitude_max: 275.25
+  event_type: marginal_temperature
+- case_id_number: 36
+  title: January 2022 Event 0522
+  start_date: 2022-01-20 00:00:00
+  end_date: 2022-01-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.25
+      latitude_max: 23.5
+      longitude_min: 348.25
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 37
+  title: January 2022 Event 0523
+  start_date: 2022-01-21 00:00:00
+  end_date: 2022-01-26 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -74.25
+      latitude_max: -67.0
+      longitude_min: 116.5
+      longitude_max: 170.5
+  event_type: marginal_temperature
+- case_id_number: 38
+  title: March 2022 Event 0552
+  start_date: 2022-03-01 00:00:00
+  end_date: 2022-03-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 54.25
+      latitude_max: 68.5
+      longitude_min: 280.75
+      longitude_max: 299.75
+  event_type: marginal_temperature
+- case_id_number: 39
+  title: May 2022 Event 0598
+  start_date: 2022-05-14 00:00:00
+  end_date: 2022-05-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 55.25
+      latitude_max: 69.5
+      longitude_min: 280.75
+      longitude_max: 291.75
+  event_type: marginal_temperature
+- case_id_number: 40
+  title: June 2022 Event 0614
+  start_date: 2022-06-10 00:00:00
+  end_date: 2022-06-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -74.5
+      latitude_max: -66.0
+      longitude_min: 99.0
+      longitude_max: 145.0
+  event_type: marginal_temperature
+- case_id_number: 41
+  title: June 2022 Event 0616
+  start_date: 2022-06-16 00:00:00
+  end_date: 2022-06-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -86.0
+      latitude_max: -66.25
+      longitude_min: 86.75
+      longitude_max: 153.0
+  event_type: marginal_temperature
+- case_id_number: 42
+  title: July 2022 Event 0628
+  start_date: 2022-07-11 00:00:00
+  end_date: 2022-07-17 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -84.75
+      latitude_max: -75.0
+      longitude_min: 201.75
+      longitude_max: 250.25
+  event_type: marginal_temperature
+- case_id_number: 43
+  title: July 2022 Event 0633
+  start_date: 2022-07-25 00:00:00
+  end_date: 2022-07-30 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 41.75
+      latitude_max: 57.75
+      longitude_min: 33.75
+      longitude_max: 66.75
+  event_type: marginal_temperature
+- case_id_number: 44
+  title: August 2022 Event 0646
+  start_date: 2022-08-29 00:00:00
+  end_date: 2022-09-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 66.75
+      latitude_max: 77.5
+      longitude_min: 73.25
+      longitude_max: 111.0
+  event_type: marginal_temperature
+- case_id_number: 45
+  title: September 2022 Event 0652
+  start_date: 2022-09-10 00:00:00
+  end_date: 2022-09-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 61.5
+      latitude_max: 75.25
+      longitude_min: 219.5
+      longitude_max: 263.75
+  event_type: marginal_temperature
+- case_id_number: 46
+  title: September 2022 Event 0659
+  start_date: 2022-09-18 00:00:00
+  end_date: 2022-09-24 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 58.0
+      latitude_max: 71.25
+      longitude_min: 192.25
+      longitude_max: 231.25
+  event_type: marginal_temperature
+- case_id_number: 47
+  title: October 2022 Event 0674
+  start_date: 2022-10-06 00:00:00
+  end_date: 2022-10-11 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -89.25
+      latitude_max: -70.75
+      longitude_min: 0.0
+      longitude_max: 118.75
+  event_type: marginal_temperature
+- case_id_number: 48
+  title: October 2022 Event 0675
+  start_date: 2022-10-09 00:00:00
+  end_date: 2022-10-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -88.75
+      latitude_max: -74.5
+      longitude_min: 242.75
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 49
+  title: October 2022 Event 0693
+  start_date: 2022-10-28 00:00:00
+  end_date: 2022-11-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 48.0
+      latitude_max: 69.75
+      longitude_min: 24.75
+      longitude_max: 78.0
+  event_type: marginal_temperature
+- case_id_number: 50
+  title: November 2022 Event 0712
+  start_date: 2022-11-23 00:00:00
+  end_date: 2022-11-29 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 41.25
+      latitude_max: 70.25
+      longitude_min: 0.0
+      longitude_max: 108.0
+  event_type: marginal_temperature
+- case_id_number: 51
+  title: December 2022 Event 0732
+  start_date: 2022-12-19 00:00:00
+  end_date: 2022-12-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 44.25
+      latitude_max: 69.0
+      longitude_min: 231.25
+      longitude_max: 302.75
+  event_type: marginal_temperature
+- case_id_number: 52
+  title: December 2022 Event 0742
+  start_date: 2022-12-24 00:00:00
+  end_date: 2022-12-29 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 58.0
+      latitude_max: 71.25
+      longitude_min: 192.25
+      longitude_max: 218.75
+  event_type: marginal_temperature
+- case_id_number: 53
+  title: January 2023 Event 0764
+  start_date: 2023-01-14 00:00:00
+  end_date: 2023-01-19 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -78.75
+      latitude_max: -67.25
+      longitude_min: 48.0
+      longitude_max: 114.25
+  event_type: marginal_temperature
+- case_id_number: 54
+  title: February 2023 Event 0778
+  start_date: 2023-02-07 00:00:00
+  end_date: 2023-02-12 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -84.0
+      latitude_max: -72.0
+      longitude_min: 0.0
+      longitude_max: 14.75
+  event_type: marginal_temperature
+- case_id_number: 55
+  title: February 2023 Event 0780
+  start_date: 2023-02-13 00:00:00
+  end_date: 2023-02-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -87.75
+      latitude_max: -74.25
+      longitude_min: 0.0
+      longitude_max: 266.25
+  event_type: marginal_temperature
+- case_id_number: 56
+  title: June 2023 Event 0858
+  start_date: 2023-06-11 00:00:00
+  end_date: 2023-06-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -90.0
+      latitude_max: -66.5
+      longitude_min: 0.0
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 57
+  title: August 2023 Event 0879
+  start_date: 2023-08-15 00:00:00
+  end_date: 2023-08-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -88.25
+      latitude_max: -67.75
+      longitude_min: 52.0
+      longitude_max: 149.25
+  event_type: marginal_temperature
+- case_id_number: 58
+  title: October 2023 Event 0909
+  start_date: 2023-10-06 00:00:00
+  end_date: 2023-10-11 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.25
+      latitude_max: 35.25
+      longitude_min: 0.0
+      longitude_max: 13.5
+  event_type: marginal_temperature
+- case_id_number: 59
+  title: October 2023 Event 0910
+  start_date: 2023-10-06 00:00:00
+  end_date: 2023-10-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 19.75
+      latitude_max: 32.25
+      longitude_min: 351.5
+      longitude_max: 359.75
+  event_type: marginal_temperature
+- case_id_number: 60
+  title: October 2023 Event 0914
+  start_date: 2023-10-14 00:00:00
+  end_date: 2023-10-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -29.0
+      latitude_max: -21.25
+      longitude_min: 121.5
+      longitude_max: 133.5
+  event_type: marginal_temperature
+- case_id_number: 61
+  title: October 2023 Event 0926
+  start_date: 2023-10-29 00:00:00
+  end_date: 2023-11-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 45.25
+      latitude_max: 67.25
+      longitude_min: 240.0
+      longitude_max: 276.25
+  event_type: marginal_temperature
+- case_id_number: 62
+  title: November 2023 Event 0943
+  start_date: 2023-11-26 00:00:00
+  end_date: 2023-12-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -88.25
+      latitude_max: -67.0
+      longitude_min: 32.25
+      longitude_max: 169.25
+  event_type: marginal_temperature
+- case_id_number: 63
+  title: December 2023 Event 0975
+  start_date: 2023-12-24 00:00:00
+  end_date: 2023-12-29 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 55.75
+      latitude_max: 71.0
+      longitude_min: 5.5
+      longitude_max: 62.25
+  event_type: marginal_temperature
+- case_id_number: 64
+  title: January 2024 Event 1006
+  start_date: 2024-01-26 00:00:00
+  end_date: 2024-02-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -79.5
+      latitude_max: -69.0
+      longitude_min: 68.25
+      longitude_max: 107.75
+  event_type: marginal_temperature
+- case_id_number: 65
+  title: February 2024 Event 1041
+  start_date: 2024-02-29 00:00:00
+  end_date: 2024-03-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 53.75
+      latitude_max: 63.5
+      longitude_min: 280.75
+      longitude_max: 299.5
+  event_type: marginal_temperature
+- case_id_number: 66
+  title: March 2024 Event 1050
+  start_date: 2024-03-09 00:00:00
+  end_date: 2024-03-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -76.5
+      latitude_max: -71.25
+      longitude_min: 25.5
+      longitude_max: 64.5
+  event_type: marginal_temperature
+- case_id_number: 67
+  title: April 2024 Event 1068
+  start_date: 2024-04-27 00:00:00
+  end_date: 2024-05-02 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -30.0
+      latitude_max: -14.0
+      longitude_min: 116.0
+      longitude_max: 140.25
+  event_type: marginal_temperature
+- case_id_number: 68
+  title: May 2024 Event 1081
+  start_date: 2024-05-13 00:00:00
+  end_date: 2024-05-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -84.5
+      latitude_max: -75.25
+      longitude_min: 201.75
+      longitude_max: 235.0
+  event_type: marginal_temperature
+- case_id_number: 69
+  title: June 2024 Event 1096
+  start_date: 2024-06-13 00:00:00
+  end_date: 2024-06-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 73.75
+      latitude_max: 84.5
+      longitude_min: 299.75
+      longitude_max: 347.5
+  event_type: marginal_temperature
+- case_id_number: 70
+  title: June 2024 Event 1102
+  start_date: 2024-06-26 00:00:00
+  end_date: 2024-07-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -80.25
+      latitude_max: -66.25
+      longitude_min: 84.0
+      longitude_max: 145.25
+  event_type: marginal_temperature
+- case_id_number: 71
+  title: July 2024 Event 1106
+  start_date: 2024-07-19 00:00:00
+  end_date: 2024-07-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -79.75
+      latitude_max: -66.5
+      longitude_min: 26.75
+      longitude_max: 131.25
+  event_type: marginal_temperature
+- case_id_number: 72
+  title: August 2024 Event 1125
+  start_date: 2024-08-29 00:00:00
+  end_date: 2024-09-06 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -83.75
+      latitude_max: -65.75
+      longitude_min: 1.5
+      longitude_max: 161.75
+  event_type: marginal_temperature
+- case_id_number: 73
+  title: September 2024 Event 1135
+  start_date: 2024-09-19 00:00:00
+  end_date: 2024-09-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 65.75
+      latitude_max: 77.5
+      longitude_min: 73.25
+      longitude_max: 129.0
+  event_type: marginal_temperature
+- case_id_number: 74
+  title: October 2024 Event 1163
+  start_date: 2024-10-29 00:00:00
+  end_date: 2024-11-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -89.75
+      latitude_max: -71.25
+      longitude_min: 0.0
+      longitude_max: 267.25
+  event_type: marginal_temperature
+- case_id_number: 75
+  title: November 2024 Event 1172
+  start_date: 2024-11-16 00:00:00
+  end_date: 2024-11-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 61.25
+      latitude_max: 78.5
+      longitude_min: 297.75
+      longitude_max: 340.25
+  event_type: marginal_temperature
+- case_id_number: 76
+  title: December 2024 Event 1196
+  start_date: 2024-12-17 00:00:00
+  end_date: 2024-12-22 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.25
+      latitude_max: 51.25
+      longitude_min: 48.0
+      longitude_max: 75.0
+  event_type: marginal_temperature

From 6206f6773bf3f1f0c6f61caa6cf3ec0da3ba4d07 Mon Sep 17 00:00:00 2001
From: taylor <mandelbaum.taylor@gmail.com>
Date: Mon, 13 Apr 2026 00:22:21 +0000
Subject: [PATCH 58/72] update with more explicit blob criteria

---
 data_prep/temperature_bounds_global.py | 62 +++++++++++++++++---------
 1 file changed, 40 insertions(+), 22 deletions(-)

diff --git a/data_prep/temperature_bounds_global.py b/data_prep/temperature_bounds_global.py
index b3bc6038..6b6512f3 100644
--- a/data_prep/temperature_bounds_global.py
+++ b/data_prep/temperature_bounds_global.py
@@ -533,13 +533,15 @@ def detect_events(
 ) -> List[Dict]:
     """Track spatiotemporal events from a filtered boolean mask.
 
-    New events are only seeded for blobs whose area meets or exceeds
-    ``min_seed_area_km2`` on the current day AND on each of the prior
-    ``MIN_CONSECUTIVE_DAYS - 1`` days (strict joint-area check). The
-    overlap of the current-day blob footprint with each previous day's
-    filtered mask must individually satisfy the threshold, ensuring the
-    contiguous region was jointly >= ``min_seed_area_km2`` for all
-    MIN_CONSECUTIVE_DAYS days. Blobs that fail but overlap an already-
+    New events are only seeded when the current-day blob AND the prior
+    ``MIN_CONSECUTIVE_DAYS - 1`` days share a single contiguous region
+    of >= ``min_seed_area_km2``. The joint intersection of the blob
+    footprint with ALL prior filtered masks is computed at once; the
+    largest connected component in that intersection must meet the
+    area threshold. This ensures every seeding point was continuously
+    active for the full window—checking each prior day independently
+    would allow scattered non-contiguous cells to satisfy the threshold
+    across different days. Blobs that fail but overlap an already-
     established event still extend it.
 
     Args:
@@ -618,18 +620,29 @@ def detect_events(
                 if min_seed_area_km2 > 0:
                     if di < MIN_CONSECUTIVE_DAYS - 1:
                         continue
-                    joint_ok = True
+                    # Require a single contiguous region that was active
+                    # on the current day AND every one of the prior
+                    # MIN_CONSECUTIVE_DAYS-1 days. Checking each prior
+                    # day independently would allow non-contiguous,
+                    # scattered cells to satisfy the threshold across
+                    # different days. The joint intersection enforces
+                    # that every point in the qualifying region persisted
+                    # continuously for the full window.
+                    joint_mask = om.copy()
                     for k in range(1, MIN_CONSECUTIVE_DAYS):
-                        overlap = om & filtered_mask[di - k]
-                        prev_area = (
-                            float(area_grid[overlap].sum())
-                            if area_grid is not None
-                            else float(overlap.sum())
-                        )
-                        if prev_area < min_seed_area_km2:
-                            joint_ok = False
-                            break
-                    if not joint_ok:
+                        joint_mask &= filtered_mask[di - k]
+                    if not joint_mask.any():
+                        continue
+                    j_lbl, j_n = ndimage.label(joint_mask)
+                    if j_n == 0:
+                        continue
+                    best_area = max(
+                        float(area_grid[j_lbl == c].sum())
+                        if area_grid is not None
+                        else float((j_lbl == c).sum())
+                        for c in range(1, j_n + 1)
+                    )
+                    if best_area < min_seed_area_km2:
                         continue
                 eid = next_id
                 next_id += 1
@@ -932,7 +945,7 @@ def plot_events_high_consec(
     df: pd.DataFrame,
     title: str,
     output_path: str,
-    min_consec: int = 6,
+    min_consec: int = 5,
 ) -> None:
     """Plot events with >= min_consec days, coloured by consecutive days.
 
@@ -1007,7 +1020,7 @@ def plot_event_consec_maps(
     lats: np.ndarray,
     lons: np.ndarray,
     stem: str,
-    min_consec: int = 6,
+    min_consec: int = 3,
 ) -> None:
     """Plot a per-event consecutive-days map for each qualifying event.
 
@@ -1189,8 +1202,13 @@ def main():
     parser.add_argument(
         "--lat-min",
         type=float,
-        default=-90.0,
-        help="Minimum latitude to include in detection. Default -90.0",
+        default=-60.0,
+        help=(
+            "Minimum latitude to include in detection. Default -60.0 "
+            "(excludes Antarctica, whose polar grid geometry produces "
+            "full-circumference ring blobs that trivially meet the area "
+            "threshold). Pass -90.0 explicitly to include Antarctica."
+        ),
     )
     parser.add_argument(
         "--lat-max",

From 4d39b1493d0431b0a4e873a754eba2859ee4997d Mon Sep 17 00:00:00 2001
From: taylor <mandelbaum.taylor@gmail.com>
Date: Mon, 13 Apr 2026 16:09:38 +0000
Subject: [PATCH 59/72] add PeriodicBoundaryIndex (h/t TomNicholas) to avoid
 lon shenanigans

---
 data_prep/plot_temperature_events.py | 130 +++++++++++++++++++++------
 1 file changed, 103 insertions(+), 27 deletions(-)

diff --git a/data_prep/plot_temperature_events.py b/data_prep/plot_temperature_events.py
index 745bfb6d..aa644fb2 100644
--- a/data_prep/plot_temperature_events.py
+++ b/data_prep/plot_temperature_events.py
@@ -19,7 +19,7 @@
 import operator as op_module
 import pathlib
 import time as time_module
-from typing import Callable, Literal, cast
+from typing import Any, Callable, Literal, cast
 
 import cartopy.crs as ccrs
 import cartopy.feature as cfeature
@@ -31,9 +31,96 @@
 import regionmask
 import xarray as xr
 from mpl_toolkits.axes_grid1 import make_axes_locatable
+from xarray.core.indexes import IndexSelResult, PandasIndex, _query_slice
+from xarray.core.indexing import _expand_slice
 
 from extremeweatherbench import cases, defaults, inputs
 
+
+class PeriodicBoundaryIndex(PandasIndex):
+    """
+    An index representing any 1D periodic numberline.
+
+    Implementation subclasses a normal xarray PandasIndex object but intercepts indexer queries.
+    """
+
+    period: float
+    _min: float
+    _max: float
+
+    __slots__ = ("index", "dim", "coord_dtype", "period", "_max", "_min")
+
+    def __init__(self, *args, period=360, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.period = period
+        self._min = self.index.min()
+        self._max = self.index.max()
+
+    @classmethod
+    def from_variables(self, variables, options):
+        obj = super().from_variables(variables, options={})
+        obj.period = options.get("period", obj.period)
+        return obj
+
+    def _wrap_periodically(self, label_value: float) -> float:
+        return self._min + (label_value - self._max) % self.period
+
+    def _split_slice_across_boundary(self, label: slice) -> np.ndarray:
+        """
+        Splits a slice into two slices, one either side of the boundary,
+        finds the corresponding indices, concatenates them, and returns them,
+        ready to be passed to .isel().
+        """
+        first_slice = slice(label.start, self._max, label.step)
+        second_slice = slice(self._min, label.stop, label.step)
+
+        first_as_index_slice = _query_slice(self.index, first_slice)
+        second_as_index_slice = _query_slice(self.index, second_slice)
+
+        first_as_indices = _expand_slice(first_as_index_slice, self.index.size)
+        second_as_indices = _expand_slice(second_as_index_slice, self.index.size)
+
+        wrapped_indices = np.concatenate([first_as_indices, second_as_indices])
+        return wrapped_indices
+
+    def sel(
+        self, labels: dict[Any, Any], method=None, tolerance=None
+    ) -> IndexSelResult:
+        """Remaps labels outside of the indexes' range back to integer indices inside the range."""
+
+        assert len(labels) == 1
+        coord_name, label = next(iter(labels.items()))
+
+        if isinstance(label, slice):
+            start, stop, step = label.start, label.stop, label.step
+            if stop < start:
+                return super().sel({coord_name: []})
+
+            assert self._min < self._max
+
+            wrapped_start = self._wrap_periodically(label.start)
+            wrapped_stop = self._wrap_periodically(label.stop)
+            wrapped_label = slice(wrapped_start, wrapped_stop, step)
+
+            if wrapped_start < wrapped_stop:
+                # simple case of slice not crossing boundary
+                return super().sel({coord_name: wrapped_label})
+            else:  # wrapped_stop < wrapped_start:
+                # nasty case of slice crossing boundary
+                wrapped_indices = self._split_slice_across_boundary(wrapped_label)
+                return IndexSelResult({self.dim: wrapped_indices})
+
+        else:
+            # just a scalar / array of scalars
+            wrapped_label = self._wrap_periodically(label)  # type: ignore
+            return super().sel(
+                {coord_name: wrapped_label}, method=method, tolerance=tolerance
+            )
+
+    def __repr__(self) -> str:
+        return f"PeriodicBoundaryIndex(period={self.period})"
+
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s %(name)s %(levelname)s %(message)s",
@@ -70,9 +157,6 @@ def resolve_op(op_str: str) -> Callable:
     return _OP_MAP[op_str]
 
 
-# ── shared ERA5 utilities ─────────────────────────────────────────────
-
-
 def detect_time_dim(obj: xr.Dataset | xr.DataArray) -> str:
     """Return the name of the time dimension.
 
@@ -162,17 +246,12 @@ def _align_climatology(
     return xr.DataArray(selected, dims=daily.dims, coords=daily.coords)
 
 
-# ── constants for bounding-box overview ──────────────────────────────
-
 _HW_COLOR = "#d73027"
 _FZ_COLOR = "#4575b4"
 _BOX_ALPHA = 0.35
 _BOX_EDGE_ALPHA = 0.85
 
 
-# ── shared helpers ───────────────────────────────────────────────────
-
-
 def _to_plot_lon(lon: float) -> float:
     """Wrap 0-360 longitude to -180..180 for PlateCarree.
 
@@ -239,9 +318,6 @@ def _add_map_features(ax) -> None:
     )
 
 
-# ── consecutive-days map ─────────────────────────────────────────────
-
-
 def plot_consecutive_map(
     consec: np.ndarray,
     lats: np.ndarray,
@@ -340,9 +416,6 @@ def plot_consecutive_map(
         logger.info("Saved consecutive-days plot to %s", output_path)
 
 
-# ── bounding-box overview ─────────────────────────────────────────────
-
-
 def plot_event_bounds(
     df: pd.DataFrame,
     csv_path: str,
@@ -449,9 +522,6 @@ def plot_event_bounds(
     logger.info("Saved bounds plot to %s", out_path)
 
 
-# ── case-level data loading ───────────────────────────────────────────
-
-
 def load_case(case_id_number: int) -> cases.IndividualCase:
     """Load a single case from events.yaml by case_id_number.
 
@@ -515,17 +585,24 @@ def compute_consecutive_field(
     bounds = single_case.location.as_geopandas().total_bounds
     lon_min, lat_min, lon_max, lat_max = bounds
 
-    if lon_min < 0:
+    if lon_min < 0 and lon_max > 0:
         lon_min += 360
+        if lon_max > 0:
+            lon_max += 360
     if lon_max < 0:
         lon_max += 360
 
     logger.info("Opening ERA5 for %s to %s ...", start, end)
     t2m = open_era5_t2m(start, end)
+    t2m = t2m.drop_indexes("longitude").set_xindex(
+        "longitude", index_cls=PeriodicBoundaryIndex, period=360
+    )
+
     t2m = t2m.sel(
         latitude=slice(lat_min, lat_max),
         longitude=slice(lon_min, lon_max),
     )
+
     logger.info("  Subset: %s", dict(t2m.sizes))
 
     tdim = detect_time_dim(t2m)
@@ -536,13 +613,14 @@ def compute_consecutive_field(
         op_str,
     )
     clim = defaults.get_climatology(quantile).sortby("latitude")
-
+    clim = clim.drop_indexes("longitude").set_xindex(
+        "longitude", index_cls=PeriodicBoundaryIndex, period=360
+    )
     clim = clim.sel(
         latitude=t2m.latitude,
         longitude=t2m.longitude,
         method="nearest",
     )
-
     doy = t2m[tdim].dt.dayofyear
     hour = t2m[tdim].dt.hour
     max_clim_doy = int(clim.dayofyear.max())
@@ -555,24 +633,22 @@ def compute_consecutive_field(
 
     logger.info("Computing 6-hourly exceedance...")
     exc_6h = cmp(t2m, clim_aligned)
-
+    print("exc_6h: ")
+    print(exc_6h)
     daily_all_pass = exc_6h.resample({tdim: "1D"}).min().astype(bool)
-
+    print("daily_all_pass: ")
+    print(daily_all_pass)
     logger.info("Building land mask...")
     land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
     land_mask = land.mask(daily_all_pass.longitude, daily_all_pass.latitude) == 0
 
     exc = daily_all_pass & land_mask
     mask_np = exc.compute().values.astype(bool)
-
     logger.info("Computing max consecutive days...")
     consec = max_consecutive_days(mask_np)
     return consec, daily_all_pass.latitude.values, daily_all_pass.longitude.values
 
 
-# ── CLI ───────────────────────────────────────────────────────────────
-
-
 def _default_output(case_id_number: int, event_type: str) -> str:
     kind = "heatwave" if event_type == "heat_wave" else "cold_snap"
     return f"case_{case_id_number}_consecutive_{kind}_days.png"

From fa0dcf782e16437e6ee056cfea166fddd93c729e Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Mon, 13 Apr 2026 18:00:52 +0000
Subject: [PATCH 60/72] rename marginal severe with underscores; add marginal
 temperature events for p15-p85 cases without antarctica

---
 .../marginal_severe_convection_cases.yaml     | 1812 +++++++++++++++++
 .../data/marginal_temperature_events.yaml     | 1266 ++++++++----
 2 files changed, 2631 insertions(+), 447 deletions(-)
 create mode 100644 src/extremeweatherbench/data/marginal_severe_convection_cases.yaml

diff --git a/src/extremeweatherbench/data/marginal_severe_convection_cases.yaml b/src/extremeweatherbench/data/marginal_severe_convection_cases.yaml
new file mode 100644
index 00000000..f71a3f4c
--- /dev/null
+++ b/src/extremeweatherbench/data/marginal_severe_convection_cases.yaml
@@ -0,0 +1,1812 @@
+- case_id_number: 8000
+  end_date: 2022-04-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
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+  start_date: 2020-04-03 12:00:00
+  title: 2020-04-03 Severe Convection Non-Event
+- case_id_number: 8115
+  end_date: 2024-11-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-11-28 12:00:00
+  title: 2024-11-28 Severe Convection Non-Event
+- case_id_number: 8116
+  end_date: 2021-10-08 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-10-07 12:00:00
+  title: 2021-10-07 Severe Convection Non-Event
+- case_id_number: 8117
+  end_date: 2020-11-10 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-11-09 12:00:00
+  title: 2020-11-09 Severe Convection Non-Event
+- case_id_number: 8118
+  end_date: 2021-04-20 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-04-19 12:00:00
+  title: 2021-04-19 Severe Convection Non-Event
+- case_id_number: 8119
+  end_date: 2022-09-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-09-21 12:00:00
+  title: 2022-09-21 Severe Convection Non-Event
+- case_id_number: 8120
+  end_date: 2021-06-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-06-28 12:00:00
+  title: 2021-06-28 Severe Convection Non-Event
+- case_id_number: 8121
+  end_date: 2023-03-14 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-03-13 12:00:00
+  title: 2023-03-13 Severe Convection Non-Event
+- case_id_number: 8122
+  end_date: 2024-11-09 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-11-08 12:00:00
+  title: 2024-11-08 Severe Convection Non-Event
+- case_id_number: 8123
+  end_date: 2023-09-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-09-20 12:00:00
+  title: 2023-09-20 Severe Convection Non-Event
+- case_id_number: 8124
+  end_date: 2021-01-25 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-01-24 12:00:00
+  title: 2021-01-24 Severe Convection Non-Event
+- case_id_number: 8125
+  end_date: 2024-03-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-03-03 12:00:00
+  title: 2024-03-03 Severe Convection Non-Event
+- case_id_number: 8126
+  end_date: 2024-12-19 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-12-18 12:00:00
+  title: 2024-12-18 Severe Convection Non-Event
+- case_id_number: 8127
+  end_date: 2021-07-03 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-07-02 12:00:00
+  title: 2021-07-02 Severe Convection Non-Event
+- case_id_number: 8128
+  end_date: 2024-10-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-10-20 12:00:00
+  title: 2024-10-20 Severe Convection Non-Event
+- case_id_number: 8129
+  end_date: 2021-09-18 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-17 12:00:00
+  title: 2021-09-17 Severe Convection Non-Event
+- case_id_number: 8130
+  end_date: 2024-09-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-09-20 12:00:00
+  title: 2024-09-20 Severe Convection Non-Event
+- case_id_number: 8131
+  end_date: 2021-09-05 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-09-04 12:00:00
+  title: 2021-09-04 Severe Convection Non-Event
+- case_id_number: 8132
+  end_date: 2023-09-29 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-09-28 12:00:00
+  title: 2023-09-28 Severe Convection Non-Event
+- case_id_number: 8133
+  end_date: 2023-09-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-09-03 12:00:00
+  title: 2023-09-03 Severe Convection Non-Event
+- case_id_number: 8134
+  end_date: 2020-05-11 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-05-10 12:00:00
+  title: 2020-05-10 Severe Convection Non-Event
+- case_id_number: 8135
+  end_date: 2023-05-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-05-21 12:00:00
+  title: 2023-05-21 Severe Convection Non-Event
+- case_id_number: 8136
+  end_date: 2025-01-01 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-12-31 12:00:00
+  title: 2024-12-31 Severe Convection Non-Event
+- case_id_number: 8137
+  end_date: 2021-07-06 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2021-07-05 12:00:00
+  title: 2021-07-05 Severe Convection Non-Event
+- case_id_number: 8138
+  end_date: 2024-02-22 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-02-21 12:00:00
+  title: 2024-02-21 Severe Convection Non-Event
+- case_id_number: 8139
+  end_date: 2022-03-21 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-03-20 12:00:00
+  title: 2022-03-20 Severe Convection Non-Event
+- case_id_number: 8140
+  end_date: 2023-01-08 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-01-07 12:00:00
+  title: 2023-01-07 Severe Convection Non-Event
+- case_id_number: 8141
+  end_date: 2024-10-04 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-10-03 12:00:00
+  title: 2024-10-03 Severe Convection Non-Event
+- case_id_number: 8142
+  end_date: 2023-09-01 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2023-08-31 12:00:00
+  title: 2023-08-31 Severe Convection Non-Event
+- case_id_number: 8143
+  end_date: 2022-08-10 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-08-09 12:00:00
+  title: 2022-08-09 Severe Convection Non-Event
+- case_id_number: 8144
+  end_date: 2024-10-20 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2024-10-19 12:00:00
+  title: 2024-10-19 Severe Convection Non-Event
+- case_id_number: 8145
+  end_date: 2020-07-23 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-07-22 12:00:00
+  title: 2020-07-22 Severe Convection Non-Event
+- case_id_number: 8146
+  end_date: 2022-09-17 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-09-16 12:00:00
+  title: 2022-09-16 Severe Convection Non-Event
+- case_id_number: 8147
+  end_date: 2022-08-18 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-08-17 12:00:00
+  title: 2022-08-17 Severe Convection Non-Event
+- case_id_number: 8148
+  end_date: 2020-04-05 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-04-04 12:00:00
+  title: 2020-04-04 Severe Convection Non-Event
+- case_id_number: 8149
+  end_date: 2022-10-28 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2022-10-27 12:00:00
+  title: 2022-10-27 Severe Convection Non-Event
+- case_id_number: 8150
+  end_date: 2020-09-28 12:00:00
+  event_type: severe_convection
+  location:
+    parameters:
+      latitude_max: 50
+      latitude_min: 30
+      longitude_max: -80
+      longitude_min: -110
+    type: bounded_region
+  start_date: 2020-09-27 12:00:00
+  title: 2020-09-27 Severe Convection Non-Event
diff --git a/src/extremeweatherbench/data/marginal_temperature_events.yaml b/src/extremeweatherbench/data/marginal_temperature_events.yaml
index a50d4781..c7051de9 100644
--- a/src/extremeweatherbench/data/marginal_temperature_events.yaml
+++ b/src/extremeweatherbench/data/marginal_temperature_events.yaml
@@ -1,5 +1,53 @@
 - case_id_number: 1
-  title: February 2020 Event 0036
+  title: January 2020 Event 0016
+  start_date: 2020-01-30 00:00:00
+  end_date: 2020-02-03 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 5.25
+      latitude_max: 30.25
+      longitude_min: 11.0
+      longitude_max: 38.0
+  event_type: marginal_temperature
+- case_id_number: 2
+  title: February 2020 Event 0018
+  start_date: 2020-02-01 00:00:00
+  end_date: 2020-02-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 5.75
+      latitude_max: 29.5
+      longitude_min: 0.0
+      longitude_max: 38.0
+  event_type: marginal_temperature
+- case_id_number: 3
+  title: February 2020 Event 0019
+  start_date: 2020-02-01 00:00:00
+  end_date: 2020-02-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 68.75
+      latitude_max: 77.0
+      longitude_min: 87.0
+      longitude_max: 128.75
+  event_type: marginal_temperature
+- case_id_number: 4
+  title: February 2020 Event 0023
+  start_date: 2020-02-05 00:00:00
+  end_date: 2020-02-09 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 63.0
+      latitude_max: 81.0
+      longitude_min: 296.5
+      longitude_max: 324.75
+  event_type: marginal_temperature
+- case_id_number: 5
+  title: February 2020 Event 0025
   start_date: 2020-02-09 00:00:00
   end_date: 2020-02-14 00:00:00
   location:
@@ -10,21 +58,45 @@
       longitude_min: 28.25
       longitude_max: 58.25
   event_type: marginal_temperature
-- case_id_number: 2
-  title: February 2020 Event 0043
-  start_date: 2020-02-17 00:00:00
-  end_date: 2020-02-22 00:00:00
+- case_id_number: 6
+  title: February 2020 Event 0034
+  start_date: 2020-02-22 00:00:00
+  end_date: 2020-02-26 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -90.0
-      latitude_max: -68.0
+      latitude_min: 60.25
+      latitude_max: 76.75
+      longitude_min: 296.5
+      longitude_max: 317.5
+  event_type: marginal_temperature
+- case_id_number: 7
+  title: March 2020 Event 0037
+  start_date: 2020-03-01 00:00:00
+  end_date: 2020-03-05 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 42.75
+      latitude_max: 53.5
       longitude_min: 0.0
-      longitude_max: 359.75
+      longitude_max: 16.25
   event_type: marginal_temperature
-- case_id_number: 3
-  title: April 2020 Event 0074
-  start_date: 2020-04-06 00:00:00
+- case_id_number: 8
+  title: April 2020 Event 0047
+  start_date: 2020-04-03 00:00:00
+  end_date: 2020-04-07 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 66.5
+      latitude_max: 77.75
+      longitude_min: 293.5
+      longitude_max: 339.5
+  event_type: marginal_temperature
+- case_id_number: 9
+  title: April 2020 Event 0048
+  start_date: 2020-04-07 00:00:00
   end_date: 2020-04-11 00:00:00
   location:
     type: bounded_region
@@ -34,9 +106,9 @@
       longitude_min: 228.0
       longitude_max: 267.0
   event_type: marginal_temperature
-- case_id_number: 4
-  title: May 2020 Event 0088
-  start_date: 2020-05-09 00:00:00
+- case_id_number: 10
+  title: May 2020 Event 0053
+  start_date: 2020-05-10 00:00:00
   end_date: 2020-05-14 00:00:00
   location:
     type: bounded_region
@@ -46,45 +118,21 @@
       longitude_min: 118.25
       longitude_max: 140.0
   event_type: marginal_temperature
-- case_id_number: 5
-  title: May 2020 Event 0096
-  start_date: 2020-05-21 00:00:00
-  end_date: 2020-05-27 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -84.5
-      latitude_max: -75.25
-      longitude_min: 201.75
-      longitude_max: 237.0
-  event_type: marginal_temperature
-- case_id_number: 6
-  title: May 2020 Event 0099
-  start_date: 2020-05-28 00:00:00
-  end_date: 2020-06-02 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -82.25
-      latitude_max: -74.5
-      longitude_min: 201.75
-      longitude_max: 237.5
-  event_type: marginal_temperature
-- case_id_number: 7
-  title: May 2020 Event 0103
+- case_id_number: 11
+  title: May 2020 Event 0060
   start_date: 2020-05-30 00:00:00
   end_date: 2020-06-07 00:00:00
   location:
     type: bounded_region
     parameters:
       latitude_min: 67.75
-      latitude_max: 78.5
-      longitude_min: 236.0
-      longitude_max: 279.0
+      latitude_max: 75.5
+      longitude_min: 235.0
+      longitude_max: 259.0
   event_type: marginal_temperature
-- case_id_number: 8
-  title: June 2020 Event 0104
-  start_date: 2020-06-02 00:00:00
+- case_id_number: 12
+  title: June 2020 Event 0062
+  start_date: 2020-06-03 00:00:00
   end_date: 2020-06-07 00:00:00
   location:
     type: bounded_region
@@ -94,20 +142,20 @@
       longitude_min: 119.5
       longitude_max: 140.0
   event_type: marginal_temperature
-- case_id_number: 9
-  title: July 2020 Event 0115
-  start_date: 2020-07-13 00:00:00
-  end_date: 2020-07-22 00:00:00
+- case_id_number: 13
+  title: June 2020 Event 0064
+  start_date: 2020-06-11 00:00:00
+  end_date: 2020-06-15 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -78.25
-      latitude_max: -66.0
-      longitude_min: 93.5
-      longitude_max: 162.0
+      latitude_min: 61.0
+      latitude_max: 74.25
+      longitude_min: 71.0
+      longitude_max: 116.75
   event_type: marginal_temperature
-- case_id_number: 10
-  title: July 2020 Event 0116
+- case_id_number: 14
+  title: July 2020 Event 0068
   start_date: 2020-07-13 00:00:00
   end_date: 2020-07-23 00:00:00
   location:
@@ -118,45 +166,33 @@
       longitude_min: 72.25
       longitude_max: 97.25
   event_type: marginal_temperature
-- case_id_number: 11
-  title: August 2020 Event 0126
-  start_date: 2020-08-05 00:00:00
-  end_date: 2020-08-12 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -87.75
-      latitude_max: -67.0
-      longitude_min: 90.75
-      longitude_max: 240.0
-  event_type: marginal_temperature
-- case_id_number: 12
-  title: September 2020 Event 0153
-  start_date: 2020-09-26 00:00:00
-  end_date: 2020-10-04 00:00:00
+- case_id_number: 15
+  title: September 2020 Event 0076
+  start_date: 2020-09-11 00:00:00
+  end_date: 2020-09-15 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -90.0
-      latitude_max: -70.0
-      longitude_min: 0.0
-      longitude_max: 359.75
+      latitude_min: 54.5
+      latitude_max: 71.0
+      longitude_min: 12.75
+      longitude_max: 50.75
   event_type: marginal_temperature
-- case_id_number: 13
-  title: September 2020 Event 0154
-  start_date: 2020-09-26 00:00:00
-  end_date: 2020-10-01 00:00:00
+- case_id_number: 16
+  title: September 2020 Event 0079
+  start_date: 2020-09-28 00:00:00
+  end_date: 2020-10-02 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -85.75
-      latitude_max: -74.5
-      longitude_min: 324.25
-      longitude_max: 359.75
+      latitude_min: 56.0
+      latitude_max: 64.25
+      longitude_min: 248.75
+      longitude_max: 269.5
   event_type: marginal_temperature
-- case_id_number: 14
-  title: October 2020 Event 0160
-  start_date: 2020-10-05 00:00:00
+- case_id_number: 17
+  title: October 2020 Event 0081
+  start_date: 2020-10-06 00:00:00
   end_date: 2020-10-10 00:00:00
   location:
     type: bounded_region
@@ -166,20 +202,20 @@
       longitude_min: 62.25
       longitude_max: 99.0
   event_type: marginal_temperature
-- case_id_number: 15
-  title: October 2020 Event 0166
-  start_date: 2020-10-12 00:00:00
-  end_date: 2020-10-17 00:00:00
+- case_id_number: 18
+  title: October 2020 Event 0082
+  start_date: 2020-10-09 00:00:00
+  end_date: 2020-10-13 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -84.5
-      latitude_max: -75.25
-      longitude_min: 201.75
-      longitude_max: 228.0
+      latitude_min: 19.75
+      latitude_max: 28.25
+      longitude_min: 10.5
+      longitude_max: 24.25
   event_type: marginal_temperature
-- case_id_number: 16
-  title: October 2020 Event 0171
+- case_id_number: 19
+  title: October 2020 Event 0084
   start_date: 2020-10-19 00:00:00
   end_date: 2020-10-24 00:00:00
   location:
@@ -190,21 +226,69 @@
       longitude_min: 281.75
       longitude_max: 299.5
   event_type: marginal_temperature
-- case_id_number: 17
-  title: October 2020 Event 0172
-  start_date: 2020-10-21 00:00:00
-  end_date: 2020-10-26 00:00:00
+- case_id_number: 20
+  title: November 2020 Event 0087
+  start_date: 2020-11-01 00:00:00
+  end_date: 2020-11-06 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -87.0
-      latitude_max: -68.0
+      latitude_min: 14.0
+      latitude_max: 23.75
       longitude_min: 0.0
-      longitude_max: 128.0
+      longitude_max: 11.25
   event_type: marginal_temperature
-- case_id_number: 18
-  title: December 2020 Event 0215
-  start_date: 2020-12-05 00:00:00
+- case_id_number: 21
+  title: November 2020 Event 0088
+  start_date: 2020-11-04 00:00:00
+  end_date: 2020-11-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.5
+      latitude_max: 54.0
+      longitude_min: 13.25
+      longitude_max: 32.75
+  event_type: marginal_temperature
+- case_id_number: 22
+  title: November 2020 Event 0092
+  start_date: 2020-11-09 00:00:00
+  end_date: 2020-11-13 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.25
+      latitude_max: 73.75
+      longitude_min: 12.25
+      longitude_max: 116.75
+  event_type: marginal_temperature
+- case_id_number: 23
+  title: December 2020 Event 0108
+  start_date: 2020-12-03 00:00:00
+  end_date: 2020-12-07 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 37.0
+      latitude_max: 57.75
+      longitude_min: 265.0
+      longitude_max: 294.75
+  event_type: marginal_temperature
+- case_id_number: 24
+  title: December 2020 Event 0109
+  start_date: 2020-12-04 00:00:00
+  end_date: 2020-12-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 57.0
+      latitude_max: 71.25
+      longitude_min: 192.25
+      longitude_max: 230.25
+  event_type: marginal_temperature
+- case_id_number: 25
+  title: December 2020 Event 0111
+  start_date: 2020-12-06 00:00:00
   end_date: 2020-12-10 00:00:00
   location:
     type: bounded_region
@@ -214,81 +298,81 @@
       longitude_min: 306.5
       longitude_max: 341.5
   event_type: marginal_temperature
-- case_id_number: 19
-  title: December 2020 Event 0236
+- case_id_number: 26
+  title: December 2020 Event 0115
+  start_date: 2020-12-13 00:00:00
+  end_date: 2020-12-17 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 65.25
+      latitude_max: 81.25
+      longitude_min: 289.5
+      longitude_max: 344.25
+  event_type: marginal_temperature
+- case_id_number: 27
+  title: December 2020 Event 0126
   start_date: 2020-12-27 00:00:00
   end_date: 2021-01-01 00:00:00
   location:
     type: bounded_region
     parameters:
       latitude_min: 43.5
-      latitude_max: 60.0
+      latitude_max: 59.0
       longitude_min: 0.0
       longitude_max: 27.5
   event_type: marginal_temperature
-- case_id_number: 20
-  title: February 2021 Event 0273
-  start_date: 2021-02-09 00:00:00
-  end_date: 2021-02-18 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -81.0
-      latitude_max: -69.5
-      longitude_min: 46.25
-      longitude_max: 76.75
-  event_type: marginal_temperature
-- case_id_number: 21
-  title: March 2021 Event 0315
-  start_date: 2021-03-26 00:00:00
-  end_date: 2021-04-03 00:00:00
+- case_id_number: 28
+  title: January 2021 Event 0138
+  start_date: 2021-01-20 00:00:00
+  end_date: 2021-01-24 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -78.75
-      latitude_max: -66.5
-      longitude_min: 88.0
-      longitude_max: 159.75
+      latitude_min: 60.25
+      latitude_max: 75.5
+      longitude_min: 306.75
+      longitude_max: 329.75
   event_type: marginal_temperature
-- case_id_number: 22
-  title: May 2021 Event 0342
-  start_date: 2021-05-08 00:00:00
-  end_date: 2021-05-17 00:00:00
+- case_id_number: 29
+  title: February 2021 Event 0157
+  start_date: 2021-02-21 00:00:00
+  end_date: 2021-02-26 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -89.75
-      latitude_max: -75.5
-      longitude_min: 0.0
-      longitude_max: 265.25
+      latitude_min: 68.25
+      latitude_max: 74.0
+      longitude_min: 73.25
+      longitude_max: 118.75
   event_type: marginal_temperature
-- case_id_number: 23
-  title: May 2021 Event 0356
-  start_date: 2021-05-29 00:00:00
-  end_date: 2021-06-04 00:00:00
+- case_id_number: 30
+  title: March 2021 Event 0168
+  start_date: 2021-03-11 00:00:00
+  end_date: 2021-03-15 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -86.5
-      latitude_max: -65.75
-      longitude_min: 91.25
-      longitude_max: 147.75
+      latitude_min: 57.0
+      latitude_max: 74.25
+      longitude_min: 76.75
+      longitude_max: 123.25
   event_type: marginal_temperature
-- case_id_number: 24
-  title: June 2021 Event 0364
-  start_date: 2021-06-10 00:00:00
-  end_date: 2021-06-15 00:00:00
+- case_id_number: 31
+  title: April 2021 Event 0182
+  start_date: 2021-04-18 00:00:00
+  end_date: 2021-04-22 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -80.5
-      latitude_max: -72.0
-      longitude_min: 56.0
-      longitude_max: 108.0
+      latitude_min: 75.0
+      latitude_max: 84.5
+      longitude_min: 292.0
+      longitude_max: 339.75
   event_type: marginal_temperature
-- case_id_number: 25
-  title: June 2021 Event 0372
-  start_date: 2021-06-20 00:00:00
+- case_id_number: 32
+  title: June 2021 Event 0208
+  start_date: 2021-06-21 00:00:00
   end_date: 2021-06-25 00:00:00
   location:
     type: bounded_region
@@ -298,44 +382,32 @@
       longitude_min: 70.75
       longitude_max: 94.5
   event_type: marginal_temperature
-- case_id_number: 26
-  title: June 2021 Event 0376
-  start_date: 2021-06-28 00:00:00
-  end_date: 2021-07-03 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -84.75
-      latitude_max: -72.75
-      longitude_min: 201.75
-      longitude_max: 292.75
-  event_type: marginal_temperature
-- case_id_number: 27
-  title: July 2021 Event 0378
-  start_date: 2021-07-03 00:00:00
-  end_date: 2021-07-09 00:00:00
+- case_id_number: 33
+  title: July 2021 Event 0215
+  start_date: 2021-07-24 00:00:00
+  end_date: 2021-07-28 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -89.5
-      latitude_max: -68.25
+      latitude_min: 23.75
+      latitude_max: 35.25
       longitude_min: 0.0
-      longitude_max: 164.5
+      longitude_max: 18.0
   event_type: marginal_temperature
-- case_id_number: 28
-  title: October 2021 Event 0421
-  start_date: 2021-10-03 00:00:00
-  end_date: 2021-10-09 00:00:00
+- case_id_number: 34
+  title: August 2021 Event 0216
+  start_date: 2021-08-22 00:00:00
+  end_date: 2021-08-26 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -90.0
-      latitude_max: -71.5
-      longitude_min: 0.0
-      longitude_max: 359.75
+      latitude_min: 67.75
+      latitude_max: 77.5
+      longitude_min: 80.75
+      longitude_max: 113.75
   event_type: marginal_temperature
-- case_id_number: 29
-  title: November 2021 Event 0450
+- case_id_number: 35
+  title: November 2021 Event 0236
   start_date: 2021-11-09 00:00:00
   end_date: 2021-11-15 00:00:00
   location:
@@ -346,9 +418,33 @@
       longitude_min: 27.0
       longitude_max: 122.5
   event_type: marginal_temperature
-- case_id_number: 30
-  title: December 2021 Event 0483
-  start_date: 2021-12-19 00:00:00
+- case_id_number: 36
+  title: November 2021 Event 0240
+  start_date: 2021-11-19 00:00:00
+  end_date: 2021-11-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 43.25
+      latitude_max: 57.5
+      longitude_min: 0.0
+      longitude_max: 26.75
+  event_type: marginal_temperature
+- case_id_number: 37
+  title: December 2021 Event 0248
+  start_date: 2021-12-07 00:00:00
+  end_date: 2021-12-11 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 10.0
+      latitude_max: 21.75
+      longitude_min: 4.0
+      longitude_max: 31.25
+  event_type: marginal_temperature
+- case_id_number: 38
+  title: December 2021 Event 0258
+  start_date: 2021-12-20 00:00:00
   end_date: 2021-12-25 00:00:00
   location:
     type: bounded_region
@@ -358,20 +454,32 @@
       longitude_min: 0.0
       longitude_max: 11.0
   event_type: marginal_temperature
-- case_id_number: 31
-  title: December 2021 Event 0485
-  start_date: 2021-12-21 00:00:00
+- case_id_number: 39
+  title: December 2021 Event 0260
+  start_date: 2021-12-22 00:00:00
   end_date: 2021-12-26 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -80.5
-      latitude_max: -66.75
-      longitude_min: 51.25
-      longitude_max: 159.75
+      latitude_min: 57.5
+      latitude_max: 73.0
+      longitude_min: 16.75
+      longitude_max: 76.75
   event_type: marginal_temperature
-- case_id_number: 32
-  title: January 2022 Event 0500
+- case_id_number: 40
+  title: December 2021 Event 0264
+  start_date: 2021-12-27 00:00:00
+  end_date: 2021-12-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 60.75
+      latitude_max: 71.0
+      longitude_min: 5.25
+      longitude_max: 47.0
+  event_type: marginal_temperature
+- case_id_number: 41
+  title: January 2022 Event 0270
   start_date: 2022-01-02 00:00:00
   end_date: 2022-01-07 00:00:00
   location:
@@ -382,44 +490,56 @@
       longitude_min: 280.5
       longitude_max: 298.5
   event_type: marginal_temperature
-- case_id_number: 33
-  title: January 2022 Event 0505
+- case_id_number: 42
+  title: January 2022 Event 0273
   start_date: 2022-01-08 00:00:00
   end_date: 2022-01-13 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -90.0
-      latitude_max: -71.25
-      longitude_min: 0.0
-      longitude_max: 231.5
+      latitude_min: 54.5
+      latitude_max: 63.5
+      longitude_min: 280.75
+      longitude_max: 293.0
   event_type: marginal_temperature
-- case_id_number: 34
-  title: January 2022 Event 0507
+- case_id_number: 43
+  title: January 2022 Event 0274
   start_date: 2022-01-08 00:00:00
-  end_date: 2022-01-13 00:00:00
+  end_date: 2022-01-12 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 54.5
-      latitude_max: 63.5
-      longitude_min: 281.75
-      longitude_max: 293.0
+      latitude_min: 56.25
+      latitude_max: 69.5
+      longitude_min: 105.25
+      longitude_max: 134.75
   event_type: marginal_temperature
-- case_id_number: 35
-  title: January 2022 Event 0520
+- case_id_number: 44
+  title: January 2022 Event 0280
+  start_date: 2022-01-14 00:00:00
+  end_date: 2022-01-18 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 35.0
+      latitude_max: 48.25
+      longitude_min: 237.5
+      longitude_max: 252.5
+  event_type: marginal_temperature
+- case_id_number: 45
+  title: January 2022 Event 0282
   start_date: 2022-01-18 00:00:00
   end_date: 2022-01-23 00:00:00
   location:
     type: bounded_region
     parameters:
       latitude_min: 57.75
-      latitude_max: 69.5
+      latitude_max: 80.5
       longitude_min: 247.5
       longitude_max: 275.25
   event_type: marginal_temperature
-- case_id_number: 36
-  title: January 2022 Event 0522
+- case_id_number: 46
+  title: January 2022 Event 0284
   start_date: 2022-01-20 00:00:00
   end_date: 2022-01-25 00:00:00
   location:
@@ -430,20 +550,8 @@
       longitude_min: 348.25
       longitude_max: 359.75
   event_type: marginal_temperature
-- case_id_number: 37
-  title: January 2022 Event 0523
-  start_date: 2022-01-21 00:00:00
-  end_date: 2022-01-26 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -74.25
-      latitude_max: -67.0
-      longitude_min: 116.5
-      longitude_max: 170.5
-  event_type: marginal_temperature
-- case_id_number: 38
-  title: March 2022 Event 0552
+- case_id_number: 47
+  title: March 2022 Event 0306
   start_date: 2022-03-01 00:00:00
   end_date: 2022-03-06 00:00:00
   location:
@@ -454,57 +562,57 @@
       longitude_min: 280.75
       longitude_max: 299.75
   event_type: marginal_temperature
-- case_id_number: 39
-  title: May 2022 Event 0598
-  start_date: 2022-05-14 00:00:00
-  end_date: 2022-05-21 00:00:00
+- case_id_number: 48
+  title: March 2022 Event 0309
+  start_date: 2022-03-07 00:00:00
+  end_date: 2022-03-11 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 55.25
-      latitude_max: 69.5
-      longitude_min: 280.75
-      longitude_max: 291.75
+      latitude_min: 51.5
+      latitude_max: 61.75
+      longitude_min: 281.75
+      longitude_max: 303.75
   event_type: marginal_temperature
-- case_id_number: 40
-  title: June 2022 Event 0614
-  start_date: 2022-06-10 00:00:00
-  end_date: 2022-06-15 00:00:00
+- case_id_number: 49
+  title: April 2022 Event 0322
+  start_date: 2022-04-06 00:00:00
+  end_date: 2022-04-10 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -74.5
-      latitude_max: -66.0
-      longitude_min: 99.0
-      longitude_max: 145.0
+      latitude_min: 66.25
+      latitude_max: 77.5
+      longitude_min: 73.25
+      longitude_max: 118.75
   event_type: marginal_temperature
-- case_id_number: 41
-  title: June 2022 Event 0616
-  start_date: 2022-06-16 00:00:00
-  end_date: 2022-06-22 00:00:00
+- case_id_number: 50
+  title: May 2022 Event 0332
+  start_date: 2022-05-14 00:00:00
+  end_date: 2022-05-21 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -86.0
-      latitude_max: -66.25
-      longitude_min: 86.75
-      longitude_max: 153.0
+      latitude_min: 55.25
+      latitude_max: 69.5
+      longitude_min: 280.75
+      longitude_max: 291.75
   event_type: marginal_temperature
-- case_id_number: 42
-  title: July 2022 Event 0628
-  start_date: 2022-07-11 00:00:00
-  end_date: 2022-07-17 00:00:00
+- case_id_number: 51
+  title: June 2022 Event 0341
+  start_date: 2022-06-27 00:00:00
+  end_date: 2022-07-01 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -84.75
-      latitude_max: -75.0
-      longitude_min: 201.75
-      longitude_max: 250.25
+      latitude_min: 18.25
+      latitude_max: 26.25
+      longitude_min: 20.0
+      longitude_max: 28.5
   event_type: marginal_temperature
-- case_id_number: 43
-  title: July 2022 Event 0633
-  start_date: 2022-07-25 00:00:00
+- case_id_number: 52
+  title: July 2022 Event 0345
+  start_date: 2022-07-26 00:00:00
   end_date: 2022-07-30 00:00:00
   location:
     type: bounded_region
@@ -514,21 +622,9 @@
       longitude_min: 33.75
       longitude_max: 66.75
   event_type: marginal_temperature
-- case_id_number: 44
-  title: August 2022 Event 0646
-  start_date: 2022-08-29 00:00:00
-  end_date: 2022-09-03 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 66.75
-      latitude_max: 77.5
-      longitude_min: 73.25
-      longitude_max: 111.0
-  event_type: marginal_temperature
-- case_id_number: 45
-  title: September 2022 Event 0652
-  start_date: 2022-09-10 00:00:00
+- case_id_number: 53
+  title: September 2022 Event 0355
+  start_date: 2022-09-11 00:00:00
   end_date: 2022-09-15 00:00:00
   location:
     type: bounded_region
@@ -538,8 +634,8 @@
       longitude_min: 219.5
       longitude_max: 263.75
   event_type: marginal_temperature
-- case_id_number: 46
-  title: September 2022 Event 0659
+- case_id_number: 54
+  title: September 2022 Event 0358
   start_date: 2022-09-18 00:00:00
   end_date: 2022-09-24 00:00:00
   location:
@@ -550,32 +646,44 @@
       longitude_min: 192.25
       longitude_max: 231.25
   event_type: marginal_temperature
-- case_id_number: 47
-  title: October 2022 Event 0674
-  start_date: 2022-10-06 00:00:00
-  end_date: 2022-10-11 00:00:00
+- case_id_number: 55
+  title: September 2022 Event 0361
+  start_date: 2022-09-29 00:00:00
+  end_date: 2022-10-03 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -89.25
-      latitude_max: -70.75
-      longitude_min: 0.0
-      longitude_max: 118.75
+      latitude_min: 63.75
+      latitude_max: 71.25
+      longitude_min: 193.25
+      longitude_max: 226.75
   event_type: marginal_temperature
-- case_id_number: 48
-  title: October 2022 Event 0675
-  start_date: 2022-10-09 00:00:00
-  end_date: 2022-10-14 00:00:00
+- case_id_number: 56
+  title: October 2022 Event 0372
+  start_date: 2022-10-18 00:00:00
+  end_date: 2022-10-22 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -88.75
-      latitude_max: -74.5
-      longitude_min: 242.75
-      longitude_max: 359.75
+      latitude_min: 49.75
+      latitude_max: 67.5
+      longitude_min: 39.5
+      longitude_max: 74.25
   event_type: marginal_temperature
-- case_id_number: 49
-  title: October 2022 Event 0693
+- case_id_number: 57
+  title: October 2022 Event 0373
+  start_date: 2022-10-19 00:00:00
+  end_date: 2022-10-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 13.25
+      latitude_max: 24.25
+      longitude_min: 19.0
+      longitude_max: 31.0
+  event_type: marginal_temperature
+- case_id_number: 58
+  title: October 2022 Event 0378
   start_date: 2022-10-28 00:00:00
   end_date: 2022-11-05 00:00:00
   location:
@@ -586,9 +694,21 @@
       longitude_min: 24.75
       longitude_max: 78.0
   event_type: marginal_temperature
-- case_id_number: 50
-  title: November 2022 Event 0712
-  start_date: 2022-11-23 00:00:00
+- case_id_number: 59
+  title: November 2022 Event 0386
+  start_date: 2022-11-17 00:00:00
+  end_date: 2022-11-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 12.5
+      latitude_max: 28.5
+      longitude_min: 0.0
+      longitude_max: 31.5
+  event_type: marginal_temperature
+- case_id_number: 60
+  title: November 2022 Event 0391
+  start_date: 2022-11-25 00:00:00
   end_date: 2022-11-29 00:00:00
   location:
     type: bounded_region
@@ -598,8 +718,20 @@
       longitude_min: 0.0
       longitude_max: 108.0
   event_type: marginal_temperature
-- case_id_number: 51
-  title: December 2022 Event 0732
+- case_id_number: 61
+  title: December 2022 Event 0394
+  start_date: 2022-12-10 00:00:00
+  end_date: 2022-12-14 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 59.5
+      latitude_max: 74.25
+      longitude_min: 73.25
+      longitude_max: 103.5
+  event_type: marginal_temperature
+- case_id_number: 62
+  title: December 2022 Event 0401
   start_date: 2022-12-19 00:00:00
   end_date: 2022-12-27 00:00:00
   location:
@@ -610,8 +742,20 @@
       longitude_min: 231.25
       longitude_max: 302.75
   event_type: marginal_temperature
-- case_id_number: 52
-  title: December 2022 Event 0742
+- case_id_number: 63
+  title: December 2022 Event 0405
+  start_date: 2022-12-21 00:00:00
+  end_date: 2022-12-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.75
+      latitude_max: 51.5
+      longitude_min: 43.75
+      longitude_max: 72.75
+  event_type: marginal_temperature
+- case_id_number: 64
+  title: December 2022 Event 0408
   start_date: 2022-12-24 00:00:00
   end_date: 2022-12-29 00:00:00
   location:
@@ -622,80 +766,200 @@
       longitude_min: 192.25
       longitude_max: 218.75
   event_type: marginal_temperature
-- case_id_number: 53
-  title: January 2023 Event 0764
-  start_date: 2023-01-14 00:00:00
-  end_date: 2023-01-19 00:00:00
+- case_id_number: 65
+  title: January 2023 Event 0412
+  start_date: 2023-01-01 00:00:00
+  end_date: 2023-01-05 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -78.75
-      latitude_max: -67.25
-      longitude_min: 48.0
-      longitude_max: 114.25
+      latitude_min: 62.0
+      latitude_max: 69.75
+      longitude_min: 166.5
+      longitude_max: 190.0
   event_type: marginal_temperature
-- case_id_number: 54
-  title: February 2023 Event 0778
-  start_date: 2023-02-07 00:00:00
-  end_date: 2023-02-12 00:00:00
+- case_id_number: 66
+  title: January 2023 Event 0427
+  start_date: 2023-01-20 00:00:00
+  end_date: 2023-01-24 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -84.0
-      latitude_max: -72.0
-      longitude_min: 0.0
-      longitude_max: 14.75
+      latitude_min: 42.5
+      latitude_max: 59.5
+      longitude_min: 4.25
+      longitude_max: 31.75
   event_type: marginal_temperature
-- case_id_number: 55
-  title: February 2023 Event 0780
+- case_id_number: 67
+  title: February 2023 Event 0436
   start_date: 2023-02-13 00:00:00
-  end_date: 2023-02-18 00:00:00
+  end_date: 2023-02-17 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -87.75
-      latitude_max: -74.25
-      longitude_min: 0.0
-      longitude_max: 266.25
+      latitude_min: 55.5
+      latitude_max: 67.0
+      longitude_min: 135.0
+      longitude_max: 179.5
   event_type: marginal_temperature
-- case_id_number: 56
-  title: June 2023 Event 0858
-  start_date: 2023-06-11 00:00:00
-  end_date: 2023-06-18 00:00:00
+- case_id_number: 68
+  title: February 2023 Event 0438
+  start_date: 2023-02-21 00:00:00
+  end_date: 2023-02-25 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -90.0
-      latitude_max: -66.5
-      longitude_min: 0.0
-      longitude_max: 359.75
+      latitude_min: 51.5
+      latitude_max: 69.25
+      longitude_min: 5.25
+      longitude_max: 24.75
   event_type: marginal_temperature
-- case_id_number: 57
-  title: August 2023 Event 0879
-  start_date: 2023-08-15 00:00:00
-  end_date: 2023-08-21 00:00:00
+- case_id_number: 69
+  title: February 2023 Event 0439
+  start_date: 2023-02-21 00:00:00
+  end_date: 2023-02-25 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -88.25
-      latitude_max: -67.75
-      longitude_min: 52.0
-      longitude_max: 149.25
+      latitude_min: -26.25
+      latitude_max: -17.75
+      longitude_min: 116.25
+      longitude_max: 137.5
   event_type: marginal_temperature
-- case_id_number: 58
-  title: October 2023 Event 0909
-  start_date: 2023-10-06 00:00:00
-  end_date: 2023-10-11 00:00:00
+- case_id_number: 70
+  title: February 2023 Event 0440
+  start_date: 2023-02-23 00:00:00
+  end_date: 2023-02-27 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: 25.25
-      latitude_max: 35.25
-      longitude_min: 0.0
-      longitude_max: 13.5
+      latitude_min: 75.25
+      latitude_max: 80.5
+      longitude_min: 286.75
+      longitude_max: 331.5
   event_type: marginal_temperature
-- case_id_number: 59
-  title: October 2023 Event 0910
+- case_id_number: 71
+  title: February 2023 Event 0443
+  start_date: 2023-02-28 00:00:00
+  end_date: 2023-03-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 52.0
+      latitude_max: 62.25
+      longitude_min: 281.75
+      longitude_max: 300.25
+  event_type: marginal_temperature
+- case_id_number: 72
+  title: March 2023 Event 0458
+  start_date: 2023-03-17 00:00:00
+  end_date: 2023-03-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 50.25
+      latitude_max: 62.25
+      longitude_min: 280.5
+      longitude_max: 301.5
+  event_type: marginal_temperature
+- case_id_number: 73
+  title: May 2023 Event 0471
+  start_date: 2023-05-08 00:00:00
+  end_date: 2023-05-12 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -30.25
+      latitude_max: -20.5
+      longitude_min: 117.0
+      longitude_max: 134.25
+  event_type: marginal_temperature
+- case_id_number: 74
+  title: May 2023 Event 0474
+  start_date: 2023-05-16 00:00:00
+  end_date: 2023-05-20 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 56.0
+      latitude_max: 72.0
+      longitude_min: 283.75
+      longitude_max: 298.0
+  event_type: marginal_temperature
+- case_id_number: 75
+  title: May 2023 Event 0476
+  start_date: 2023-05-17 00:00:00
+  end_date: 2023-05-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 54.5
+      latitude_max: 71.25
+      longitude_min: 280.75
+      longitude_max: 298.25
+  event_type: marginal_temperature
+- case_id_number: 76
+  title: May 2023 Event 0480
+  start_date: 2023-05-26 00:00:00
+  end_date: 2023-05-30 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 43.25
+      latitude_max: 51.0
+      longitude_min: 28.75
+      longitude_max: 42.25
+  event_type: marginal_temperature
+- case_id_number: 77
+  title: June 2023 Event 0488
+  start_date: 2023-06-23 00:00:00
+  end_date: 2023-06-27 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 17.0
+      latitude_max: 31.75
+      longitude_min: 15.75
+      longitude_max: 38.25
+  event_type: marginal_temperature
+- case_id_number: 78
+  title: July 2023 Event 0493
+  start_date: 2023-07-30 00:00:00
+  end_date: 2023-08-04 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 66.75
+      latitude_max: 76.75
+      longitude_min: 299.0
+      longitude_max: 336.0
+  event_type: marginal_temperature
+- case_id_number: 79
+  title: August 2023 Event 0495
+  start_date: 2023-08-21 00:00:00
+  end_date: 2023-08-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: -21.75
+      latitude_max: -15.0
+      longitude_min: 123.25
+      longitude_max: 133.75
+  event_type: marginal_temperature
+- case_id_number: 80
+  title: September 2023 Event 0499
+  start_date: 2023-09-17 00:00:00
+  end_date: 2023-09-21 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 60.0
+      latitude_max: 70.75
+      longitude_min: 192.25
+      longitude_max: 232.5
+  event_type: marginal_temperature
+- case_id_number: 81
+  title: October 2023 Event 0508
   start_date: 2023-10-06 00:00:00
   end_date: 2023-10-14 00:00:00
   location:
@@ -706,9 +970,21 @@
       longitude_min: 351.5
       longitude_max: 359.75
   event_type: marginal_temperature
-- case_id_number: 60
-  title: October 2023 Event 0914
-  start_date: 2023-10-14 00:00:00
+- case_id_number: 82
+  title: October 2023 Event 0509
+  start_date: 2023-10-06 00:00:00
+  end_date: 2023-10-11 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 25.25
+      latitude_max: 35.25
+      longitude_min: 0.0
+      longitude_max: 13.5
+  event_type: marginal_temperature
+- case_id_number: 83
+  title: October 2023 Event 0512
+  start_date: 2023-10-15 00:00:00
   end_date: 2023-10-21 00:00:00
   location:
     type: bounded_region
@@ -718,8 +994,20 @@
       longitude_min: 121.5
       longitude_max: 133.5
   event_type: marginal_temperature
-- case_id_number: 61
-  title: October 2023 Event 0926
+- case_id_number: 84
+  title: October 2023 Event 0515
+  start_date: 2023-10-24 00:00:00
+  end_date: 2023-10-28 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 51.0
+      latitude_max: 62.5
+      longitude_min: 280.5
+      longitude_max: 297.25
+  event_type: marginal_temperature
+- case_id_number: 85
+  title: October 2023 Event 0517
   start_date: 2023-10-29 00:00:00
   end_date: 2023-11-04 00:00:00
   location:
@@ -730,20 +1018,32 @@
       longitude_min: 240.0
       longitude_max: 276.25
   event_type: marginal_temperature
-- case_id_number: 62
-  title: November 2023 Event 0943
-  start_date: 2023-11-26 00:00:00
-  end_date: 2023-12-01 00:00:00
+- case_id_number: 86
+  title: December 2023 Event 0530
+  start_date: 2023-12-04 00:00:00
+  end_date: 2023-12-08 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 67.75
+      latitude_max: 73.75
+      longitude_min: 73.25
+      longitude_max: 97.0
+  event_type: marginal_temperature
+- case_id_number: 87
+  title: December 2023 Event 0537
+  start_date: 2023-12-11 00:00:00
+  end_date: 2023-12-15 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -88.25
-      latitude_max: -67.0
-      longitude_min: 32.25
-      longitude_max: 169.25
+      latitude_min: 66.5
+      latitude_max: 72.75
+      longitude_min: 73.25
+      longitude_max: 98.0
   event_type: marginal_temperature
-- case_id_number: 63
-  title: December 2023 Event 0975
+- case_id_number: 88
+  title: December 2023 Event 0544
   start_date: 2023-12-24 00:00:00
   end_date: 2023-12-29 00:00:00
   location:
@@ -754,20 +1054,68 @@
       longitude_min: 5.5
       longitude_max: 62.25
   event_type: marginal_temperature
-- case_id_number: 64
-  title: January 2024 Event 1006
-  start_date: 2024-01-26 00:00:00
-  end_date: 2024-02-01 00:00:00
+- case_id_number: 89
+  title: January 2024 Event 0548
+  start_date: 2024-01-01 00:00:00
+  end_date: 2024-01-05 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -79.5
-      latitude_max: -69.0
-      longitude_min: 68.25
-      longitude_max: 107.75
+      latitude_min: 5.5
+      latitude_max: 22.75
+      longitude_min: 20.75
+      longitude_max: 37.5
   event_type: marginal_temperature
-- case_id_number: 65
-  title: February 2024 Event 1041
+- case_id_number: 90
+  title: January 2024 Event 0555
+  start_date: 2024-01-11 00:00:00
+  end_date: 2024-01-15 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 60.0
+      latitude_max: 69.75
+      longitude_min: 163.5
+      longitude_max: 179.25
+  event_type: marginal_temperature
+- case_id_number: 91
+  title: February 2024 Event 0588
+  start_date: 2024-02-24 00:00:00
+  end_date: 2024-02-28 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 62.75
+      latitude_max: 73.5
+      longitude_min: 270.25
+      longitude_max: 298.0
+  event_type: marginal_temperature
+- case_id_number: 92
+  title: February 2024 Event 0590
+  start_date: 2024-02-25 00:00:00
+  end_date: 2024-02-29 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 41.25
+      latitude_max: 59.5
+      longitude_min: 32.5
+      longitude_max: 73.5
+  event_type: marginal_temperature
+- case_id_number: 93
+  title: February 2024 Event 0592
+  start_date: 2024-02-25 00:00:00
+  end_date: 2024-02-29 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 60.25
+      latitude_max: 81.5
+      longitude_min: 296.0
+      longitude_max: 320.25
+  event_type: marginal_temperature
+- case_id_number: 94
+  title: February 2024 Event 0593
   start_date: 2024-02-29 00:00:00
   end_date: 2024-03-08 00:00:00
   location:
@@ -778,20 +1126,8 @@
       longitude_min: 280.75
       longitude_max: 299.5
   event_type: marginal_temperature
-- case_id_number: 66
-  title: March 2024 Event 1050
-  start_date: 2024-03-09 00:00:00
-  end_date: 2024-03-14 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -76.5
-      latitude_max: -71.25
-      longitude_min: 25.5
-      longitude_max: 64.5
-  event_type: marginal_temperature
-- case_id_number: 67
-  title: April 2024 Event 1068
+- case_id_number: 95
+  title: April 2024 Event 0611
   start_date: 2024-04-27 00:00:00
   end_date: 2024-05-02 00:00:00
   location:
@@ -802,20 +1138,32 @@
       longitude_min: 116.0
       longitude_max: 140.25
   event_type: marginal_temperature
-- case_id_number: 68
-  title: May 2024 Event 1081
-  start_date: 2024-05-13 00:00:00
-  end_date: 2024-05-18 00:00:00
+- case_id_number: 96
+  title: May 2024 Event 0614
+  start_date: 2024-05-07 00:00:00
+  end_date: 2024-05-11 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -84.5
-      latitude_max: -75.25
-      longitude_min: 201.75
-      longitude_max: 235.0
+      latitude_min: 67.0
+      latitude_max: 76.25
+      longitude_min: 73.25
+      longitude_max: 138.75
   event_type: marginal_temperature
-- case_id_number: 69
-  title: June 2024 Event 1096
+- case_id_number: 97
+  title: May 2024 Event 0616
+  start_date: 2024-05-19 00:00:00
+  end_date: 2024-05-23 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 53.75
+      latitude_max: 62.5
+      longitude_min: 282.0
+      longitude_max: 297.0
+  event_type: marginal_temperature
+- case_id_number: 98
+  title: June 2024 Event 0621
   start_date: 2024-06-13 00:00:00
   end_date: 2024-06-18 00:00:00
   location:
@@ -826,44 +1174,32 @@
       longitude_min: 299.75
       longitude_max: 347.5
   event_type: marginal_temperature
-- case_id_number: 70
-  title: June 2024 Event 1102
-  start_date: 2024-06-26 00:00:00
-  end_date: 2024-07-03 00:00:00
+- case_id_number: 99
+  title: June 2024 Event 0622
+  start_date: 2024-06-15 00:00:00
+  end_date: 2024-06-19 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -80.25
-      latitude_max: -66.25
-      longitude_min: 84.0
-      longitude_max: 145.25
+      latitude_min: 62.0
+      latitude_max: 77.75
+      longitude_min: 250.25
+      longitude_max: 273.75
   event_type: marginal_temperature
-- case_id_number: 71
-  title: July 2024 Event 1106
-  start_date: 2024-07-19 00:00:00
-  end_date: 2024-07-27 00:00:00
+- case_id_number: 100
+  title: August 2024 Event 0624
+  start_date: 2024-08-07 00:00:00
+  end_date: 2024-08-11 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -79.75
-      latitude_max: -66.5
-      longitude_min: 26.75
-      longitude_max: 131.25
-  event_type: marginal_temperature
-- case_id_number: 72
-  title: August 2024 Event 1125
-  start_date: 2024-08-29 00:00:00
-  end_date: 2024-09-06 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -83.75
-      latitude_max: -65.75
+      latitude_min: 27.5
+      latitude_max: 35.0
       longitude_min: 1.5
-      longitude_max: 161.75
+      longitude_max: 16.5
   event_type: marginal_temperature
-- case_id_number: 73
-  title: September 2024 Event 1135
+- case_id_number: 101
+  title: September 2024 Event 0636
   start_date: 2024-09-19 00:00:00
   end_date: 2024-09-25 00:00:00
   location:
@@ -874,21 +1210,45 @@
       longitude_min: 73.25
       longitude_max: 129.0
   event_type: marginal_temperature
-- case_id_number: 74
-  title: October 2024 Event 1163
-  start_date: 2024-10-29 00:00:00
-  end_date: 2024-11-03 00:00:00
+- case_id_number: 102
+  title: October 2024 Event 0641
+  start_date: 2024-10-06 00:00:00
+  end_date: 2024-10-10 00:00:00
   location:
     type: bounded_region
     parameters:
-      latitude_min: -89.75
-      latitude_max: -71.25
-      longitude_min: 0.0
-      longitude_max: 267.25
+      latitude_min: 61.25
+      latitude_max: 70.75
+      longitude_min: 154.75
+      longitude_max: 190.0
   event_type: marginal_temperature
-- case_id_number: 75
-  title: November 2024 Event 1172
-  start_date: 2024-11-16 00:00:00
+- case_id_number: 103
+  title: October 2024 Event 0646
+  start_date: 2024-10-27 00:00:00
+  end_date: 2024-10-31 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 40.5
+      latitude_max: 61.5
+      longitude_min: 233.0
+      longitude_max: 264.0
+  event_type: marginal_temperature
+- case_id_number: 104
+  title: October 2024 Event 0647
+  start_date: 2024-10-28 00:00:00
+  end_date: 2024-11-01 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 56.0
+      latitude_max: 69.25
+      longitude_min: 192.25
+      longitude_max: 230.25
+  event_type: marginal_temperature
+- case_id_number: 105
+  title: November 2024 Event 0656
+  start_date: 2024-11-17 00:00:00
   end_date: 2024-11-21 00:00:00
   location:
     type: bounded_region
@@ -898,8 +1258,20 @@
       longitude_min: 297.75
       longitude_max: 340.25
   event_type: marginal_temperature
-- case_id_number: 76
-  title: December 2024 Event 1196
+- case_id_number: 106
+  title: November 2024 Event 0659
+  start_date: 2024-11-21 00:00:00
+  end_date: 2024-11-25 00:00:00
+  location:
+    type: bounded_region
+    parameters:
+      latitude_min: 41.25
+      latitude_max: 69.75
+      longitude_min: 237.25
+      longitude_max: 291.0
+  event_type: marginal_temperature
+- case_id_number: 107
+  title: December 2024 Event 0674
   start_date: 2024-12-17 00:00:00
   end_date: 2024-12-22 00:00:00
   location:

From e78650365d97e51c6a862006760245d4f3f8ce71 Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Mon, 13 Apr 2026 18:01:51 +0000
Subject: [PATCH 61/72] remove marginal severe with dashes

---
 .../marginal-severe-convection-cases.yaml     | 1812 -----------------
 1 file changed, 1812 deletions(-)
 delete mode 100644 src/extremeweatherbench/data/marginal-severe-convection-cases.yaml

diff --git a/src/extremeweatherbench/data/marginal-severe-convection-cases.yaml b/src/extremeweatherbench/data/marginal-severe-convection-cases.yaml
deleted file mode 100644
index f71a3f4c..00000000
--- a/src/extremeweatherbench/data/marginal-severe-convection-cases.yaml
+++ /dev/null
@@ -1,1812 +0,0 @@
-- case_id_number: 8000
-  end_date: 2022-04-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-04-16 12:00:00
-  title: 2022-04-16 Severe Convection Non-Event
-- case_id_number: 8001
-  end_date: 2022-09-03 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-02 12:00:00
-  title: 2022-09-02 Severe Convection Non-Event
-- case_id_number: 8002
-  end_date: 2022-10-11 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-10-10 12:00:00
-  title: 2022-10-10 Severe Convection Non-Event
-- case_id_number: 8003
-  end_date: 2022-09-06 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-05 12:00:00
-  title: 2022-09-05 Severe Convection Non-Event
-- case_id_number: 8004
-  end_date: 2023-11-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-11-06 12:00:00
-  title: 2023-11-06 Severe Convection Non-Event
-- case_id_number: 8005
-  end_date: 2024-02-20 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-02-19 12:00:00
-  title: 2024-02-19 Severe Convection Non-Event
-- case_id_number: 8006
-  end_date: 2022-04-20 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-04-19 12:00:00
-  title: 2022-04-19 Severe Convection Non-Event
-- case_id_number: 8007
-  end_date: 2022-04-19 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-04-18 12:00:00
-  title: 2022-04-18 Severe Convection Non-Event
-- case_id_number: 8008
-  end_date: 2021-02-11 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-02-10 12:00:00
-  title: 2021-02-10 Severe Convection Non-Event
-- case_id_number: 8009
-  end_date: 2023-02-23 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-02-22 12:00:00
-  title: 2023-02-22 Severe Convection Non-Event
-- case_id_number: 8010
-  end_date: 2021-03-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-03-06 12:00:00
-  title: 2021-03-06 Severe Convection Non-Event
-- case_id_number: 8011
-  end_date: 2021-10-24 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-10-23 12:00:00
-  title: 2021-10-23 Severe Convection Non-Event
-- case_id_number: 8012
-  end_date: 2020-03-15 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-14 12:00:00
-  title: 2020-03-14 Severe Convection Non-Event
-- case_id_number: 8013
-  end_date: 2022-07-25 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-07-24 12:00:00
-  title: 2022-07-24 Severe Convection Non-Event
-- case_id_number: 8014
-  end_date: 2023-09-19 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-09-18 12:00:00
-  title: 2023-09-18 Severe Convection Non-Event
-- case_id_number: 8015
-  end_date: 2021-10-01 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-30 12:00:00
-  title: 2021-09-30 Severe Convection Non-Event
-- case_id_number: 8016
-  end_date: 2021-09-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-06 12:00:00
-  title: 2021-09-06 Severe Convection Non-Event
-- case_id_number: 8017
-  end_date: 2022-09-08 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-07 12:00:00
-  title: 2022-09-07 Severe Convection Non-Event
-- case_id_number: 8018
-  end_date: 2022-08-08 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-08-07 12:00:00
-  title: 2022-08-07 Severe Convection Non-Event
-- case_id_number: 8019
-  end_date: 2024-04-23 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-04-22 12:00:00
-  title: 2024-04-22 Severe Convection Non-Event
-- case_id_number: 8020
-  end_date: 2021-09-11 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-10 12:00:00
-  title: 2021-09-10 Severe Convection Non-Event
-- case_id_number: 8021
-  end_date: 2022-11-26 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-11-25 12:00:00
-  title: 2022-11-25 Severe Convection Non-Event
-- case_id_number: 8022
-  end_date: 2023-08-29 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-08-28 12:00:00
-  title: 2023-08-28 Severe Convection Non-Event
-- case_id_number: 8023
-  end_date: 2024-08-25 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-08-24 12:00:00
-  title: 2024-08-24 Severe Convection Non-Event
-- case_id_number: 8024
-  end_date: 2023-04-25 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-04-24 12:00:00
-  title: 2023-04-24 Severe Convection Non-Event
-- case_id_number: 8025
-  end_date: 2024-01-27 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-01-26 12:00:00
-  title: 2024-01-26 Severe Convection Non-Event
-- case_id_number: 8026
-  end_date: 2021-03-12 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-03-11 12:00:00
-  title: 2021-03-11 Severe Convection Non-Event
-- case_id_number: 8027
-  end_date: 2023-03-09 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-03-08 12:00:00
-  title: 2023-03-08 Severe Convection Non-Event
-- case_id_number: 8028
-  end_date: 2020-06-16 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-06-15 12:00:00
-  title: 2020-06-15 Severe Convection Non-Event
-- case_id_number: 8029
-  end_date: 2024-04-20 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-04-19 12:00:00
-  title: 2024-04-19 Severe Convection Non-Event
-- case_id_number: 8030
-  end_date: 2022-10-24 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-10-23 12:00:00
-  title: 2022-10-23 Severe Convection Non-Event
-- case_id_number: 8031
-  end_date: 2023-03-29 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-03-28 12:00:00
-  title: 2023-03-28 Severe Convection Non-Event
-- case_id_number: 8032
-  end_date: 2020-11-15 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-11-14 12:00:00
-  title: 2020-11-14 Severe Convection Non-Event
-- case_id_number: 8033
-  end_date: 2021-09-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-03 12:00:00
-  title: 2021-09-03 Severe Convection Non-Event
-- case_id_number: 8034
-  end_date: 2024-03-18 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-03-17 12:00:00
-  title: 2024-03-17 Severe Convection Non-Event
-- case_id_number: 8035
-  end_date: 2024-03-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-03-06 12:00:00
-  title: 2024-03-06 Severe Convection Non-Event
-- case_id_number: 8036
-  end_date: 2022-10-02 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-10-01 12:00:00
-  title: 2022-10-01 Severe Convection Non-Event
-- case_id_number: 8037
-  end_date: 2021-04-27 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-04-26 12:00:00
-  title: 2021-04-26 Severe Convection Non-Event
-- case_id_number: 8038
-  end_date: 2020-03-23 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-22 12:00:00
-  title: 2020-03-22 Severe Convection Non-Event
-- case_id_number: 8039
-  end_date: 2022-08-14 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-08-13 12:00:00
-  title: 2022-08-13 Severe Convection Non-Event
-- case_id_number: 8040
-  end_date: 2023-04-24 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-04-23 12:00:00
-  title: 2023-04-23 Severe Convection Non-Event
-- case_id_number: 8041
-  end_date: 2021-08-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-08-06 12:00:00
-  title: 2021-08-06 Severe Convection Non-Event
-- case_id_number: 8042
-  end_date: 2023-05-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-05-03 12:00:00
-  title: 2023-05-03 Severe Convection Non-Event
-- case_id_number: 8043
-  end_date: 2023-08-22 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-08-21 12:00:00
-  title: 2023-08-21 Severe Convection Non-Event
-- case_id_number: 8044
-  end_date: 2021-09-22 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-21 12:00:00
-  title: 2021-09-21 Severe Convection Non-Event
-- case_id_number: 8045
-  end_date: 2020-06-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-06-16 12:00:00
-  title: 2020-06-16 Severe Convection Non-Event
-- case_id_number: 8046
-  end_date: 2022-10-12 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-10-11 12:00:00
-  title: 2022-10-11 Severe Convection Non-Event
-- case_id_number: 8047
-  end_date: 2020-03-02 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-01 12:00:00
-  title: 2020-03-01 Severe Convection Non-Event
-- case_id_number: 8048
-  end_date: 2023-02-08 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-02-07 12:00:00
-  title: 2023-02-07 Severe Convection Non-Event
-- case_id_number: 8049
-  end_date: 2024-08-26 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-08-25 12:00:00
-  title: 2024-08-25 Severe Convection Non-Event
-- case_id_number: 8050
-  end_date: 2022-08-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-08-16 12:00:00
-  title: 2022-08-16 Severe Convection Non-Event
-- case_id_number: 8051
-  end_date: 2023-04-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-04-16 12:00:00
-  title: 2023-04-16 Severe Convection Non-Event
-- case_id_number: 8052
-  end_date: 2021-04-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-04-16 12:00:00
-  title: 2021-04-16 Severe Convection Non-Event
-- case_id_number: 8053
-  end_date: 2023-08-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-08-16 12:00:00
-  title: 2023-08-16 Severe Convection Non-Event
-- case_id_number: 8054
-  end_date: 2024-02-13 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-02-12 12:00:00
-  title: 2024-02-12 Severe Convection Non-Event
-- case_id_number: 8055
-  end_date: 2023-10-11 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-10-10 12:00:00
-  title: 2023-10-10 Severe Convection Non-Event
-- case_id_number: 8056
-  end_date: 2022-09-09 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-08 12:00:00
-  title: 2022-09-08 Severe Convection Non-Event
-- case_id_number: 8057
-  end_date: 2020-03-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-20 12:00:00
-  title: 2020-03-20 Severe Convection Non-Event
-- case_id_number: 8058
-  end_date: 2023-09-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-09-06 12:00:00
-  title: 2023-09-06 Severe Convection Non-Event
-- case_id_number: 8059
-  end_date: 2021-09-20 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-19 12:00:00
-  title: 2021-09-19 Severe Convection Non-Event
-- case_id_number: 8060
-  end_date: 2020-05-01 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-04-30 12:00:00
-  title: 2020-04-30 Severe Convection Non-Event
-- case_id_number: 8061
-  end_date: 2024-06-12 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-06-11 12:00:00
-  title: 2024-06-11 Severe Convection Non-Event
-- case_id_number: 8062
-  end_date: 2023-09-13 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-09-12 12:00:00
-  title: 2023-09-12 Severe Convection Non-Event
-- case_id_number: 8063
-  end_date: 2024-01-24 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-01-23 12:00:00
-  title: 2024-01-23 Severe Convection Non-Event
-- case_id_number: 8064
-  end_date: 2022-09-25 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-24 12:00:00
-  title: 2022-09-24 Severe Convection Non-Event
-- case_id_number: 8065
-  end_date: 2022-04-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-04-20 12:00:00
-  title: 2022-04-20 Severe Convection Non-Event
-- case_id_number: 8066
-  end_date: 2020-07-27 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-07-26 12:00:00
-  title: 2020-07-26 Severe Convection Non-Event
-- case_id_number: 8067
-  end_date: 2020-09-09 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-09-08 12:00:00
-  title: 2020-09-08 Severe Convection Non-Event
-- case_id_number: 8068
-  end_date: 2021-10-02 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-10-01 12:00:00
-  title: 2021-10-01 Severe Convection Non-Event
-- case_id_number: 8069
-  end_date: 2022-11-28 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-11-27 12:00:00
-  title: 2022-11-27 Severe Convection Non-Event
-- case_id_number: 8070
-  end_date: 2022-04-03 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-04-02 12:00:00
-  title: 2022-04-02 Severe Convection Non-Event
-- case_id_number: 8071
-  end_date: 2024-02-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-02-16 12:00:00
-  title: 2024-02-16 Severe Convection Non-Event
-- case_id_number: 8072
-  end_date: 2020-03-24 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-23 12:00:00
-  title: 2020-03-23 Severe Convection Non-Event
-- case_id_number: 8073
-  end_date: 2023-10-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-10-20 12:00:00
-  title: 2023-10-20 Severe Convection Non-Event
-- case_id_number: 8074
-  end_date: 2022-09-30 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-29 12:00:00
-  title: 2022-09-29 Severe Convection Non-Event
-- case_id_number: 8075
-  end_date: 2021-08-02 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-08-01 12:00:00
-  title: 2021-08-01 Severe Convection Non-Event
-- case_id_number: 8076
-  end_date: 2020-08-13 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-08-12 12:00:00
-  title: 2020-08-12 Severe Convection Non-Event
-- case_id_number: 8077
-  end_date: 2023-03-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-03-06 12:00:00
-  title: 2023-03-06 Severe Convection Non-Event
-- case_id_number: 8078
-  end_date: 2023-05-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-05-20 12:00:00
-  title: 2023-05-20 Severe Convection Non-Event
-- case_id_number: 8079
-  end_date: 2020-11-27 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-11-26 12:00:00
-  title: 2020-11-26 Severe Convection Non-Event
-- case_id_number: 8080
-  end_date: 2024-03-30 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-03-29 12:00:00
-  title: 2024-03-29 Severe Convection Non-Event
-- case_id_number: 8081
-  end_date: 2020-07-31 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-07-30 12:00:00
-  title: 2020-07-30 Severe Convection Non-Event
-- case_id_number: 8082
-  end_date: 2024-08-03 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-08-02 12:00:00
-  title: 2024-08-02 Severe Convection Non-Event
-- case_id_number: 8083
-  end_date: 2022-09-13 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-12 12:00:00
-  title: 2022-09-12 Severe Convection Non-Event
-- case_id_number: 8084
-  end_date: 2020-03-11 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-10 12:00:00
-  title: 2020-03-10 Severe Convection Non-Event
-- case_id_number: 8085
-  end_date: 2022-08-07 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-08-06 12:00:00
-  title: 2022-08-06 Severe Convection Non-Event
-- case_id_number: 8086
-  end_date: 2021-09-25 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-24 12:00:00
-  title: 2021-09-24 Severe Convection Non-Event
-- case_id_number: 8087
-  end_date: 2024-04-24 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-04-23 12:00:00
-  title: 2024-04-23 Severe Convection Non-Event
-- case_id_number: 8088
-  end_date: 2021-08-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-08-03 12:00:00
-  title: 2021-08-03 Severe Convection Non-Event
-- case_id_number: 8089
-  end_date: 2023-04-08 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-04-07 12:00:00
-  title: 2023-04-07 Severe Convection Non-Event
-- case_id_number: 8090
-  end_date: 2024-10-22 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-10-21 12:00:00
-  title: 2024-10-21 Severe Convection Non-Event
-- case_id_number: 8091
-  end_date: 2024-08-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-08-20 12:00:00
-  title: 2024-08-20 Severe Convection Non-Event
-- case_id_number: 8092
-  end_date: 2024-09-09 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-09-08 12:00:00
-  title: 2024-09-08 Severe Convection Non-Event
-- case_id_number: 8093
-  end_date: 2020-04-27 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-04-26 12:00:00
-  title: 2020-04-26 Severe Convection Non-Event
-- case_id_number: 8094
-  end_date: 2020-07-29 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-07-28 12:00:00
-  title: 2020-07-28 Severe Convection Non-Event
-- case_id_number: 8095
-  end_date: 2021-11-22 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-11-21 12:00:00
-  title: 2021-11-21 Severe Convection Non-Event
-- case_id_number: 8096
-  end_date: 2021-02-09 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-02-08 12:00:00
-  title: 2021-02-08 Severe Convection Non-Event
-- case_id_number: 8097
-  end_date: 2020-06-12 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-06-11 12:00:00
-  title: 2020-06-11 Severe Convection Non-Event
-- case_id_number: 8098
-  end_date: 2024-03-08 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-03-07 12:00:00
-  title: 2024-03-07 Severe Convection Non-Event
-- case_id_number: 8099
-  end_date: 2020-03-12 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-11 12:00:00
-  title: 2020-03-11 Severe Convection Non-Event
-- case_id_number: 8100
-  end_date: 2021-09-30 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-29 12:00:00
-  title: 2021-09-29 Severe Convection Non-Event
-- case_id_number: 8101
-  end_date: 2021-04-12 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-04-11 12:00:00
-  title: 2021-04-11 Severe Convection Non-Event
-- case_id_number: 8102
-  end_date: 2022-01-20 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-01-19 12:00:00
-  title: 2022-01-19 Severe Convection Non-Event
-- case_id_number: 8103
-  end_date: 2020-10-23 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-10-22 12:00:00
-  title: 2020-10-22 Severe Convection Non-Event
-- case_id_number: 8104
-  end_date: 2024-03-28 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-03-27 12:00:00
-  title: 2024-03-27 Severe Convection Non-Event
-- case_id_number: 8105
-  end_date: 2023-03-10 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-03-09 12:00:00
-  title: 2023-03-09 Severe Convection Non-Event
-- case_id_number: 8106
-  end_date: 2024-08-02 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-08-01 12:00:00
-  title: 2024-08-01 Severe Convection Non-Event
-- case_id_number: 8107
-  end_date: 2020-04-03 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-04-02 12:00:00
-  title: 2020-04-02 Severe Convection Non-Event
-- case_id_number: 8108
-  end_date: 2022-10-03 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-10-02 12:00:00
-  title: 2022-10-02 Severe Convection Non-Event
-- case_id_number: 8109
-  end_date: 2022-09-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-03 12:00:00
-  title: 2022-09-03 Severe Convection Non-Event
-- case_id_number: 8110
-  end_date: 2020-09-24 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-09-23 12:00:00
-  title: 2020-09-23 Severe Convection Non-Event
-- case_id_number: 8111
-  end_date: 2020-03-30 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-03-29 12:00:00
-  title: 2020-03-29 Severe Convection Non-Event
-- case_id_number: 8112
-  end_date: 2024-09-14 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-09-13 12:00:00
-  title: 2024-09-13 Severe Convection Non-Event
-- case_id_number: 8113
-  end_date: 2023-08-03 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-08-02 12:00:00
-  title: 2023-08-02 Severe Convection Non-Event
-- case_id_number: 8114
-  end_date: 2020-04-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-04-03 12:00:00
-  title: 2020-04-03 Severe Convection Non-Event
-- case_id_number: 8115
-  end_date: 2024-11-29 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-11-28 12:00:00
-  title: 2024-11-28 Severe Convection Non-Event
-- case_id_number: 8116
-  end_date: 2021-10-08 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-10-07 12:00:00
-  title: 2021-10-07 Severe Convection Non-Event
-- case_id_number: 8117
-  end_date: 2020-11-10 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-11-09 12:00:00
-  title: 2020-11-09 Severe Convection Non-Event
-- case_id_number: 8118
-  end_date: 2021-04-20 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-04-19 12:00:00
-  title: 2021-04-19 Severe Convection Non-Event
-- case_id_number: 8119
-  end_date: 2022-09-22 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-21 12:00:00
-  title: 2022-09-21 Severe Convection Non-Event
-- case_id_number: 8120
-  end_date: 2021-06-29 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-06-28 12:00:00
-  title: 2021-06-28 Severe Convection Non-Event
-- case_id_number: 8121
-  end_date: 2023-03-14 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-03-13 12:00:00
-  title: 2023-03-13 Severe Convection Non-Event
-- case_id_number: 8122
-  end_date: 2024-11-09 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-11-08 12:00:00
-  title: 2024-11-08 Severe Convection Non-Event
-- case_id_number: 8123
-  end_date: 2023-09-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-09-20 12:00:00
-  title: 2023-09-20 Severe Convection Non-Event
-- case_id_number: 8124
-  end_date: 2021-01-25 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-01-24 12:00:00
-  title: 2021-01-24 Severe Convection Non-Event
-- case_id_number: 8125
-  end_date: 2024-03-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-03-03 12:00:00
-  title: 2024-03-03 Severe Convection Non-Event
-- case_id_number: 8126
-  end_date: 2024-12-19 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-12-18 12:00:00
-  title: 2024-12-18 Severe Convection Non-Event
-- case_id_number: 8127
-  end_date: 2021-07-03 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-07-02 12:00:00
-  title: 2021-07-02 Severe Convection Non-Event
-- case_id_number: 8128
-  end_date: 2024-10-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-10-20 12:00:00
-  title: 2024-10-20 Severe Convection Non-Event
-- case_id_number: 8129
-  end_date: 2021-09-18 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-17 12:00:00
-  title: 2021-09-17 Severe Convection Non-Event
-- case_id_number: 8130
-  end_date: 2024-09-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-09-20 12:00:00
-  title: 2024-09-20 Severe Convection Non-Event
-- case_id_number: 8131
-  end_date: 2021-09-05 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-09-04 12:00:00
-  title: 2021-09-04 Severe Convection Non-Event
-- case_id_number: 8132
-  end_date: 2023-09-29 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-09-28 12:00:00
-  title: 2023-09-28 Severe Convection Non-Event
-- case_id_number: 8133
-  end_date: 2023-09-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-09-03 12:00:00
-  title: 2023-09-03 Severe Convection Non-Event
-- case_id_number: 8134
-  end_date: 2020-05-11 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-05-10 12:00:00
-  title: 2020-05-10 Severe Convection Non-Event
-- case_id_number: 8135
-  end_date: 2023-05-22 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-05-21 12:00:00
-  title: 2023-05-21 Severe Convection Non-Event
-- case_id_number: 8136
-  end_date: 2025-01-01 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-12-31 12:00:00
-  title: 2024-12-31 Severe Convection Non-Event
-- case_id_number: 8137
-  end_date: 2021-07-06 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2021-07-05 12:00:00
-  title: 2021-07-05 Severe Convection Non-Event
-- case_id_number: 8138
-  end_date: 2024-02-22 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-02-21 12:00:00
-  title: 2024-02-21 Severe Convection Non-Event
-- case_id_number: 8139
-  end_date: 2022-03-21 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-03-20 12:00:00
-  title: 2022-03-20 Severe Convection Non-Event
-- case_id_number: 8140
-  end_date: 2023-01-08 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-01-07 12:00:00
-  title: 2023-01-07 Severe Convection Non-Event
-- case_id_number: 8141
-  end_date: 2024-10-04 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-10-03 12:00:00
-  title: 2024-10-03 Severe Convection Non-Event
-- case_id_number: 8142
-  end_date: 2023-09-01 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2023-08-31 12:00:00
-  title: 2023-08-31 Severe Convection Non-Event
-- case_id_number: 8143
-  end_date: 2022-08-10 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-08-09 12:00:00
-  title: 2022-08-09 Severe Convection Non-Event
-- case_id_number: 8144
-  end_date: 2024-10-20 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2024-10-19 12:00:00
-  title: 2024-10-19 Severe Convection Non-Event
-- case_id_number: 8145
-  end_date: 2020-07-23 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-07-22 12:00:00
-  title: 2020-07-22 Severe Convection Non-Event
-- case_id_number: 8146
-  end_date: 2022-09-17 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-09-16 12:00:00
-  title: 2022-09-16 Severe Convection Non-Event
-- case_id_number: 8147
-  end_date: 2022-08-18 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-08-17 12:00:00
-  title: 2022-08-17 Severe Convection Non-Event
-- case_id_number: 8148
-  end_date: 2020-04-05 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-04-04 12:00:00
-  title: 2020-04-04 Severe Convection Non-Event
-- case_id_number: 8149
-  end_date: 2022-10-28 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2022-10-27 12:00:00
-  title: 2022-10-27 Severe Convection Non-Event
-- case_id_number: 8150
-  end_date: 2020-09-28 12:00:00
-  event_type: severe_convection
-  location:
-    parameters:
-      latitude_max: 50
-      latitude_min: 30
-      longitude_max: -80
-      longitude_min: -110
-    type: bounded_region
-  start_date: 2020-09-27 12:00:00
-  title: 2020-09-27 Severe Convection Non-Event

From 7571d1aa8ed108548633d366909b80ee4a207935 Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Tue, 14 Apr 2026 16:23:43 +0000
Subject: [PATCH 62/72] update nz heatwave (200k km2 limit doesnt work on small
 island)

---
 src/extremeweatherbench/data/events.yaml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/extremeweatherbench/data/events.yaml b/src/extremeweatherbench/data/events.yaml
index cdd7659e..d5dd5b54 100644
--- a/src/extremeweatherbench/data/events.yaml
+++ b/src/extremeweatherbench/data/events.yaml
@@ -329,10 +329,10 @@
   location:
     type: bounded_region
     parameters:
-      latitude_min: -46.5
-      latitude_max: -41.5
-      longitude_min: 149.0
-      longitude_max: 149.0
+      latitude_min: -48
+      latitude_max: -35.75
+      longitude_min: 165.5
+      longitude_max: 178.25
   event_type: heat_wave
 - case_id_number: 29
   title: 2020 Australia

From 9d0e89765e2fb3f7e13d4b742977eddb8f8685f8 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Tue, 14 Apr 2026 14:02:53 -0400
Subject: [PATCH 63/72] swap marginal temp event type to heat wave

---
 .../data/marginal_temperature_events.yaml     | 214 +++++++++---------
 1 file changed, 107 insertions(+), 107 deletions(-)

diff --git a/src/extremeweatherbench/data/marginal_temperature_events.yaml b/src/extremeweatherbench/data/marginal_temperature_events.yaml
index c7051de9..df89800a 100644
--- a/src/extremeweatherbench/data/marginal_temperature_events.yaml
+++ b/src/extremeweatherbench/data/marginal_temperature_events.yaml
@@ -9,7 +9,7 @@
       latitude_max: 30.25
       longitude_min: 11.0
       longitude_max: 38.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 2
   title: February 2020 Event 0018
   start_date: 2020-02-01 00:00:00
@@ -21,7 +21,7 @@
       latitude_max: 29.5
       longitude_min: 0.0
       longitude_max: 38.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 3
   title: February 2020 Event 0019
   start_date: 2020-02-01 00:00:00
@@ -33,7 +33,7 @@
       latitude_max: 77.0
       longitude_min: 87.0
       longitude_max: 128.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 4
   title: February 2020 Event 0023
   start_date: 2020-02-05 00:00:00
@@ -45,7 +45,7 @@
       latitude_max: 81.0
       longitude_min: 296.5
       longitude_max: 324.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 5
   title: February 2020 Event 0025
   start_date: 2020-02-09 00:00:00
@@ -57,7 +57,7 @@
       latitude_max: 60.5
       longitude_min: 28.25
       longitude_max: 58.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 6
   title: February 2020 Event 0034
   start_date: 2020-02-22 00:00:00
@@ -69,7 +69,7 @@
       latitude_max: 76.75
       longitude_min: 296.5
       longitude_max: 317.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 7
   title: March 2020 Event 0037
   start_date: 2020-03-01 00:00:00
@@ -81,7 +81,7 @@
       latitude_max: 53.5
       longitude_min: 0.0
       longitude_max: 16.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 8
   title: April 2020 Event 0047
   start_date: 2020-04-03 00:00:00
@@ -93,7 +93,7 @@
       latitude_max: 77.75
       longitude_min: 293.5
       longitude_max: 339.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 9
   title: April 2020 Event 0048
   start_date: 2020-04-07 00:00:00
@@ -105,7 +105,7 @@
       latitude_max: 70.25
       longitude_min: 228.0
       longitude_max: 267.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 10
   title: May 2020 Event 0053
   start_date: 2020-05-10 00:00:00
@@ -117,7 +117,7 @@
       latitude_max: -14.75
       longitude_min: 118.25
       longitude_max: 140.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 11
   title: May 2020 Event 0060
   start_date: 2020-05-30 00:00:00
@@ -129,7 +129,7 @@
       latitude_max: 75.5
       longitude_min: 235.0
       longitude_max: 259.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 12
   title: June 2020 Event 0062
   start_date: 2020-06-03 00:00:00
@@ -141,7 +141,7 @@
       latitude_max: -15.5
       longitude_min: 119.5
       longitude_max: 140.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 13
   title: June 2020 Event 0064
   start_date: 2020-06-11 00:00:00
@@ -153,7 +153,7 @@
       latitude_max: 74.25
       longitude_min: 71.0
       longitude_max: 116.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 14
   title: July 2020 Event 0068
   start_date: 2020-07-13 00:00:00
@@ -165,7 +165,7 @@
       latitude_max: 75.5
       longitude_min: 72.25
       longitude_max: 97.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 15
   title: September 2020 Event 0076
   start_date: 2020-09-11 00:00:00
@@ -177,7 +177,7 @@
       latitude_max: 71.0
       longitude_min: 12.75
       longitude_max: 50.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 16
   title: September 2020 Event 0079
   start_date: 2020-09-28 00:00:00
@@ -189,7 +189,7 @@
       latitude_max: 64.25
       longitude_min: 248.75
       longitude_max: 269.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 17
   title: October 2020 Event 0081
   start_date: 2020-10-06 00:00:00
@@ -201,7 +201,7 @@
       latitude_max: 71.5
       longitude_min: 62.25
       longitude_max: 99.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 18
   title: October 2020 Event 0082
   start_date: 2020-10-09 00:00:00
@@ -213,7 +213,7 @@
       latitude_max: 28.25
       longitude_min: 10.5
       longitude_max: 24.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 19
   title: October 2020 Event 0084
   start_date: 2020-10-19 00:00:00
@@ -225,7 +225,7 @@
       latitude_max: 67.5
       longitude_min: 281.75
       longitude_max: 299.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 20
   title: November 2020 Event 0087
   start_date: 2020-11-01 00:00:00
@@ -237,7 +237,7 @@
       latitude_max: 23.75
       longitude_min: 0.0
       longitude_max: 11.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 21
   title: November 2020 Event 0088
   start_date: 2020-11-04 00:00:00
@@ -249,7 +249,7 @@
       latitude_max: 54.0
       longitude_min: 13.25
       longitude_max: 32.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 22
   title: November 2020 Event 0092
   start_date: 2020-11-09 00:00:00
@@ -261,7 +261,7 @@
       latitude_max: 73.75
       longitude_min: 12.25
       longitude_max: 116.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 23
   title: December 2020 Event 0108
   start_date: 2020-12-03 00:00:00
@@ -273,7 +273,7 @@
       latitude_max: 57.75
       longitude_min: 265.0
       longitude_max: 294.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 24
   title: December 2020 Event 0109
   start_date: 2020-12-04 00:00:00
@@ -285,7 +285,7 @@
       latitude_max: 71.25
       longitude_min: 192.25
       longitude_max: 230.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 25
   title: December 2020 Event 0111
   start_date: 2020-12-06 00:00:00
@@ -297,7 +297,7 @@
       latitude_max: 78.5
       longitude_min: 306.5
       longitude_max: 341.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 26
   title: December 2020 Event 0115
   start_date: 2020-12-13 00:00:00
@@ -309,7 +309,7 @@
       latitude_max: 81.25
       longitude_min: 289.5
       longitude_max: 344.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 27
   title: December 2020 Event 0126
   start_date: 2020-12-27 00:00:00
@@ -321,7 +321,7 @@
       latitude_max: 59.0
       longitude_min: 0.0
       longitude_max: 27.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 28
   title: January 2021 Event 0138
   start_date: 2021-01-20 00:00:00
@@ -333,7 +333,7 @@
       latitude_max: 75.5
       longitude_min: 306.75
       longitude_max: 329.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 29
   title: February 2021 Event 0157
   start_date: 2021-02-21 00:00:00
@@ -345,7 +345,7 @@
       latitude_max: 74.0
       longitude_min: 73.25
       longitude_max: 118.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 30
   title: March 2021 Event 0168
   start_date: 2021-03-11 00:00:00
@@ -357,7 +357,7 @@
       latitude_max: 74.25
       longitude_min: 76.75
       longitude_max: 123.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 31
   title: April 2021 Event 0182
   start_date: 2021-04-18 00:00:00
@@ -369,7 +369,7 @@
       latitude_max: 84.5
       longitude_min: 292.0
       longitude_max: 339.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 32
   title: June 2021 Event 0208
   start_date: 2021-06-21 00:00:00
@@ -381,7 +381,7 @@
       latitude_max: 74.0
       longitude_min: 70.75
       longitude_max: 94.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 33
   title: July 2021 Event 0215
   start_date: 2021-07-24 00:00:00
@@ -393,7 +393,7 @@
       latitude_max: 35.25
       longitude_min: 0.0
       longitude_max: 18.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 34
   title: August 2021 Event 0216
   start_date: 2021-08-22 00:00:00
@@ -405,7 +405,7 @@
       latitude_max: 77.5
       longitude_min: 80.75
       longitude_max: 113.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 35
   title: November 2021 Event 0236
   start_date: 2021-11-09 00:00:00
@@ -417,7 +417,7 @@
       latitude_max: 74.0
       longitude_min: 27.0
       longitude_max: 122.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 36
   title: November 2021 Event 0240
   start_date: 2021-11-19 00:00:00
@@ -429,7 +429,7 @@
       latitude_max: 57.5
       longitude_min: 0.0
       longitude_max: 26.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 37
   title: December 2021 Event 0248
   start_date: 2021-12-07 00:00:00
@@ -441,7 +441,7 @@
       latitude_max: 21.75
       longitude_min: 4.0
       longitude_max: 31.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 38
   title: December 2021 Event 0258
   start_date: 2021-12-20 00:00:00
@@ -453,7 +453,7 @@
       latitude_max: 23.75
       longitude_min: 0.0
       longitude_max: 11.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 39
   title: December 2021 Event 0260
   start_date: 2021-12-22 00:00:00
@@ -465,7 +465,7 @@
       latitude_max: 73.0
       longitude_min: 16.75
       longitude_max: 76.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 40
   title: December 2021 Event 0264
   start_date: 2021-12-27 00:00:00
@@ -477,7 +477,7 @@
       latitude_max: 71.0
       longitude_min: 5.25
       longitude_max: 47.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 41
   title: January 2022 Event 0270
   start_date: 2022-01-02 00:00:00
@@ -489,7 +489,7 @@
       latitude_max: 62.5
       longitude_min: 280.5
       longitude_max: 298.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 42
   title: January 2022 Event 0273
   start_date: 2022-01-08 00:00:00
@@ -501,7 +501,7 @@
       latitude_max: 63.5
       longitude_min: 280.75
       longitude_max: 293.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 43
   title: January 2022 Event 0274
   start_date: 2022-01-08 00:00:00
@@ -513,7 +513,7 @@
       latitude_max: 69.5
       longitude_min: 105.25
       longitude_max: 134.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 44
   title: January 2022 Event 0280
   start_date: 2022-01-14 00:00:00
@@ -525,7 +525,7 @@
       latitude_max: 48.25
       longitude_min: 237.5
       longitude_max: 252.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 45
   title: January 2022 Event 0282
   start_date: 2022-01-18 00:00:00
@@ -537,7 +537,7 @@
       latitude_max: 80.5
       longitude_min: 247.5
       longitude_max: 275.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 46
   title: January 2022 Event 0284
   start_date: 2022-01-20 00:00:00
@@ -549,7 +549,7 @@
       latitude_max: 23.5
       longitude_min: 348.25
       longitude_max: 359.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 47
   title: March 2022 Event 0306
   start_date: 2022-03-01 00:00:00
@@ -561,7 +561,7 @@
       latitude_max: 68.5
       longitude_min: 280.75
       longitude_max: 299.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 48
   title: March 2022 Event 0309
   start_date: 2022-03-07 00:00:00
@@ -573,7 +573,7 @@
       latitude_max: 61.75
       longitude_min: 281.75
       longitude_max: 303.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 49
   title: April 2022 Event 0322
   start_date: 2022-04-06 00:00:00
@@ -585,7 +585,7 @@
       latitude_max: 77.5
       longitude_min: 73.25
       longitude_max: 118.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 50
   title: May 2022 Event 0332
   start_date: 2022-05-14 00:00:00
@@ -597,7 +597,7 @@
       latitude_max: 69.5
       longitude_min: 280.75
       longitude_max: 291.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 51
   title: June 2022 Event 0341
   start_date: 2022-06-27 00:00:00
@@ -609,7 +609,7 @@
       latitude_max: 26.25
       longitude_min: 20.0
       longitude_max: 28.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 52
   title: July 2022 Event 0345
   start_date: 2022-07-26 00:00:00
@@ -621,7 +621,7 @@
       latitude_max: 57.75
       longitude_min: 33.75
       longitude_max: 66.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 53
   title: September 2022 Event 0355
   start_date: 2022-09-11 00:00:00
@@ -633,7 +633,7 @@
       latitude_max: 75.25
       longitude_min: 219.5
       longitude_max: 263.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 54
   title: September 2022 Event 0358
   start_date: 2022-09-18 00:00:00
@@ -645,7 +645,7 @@
       latitude_max: 71.25
       longitude_min: 192.25
       longitude_max: 231.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 55
   title: September 2022 Event 0361
   start_date: 2022-09-29 00:00:00
@@ -657,7 +657,7 @@
       latitude_max: 71.25
       longitude_min: 193.25
       longitude_max: 226.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 56
   title: October 2022 Event 0372
   start_date: 2022-10-18 00:00:00
@@ -669,7 +669,7 @@
       latitude_max: 67.5
       longitude_min: 39.5
       longitude_max: 74.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 57
   title: October 2022 Event 0373
   start_date: 2022-10-19 00:00:00
@@ -681,7 +681,7 @@
       latitude_max: 24.25
       longitude_min: 19.0
       longitude_max: 31.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 58
   title: October 2022 Event 0378
   start_date: 2022-10-28 00:00:00
@@ -693,7 +693,7 @@
       latitude_max: 69.75
       longitude_min: 24.75
       longitude_max: 78.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 59
   title: November 2022 Event 0386
   start_date: 2022-11-17 00:00:00
@@ -705,7 +705,7 @@
       latitude_max: 28.5
       longitude_min: 0.0
       longitude_max: 31.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 60
   title: November 2022 Event 0391
   start_date: 2022-11-25 00:00:00
@@ -717,7 +717,7 @@
       latitude_max: 70.25
       longitude_min: 0.0
       longitude_max: 108.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 61
   title: December 2022 Event 0394
   start_date: 2022-12-10 00:00:00
@@ -729,7 +729,7 @@
       latitude_max: 74.25
       longitude_min: 73.25
       longitude_max: 103.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 62
   title: December 2022 Event 0401
   start_date: 2022-12-19 00:00:00
@@ -741,7 +741,7 @@
       latitude_max: 69.0
       longitude_min: 231.25
       longitude_max: 302.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 63
   title: December 2022 Event 0405
   start_date: 2022-12-21 00:00:00
@@ -753,7 +753,7 @@
       latitude_max: 51.5
       longitude_min: 43.75
       longitude_max: 72.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 64
   title: December 2022 Event 0408
   start_date: 2022-12-24 00:00:00
@@ -765,7 +765,7 @@
       latitude_max: 71.25
       longitude_min: 192.25
       longitude_max: 218.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 65
   title: January 2023 Event 0412
   start_date: 2023-01-01 00:00:00
@@ -777,7 +777,7 @@
       latitude_max: 69.75
       longitude_min: 166.5
       longitude_max: 190.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 66
   title: January 2023 Event 0427
   start_date: 2023-01-20 00:00:00
@@ -789,7 +789,7 @@
       latitude_max: 59.5
       longitude_min: 4.25
       longitude_max: 31.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 67
   title: February 2023 Event 0436
   start_date: 2023-02-13 00:00:00
@@ -801,7 +801,7 @@
       latitude_max: 67.0
       longitude_min: 135.0
       longitude_max: 179.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 68
   title: February 2023 Event 0438
   start_date: 2023-02-21 00:00:00
@@ -813,7 +813,7 @@
       latitude_max: 69.25
       longitude_min: 5.25
       longitude_max: 24.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 69
   title: February 2023 Event 0439
   start_date: 2023-02-21 00:00:00
@@ -825,7 +825,7 @@
       latitude_max: -17.75
       longitude_min: 116.25
       longitude_max: 137.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 70
   title: February 2023 Event 0440
   start_date: 2023-02-23 00:00:00
@@ -837,7 +837,7 @@
       latitude_max: 80.5
       longitude_min: 286.75
       longitude_max: 331.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 71
   title: February 2023 Event 0443
   start_date: 2023-02-28 00:00:00
@@ -849,7 +849,7 @@
       latitude_max: 62.25
       longitude_min: 281.75
       longitude_max: 300.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 72
   title: March 2023 Event 0458
   start_date: 2023-03-17 00:00:00
@@ -861,7 +861,7 @@
       latitude_max: 62.25
       longitude_min: 280.5
       longitude_max: 301.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 73
   title: May 2023 Event 0471
   start_date: 2023-05-08 00:00:00
@@ -873,7 +873,7 @@
       latitude_max: -20.5
       longitude_min: 117.0
       longitude_max: 134.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 74
   title: May 2023 Event 0474
   start_date: 2023-05-16 00:00:00
@@ -885,7 +885,7 @@
       latitude_max: 72.0
       longitude_min: 283.75
       longitude_max: 298.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 75
   title: May 2023 Event 0476
   start_date: 2023-05-17 00:00:00
@@ -897,7 +897,7 @@
       latitude_max: 71.25
       longitude_min: 280.75
       longitude_max: 298.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 76
   title: May 2023 Event 0480
   start_date: 2023-05-26 00:00:00
@@ -909,7 +909,7 @@
       latitude_max: 51.0
       longitude_min: 28.75
       longitude_max: 42.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 77
   title: June 2023 Event 0488
   start_date: 2023-06-23 00:00:00
@@ -921,7 +921,7 @@
       latitude_max: 31.75
       longitude_min: 15.75
       longitude_max: 38.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 78
   title: July 2023 Event 0493
   start_date: 2023-07-30 00:00:00
@@ -933,7 +933,7 @@
       latitude_max: 76.75
       longitude_min: 299.0
       longitude_max: 336.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 79
   title: August 2023 Event 0495
   start_date: 2023-08-21 00:00:00
@@ -945,7 +945,7 @@
       latitude_max: -15.0
       longitude_min: 123.25
       longitude_max: 133.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 80
   title: September 2023 Event 0499
   start_date: 2023-09-17 00:00:00
@@ -957,7 +957,7 @@
       latitude_max: 70.75
       longitude_min: 192.25
       longitude_max: 232.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 81
   title: October 2023 Event 0508
   start_date: 2023-10-06 00:00:00
@@ -969,7 +969,7 @@
       latitude_max: 32.25
       longitude_min: 351.5
       longitude_max: 359.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 82
   title: October 2023 Event 0509
   start_date: 2023-10-06 00:00:00
@@ -981,7 +981,7 @@
       latitude_max: 35.25
       longitude_min: 0.0
       longitude_max: 13.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 83
   title: October 2023 Event 0512
   start_date: 2023-10-15 00:00:00
@@ -993,7 +993,7 @@
       latitude_max: -21.25
       longitude_min: 121.5
       longitude_max: 133.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 84
   title: October 2023 Event 0515
   start_date: 2023-10-24 00:00:00
@@ -1005,7 +1005,7 @@
       latitude_max: 62.5
       longitude_min: 280.5
       longitude_max: 297.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 85
   title: October 2023 Event 0517
   start_date: 2023-10-29 00:00:00
@@ -1017,7 +1017,7 @@
       latitude_max: 67.25
       longitude_min: 240.0
       longitude_max: 276.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 86
   title: December 2023 Event 0530
   start_date: 2023-12-04 00:00:00
@@ -1029,7 +1029,7 @@
       latitude_max: 73.75
       longitude_min: 73.25
       longitude_max: 97.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 87
   title: December 2023 Event 0537
   start_date: 2023-12-11 00:00:00
@@ -1041,7 +1041,7 @@
       latitude_max: 72.75
       longitude_min: 73.25
       longitude_max: 98.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 88
   title: December 2023 Event 0544
   start_date: 2023-12-24 00:00:00
@@ -1053,7 +1053,7 @@
       latitude_max: 71.0
       longitude_min: 5.5
       longitude_max: 62.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 89
   title: January 2024 Event 0548
   start_date: 2024-01-01 00:00:00
@@ -1065,7 +1065,7 @@
       latitude_max: 22.75
       longitude_min: 20.75
       longitude_max: 37.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 90
   title: January 2024 Event 0555
   start_date: 2024-01-11 00:00:00
@@ -1077,7 +1077,7 @@
       latitude_max: 69.75
       longitude_min: 163.5
       longitude_max: 179.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 91
   title: February 2024 Event 0588
   start_date: 2024-02-24 00:00:00
@@ -1089,7 +1089,7 @@
       latitude_max: 73.5
       longitude_min: 270.25
       longitude_max: 298.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 92
   title: February 2024 Event 0590
   start_date: 2024-02-25 00:00:00
@@ -1101,7 +1101,7 @@
       latitude_max: 59.5
       longitude_min: 32.5
       longitude_max: 73.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 93
   title: February 2024 Event 0592
   start_date: 2024-02-25 00:00:00
@@ -1113,7 +1113,7 @@
       latitude_max: 81.5
       longitude_min: 296.0
       longitude_max: 320.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 94
   title: February 2024 Event 0593
   start_date: 2024-02-29 00:00:00
@@ -1125,7 +1125,7 @@
       latitude_max: 63.5
       longitude_min: 280.75
       longitude_max: 299.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 95
   title: April 2024 Event 0611
   start_date: 2024-04-27 00:00:00
@@ -1137,7 +1137,7 @@
       latitude_max: -14.0
       longitude_min: 116.0
       longitude_max: 140.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 96
   title: May 2024 Event 0614
   start_date: 2024-05-07 00:00:00
@@ -1149,7 +1149,7 @@
       latitude_max: 76.25
       longitude_min: 73.25
       longitude_max: 138.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 97
   title: May 2024 Event 0616
   start_date: 2024-05-19 00:00:00
@@ -1161,7 +1161,7 @@
       latitude_max: 62.5
       longitude_min: 282.0
       longitude_max: 297.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 98
   title: June 2024 Event 0621
   start_date: 2024-06-13 00:00:00
@@ -1173,7 +1173,7 @@
       latitude_max: 84.5
       longitude_min: 299.75
       longitude_max: 347.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 99
   title: June 2024 Event 0622
   start_date: 2024-06-15 00:00:00
@@ -1185,7 +1185,7 @@
       latitude_max: 77.75
       longitude_min: 250.25
       longitude_max: 273.75
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 100
   title: August 2024 Event 0624
   start_date: 2024-08-07 00:00:00
@@ -1197,7 +1197,7 @@
       latitude_max: 35.0
       longitude_min: 1.5
       longitude_max: 16.5
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 101
   title: September 2024 Event 0636
   start_date: 2024-09-19 00:00:00
@@ -1209,7 +1209,7 @@
       latitude_max: 77.5
       longitude_min: 73.25
       longitude_max: 129.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 102
   title: October 2024 Event 0641
   start_date: 2024-10-06 00:00:00
@@ -1221,7 +1221,7 @@
       latitude_max: 70.75
       longitude_min: 154.75
       longitude_max: 190.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 103
   title: October 2024 Event 0646
   start_date: 2024-10-27 00:00:00
@@ -1233,7 +1233,7 @@
       latitude_max: 61.5
       longitude_min: 233.0
       longitude_max: 264.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 104
   title: October 2024 Event 0647
   start_date: 2024-10-28 00:00:00
@@ -1245,7 +1245,7 @@
       latitude_max: 69.25
       longitude_min: 192.25
       longitude_max: 230.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 105
   title: November 2024 Event 0656
   start_date: 2024-11-17 00:00:00
@@ -1257,7 +1257,7 @@
       latitude_max: 78.5
       longitude_min: 297.75
       longitude_max: 340.25
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 106
   title: November 2024 Event 0659
   start_date: 2024-11-21 00:00:00
@@ -1269,7 +1269,7 @@
       latitude_max: 69.75
       longitude_min: 237.25
       longitude_max: 291.0
-  event_type: marginal_temperature
+  event_type: heat_wave
 - case_id_number: 107
   title: December 2024 Event 0674
   start_date: 2024-12-17 00:00:00
@@ -1281,4 +1281,4 @@
       latitude_max: 51.25
       longitude_min: 48.0
       longitude_max: 75.0
-  event_type: marginal_temperature
+  event_type: heat_wave

From 15c1e3b4e4629819642e0fa2b0f6d2cdffebc68f Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Wed, 15 Apr 2026 18:59:46 -0400
Subject: [PATCH 64/72] remove no landfalling tc's (-8 TCs) (#361)

---
 src/extremeweatherbench/data/events.yaml | 96 ------------------------
 1 file changed, 96 deletions(-)

diff --git a/src/extremeweatherbench/data/events.yaml b/src/extremeweatherbench/data/events.yaml
index d5dd5b54..053990e2 100644
--- a/src/extremeweatherbench/data/events.yaml
+++ b/src/extremeweatherbench/data/events.yaml
@@ -1870,18 +1870,6 @@
       longitude_min: 254.5
       longitude_max: 263.9
   event_type: tropical_cyclone
-- case_id_number: 157
-  title: Hone
-  start_date: 2024-08-20 00:00:00
-  end_date: 2024-09-03 06:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 13.2
-      latitude_max: 32.0
-      longitude_min: 175.8
-      longitude_max: 224.8
-  event_type: tropical_cyclone
 - case_id_number: 158
   title: Trami
   start_date: 2024-10-18 00:00:00
@@ -1978,18 +1966,6 @@
       longitude_min: 116.8
       longitude_max: 130.6
   event_type: tropical_cyclone
-- case_id_number: 166
-  title: Asna
-  start_date: 2024-08-23 00:00:00
-  end_date: 2024-09-05 06:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 17.5
-      latitude_max: 26.8
-      longitude_min: 58.3
-      longitude_max: 79.7
-  event_type: tropical_cyclone
 - case_id_number: 167
   title: Kirrily
   start_date: 2024-01-15 00:00:00
@@ -2302,18 +2278,6 @@
       longitude_min: 28.8
       longitude_max: 121.4
   event_type: tropical_cyclone
-- case_id_number: 193
-  title: Jasper
-  start_date: 2023-11-30 12:00:00
-  end_date: 2023-12-19 00:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: -20.0
-      latitude_max: -5.3
-      longitude_min: 139.0
-      longitude_max: 167.2
-  event_type: tropical_cyclone
 - case_id_number: 194
   title: Alex
   start_date: 2022-05-31 18:00:00
@@ -2542,18 +2506,6 @@
       longitude_min: 127.3
       longitude_max: 161.5
   event_type: tropical_cyclone
-- case_id_number: 213
-  title: Blas
-  start_date: 2022-06-12 06:00:00
-  end_date: 2022-06-24 18:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 11.4
-      latitude_max: 21.5
-      longitude_min: 240.9
-      longitude_max: 260.3
-  event_type: tropical_cyclone
 - case_id_number: 214
   title: Elsa
   start_date: 2021-06-28 18:00:00
@@ -2686,18 +2638,6 @@
       longitude_min: 248.4
       longitude_max: 259.9
   event_type: tropical_cyclone
-- case_id_number: 225
-  title: Surigae (Bising)
-  start_date: 2021-04-09 12:00:00
-  end_date: 2021-05-02 18:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 3.3
-      latitude_max: 49.7
-      longitude_min: 122.5
-      longitude_max: 186.8
-  event_type: tropical_cyclone
 - case_id_number: 226
   title: In-fa (Fabian)
   start_date: 2021-07-14 00:00:00
@@ -2722,18 +2662,6 @@
       longitude_min: 103.4
       longitude_max: 136.5
   event_type: tropical_cyclone
-- case_id_number: 228
-  title: Rai
-  start_date: 2021-12-09 12:00:00
-  end_date: 2021-12-23 12:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 3.1
-      latitude_max: 23.9
-      longitude_min: 108.3
-      longitude_max: 147.3
-  event_type: tropical_cyclone
 - case_id_number: 229
   title: Tauktae
   start_date: 2021-05-11 06:00:00
@@ -2758,18 +2686,6 @@
       longitude_min: 82.5
       longitude_max: 92.3
   event_type: tropical_cyclone
-- case_id_number: 231
-  title: Gulab and Shaheen
-  start_date: 2021-09-21 18:00:00
-  end_date: 2021-10-06 06:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 15.9
-      latitude_max: 26.6
-      longitude_min: 53.7
-      longitude_max: 96.5
-  event_type: tropical_cyclone
 - case_id_number: 232
   title: Eloise
   start_date: 2021-01-09 18:00:00
@@ -2926,18 +2842,6 @@
       longitude_min: 268.7
       longitude_max: 291.4
   event_type: tropical_cyclone
-- case_id_number: 245
-  title: Genevieve
-  start_date: 2020-08-14 12:00:00
-  end_date: 2020-08-24 06:00:00
-  location:
-    type: bounded_region
-    parameters:
-      latitude_min: 9.1
-      latitude_max: 29.2
-      longitude_min: 240.3
-      longitude_max: 265.8
-  event_type: tropical_cyclone
 - case_id_number: 246
   title: Hagupit (Dindo)
   start_date: 2020-07-29 06:00:00

From 9f9dff835aba30d9a214a75c0a91d670fbf5d906 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 16 Apr 2026 17:11:36 -0400
Subject: [PATCH 65/72] Manage landfall edge cases (#362)

* modify criteria slightly

* make event_type heat_wave in marginal temp events

* update tracker to fill in-between lead time points for a valid track

* update defaults

* update  return next landfall logic to metric

* add neighborhood max wind func and add track gap filler

* update stitching criteria and tests

* add extra column allowance

* add a bunch of landfall methods

* add empty init time array method

* update landfall metrics methods and add metadata inclusion (small data increase. lots of value)

* ruff

* add landfall filter to within window of valid landfall

* cleanup
---
 src/extremeweatherbench/calc.py               | 452 +++++++-
 src/extremeweatherbench/derived.py            |  25 +-
 src/extremeweatherbench/evaluate.py           |   8 +-
 .../events/tropical_cyclone.py                | 249 ++++-
 src/extremeweatherbench/metrics.py            | 303 ++++--
 src/extremeweatherbench/utils.py              |   9 +
 tests/test_calc.py                            | 192 ++++
 tests/test_metrics.py                         | 984 ++++++++++++++++++
 tests/test_tropical_cyclone.py                | 306 ++++++
 9 files changed, 2358 insertions(+), 170 deletions(-)

diff --git a/src/extremeweatherbench/calc.py b/src/extremeweatherbench/calc.py
index f5f35e61..673fd805 100644
--- a/src/extremeweatherbench/calc.py
+++ b/src/extremeweatherbench/calc.py
@@ -16,7 +16,7 @@
 epsilon: float = 0.6219569100577033  # Ratio of molecular weights (H2O/dry air)
 sat_press_0c: float = 6.112  # Saturation vapor pressure at 0°C (hPa)
 g0: float = 9.80665  # Standard gravity (m/s^2)
-
+_ns_per_hour = 3_600_000_000_000
 logger = logging.getLogger("extremeweatherbench.calc")
 logger.setLevel(logging.INFO)
 
@@ -488,68 +488,200 @@ def find_landfalls(
     return _deduplicate_landfalls(landfalls)
 
 
+def _filter_minor_landfalls(
+    target_landfalls: xr.DataArray,
+    min_separation_hours: float,
+) -> xr.DataArray:
+    """Remove target landfalls within min_separation_hours of the prior one.
+
+    For multi-landfall storms, rapid sequential crossings (e.g., a TC
+    clipping several Caribbean islands within hours of each other) are
+    treated as a single landfall event. Only the first crossing in each
+    rapid sequence is kept; later crossings within min_separation_hours
+    are dropped.
+
+    Args:
+        target_landfalls: DataArray with ``landfall`` dim and
+            ``valid_time`` coord, as returned by find_landfalls.
+        min_separation_hours: Minimum gap (hours) between consecutive
+            retained landfalls.
+
+    Returns:
+        Filtered DataArray with minor landfalls removed.
+    """
+    if len(target_landfalls) <= 1:
+        return target_landfalls
+    ns = (
+        target_landfalls.coords["valid_time"]
+        .values.astype("datetime64[ns]")
+        .astype(np.int64)
+    )
+    min_gap_ns = int(min_separation_hours * _ns_per_hour)
+    # Greedy reset-anchor walk: each kept landfall becomes the new
+    # reference, so this cannot be a plain ``np.diff`` threshold.
+    keep_idx = [0]
+    last_ns = ns[0]
+    for i in range(1, len(ns)):
+        if ns[i] - last_ns >= min_gap_ns:
+            keep_idx.append(i)
+            last_ns = ns[i]
+    return target_landfalls.isel(landfall=keep_idx)
+
+
+def _landfall_point_to_init_row(
+    da: xr.DataArray,
+    init_time: np.datetime64,
+) -> xr.DataArray:
+    """Promote a scalar landfall point to (init_time,) with coords."""
+    lat = float(da.coords["latitude"].values)
+    lon = float(da.coords["longitude"].values)
+    vt = da.coords["valid_time"].values
+    da = da.expand_dims("init_time")
+    return da.assign_coords(
+        init_time=("init_time", [init_time]),
+        latitude=("init_time", [lat]),
+        longitude=("init_time", [lon]),
+        valid_time=("init_time", [vt]),
+    )
+
+
 def find_next_landfall_for_init_time(
     forecast_landfalls: xr.DataArray,
     target_landfalls: xr.DataArray,
+    max_lead_time: Optional[np.timedelta64] = None,
+    min_target_separation_hours: float = 0.0,
+    max_time_mismatch_hours: Optional[float] = None,
+    track_start_times: Optional[dict[np.datetime64, np.datetime64]] = None,
 ) -> xr.DataArray:
-    """Find unique next upcoming landfalls from target data.
+    """Match forecast landfalls to the closest target landfall.
 
-    For each forecast initialization time, finds the next landfall
-    event in the target data that occurs after that init_time. Only
-    unique landfalls are returned (multiple init_times mapping to the
-    same landfall are deduplicated). Init_times without future
-    landfalls are excluded.
+    For each forecast initialization time, finds the target landfall
+    whose valid_time is closest to the forecast's predicted landfall
+    time, constrained to be after the forecast track's actual start
+    time (and optionally within max_lead_time). This ensures that
+    multi-landfall storms pair the model's prediction with the
+    observed event it is actually attempting to forecast, rather
+    than a chronologically earlier landfall the model cannot
+    represent.
 
     Args:
         forecast_landfalls: Forecast landfall DataArray with init_time
-            dimension (from find_landfalls)
+            dimension (from find_landfalls). Must have valid_time
+            coordinate indicating when the model predicts landfall.
         target_landfalls: Target landfall DataArray from find_landfalls
-            with return_all=True (has landfall dimension)
+            with landfall dimension and valid_time coordinate.
+        max_lead_time: If provided, only consider target landfalls
+            within this duration after each init_time.
+        min_target_separation_hours: If > 0, pre-filter target
+            landfalls so that consecutive retained landfalls are at
+            least this many hours apart.
+        max_time_mismatch_hours: If provided, discard matched pairs
+            where the absolute difference between the forecast's
+            predicted landfall valid_time and the matched target
+            landfall valid_time exceeds this threshold (hours).
+        track_start_times: Optional dict mapping init_time to the
+            earliest valid_time with non-NaN forecast track data
+            (from ``get_forecast_track_start_times``). When
+            provided, only target landfalls after the track start
+            are considered. Falls back to init_time when not
+            provided or when a given init_time is missing.
 
     Returns:
-        DataArray with unique next landfalls indexed by init_time. Returns
-        a zero-length DataArray if no future landfalls exist.
+        DataArray with matched target landfalls indexed by init_time.
+        Returns a zero-length DataArray if no matches exist.
     """
-    # Extract init_times from forecast landfalls
+    if min_target_separation_hours > 0:
+        target_landfalls = _filter_minor_landfalls(
+            target_landfalls, min_target_separation_hours
+        )
+        if len(target_landfalls) == 0:
+            return utils._empty_init_time_array()
+
     if "init_time" in forecast_landfalls.dims:
         init_times = forecast_landfalls.init_time.values
     elif "init_time" in forecast_landfalls.coords:
-        # init_time as coordinate (scalar case)
         init_times = np.array([forecast_landfalls.coords["init_time"].values])
     else:
         raise KeyError("Missing init_time dimension or coordinate.")
 
-    # Get target landfall times
+    has_forecast_vt = "valid_time" in forecast_landfalls.coords
+    forecast_vt_map: dict[np.datetime64, np.datetime64] = {}
+    if has_forecast_vt:
+        if (
+            "landfall" in forecast_landfalls.dims
+            and "init_time" in forecast_landfalls.dims
+        ):
+            # 2D: Map each init to earliest forecast landfall
+            # valid_time. valid_time may be 1D (landfall,) or
+            # 2D (init_time, landfall).
+            lf_vts = forecast_landfalls.coords["valid_time"].values
+            vt_2d = lf_vts.ndim == 2
+            data = forecast_landfalls.values
+            n_lf = forecast_landfalls.sizes["landfall"]
+            for j in range(n_lf):
+                col = data[:, j]
+                non_nan = np.where(~np.isnan(col))[0]
+                if len(non_nan) == 0:
+                    continue
+                owner = init_times[non_nan[0]]
+                vt = lf_vts[non_nan[0], j] if vt_2d else lf_vts[j]
+                if owner not in forecast_vt_map or vt < forecast_vt_map[owner]:
+                    forecast_vt_map[owner] = vt
+        else:
+            # 1D / scalar: valid_time aligns with init_time.
+            fvts = np.atleast_1d(forecast_landfalls.coords["valid_time"].values)
+            for idx, it_val in enumerate(init_times):
+                if idx < len(fvts):
+                    forecast_vt_map[it_val] = fvts[idx]
+
     target_times = target_landfalls.coords["valid_time"].values
 
-    # Use searchsorted to find next landfall index for each init_time
-    next_indices = np.searchsorted(target_times, init_times, side="right")
+    results = []
+
+    for init_time in init_times:
+        # With track_start_times, cutoff is when the forecast
+        # has data (>= because the track exists at cutoff).
+        # Without, cutoff is the nominal init_time which
+        # predates any forecast output (strict >).
+        if track_start_times is not None:
+            cutoff = track_start_times.get(init_time, init_time)
+            future_mask = target_times >= cutoff
+        else:
+            future_mask = target_times > init_time
 
-    # Create mask for init_times with future landfalls
-    valid_mask = next_indices < len(target_times)
+        if max_lead_time is not None:
+            horizon = init_time + max_lead_time
+            future_mask &= target_times <= horizon
 
-    # Build result for all init_times, keeping only unique landfalls
-    results = []
+        if not future_mask.any():
+            continue
+
+        future_indices = np.where(future_mask)[0]
+        future_times = target_times[future_indices]
 
-    for i, init_time in enumerate(init_times):
-        if valid_mask[i]:
-            # There is a future landfall for this init_time
-            landfall_idx = next_indices[i]
-            landfall = target_landfalls.isel(landfall=landfall_idx)
-            landfall = landfall.expand_dims("init_time", axis=0)
-            landfall = landfall.assign_coords(init_time=("init_time", [init_time]))
+        fc_vt = forecast_vt_map.get(init_time)
+        if fc_vt is not None:
+            best = future_indices[np.argmin(np.abs(future_times - fc_vt))]
+        else:
+            best = future_indices[0]
+
+        if max_time_mismatch_hours is not None and fc_vt is not None:
+            target_vt = target_times[int(best)]
+            mismatch_h = abs(np.int64(fc_vt) - np.int64(target_vt)) / _ns_per_hour
+            if mismatch_h > max_time_mismatch_hours:
+                continue
 
-            results.append(landfall)
+        landfall = target_landfalls.isel(landfall=int(best))
+        results.append(_landfall_point_to_init_row(landfall, init_time))
 
-    # Combine landfalls indexed by init_time or return empty if no results
     if not results:
-        return xr.DataArray(
-            np.array([], dtype=float),
-            dims=["init_time"],
-            coords={"init_time": np.array([], dtype="datetime64[ns]")},
-        )
+        return utils._empty_init_time_array()
     return xr.concat(
-        results, dim="init_time", coords="different", compat="equals", join="outer"
+        results,
+        dim="init_time",
+        coords="different",
+        compat="equals",
+        join="outer",
     )
 
 
@@ -863,6 +995,240 @@ def _deduplicate_landfalls(
     return landfalls.isel(landfall=kept_indices)
 
 
+def get_forecast_track_start_times(
+    forecast: xr.DataArray,
+) -> dict[np.datetime64, np.datetime64]:
+    """Map each init_time to the earliest valid_time with data.
+
+    TC tracker output contains NaN at lead times before the
+    storm is detected. This returns the valid_time of each
+    init_time's first non-NaN data point, telling us when the
+    forecast track actually begins.
+
+    Args:
+        forecast: Forecast DataArray with ``lead_time`` and
+            ``valid_time`` dimensions.
+
+    Returns:
+        Dict mapping ``init_time`` → earliest ``valid_time``
+        with non-NaN track data for that initialization.
+    """
+    if "lead_time" not in forecast.dims:
+        return {}
+
+    lt_vals = forecast.lead_time.values
+    if len(lt_vals) == 0:
+        return {}
+    if not isinstance(lt_vals[0], np.timedelta64):
+        lt_vals = np.array([np.timedelta64(int(lt), "h") for lt in lt_vals])
+
+    vt_vals = forecast.valid_time.values
+    data = forecast.values
+
+    start_times: dict[np.datetime64, np.datetime64] = {}
+    for j, vt in enumerate(vt_vals):
+        for i, lt in enumerate(lt_vals):
+            if not np.isnan(data[i, j]):
+                init = vt - lt
+                if init not in start_times or vt < start_times[init]:
+                    start_times[init] = vt
+
+    return start_times
+
+
+def filter_inits_by_track_start(
+    forecast_landfalls: xr.DataArray,
+    first_valid_time: np.datetime64,
+    track_starts: dict[np.datetime64, np.datetime64],
+) -> xr.DataArray:
+    """Drop init_times whose track starts after a target event.
+
+    Keeps init_times where the track starts at or before
+    ``first_valid_time``, or where the track start is unknown.
+
+    Args:
+        forecast_landfalls: DataArray with ``init_time`` dim.
+        first_valid_time: Target landfall valid_time cutoff.
+        track_starts: Dict from ``get_forecast_track_start_times``.
+
+    Returns:
+        Filtered DataArray (may be empty).
+    """
+    keep = [
+        it
+        for it in forecast_landfalls.init_time.values
+        if track_starts.get(it, first_valid_time) <= first_valid_time
+    ]
+    if len(keep) < len(forecast_landfalls.init_time):
+        return forecast_landfalls.sel(init_time=np.array(keep))
+    return forecast_landfalls
+
+
+def _landfall_pair_time_mismatch(
+    forecast_landfalls: xr.DataArray,
+    target_landfalls: xr.DataArray,
+) -> Optional[tuple[np.ndarray, np.ndarray, np.ndarray]]:
+    """Return ``(fc_vt, tgt_vt, |fc_vt - tgt_vt|)`` as numpy arrays.
+
+    Returns ``None`` if either array lacks the ``init_time`` dim or
+    the ``valid_time`` coord needed for pairwise timing filters.
+    """
+    if not (
+        "init_time" in forecast_landfalls.dims
+        and "valid_time" in forecast_landfalls.coords
+        and "valid_time" in target_landfalls.coords
+    ):
+        return None
+    fc_vt = forecast_landfalls.coords["valid_time"].values
+    tgt_vt = target_landfalls.coords["valid_time"].values
+    return fc_vt, tgt_vt, np.abs(fc_vt - tgt_vt)
+
+
+def _apply_landfall_keep_mask(
+    forecast_landfalls: xr.DataArray,
+    target_landfalls: xr.DataArray,
+    keep: np.ndarray,
+) -> tuple[xr.DataArray, xr.DataArray]:
+    """Select init_times where ``keep`` is True."""
+    if keep.all():
+        return forecast_landfalls, target_landfalls
+    if not keep.any():
+        return (
+            utils._empty_init_time_array(),
+            utils._empty_init_time_array(),
+        )
+    idx = np.where(keep)[0]
+    return (
+        forecast_landfalls.isel(init_time=idx),
+        target_landfalls.isel(init_time=idx),
+    )
+
+
+def filter_by_landfall_time_window(
+    forecast_landfalls: xr.DataArray,
+    target_landfalls: xr.DataArray,
+    window_hours: float = 24.0,
+) -> tuple[xr.DataArray, xr.DataArray]:
+    """Drop pairs where forecast and target landfall times
+    differ by more than ``window_hours``.
+
+    Keeps:
+
+        abs(fc_valid_time - tgt_valid_time) <= window_hours
+
+    Args:
+        forecast_landfalls: ``(init_time,)`` with
+            ``valid_time`` coord.
+        target_landfalls: Same shape/coords.
+        window_hours: Max allowed mismatch in hours.
+
+    Returns:
+        Filtered (forecast, target) pair. May be empty.
+    """
+    pair = _landfall_pair_time_mismatch(forecast_landfalls, target_landfalls)
+    if pair is None:
+        return forecast_landfalls, target_landfalls
+    _fc_vt, _tgt_vt, mismatch = pair
+
+    threshold = np.timedelta64(int(window_hours * 3600), "s")
+
+    return _apply_landfall_keep_mask(
+        forecast_landfalls, target_landfalls, mismatch <= threshold
+    )
+
+
+def filter_by_landfall_time_tolerance(
+    forecast_landfalls: xr.DataArray,
+    target_landfalls: xr.DataArray,
+    tolerance_fraction: float = 0.5,
+) -> tuple[xr.DataArray, xr.DataArray]:
+    """Drop pairs where forecast landfall timing error exceeds
+    a fraction of the lead time.
+
+    Keeps::
+
+        abs(fc_vt - tgt_vt) <= fraction * (tgt_vt - init_time)
+
+    A 2-day forecast predicting landfall 4 days late would be
+    dropped with the default fraction of 0.5.
+
+    Args:
+        forecast_landfalls: ``(init_time,)`` with
+            ``valid_time`` coord.
+        target_landfalls: Same shape/coords.
+        tolerance_fraction: Multiplier on the lead time
+            that sets the max allowed mismatch.
+
+    Returns:
+        Filtered (forecast, target) pair. May be empty.
+    """
+    pair = _landfall_pair_time_mismatch(forecast_landfalls, target_landfalls)
+    if pair is None:
+        return forecast_landfalls, target_landfalls
+    _fc_vt, tgt_vt, mismatch = pair
+
+    init_vals = forecast_landfalls.coords["init_time"].values
+    threshold = tolerance_fraction * np.abs(tgt_vt - init_vals)
+
+    return _apply_landfall_keep_mask(
+        forecast_landfalls, target_landfalls, mismatch <= threshold
+    )
+
+
+def select_first_forecast_landfall_per_init(
+    forecast_landfalls: xr.DataArray,
+) -> xr.DataArray:
+    """Reduce forecast landfalls to one per init_time.
+
+    For each init_time, keeps the forecast landfall with the
+    earliest valid_time. This prevents downstream metrics from
+    averaging distances across multiple forecast landfalls
+    (e.g. island-hopping detections) for a single init_time.
+
+    Args:
+        forecast_landfalls: DataArray with ``init_time`` and
+            ``landfall`` dimensions, as returned by
+            ``find_landfalls`` on forecast data.
+
+    Returns:
+        DataArray with one landfall per init_time, indexed by
+        ``init_time`` with ``landfall`` dim removed.
+    """
+    if "init_time" not in forecast_landfalls.dims:
+        return forecast_landfalls
+    if "landfall" not in forecast_landfalls.dims:
+        return forecast_landfalls
+
+    init_times = forecast_landfalls.init_time.values
+    vt_vals = forecast_landfalls.coords["valid_time"].values
+    data = forecast_landfalls.values
+    vt_2d = vt_vals.ndim == 2
+
+    results = []
+    for i, it in enumerate(init_times):
+        row = data[i, :]
+        non_nan = np.where(~np.isnan(row))[0]
+        if len(non_nan) == 0:
+            continue
+        row_vts = vt_vals[i, non_nan] if vt_2d else vt_vals[non_nan]
+        earliest_j = non_nan[np.argmin(row_vts)]
+        pt = forecast_landfalls.isel(
+            init_time=i,
+            landfall=earliest_j,
+        )
+        results.append(_landfall_point_to_init_row(pt, it))
+
+    if not results:
+        return utils._empty_init_time_array()
+    return xr.concat(
+        results,
+        dim="init_time",
+        coords="different",
+        compat="equals",
+        join="outer",
+    )
+
+
 def broadcast_first_target_to_init_times(
     init_times: xr.DataArray,
     target: xr.DataArray,
@@ -879,15 +1245,17 @@ def broadcast_first_target_to_init_times(
     landfall) before passing it here.
 
     Args:
-        target: ``(landfall,)`` DataArray from ``find_landfalls`` for
-            observations. Must contain exactly one element.
-        forecast: ``(init_time,)`` DataArray from ``find_landfalls`` for
-            the forecast, used to determine which init_times the broadcast
-            target should span.
+        init_times: ``init_time`` coordinate from the forecast
+            landfall DataArray, determines which init_times the
+            broadcast target should span.
+        target: ``(landfall,)`` DataArray from ``find_landfalls``
+            for observations. Must contain at least one element
+            (uses the first).
 
     Returns:
-        ``(init_time,)`` DataArray with the target value, latitude,
-        longitude, and valid_time repeated across all forecast init_times.
+        ``(init_time,)`` DataArray with the target value,
+        latitude, longitude, and valid_time repeated across
+        all provided init_times.
     """
     t = target.isel(landfall=0)
     n = len(init_times)
diff --git a/src/extremeweatherbench/derived.py b/src/extremeweatherbench/derived.py
index 263d7d65..5cd33377 100644
--- a/src/extremeweatherbench/derived.py
+++ b/src/extremeweatherbench/derived.py
@@ -135,7 +135,7 @@ def __init__(
         use_contour_validation: bool = True,
         timestep_count_wind_minimum: int = 10,
         latitude_max_degrees: float = 50.0,
-        surface_pressure_threshold: float = 102000.0,
+        surface_pressure_threshold: float = 101000.0,
         orography: Optional[xr.DataArray] = None,
         max_gc_distance_slp_contour_degrees: float = 5.5,
         max_gc_distance_dz_contour_degrees: float = 6.5,
@@ -153,24 +153,25 @@ def __init__(
                 Defaults to 200.0.
             dz_contour_magnitude: Geopotential thickness contour threshold in m.
                 Defaults to -6.0.
-            min_distance_between_peaks_degrees: Minimum GCD distance between
-                detected peaks in degrees. Converted to grid points at runtime
-                using the actual model resolution. Defaults to 1.0 degree.
+            min_distance_between_peaks_degrees: Minimum distance
+                between detected peaks in degrees. Defaults
+                to 1.0.
             max_spatial_distance_degrees: Maximum distance in degrees for track matching.
                 Defaults to 5.0.
             max_temporal_hours: Maximum hours between detections for track continuity.
                 Defaults to 48.0.
             use_contour_validation: Whether to apply closed contour validation.
                 Defaults to True.
-            timestep_count_wind_minimum: Minimum number of lead times where the
-                neighbourhood peak wind (max within wind_search_radius_degrees)
-                is >= 10 m/s for a track to be retained. Defaults to 10.
+            timestep_count_wind_minimum: Minimum number of lead times where
+                the neighbourhood peak wind (max within
+                wind_search_radius_degrees) is >= 10 m/s for a track to be
+                retained. Defaults to 10.
             latitude_max_degrees: Maximum latitude in degrees for TC detection.
                 Defaults to 50.0.
             surface_pressure_threshold: Maximum SLP (Pa) a candidate grid
                 point may have to be considered for peak detection. Defaults
-                to 101325.0 Pa (standard atmosphere), so only below-average
-                pressure cells are examined.
+                to 101000.0 Pa, so only below-average pressure cells are
+                examined.
             orography: Optional orography DataArray for terrain filtering.
                 Defaults to None.
             max_gc_distance_slp_contour_degrees: Maximum great circle distance for
@@ -180,9 +181,9 @@ def __init__(
                 degrees.
             orography_filter_threshold: Orography filter threshold in meters.
                 Defaults to 150.0.
-            wind_search_radius_degrees: GCD radius in degrees for neighbourhood
-                wind sampling, per TempestExtremes 2.1. Converted to grid
-                points at runtime. Defaults to 2.0 degrees.
+            wind_search_radius_degrees: GCD radius in degrees for
+                neighbourhood wind sampling, per TempestExtremes 2.1.
+                Converted to grid points at runtime. Defaults to 2.0.
         """
         super().__init__(output_variables=output_variables, name=name)
         self.slp_contour_magnitude = slp_contour_magnitude
diff --git a/src/extremeweatherbench/evaluate.py b/src/extremeweatherbench/evaluate.py
index bcb87b3e..fb58ceb5 100644
--- a/src/extremeweatherbench/evaluate.py
+++ b/src/extremeweatherbench/evaluate.py
@@ -575,9 +575,11 @@ def _ensure_output_schema(df: pd.DataFrame, **metadata) -> pd.DataFrame:
     if missing_cols:
         logger.warning("Missing expected columns: %s.", missing_cols)
 
-    # Ensure all OUTPUT_COLUMNS are present (missing ones will be NaN)
-    # and reorder to match OUTPUT_COLUMNS specification
-    return df.reindex(columns=OUTPUT_COLUMNS)
+    # Ensure all OUTPUT_COLUMNS are present (missing ones will be NaN),
+    # reorder to match OUTPUT_COLUMNS specification, and preserve any
+    # extra columns (e.g. landfall metadata) appended at the end.
+    extra_cols = [c for c in df.columns if c not in OUTPUT_COLUMNS]
+    return df.reindex(columns=OUTPUT_COLUMNS + extra_cols)
 
 
 def _evaluate_metric_and_return_df(
diff --git a/src/extremeweatherbench/events/tropical_cyclone.py b/src/extremeweatherbench/events/tropical_cyclone.py
index 97f43be2..d70fd2bc 100644
--- a/src/extremeweatherbench/events/tropical_cyclone.py
+++ b/src/extremeweatherbench/events/tropical_cyclone.py
@@ -20,6 +20,22 @@
 Location = namedtuple("Location", ["latitude", "longitude"])
 
 
+def _neighbourhood_max_wind(
+    wind_slice: npt.NDArray,
+    row: int,
+    col: int,
+    radius_gridpts: int,
+) -> float:
+    """Return max wind speed in a box of ±radius around (row, col)."""
+    nr, nc = wind_slice.shape
+    return float(
+        wind_slice[
+            max(0, row - radius_gridpts) : min(nr, row + radius_gridpts + 1),
+            max(0, col - radius_gridpts) : min(nc, col + radius_gridpts + 1),
+        ].max()
+    )
+
+
 def _degrees_to_gridpoints(
     degrees: float,
     lat_coords: npt.NDArray,
@@ -62,7 +78,7 @@ def generate_tc_tracks_by_init_time(
     use_contour_validation: bool = True,
     timestep_count_wind_minimum: int = 10,
     latitude_max_degrees: float = 50.0,
-    surface_pressure_threshold: float = 102000.0,
+    surface_pressure_threshold: float = 101000.0,
     orography: Optional[xr.DataArray] = None,
     max_gc_distance_slp_contour_degrees: float = 5.5,
     max_gc_distance_dz_contour_degrees: float = 6.5,
@@ -94,21 +110,20 @@ def generate_tc_tracks_by_init_time(
             200.0 Pa
         dz_contour_magnitude: DZ contour threshold for validation in m. Defaults to
             -6.0 m
-        min_distance_between_peaks_degrees: Minimum GCD distance between detected
-            peaks in degrees. Converted to grid points at runtime using the
-            actual grid resolution. Defaults to 1.0 degree
+        min_distance_between_peaks_degrees: Minimum distance
+            between detected peaks in degrees. Defaults to 1.0
         max_spatial_distance_degrees: Max spatial distance for TC track data filtering
             in degrees. Defaults to 5.0 degrees
         max_temporal_hours: Maximum temporal window from init_time in hours. Defaults
             to 48.0 hours
         use_contour_validation: Whether to use contour validation. Defaults to True
-        timestep_count_wind_minimum: Minimum number of lead times where the neighbourhood
-            peak wind speed (max within wind_search_radius_degrees) is >= 10 m/s
-            for a track to be considered valid. Defaults to 2
+        timestep_count_wind_minimum: Minimum number of lead times where the
+            neighbourhood peak wind speed (max within wind_search_radius_degrees)
+            is >= 10 m/s for a track to be considered valid. Defaults to 10
         latitude_max_degrees: Maximum latitude for valid tracks in degrees. Defaults
             to 50.0 degrees
         surface_pressure_threshold: Maximum surface pressure threshold for valid peaks
-            in Pa. Defaults to 100500.0 Pa
+            in Pa. Defaults to 101000.0 Pa
         orography: Orography dataarray in m. Defaults to None
         max_gc_distance_slp_contour_degrees: Max great circle distance for SLP contour
             in degrees. Defaults to 5.5 degrees
@@ -117,9 +132,9 @@ def generate_tc_tracks_by_init_time(
         orography_filter_threshold: Threshold for the orography filter's max height
             in m. Defaults to 150.0 m
         wind_search_radius_degrees: GCD radius in degrees used to sample the
-            neighbourhood maximum wind speed around each detected peak, following
-            TempestExtremes 2.1. Converted to grid points at runtime. Defaults
-            to 2.0 degrees
+            neighbourhood maximum wind speed around each detected peak,
+            following TempestExtremes 2.1. Converted to grid points at
+            runtime. Defaults to 2.0 degrees
 
     Returns:
         xarray Dataset with detected tropical cyclone tracks
@@ -182,7 +197,7 @@ def generate_tc_tracks_by_init_time(
             "latitude": latitude.values,
             "longitude": longitude.values,
             "tc_track_data_df": tc_track_data_df,
-            "min_distance_between_peaks_degrees": min_distance_between_peaks_degrees,
+            "min_distance_between_peaks": min_distance_between_peaks_degrees,
             "max_spatial_distance_degrees": max_spatial_distance_degrees,
             "max_temporal_hours": max_temporal_hours,
             "slp_contour_magnitude": slp_contour_magnitude,
@@ -195,7 +210,6 @@ def generate_tc_tracks_by_init_time(
             "max_gc_distance_slp_contour_degrees": max_gc_distance_slp_contour_degrees,
             "max_gc_distance_dz_contour_degrees": max_gc_distance_dz_contour_degrees,
             "orography_filter_threshold": orography_filter_threshold,
-            "wind_search_radius_degrees": wind_search_radius_degrees,
         },
         input_core_dims=[
             ["lead_time", "latitude", "longitude"],
@@ -260,7 +274,7 @@ def _process_single_init_time(
     latitude: npt.NDArray,
     longitude: npt.NDArray,
     tc_track_data_df: pd.DataFrame,
-    min_distance_between_peaks_degrees: float,
+    min_distance_between_peaks: float,
     max_spatial_distance_degrees: float,
     max_temporal_hours: float,
     slp_contour_magnitude: float,
@@ -321,7 +335,7 @@ def _process_single_init_time(
     # Convert degree-based spatial parameters to grid points using the
     # actual grid resolution so the code is resolution-agnostic.
     min_distance_between_peaks_gridpts = _degrees_to_gridpoints(
-        min_distance_between_peaks_degrees, latitude, longitude
+        min_distance_between_peaks, latitude, longitude
     )
     wind_search_radius_gridpts = _degrees_to_gridpoints(
         wind_search_radius_degrees, latitude, longitude
@@ -403,17 +417,14 @@ def _process_single_init_time(
             peak_lats = latitude[peaks[:, 0]]
             peak_lons = longitude[peaks[:, 1]]
             peak_slps = slp_slice[peaks[:, 0], peaks[:, 1]]
-            # Sample max wind within wind_search_radius_degrees of each
-            # detected peak (per TempestExtremes 2.1) to capture the eyewall
-            # rather than the low-wind eye at the SLP minimum.
-            _nrows, _ncols = wind_slice.shape
-            _nb = wind_search_radius_gridpts
             peak_winds = np.array(
                 [
-                    wind_slice[
-                        max(0, r - _nb) : min(_nrows, r + _nb + 1),
-                        max(0, c - _nb) : min(_ncols, c + _nb + 1),
-                    ].max()
+                    _neighbourhood_max_wind(
+                        wind_slice,
+                        r,
+                        c,
+                        wind_search_radius_gridpts,
+                    )
                     for r, c in peaks
                 ]
             )
@@ -513,6 +524,20 @@ def _process_single_init_time(
         )
         detections = filtered_detections
 
+    # Fill gaps in surviving tracks using SLP minima from
+    # the forecast fields as a fallback when the standard
+    # peak-finding/validation failed at intermediate leads.
+    detections = _fill_track_gaps_from_fields(
+        detections,
+        slp_data,
+        wind_data,
+        lead_time_indices_seq,
+        valid_time_indices_seq,
+        latitude,
+        longitude,
+        wind_search_radius_gridpts,
+    )
+
     return {"detections": detections}
 
 
@@ -737,16 +762,16 @@ def _find_peaks_for_time_slice(
     min_distance_between_peaks: int,
     lat_coords: npt.NDArray,
     lon_coords: npt.NDArray,
-    max_spatial_distance_degrees: float,
-    max_temporal_hours: float,
-    slp_contour_magnitude: float,
-    dz_contour_magnitude: float,
-    use_contour_validation: bool,
-    is_first_timestep: bool,
-    surface_pressure_threshold: float,
-    latitude_max_degrees: float,
-    max_gc_distance_slp_contour_degrees: float,
-    max_gc_distance_dz_contour_degrees: float,
+    max_spatial_distance_degrees: float = 5.0,
+    max_temporal_hours: float = 48.0,
+    slp_contour_magnitude: float = 200.0,
+    dz_contour_magnitude: float = -6.0,
+    use_contour_validation: bool = True,
+    is_first_timestep: bool = False,
+    surface_pressure_threshold: float = 101000.0,
+    latitude_max_degrees: float = 50.0,
+    max_gc_distance_slp_contour_degrees: float = 5.5,
+    max_gc_distance_dz_contour_degrees: float = 6.5,
 ) -> npt.NDArray:
     """Find peaks with tropical cyclone track data filtering.
 
@@ -760,8 +785,8 @@ def _find_peaks_for_time_slice(
         current_valid_time: Current valid time being processed
         tc_track_data_df: Tropical cyclone track data
         min_distance_between_peaks: Minimum distance between peaks in grid points
-        lat_coords: Latitude coordinates in degrees. Defaults to None
-        lon_coords: Longitude coordinates in degrees. Defaults to None
+        lat_coords: 1-D latitude coordinate array in degrees
+        lon_coords: 1-D longitude coordinate array in degrees
         max_spatial_distance_degrees: Max spatial distance in degrees. Defaults to
             5.0 degrees
         max_temporal_hours: Max temporal buffer for genesis in hours. Defaults to
@@ -771,7 +796,7 @@ def _find_peaks_for_time_slice(
         use_contour_validation: Whether to use contour validation. Defaults to True
         is_first_timestep: If True, applies temporal buffer. Defaults to False
         surface_pressure_threshold: Maximum surface pressure threshold for valid peaks
-            in Pa. Defaults to 100500.0 Pa
+            in Pa. Defaults to 101000.0 Pa
         latitude_max_degrees: Maximum latitude for valid tracks in degrees. Defaults
             to 50.0 degrees
         max_gc_distance_slp_contour_degrees: Max great circle distance for SLP contour
@@ -926,6 +951,156 @@ def _create_spatial_mask(
     return spatial_mask
 
 
+def _fill_track_gaps_from_fields(
+    detections: list[dict],
+    slp_data: npt.NDArray,
+    wind_data: npt.NDArray,
+    lead_time_indices_seq: npt.NDArray,
+    valid_time_indices_seq: npt.NDArray,
+    latitude: npt.NDArray,
+    longitude: npt.NDArray,
+    wind_search_radius_gridpts: int,
+    search_radius_degrees: float = 3.0,
+    n_candidates: int = 5,
+) -> list[dict]:
+    """Fill track gaps using SLP minima from forecast fields.
+
+    When the tracker loses a storm (e.g. over land), this
+    finds the SLP minimum closest to the expected track
+    position for each missing timestep, ensuring continuous
+    tracks between the first and last detection.
+
+    The algorithm picks the top-N lowest-SLP cells within
+    the search radius, then selects the one nearest to the
+    interpolated expected position. Each filled point
+    updates the "previous" anchor so successive gap fills
+    stay physically consistent along the track.
+
+    Args:
+        detections: Existing detections from the tracking pass.
+        slp_data: Full SLP array (lead_time, lat, lon) in Pa.
+        wind_data: Full wind array (lead_time, lat, lon) in m/s.
+        lead_time_indices_seq: Ordered lead_time indices for
+            each timestep of this init_time.
+        valid_time_indices_seq: Ordered valid_time indices for
+            each timestep.
+        latitude: 1-D latitude coordinate array (degrees).
+        longitude: 1-D longitude coordinate array (degrees).
+        wind_search_radius_gridpts: Neighbourhood radius in
+            grid points for sampling peak wind.
+        search_radius_degrees: Max distance from the expected
+            position to search for SLP minima (degrees).
+        n_candidates: Number of lowest-SLP cells to consider
+            when choosing the closest to expected position.
+
+    Returns:
+        detections list with gap-fill entries appended.
+    """
+    if not detections:
+        return detections
+
+    ts_lookup: dict[tuple[int, int], int] = {}
+    for ts_idx in range(len(lead_time_indices_seq)):
+        key = (
+            int(lead_time_indices_seq[ts_idx]),
+            int(valid_time_indices_seq[ts_idx]),
+        )
+        ts_lookup[key] = ts_idx
+
+    # Group detections by track_id -> [(ts_idx, det), ...]
+    track_timeline: dict[int, list[tuple[int, dict]]] = {}
+    for det in detections:
+        key = (
+            int(det["lead_time_index"]),
+            int(det["valid_time_index"]),
+        )
+        maybe_ts = ts_lookup.get(key)
+        if maybe_ts is not None:
+            track_timeline.setdefault(det["track_id"], []).append((maybe_ts, det))
+
+    new_detections: list[dict] = []
+
+    for tid, ts_dets in track_timeline.items():
+        ts_dets.sort(key=lambda x: x[0])
+        first_ts = ts_dets[0][0]
+        last_ts = ts_dets[-1][0]
+
+        if last_ts - first_ts <= 1:
+            continue
+
+        # Mutable map so gap-fills update the "previous"
+        # anchor for subsequent gaps.
+        ts_to_det: dict[int, dict] = {ts: d for ts, d in ts_dets}
+
+        for ts_idx in range(first_ts + 1, last_ts):
+            if ts_idx in ts_to_det:
+                continue
+
+            prev_det = next_det = None
+            for lb in range(ts_idx - 1, first_ts - 1, -1):
+                if lb in ts_to_det:
+                    prev_det = (lb, ts_to_det[lb])
+                    break
+            for lf in range(ts_idx + 1, last_ts + 1):
+                if lf in ts_to_det:
+                    next_det = (lf, ts_to_det[lf])
+                    break
+
+            if prev_det is None or next_det is None:
+                continue
+
+            p_ts, p_d = prev_det
+            n_ts, n_d = next_det
+            frac = (ts_idx - p_ts) / (n_ts - p_ts)
+            exp_lat = p_d["latitude"] + frac * (n_d["latitude"] - p_d["latitude"])
+            exp_lon = p_d["longitude"] + frac * (n_d["longitude"] - p_d["longitude"])
+
+            _lt_idx = lead_time_indices_seq[ts_idx]
+            slp_slice = slp_data[_lt_idx]
+            wind_slice = wind_data[_lt_idx]
+
+            dlat = latitude[:, np.newaxis] - exp_lat
+            dlon = longitude[np.newaxis, :] - exp_lon
+            dlon = np.mod(dlon + 180, 360) - 180
+            cos_lat = np.cos(np.radians(exp_lat))
+            dist = np.sqrt(dlat**2 + (dlon * cos_lat) ** 2)
+
+            mask = dist <= search_radius_degrees
+            n_valid = int(np.count_nonzero(mask))
+            if n_valid == 0:
+                continue
+
+            slp_masked = np.where(mask, slp_slice, np.inf)
+
+            k = min(n_candidates, n_valid)
+            flat_idx = np.argpartition(slp_masked.ravel(), k)[:k]
+            cand_r, cand_c = np.unravel_index(flat_idx, slp_masked.shape)
+            best = int(np.argmin(dist[cand_r, cand_c]))
+            r, c = int(cand_r[best]), int(cand_c[best])
+
+            peak_wind = _neighbourhood_max_wind(
+                wind_slice,
+                r,
+                c,
+                wind_search_radius_gridpts,
+            )
+
+            filled = {
+                "lead_time_index": int(_lt_idx),
+                "valid_time_index": int(valid_time_indices_seq[ts_idx]),
+                "track_id": tid,
+                "latitude": float(latitude[r]),
+                "longitude": float(longitude[c]),
+                "slp": float(slp_slice[r, c]),
+                "wind": float(peak_wind),
+            }
+            new_detections.append(filled)
+            ts_to_det[ts_idx] = filled
+
+    detections.extend(new_detections)
+    return detections
+
+
 def _convert_detections_to_dataset(
     n_detections: int,
     lead_time_indices: npt.NDArray,
diff --git a/src/extremeweatherbench/metrics.py b/src/extremeweatherbench/metrics.py
index 1a1fe673..c269d2ab 100644
--- a/src/extremeweatherbench/metrics.py
+++ b/src/extremeweatherbench/metrics.py
@@ -1650,32 +1650,39 @@ def __init__(
         forecast_variable: Optional[str | derived.DerivedVariable] = None,
         target_variable: Optional[str | derived.DerivedVariable] = None,
         metrics: Optional[list[Type["LandfallMetric"]]] = None,
+        min_target_separation_hours: float = 0.0,
+        max_time_mismatch_hours: Optional[float] = None,
+        landfall_time_filter: Optional[tuple[str, float]] = ("window", 24.0),
         *args,
         **kwargs,
     ):
-        """Initialize LandfallMetric.
+        """Configure landfall detection and filtering.
 
-        Landfalls are detected using the calc.find_landfalls function, which utilizes a
-        land geometry and line segments based on coordinates to determine intersections.
-
-        Using approach, "first" will calculate the first detected landfall for an entire
-        forecast, i.e. later landfalls in a multi-landfall event will not be considered.
-        "next" will calculate the next landfall for each init_time.
-        Using Ida as an example (case 220), "first" would only run calculations for the
-        first landfall in Cuba, ignoring the later US landfall. "next" would run
-        calculations for the first landfall in Cuba, then the next landfall in the US,
-        etc. based on the init_time and when landfall occurred.
+        ``approach="first"`` evaluates the first observed
+        landfall only; ``approach="next"`` matches each
+        init_time to the closest future observed landfall.
 
         Args:
-            name: The name of the metric. Defaults to "landfall_metrics" for the base
-                class.
-            preserve_dims: The dimensions to preserve. Defaults to "init_time".
-            approach: The approach to use for landfall detection. Defaults to "first".
-            exclude_post_landfall: Whether to exclude post-landfall data. Defaults to
-                False.
-            forecast_variable: The forecast variable to use. Defaults to None.
-            target_variable: The target variable to use. Defaults to None.
-            metrics: A list of metrics to use as a composite. Defaults to None.
+            name: Metric name.
+            preserve_dims: Dims to keep in the result.
+            approach: ``"first"`` or ``"next"``.
+            exclude_post_landfall: Drop post-landfall data.
+            forecast_variable: Forecast variable name.
+            target_variable: Target variable name.
+            metrics: Sub-metric classes for composite mode.
+            min_target_separation_hours: Min hours between
+                consecutive target landfalls. Filters rapid
+                island crossings. Only for ``"next"``.
+            max_time_mismatch_hours: Max hours between
+                matched forecast/target landfall times.
+                Only for ``"next"``.
+            landfall_time_filter: How to filter paired
+                forecast/target landfalls by timing.
+                ``("window", hours)`` keeps pairs within
+                a fixed ± window.
+                ``("proportional", fraction)`` keeps pairs
+                where timing error < fraction * lead time.
+                ``None`` disables filtering.
         """
         super().__init__(
             name=name,
@@ -1687,24 +1694,22 @@ def __init__(
         )
         self.approach = approach
         self.exclude_post_landfall = exclude_post_landfall
+        self.min_target_separation_hours = min_target_separation_hours
+        self.max_time_mismatch_hours = max_time_mismatch_hours
+        self.landfall_time_filter = landfall_time_filter
         self.metrics = metrics or []
-
-        # If metrics provided, instantiate them
-        if self.metrics is not None:
-            self._metric_instances = [
-                (
-                    metric_cls(
-                        preserve_dims=self.preserve_dims,
-                        forecast_variable=self.forecast_variable,
-                        target_variable=self.target_variable,
-                    )
-                    if isinstance(metric_cls, type)
-                    else metric_cls
+        self._metric_instances = [
+            (
+                metric_cls(
+                    preserve_dims=self.preserve_dims,
+                    forecast_variable=self.forecast_variable,
+                    target_variable=self.target_variable,
                 )
-                for metric_cls in self.metrics
-            ]
-        else:
-            self._metric_instances = []
+                if isinstance(metric_cls, type)
+                else metric_cls
+            )
+            for metric_cls in self.metrics
+        ]
 
     def __call__(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any
@@ -1718,25 +1723,32 @@ def __call__(
         """
         return self.compute_metric(forecast, target, **kwargs)
 
+    def _nan_landfall_pair(self) -> tuple[xr.DataArray, xr.DataArray]:
+        """Return a (NaN, NaN) forecast/target landfall pair."""
+        a = utils._create_nan_dataarray(self.preserve_dims)
+        return (a, a.copy())
+
     def maybe_compute_landfalls(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any
     ) -> tuple[xr.DataArray, xr.DataArray]:
-        """Compute landfalls for a given forecast and target dataarray.
+        """Detect and pair forecast/target landfalls.
 
-        This function computes the landfalls for a given forecast and target dataarray
-        using calc.find_landfalls. Currently, this access pattern doesn't include
-        passing land geometry in, but calc.find_landfalls will use NaturalEarth's 10m
-        land geometry by default.
+        Uses ``calc.find_landfalls`` (NaturalEarth 10m land
+        geometry) then applies approach-specific matching and
+        the configured time filters.
+
+        Pre-computed landfalls can be passed via ``kwargs``
+        keys ``forecast_landfall`` and ``target_landfall``
+        to skip detection.
 
         Args:
-            forecast: The forecast DataArray
-            target: The target DataArray
-            **kwargs: Additional keyword arguments, may include pre-computed
-                forecast_landfall and target_landfall
+            forecast: Forecast DataArray.
+            target: Target DataArray.
+            **kwargs: May include pre-computed landfalls.
 
         Returns:
-            Tuple of (forecast_landfall, target_landfall). If no landfalls are found,
-            returns NaN DataArrays with init_time dimension.
+            ``(forecast_landfall, target_landfall)`` tuple,
+            or NaN arrays if no valid landfalls remain.
         """
         forecast_landfall, target_landfall = (
             kwargs.get("forecast_landfall", None),
@@ -1749,44 +1761,120 @@ def maybe_compute_landfalls(
         # For "next" approach: get all target landfalls, then filter
         return_next_landfall = self.approach == "next"
 
-        # Get first forecast landfall per init_time
         forecast_landfalls = calc.find_landfalls(forecast)
 
-        # find_landfalls never returns None; an empty array means no landfalls.
         if len(forecast_landfalls) == 0:
-            nan_landfalls = utils._create_nan_dataarray(self.preserve_dims)
-            return (nan_landfalls, nan_landfalls.copy())
+            return self._nan_landfall_pair()
 
-        # Get all target landfalls
         target_landfalls = calc.find_landfalls(target)
 
         if len(target_landfalls) == 0:
-            nan_landfalls = utils._create_nan_dataarray(self.preserve_dims)
-            return (nan_landfalls, nan_landfalls.copy())
+            return self._nan_landfall_pair()
 
         if return_next_landfall:
-            # Find next target landfall for each init_time
+            max_lead = None
+            track_starts = None
+            if "lead_time" in forecast.dims:
+                max_lead = forecast.lead_time.values.max()
+                if not isinstance(max_lead, np.timedelta64):
+                    max_lead = np.timedelta64(int(max_lead), "h")
+                track_starts = calc.get_forecast_track_start_times(forecast)
             target_landfalls = calc.find_next_landfall_for_init_time(
-                forecast_landfalls, target_landfalls
+                forecast_landfalls,
+                target_landfalls,
+                max_lead_time=max_lead,
+                min_target_separation_hours=self.min_target_separation_hours,
+                max_time_mismatch_hours=self.max_time_mismatch_hours,
+                track_start_times=track_starts,
             )
             if len(target_landfalls) == 0:
-                nan_landfalls = utils._create_nan_dataarray(self.preserve_dims)
-                return (nan_landfalls, nan_landfalls.copy())
+                return self._nan_landfall_pair()
+            # Reduce to first forecast landfall per init_time so
+            # downstream metrics compare one-to-one instead of
+            # averaging across multiple forecast landfalls.
+            forecast_landfalls = calc.select_first_forecast_landfall_per_init(
+                forecast_landfalls
+            )
+            common = utils.find_common_init_times(forecast_landfalls, target_landfalls)
+            if len(common) == 0:
+                return self._nan_landfall_pair()
+            forecast_landfalls = forecast_landfalls.sel(init_time=common)
+            target_landfalls = target_landfalls.sel(init_time=common)
         else:
-            # First approach: target is (landfall,) with one observed event.
-            # Drop forecast init_times at or after the landfall to prevent
-            # data leakage, then broadcast the single target to (init_time,)
-            # so sub-metrics receive consistent arrays.
-            first_valid_time = (
-                target_landfalls.coords["valid_time"].isel(landfall=0).values
+            # "first" approach: compare to the earliest IBTrACS
+            # landfall that the forecast can actually cover.
+            # If the first landfall has no forecast data (e.g.
+            # tracker misses a Cuba crossing), fall back to the
+            # next landfall with coverage.
+            reduced_forecast = calc.select_first_forecast_landfall_per_init(
+                forecast_landfalls
             )
-            forecast_landfalls = forecast_landfalls.where(
-                forecast_landfalls.init_time < first_valid_time,
-                drop=True,
+            track_starts = None
+            if "lead_time" in forecast.dims:
+                track_starts = calc.get_forecast_track_start_times(forecast)
+
+            n_targets = target_landfalls.sizes.get("landfall", 1)
+            selected_idx = None
+            for lf_idx in range(n_targets):
+                candidate_vt = (
+                    target_landfalls.coords["valid_time"].isel(landfall=lf_idx).values
+                )
+                candidate_fc = reduced_forecast.where(
+                    reduced_forecast.init_time < candidate_vt,
+                    drop=True,
+                )
+                if len(candidate_fc) == 0:
+                    continue
+                if track_starts is not None:
+                    candidate_fc = calc.filter_inits_by_track_start(
+                        candidate_fc,
+                        candidate_vt,
+                        track_starts,
+                    )
+                if len(candidate_fc) > 0:
+                    selected_idx = lf_idx
+                    forecast_landfalls = candidate_fc
+                    break
+
+            if selected_idx is None:
+                return self._nan_landfall_pair()
+
+            # slice keeps the landfall dim (length 1) so
+            # broadcast_first_target_to_init_times can
+            # .isel(landfall=0) on it.
+            selected_target = target_landfalls.isel(
+                landfall=slice(selected_idx, selected_idx + 1)
             )
             target_landfalls = calc.broadcast_first_target_to_init_times(
-                forecast_landfalls.init_time, target_landfalls
+                forecast_landfalls.init_time,
+                selected_target,
             )
+
+        if self.landfall_time_filter is not None:
+            method, value = self.landfall_time_filter
+            if method == "window":
+                forecast_landfalls, target_landfalls = (
+                    calc.filter_by_landfall_time_window(
+                        forecast_landfalls,
+                        target_landfalls,
+                        window_hours=value,
+                    )
+                )
+            elif method == "proportional":
+                forecast_landfalls, target_landfalls = (
+                    calc.filter_by_landfall_time_tolerance(
+                        forecast_landfalls,
+                        target_landfalls,
+                        tolerance_fraction=value,
+                    )
+                )
+            else:
+                raise ValueError(
+                    f'Unknown filter method {method!r}. Use "window" or "proportional".'
+                )
+            if len(forecast_landfalls) == 0:
+                return self._nan_landfall_pair()
+
         return forecast_landfalls, target_landfalls
 
     def maybe_prepare_composite_kwargs(
@@ -1818,6 +1906,49 @@ def maybe_prepare_composite_kwargs(
 
         return kwargs
 
+    @staticmethod
+    def _attach_landfall_metadata(
+        result: xr.DataArray,
+        forecast_landfall: xr.DataArray,
+        target_landfall: xr.DataArray,
+    ) -> xr.DataArray:
+        """Attach forecast/target landfall coords to a result.
+
+        Adds latitude, longitude, and valid_time for both the
+        forecast and target landfalls as coordinates on the
+        result DataArray. These become extra columns when
+        converted to a DataFrame in the evaluation pipeline.
+        """
+        if "init_time" not in result.dims:
+            return result
+        coord_map = {}
+        for prefix, lf in (
+            ("forecast_landfall", forecast_landfall),
+            ("target_landfall", target_landfall),
+        ):
+            for coord in ("latitude", "longitude", "valid_time"):
+                if coord in lf.coords:
+                    vals = lf.coords[coord].values
+                    if vals.ndim == 0:
+                        vals = np.full(
+                            len(result.init_time),
+                            vals,
+                        )
+                    coord_map[f"{prefix}_{coord}"] = (
+                        "init_time",
+                        vals,
+                    )
+        return result.assign_coords(coord_map)
+
+    @staticmethod
+    def _reduce_landfall_dim(
+        result: xr.DataArray,
+    ) -> xr.DataArray:
+        """Mean over landfall dim if present."""
+        if "landfall" in result.dims:
+            return result.mean(dim="landfall")
+        return result
+
     def _compute_metric(
         self,
         forecast: xr.DataArray,
@@ -2004,7 +2135,8 @@ def calculate_displacement(
         )
         if not isinstance(distances, xr.DataArray):
             raise TypeError("haversine_distance returned a scalar; expected DataArray")
-        return distances.where(forecast_landfall.notnull()).mean(dim="landfall")
+        result = distances.where(forecast_landfall.notnull())
+        return self._reduce_landfall_dim(result)
 
     def _compute_metric(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any
@@ -2017,11 +2149,16 @@ def _compute_metric(
             forecast_landfall
         ) or not utils.is_valid_landfall(target_landfall):
             return utils._create_nan_dataarray(self.preserve_dims)
-        return self.calculate_displacement(
+        result = self.calculate_displacement(
             forecast_landfall,
             target_landfall,
             units=self.units,
         )
+        return self._attach_landfall_metadata(
+            result,
+            forecast_landfall,
+            target_landfall,
+        )
 
 
 class LandfallTimeMeanError(LandfallMetric):
@@ -2076,7 +2213,8 @@ def calculate_time_difference(
         time_diffs = (
             forecast_landfall.valid_time - target_landfall.valid_time
         ) / np.timedelta64(1, "h")
-        return time_diffs.where(forecast_landfall.notnull()).mean(dim="landfall")
+        result = time_diffs.where(forecast_landfall.notnull())
+        return self._reduce_landfall_dim(result)
 
     def _compute_metric(
         self, forecast: xr.DataArray, target: xr.DataArray, **kwargs: Any
@@ -2089,7 +2227,15 @@ def _compute_metric(
             forecast_landfall
         ) or not utils.is_valid_landfall(target_landfall):
             return utils._create_nan_dataarray(self.preserve_dims)
-        return self.calculate_time_difference(forecast_landfall, target_landfall)
+        result = self.calculate_time_difference(
+            forecast_landfall,
+            target_landfall,
+        )
+        return self._attach_landfall_metadata(
+            result,
+            forecast_landfall,
+            target_landfall,
+        )
 
 
 class LandfallIntensityMeanAbsoluteError(LandfallMetric, MeanAbsoluteError):
@@ -2131,8 +2277,13 @@ def _compute_metric(
         ) or not utils.is_valid_landfall(target_landfall):
             return utils._create_nan_dataarray(self.preserve_dims)
 
-        return (
-            np.abs(forecast_landfall - target_landfall)
-            .where(forecast_landfall.notnull())
-            .mean(dim="landfall")
+        result = self._reduce_landfall_dim(
+            np.abs(forecast_landfall - target_landfall).where(
+                forecast_landfall.notnull()
+            )
+        )
+        return self._attach_landfall_metadata(
+            result,
+            forecast_landfall,
+            target_landfall,
         )
diff --git a/src/extremeweatherbench/utils.py b/src/extremeweatherbench/utils.py
index e193e8fc..8f085768 100644
--- a/src/extremeweatherbench/utils.py
+++ b/src/extremeweatherbench/utils.py
@@ -32,6 +32,15 @@
 }
 
 
+def _empty_init_time_array() -> xr.DataArray:
+    """Empty float DataArray with an init_time dimension."""
+    return xr.DataArray(
+        np.array([], dtype=float),
+        dims=["init_time"],
+        coords={"init_time": np.array([], dtype="datetime64[ns]")},
+    )
+
+
 def maybe_get_operator(
     operator_method: Union[Literal[">", ">=", "<", "<=", "==", "!="], Callable],
 ) -> Callable:
diff --git a/tests/test_calc.py b/tests/test_calc.py
index a7af8f31..7c793e3d 100644
--- a/tests/test_calc.py
+++ b/tests/test_calc.py
@@ -2282,6 +2282,198 @@ def test_find_next_landfall_no_future_landfalls(self):
         assert isinstance(result, xr.DataArray)
         assert len(result) == 0
 
+    def test_find_next_landfall_max_lead_time(self):
+        """Landfalls beyond the forecast horizon are excluded."""
+        init_times = pd.to_datetime(["2023-09-14 00:00", "2023-09-14 12:00"])
+        forecast_landfalls = xr.DataArray(
+            [35.0, 36.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": (
+                    ["init_time"],
+                    init_times + pd.Timedelta(hours=12),
+                ),
+                "latitude": (["init_time"], [24.0, 24.5]),
+                "longitude": (["init_time"], [280.0, 279.5]),
+            },
+            name="surface_wind_speed",
+        )
+
+        target_times = pd.to_datetime(["2023-09-14 18:00", "2023-09-26 00:00"])
+        target_landfalls = xr.DataArray(
+            [40.0, 45.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0, 1],
+                "valid_time": (["landfall"], target_times),
+                "latitude": (["landfall"], [26.0, 50.0]),
+                "longitude": (["landfall"], [278.0, 300.0]),
+            },
+            name="surface_wind_speed",
+        )
+
+        max_lead = np.timedelta64(240, "h")
+        result = calc.find_next_landfall_for_init_time(
+            forecast_landfalls,
+            target_landfalls,
+            max_lead_time=max_lead,
+        )
+
+        assert isinstance(result, xr.DataArray)
+        assert "init_time" in result.dims
+        vt_vals = np.atleast_1d(result.coords["valid_time"].values)
+        for vt in vt_vals:
+            assert vt == target_times[0]
+
+    def test_find_next_landfall_max_lead_time_all_excluded(self):
+        """All landfalls beyond horizon returns empty."""
+        init_times = pd.to_datetime(["2023-09-01 00:00"])
+        forecast_landfalls = xr.DataArray(
+            [35.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": (
+                    ["init_time"],
+                    init_times + pd.Timedelta(hours=6),
+                ),
+                "latitude": (["init_time"], [24.0]),
+                "longitude": (["init_time"], [280.0]),
+            },
+        )
+
+        target_times = pd.to_datetime(["2023-09-20 00:00"])
+        target_landfalls = xr.DataArray(
+            [40.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0],
+                "valid_time": (["landfall"], target_times),
+                "latitude": (["landfall"], [50.0]),
+                "longitude": (["landfall"], [300.0]),
+            },
+        )
+
+        max_lead = np.timedelta64(240, "h")
+        result = calc.find_next_landfall_for_init_time(
+            forecast_landfalls,
+            target_landfalls,
+            max_lead_time=max_lead,
+        )
+
+        assert isinstance(result, xr.DataArray)
+        assert len(result) == 0
+
+    def test_find_next_landfall_temporal_proximity(self):
+        """Forecast matches the target closest to its predicted time,
+        not the chronologically first target after init."""
+        init_times = pd.to_datetime(["2023-09-14 00:00"])
+
+        # Forecast predicts landfall at day 5 (Sep 19)
+        forecast_landfalls = xr.DataArray(
+            [35.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": (
+                    ["init_time"],
+                    pd.to_datetime(["2023-09-19 00:00"]),
+                ),
+                "latitude": (["init_time"], [30.0]),
+                "longitude": (["init_time"], [270.0]),
+            },
+            name="surface_wind_speed",
+        )
+
+        # Two observed landfalls: one at day 1, one at day 5
+        target_times = pd.to_datetime(["2023-09-15 00:00", "2023-09-19 06:00"])
+        target_landfalls = xr.DataArray(
+            [40.0, 45.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0, 1],
+                "valid_time": (["landfall"], target_times),
+                "latitude": (["landfall"], [25.0, 30.0]),
+                "longitude": (["landfall"], [280.0, 270.0]),
+            },
+            name="surface_wind_speed",
+        )
+
+        result = calc.find_next_landfall_for_init_time(
+            forecast_landfalls,
+            target_landfalls,
+        )
+
+        # Should match target #1 (Sep 19, closest to forecast)
+        # not target #0 (Sep 15, chronologically first).
+        matched_vt = np.atleast_1d(result.coords["valid_time"].values)
+        assert matched_vt[0] == target_times[1]
+
+    def test_find_next_landfall_2d_forecast(self):
+        """Forecast with (init_time, landfall) dims where
+        init_time dim is larger than landfall dim (e.g. after
+        deduplication). Regression test for IndexError."""
+        it1 = np.datetime64("2023-09-14T00:00")
+        it2 = np.datetime64("2023-09-14T12:00")
+        it3 = np.datetime64("2023-09-15T00:00")
+
+        # 2D forecast: 3 init_times, 2 landfalls.
+        # Landfall 0 came from it1, landfall 1 from it2.
+        # it3 has no landfalls (NaN row).
+        forecast_landfalls = xr.DataArray(
+            [[35.0, np.nan], [np.nan, 36.0], [np.nan, np.nan]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [it1, it2, it3],
+                "landfall": [0, 1],
+                "valid_time": (
+                    "landfall",
+                    pd.to_datetime(["2023-09-16 00:00", "2023-09-17 00:00"]),
+                ),
+                "latitude": ("landfall", [25.0, 26.0]),
+                "longitude": ("landfall", [280.0, 279.0]),
+            },
+            name="surface_wind_speed",
+        )
+
+        target_times = pd.to_datetime(
+            ["2023-09-15 06:00", "2023-09-16 06:00", "2023-09-17 06:00"]
+        )
+        target_landfalls = xr.DataArray(
+            [40.0, 42.0, 44.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0, 1, 2],
+                "valid_time": ("landfall", target_times),
+                "latitude": ("landfall", [24.0, 25.0, 26.0]),
+                "longitude": ("landfall", [281.0, 280.0, 279.0]),
+            },
+            name="surface_wind_speed",
+        )
+
+        result = calc.find_next_landfall_for_init_time(
+            forecast_landfalls,
+            target_landfalls,
+        )
+
+        # it1 forecasts landfall at Sep 16 -> closest target
+        # is Sep 16 06:00 (target #1).
+        # it2 forecasts landfall at Sep 17 -> closest target
+        # is Sep 17 06:00 (target #2).
+        # it3 has no forecast landfall -> falls back to
+        # chronologically next after Sep 15, which is
+        # Sep 15 06:00 (target #0).
+        result_vts = {
+            pd.Timestamp(it): pd.Timestamp(
+                result.sel(init_time=it).coords["valid_time"].values
+            )
+            for it in result.init_time.values
+        }
+        assert result_vts[pd.Timestamp(it1)] == target_times[1]
+        assert result_vts[pd.Timestamp(it2)] == target_times[2]
+        assert result_vts[pd.Timestamp(it3)] == target_times[0]
+
 
 class TestLandfallMetricAlignment:
     """Test that landfall metrics properly align forecast and target."""
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 24b1e97a..da1bbbbd 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -2350,6 +2350,542 @@ def mock_find_func(track_data, return_next_landfall=False):
             # Allow some tolerance for floating point calculations
             assert 39.0 < result.values[0] < 41.0
 
+    def test_multi_landfall_displacement_uses_first_per_init(self):
+        """Verify displacement uses only the first forecast landfall per
+        init_time, not an average across multiple forecast landfalls.
+
+        Mimics TC Yagi's pattern: Philippines landfall (~16N, 120E) then
+        Vietnam landfall (~20N, 106E). The target is the Philippines
+        landfall. Without the fix, the displacement would be inflated
+        by averaging in the Vietnam forecast landfall distance (~1500 km).
+        With the fix, only the first (Philippines) forecast landfall
+        is used, giving a small displacement (~15 km).
+        """
+        target = xr.Dataset(
+            {
+                "latitude": (["valid_time"], [16.0]),
+                "longitude": (["valid_time"], [120.0]),
+                "surface_wind_speed": (["valid_time"], [55.0]),
+            },
+            coords={
+                "valid_time": [pd.Timestamp("2024-09-01 12:00")],
+            },
+        )
+
+        forecast = xr.Dataset(
+            {
+                "latitude": (
+                    ["lead_time", "valid_time"],
+                    [[16.0]],
+                ),
+                "longitude": (
+                    ["lead_time", "valid_time"],
+                    [[120.0]],
+                ),
+                "surface_wind_speed": (
+                    ["lead_time", "valid_time"],
+                    [[52.0]],
+                ),
+            },
+            coords={
+                "lead_time": [24],
+                "valid_time": [
+                    pd.Timestamp("2024-09-01 12:00"),
+                ],
+            },
+        )
+
+        init_ts = pd.Timestamp("2024-08-31 12:00")
+
+        # Target: single observed landfall near Philippines
+        mock_target_landfall = xr.DataArray(
+            [55.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0],
+                "latitude": ("landfall", [16.0]),
+                "longitude": ("landfall", [120.0]),
+                "valid_time": (
+                    "landfall",
+                    [pd.Timestamp("2024-09-01 12:00")],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        # Forecast: TWO landfalls for the same init_time.
+        # Landfall 0 = Philippines (16.1N, 120.1E, close)
+        # Landfall 1 = Vietnam (20N, 106E, ~1500 km away)
+        mock_forecast_landfall = xr.DataArray(
+            [[52.0, 45.0]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [init_ts],
+                "landfall": [0, 1],
+                "latitude": (
+                    ["init_time", "landfall"],
+                    [[16.1, 20.0]],
+                ),
+                "longitude": (
+                    ["init_time", "landfall"],
+                    [[120.1, 106.0]],
+                ),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [
+                        [
+                            pd.Timestamp("2024-09-01 12:00"),
+                            pd.Timestamp("2024-09-07 06:00"),
+                        ]
+                    ],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        with mock.patch.object(calc, "find_landfalls") as mock_find:
+
+            def side_effect(track_data, **kw):
+                if "lead_time" not in track_data.dims:
+                    return mock_target_landfall
+                return mock_forecast_landfall
+
+            mock_find.side_effect = side_effect
+
+            metric = metrics.LandfallDisplacement(
+                approach="first",
+            )
+            result = metric._compute_metric(
+                forecast["surface_wind_speed"],
+                target["surface_wind_speed"],
+            )
+
+            assert isinstance(result, xr.DataArray)
+            assert result.dims == ("init_time",)
+            displacement_km = result.values[0]
+            # First forecast landfall is ~15 km from target.
+            # If the bug were present (averaging both landfalls),
+            # the displacement would be ~750 km.
+            assert displacement_km < 50, (
+                f"Displacement {displacement_km:.1f} km is too large; "
+                f"multi-landfall averaging bug is likely present"
+            )
+
+            # Verify landfall metadata is attached
+            expected_coords = [
+                "forecast_landfall_latitude",
+                "forecast_landfall_longitude",
+                "forecast_landfall_valid_time",
+                "target_landfall_latitude",
+                "target_landfall_longitude",
+                "target_landfall_valid_time",
+            ]
+            for coord_name in expected_coords:
+                assert coord_name in result.coords, (
+                    f"Missing metadata coord: {coord_name}"
+                )
+            assert abs(float(result.forecast_landfall_latitude) - 16.1) < 0.01
+            assert abs(float(result.target_landfall_latitude) - 16.0) < 0.01
+
+    def test_first_approach_skips_init_with_late_track_start(self):
+        """Verify approach='first' filters out init_times whose
+        forecast track starts after the first target landfall.
+
+        Mimics TC Debby (case 154): the target's first landfall is
+        Cuba at Aug 3 17Z, but the forecast track for init Aug 2
+        12Z doesn't start until Aug 3 18Z (tracker doesn't detect
+        the TC until then). That init_time should be excluded
+        because the forecast can't predict the Cuba landfall.
+
+        The forecast grid has two init_times sharing the same
+        valid_time column (Aug 3 18Z): init Aug 3 18Z at lt=0
+        and init Aug 2 12Z at lt=30. This tests that the
+        track-start detection scans all cells in the
+        (lead_time, valid_time) grid, not just the first
+        non-NaN per column.
+        """
+        forecast = xr.Dataset(
+            {
+                "surface_wind_speed": (
+                    ["lead_time", "valid_time"],
+                    [
+                        [np.nan, 40.0],
+                        [np.nan, 35.0],
+                    ],
+                ),
+                "latitude": (
+                    ["lead_time", "valid_time"],
+                    [
+                        [np.nan, 25.0],
+                        [np.nan, 29.0],
+                    ],
+                ),
+                "longitude": (
+                    ["lead_time", "valid_time"],
+                    [
+                        [np.nan, -80.0],
+                        [np.nan, -83.0],
+                    ],
+                ),
+            },
+            coords={
+                "lead_time": [0, 30],
+                "valid_time": [
+                    pd.Timestamp("2024-08-02 12:00"),
+                    pd.Timestamp("2024-08-03 18:00"),
+                ],
+            },
+        )
+
+        target = xr.Dataset(
+            {
+                "surface_wind_speed": (["valid_time"], [55.0]),
+                "latitude": (["valid_time"], [22.0]),
+                "longitude": (["valid_time"], [-79.5]),
+            },
+            coords={
+                "valid_time": [
+                    pd.Timestamp("2024-08-03 17:00"),
+                ],
+            },
+        )
+
+        # Target: Cuba landfall at Aug 3 17Z
+        mock_target_landfall = xr.DataArray(
+            [55.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0],
+                "latitude": ("landfall", [22.0]),
+                "longitude": ("landfall", [-79.5]),
+                "valid_time": (
+                    "landfall",
+                    [pd.Timestamp("2024-08-03 17:00")],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        init_ts = pd.Timestamp("2024-08-02 12:00")
+
+        # Forecast landfall: Florida at Aug 5
+        # (only landfall the tracker can see)
+        mock_forecast_landfall = xr.DataArray(
+            [[35.0]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [init_ts],
+                "landfall": [0],
+                "latitude": (
+                    ["init_time", "landfall"],
+                    [[29.0]],
+                ),
+                "longitude": (
+                    ["init_time", "landfall"],
+                    [[-83.0]],
+                ),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [[pd.Timestamp("2024-08-05 00:00")]],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        with mock.patch.object(calc, "find_landfalls") as mock_find:
+
+            def side_effect(track_data, **kw):
+                if "lead_time" not in track_data.dims:
+                    return mock_target_landfall
+                return mock_forecast_landfall
+
+            mock_find.side_effect = side_effect
+
+            metric = metrics.LandfallDisplacement(
+                approach="first",
+            )
+            result = metric._compute_metric(
+                forecast["surface_wind_speed"],
+                target["surface_wind_speed"],
+            )
+
+            # The init_time should be filtered out because
+            # the track starts at Aug 3 18Z, AFTER the Cuba
+            # landfall at Aug 3 17Z. Result should be NaN
+            # (no valid init_times remain).
+            assert np.isnan(result.values).all(), (
+                "Init with track starting after target "
+                "landfall should have been filtered out"
+            )
+
+    def test_first_approach_falls_back_to_next_target(self):
+        """When the first IBTrACS landfall has no forecast
+        coverage, fall back to the next one that does.
+
+        Mimics TC Debby: Cuba landfall at Aug 3 17Z has no
+        forecast data (track starts Aug 3 18Z). Florida
+        landfall at Aug 5 15Z does. The metric should compare
+        against the Florida landfall instead of returning NaN.
+        """
+        forecast = xr.Dataset(
+            {
+                "surface_wind_speed": (
+                    ["lead_time", "valid_time"],
+                    [
+                        [np.nan, 40.0],
+                        [np.nan, 35.0],
+                    ],
+                ),
+                "latitude": (
+                    ["lead_time", "valid_time"],
+                    [
+                        [np.nan, 25.0],
+                        [np.nan, 29.0],
+                    ],
+                ),
+                "longitude": (
+                    ["lead_time", "valid_time"],
+                    [
+                        [np.nan, -80.0],
+                        [np.nan, -83.0],
+                    ],
+                ),
+            },
+            coords={
+                "lead_time": [0, 30],
+                "valid_time": [
+                    pd.Timestamp("2024-08-02 12:00"),
+                    pd.Timestamp("2024-08-03 18:00"),
+                ],
+            },
+        )
+
+        target = xr.Dataset(
+            {
+                "surface_wind_speed": (
+                    ["valid_time"],
+                    [55.0, 50.0],
+                ),
+                "latitude": (
+                    ["valid_time"],
+                    [22.0, 29.5],
+                ),
+                "longitude": (
+                    ["valid_time"],
+                    [-79.5, -83.5],
+                ),
+            },
+            coords={
+                "valid_time": [
+                    pd.Timestamp("2024-08-03 17:00"),
+                    pd.Timestamp("2024-08-05 15:00"),
+                ],
+            },
+        )
+
+        # Two target landfalls: Cuba (Aug 3 17Z), Florida
+        # (Aug 5 15Z)
+        mock_target_landfall = xr.DataArray(
+            [55.0, 50.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0, 1],
+                "latitude": ("landfall", [22.0, 29.5]),
+                "longitude": (
+                    "landfall",
+                    [-79.5, -83.5],
+                ),
+                "valid_time": (
+                    "landfall",
+                    [
+                        pd.Timestamp("2024-08-03 17:00"),
+                        pd.Timestamp("2024-08-05 15:00"),
+                    ],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        init_ts = pd.Timestamp("2024-08-02 12:00")
+
+        mock_forecast_landfall = xr.DataArray(
+            [[35.0]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [init_ts],
+                "landfall": [0],
+                "latitude": (
+                    ["init_time", "landfall"],
+                    [[29.0]],
+                ),
+                "longitude": (
+                    ["init_time", "landfall"],
+                    [[-83.0]],
+                ),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [[pd.Timestamp("2024-08-05 00:00")]],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        with mock.patch.object(calc, "find_landfalls") as mock_find:
+
+            def side_effect(track_data, **kw):
+                if "lead_time" not in track_data.dims:
+                    return mock_target_landfall
+                return mock_forecast_landfall
+
+            mock_find.side_effect = side_effect
+
+            metric = metrics.LandfallDisplacement(
+                approach="first",
+            )
+            result = metric._compute_metric(
+                forecast["surface_wind_speed"],
+                target["surface_wind_speed"],
+            )
+
+            assert isinstance(result, xr.DataArray)
+            assert not np.isnan(result.values).all(), (
+                "Should fall back to Florida landfall, not return NaN"
+            )
+            # Target is Florida (29.5N, -83.5W),
+            # forecast is (29.0N, -83.0W) — close match
+            assert result.values[0] < 100, (
+                f"Displacement {result.values[0]:.1f} km should be small (FL vs FL)"
+            )
+            # Verify metadata uses the Florida target
+            assert abs(float(result.target_landfall_latitude) - 29.5) < 0.01
+
+    def test_multi_landfall_displacement_next_approach(self):
+        """Verify approach='next' also uses first forecast landfall
+        per init_time after matching to the correct target.
+
+        Two target landfalls (Philippines, Vietnam) and two forecast
+        landfalls per init_time. find_next_landfall_for_init_time
+        runs with real logic to match by closest valid_time. Then
+        select_first_forecast_landfall_per_init keeps only the
+        earliest forecast landfall, preventing averaging inflation.
+        """
+        target = xr.Dataset(
+            {
+                "latitude": (["valid_time"], [16.0]),
+                "longitude": (["valid_time"], [120.0]),
+                "surface_wind_speed": (["valid_time"], [55.0]),
+            },
+            coords={
+                "valid_time": [pd.Timestamp("2024-09-01 12:00")],
+            },
+        )
+
+        forecast = xr.Dataset(
+            {
+                "latitude": (
+                    ["lead_time", "valid_time"],
+                    [[16.0]],
+                ),
+                "longitude": (
+                    ["lead_time", "valid_time"],
+                    [[120.0]],
+                ),
+                "surface_wind_speed": (
+                    ["lead_time", "valid_time"],
+                    [[52.0]],
+                ),
+            },
+            coords={
+                "lead_time": [24],
+                "valid_time": [
+                    pd.Timestamp("2024-09-01 12:00"),
+                ],
+            },
+        )
+
+        init_ts = pd.Timestamp("2024-08-31 12:00")
+
+        # Target: TWO observed landfalls (Philippines then Vietnam)
+        mock_target_landfall = xr.DataArray(
+            [55.0, 48.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0, 1],
+                "latitude": ("landfall", [16.0, 20.0]),
+                "longitude": ("landfall", [120.0, 106.0]),
+                "valid_time": (
+                    "landfall",
+                    [
+                        pd.Timestamp("2024-09-01 12:00"),
+                        pd.Timestamp("2024-09-07 06:00"),
+                    ],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        # Forecast: TWO landfalls for the same init_time.
+        # Landfall 0 = Philippines (16.1N, 120.1E) at Sep 1
+        # Landfall 1 = Vietnam (20N, 106E) at Sep 7
+        mock_forecast_landfall = xr.DataArray(
+            [[52.0, 45.0]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [init_ts],
+                "landfall": [0, 1],
+                "latitude": (
+                    ["init_time", "landfall"],
+                    [[16.1, 20.0]],
+                ),
+                "longitude": (
+                    ["init_time", "landfall"],
+                    [[120.1, 106.0]],
+                ),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [
+                        [
+                            pd.Timestamp("2024-09-01 12:00"),
+                            pd.Timestamp("2024-09-07 06:00"),
+                        ]
+                    ],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        with mock.patch.object(calc, "find_landfalls") as mock_find:
+
+            def side_effect(track_data, **kw):
+                if "lead_time" not in track_data.dims:
+                    return mock_target_landfall
+                return mock_forecast_landfall
+
+            mock_find.side_effect = side_effect
+
+            metric = metrics.LandfallDisplacement(
+                approach="next",
+            )
+            result = metric._compute_metric(
+                forecast["surface_wind_speed"],
+                target["surface_wind_speed"],
+            )
+
+            assert isinstance(result, xr.DataArray)
+            assert result.dims == ("init_time",)
+            displacement_km = result.values[0]
+            # find_next_landfall_for_init_time matches the
+            # forecast (Philippines, Sep 1) to the target
+            # (Philippines, Sep 1) by closest valid_time.
+            # select_first_forecast_landfall_per_init then
+            # keeps only the first forecast landfall (~15 km).
+            # Without the fix, averaging would give ~750 km.
+            assert displacement_km < 50, (
+                f"Displacement {displacement_km:.1f} km is too "
+                f"large; multi-landfall averaging bug is "
+                f"likely present (next approach)"
+            )
+
     def test_landfall_intensity_mae_with_known_values(self):
         """Test LandfallIntensityMeanAbsoluteError with manually calculated expected
         values."""
@@ -2942,6 +3478,454 @@ def mock_find_func(track_data, return_next_landfall=False):
             assert isinstance(forecast_landfall, xr.DataArray)
             assert isinstance(target_landfall, xr.DataArray)
 
+    def test_filter_by_landfall_time_tolerance(self):
+        """Proportional time-tolerance filter on paired landfalls.
+
+        Rule: keep iff
+          abs(fc_vt - tgt_vt) <= 0.5 * (tgt_vt - init_time)
+        """
+        d = np.datetime64
+
+        # init day-1, target day-3, fc day-5 → mismatch 2d,
+        # threshold 0.5*2d=1d → DROP
+        # init day-1, target day-3, fc day-3.5 → mismatch 0.5d,
+        # threshold 1d → KEEP
+        # init day-1, target day-3, fc day-4 → mismatch 1d,
+        # threshold 1d → KEEP (<=)
+        init_times = np.array(
+            [
+                d("2024-08-01"),
+                d("2024-08-01"),
+                d("2024-08-01"),
+            ]
+        )
+        tgt_vts = np.array(
+            [
+                d("2024-08-03"),
+                d("2024-08-03"),
+                d("2024-08-03"),
+            ]
+        )
+        fc_vts = np.array(
+            [
+                d("2024-08-05"),
+                d("2024-08-03T12"),
+                d("2024-08-04"),
+            ]
+        )
+
+        fc = xr.DataArray(
+            [1.0, 2.0, 3.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", fc_vts),
+                "latitude": ("init_time", [25.0, 26.0, 27.0]),
+                "longitude": ("init_time", [-80.0, -81.0, -82.0]),
+            },
+        )
+        tgt = xr.DataArray(
+            [10.0, 20.0, 30.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", tgt_vts),
+                "latitude": ("init_time", [25.5, 26.5, 27.5]),
+                "longitude": ("init_time", [-80.5, -81.5, -82.5]),
+            },
+        )
+
+        fc_out, tgt_out = calc.filter_by_landfall_time_tolerance(fc, tgt)
+
+        assert len(fc_out) == 2
+        np.testing.assert_array_equal(fc_out.values, [2.0, 3.0])
+        np.testing.assert_array_equal(tgt_out.values, [20.0, 30.0])
+
+    def test_filter_by_landfall_time_tolerance_all_dropped(self):
+        """All init_times fail → returns empty arrays."""
+        d = np.datetime64
+        init_times = np.array([d("2024-08-01")])
+        fc = xr.DataArray(
+            [1.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", [d("2024-08-07")]),
+                "latitude": ("init_time", [25.0]),
+                "longitude": ("init_time", [-80.0]),
+            },
+        )
+        tgt = xr.DataArray(
+            [10.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", [d("2024-08-03")]),
+                "latitude": ("init_time", [25.5]),
+                "longitude": ("init_time", [-80.5]),
+            },
+        )
+
+        fc_out, tgt_out = calc.filter_by_landfall_time_tolerance(fc, tgt)
+        assert len(fc_out) == 0
+        assert len(tgt_out) == 0
+
+    def test_filter_by_landfall_time_tolerance_all_pass(self):
+        """All init_times pass → arrays returned unchanged."""
+        d = np.datetime64
+        init_times = np.array([d("2024-08-01")])
+        fc = xr.DataArray(
+            [1.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", [d("2024-08-03")]),
+                "latitude": ("init_time", [25.0]),
+                "longitude": ("init_time", [-80.0]),
+            },
+        )
+        tgt = xr.DataArray(
+            [10.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", [d("2024-08-03")]),
+                "latitude": ("init_time", [25.5]),
+                "longitude": ("init_time", [-80.5]),
+            },
+        )
+
+        fc_out, tgt_out = calc.filter_by_landfall_time_tolerance(fc, tgt)
+        assert len(fc_out) == 1
+        xr.testing.assert_equal(fc_out, fc)
+        xr.testing.assert_equal(tgt_out, tgt)
+
+    def test_filter_by_landfall_time_window(self):
+        """Fixed +-24h window filter on paired landfalls."""
+        d = np.datetime64
+
+        # init day-1, target day-3, fc day-5 → mismatch 48h → DROP
+        # init day-1, target day-3, fc day-3T12 → mismatch 12h → KEEP
+        # init day-1, target day-3, fc day-4 → mismatch 24h → KEEP (<=)
+        init_times = np.array(
+            [
+                d("2024-08-01"),
+                d("2024-08-01"),
+                d("2024-08-01"),
+            ]
+        )
+        tgt_vts = np.array(
+            [
+                d("2024-08-03"),
+                d("2024-08-03"),
+                d("2024-08-03"),
+            ]
+        )
+        fc_vts = np.array(
+            [
+                d("2024-08-05"),
+                d("2024-08-03T12"),
+                d("2024-08-04"),
+            ]
+        )
+
+        fc = xr.DataArray(
+            [1.0, 2.0, 3.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", fc_vts),
+                "latitude": ("init_time", [25.0, 26.0, 27.0]),
+                "longitude": ("init_time", [-80.0, -81.0, -82.0]),
+            },
+        )
+        tgt = xr.DataArray(
+            [10.0, 20.0, 30.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", tgt_vts),
+                "latitude": ("init_time", [25.5, 26.5, 27.5]),
+                "longitude": ("init_time", [-80.5, -81.5, -82.5]),
+            },
+        )
+
+        fc_out, tgt_out = calc.filter_by_landfall_time_window(
+            fc, tgt, window_hours=24.0
+        )
+
+        assert len(fc_out) == 2
+        np.testing.assert_array_equal(fc_out.values, [2.0, 3.0])
+        np.testing.assert_array_equal(tgt_out.values, [20.0, 30.0])
+
+    def test_filter_by_landfall_time_window_custom(self):
+        """Custom 12h window keeps only the closest."""
+        d = np.datetime64
+        init_times = np.array([d("2024-08-01"), d("2024-08-01")])
+        tgt_vts = np.array([d("2024-08-03"), d("2024-08-03")])
+        fc_vts = np.array([d("2024-08-04"), d("2024-08-03T06")])
+
+        fc = xr.DataArray(
+            [1.0, 2.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", fc_vts),
+                "latitude": ("init_time", [25.0, 26.0]),
+                "longitude": ("init_time", [-80.0, -81.0]),
+            },
+        )
+        tgt = xr.DataArray(
+            [10.0, 20.0],
+            dims=["init_time"],
+            coords={
+                "init_time": init_times,
+                "valid_time": ("init_time", tgt_vts),
+                "latitude": ("init_time", [25.5, 26.5]),
+                "longitude": ("init_time", [-80.5, -81.5]),
+            },
+        )
+
+        fc_out, tgt_out = calc.filter_by_landfall_time_window(
+            fc, tgt, window_hours=12.0
+        )
+        assert len(fc_out) == 1
+        np.testing.assert_array_equal(fc_out.values, [2.0])
+
+    def test_landfall_metric_default_filter(self):
+        """LandfallMetric defaults to 24h window filter."""
+        m = metrics.LandfallDisplacement(approach="first")
+        assert m.landfall_time_filter == ("window", 24.0)
+
+    def test_time_window_with_mismatched_init_times_next(self):
+        """approach='next' where forecast has more init_times
+        than target matches.  Regression test for the ValueError
+        caused by shape (N,) vs (M,) when N != M.
+        """
+        ts = pd.Timestamp
+        # 3 init_times, but only the first two have matching
+        # target landfalls (the third forecast init is too late).
+        init_a = ts("2024-08-28 00:00")
+        init_b = ts("2024-08-29 00:00")
+        init_c = ts("2024-08-30 00:00")
+
+        target = xr.Dataset(
+            {
+                "latitude": ("valid_time", [25.0]),
+                "longitude": ("valid_time", [-80.0]),
+                "surface_wind_speed": ("valid_time", [50.0]),
+            },
+            coords={
+                "valid_time": [ts("2024-08-30 06:00")],
+            },
+        )
+
+        forecast = xr.Dataset(
+            {
+                "latitude": (
+                    ["lead_time", "valid_time"],
+                    [[25.0, 25.0, 25.0]],
+                ),
+                "longitude": (
+                    ["lead_time", "valid_time"],
+                    [[-80.0, -80.0, -80.0]],
+                ),
+                "surface_wind_speed": (
+                    ["lead_time", "valid_time"],
+                    [[50.0, 50.0, 50.0]],
+                ),
+            },
+            coords={
+                "lead_time": [24],
+                "valid_time": [
+                    ts("2024-08-28 00:00"),
+                    ts("2024-08-29 00:00"),
+                    ts("2024-08-30 00:00"),
+                ],
+            },
+        )
+
+        # Target: one observed landfall
+        mock_target = xr.DataArray(
+            [50.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0],
+                "latitude": ("landfall", [25.0]),
+                "longitude": ("landfall", [-80.0]),
+                "valid_time": (
+                    "landfall",
+                    [ts("2024-08-30 06:00")],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        # Forecast: 3 init_times each with one landfall, but
+        # init_c's landfall time is 4 days late so it won't
+        # match any target in find_next_landfall_for_init_time.
+        mock_forecast = xr.DataArray(
+            [[48.0], [49.0], [51.0]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [init_a, init_b, init_c],
+                "landfall": [0],
+                "latitude": (
+                    ["init_time", "landfall"],
+                    [[25.1], [25.05], [30.0]],
+                ),
+                "longitude": (
+                    ["init_time", "landfall"],
+                    [[-80.1], [-80.05], [-75.0]],
+                ),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [
+                        [ts("2024-08-30 06:00")],
+                        [ts("2024-08-30 06:00")],
+                        [ts("2024-09-03 00:00")],
+                    ],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        with mock.patch.object(calc, "find_landfalls") as mf:
+
+            def side_effect(track_data, **kw):
+                if "lead_time" not in track_data.dims:
+                    return mock_target
+                return mock_forecast
+
+            mf.side_effect = side_effect
+
+            metric = metrics.LandfallDisplacement(
+                approach="next",
+                landfall_time_filter=("window", 24.0),
+            )
+            result = metric._compute_metric(
+                forecast["surface_wind_speed"],
+                target["surface_wind_speed"],
+            )
+
+            assert isinstance(result, xr.DataArray)
+            assert "init_time" in result.dims
+            # init_c should have been dropped (no target match
+            # or window mismatch), so at most 2 init_times.
+            assert len(result.init_time) <= 2
+
+    def test_time_window_with_first_approach(self):
+        """approach='first' with time window filter succeeds
+        and drops init_times whose forecast landfall is far
+        from the target.
+        """
+        ts = pd.Timestamp
+        init_a = ts("2024-08-28 00:00")
+        init_b = ts("2024-08-29 00:00")
+
+        target = xr.Dataset(
+            {
+                "latitude": ("valid_time", [25.0]),
+                "longitude": ("valid_time", [-80.0]),
+                "surface_wind_speed": ("valid_time", [50.0]),
+            },
+            coords={
+                "valid_time": [ts("2024-08-31 00:00")],
+            },
+        )
+
+        forecast = xr.Dataset(
+            {
+                "latitude": (
+                    ["lead_time", "valid_time"],
+                    [[25.0, 25.0]],
+                ),
+                "longitude": (
+                    ["lead_time", "valid_time"],
+                    [[-80.0, -80.0]],
+                ),
+                "surface_wind_speed": (
+                    ["lead_time", "valid_time"],
+                    [[50.0, 50.0]],
+                ),
+            },
+            coords={
+                "lead_time": [24],
+                "valid_time": [
+                    ts("2024-08-28 00:00"),
+                    ts("2024-08-29 00:00"),
+                ],
+            },
+        )
+
+        # Target: one observed landfall at Aug 31 00Z
+        mock_target = xr.DataArray(
+            [50.0],
+            dims=["landfall"],
+            coords={
+                "landfall": [0],
+                "latitude": ("landfall", [25.0]),
+                "longitude": ("landfall", [-80.0]),
+                "valid_time": (
+                    "landfall",
+                    [ts("2024-08-31 00:00")],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        # Forecast: init_a landfall close to target (Aug 31 12Z
+        # = 12h off), init_b landfall 3 days late (Sep 3).
+        mock_forecast = xr.DataArray(
+            [[48.0], [51.0]],
+            dims=["init_time", "landfall"],
+            coords={
+                "init_time": [init_a, init_b],
+                "landfall": [0],
+                "latitude": (
+                    ["init_time", "landfall"],
+                    [[25.1], [30.0]],
+                ),
+                "longitude": (
+                    ["init_time", "landfall"],
+                    [[-80.1], [-75.0]],
+                ),
+                "valid_time": (
+                    ["init_time", "landfall"],
+                    [
+                        [ts("2024-08-31 12:00")],
+                        [ts("2024-09-03 00:00")],
+                    ],
+                ),
+            },
+            name="surface_wind_speed",
+        )
+
+        with mock.patch.object(calc, "find_landfalls") as mf:
+
+            def side_effect(track_data, **kw):
+                if "lead_time" not in track_data.dims:
+                    return mock_target
+                return mock_forecast
+
+            mf.side_effect = side_effect
+
+            metric = metrics.LandfallDisplacement(
+                approach="first",
+                landfall_time_filter=("window", 24.0),
+            )
+            result = metric._compute_metric(
+                forecast["surface_wind_speed"],
+                target["surface_wind_speed"],
+            )
+
+            assert isinstance(result, xr.DataArray)
+            assert "init_time" in result.dims
+            # init_b's forecast landfall (Sep 3) is 3 days from
+            # target (Aug 31) — exceeds 24h → dropped.
+            assert len(result.init_time) == 1
+            assert result.init_time.values[0] == init_a
+
 
 class TestThresholdMetricComposite:
     """Tests for ThresholdMetric composite functionality."""
diff --git a/tests/test_tropical_cyclone.py b/tests/test_tropical_cyclone.py
index 8de5ec15..b5b81d7a 100644
--- a/tests/test_tropical_cyclone.py
+++ b/tests/test_tropical_cyclone.py
@@ -962,3 +962,309 @@ def test_boundary_is_sharp(self, tc_ds, ibt):
         result_fail = self._run(tc_ds, ibt, self.N_WIND_STRONG + 1)
         assert result_pass.sizes.get("valid_time", 0) > 0
         assert result_fail.sizes.get("valid_time", 0) == 0
+
+
+class TestFillTrackGapsFromFields:
+    """Tests for _fill_track_gaps_from_fields SLP fallback."""
+
+    def test_fills_gap_with_slp_minimum(self):
+        """A gap between two detections is filled by finding
+        the SLP minimum near the expected position."""
+        n_lat, n_lon = 31, 31
+        lat = np.linspace(0, 30, n_lat)
+        lon = np.linspace(110, 140, n_lon)
+
+        # 3 timesteps; detection at ts 0 and ts 2, gap at 1
+        lt_seq = np.array([0, 1, 2])
+        vt_seq = np.array([0, 1, 2])
+        tid = 5000
+
+        detections = [
+            {
+                "lead_time_index": 0,
+                "valid_time_index": 0,
+                "track_id": tid,
+                "latitude": 15.0,
+                "longitude": 125.0,
+                "slp": 99800.0,
+                "wind": 20.0,
+            },
+            {
+                "lead_time_index": 2,
+                "valid_time_index": 2,
+                "track_id": tid,
+                "latitude": 15.0,
+                "longitude": 125.0,
+                "slp": 99600.0,
+                "wind": 22.0,
+            },
+        ]
+
+        # SLP field at ts 1: minimum at (15, 125)
+        min_r = np.argmin(np.abs(lat - 15.0))
+        min_c = np.argmin(np.abs(lon - 125.0))
+        slp_all = np.full((3, n_lat, n_lon), 101000.0)
+        slp_all[1, min_r, min_c] = 99700.0
+        wind_all = np.full((3, n_lat, n_lon), 5.0)
+        wind_all[1, min_r, min_c] = 18.0
+
+        result = tropical_cyclone._fill_track_gaps_from_fields(
+            detections,
+            slp_all,
+            wind_all,
+            lt_seq,
+            vt_seq,
+            lat,
+            lon,
+            wind_search_radius_gridpts=1,
+        )
+
+        # Should now have 3 detections
+        assert len(result) == 3
+        gap_det = [d for d in result if d["lead_time_index"] == 1]
+        assert len(gap_det) == 1
+        assert gap_det[0]["track_id"] == tid
+        np.testing.assert_allclose(gap_det[0]["latitude"], lat[min_r], atol=0.5)
+        np.testing.assert_allclose(gap_det[0]["longitude"], lon[min_c], atol=0.5)
+
+    def test_no_fill_beyond_endpoints(self):
+        """Timesteps outside the first/last detection are
+        NOT filled."""
+        n_lat, n_lon = 11, 11
+        lat = np.linspace(10, 20, n_lat)
+        lon = np.linspace(120, 130, n_lon)
+
+        lt_seq = np.array([0, 1, 2, 3, 4])
+        vt_seq = np.array([0, 1, 2, 3, 4])
+        tid = 100
+
+        detections = [
+            {
+                "lead_time_index": 1,
+                "valid_time_index": 1,
+                "track_id": tid,
+                "latitude": 15.0,
+                "longitude": 125.0,
+                "slp": 99800.0,
+                "wind": 20.0,
+            },
+            {
+                "lead_time_index": 3,
+                "valid_time_index": 3,
+                "track_id": tid,
+                "latitude": 15.0,
+                "longitude": 125.0,
+                "slp": 99600.0,
+                "wind": 22.0,
+            },
+        ]
+
+        slp_all = np.full((5, n_lat, n_lon), 101000.0)
+        slp_all[:, 5, 5] = 99500.0
+        wind_all = np.full((5, n_lat, n_lon), 5.0)
+
+        result = tropical_cyclone._fill_track_gaps_from_fields(
+            detections,
+            slp_all,
+            wind_all,
+            lt_seq,
+            vt_seq,
+            lat,
+            lon,
+            wind_search_radius_gridpts=1,
+        )
+
+        filled_lts = {d["lead_time_index"] for d in result}
+        # ts 0 and ts 4 should NOT be filled
+        assert 0 not in filled_lts
+        assert 4 not in filled_lts
+        # ts 2 should be filled
+        assert 2 in filled_lts
+
+    def test_empty_detections(self):
+        """No detections should return empty list."""
+        result = tropical_cyclone._fill_track_gaps_from_fields(
+            [],
+            np.zeros((1, 5, 5)),
+            np.zeros((1, 5, 5)),
+            np.array([0]),
+            np.array([0]),
+            np.linspace(0, 10, 5),
+            np.linspace(0, 10, 5),
+            wind_search_radius_gridpts=1,
+        )
+        assert result == []
+
+    def test_prefers_closest_candidate_over_deepest(self):
+        """Among low-SLP candidates, pick the one nearest
+        to expected position, not the absolute minimum."""
+        n_lat, n_lon = 31, 31
+        lat = np.linspace(0, 30, n_lat)
+        lon = np.linspace(110, 140, n_lon)
+
+        lt_seq = np.array([0, 1, 2])
+        vt_seq = np.array([0, 1, 2])
+        tid = 42
+
+        detections = [
+            {
+                "lead_time_index": 0,
+                "valid_time_index": 0,
+                "track_id": tid,
+                "latitude": 15.0,
+                "longitude": 125.0,
+                "slp": 99800.0,
+                "wind": 20.0,
+            },
+            {
+                "lead_time_index": 2,
+                "valid_time_index": 2,
+                "track_id": tid,
+                "latitude": 15.0,
+                "longitude": 125.0,
+                "slp": 99600.0,
+                "wind": 22.0,
+            },
+        ]
+
+        near_r = np.argmin(np.abs(lat - 15.0))
+        near_c = np.argmin(np.abs(lon - 125.0))
+        far_r = np.argmin(np.abs(lat - 17.0))
+        far_c = np.argmin(np.abs(lon - 127.0))
+
+        slp_all = np.full((3, n_lat, n_lon), 101000.0)
+        # Near candidate: moderate low
+        slp_all[1, near_r, near_c] = 99700.0
+        # Far candidate: deeper low but farther away
+        slp_all[1, far_r, far_c] = 99200.0
+
+        wind_all = np.full((3, n_lat, n_lon), 5.0)
+        wind_all[1, near_r, near_c] = 18.0
+        wind_all[1, far_r, far_c] = 25.0
+
+        result = tropical_cyclone._fill_track_gaps_from_fields(
+            detections,
+            slp_all,
+            wind_all,
+            lt_seq,
+            vt_seq,
+            lat,
+            lon,
+            wind_search_radius_gridpts=1,
+        )
+
+        gap = [d for d in result if d["lead_time_index"] == 1]
+        assert len(gap) == 1
+        np.testing.assert_allclose(
+            gap[0]["latitude"],
+            lat[near_r],
+            atol=0.5,
+        )
+        np.testing.assert_allclose(
+            gap[0]["longitude"],
+            lon[near_c],
+            atol=0.5,
+        )
+
+    def test_chaining_uses_filled_points(self):
+        """Successive gap fills should chain through
+        previously filled positions, not always interpolate
+        from the original bracketing detections.
+
+        Setup: endpoints at lat 10 (ts 0) and lat 18 (ts 3).
+        The SLP min at ts 1 sits at lat 14, which becomes
+        the "previous" anchor for ts 2. With chaining, ts 2
+        interpolates between lat 14 and 18 (expect ~16);
+        without chaining it would use 10 and 18 (expect ~14.7).
+        We place the ts 2 SLP min at lat 16 so it is only
+        chosen when the chained anchor shifts the expected
+        position northward."""
+        n_lat, n_lon = 41, 21
+        lat = np.linspace(8, 20, n_lat)
+        lon = np.linspace(123, 127, n_lon)
+
+        lt_seq = np.arange(4)
+        vt_seq = np.arange(4)
+        tid = 99
+
+        detections = [
+            {
+                "lead_time_index": 0,
+                "valid_time_index": 0,
+                "track_id": tid,
+                "latitude": 10.0,
+                "longitude": 125.0,
+                "slp": 99800.0,
+                "wind": 20.0,
+            },
+            {
+                "lead_time_index": 3,
+                "valid_time_index": 3,
+                "track_id": tid,
+                "latitude": 18.0,
+                "longitude": 125.0,
+                "slp": 99600.0,
+                "wind": 22.0,
+            },
+        ]
+
+        slp_all = np.full((4, n_lat, n_lon), 101000.0)
+        wind_all = np.full((4, n_lat, n_lon), 5.0)
+        c_mid = np.argmin(np.abs(lon - 125.0))
+
+        # ts 1: SLP min at lat 14
+        r_ts1 = np.argmin(np.abs(lat - 14.0))
+        slp_all[1, r_ts1, c_mid] = 99700.0
+        wind_all[1, r_ts1, c_mid] = 15.0
+
+        # ts 2: SLP min at lat 16 (only reachable via
+        # chained anchor at lat 14, not via endpoint
+        # midpoint at lat ~14.7)
+        r_ts2 = np.argmin(np.abs(lat - 16.0))
+        slp_all[2, r_ts2, c_mid] = 99700.0
+        wind_all[2, r_ts2, c_mid] = 15.0
+
+        result = tropical_cyclone._fill_track_gaps_from_fields(
+            detections,
+            slp_all,
+            wind_all,
+            lt_seq,
+            vt_seq,
+            lat,
+            lon,
+            wind_search_radius_gridpts=1,
+        )
+
+        assert len(result) == 4
+        ts2_fill = [d for d in result if d["lead_time_index"] == 2]
+        assert len(ts2_fill) == 1
+        np.testing.assert_allclose(
+            ts2_fill[0]["latitude"],
+            lat[r_ts2],
+            atol=0.5,
+        )
+
+    def test_single_detection_no_fill(self):
+        """A single detection should not trigger gap fill."""
+        detections = [
+            {
+                "lead_time_index": 0,
+                "valid_time_index": 0,
+                "track_id": 1,
+                "latitude": 15.0,
+                "longitude": 125.0,
+                "slp": 99800.0,
+                "wind": 20.0,
+            },
+        ]
+        result = tropical_cyclone._fill_track_gaps_from_fields(
+            detections,
+            np.full((3, 5, 5), 101000.0),
+            np.full((3, 5, 5), 5.0),
+            np.array([0, 1, 2]),
+            np.array([0, 1, 2]),
+            np.linspace(0, 10, 5),
+            np.linspace(0, 10, 5),
+            wind_search_radius_gridpts=1,
+        )
+        assert len(result) == 1

From 9fad797c4d7673f3040644b3e078848541d3c981 Mon Sep 17 00:00:00 2001
From: aaTman <mandelbaum.taylor@gmail.com>
Date: Mon, 20 Apr 2026 16:47:45 +0000
Subject: [PATCH 66/72] update consecutive days label formatting

---
 data_prep/plot_temperature_events.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_prep/plot_temperature_events.py b/data_prep/plot_temperature_events.py
index aa644fb2..168aa9f3 100644
--- a/data_prep/plot_temperature_events.py
+++ b/data_prep/plot_temperature_events.py
@@ -403,7 +403,7 @@ def plot_consecutive_map(
     )
     cbar = fig.colorbar(im, cax=cax)
     cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
-    cbar.set_label("Consecutive Days", fontsize=12, labelpad=6)
+    cbar.set_label("Consecutive Days", fontsize=12, labelpad=12, rotation=270)
     cbar.ax.yaxis.set_label_position("left")
 
     ax.set_title(title, loc="left", fontsize=13)

From 7886f1dd76c330a562d1460f60ffb603a88f56ab Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Mon, 20 Apr 2026 20:59:14 -0400
Subject: [PATCH 67/72] move label position to right for consecutive plots

---
 data_prep/plot_temperature_events.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_prep/plot_temperature_events.py b/data_prep/plot_temperature_events.py
index 168aa9f3..f5b31fbe 100644
--- a/data_prep/plot_temperature_events.py
+++ b/data_prep/plot_temperature_events.py
@@ -404,7 +404,7 @@ def plot_consecutive_map(
     cbar = fig.colorbar(im, cax=cax)
     cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
     cbar.set_label("Consecutive Days", fontsize=12, labelpad=12, rotation=270)
-    cbar.ax.yaxis.set_label_position("left")
+    cbar.ax.yaxis.set_label_position("right")
 
     ax.set_title(title, loc="left", fontsize=13)
 

From f5420ca7d7629e81522b75a0bdb824ed21260a9a Mon Sep 17 00:00:00 2001
From: taylor <mandelbaum.taylor@gmail.com>
Date: Wed, 22 Apr 2026 00:24:27 +0000
Subject: [PATCH 68/72] update TC count in readme after removing non-landfall
 TCs

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 8d0ec92f..4a47fc1e 100644
--- a/README.md
+++ b/README.md
@@ -15,10 +15,10 @@ EWB has cases broken down by multiple event types within `src/extremeweatherbenc
 | ---------- | --------------- | 
 | 🌇 Heat Waves | 46 |
 | 🧊 Freezes | 14 |
-| 🌀 Tropical Cyclones | 106 |
+| 🌀 Tropical Cyclones |  98 |
 | ☔️ Atmospheric Rivers | 56 |
 | 🌪️ Severe Convection | 115 | 
-| **Total Cases** | 337 |
+| **Total Cases** | 329 |
 
 # EWB paper and talks
 

From 296ada41e66b9956993f5384e04b181061756eeb Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Tue, 21 Apr 2026 21:24:45 -0400
Subject: [PATCH 69/72] Add > 15 degree latitude filter for ARs (#365)

* add > 15 degree filter for ARs

* ruff
---
 .../events/atmospheric_river.py                   | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/src/extremeweatherbench/events/atmospheric_river.py b/src/extremeweatherbench/events/atmospheric_river.py
index 4346838d..8e594336 100644
--- a/src/extremeweatherbench/events/atmospheric_river.py
+++ b/src/extremeweatherbench/events/atmospheric_river.py
@@ -75,8 +75,21 @@ def atmospheric_river_mask(
         np.where((label_counts >= min_size_gridpoints) & (unique_labels != 0))
     ]
 
+    # Filter out tropical ARs: the mean latitude of each labeled feature
+    # must be > 15 degrees N or S of the equator.
+    latitudes = ivt.coords["latitude"].values
+    lat_axis = list(coords_dict.keys()).index("latitude")
+    lat_shape = [1] * labeled_array.ndim
+    lat_shape[lat_axis] = len(latitudes)
+    lat_grid = np.broadcast_to(latitudes.reshape(lat_shape), labeled_array.shape)
+    valid_labels = [
+        label
+        for label in size_valid_labels
+        if abs(lat_grid[labeled_array == label].mean()) > 15
+    ]
+
     # Create final mask using valid features
-    feature_mask = np.isin(labeled_array, size_valid_labels)
+    feature_mask = np.isin(labeled_array, valid_labels)
 
     # Final result with size threshold applied
     ar_mask = xr.DataArray(

From 3b6506b736fd15a0d1d2ddac31132cd1e0131283 Mon Sep 17 00:00:00 2001
From: Taylor Mandelbaum <mandelbaum.taylor@gmail.com>
Date: Thu, 23 Apr 2026 14:51:58 -0400
Subject: [PATCH 70/72] Consolidate temperature data_prep scripts into
 temperature_events.py (#364)

* refactor: consolidate temp data_prep scripts into temperature_events.py

Merges heat_cold_bounds_case.py, plot_temperature_events.py, and
temperature_bounds_global.py into a single temperature_events.py with
three subcommands (plot / case / global). Preserves develop behavior
exactly: original _wrap_periodically formula, no ndimage landmass
isolation, numpy-based detect_events / expand_event_bounds /
include_temps_with_events, and no --event-quantiles flag.

Made-with: Cursor

* ruff

* cleanup unused funcs and args

* consistent operators

* ruff
---
 data_prep/heat_cold_bounds_case.py     |  954 --------
 data_prep/plot_temperature_events.py   |  735 -------
 data_prep/temperature_bounds_global.py | 1531 -------------
 data_prep/temperature_events.py        | 2756 ++++++++++++++++++++++++
 4 files changed, 2756 insertions(+), 3220 deletions(-)
 delete mode 100644 data_prep/heat_cold_bounds_case.py
 delete mode 100644 data_prep/plot_temperature_events.py
 delete mode 100644 data_prep/temperature_bounds_global.py
 create mode 100644 data_prep/temperature_events.py

diff --git a/data_prep/heat_cold_bounds_case.py b/data_prep/heat_cold_bounds_case.py
deleted file mode 100644
index dba02627..00000000
--- a/data_prep/heat_cold_bounds_case.py
+++ /dev/null
@@ -1,954 +0,0 @@
-"""Validate and expand heat wave / freeze bounding boxes.
-
-Reads heat_wave and freeze cases from base_temp_events.yaml (which uses
-centered_region),
-then:
-  1. Uses the centered_region center as the initial box.
-  2. Grows the time window forward from start_date-3 one day at a
-     time until the single-day exceedance fraction drops below 50 %
-     of its peak.
-  3. Iteratively grows the spatial box by 2 degrees in each direction
-     until fewer than 50 % of grid points on each edge exceed the
-     climatological threshold (or 10 iterations).
-  4. Writes the final bounds as bounded_region entries into
-     events.yaml.
-
-Usage:
-    python heat_cold_bounds_case.py \\
-        --output heat_cold_yaml.csv --n-workers 4
-"""
-
-import argparse
-import importlib
-import logging
-import pathlib
-import time as time_module
-from typing import Dict, List, Literal, Optional, cast
-
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-import joblib
-import matplotlib.colors as mcolors
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import regionmask
-import scipy.ndimage as ndimage
-import xarray as xr
-from dask.distributed import Client, LocalCluster
-from plot_temperature_events import (
-    VALID_QUANTILES,
-    detect_time_dim,
-    max_consecutive_days,
-    plot_consecutive_map,
-    resolve_op,
-)
-from ruamel.yaml import YAML
-
-from extremeweatherbench import cases, defaults, inputs, regions
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(name)s %(levelname)s %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-MIN_CONSECUTIVE_DAYS = 3
-EXPANSION_DEGREES = 1
-MAX_ITERATIONS = 20
-EDGE_VALIDITY_THRESHOLD = 0.5
-MIN_GRIDPOINTS = 500
-TEMPORAL_LOAD_BUFFER_DAYS = 14
-MAX_TEMPORAL_DAYS = 21
-
-
-def _apply_consecutive_filter(
-    mask: np.ndarray,
-    min_days: int = MIN_CONSECUTIVE_DAYS,
-    max_grace_days: int = 1,
-) -> np.ndarray:
-    """Keep runs of ``min_days``+ True days along axis 0.
-
-    After ``min_days`` strict consecutive True days are established,
-    gaps of up to ``max_grace_days`` are bridged so the event can
-    continue. Runs that never reach ``min_days`` strict consecutive
-    True days are discarded.
-
-    Args:
-        mask: Boolean array of shape (time, lat, lon).
-        min_days: Minimum run length required to qualify as an
-            event. Default is 3 (MIN_CONSECUTIVE_DAYS).
-        max_grace_days: Maximum gap length to bridge after the
-            minimum run is established. Default is 1.
-
-    Returns:
-        Boolean array of the same shape with only qualifying runs
-        retained.
-    """
-    struct = np.zeros((min_days, 1, 1), dtype=bool)
-    struct[:, 0, 0] = True
-
-    # Strict 3-day runs (no grace)
-    strict = (
-        ndimage.binary_dilation(
-            ndimage.binary_erosion(
-                mask,
-                structure=struct,
-                border_value=False,
-            ),
-            structure=struct,
-            border_value=False,
-        )
-        & mask
-    )
-
-    if max_grace_days <= 0:
-        return strict
-
-    # Fill gaps of ≤ max_grace_days
-    close_k = np.zeros((2 * max_grace_days + 1, 1, 1), dtype=bool)
-    close_k[:, 0, 0] = True
-    filled = ndimage.binary_closing(
-        mask,
-        structure=close_k,
-        border_value=False,
-    )
-
-    # Runs of min_days+ in the gap-filled mask
-    filled_runs = (
-        ndimage.binary_dilation(
-            ndimage.binary_erosion(
-                filled,
-                structure=struct,
-                border_value=False,
-            ),
-            structure=struct,
-            border_value=False,
-        )
-        & filled
-    )
-
-    # Label each temporal run independently per grid point
-    lbl_struct = np.zeros((3, 3, 3), dtype=int)
-    lbl_struct[0, 1, 1] = 1
-    lbl_struct[1, 1, 1] = 1
-    lbl_struct[2, 1, 1] = 1
-    labels, _ = ndimage.label(filled_runs, structure=lbl_struct)
-
-    # Keep only runs that contain a strict 3-day block
-    valid = np.unique(labels[strict & (labels > 0)])
-    return np.isin(labels, valid) & filled_runs
-
-
-def _edge_valid_fraction(
-    mask_2d: np.ndarray,
-    land_2d: np.ndarray,
-    edge: str,
-    band_pts: int,
-) -> float:
-    """Return the fraction of land grid points on an edge that are active.
-
-    Ocean/masked points are excluded from both numerator and denominator
-    so coastal edges aren't penalised.
-
-    Args:
-        mask_2d: 2-D boolean activity array (lat, lon).
-        land_2d: 2-D boolean land mask (lat, lon); True = land.
-        edge: One of "north", "south", "east", "west".
-        band_pts: Width of the edge strip in grid points.
-
-    Returns:
-        Fraction in [0, 1] of land points in the strip that are active,
-        or 0.0 if the strip contains no land points.
-
-    Raises:
-        ValueError: If edge is not one of the recognised values.
-    """
-    if edge == "north":
-        strip = mask_2d[-band_pts:, :]
-        land_strip = land_2d[-band_pts:, :]
-    elif edge == "south":
-        strip = mask_2d[:band_pts, :]
-        land_strip = land_2d[:band_pts, :]
-    elif edge == "west":
-        strip = mask_2d[:, :band_pts]
-        land_strip = land_2d[:, :band_pts]
-    elif edge == "east":
-        strip = mask_2d[:, -band_pts:]
-        land_strip = land_2d[:, -band_pts:]
-    else:
-        raise ValueError(f"Unknown edge: {edge}")
-
-    n_land = int(land_strip.sum())
-    if n_land == 0:
-        return 0.0
-    return float((strip & land_strip).sum()) / n_land
-
-
-def _to_plot_lon(lon: float) -> float:
-    """Convert 0-360 longitude to -180..180 for plotting.
-
-    Args:
-        lon: Longitude in 0-360 degrees.
-
-    Returns:
-        Longitude in -180..180 degrees.
-    """
-    return lon - 360 if lon > 180 else lon
-
-
-def plot_peak_day_with_bounds(
-    peak_mask: np.ndarray,
-    all_lats: np.ndarray,
-    all_lons: np.ndarray,
-    initial_box: tuple,
-    final_box: tuple,
-    event_type: str,
-    title: str,
-    output_path: str,
-) -> None:
-    """Plot the peak-footprint day with initial and final bounding boxes.
-
-    Args:
-        peak_mask: 2-D boolean array (lat, lon) for the peak day.
-        all_lats: 1-D latitude array matching peak_mask rows.
-        all_lons: 1-D longitude array (0-360) matching peak_mask cols.
-        initial_box: (lat_min, lat_max, lon_min, lon_max) of the
-            initial centered region.
-        final_box: (lat_min, lat_max, lon_min, lon_max) after spatial
-            expansion.
-        event_type: ``"heat_wave"`` or ``"freeze"``.
-        title: Figure title.
-        output_path: Destination PNG file path.
-    """
-    plot_lons = np.array([_to_plot_lon(x) for x in all_lons])
-    lon_min = float(plot_lons.min()) - 1
-    lon_max = float(plot_lons.max()) + 1
-    lat_min = float(all_lats.min()) - 1
-    lat_max = float(all_lats.max()) + 1
-
-    cmap = mcolors.ListedColormap(
-        ["white", "firebrick" if event_type == "heat_wave" else "steelblue"]
-    )
-    norm = mcolors.BoundaryNorm([0, 0.5, 1], cmap.N)
-
-    fig, ax = plt.subplots(
-        subplot_kw={"projection": ccrs.PlateCarree()},
-        figsize=(10, 8),
-    )
-    ax.set_extent(
-        [lon_min, lon_max, lat_min, lat_max],
-        crs=ccrs.PlateCarree(),
-    )
-    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
-
-    ax.pcolormesh(
-        plot_lons,
-        all_lats,
-        peak_mask.astype(float),
-        cmap=cmap,
-        norm=norm,
-        transform=ccrs.PlateCarree(),
-        shading="auto",
-    )
-    ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
-    ax.add_feature(cfeature.BORDERS, linewidth=0.3)
-
-    def _add_rect(box, color, label, ls="-"):
-        blat_min, blat_max, blon_min, blon_max = box
-        blon_min = _to_plot_lon(blon_min)
-        blon_max = _to_plot_lon(blon_max)
-        rect = mpatches.Rectangle(
-            (blon_min, blat_min),
-            blon_max - blon_min,
-            blat_max - blat_min,
-            linewidth=2,
-            edgecolor=color,
-            facecolor="none",
-            linestyle=ls,
-            transform=ccrs.PlateCarree(),
-            label=label,
-        )
-        ax.add_patch(rect)
-
-    _add_rect(initial_box, "blue", "Initial box", ls="--")
-    _add_rect(final_box, "green", "Final bounds", ls="-")
-
-    ax.legend(loc="lower left", fontsize=9)
-    ax.set_title(title, loc="left", fontsize=12)
-    fig.savefig(output_path, dpi=200, bbox_inches="tight")
-    plt.close(fig)
-    logger.info("  Saved peak-day plot: %s", output_path)
-
-
-def process_event(
-    single_case: cases.IndividualCase,
-    out_dir: pathlib.Path = pathlib.Path("."),
-    quantile: float | None = None,
-    op_str: str | None = None,
-) -> Optional[Dict]:
-    """Process one event: find the time window then expand bounds.
-
-    Args:
-        single_case: The individual case to process; must have a
-            CenteredRegion location.
-        out_dir: Directory in which plots are saved. Default is
-            pathlib.Path(".").
-        quantile: Climatology quantile. Default is None, which
-            resolves to 0.85 for heat_wave or 0.15 for freeze.
-        op_str: Comparison operator string (e.g. ">", ">=", "<",
-            "<="). Default is None, which resolves to ">" for
-            heat_wave or "<" for freeze.
-
-    Returns:
-        A dict with keys event_type, start_date, end_date,
-        latitude_min/max, longitude_min/max, case_id, title, and
-        _peak_gridpoints, or None if the case was skipped.
-    """
-    is_heatwave = single_case.event_type == "heat_wave"
-    logger.info(
-        "Processing case %d: %s (%s)",
-        single_case.case_id_number,
-        single_case.title,
-        single_case.event_type,
-    )
-
-    loc = single_case.location
-    if not isinstance(loc, regions.CenteredRegion):
-        logger.warning(
-            "  Case %d location is %s, not CenteredRegion — skipping",
-            single_case.case_id_number,
-            type(loc).__name__,
-        )
-        return None
-
-    center_lat = loc.latitude
-    center_lon = loc.longitude
-    if isinstance(loc.bounding_box_degrees, tuple):
-        half_lat = loc.bounding_box_degrees[0] / 2.0
-        half_lon = loc.bounding_box_degrees[1] / 2.0
-    else:
-        half_lat = loc.bounding_box_degrees / 2.0
-        half_lon = loc.bounding_box_degrees / 2.0
-
-    box_lat_min = center_lat - half_lat
-    box_lat_max = center_lat + half_lat
-    box_lon_min = center_lon - half_lon
-    box_lon_max = center_lon + half_lon
-
-    logger.info(
-        "  Initial box (%.1f°): center (%.2f, %.2f)"
-        "  lat [%.2f, %.2f], lon [%.2f, %.2f]",
-        loc.bounding_box_degrees
-        if not isinstance(loc.bounding_box_degrees, tuple)
-        else loc.bounding_box_degrees[0],
-        center_lat,
-        center_lon,
-        box_lat_min,
-        box_lat_max,
-        box_lon_min,
-        box_lon_max,
-    )
-
-    # Time range and spatial pre-fetch extent
-    start_date = pd.Timestamp(single_case.start_date) - pd.Timedelta(days=3)
-    end_date = pd.Timestamp(single_case.end_date) + pd.Timedelta(
-        days=TEMPORAL_LOAD_BUFFER_DAYS
-    )
-
-    pot_lat_min = max(
-        -90,
-        box_lat_min - MAX_ITERATIONS * EXPANSION_DEGREES,
-    )
-    pot_lat_max = min(
-        90,
-        box_lat_max + MAX_ITERATIONS * EXPANSION_DEGREES,
-    )
-    pot_lon_min = box_lon_min - MAX_ITERATIONS * EXPANSION_DEGREES
-    pot_lon_max = box_lon_max + MAX_ITERATIONS * EXPANSION_DEGREES
-
-    ds = xr.open_zarr(
-        inputs.ARCO_ERA5_FULL_URI,
-        storage_options={"token": "anon"},
-        chunks={},
-    )
-    tdim = detect_time_dim(ds)
-    t2m = ds["2m_temperature"].sel(
-        {tdim: slice(str(start_date), str(end_date))},
-    )
-    six_hourly = t2m[tdim].dt.hour.isin([0, 6, 12, 18])
-    t2m = t2m.sel({tdim: six_hourly}).sortby("latitude")
-
-    if float(t2m.longitude.max()) > 180:
-        t2m = t2m.assign_coords(
-            longitude=(t2m.longitude.values + 180) % 360 - 180,
-        ).sortby("longitude")
-
-    # Convert center lon to match ERA5 convention for slicing
-    pot_lon_min_era = pot_lon_min
-    pot_lon_max_era = pot_lon_max
-    if pot_lon_min_era > 180:
-        pot_lon_min_era -= 360
-    if pot_lon_max_era > 180:
-        pot_lon_max_era -= 360
-
-    t2m = t2m.sel(
-        latitude=slice(pot_lat_min, pot_lat_max),
-        longitude=slice(
-            min(pot_lon_min_era, pot_lon_max_era),
-            max(pot_lon_min_era, pot_lon_max_era),
-        ),
-    )
-
-    if t2m.latitude.size == 0 or t2m.longitude.size == 0:
-        logger.warning(
-            "  Case %d: empty spatial selection — skipping",
-            single_case.case_id_number,
-        )
-        return None
-
-    # 6-hourly exceedance then daily all-pass
-    if quantile is None:
-        quantile = 0.85 if is_heatwave else 0.15
-    if op_str is None:
-        op_str = ">" if is_heatwave else "<"
-    cmp = resolve_op(op_str)
-
-    clim = defaults.get_climatology(quantile).sortby("latitude")
-
-    if float(clim.longitude.max()) > 180:
-        clim = clim.assign_coords(
-            longitude=(clim.longitude.values + 180) % 360 - 180,
-        ).sortby("longitude")
-
-    doy = t2m[tdim].dt.dayofyear
-    hour = t2m[tdim].dt.hour
-    max_clim_doy = int(clim.dayofyear.max())
-    doy_capped = doy.clip(max=max_clim_doy)
-
-    clim_aligned = clim.sel(
-        dayofyear=doy_capped,
-        hour=hour,
-    ).reindex_like(t2m, method="nearest")
-
-    exc_6h = cmp(t2m, clim_aligned)
-
-    daily_all_pass = exc_6h.resample({tdim: "1D"}).min().astype(bool)
-
-    land_reg = regionmask.defined_regions.natural_earth_v5_0_0.land_110
-    land_mask = (
-        land_reg.mask(
-            daily_all_pass.longitude,
-            daily_all_pass.latitude,
-        )
-        == 0
-    )
-
-    exc_mask = daily_all_pass & land_mask
-
-    logger.info("  Computing exceedance mask...")
-    mask_np = exc_mask.compute().values.astype(bool)
-
-    all_lats = daily_all_pass.latitude.values
-    all_lons = daily_all_pass.longitude.values
-    grid_res = np.abs(np.diff(all_lats[:2]))[0] if len(all_lats) > 1 else 0.25
-    band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
-
-    def _lat_idx(val: float) -> int:
-        return int(np.argmin(np.abs(all_lats - val)))
-
-    def _lon_idx(val: float) -> int:
-        return int(np.argmin(np.abs(all_lons - val)))
-
-    # Convert box bounds to ERA5 lon convention for index lookup
-    box_lon_min_era = box_lon_min
-    box_lon_max_era = box_lon_max
-    if box_lon_min_era > 180:
-        box_lon_min_era -= 360
-    if box_lon_max_era > 180:
-        box_lon_max_era -= 360
-
-    idx_s0 = _lat_idx(box_lat_min)
-    idx_n0 = _lat_idx(box_lat_max)
-    idx_w0 = _lon_idx(box_lon_min_era)
-    idx_e0 = _lon_idx(box_lon_max_era)
-
-    # Temporal iteration
-    # Land mask within the initial box (exclude ocean from denominator)
-    land_mask_np = land_mask.compute().values.astype(bool)
-    box_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
-    n_land_pts = int(box_land.sum())
-    if n_land_pts == 0:
-        logger.warning(
-            "  Case %d: no land points in initial box — skipping",
-            single_case.case_id_number,
-        )
-        return None
-
-    n_days_total = min(mask_np.shape[0], MAX_TEMPORAL_DAYS)
-    final_t = n_days_total
-    established = False
-
-    for t in range(n_days_total):
-        if t < 2:
-            continue
-
-        # "Currently active": last 3 days all exceed threshold
-        box_sl = (
-            slice(idx_s0, idx_n0 + 1),
-            slice(idx_w0, idx_e0 + 1),
-        )
-        currently_active = (
-            mask_np[t][box_sl] & mask_np[t - 1][box_sl] & mask_np[t - 2][box_sl]
-        )
-        active_land = int((currently_active & box_land).sum())
-        frac = active_land / n_land_pts
-
-        if t == 2 and frac < EDGE_VALIDITY_THRESHOLD:
-            logger.warning(
-                "  Day 3: only %.1f%% of land points have 3 consecutive days (< 50%%)",
-                frac * 100,
-            )
-
-        if not established:
-            if frac >= EDGE_VALIDITY_THRESHOLD:
-                established = True
-                logger.info(
-                    "  Event established at day %d (%.1f%% of land points active)",
-                    t,
-                    frac * 100,
-                )
-        else:
-            if frac < EDGE_VALIDITY_THRESHOLD:
-                final_t = t + 1
-                logger.info(
-                    "  Temporal stop at day %d (%.1f%% < 50%% of land points)",
-                    t,
-                    frac * 100,
-                )
-                break
-
-        if t == 9:
-            logger.warning(
-                "  Exceeded 10 days (frac %.1f%%), continuing...",
-                frac * 100,
-            )
-
-    if not established:
-        logger.warning(
-            "  Case %d: event never reached 50%% of land points — using all %d days",
-            single_case.case_id_number,
-            final_t,
-        )
-
-    logger.info(
-        "  Using %d of %d available days",
-        final_t,
-        mask_np.shape[0],
-    )
-
-    mask_np = mask_np[:final_t]
-    filtered = _apply_consecutive_filter(mask_np)
-
-    # Spatial expansion on peak-footprint day
-    # Find the timestep(s) with the most active grid points.
-    # If tied: 2nd of 2, middle of odd count, first-middle of even.
-    daily_counts = filtered.sum(axis=(1, 2))
-    max_count = daily_counts.max()
-    (tied_days,) = np.where(daily_counts == max_count)
-    n_tied = len(tied_days)
-    if n_tied <= 2:
-        peak_day = int(tied_days[-1]) if n_tied == 2 else int(tied_days[0])
-    else:
-        peak_day = int(tied_days[(n_tied - 1) // 2])
-    peak_mask = filtered[peak_day]
-    logger.info(
-        "  Peak footprint on day %d (%d active grid points, %d tied days)",
-        peak_day,
-        int(max_count),
-        n_tied,
-    )
-
-    idx_s = idx_s0
-    idx_n = idx_n0
-    idx_w = idx_w0
-    idx_e = idx_e0
-
-    # Pre-check: disable edges that are >= 95% ocean
-    # in the initial box (prevents expansion through water).
-    init_region_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
-    edges_active = {}
-    for edge in ("north", "south", "east", "west"):
-        if edge == "north":
-            strip = init_region_land[-band_pts:, :]
-        elif edge == "south":
-            strip = init_region_land[:band_pts, :]
-        elif edge == "west":
-            strip = init_region_land[:, :band_pts]
-        else:
-            strip = init_region_land[:, -band_pts:]
-        land_frac = strip.sum() / max(strip.size, 1)
-        edges_active[edge] = land_frac >= 0.25
-        if not edges_active[edge]:
-            logger.info(
-                "  %s edge disabled (%.1f%% land in initial box)",
-                edge.capitalize(),
-                land_frac * 100,
-            )
-    n_iter = 0
-
-    for iteration in range(MAX_ITERATIONS):
-        n_iter = iteration + 1
-        region = peak_mask[idx_s : idx_n + 1, idx_w : idx_e + 1]
-        land_region = land_mask_np[idx_s : idx_n + 1, idx_w : idx_e + 1]
-
-        all_done = True
-        for edge in list(edges_active.keys()):
-            if not edges_active[edge]:
-                continue
-
-            frac = _edge_valid_fraction(
-                region,
-                land_region,
-                edge,
-                band_pts,
-            )
-            if frac < EDGE_VALIDITY_THRESHOLD:
-                edges_active[edge] = False
-            else:
-                all_done = False
-                if edge == "north":
-                    idx_n = min(
-                        len(all_lats) - 1,
-                        idx_n + band_pts,
-                    )
-                elif edge == "south":
-                    idx_s = max(0, idx_s - band_pts)
-                elif edge == "east":
-                    idx_e = min(
-                        len(all_lons) - 1,
-                        idx_e + band_pts,
-                    )
-                elif edge == "west":
-                    idx_w = max(0, idx_w - band_pts)
-
-        if all_done:
-            logger.info(
-                "  Spatial expansion converged at iteration %d",
-                n_iter,
-            )
-            break
-    else:
-        logger.info(
-            "  Reached max spatial iterations (%d)",
-            MAX_ITERATIONS,
-        )
-
-    fin_lats = all_lats[idx_s : idx_n + 1]
-    fin_lons = all_lons[idx_w : idx_e + 1]
-    fin_filtered = filtered[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
-    consec = max_consecutive_days(fin_filtered)
-    peak_gridpoints = int((consec >= MIN_CONSECUTIVE_DAYS).sum())
-
-    result = {
-        "case_id": single_case.case_id_number,
-        "title": single_case.title,
-        "event_type": single_case.event_type,
-        "start_date": str(single_case.start_date),
-        "end_date": str(single_case.end_date),
-        "latitude_min": float(all_lats[idx_s]),
-        "latitude_max": float(all_lats[idx_n]),
-        "longitude_min": float(all_lons[idx_w]),
-        "longitude_max": float(all_lons[idx_e]),
-        "n_iterations": n_iter,
-        "final_days": final_t,
-        "_consec": consec,
-        "_lats": fin_lats,
-        "_lons": fin_lons,
-        "_peak_gridpoints": peak_gridpoints,
-    }
-    logger.info(
-        "  Final bounds: lat [%.2f, %.2f], lon [%.2f, %.2f] (%d days, %d iterations)",
-        result["latitude_min"],
-        result["latitude_max"],
-        result["longitude_min"],
-        result["longitude_max"],
-        final_t,
-        n_iter,
-    )
-
-    kind = "heatwave" if is_heatwave else "freeze"
-    start = result["start_date"][:10]
-    end = result["end_date"][:10]
-    out_png = str(out_dir / f"case_{result['case_id']}_consecutive_{kind}_days.png")
-    plot_consecutive_map(
-        consec,
-        fin_lats,
-        fin_lons,
-        cast(Literal["heat_wave", "cold_snap"], single_case.event_type),
-        title=(
-            f"Consecutive {kind.capitalize()} Days"
-            f" — {result['title']}\n{start} to {end}"
-        ),
-        output_path=out_png,
-    )
-    logger.info("  Saved plot: %s", out_png)
-
-    initial_box = (box_lat_min, box_lat_max, box_lon_min, box_lon_max)
-    final_box = (
-        result["latitude_min"],
-        result["latitude_max"],
-        result["longitude_min"],
-        result["longitude_max"],
-    )
-    peak_png = str(out_dir / f"case_{result['case_id']}_peak_day_{kind}.png")
-    plot_peak_day_with_bounds(
-        peak_mask,
-        all_lats,
-        all_lons,
-        initial_box,
-        final_box,
-        single_case.event_type,
-        title=(
-            f"Peak Footprint (day {peak_day}) — {result['title']}\n{start} to {end}"
-        ),
-        output_path=peak_png,
-    )
-
-    return result
-
-
-def results_to_dataframe(results: List[Dict]) -> pd.DataFrame:
-    """Convert result dicts to a labelled DataFrame.
-
-    Args:
-        results: List of dicts returned by process_event (may
-            contain None entries which are ignored). Each dict must
-            have keys event_type, start_date, end_date,
-            latitude_min/max, longitude_min/max, case_id, title, and
-            _peak_gridpoints.
-
-    Returns:
-        DataFrame with columns label, case_id, title, event_type,
-        start_date, end_date, latitude_min, latitude_max,
-        longitude_min, longitude_max, sorted by start_date.
-        Events below MIN_GRIDPOINTS are excluded.
-    """
-    columns = [
-        "label",
-        "case_id",
-        "title",
-        "event_type",
-        "start_date",
-        "end_date",
-        "latitude_min",
-        "latitude_max",
-        "longitude_min",
-        "longitude_max",
-    ]
-    if not results:
-        return pd.DataFrame(columns=columns)
-
-    valid = [r for r in results if r is not None]
-    n_before = len(valid)
-    valid = [r for r in valid if r["_peak_gridpoints"] >= MIN_GRIDPOINTS]
-    logger.info(
-        "  Filtered %d events below %d gridpoints; %d remain",
-        n_before - len(valid),
-        MIN_GRIDPOINTS,
-        len(valid),
-    )
-
-    rows = [
-        {
-            "event_type": r["event_type"],
-            "start_date": r["start_date"],
-            "end_date": r["end_date"],
-            "latitude_min": r["latitude_min"],
-            "latitude_max": r["latitude_max"],
-            "longitude_min": r["longitude_min"],
-            "longitude_max": r["longitude_max"],
-        }
-        for r in valid
-    ]
-    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
-    df.insert(0, "label", range(1, len(df) + 1))
-    return df
-
-
-def write_bounds_to_yaml(
-    results: List[Dict],
-    yaml_path: pathlib.Path,
-) -> None:
-    """Update events.yaml with computed bounded_region bounds.
-
-    Uses ruamel.yaml round-trip mode so indentation, quoting style,
-    key order, blank lines, and comments are preserved exactly.
-    Only heat_wave / freeze cases with a valid result are modified.
-
-    Args:
-        results: List of dicts returned by process_event (None
-            entries are ignored).
-        yaml_path: Path to the events.yaml file to update in-place.
-    """
-    result_map = {r["case_id"]: r for r in results if r is not None}
-    if not result_map:
-        logger.warning("write_bounds_to_yaml: no valid results, skipping.")
-        return
-
-    yaml = YAML(typ="rt")
-    yaml.preserve_quotes = True
-
-    with yaml_path.open("r") as fh:
-        data = yaml.load(fh)
-
-    updated = 0
-    for entry in data:
-        cid = entry.get("case_id_number")
-        if cid not in result_map:
-            continue
-        r = result_map[cid]
-        params = entry["location"]["parameters"]
-        params["latitude_min"] = round(r["latitude_min"], 2)
-        params["latitude_max"] = round(r["latitude_max"], 2)
-        params["longitude_min"] = round(r["longitude_min"], 2)
-        params["longitude_max"] = round(r["longitude_max"], 2)
-        updated += 1
-
-    with yaml_path.open("w") as fh:
-        yaml.dump(data, fh)
-
-    logger.info(
-        "write_bounds_to_yaml: updated %d cases in %s",
-        updated,
-        yaml_path,
-    )
-
-
-def _load_base_temp_events() -> list[cases.IndividualCase]:
-    """Load all cases from base_temp_events.yaml.
-
-    Returns:
-        List of IndividualCase objects parsed from the bundled
-        base_temp_events.yaml resource file.
-    """
-    import extremeweatherbench.data
-
-    old_yaml = importlib.resources.files(
-        extremeweatherbench.data,
-    ).joinpath("base_temp_events.yaml")
-    with importlib.resources.as_file(old_yaml) as f:
-        raw = cases.read_incoming_yaml(f)
-    return cases.load_individual_cases(raw)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description=(
-            "Validate / expand heat wave and freeze bounds"
-            " using base_temp_events.yaml centered_region data."
-        ),
-    )
-    parser.add_argument(
-        "--output",
-        default="heat_cold_yaml.csv",
-        help="Output CSV path",
-    )
-    parser.add_argument(
-        "--n-workers",
-        type=int,
-        default=4,
-        help="Number of parallel workers (joblib)",
-    )
-    parser.add_argument(
-        "--case-min",
-        type=int,
-        default=None,
-        help="Minimum case_id_number to process (inclusive)",
-    )
-    parser.add_argument(
-        "--case-max",
-        type=int,
-        default=None,
-        help="Maximum case_id_number to process (inclusive)",
-    )
-    parser.add_argument(
-        "--quantile",
-        type=float,
-        default=None,
-        help=(
-            "Climatology quantile "
-            f"({VALID_QUANTILES}; "
-            "default: 0.85 for heat_wave, 0.15 for freeze)"
-        ),
-    )
-    parser.add_argument(
-        "--operator",
-        default=None,
-        help=(
-            "Comparison operator "
-            "(>, >=, <, <=, ==; "
-            "default: > for heat_wave, < for freeze)"
-        ),
-    )
-    args = parser.parse_args()
-
-    wall_start = time_module.time()
-
-    client = Client(LocalCluster(n_workers=args.n_workers))
-    logger.info("Dask dashboard: %s", client.dashboard_link)
-
-    old_cases = _load_base_temp_events()
-    hw_fz = [
-        e
-        for e in old_cases
-        if e.event_type in ("heat_wave", "freeze")
-        and (args.case_min is None or e.case_id_number >= args.case_min)
-        and (args.case_max is None or e.case_id_number <= args.case_max)
-    ]
-    logger.info(
-        "Found %d heat_wave / freeze events in base_temp_events.yaml",
-        len(hw_fz),
-    )
-
-    out_dir = pathlib.Path(args.output).parent
-
-    results = joblib.Parallel(n_jobs=args.n_workers)(
-        joblib.delayed(process_event)(
-            c,
-            out_dir,
-            quantile=args.quantile,
-            op_str=args.operator,
-        )
-        for c in hw_fz
-    )
-
-    df = results_to_dataframe(results)
-    out_path = pathlib.Path(args.output)
-    if out_path.exists():
-        df.to_csv(out_path, index=False, mode="a", header=False)
-    else:
-        df.to_csv(out_path, index=False)
-
-    yaml_path = (
-        pathlib.Path(__file__).parent.parent
-        / "src"
-        / "extremeweatherbench"
-        / "data"
-        / "events.yaml"
-    )
-    write_bounds_to_yaml(results, yaml_path)
-
-    elapsed = time_module.time() - wall_start
-    logger.info(
-        "Done: %d events -> %s (%.1f s)",
-        len(df),
-        args.output,
-        elapsed,
-    )
-    client.close()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/data_prep/plot_temperature_events.py b/data_prep/plot_temperature_events.py
deleted file mode 100644
index f5b31fbe..00000000
--- a/data_prep/plot_temperature_events.py
+++ /dev/null
@@ -1,735 +0,0 @@
-#!/usr/bin/env python3
-"""Plotting utilities and CLI for heat wave and cold snap events.
-
-Handles both event types from a single entry point. The event type
-is auto-detected from events.yaml; no flag required.
-
-Exported functions used by temperature_bounds_global.py and
-heat_cold_bounds_case.py:
-    max_consecutive_days  -- compute field from boolean mask
-    plot_consecutive_map  -- pcolormesh map (Reds/Blues, discrete)
-
-CLI usage:
-    python plot_temperature_events.py --case-id-number 2
-    python plot_temperature_events.py --case-id-number 30 --output my_plot.png
-"""
-
-import argparse
-import logging
-import operator as op_module
-import pathlib
-import time as time_module
-from typing import Any, Callable, Literal, cast
-
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-import matplotlib.colors as mcolors
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import regionmask
-import xarray as xr
-from mpl_toolkits.axes_grid1 import make_axes_locatable
-from xarray.core.indexes import IndexSelResult, PandasIndex, _query_slice
-from xarray.core.indexing import _expand_slice
-
-from extremeweatherbench import cases, defaults, inputs
-
-
-class PeriodicBoundaryIndex(PandasIndex):
-    """
-    An index representing any 1D periodic numberline.
-
-    Implementation subclasses a normal xarray PandasIndex object but intercepts indexer queries.
-    """
-
-    period: float
-    _min: float
-    _max: float
-
-    __slots__ = ("index", "dim", "coord_dtype", "period", "_max", "_min")
-
-    def __init__(self, *args, period=360, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.period = period
-        self._min = self.index.min()
-        self._max = self.index.max()
-
-    @classmethod
-    def from_variables(self, variables, options):
-        obj = super().from_variables(variables, options={})
-        obj.period = options.get("period", obj.period)
-        return obj
-
-    def _wrap_periodically(self, label_value: float) -> float:
-        return self._min + (label_value - self._max) % self.period
-
-    def _split_slice_across_boundary(self, label: slice) -> np.ndarray:
-        """
-        Splits a slice into two slices, one either side of the boundary,
-        finds the corresponding indices, concatenates them, and returns them,
-        ready to be passed to .isel().
-        """
-        first_slice = slice(label.start, self._max, label.step)
-        second_slice = slice(self._min, label.stop, label.step)
-
-        first_as_index_slice = _query_slice(self.index, first_slice)
-        second_as_index_slice = _query_slice(self.index, second_slice)
-
-        first_as_indices = _expand_slice(first_as_index_slice, self.index.size)
-        second_as_indices = _expand_slice(second_as_index_slice, self.index.size)
-
-        wrapped_indices = np.concatenate([first_as_indices, second_as_indices])
-        return wrapped_indices
-
-    def sel(
-        self, labels: dict[Any, Any], method=None, tolerance=None
-    ) -> IndexSelResult:
-        """Remaps labels outside of the indexes' range back to integer indices inside the range."""
-
-        assert len(labels) == 1
-        coord_name, label = next(iter(labels.items()))
-
-        if isinstance(label, slice):
-            start, stop, step = label.start, label.stop, label.step
-            if stop < start:
-                return super().sel({coord_name: []})
-
-            assert self._min < self._max
-
-            wrapped_start = self._wrap_periodically(label.start)
-            wrapped_stop = self._wrap_periodically(label.stop)
-            wrapped_label = slice(wrapped_start, wrapped_stop, step)
-
-            if wrapped_start < wrapped_stop:
-                # simple case of slice not crossing boundary
-                return super().sel({coord_name: wrapped_label})
-            else:  # wrapped_stop < wrapped_start:
-                # nasty case of slice crossing boundary
-                wrapped_indices = self._split_slice_across_boundary(wrapped_label)
-                return IndexSelResult({self.dim: wrapped_indices})
-
-        else:
-            # just a scalar / array of scalars
-            wrapped_label = self._wrap_periodically(label)  # type: ignore
-            return super().sel(
-                {coord_name: wrapped_label}, method=method, tolerance=tolerance
-            )
-
-    def __repr__(self) -> str:
-        return f"PeriodicBoundaryIndex(period={self.period})"
-
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(name)s %(levelname)s %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-MIN_CONSECUTIVE_DAYS = 3
-
-_OP_MAP: dict[str, Callable] = {
-    ">": op_module.gt,
-    ">=": op_module.ge,
-    "<": op_module.lt,
-    "<=": op_module.le,
-    "==": op_module.eq,
-}
-
-VALID_QUANTILES = [0.10, 0.15, 0.25, 0.50, 0.75, 0.85, 0.90]
-
-
-def resolve_op(op_str: str) -> Callable:
-    """Convert a string operator to a callable.
-
-    Args:
-        op_str: One of ">", ">=", "<", "<=", "==".
-
-    Returns:
-        The corresponding operator callable.
-
-    Raises:
-        ValueError: If op_str is not a recognised operator.
-    """
-    if op_str not in _OP_MAP:
-        raise ValueError(f"Unknown operator {op_str!r}; choose from {list(_OP_MAP)}")
-    return _OP_MAP[op_str]
-
-
-def detect_time_dim(obj: xr.Dataset | xr.DataArray) -> str:
-    """Return the name of the time dimension.
-
-    Args:
-        obj: An xarray Dataset or DataArray.
-
-    Returns:
-        The name of the first matching time dimension.
-
-    Raises:
-        ValueError: If no recognised time dimension is found.
-    """
-    for name in ("valid_time", "time"):
-        if name in obj.dims:
-            return name
-    raise ValueError(f"No time dimension found. Available: {list(obj.dims)}")
-
-
-def open_era5_t2m(start_date: str, end_date: str) -> xr.DataArray:
-    """Open ERA5 2m temperature lazily for a date range.
-
-    Selects 6-hourly timesteps (0/6/12/18 UTC) to match the
-    climatology base and sorts latitude to ascending order.
-
-    Args:
-        start_date: Inclusive start date string (YYYY-MM-DD).
-        end_date: Inclusive end date string (YYYY-MM-DD).
-
-    Returns:
-        Lazy DataArray of 2m temperature with ascending latitude.
-    """
-    ds = xr.open_zarr(
-        inputs.ARCO_ERA5_FULL_URI,
-        storage_options={"token": "anon"},
-        chunks=None,
-    )
-    tdim = detect_time_dim(ds)
-    t2m = ds["2m_temperature"].sel({tdim: slice(start_date, end_date)})
-    six_hourly = t2m[tdim].dt.hour.isin([0, 6, 12, 18])
-    t2m = t2m.sel({tdim: six_hourly})
-    t2m = t2m.chunk({"latitude": -1, "longitude": -1})
-    return t2m.sortby("latitude")
-
-
-def _align_climatology(
-    clim: xr.DataArray,
-    daily: xr.DataArray,
-    tdim: str,
-) -> xr.DataArray:
-    """Align a dayofyear-indexed climatology to daily ERA5 data.
-
-    Computes the climatology into memory (366 x lat x lon) to avoid
-    dask chunk multiplication, then indexes by dayofyear via numpy.
-
-    Args:
-        clim: Climatology DataArray indexed by dayofyear.
-        daily: Daily ERA5 DataArray to align against.
-        tdim: Name of the time dimension in daily.
-
-    Returns:
-        DataArray with the same shape as daily, containing the
-        climatology value for each timestep's day-of-year.
-    """
-    clim_vals = clim.compute().values
-    clim_doy = clim.dayofyear.values
-    doy = daily[tdim].dt.dayofyear.values
-    max_doy = int(clim_doy.max())
-    doy_idx = np.clip(doy, 1, max_doy) - int(clim_doy.min())
-
-    clim_lats = clim.latitude.values
-    clim_lons = clim.longitude.values
-    daily_lats = daily.latitude.values
-    daily_lons = daily.longitude.values
-
-    if (
-        clim_lats.shape == daily_lats.shape
-        and np.allclose(clim_lats, daily_lats)
-        and clim_lons.shape == daily_lons.shape
-        and np.allclose(clim_lons, daily_lons)
-    ):
-        selected = clim_vals[doy_idx]
-    else:
-        lat_idx = np.array([np.argmin(np.abs(clim_lats - dl)) for dl in daily_lats])
-        lon_idx = np.array([np.argmin(np.abs(clim_lons - dl)) for dl in daily_lons])
-        selected = clim_vals[np.ix_(doy_idx, lat_idx, lon_idx)]
-
-    return xr.DataArray(selected, dims=daily.dims, coords=daily.coords)
-
-
-_HW_COLOR = "#d73027"
-_FZ_COLOR = "#4575b4"
-_BOX_ALPHA = 0.35
-_BOX_EDGE_ALPHA = 0.85
-
-
-def _to_plot_lon(lon: float) -> float:
-    """Wrap 0-360 longitude to -180..180 for PlateCarree.
-
-    Args:
-        lon: Longitude in 0-360 degrees.
-
-    Returns:
-        Longitude in -180..180 degrees.
-    """
-    return lon - 360.0 if lon > 180.0 else lon
-
-
-def max_consecutive_days(mask_3d: np.ndarray) -> np.ndarray:
-    """Compute max consecutive True days per grid point.
-
-    Args:
-        mask_3d: Boolean array of shape (time, lat, lon).
-
-    Returns:
-        Int32 array of shape (lat, lon) with the maximum number of
-        consecutive True values along axis 0 for each grid point.
-    """
-    nt, nlat, nlon = mask_3d.shape
-    flat = mask_3d.reshape(nt, -1).astype(np.int8)
-    result = np.zeros(flat.shape[1], dtype=np.int32)
-    for k in range(flat.shape[1]):
-        col = flat[:, k]
-        if not col.any():
-            continue
-        d = np.diff(np.concatenate(([0], col, [0])))
-        starts = np.where(d == 1)[0]
-        ends = np.where(d == -1)[0]
-        result[k] = int((ends - starts).max())
-    return result.reshape(nlat, nlon)
-
-
-def _add_map_features(ax) -> None:
-    """Add standard cartopy features to an axis.
-
-    Args:
-        ax: A cartopy GeoAxes instance.
-    """
-    ax.coastlines(linewidth=0.5, zorder=10)
-    ax.add_feature(
-        cfeature.BORDERS,
-        linewidth=0.6,
-        edgecolor="grey",
-    )
-    ax.add_feature(
-        cfeature.STATES,
-        linewidth=0.4,
-        edgecolor="lightgrey",
-    )
-    ax.add_feature(
-        cfeature.LAKES,
-        linewidth=0.3,
-        edgecolor="none",
-        facecolor="none",
-    )
-    ax.add_feature(
-        cfeature.RIVERS,
-        linewidth=0.2,
-        edgecolor="none",
-    )
-
-
-def plot_consecutive_map(
-    consec: np.ndarray,
-    lats: np.ndarray,
-    lons: np.ndarray,
-    event_type: Literal["heat_wave", "cold_snap"],
-    title: str,
-    output_path: str | None = None,
-    extent: tuple[float, float, float, float] | None = None,
-    ax=None,
-) -> None:
-    """Plot a max-consecutive-days field using the standard style.
-
-    When ``ax`` is provided the plot is drawn into that axis and
-    nothing is saved to disk; ``output_path`` is ignored.  When
-    ``ax`` is None a new figure is created and saved to
-    ``output_path``.
-
-    Args:
-        consec: int32 2-D array (nlat, nlon) of max consecutive days.
-        lats: 1-D latitude array matching consec rows.
-        lons: 1-D longitude array (0-360) matching consec columns.
-        event_type: ``'heat_wave'`` or ``'cold_snap'``.
-        title: Two-line figure title (left-aligned).
-        output_path: Destination PNG file path. Required when ax is
-            None; ignored when ax is provided.
-        extent: (lon_min, lon_max, lat_min, lat_max) in -180..180.
-            Derived from lats/lons when None. Default is None.
-        ax: Optional existing cartopy GeoAxes to draw into. When
-            provided, no figure is created or saved. Default is None.
-    """
-    plot_data = consec.astype(float)
-    plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
-
-    valid = plot_data[~np.isnan(plot_data)]
-    vmax = int(valid.max()) if valid.size > 0 else MIN_CONSECUTIVE_DAYS
-
-    cmap_name = "Reds" if event_type == "heat_wave" else "Blues"
-    n_levels = vmax - MIN_CONSECUTIVE_DAYS + 1
-    cmap = plt.colormaps.get_cmap(cmap_name).resampled(n_levels)
-    bin_edges = np.arange(MIN_CONSECUTIVE_DAYS - 0.5, vmax + 1.5, 1)
-    norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
-
-    plot_lons = np.array([_to_plot_lon(x) for x in lons])
-
-    if extent is None:
-        lon_min, lon_max = float(plot_lons.min()), float(plot_lons.max())
-        lat_min, lat_max = float(lats.min()), float(lats.max())
-    else:
-        lon_min, lon_max, lat_min, lat_max = extent
-
-    own_fig = ax is None
-    if own_fig:
-        fig, ax = plt.subplots(
-            subplot_kw={"projection": ccrs.PlateCarree()},
-            figsize=(10, 8),
-        )
-    else:
-        fig = ax.get_figure()
-
-    ax.set_extent(
-        [lon_min, lon_max, lat_min, lat_max],
-        crs=ccrs.PlateCarree(),
-    )
-    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
-
-    im = ax.pcolormesh(
-        plot_lons,
-        lats,
-        plot_data,
-        cmap=cmap,
-        norm=norm,
-        transform=ccrs.PlateCarree(),
-        shading="auto",
-    )
-    _add_map_features(ax)
-
-    divider = make_axes_locatable(ax)
-    cax = divider.append_axes(
-        "right",
-        size="3.75%",
-        pad=0.1,
-        axes_class=plt.Axes,
-    )
-    cbar = fig.colorbar(im, cax=cax)
-    cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
-    cbar.set_label("Consecutive Days", fontsize=12, labelpad=12, rotation=270)
-    cbar.ax.yaxis.set_label_position("right")
-
-    ax.set_title(title, loc="left", fontsize=13)
-
-    if own_fig:
-        if output_path is None:
-            raise ValueError("output_path is required when ax is not provided.")
-        fig.savefig(output_path, dpi=200, bbox_inches="tight")
-        plt.close(fig)
-        logger.info("Saved consecutive-days plot to %s", output_path)
-
-
-def plot_event_bounds(
-    df: pd.DataFrame,
-    csv_path: str,
-    title: str = "Detected Heat Wave and Cold Snap Events",
-) -> None:
-    """Draw bounding boxes for all events on a global Robinson map.
-
-    Saves a PNG alongside the CSV with the same stem.
-
-    Args:
-        df: DataFrame with columns event_type, longitude_min,
-            longitude_max, latitude_min, latitude_max.
-        csv_path: Path to the output CSV; the PNG is saved with the
-            same stem.
-        title: Figure title. Default is "Detected Heat Wave and
-            Cold Snap Events".
-    """
-    if df.empty:
-        logger.warning("No events to plot -- skipping bounds plot.")
-        return
-
-    out_path = str(pathlib.Path(csv_path).with_suffix(".png"))
-    hw_count = int((df["event_type"] == "heat_wave").sum())
-    fz_count = int((df["event_type"] == "cold_snap").sum())
-
-    fig, ax = plt.subplots(
-        subplot_kw={"projection": ccrs.Robinson()},
-        figsize=(14, 7),
-    )
-    ax.set_global()
-    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
-    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=0)
-    ax.coastlines(linewidth=0.5, zorder=2)
-    ax.add_feature(
-        cfeature.BORDERS,
-        linewidth=0.3,
-        edgecolor="grey",
-        zorder=2,
-    )
-
-    for _, row in df.iterrows():
-        lon0 = _to_plot_lon(float(row["longitude_min"]))
-        lon1 = _to_plot_lon(float(row["longitude_max"]))
-        lat0, lat1 = float(row["latitude_min"]), float(row["latitude_max"])
-        if lon1 < lon0:
-            lon1 += 360.0
-        color = _HW_COLOR if row["event_type"] == "heat_wave" else _FZ_COLOR
-        kw = dict(
-            width=lon1 - lon0,
-            height=lat1 - lat0,
-            transform=ccrs.PlateCarree(),
-        )
-        ax.add_patch(
-            mpatches.Rectangle(
-                xy=(lon0, lat0),
-                facecolor=color,
-                edgecolor=color,
-                alpha=_BOX_ALPHA,
-                linewidth=1.0,
-                zorder=3,
-                **kw,
-            )
-        )
-        ax.add_patch(
-            mpatches.Rectangle(
-                xy=(lon0, lat0),
-                facecolor="none",
-                edgecolor=color,
-                alpha=_BOX_EDGE_ALPHA,
-                linewidth=1.0,
-                zorder=4,
-                **kw,
-            )
-        )
-
-    legend_handles = []
-    if hw_count:
-        legend_handles.append(
-            mpatches.Patch(
-                facecolor=_HW_COLOR,
-                alpha=0.7,
-                label=f"Heat wave ({hw_count})",
-            )
-        )
-    if fz_count:
-        legend_handles.append(
-            mpatches.Patch(
-                facecolor=_FZ_COLOR,
-                alpha=0.7,
-                label=f"Cold Snap ({fz_count})",
-            )
-        )
-    if legend_handles:
-        ax.legend(
-            handles=legend_handles,
-            loc="lower left",
-            fontsize=10,
-            framealpha=0.85,
-        )
-
-    ax.set_title(title, loc="left", fontsize=13)
-    fig.savefig(out_path, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-    logger.info("Saved bounds plot to %s", out_path)
-
-
-def load_case(case_id_number: int) -> cases.IndividualCase:
-    """Load a single case from events.yaml by case_id_number.
-
-    Args:
-        case_id_number: Integer identifier for the case.
-
-    Returns:
-        The matching IndividualCase object.
-
-    Raises:
-        ValueError: If no case with the given ID exists.
-    """
-    all_cases = cases.load_ewb_events_yaml_into_case_list()
-    for c in all_cases:
-        if c.case_id_number == case_id_number:
-            return c
-    hw_ids = ", ".join(
-        str(c.case_id_number) for c in all_cases if c.event_type == "heat_wave"
-    )
-    cs_ids = ", ".join(
-        str(c.case_id_number) for c in all_cases if c.event_type == "cold_snap"
-    )
-    raise ValueError(
-        f"Case {case_id_number} not found.\n"
-        f"  heat_wave ids: {hw_ids}\n"
-        f"  cold_snap ids:    {cs_ids}"
-    )
-
-
-def compute_consecutive_field(
-    single_case: cases.IndividualCase,
-    quantile: float | None = None,
-    op_str: str | None = None,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Return (consecutive_days, lats, lons) for a case.
-
-    Args:
-        single_case: The case to compute the consecutive field for.
-        quantile: Climatology quantile. Default is None, which
-            resolves to 0.85 for heat_wave or 0.15 for
-            freeze/cold_snap.
-        op_str: Comparison operator string (e.g. ">", ">=",
-            "<", "<="). Default is None, which resolves to ">"
-            for heat_wave or "<" for freeze.
-
-    Returns:
-        A tuple of (consec, lats, lons) where consec is an int32
-        array of shape (lat, lon) containing max consecutive event
-        days, lats is the 1-D latitude array, and lons is the 1-D
-        longitude array (0-360).
-    """
-    is_heatwave = single_case.event_type == "heat_wave"
-    if quantile is None:
-        quantile = 0.85 if is_heatwave else 0.15
-    if op_str is None:
-        op_str = ">" if is_heatwave else "<"
-    cmp = resolve_op(op_str)
-
-    start = str(single_case.start_date.date())
-    end = str(single_case.end_date.date())
-    bounds = single_case.location.as_geopandas().total_bounds
-    lon_min, lat_min, lon_max, lat_max = bounds
-
-    if lon_min < 0 and lon_max > 0:
-        lon_min += 360
-        if lon_max > 0:
-            lon_max += 360
-    if lon_max < 0:
-        lon_max += 360
-
-    logger.info("Opening ERA5 for %s to %s ...", start, end)
-    t2m = open_era5_t2m(start, end)
-    t2m = t2m.drop_indexes("longitude").set_xindex(
-        "longitude", index_cls=PeriodicBoundaryIndex, period=360
-    )
-
-    t2m = t2m.sel(
-        latitude=slice(lat_min, lat_max),
-        longitude=slice(lon_min, lon_max),
-    )
-
-    logger.info("  Subset: %s", dict(t2m.sizes))
-
-    tdim = detect_time_dim(t2m)
-
-    logger.info(
-        "Loading q=%.2f climatology (op=%s)...",
-        quantile,
-        op_str,
-    )
-    clim = defaults.get_climatology(quantile).sortby("latitude")
-    clim = clim.drop_indexes("longitude").set_xindex(
-        "longitude", index_cls=PeriodicBoundaryIndex, period=360
-    )
-    clim = clim.sel(
-        latitude=t2m.latitude,
-        longitude=t2m.longitude,
-        method="nearest",
-    )
-    doy = t2m[tdim].dt.dayofyear
-    hour = t2m[tdim].dt.hour
-    max_clim_doy = int(clim.dayofyear.max())
-    doy_capped = doy.clip(max=max_clim_doy)
-
-    clim_aligned = clim.sel(
-        dayofyear=doy_capped,
-        hour=hour,
-    )
-
-    logger.info("Computing 6-hourly exceedance...")
-    exc_6h = cmp(t2m, clim_aligned)
-    print("exc_6h: ")
-    print(exc_6h)
-    daily_all_pass = exc_6h.resample({tdim: "1D"}).min().astype(bool)
-    print("daily_all_pass: ")
-    print(daily_all_pass)
-    logger.info("Building land mask...")
-    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
-    land_mask = land.mask(daily_all_pass.longitude, daily_all_pass.latitude) == 0
-
-    exc = daily_all_pass & land_mask
-    mask_np = exc.compute().values.astype(bool)
-    logger.info("Computing max consecutive days...")
-    consec = max_consecutive_days(mask_np)
-    return consec, daily_all_pass.latitude.values, daily_all_pass.longitude.values
-
-
-def _default_output(case_id_number: int, event_type: str) -> str:
-    kind = "heatwave" if event_type == "heat_wave" else "cold_snap"
-    return f"case_{case_id_number}_consecutive_{kind}_days.png"
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Plot consecutive heatwave or cold_snap days for a case from events.yaml."
-        ),
-    )
-    parser.add_argument(
-        "--case-id-number",
-        type=int,
-        required=True,
-        help="case_id_number from events.yaml",
-    )
-    parser.add_argument(
-        "--output",
-        default=None,
-        help=(
-            "Output PNG path "
-            "(default: case_N_consecutive_{heatwave|cold_snap}_days.png)"
-        ),
-    )
-    parser.add_argument(
-        "--quantile",
-        type=float,
-        default=None,
-        help=(
-            "Climatology quantile "
-            f"(one of {VALID_QUANTILES}; "
-            "default: 0.85 for heat_wave, 0.15 for freeze)"
-        ),
-    )
-    parser.add_argument(
-        "--operator",
-        default=None,
-        help=(
-            "Comparison operator string "
-            "(>, >=, <, <=, ==; "
-            "default: > for heat_wave, < for freeze)"
-        ),
-    )
-    args = parser.parse_args()
-
-    t0 = time_module.time()
-    single_case = load_case(args.case_id_number)
-    logger.info(
-        "Case %d: %s (%s)",
-        single_case.case_id_number,
-        single_case.title,
-        single_case.event_type,
-    )
-
-    if args.output is None:
-        args.output = _default_output(
-            args.case_id_number,
-            single_case.event_type,
-        )
-
-    consec, lats, lons = compute_consecutive_field(
-        single_case,
-        quantile=args.quantile,
-        op_str=args.operator,
-    )
-
-    kind = "Heat Wave" if single_case.event_type == "heat_wave" else "Cold Snap"
-    start_str = str(single_case.start_date.date())
-    end_str = str(single_case.end_date.date())
-
-    plot_consecutive_map(
-        consec,
-        lats,
-        lons,
-        cast(Literal["heat_wave", "cold_snap"], single_case.event_type),
-        title=f"Consecutive {kind} Days\n{start_str} to {end_str}",
-        output_path=args.output,
-    )
-    logger.info("Done in %.1f s", time_module.time() - t0)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/data_prep/temperature_bounds_global.py b/data_prep/temperature_bounds_global.py
deleted file mode 100644
index 6b6512f3..00000000
--- a/data_prep/temperature_bounds_global.py
+++ /dev/null
@@ -1,1531 +0,0 @@
-"""Detect temperature exceedance events globally from ERA5 reanalysis.
-
-Scans ERA5 2m temperature over an input date range and identifies
-events where the daily temperature falls within a user-specified
-climatology quantile band for 3+ consecutive days, over land.
-
-At least one of --quantile-lower or --quantile-upper must be given.
-Both can be combined to define a band (e.g. 50th-85th percentile).
-
-Bounding boxes are first derived from blob tracking, then expanded
-using the same edge-validity logic as heat_cold_bounds_case.py:
-each edge grows by 1 degree while >= 50% of its land points are
-active on the peak-footprint day.  Events terminate when their
-active area drops below 50% of peak.
-
-Usage:
-    # Anything below the 15th percentile (cold snap)
-    python temperature_bounds_global.py \\
-        --start-date 2020-01-01 --end-date 2020-12-31 \\
-        --quantile-upper 0.15 \\
-        --output cold_q15_2020.csv
-
-    # Anything above the 85th percentile (heat wave)
-    python temperature_bounds_global.py \\
-        --start-date 2023-06-01 --end-date 2023-09-01 \\
-        --quantile-lower 0.85 --operator-lower ">=" \\
-        --output heat_q85_2023.csv
-"""
-
-import argparse
-import logging
-import pathlib
-import time as time_module
-from typing import Dict, List, Literal, Optional, Tuple, cast
-
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-import dask
-import joblib
-import matplotlib.colors as mcolors
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numba as nb
-import numpy as np
-import pandas as pd
-import regionmask
-import scipy.ndimage as ndimage
-import xarray as xr
-from dask.distributed import Client, LocalCluster
-from plot_temperature_events import (
-    VALID_QUANTILES,
-    detect_time_dim,
-    max_consecutive_days,
-    open_era5_t2m,
-    plot_consecutive_map,
-    resolve_op,
-)
-
-from extremeweatherbench import defaults
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(name)s %(levelname)s %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-MIN_CONSECUTIVE_DAYS = 3
-AREA_DECLINE_FRACTION = 0.5
-MIN_GRIDPOINTS = 500
-MIN_AREA_KM2 = 200000.0
-EXPANSION_DEGREES = 1
-MAX_SPATIAL_ITERATIONS = 20
-EDGE_VALIDITY_THRESHOLD = 0.5
-
-
-def compute_grid_cell_area(
-    lats: np.ndarray,
-    lons: np.ndarray,
-) -> np.ndarray:
-    """Return a 2-D (lat, lon) array of grid-cell areas in km².
-
-    Uses the spherical-Earth approximation:
-        area = R^2 * dlat_rad * dlon_rad * cos(lat)
-
-    Args:
-        lats: 1-D latitude array in degrees.
-        lons: 1-D longitude array in degrees; used only for shape.
-
-    Returns:
-        Float64 array of shape (len(lats), len(lons)) with each
-        cell's surface area in km².
-    """
-    R_KM = 6371.0
-    dlat = float(np.abs(np.diff(lats[:2]))[0]) if len(lats) > 1 else 0.25
-    dlon = float(np.abs(np.diff(lons[:2]))[0]) if len(lons) > 1 else 0.25
-    cell_km2 = R_KM**2 * np.deg2rad(dlat) * np.deg2rad(dlon) * np.cos(np.deg2rad(lats))
-    return np.outer(cell_km2, np.ones(len(lons)))
-
-
-def get_climatology_bounds(
-    q_lower: Optional[float] = None,
-    q_upper: Optional[float] = None,
-) -> Tuple[Optional[xr.DataArray], Optional[xr.DataArray]]:
-    """Return climatology DataArrays for the lower and/or upper bound.
-
-    Args:
-        q_lower: Quantile for the lower bound. None skips it.
-        q_upper: Quantile for the upper bound. None skips it.
-
-    Returns:
-        Tuple (clim_lower, clim_upper); either may be None.
-
-    Raises:
-        ValueError: If both q_lower and q_upper are None.
-    """
-    if q_lower is None and q_upper is None:
-        raise ValueError("At least one of q_lower or q_upper must be set.")
-    clim_lower = (
-        defaults.get_climatology(q_lower).sortby("latitude")
-        if q_lower is not None
-        else None
-    )
-    clim_upper = (
-        defaults.get_climatology(q_upper).sortby("latitude")
-        if q_upper is not None
-        else None
-    )
-    return clim_lower, clim_upper
-
-
-def build_land_mask(
-    lons: xr.DataArray,
-    lats: xr.DataArray,
-) -> xr.DataArray:
-    """Build a boolean land mask (True = land) for the given grid.
-
-    Args:
-        lons: 1-D DataArray of longitudes.
-        lats: 1-D DataArray of latitudes.
-
-    Returns:
-        Boolean DataArray where True indicates a land grid point.
-    """
-    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
-    mask = land.mask(lons, lats)
-    return mask == 0
-
-
-def _edge_valid_fraction(
-    mask_2d: np.ndarray,
-    land_2d: np.ndarray,
-    edge: str,
-    band_pts: int,
-) -> float:
-    """Return the fraction of land grid points on an edge that are active.
-
-    Args:
-        mask_2d: 2-D boolean activity array (lat, lon).
-        land_2d: 2-D boolean land mask (lat, lon); True = land.
-        edge: One of "north", "south", "east", "west".
-        band_pts: Width of the edge strip in grid points.
-
-    Returns:
-        Fraction in [0, 1] of land points in the strip that are active,
-        or 0.0 if the strip contains no land points.
-
-    Raises:
-        ValueError: If edge is not one of the recognised values.
-    """
-    if edge == "north":
-        strip = mask_2d[-band_pts:, :]
-        land_strip = land_2d[-band_pts:, :]
-    elif edge == "south":
-        strip = mask_2d[:band_pts, :]
-        land_strip = land_2d[:band_pts, :]
-    elif edge == "west":
-        strip = mask_2d[:, :band_pts]
-        land_strip = land_2d[:, :band_pts]
-    elif edge == "east":
-        strip = mask_2d[:, -band_pts:]
-        land_strip = land_2d[:, -band_pts:]
-    else:
-        raise ValueError(f"Unknown edge: {edge}")
-    n_land = int(land_strip.sum())
-    if n_land == 0:
-        return 0.0
-    return float((strip & land_strip).sum()) / n_land
-
-
-def expand_event_bounds(
-    event: Dict,
-    filt_mask: np.ndarray,
-    dates: np.ndarray,
-    lats: np.ndarray,
-    lons: np.ndarray,
-    land_mask_np: np.ndarray,
-) -> Dict:
-    """Expand a detected event's bounding box using case-script logic.
-
-    Starting from the blob-tracked bounding box, expands each edge
-    outward while >= EDGE_VALIDITY_THRESHOLD of land points on that
-    edge are active on the peak-footprint day. Also computes and stores
-    max_consecutive_days and mean_consecutive_days within the expanded
-    region.
-
-    Args:
-        event: Event dict from detect_events.
-        filt_mask: Boolean array (time, lat, lon) with consecutive-day
-            filtering already applied.
-        dates: 1-D datetime64 array of daily timestamps (axis 0).
-        lats: 1-D latitude array (axis 1).
-        lons: 1-D longitude array (axis 2).
-        land_mask_np: 2-D boolean array (lat, lon); True = land.
-
-    Returns:
-        The same event dict with updated lat_min/lat_max/lon_min/
-        lon_max, max_consecutive_days, and mean_consecutive_days.
-    """
-    grid_res = float(np.abs(np.diff(lats[:2]))[0]) if len(lats) > 1 else 0.25
-    band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
-
-    start_date = np.datetime64(event["start"])
-    end_date = np.datetime64(event["end"])
-    t_mask = (dates >= start_date) & (dates <= end_date)
-    ev_filt = filt_mask[t_mask]
-
-    if ev_filt.shape[0] == 0:
-        event["max_consecutive_days"] = 0
-        event["mean_consecutive_days"] = 0.0
-        return event
-
-    def _lat_idx(val: float) -> int:
-        return int(np.argmin(np.abs(lats - val)))
-
-    def _lon_idx(val: float) -> int:
-        return int(np.argmin(np.abs(lons - val)))
-
-    idx_s0 = _lat_idx(event["lat_min"])
-    idx_n0 = _lat_idx(event["lat_max"])
-    idx_w0 = _lon_idx(event["lon_min"])
-    idx_e0 = _lon_idx(event["lon_max"])
-
-    daily_counts = ev_filt.sum(axis=(1, 2))
-    max_count = daily_counts.max()
-    tied_days = np.where(daily_counts == max_count)[0]
-    n_tied = len(tied_days)
-    if n_tied <= 2:
-        peak_day = int(tied_days[-1]) if n_tied == 2 else int(tied_days[0])
-    else:
-        peak_day = int(tied_days[(n_tied - 1) // 2])
-    peak_mask = ev_filt[peak_day]
-
-    idx_s, idx_n, idx_w, idx_e = idx_s0, idx_n0, idx_w0, idx_e0
-
-    init_region_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
-    edges_active: Dict[str, bool] = {}
-    for edge in ("north", "south", "east", "west"):
-        if edge == "north":
-            strip = init_region_land[-band_pts:, :]
-        elif edge == "south":
-            strip = init_region_land[:band_pts, :]
-        elif edge == "west":
-            strip = init_region_land[:, :band_pts]
-        else:
-            strip = init_region_land[:, -band_pts:]
-        land_frac = strip.sum() / max(strip.size, 1)
-        edges_active[edge] = bool(land_frac >= 0.25)
-
-    for _ in range(MAX_SPATIAL_ITERATIONS):
-        region = peak_mask[idx_s : idx_n + 1, idx_w : idx_e + 1]
-        land_region = land_mask_np[idx_s : idx_n + 1, idx_w : idx_e + 1]
-
-        all_done = True
-        for edge in list(edges_active.keys()):
-            if not edges_active[edge]:
-                continue
-            frac = _edge_valid_fraction(region, land_region, edge, band_pts)
-            if frac < EDGE_VALIDITY_THRESHOLD:
-                edges_active[edge] = False
-            else:
-                all_done = False
-                if edge == "north":
-                    idx_n = min(len(lats) - 1, idx_n + band_pts)
-                elif edge == "south":
-                    idx_s = max(0, idx_s - band_pts)
-                elif edge == "east":
-                    idx_e = min(len(lons) - 1, idx_e + band_pts)
-                elif edge == "west":
-                    idx_w = max(0, idx_w - band_pts)
-
-        if all_done:
-            break
-
-    fin_filt = ev_filt[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
-    consec = max_consecutive_days(fin_filt)
-    event["lat_min"] = float(lats[idx_s])
-    event["lat_max"] = float(lats[idx_n])
-    event["lon_min"] = float(lons[idx_w])
-    event["lon_max"] = float(lons[idx_e])
-    event["max_consecutive_days"] = int(consec.max()) if consec.size > 0 else 0
-    active = consec[consec >= MIN_CONSECUTIVE_DAYS]
-    event["mean_consecutive_days"] = (
-        round(float(active.mean()), 2) if active.size > 0 else 0.0
-    )
-    return event
-
-
-def build_exceedance_mask(
-    t2m: xr.DataArray,
-    clim_lower: Optional[xr.DataArray],
-    clim_upper: Optional[xr.DataArray],
-    land_mask: xr.DataArray,
-    op_lower: str = ">",
-    op_upper: str = "<",
-) -> xr.DataArray:
-    """Build a daily exceedance mask from 6-hourly temperature data.
-
-    Each 6-hourly timestep must satisfy all provided bounds. A day
-    passes only when every 6-hourly step satisfies every active bound.
-    The result is further masked to land points.
-
-    Args:
-        t2m: 6-hourly 2m temperature DataArray.
-        clim_lower: Climatology for the lower bound, or None.
-        clim_upper: Climatology for the upper bound, or None.
-        land_mask: Boolean DataArray (True = land) matching t2m grid.
-        op_lower: Comparison operator for the lower bound. Default ">".
-        op_upper: Comparison operator for the upper bound. Default "<".
-
-    Returns:
-        Daily boolean DataArray masked to land.
-    """
-    tdim = detect_time_dim(t2m)
-    doy = t2m[tdim].dt.dayofyear
-    hour = t2m[tdim].dt.hour
-    ref = clim_lower if clim_lower is not None else clim_upper
-    assert ref is not None
-    max_clim_doy = int(ref.dayofyear.max())
-    doy_capped = doy.clip(max=max_clim_doy)
-
-    mask_6h = xr.ones_like(t2m, dtype=bool)
-
-    if clim_lower is not None:
-        cmp = resolve_op(op_lower)
-        aligned = clim_lower.sel(dayofyear=doy_capped, hour=hour).reindex_like(
-            t2m, method="nearest"
-        )
-        mask_6h = mask_6h & cmp(t2m, aligned)
-
-    if clim_upper is not None:
-        cmp = resolve_op(op_upper)
-        aligned = clim_upper.sel(dayofyear=doy_capped, hour=hour).reindex_like(
-            t2m, method="nearest"
-        )
-        mask_6h = mask_6h & cmp(t2m, aligned)
-
-    return mask_6h.resample({tdim: "1D"}).min().astype(bool) & land_mask
-
-
-def apply_consecutive_filter(
-    mask: np.ndarray,
-    min_days: int = MIN_CONSECUTIVE_DAYS,
-    max_grace_days: int = 1,
-) -> np.ndarray:
-    """Keep runs of ``min_days``+ True days along axis 0.
-
-    After ``min_days`` strict consecutive True days are established,
-    gaps of up to ``max_grace_days`` are bridged so the event can
-    continue. Runs that never reach ``min_days`` strict consecutive
-    True days are discarded.
-
-    Args:
-        mask: Boolean array of shape (time, lat, lon).
-        min_days: Minimum run length. Default is 3.
-        max_grace_days: Maximum gap length to bridge. Default is 1.
-
-    Returns:
-        Boolean array of the same shape with qualifying runs only.
-    """
-    struct = np.zeros((min_days, 1, 1), dtype=bool)
-    struct[:, 0, 0] = True
-
-    strict = (
-        ndimage.binary_dilation(
-            ndimage.binary_erosion(
-                mask,
-                structure=struct,
-                border_value=False,
-            ),
-            structure=struct,
-            border_value=False,
-        )
-        & mask
-    )
-
-    if max_grace_days <= 0:
-        return strict
-
-    close_k = np.zeros((2 * max_grace_days + 1, 1, 1), dtype=bool)
-    close_k[:, 0, 0] = True
-    filled = ndimage.binary_closing(
-        mask,
-        structure=close_k,
-        border_value=False,
-    )
-
-    filled_runs = (
-        ndimage.binary_dilation(
-            ndimage.binary_erosion(
-                filled,
-                structure=struct,
-                border_value=False,
-            ),
-            structure=struct,
-            border_value=False,
-        )
-        & filled
-    )
-
-    lbl_struct = np.zeros((3, 3, 3), dtype=int)
-    lbl_struct[0, 1, 1] = 1
-    lbl_struct[1, 1, 1] = 1
-    lbl_struct[2, 1, 1] = 1
-    labels, _ = ndimage.label(filled_runs, structure=lbl_struct)
-
-    valid = np.unique(labels[strict & (labels > 0)])
-    return np.isin(labels, valid) & filled_runs
-
-
-@nb.njit(cache=True)
-def _count_overlaps_nb(
-    labels_a: np.ndarray,
-    labels_b: np.ndarray,
-    n_a: int,
-    n_b: int,
-) -> np.ndarray:
-    """Compute a pixel-count overlap matrix between two label grids.
-
-    Args:
-        labels_a: Integer label array for day t (shape lat x lon).
-        labels_b: Integer label array for day t+1 (shape lat x lon).
-        n_a: Number of blobs in labels_a.
-        n_b: Number of blobs in labels_b.
-
-    Returns:
-        Int32 array of shape (n_a, n_b) where element [i, j] is the
-        number of pixels where labels_a == i+1 and labels_b == j+1.
-    """
-    mat = np.zeros((n_a, n_b), dtype=np.int32)
-    rows, cols = labels_a.shape
-    for r in range(rows):
-        for c in range(cols):
-            a = labels_a[r, c]
-            b = labels_b[r, c]
-            if a > 0 and b > 0:
-                mat[a - 1, b - 1] += 1
-    return mat
-
-
-def _resolve_event(
-    oid: int,
-    overlap_mat: Optional[np.ndarray],
-    prev_map: Dict[int, int],
-    events: Dict[int, Dict],
-    cur_map: Dict[int, int],
-) -> Optional[int]:
-    """Find and merge prior-day events overlapping with blob oid.
-
-    Args:
-        oid: Current-day blob label (1-indexed).
-        overlap_mat: Pixel-count overlap matrix, or None.
-        prev_map: Mapping from previous-day blob label to event ID.
-        events: Mutable dict of all live events keyed by event ID.
-        cur_map: Mutable mapping from current-day blob label to event ID.
-
-    Returns:
-        The surviving event ID after merging, or None if no overlap.
-    """
-    if overlap_mat is None:
-        return None
-    col = overlap_mat[:, oid - 1]
-    prev_blob_ids = np.where(col > 0)[0] + 1
-    eids = {prev_map[p] for p in prev_blob_ids if p in prev_map}
-    alive = {e for e in eids if e in events and not events[e]["done"]}
-    if not alive:
-        return None
-
-    eid = min(alive)
-    for other in alive - {eid}:
-        merged = events.pop(other)
-        tgt = events[eid]
-        tgt["lat_min"] = min(tgt["lat_min"], merged["lat_min"])
-        tgt["lat_max"] = max(tgt["lat_max"], merged["lat_max"])
-        tgt["lon_min"] = min(tgt["lon_min"], merged["lon_min"])
-        tgt["lon_max"] = max(tgt["lon_max"], merged["lon_max"])
-        tgt["peak"] = max(tgt["peak"], merged["peak"])
-        tgt["peak_area_km2"] = max(tgt["peak_area_km2"], merged["peak_area_km2"])
-        tgt["start"] = min(tgt["start"], merged["start"])
-        for k, v in list(cur_map.items()):
-            if v == other:
-                cur_map[k] = eid
-    return eid
-
-
-def _terminate_declined_events(
-    events: Dict[int, Dict],
-    cur_map: Dict[int, int],
-) -> None:
-    """Mark events as done if absent today or below 50% of peak area.
-
-    Args:
-        events: Mutable dict of all live events keyed by event ID.
-        cur_map: Mapping from current-day blob label to event ID.
-    """
-    active = set(cur_map.values())
-    for eid, ev in events.items():
-        if ev["done"]:
-            continue
-        if eid not in active:
-            ev["done"] = True
-        elif ev["area"] < AREA_DECLINE_FRACTION * ev["peak"]:
-            ev["done"] = True
-
-
-def detect_events(
-    filtered_mask: np.ndarray,
-    dates: np.ndarray,
-    lats: np.ndarray,
-    lons: np.ndarray,
-    event_type: str,
-    area_grid: Optional[np.ndarray] = None,
-    min_seed_area_km2: float = 0.0,
-) -> List[Dict]:
-    """Track spatiotemporal events from a filtered boolean mask.
-
-    New events are only seeded when the current-day blob AND the prior
-    ``MIN_CONSECUTIVE_DAYS - 1`` days share a single contiguous region
-    of >= ``min_seed_area_km2``. The joint intersection of the blob
-    footprint with ALL prior filtered masks is computed at once; the
-    largest connected component in that intersection must meet the
-    area threshold. This ensures every seeding point was continuously
-    active for the full window—checking each prior day independently
-    would allow scattered non-contiguous cells to satisfy the threshold
-    across different days. Blobs that fail but overlap an already-
-    established event still extend it.
-
-    Args:
-        filtered_mask: Boolean array of shape (time, lat, lon) with
-            consecutive-day filtering already applied.
-        dates: 1-D array of date labels aligned with axis 0.
-        lats: 1-D latitude array aligned with axis 1.
-        lons: 1-D longitude array aligned with axis 2.
-        event_type: Label string stored in each returned event dict.
-        area_grid: Optional 2-D array (lat, lon) of grid-cell areas
-            in km².
-        min_seed_area_km2: Minimum contiguous area in km² that the
-            blob footprint must satisfy on each of MIN_CONSECUTIVE_DAYS
-            days to seed a new event. Default is 0.0 (all blobs seed).
-
-    Returns:
-        List of event dicts with keys type, start, end, lat_min,
-        lat_max, lon_min, lon_max, peak, peak_area_km2, area, done,
-        initial_bbox.
-    """
-    n_days = filtered_mask.shape[0]
-    events: Dict[int, Dict] = {}
-    next_id = 1
-    prev_labels: Optional[np.ndarray] = None
-    prev_n_obj: int = 0
-    prev_map: Dict[int, int] = {}
-
-    for di in range(n_days):
-        day = filtered_mask[di]
-
-        if not day.any():
-            for ev in events.values():
-                if not ev["done"]:
-                    ev["done"] = True
-            prev_labels = None
-            prev_n_obj = 0
-            prev_map = {}
-            continue
-
-        labels, n_obj = ndimage.label(day)
-        labels = labels.astype(np.int32)
-        cur_map: Dict[int, int] = {}
-
-        overlap_mat: Optional[np.ndarray] = None
-        if prev_labels is not None and n_obj > 0 and prev_n_obj > 0:
-            overlap_mat = _count_overlaps_nb(
-                prev_labels,
-                labels,
-                prev_n_obj,
-                n_obj,
-            )
-
-        for oid in range(1, n_obj + 1):
-            om = labels == oid
-            area = int(om.sum())
-            li, lo = np.where(om)
-            blob_area_km2 = (
-                float(area_grid[li, lo].sum()) if area_grid is not None else 0.0
-            )
-
-            eid = _resolve_event(
-                oid,
-                overlap_mat,
-                prev_map,
-                events,
-                cur_map,
-            )
-
-            if eid is None:
-                if blob_area_km2 < min_seed_area_km2:
-                    continue
-                # Strict joint-area check: the blob's footprint must
-                # also have been >= min_seed_area_km2 on each of the
-                # prior MIN_CONSECUTIVE_DAYS-1 days. Skip seeding when
-                # insufficient history is available.
-                if min_seed_area_km2 > 0:
-                    if di < MIN_CONSECUTIVE_DAYS - 1:
-                        continue
-                    # Require a single contiguous region that was active
-                    # on the current day AND every one of the prior
-                    # MIN_CONSECUTIVE_DAYS-1 days. Checking each prior
-                    # day independently would allow non-contiguous,
-                    # scattered cells to satisfy the threshold across
-                    # different days. The joint intersection enforces
-                    # that every point in the qualifying region persisted
-                    # continuously for the full window.
-                    joint_mask = om.copy()
-                    for k in range(1, MIN_CONSECUTIVE_DAYS):
-                        joint_mask &= filtered_mask[di - k]
-                    if not joint_mask.any():
-                        continue
-                    j_lbl, j_n = ndimage.label(joint_mask)
-                    if j_n == 0:
-                        continue
-                    best_area = max(
-                        float(area_grid[j_lbl == c].sum())
-                        if area_grid is not None
-                        else float((j_lbl == c).sum())
-                        for c in range(1, j_n + 1)
-                    )
-                    if best_area < min_seed_area_km2:
-                        continue
-                eid = next_id
-                next_id += 1
-                _lat_min = float(lats[li].min())
-                _lat_max = float(lats[li].max())
-                _lon_min = float(lons[lo].min())
-                _lon_max = float(lons[lo].max())
-                events[eid] = {
-                    "type": event_type,
-                    "start": dates[di],
-                    "end": dates[di],
-                    "lat_min": _lat_min,
-                    "lat_max": _lat_max,
-                    "lon_min": _lon_min,
-                    "lon_max": _lon_max,
-                    "peak": area,
-                    "peak_area_km2": blob_area_km2,
-                    "area": area,
-                    "done": False,
-                    "initial_bbox": (
-                        _lat_min,
-                        _lat_max,
-                        _lon_min,
-                        _lon_max,
-                    ),
-                }
-            else:
-                ev = events[eid]
-                ev["end"] = dates[di]
-                ev["peak_area_km2"] = max(ev["peak_area_km2"], blob_area_km2)
-                ev["lat_min"] = min(ev["lat_min"], float(lats[li].min()))
-                ev["lat_max"] = max(ev["lat_max"], float(lats[li].max()))
-                ev["lon_min"] = min(ev["lon_min"], float(lons[lo].min()))
-                ev["lon_max"] = max(ev["lon_max"], float(lons[lo].max()))
-                ev["peak"] = max(ev["peak"], area)
-                ev["area"] = area
-
-            cur_map[oid] = eid
-
-        _terminate_declined_events(events, cur_map)
-        prev_labels = labels
-        prev_n_obj = n_obj
-        prev_map = cur_map
-
-    for ev in events.values():
-        ev["done"] = True
-
-    return list(events.values())
-
-
-def include_temps_with_events(
-    events: List[Dict],
-    t2m_daily_np: np.ndarray,
-    exc_filt: np.ndarray,
-    dates: np.ndarray,
-    lats: np.ndarray,
-    lons: np.ndarray,
-) -> List[Dict]:
-    """Add mean and minimum temperature (C) to each event dict.
-
-    Uses a pre-computed global daily-mean temperature array so that
-    all events share a single ERA5 fetch rather than making one Dask
-    compute call per event. Statistics are computed over exceedant
-    (active) grid point-days within each event's expanded bounding
-    box and time window.
-
-    Args:
-        events: Event dicts with lat_min/max, lon_min/max, start, end.
-        t2m_daily_np: Float32 array (days, lat, lon) of daily-mean
-            2 m temperature in Celsius, aligned with dates/lats/lons.
-        exc_filt: Boolean array (time, lat, lon) from
-            apply_consecutive_filter, aligned with dates/lats/lons.
-        dates: 1-D datetime64 array aligned with axis 0.
-        lats: 1-D latitude array aligned with axis 1.
-        lons: 1-D longitude array aligned with axis 2.
-
-    Returns:
-        The same event list with mean_temp_c and min_temp_c added
-        (NaN when no active points exist).
-    """
-
-    def _idx(arr: np.ndarray, val: float) -> int:
-        return int(np.argmin(np.abs(arr - val)))
-
-    for ev in events:
-        start_date = np.datetime64(ev["start"])
-        end_date = np.datetime64(ev["end"])
-        t_indices = np.where((dates >= start_date) & (dates <= end_date))[0]
-
-        idx_s = _idx(lats, ev["lat_min"])
-        idx_n = _idx(lats, ev["lat_max"])
-        idx_w = _idx(lons, ev["lon_min"])
-        idx_e = _idx(lons, ev["lon_max"])
-
-        exc_ev = exc_filt[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
-        t2m_ev = t2m_daily_np[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
-
-        n_days = min(t2m_ev.shape[0], exc_ev.shape[0])
-        active_temps = t2m_ev[:n_days][exc_ev[:n_days]]
-        if active_temps.size > 0:
-            ev["mean_temp_c"] = round(float(active_temps.mean()), 2)
-            ev["min_temp_c"] = round(float(active_temps.min()), 2)
-        else:
-            ev["mean_temp_c"] = float("nan")
-            ev["min_temp_c"] = float("nan")
-
-    return events
-
-
-def events_to_dataframe(
-    events: List[Dict],
-    min_gridpoints: int = MIN_GRIDPOINTS,
-    min_area_km2: float = MIN_AREA_KM2,
-) -> pd.DataFrame:
-    """Convert event dicts to a labelled DataFrame.
-
-    Args:
-        events: Raw event dicts from detect_events (after enrichment).
-        min_gridpoints: Drop events below this peak grid-point count.
-        min_area_km2: Drop events below this peak area (km²).
-
-    Returns:
-        DataFrame with columns label, event_type, start_date,
-        end_date, latitude_min/max, longitude_min/max,
-        max_consecutive_days, mean_consecutive_days, mean_temp_c,
-        min_temp_c, sorted by start_date. Events with fewer than
-        MIN_CONSECUTIVE_DAYS consecutive days are also excluded.
-    """
-    columns = [
-        "label",
-        "event_type",
-        "start_date",
-        "end_date",
-        "latitude_min",
-        "latitude_max",
-        "longitude_min",
-        "longitude_max",
-        "max_consecutive_days",
-        "mean_consecutive_days",
-        "mean_temp_c",
-        "min_temp_c",
-    ]
-    if not events:
-        return pd.DataFrame(columns=columns)
-
-    n_before = len(events)
-    events = [
-        e
-        for e in events
-        if e["peak"] >= min_gridpoints
-        and e.get("peak_area_km2", 0.0) >= min_area_km2
-        and e.get("max_consecutive_days", 0) >= MIN_CONSECUTIVE_DAYS
-    ]
-    logger.info(
-        "  Filtered %d events (< %d pts, < %.0f km², or < %d consec days); %d remain",
-        n_before - len(events),
-        min_gridpoints,
-        min_area_km2,
-        MIN_CONSECUTIVE_DAYS,
-        len(events),
-    )
-
-    if not events:
-        return pd.DataFrame(columns=columns)
-
-    rows = [
-        {
-            "event_type": e["type"],
-            "start_date": str(e["start"]),
-            "end_date": str(e["end"]),
-            "latitude_min": e["lat_min"],
-            "latitude_max": e["lat_max"],
-            "longitude_min": e["lon_min"],
-            "longitude_max": e["lon_max"],
-            "max_consecutive_days": e.get("max_consecutive_days", 0),
-            "mean_consecutive_days": e.get("mean_consecutive_days", 0.0),
-            "mean_temp_c": e.get("mean_temp_c", float("nan")),
-            "min_temp_c": e.get("min_temp_c", float("nan")),
-        }
-        for e in events
-    ]
-    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
-    df.insert(0, "label", range(1, len(df) + 1))
-    return df
-
-
-def _add_lon_rect(
-    ax: plt.Axes,
-    lon_min_0360: float,
-    lon_max_0360: float,
-    lat_min: float,
-    lat_max: float,
-    color: tuple,
-    linewidth: float = 0.8,
-    zorder: int = 4,
-) -> None:
-    """Add a lat/lon rectangle to ax, splitting at the antimeridian if needed.
-
-    Inputs use 0-360 longitude. When the event wraps around the 0°/360°
-    boundary the bounding box is split into two rectangles so that
-    matplotlib does not draw an inverted or near-zero-width patch.
-    """
-
-    def _to_180(lon: float) -> float:
-        return lon if lon <= 180 else lon - 360
-
-    lmin = _to_180(lon_min_0360)
-    lmax = _to_180(lon_max_0360)
-
-    def _rect(x0: float, x1: float) -> mpatches.Rectangle:
-        return mpatches.Rectangle(
-            (x0, lat_min),
-            x1 - x0,
-            lat_max - lat_min,
-            linewidth=linewidth,
-            edgecolor=color,
-            facecolor=(*color[:3], 0.25),
-            transform=ccrs.PlateCarree(),
-            zorder=zorder,
-        )
-
-    if lmin <= lmax:
-        ax.add_patch(_rect(lmin, lmax))
-    else:
-        # Event wraps across the antimeridian – draw two segments.
-        ax.add_patch(_rect(lmin, 180.0))
-        ax.add_patch(_rect(-180.0, lmax))
-
-
-def plot_events_global(
-    df: pd.DataFrame,
-    event_type: str,
-    title: str,
-    output_path: str,
-) -> None:
-    """Plot detected events as bounding boxes on a global Robinson map.
-
-    Each event is drawn as a rectangle coloured by its start date.
-
-    Args:
-        df: DataFrame with latitude_min/max, longitude_min/max,
-            start_date columns.
-        event_type: 'heat_wave' or 'cold_snap' for colormap.
-        title: Figure title.
-        output_path: Destination PNG file path.
-    """
-    cmap_name = "Reds" if event_type == "heat_wave" else "Blues"
-    cmap = plt.colormaps.get_cmap(cmap_name)
-
-    fig = plt.figure(figsize=(18, 9))
-    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
-    ax.set_global()
-    ax.add_feature(cfeature.OCEAN, facecolor="lightcyan", zorder=0)
-    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=1)
-    ax.coastlines(linewidth=0.5, zorder=3)
-    ax.add_feature(cfeature.BORDERS, linewidth=0.4, edgecolor="grey", zorder=3)
-    ax.gridlines(draw_labels=False, linewidth=0.3, color="grey", alpha=0.5, zorder=2)
-
-    if df.empty:
-        ax.set_title(f"{title}\n(no events)", fontsize=11, loc="left")
-        fig.savefig(output_path, dpi=150, bbox_inches="tight")
-        plt.close(fig)
-        logger.info("Events plot saved to %s (no events)", output_path)
-        return
-
-    start_dates = pd.to_datetime(df["start_date"])
-    date_min = start_dates.min()
-    date_max = start_dates.max()
-    span = max((date_max - date_min).days, 1)
-    norm = mcolors.Normalize(vmin=0, vmax=span)
-
-    for _, row in df.iterrows():
-        days = (pd.to_datetime(row["start_date"]) - date_min).days
-        color = cmap(norm(days))
-        _add_lon_rect(
-            ax,
-            row["longitude_min"],
-            row["longitude_max"],
-            row["latitude_min"],
-            row["latitude_max"],
-            color,
-        )
-
-    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
-    sm.set_array([])
-    cbar = fig.colorbar(sm, ax=ax, orientation="vertical", fraction=0.02, pad=0.02)
-    tick_days = np.linspace(0, span, min(7, span + 1))
-    cbar.set_ticks(tick_days)
-    cbar.set_ticklabels(
-        [(date_min + pd.Timedelta(days=int(d))).strftime("%Y-%m-%d") for d in tick_days]
-    )
-    cbar.set_label("Event start date", fontsize=9)
-    ax.set_title(f"{title}\n{len(df)} events", fontsize=11, loc="left")
-    fig.savefig(output_path, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-    logger.info("Events plot saved to %s", output_path)
-
-
-def plot_events_high_consec(
-    df: pd.DataFrame,
-    title: str,
-    output_path: str,
-    min_consec: int = 5,
-) -> None:
-    """Plot events with >= min_consec days, coloured by consecutive days.
-
-    Uses the plasma colormap (neutral, not event-type specific).
-
-    Args:
-        df: DataFrame from events_to_dataframe with columns
-            latitude_min/max, longitude_min/max, max_consecutive_days.
-        title: Figure title.
-        output_path: Destination PNG file path.
-        min_consec: Minimum max_consecutive_days to include. Default 6.
-    """
-    sub = df[df["max_consecutive_days"] >= min_consec].copy()
-
-    fig = plt.figure(figsize=(18, 9))
-    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
-    ax.set_global()
-    ax.add_feature(cfeature.OCEAN, facecolor="lightcyan", zorder=0)
-    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=1)
-    ax.coastlines(linewidth=0.5, zorder=3)
-    ax.add_feature(cfeature.BORDERS, linewidth=0.4, edgecolor="grey", zorder=3)
-    ax.gridlines(draw_labels=False, linewidth=0.3, color="grey", alpha=0.5, zorder=2)
-
-    if sub.empty:
-        ax.set_title(
-            f"{title}\n(no events with >= {min_consec} consecutive days)",
-            fontsize=11,
-            loc="left",
-        )
-        fig.savefig(output_path, dpi=150, bbox_inches="tight")
-        plt.close(fig)
-        logger.info(
-            "High-consec plot saved to %s (no qualifying events)",
-            output_path,
-        )
-        return
-
-    vmin = min_consec
-    vmax = int(sub["max_consecutive_days"].max())
-    n_levels = vmax - vmin + 1
-    cmap = plt.colormaps.get_cmap("plasma_r").resampled(n_levels)
-    bin_edges = np.arange(vmin - 0.5, vmax + 1.5, 1)
-    norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
-
-    for _, row in sub.iterrows():
-        consec_val = int(row["max_consecutive_days"])
-        color = cmap(norm(consec_val))
-        _add_lon_rect(
-            ax,
-            row["longitude_min"],
-            row["longitude_max"],
-            row["latitude_min"],
-            row["latitude_max"],
-            color,
-        )
-
-    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
-    sm.set_array([])
-    cbar = fig.colorbar(sm, ax=ax, orientation="vertical", fraction=0.02, pad=0.02)
-    cbar.set_ticks(range(vmin, vmax + 1))
-    cbar.set_label("Max Consecutive Days", fontsize=9)
-    ax.set_title(f"{title}\n{len(sub)} events", fontsize=11, loc="left")
-    fig.savefig(output_path, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-    logger.info("High-consec plot saved to %s", output_path)
-
-
-def plot_event_consec_maps(
-    df: pd.DataFrame,
-    exc_filt: np.ndarray,
-    dates: np.ndarray,
-    lats: np.ndarray,
-    lons: np.ndarray,
-    stem: str,
-    min_consec: int = 3,
-) -> None:
-    """Plot a per-event consecutive-days map for each qualifying event.
-
-    For each row in df with max_consecutive_days >= min_consec, slices
-    exc_filt to the event's bounding box and time window, computes
-    per-grid-point max consecutive days, and saves a pcolormesh plot
-    using the plasma colormap.
-
-    Args:
-        df: DataFrame from events_to_dataframe with columns label,
-            start_date, end_date, latitude_min/max, longitude_min/max,
-            max_consecutive_days.
-        exc_filt: Boolean array (time, lat, lon) from
-            apply_consecutive_filter, aligned with dates/lats/lons.
-        dates: 1-D datetime64 array aligned with exc_filt axis 0.
-        lats: 1-D latitude array aligned with exc_filt axis 1.
-        lons: 1-D longitude array (0-360) aligned with exc_filt axis 2.
-        stem: Output path stem; each plot saved as
-            {stem}_event_{label:04d}_consec.png.
-        min_consec: Minimum max_consecutive_days to plot. Default 6.
-    """
-    qualifying = df[df["max_consecutive_days"] >= min_consec]
-    if qualifying.empty:
-        logger.info(
-            "No events with >= %d consecutive days to plot individually.",
-            min_consec,
-        )
-        return
-
-    logger.info(
-        "Plotting %d per-event consecutive-days maps...",
-        len(qualifying),
-    )
-
-    def _idx(arr: np.ndarray, val: float) -> int:
-        return int(np.argmin(np.abs(arr - val)))
-
-    plot_lons = np.where(lons > 180, lons - 360.0, lons)
-
-    for _, row in qualifying.iterrows():
-        label = int(row["label"])
-        start_date = np.datetime64(row["start_date"])
-        end_date = np.datetime64(row["end_date"])
-        t_indices = np.where((dates >= start_date) & (dates <= end_date))[0]
-
-        idx_s = _idx(lats, row["latitude_min"])
-        idx_n = _idx(lats, row["latitude_max"])
-        idx_w = _idx(lons, row["longitude_min"])
-        idx_e = _idx(lons, row["longitude_max"])
-
-        ev_filt = exc_filt[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
-        consec = max_consecutive_days(ev_filt)
-
-        ev_lats = lats[idx_s : idx_n + 1]
-        ev_lons = plot_lons[idx_w : idx_e + 1]
-
-        plot_data = consec.astype(float)
-        plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
-        valid = plot_data[~np.isnan(plot_data)]
-        if valid.size == 0:
-            continue
-
-        vmax = int(valid.max())
-        n_levels = vmax - MIN_CONSECUTIVE_DAYS + 1
-        cmap = plt.colormaps.get_cmap("plasma_r").resampled(n_levels)
-        bin_edges = np.arange(MIN_CONSECUTIVE_DAYS - 0.5, vmax + 1.5, 1)
-        norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
-
-        fig, ax = plt.subplots(
-            subplot_kw={"projection": ccrs.PlateCarree()},
-            figsize=(10, 8),
-        )
-        ax.set_extent(
-            [
-                float(ev_lons.min()) - 1,
-                float(ev_lons.max()) + 1,
-                float(ev_lats.min()) - 1,
-                float(ev_lats.max()) + 1,
-            ],
-            crs=ccrs.PlateCarree(),
-        )
-        ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
-        im = ax.pcolormesh(
-            ev_lons,
-            ev_lats,
-            plot_data,
-            cmap=cmap,
-            norm=norm,
-            transform=ccrs.PlateCarree(),
-            shading="auto",
-        )
-        ax.coastlines(linewidth=0.5, zorder=10)
-        ax.add_feature(cfeature.BORDERS, linewidth=0.6, edgecolor="grey")
-        ax.add_feature(cfeature.STATES, linewidth=0.4, edgecolor="lightgrey")
-
-        cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
-        cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
-        cbar.set_label("Consecutive Days", fontsize=12)
-
-        start_str = str(row["start_date"])[:10]
-        end_str = str(row["end_date"])[:10]
-        ax.set_title(
-            f"Event {label}: Consecutive Exceedance Days\n{start_str} to {end_str}",
-            loc="left",
-            fontsize=13,
-        )
-        out_path = f"{stem}_event_{label:04d}_consec.png"
-        fig.savefig(out_path, dpi=200, bbox_inches="tight")
-        plt.close(fig)
-        logger.info("  Event %04d saved to %s", label, out_path)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description=("Detect temperature exceedance events globally from ERA5."),
-    )
-    parser.add_argument(
-        "--start-date",
-        required=True,
-        help="Start date YYYY-MM-DD",
-    )
-    parser.add_argument(
-        "--end-date",
-        required=True,
-        help="End date YYYY-MM-DD",
-    )
-    parser.add_argument(
-        "--output",
-        default="events_global.csv",
-        help="Output CSV path stem (actual file will be {stem}_events.csv)",
-    )
-    parser.add_argument(
-        "--n-workers",
-        type=int,
-        default=4,
-        help="Number of dask workers",
-    )
-    parser.add_argument(
-        "--dask-batch",
-        type=int,
-        default=1,
-        help=(
-            "Number of monthly chunks to submit to Dask at once. "
-            "Default 1 (one month per batch). Dask still parallelizes "
-            "daily time steps within each month across workers. Larger "
-            "values increase cross-month parallelism but cause "
-            "rechunk-merge bottlenecks when months span many zarr "
-            "chunks."
-        ),
-    )
-    parser.add_argument(
-        "--quantile-lower",
-        type=float,
-        default=None,
-        help=(
-            f"Lower-bound climatology quantile ({VALID_QUANTILES}). "
-            "At least one of --quantile-lower or --quantile-upper required."
-        ),
-    )
-    parser.add_argument(
-        "--operator-lower",
-        default=">",
-        help="Comparison operator for the lower bound (default: >)",
-    )
-    parser.add_argument(
-        "--quantile-upper",
-        type=float,
-        default=None,
-        help=(
-            f"Upper-bound climatology quantile ({VALID_QUANTILES}). "
-            "Combine with --quantile-lower to define a band."
-        ),
-    )
-    parser.add_argument(
-        "--operator-upper",
-        default="<",
-        help="Comparison operator for the upper bound (default: <)",
-    )
-    parser.add_argument(
-        "--lat-min",
-        type=float,
-        default=-60.0,
-        help=(
-            "Minimum latitude to include in detection. Default -60.0 "
-            "(excludes Antarctica, whose polar grid geometry produces "
-            "full-circumference ring blobs that trivially meet the area "
-            "threshold). Pass -90.0 explicitly to include Antarctica."
-        ),
-    )
-    parser.add_argument(
-        "--lat-max",
-        type=float,
-        default=90.0,
-        help="Maximum latitude to include in detection. Default 90.0",
-    )
-    parser.add_argument(
-        "--chunk-months",
-        type=int,
-        default=1,
-        help=(
-            "Process the exceedance mask this many months at a time "
-            "to stay within Dask worker memory limits. Set to 0 to "
-            "compute the full date range at once. Default is 1."
-        ),
-    )
-    parser.add_argument(
-        "--min-area-km2",
-        type=float,
-        default=200_000.0,
-        help=(
-            "Minimum contiguous blob area in km² required to seed a "
-            "new event AND on each of the prior MIN_CONSECUTIVE_DAYS-1 "
-            "days (joint-area check). Always applied, including in "
-            "sensitivity mode. Default 200 000."
-        ),
-    )
-    parser.add_argument(
-        "--sensitivity-thresholds",
-        type=float,
-        nargs="+",
-        default=None,
-        metavar="KM2",
-        help=(
-            "Count surviving events at each supplied area threshold "
-            "(km²) and write a summary CSV. Events are seeded at "
-            "--min-area-km2 first; thresholds below that value will "
-            "equal the seed-filter count. Example: "
-            "--sensitivity-thresholds 200000 300000 500000 1000000"
-        ),
-    )
-    args = parser.parse_args()
-
-    if args.quantile_lower is None and args.quantile_upper is None:
-        parser.error(
-            "At least one of --quantile-lower or --quantile-upper must be provided."
-        )
-
-    ql, qu = args.quantile_lower, args.quantile_upper
-    if ql is not None and qu is not None:
-        event_type = f"q{ql:.2f}-q{qu:.2f}"
-    elif ql is not None:
-        event_type = f"q{ql:.2f}+"
-    else:
-        event_type = f"q{qu:.2f}-"
-
-    if ql is not None and ql >= 0.5:
-        plot_event_type = "heat_wave"
-    elif qu is not None and qu <= 0.5:
-        plot_event_type = "cold_snap"
-    else:
-        plot_event_type = "heat_wave"
-
-    is_sensitivity = args.sensitivity_thresholds is not None
-    if is_sensitivity:
-        below = [t for t in args.sensitivity_thresholds if t < args.min_area_km2]
-        if below:
-            logger.warning(
-                "Sensitivity thresholds %s are below the seed filter "
-                "(%.0f km²); those counts will equal the seed-filter total.",
-                below,
-                args.min_area_km2,
-            )
-
-    wall_start = time_module.time()
-    client = Client(LocalCluster(n_workers=args.n_workers))
-    logger.info("Dask dashboard: %s", client.dashboard_link)
-
-    logger.info("Pre-warming numba JIT...")
-    _tiny = np.zeros((2, 2), dtype=np.int32)
-    _tiny[0, 0] = 1
-    _tiny[1, 1] = 1
-    _count_overlaps_nb(_tiny, _tiny, 1, 1)
-    logger.info("  numba ready")
-
-    logger.info("Opening ERA5 data...")
-    t2m = open_era5_t2m(args.start_date, args.end_date)
-    if args.lat_min != -90.0 or args.lat_max != 90.0:
-        t2m = t2m.sel(latitude=slice(args.lat_min, args.lat_max))
-        logger.info(
-            "  Latitude filtered to [%.1f, %.1f]",
-            args.lat_min,
-            args.lat_max,
-        )
-    logger.info("  sizes=%s", dict(t2m.sizes))
-
-    logger.info(
-        "Loading climatology bounds (lower=%s, upper=%s)...",
-        args.quantile_lower,
-        args.quantile_upper,
-    )
-    clim_lower, clim_upper = get_climatology_bounds(
-        q_lower=args.quantile_lower,
-        q_upper=args.quantile_upper,
-    )
-
-    logger.info("Building land mask and grid-cell area array...")
-    land_mask = build_land_mask(t2m.longitude, t2m.latitude)
-    land_mask_np = land_mask.values.astype(bool)
-    area_grid = compute_grid_cell_area(t2m.latitude.values, t2m.longitude.values)
-
-    tdim = detect_time_dim(t2m)
-
-    # ── Exceedance mask + daily temperature (combined, parallel) ──────
-    # Both passes read the same ERA5 data month-by-month. Combining them
-    # into one loop and submitting all lazy chunks to dask.compute() at
-    # once lets the Dask workers process months in parallel, halving ERA5
-    # reads and eliminating sequential chunk overhead.
-    t0 = time_module.time()
-    if args.chunk_months > 0:
-        logger.info(
-            "Building lazy graphs for exceedance mask + temperature "
-            "(%d-month chunks, parallel)...",
-            args.chunk_months,
-        )
-        month_starts = pd.date_range(args.start_date, args.end_date, freq="MS")
-        chunk_labels = []
-        exc_lazy_list = []
-        t2m_lazy_list = []
-        for i in range(0, len(month_starts), args.chunk_months):
-            ms = month_starts[i]
-            me = month_starts[min(i + args.chunk_months, len(month_starts)) - 1]
-            me = me + pd.offsets.MonthEnd(1)
-            chunk_labels.append(f"{ms.strftime('%Y-%m')} - {me.strftime('%Y-%m')}")
-            t2m_chunk = t2m.sel({tdim: slice(str(ms.date()), str(me.date()))})
-            exc_lazy_list.append(
-                build_exceedance_mask(
-                    t2m_chunk,
-                    clim_lower,
-                    clim_upper,
-                    land_mask,
-                    op_lower=args.operator_lower,
-                    op_upper=args.operator_upper,
-                )
-            )
-            t2m_lazy_list.append(t2m_chunk.resample({tdim: "1D"}).mean())
-
-        n_chunks = len(exc_lazy_list)
-        batch = args.dask_batch
-        logger.info(
-            "  Computing %d chunks, %d per batch, via Dask...",
-            n_chunks,
-            batch,
-        )
-        exc_parts: list = []
-        t2m_parts: list = []
-        for b0 in range(0, n_chunks, batch):
-            b1 = min(b0 + batch, n_chunks)
-            b_exc = exc_lazy_list[b0:b1]
-            b_t2m = t2m_lazy_list[b0:b1]
-            b_sz = b1 - b0
-            b_results = dask.compute(*b_exc, *b_t2m)
-            exc_parts.extend(b_results[:b_sz])
-            t2m_parts.extend(b_results[b_sz:])
-            for label in chunk_labels[b0:b1]:
-                logger.info("  chunk %s done", label)
-
-        exc_da = xr.concat(exc_parts, dim=tdim)
-        t2m_daily_da = xr.concat(t2m_parts, dim=tdim)
-        del exc_parts, t2m_parts
-    else:
-        logger.info(
-            "Building lazy graphs (all-at-once) for exceedance mask + temperature..."
-        )
-        exc_lazy = build_exceedance_mask(
-            t2m,
-            clim_lower,
-            clim_upper,
-            land_mask,
-            op_lower=args.operator_lower,
-            op_upper=args.operator_upper,
-        )
-        t2m_lazy = t2m.resample({tdim: "1D"}).mean()
-        exc_da, t2m_daily_da = dask.compute(exc_lazy, t2m_lazy)
-
-    logger.info(
-        "  exceedance mask + temperature done in %.1f s",
-        time_module.time() - t0,
-    )
-
-    dates = exc_da[tdim].values
-    lats = exc_da.latitude.values
-    lons = exc_da.longitude.values
-    exc_np = exc_da.values.astype(bool)
-    del exc_da
-
-    t2m_daily_np = (t2m_daily_da.values - 273.15).astype(np.float32)
-    del t2m_daily_da
-
-    logger.info(
-        "Applying %d-day consecutive filter...",
-        MIN_CONSECUTIVE_DAYS,
-    )
-    exc_filt = apply_consecutive_filter(exc_np)
-    logger.info(
-        "  %d -> %d True cells",
-        exc_np.sum(),
-        exc_filt.sum(),
-    )
-    del exc_np
-
-    logger.info(
-        "Detecting events (min_seed_area_km2=%.0f)...",
-        args.min_area_km2,
-    )
-    t0 = time_module.time()
-    events = detect_events(
-        exc_filt,
-        dates,
-        lats,
-        lons,
-        event_type,
-        area_grid=area_grid,
-        min_seed_area_km2=args.min_area_km2,
-    )
-    logger.info("  %d raw events (%.1f s)", len(events), time_module.time() - t0)
-
-    logger.info(
-        "Expanding event bounds (%d-deg steps)...",
-        EXPANSION_DEGREES,
-    )
-    t0 = time_module.time()
-    events = joblib.Parallel(n_jobs=-1, prefer="threads")(
-        joblib.delayed(expand_event_bounds)(
-            ev, exc_filt, dates, lats, lons, land_mask_np
-        )
-        for ev in events
-    )
-    logger.info("  done in %.1f s", time_module.time() - t0)
-
-    logger.info("Computing per-event temperature statistics...")
-    t0 = time_module.time()
-    events = include_temps_with_events(
-        events, t2m_daily_np, exc_filt, dates, lats, lons
-    )
-    del t2m_daily_np
-    logger.info("  done in %.1f s", time_module.time() - t0)
-
-    stem = str(pathlib.Path(args.output).with_suffix(""))
-
-    df = events_to_dataframe(events, min_area_km2=args.min_area_km2)
-    events_csv = f"{stem}_events.csv"
-    df.to_csv(events_csv, index=False)
-    logger.info("%d events written to %s", len(df), events_csv)
-
-    # ── Global consecutive-days map ───────────────────────────────────
-    logger.info("Computing max-consecutive-days plot...")
-    consec = max_consecutive_days(exc_filt)
-    plot_consecutive_map(
-        consec,
-        lats,
-        lons,
-        cast(Literal["heat_wave", "cold_snap"], plot_event_type),
-        title=(
-            f"Consecutive Exceedance Days ({event_type})"
-            f"\n{args.start_date} to {args.end_date}"
-        ),
-        output_path=f"{stem}_consec.png",
-    )
-    del consec
-
-    # ── Overview and high-consec bounding-box plots ───────────────────
-    plot_events_global(
-        df,
-        event_type=plot_event_type,
-        title=(
-            f"Detected events ({event_type.replace('_', ' ')})"
-            f"\n{args.start_date} to {args.end_date}"
-        ),
-        output_path=f"{stem}_events.png",
-    )
-    plot_events_high_consec(
-        df,
-        title=(
-            f"Events \u22656 consecutive days ({event_type.replace('_', ' ')})"
-            f"\n{args.start_date} to {args.end_date}"
-        ),
-        output_path=f"{stem}_high_consec.png",
-    )
-
-    # ── Per-event consecutive-days maps (>= 6 consec days) ───────────
-    plot_event_consec_maps(df, exc_filt, dates, lats, lons, stem)
-
-    del exc_filt
-
-    # ── Sensitivity table ─────────────────────────────────────────────
-    if is_sensitivity:
-        thresholds = sorted(args.sensitivity_thresholds)
-        logger.info(
-            "Sensitivity test: counting events at %d thresholds...",
-            len(thresholds),
-        )
-        rows = []
-        for thresh in thresholds:
-            df_thresh = events_to_dataframe(events, min_area_km2=thresh)
-            rows.append({"area_threshold_km2": thresh, "n_events": len(df_thresh)})
-        sens_df = pd.DataFrame(rows)
-        print(sens_df.to_string(index=False))
-        sens_path = f"{stem}_sensitivity.csv"
-        sens_df.to_csv(sens_path, index=False)
-        logger.info("Sensitivity table saved to %s", sens_path)
-
-    elapsed = time_module.time() - wall_start
-    logger.info("Done in %.1f s", elapsed)
-    client.close()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/data_prep/temperature_events.py b/data_prep/temperature_events.py
new file mode 100644
index 00000000..808457e2
--- /dev/null
+++ b/data_prep/temperature_events.py
@@ -0,0 +1,2756 @@
+#!/usr/bin/env python3
+"""ERA5 temperature-event tooling: detection, expansion, and plotting.
+
+Three subcommands, each producing csv and/or png output:
+
+  plot    Render the consecutive-exceedance-days map for a single
+          case_id_number from events.yaml.
+
+  case    Re-expand the bounding boxes of curated heat_wave / freeze
+          cases in base_temp_events.yaml and write the refreshed
+          bounds back to events.yaml.
+
+  global  Scan ERA5 2m temperature over a date range, detect events
+          that stay within a climatology-quantile band for at least
+          MIN_CONSECUTIVE_DAYS days over land, and write a csv plus
+          a set of summary plots.
+
+Examples:
+
+  python temperature_events.py plot --case-id-number 30 \\
+      --quantile 0.85 --operator ">="
+
+  python temperature_events.py case \\
+      --output heat_cold_yaml.csv --n-workers 4
+
+  python temperature_events.py global \\
+      --start-date 2023-06-01 --end-date 2023-09-01 \\
+      --quantile-lower 0.85 --operator-lower ">=" \\
+      --output heat_q85_2023.csv
+"""
+
+import argparse
+import importlib
+import logging
+import operator as op_module
+import pathlib
+import time as time_module
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, cast
+
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import dask
+import joblib
+import matplotlib.colors as mcolors
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numba as nb
+import numpy as np
+import pandas as pd
+import regionmask
+import scipy.ndimage as ndimage
+import xarray as xr
+from dask.distributed import Client, LocalCluster
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from ruamel.yaml import YAML
+from xarray.core.indexes import IndexSelResult, PandasIndex, _query_slice
+from xarray.core.indexing import _expand_slice
+
+from extremeweatherbench import cases, defaults, inputs, regions
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(name)s %(levelname)s %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+MIN_CONSECUTIVE_DAYS = 3
+MIN_GRIDPOINTS = 500
+MIN_AREA_KM2 = 200000.0
+EXPANSION_DEGREES = 1
+MAX_SPATIAL_ITERATIONS = 20
+EDGE_VALIDITY_THRESHOLD = 0.5
+AREA_DECLINE_FRACTION = 0.5
+TEMPORAL_LOAD_BUFFER_DAYS = 14
+MAX_TEMPORAL_DAYS = 21
+
+VALID_QUANTILES = [0.10, 0.15, 0.25, 0.50, 0.75, 0.85, 0.90]
+
+_OP_MAP: dict[str, Callable] = {
+    ">": op_module.gt,
+    ">=": op_module.ge,
+    "<": op_module.lt,
+    "<=": op_module.le,
+    "==": op_module.eq,
+}
+
+_HW_COLOR = "#d73027"
+_FZ_COLOR = "#4575b4"
+_BOX_ALPHA = 0.35
+_BOX_EDGE_ALPHA = 0.85
+
+
+class PeriodicBoundaryIndex(PandasIndex):
+    """xarray index for a 1-D coordinate that wraps at a period.
+
+    Subclasses PandasIndex and intercepts slice queries so a selection
+    that crosses the period boundary (e.g. longitude 350 to 10) is
+    returned as two concatenated index arrays rather than an empty
+    slice.
+    """
+
+    period: float
+    _min: float
+    _max: float
+
+    __slots__ = ("index", "dim", "coord_dtype", "period", "_max", "_min")
+
+    def __init__(self, *args, period=360, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.period = period
+        self._min = self.index.min()
+        self._max = self.index.max()
+
+    @classmethod
+    def from_variables(self, variables, options):
+        obj = super().from_variables(variables, options={})
+        obj.period = options.get("period", obj.period)
+        return obj
+
+    def _wrap_periodically(self, label_value: float) -> float:
+        return self._min + (label_value - self._max) % self.period
+
+    def _split_slice_across_boundary(self, label: slice) -> np.ndarray:
+        """Return concatenated integer indices for a slice that wraps."""
+        first_slice = slice(label.start, self._max, label.step)
+        second_slice = slice(self._min, label.stop, label.step)
+
+        first_as_index_slice = _query_slice(self.index, first_slice)
+        second_as_index_slice = _query_slice(self.index, second_slice)
+
+        first_as_indices = _expand_slice(first_as_index_slice, self.index.size)
+        second_as_indices = _expand_slice(second_as_index_slice, self.index.size)
+
+        return np.concatenate([first_as_indices, second_as_indices])
+
+    def sel(
+        self, labels: dict[Any, Any], method=None, tolerance=None
+    ) -> IndexSelResult:
+        """Remap out-of-range labels back into the index range."""
+        assert len(labels) == 1
+        coord_name, label = next(iter(labels.items()))
+
+        if isinstance(label, slice):
+            start, stop, step = label.start, label.stop, label.step
+            if stop < start:
+                return super().sel({coord_name: []})
+
+            assert self._min < self._max
+
+            wrapped_start = self._wrap_periodically(label.start)
+            wrapped_stop = self._wrap_periodically(label.stop)
+            wrapped_label = slice(wrapped_start, wrapped_stop, step)
+
+            if wrapped_start < wrapped_stop:
+                return super().sel({coord_name: wrapped_label})
+            # Slice crosses the wrap boundary; split in two.
+            wrapped_indices = self._split_slice_across_boundary(wrapped_label)
+            return IndexSelResult({self.dim: wrapped_indices})
+
+        wrapped_label = self._wrap_periodically(label)  # type: ignore
+        return super().sel(
+            {coord_name: wrapped_label}, method=method, tolerance=tolerance
+        )
+
+    def __repr__(self) -> str:
+        return f"PeriodicBoundaryIndex(period={self.period})"
+
+
+def resolve_op(op_str: str) -> Callable:
+    """Return the callable for a comparison-operator string.
+
+    Args:
+        op_str: One of ``>``, ``>=``, ``<``, ``<=``, ``==``.
+
+    Raises:
+        ValueError: If ``op_str`` is not recognised.
+    """
+    if op_str not in _OP_MAP:
+        raise ValueError(f"Unknown operator {op_str!r}; choose from {list(_OP_MAP)}")
+    return _OP_MAP[op_str]
+
+
+def detect_time_dim(obj: xr.Dataset | xr.DataArray) -> str:
+    """Return the name of the time dimension (``valid_time`` or ``time``)."""
+    for name in ("valid_time", "time"):
+        if name in obj.dims:
+            return name
+    raise ValueError(f"No time dimension found. Available: {list(obj.dims)}")
+
+
+def _to_plot_lon(lon: float) -> float:
+    """Wrap 0-360 longitude to -180..180 for PlateCarree plotting."""
+    return lon - 360.0 if lon > 180.0 else lon
+
+
+def max_consecutive_days(mask_3d: np.ndarray) -> np.ndarray:
+    """Return the per-grid-point max run length of True along axis 0.
+
+    Args:
+        mask_3d: Boolean array of shape (time, lat, lon).
+
+    Returns:
+        Int32 array of shape (lat, lon) holding the longest run of
+        consecutive True values along the time axis for each cell.
+    """
+    nt, nlat, nlon = mask_3d.shape
+    flat = mask_3d.reshape(nt, -1).astype(np.int8)
+    result = np.zeros(flat.shape[1], dtype=np.int32)
+    for k in range(flat.shape[1]):
+        col = flat[:, k]
+        if not col.any():
+            continue
+        d = np.diff(np.concatenate(([0], col, [0])))
+        starts = np.where(d == 1)[0]
+        ends = np.where(d == -1)[0]
+        result[k] = int((ends - starts).max())
+    return result.reshape(nlat, nlon)
+
+
+def _consecutive_filter_np(
+    mask: np.ndarray,
+    min_days: int,
+    max_grace_days: int,
+) -> np.ndarray:
+    struct = np.zeros((min_days, 1, 1), dtype=bool)
+    struct[:, 0, 0] = True
+
+    # Strict min_days-long runs with no grace applied.
+    strict = (
+        ndimage.binary_dilation(
+            ndimage.binary_erosion(
+                mask,
+                structure=struct,
+                border_value=False,
+            ),
+            structure=struct,
+            border_value=False,
+        )
+        & mask
+    )
+
+    if max_grace_days <= 0:
+        return strict
+
+    # Bridge gaps of up to max_grace_days days.
+    close_k = np.zeros((2 * max_grace_days + 1, 1, 1), dtype=bool)
+    close_k[:, 0, 0] = True
+    filled = ndimage.binary_closing(
+        mask,
+        structure=close_k,
+        border_value=False,
+    )
+
+    filled_runs = (
+        ndimage.binary_dilation(
+            ndimage.binary_erosion(
+                filled,
+                structure=struct,
+                border_value=False,
+            ),
+            structure=struct,
+            border_value=False,
+        )
+        & filled
+    )
+
+    # Label each temporal run independently per grid point.
+    lbl_struct = np.zeros((3, 3, 3), dtype=int)
+    lbl_struct[0, 1, 1] = 1
+    lbl_struct[1, 1, 1] = 1
+    lbl_struct[2, 1, 1] = 1
+    labels, _ = ndimage.label(filled_runs, structure=lbl_struct)
+
+    # Keep only runs that contain a strict min_days-day block.
+    valid = np.unique(labels[strict & (labels > 0)])
+    return np.isin(labels, valid) & filled_runs
+
+
+def open_era5_t2m(start_date: str, end_date: str) -> xr.DataArray:
+    """Open ERA5 2m temperature lazily for an inclusive date range.
+
+    Restricts to 6-hourly timesteps (0/6/12/18 UTC) to match the
+    climatology base and sorts latitude to ascending order.
+
+    Args:
+        start_date: Inclusive start date string (YYYY-MM-DD).
+        end_date: Inclusive end date string (YYYY-MM-DD).
+    """
+    ds = xr.open_zarr(
+        inputs.ARCO_ERA5_FULL_URI,
+        storage_options={"token": "anon"},
+        chunks=None,
+    )
+    tdim = detect_time_dim(ds)
+    t2m = ds["2m_temperature"].sel({tdim: slice(start_date, end_date)})
+    six_hourly = t2m[tdim].dt.hour.isin([0, 6, 12, 18])
+    t2m = t2m.sel({tdim: six_hourly})
+    t2m = t2m.chunk({"latitude": -1, "longitude": -1})
+    return t2m.sortby("latitude")
+
+
+def get_climatology_bounds(
+    q_lower: Optional[float] = None,
+    q_upper: Optional[float] = None,
+) -> Tuple[Optional[xr.DataArray], Optional[xr.DataArray]]:
+    """Return climatology DataArrays for the lower and/or upper bound.
+
+    Args:
+        q_lower: Quantile for the lower bound, or None.
+        q_upper: Quantile for the upper bound, or None.
+
+    Raises:
+        ValueError: If both ``q_lower`` and ``q_upper`` are None.
+    """
+    if q_lower is None and q_upper is None:
+        raise ValueError("At least one of q_lower or q_upper must be set.")
+    clim_lower = (
+        defaults.get_climatology(q_lower).sortby("latitude")
+        if q_lower is not None
+        else None
+    )
+    clim_upper = (
+        defaults.get_climatology(q_upper).sortby("latitude")
+        if q_upper is not None
+        else None
+    )
+    return clim_lower, clim_upper
+
+
+def build_land_mask(
+    lons: xr.DataArray,
+    lats: xr.DataArray,
+) -> xr.DataArray:
+    """Return a boolean DataArray (True = land) for the given grid."""
+    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+    mask = land.mask(lons, lats)
+    return mask == 0
+
+
+def compute_grid_cell_area(
+    lats: np.ndarray,
+    lons: np.ndarray,
+) -> np.ndarray:
+    """Return a (lat, lon) array of grid-cell areas in km^2.
+
+    Uses the spherical-Earth approximation
+    ``area = R^2 * dlat_rad * dlon_rad * cos(lat)``.
+
+    Args:
+        lats: 1-D latitude array in degrees.
+        lons: 1-D longitude array in degrees; only its length is used.
+    """
+    R_KM = 6371.0
+    dlat = float(np.abs(np.diff(lats[:2]))[0]) if len(lats) > 1 else 0.25
+    dlon = float(np.abs(np.diff(lons[:2]))[0]) if len(lons) > 1 else 0.25
+    cell_km2 = R_KM**2 * np.deg2rad(dlat) * np.deg2rad(dlon) * np.cos(np.deg2rad(lats))
+    return np.outer(cell_km2, np.ones(len(lons)))
+
+
+def build_exceedance_mask(
+    t2m: xr.DataArray,
+    clim_lower: Optional[xr.DataArray],
+    clim_upper: Optional[xr.DataArray],
+    land_mask: xr.DataArray,
+    op_lower: str = ">",
+    op_upper: str = "<",
+) -> xr.DataArray:
+    """Build a daily land-only mask of passed-bound steps.
+
+    A day passes only when every 6-hourly step satisfies every
+    provided bound. The result is then masked to land.
+
+    Args:
+        t2m: 6-hourly 2m temperature DataArray.
+        clim_lower: Climatology for the lower bound, or None.
+        clim_upper: Climatology for the upper bound, or None.
+        land_mask: Boolean DataArray (True = land) on t2m's grid.
+        op_lower: Operator for the lower bound. Default ``>``.
+        op_upper: Operator for the upper bound. Default ``<``.
+    """
+    tdim = detect_time_dim(t2m)
+    doy = t2m[tdim].dt.dayofyear
+    hour = t2m[tdim].dt.hour
+    ref = clim_lower if clim_lower is not None else clim_upper
+    assert ref is not None
+    max_clim_doy = int(ref.dayofyear.max())
+    doy_capped = doy.clip(max=max_clim_doy)
+
+    mask_6h = xr.ones_like(t2m, dtype=bool)
+
+    if clim_lower is not None:
+        cmp = resolve_op(op_lower)
+        aligned = clim_lower.sel(dayofyear=doy_capped, hour=hour).reindex_like(
+            t2m, method="nearest"
+        )
+        mask_6h = mask_6h & cmp(t2m, aligned)
+
+    if clim_upper is not None:
+        cmp = resolve_op(op_upper)
+        aligned = clim_upper.sel(dayofyear=doy_capped, hour=hour).reindex_like(
+            t2m, method="nearest"
+        )
+        mask_6h = mask_6h & cmp(t2m, aligned)
+
+    return mask_6h.resample({tdim: "1D"}).min().astype(bool) & land_mask
+
+
+def _nearest_1d_index(arr: np.ndarray, val: float) -> int:
+    """Return the index of the nearest element in a 1-D array."""
+    return int(np.argmin(np.abs(arr - val)))
+
+
+def _peak_day_index(daily_counts: np.ndarray) -> int:
+    """Return the index of the peak-footprint day.
+
+    Ties broken as: last of two, middle of odd ≥ 3, first-middle of
+    even ≥ 4 (index ``(n_tied - 1) // 2``).
+    """
+    tied_days = np.where(daily_counts == daily_counts.max())[0]
+    n = len(tied_days)
+    if n <= 2:
+        return int(tied_days[-1]) if n == 2 else int(tied_days[0])
+    return int(tied_days[(n - 1) // 2])
+
+
+def _initial_edges_active(
+    init_region_land: np.ndarray,
+    band_pts: int,
+) -> Dict[str, bool]:
+    """Return edges that have ≥ 25% land in the initial box band.
+
+    Edges that are mostly ocean are disabled before the expansion loop
+    so the box does not slide into open water from the start.
+    """
+    active: Dict[str, bool] = {}
+    for edge in ("north", "south", "east", "west"):
+        if edge == "north":
+            strip = init_region_land[-band_pts:, :]
+        elif edge == "south":
+            strip = init_region_land[:band_pts, :]
+        elif edge == "west":
+            strip = init_region_land[:, :band_pts]
+        else:
+            strip = init_region_land[:, -band_pts:]
+        active[edge] = bool(strip.sum() / max(strip.size, 1) >= 0.25)
+    return active
+
+
+def _edge_valid_fraction(
+    mask_2d: np.ndarray,
+    land_2d: np.ndarray,
+    edge: str,
+    band_pts: int,
+) -> float:
+    """Return the fraction of land grid points on an edge that are active.
+
+    Args:
+        mask_2d: 2-D boolean activity array (lat, lon).
+        land_2d: 2-D boolean land mask (lat, lon); True = land.
+        edge: One of ``north``, ``south``, ``east``, ``west``.
+        band_pts: Width of the edge strip in grid points.
+
+    Returns:
+        Fraction in [0, 1] of land points in the strip that are
+        active, or 0.0 if the strip contains no land points.
+
+    Raises:
+        ValueError: If ``edge`` is not one of the recognised values.
+    """
+    if edge == "north":
+        strip = mask_2d[-band_pts:, :]
+        land_strip = land_2d[-band_pts:, :]
+    elif edge == "south":
+        strip = mask_2d[:band_pts, :]
+        land_strip = land_2d[:band_pts, :]
+    elif edge == "west":
+        strip = mask_2d[:, :band_pts]
+        land_strip = land_2d[:, :band_pts]
+    elif edge == "east":
+        strip = mask_2d[:, -band_pts:]
+        land_strip = land_2d[:, -band_pts:]
+    else:
+        raise ValueError(f"Unknown edge: {edge}")
+    n_land = int(land_strip.sum())
+    if n_land == 0:
+        return 0.0
+    return float((strip & land_strip).sum()) / n_land
+
+
+def expand_event_bounds(
+    event: Dict,
+    filt_mask: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    land_mask_np: np.ndarray,
+) -> Dict:
+    """Expand a detected event's bounding box using case-script logic.
+
+    Starting from the blob-tracked bounding box, expands each edge
+    outward while >= EDGE_VALIDITY_THRESHOLD of land points on that
+    edge are active on the peak-footprint day. Also computes and stores
+    max_consecutive_days and mean_consecutive_days within the expanded
+    region.
+
+    Args:
+        event: Event dict from detect_events.
+        filt_mask: Boolean array (time, lat, lon) with consecutive-day
+            filtering already applied.
+        dates: 1-D datetime64 array of daily timestamps (axis 0).
+        lats: 1-D latitude array (axis 1).
+        lons: 1-D longitude array (axis 2).
+        land_mask_np: 2-D boolean array (lat, lon); True = land.
+
+    Returns:
+        The same event dict with updated lat_min/lat_max/lon_min/
+        lon_max, max_consecutive_days, and mean_consecutive_days.
+    """
+    grid_res = float(np.abs(np.diff(lats[:2]))[0]) if len(lats) > 1 else 0.25
+    band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
+
+    start_date = np.datetime64(event["start"])
+    end_date = np.datetime64(event["end"])
+    t_mask = (dates >= start_date) & (dates <= end_date)
+    ev_filt = filt_mask[t_mask]
+
+    if ev_filt.shape[0] == 0:
+        event["max_consecutive_days"] = 0
+        event["mean_consecutive_days"] = 0.0
+        return event
+
+    idx_s0 = _nearest_1d_index(lats, event["lat_min"])
+    idx_n0 = _nearest_1d_index(lats, event["lat_max"])
+    idx_w0 = _nearest_1d_index(lons, event["lon_min"])
+    idx_e0 = _nearest_1d_index(lons, event["lon_max"])
+
+    peak_day = _peak_day_index(ev_filt.sum(axis=(1, 2)))
+    peak_mask = ev_filt[peak_day]
+
+    idx_s, idx_n, idx_w, idx_e = idx_s0, idx_n0, idx_w0, idx_e0
+
+    init_region_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
+    edges_active = _initial_edges_active(init_region_land, band_pts)
+
+    for _ in range(MAX_SPATIAL_ITERATIONS):
+        region = peak_mask[idx_s : idx_n + 1, idx_w : idx_e + 1]
+        land_region = land_mask_np[idx_s : idx_n + 1, idx_w : idx_e + 1]
+
+        all_done = True
+        for edge in list(edges_active.keys()):
+            if not edges_active[edge]:
+                continue
+            frac = _edge_valid_fraction(region, land_region, edge, band_pts)
+            if frac < EDGE_VALIDITY_THRESHOLD:
+                edges_active[edge] = False
+            else:
+                all_done = False
+                if edge == "north":
+                    idx_n = min(len(lats) - 1, idx_n + band_pts)
+                elif edge == "south":
+                    idx_s = max(0, idx_s - band_pts)
+                elif edge == "east":
+                    idx_e = min(len(lons) - 1, idx_e + band_pts)
+                elif edge == "west":
+                    idx_w = max(0, idx_w - band_pts)
+
+        if all_done:
+            break
+
+    fin_filt = ev_filt[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+    consec = max_consecutive_days(fin_filt)
+    event["lat_min"] = float(lats[idx_s])
+    event["lat_max"] = float(lats[idx_n])
+    event["lon_min"] = float(lons[idx_w])
+    event["lon_max"] = float(lons[idx_e])
+    event["max_consecutive_days"] = int(consec.max()) if consec.size > 0 else 0
+    active = consec[consec >= MIN_CONSECUTIVE_DAYS]
+    event["mean_consecutive_days"] = (
+        round(float(active.mean()), 2) if active.size > 0 else 0.0
+    )
+    return event
+
+
+def process_event(
+    single_case: cases.IndividualCase,
+    out_dir: pathlib.Path = pathlib.Path("."),
+    quantile: float | None = None,
+    op_str: str | None = None,
+) -> Optional[Dict]:
+    """Re-expand one curated case's time window and spatial bounds.
+
+    Grows the time window forward from ``start_date - 3`` one day at
+    a time until the single-day exceedance fraction drops below 50%
+    of its peak, then grows each spatial edge by
+    ``EXPANSION_DEGREES`` per iteration while the edge-validity
+    threshold is met.
+
+    Args:
+        single_case: Case with a ``CenteredRegion`` location.
+        out_dir: Directory to write PNG plots into.
+        quantile: Climatology quantile. Defaults to 0.85 for
+            heat_wave or 0.15 for freeze when not provided.
+        op_str: Comparison operator (``>``, ``>=``, ``<``, ``<=``).
+            Defaults to ``>`` for heat_wave or ``<`` for freeze.
+
+    Returns:
+        A result dict (see call sites) or None if the case was
+        skipped (wrong location type, empty grid, or no land).
+    """
+    is_heatwave = single_case.event_type == "heat_wave"
+    logger.info(
+        "Processing case %d: %s (%s)",
+        single_case.case_id_number,
+        single_case.title,
+        single_case.event_type,
+    )
+
+    loc = single_case.location
+    if not isinstance(loc, regions.CenteredRegion):
+        logger.warning(
+            "  Case %d location is %s, not CenteredRegion — skipping",
+            single_case.case_id_number,
+            type(loc).__name__,
+        )
+        return None
+
+    center_lat = loc.latitude
+    center_lon = loc.longitude
+    if isinstance(loc.bounding_box_degrees, tuple):
+        half_lat = loc.bounding_box_degrees[0] / 2.0
+        half_lon = loc.bounding_box_degrees[1] / 2.0
+    else:
+        half_lat = loc.bounding_box_degrees / 2.0
+        half_lon = loc.bounding_box_degrees / 2.0
+
+    box_lat_min = center_lat - half_lat
+    box_lat_max = center_lat + half_lat
+    box_lon_min = center_lon - half_lon
+    box_lon_max = center_lon + half_lon
+
+    logger.info(
+        "  Initial box (%.1f°): center (%.2f, %.2f)"
+        "  lat [%.2f, %.2f], lon [%.2f, %.2f]",
+        loc.bounding_box_degrees
+        if not isinstance(loc.bounding_box_degrees, tuple)
+        else loc.bounding_box_degrees[0],
+        center_lat,
+        center_lon,
+        box_lat_min,
+        box_lat_max,
+        box_lon_min,
+        box_lon_max,
+    )
+
+    start_date = pd.Timestamp(single_case.start_date) - pd.Timedelta(days=3)
+    end_date = pd.Timestamp(single_case.end_date) + pd.Timedelta(
+        days=TEMPORAL_LOAD_BUFFER_DAYS
+    )
+
+    pot_lat_min = max(
+        -90,
+        box_lat_min - MAX_SPATIAL_ITERATIONS * EXPANSION_DEGREES,
+    )
+    pot_lat_max = min(
+        90,
+        box_lat_max + MAX_SPATIAL_ITERATIONS * EXPANSION_DEGREES,
+    )
+    pot_lon_min = box_lon_min - MAX_SPATIAL_ITERATIONS * EXPANSION_DEGREES
+    pot_lon_max = box_lon_max + MAX_SPATIAL_ITERATIONS * EXPANSION_DEGREES
+
+    ds = xr.open_zarr(
+        inputs.ARCO_ERA5_FULL_URI,
+        storage_options={"token": "anon"},
+        chunks={},
+    )
+    tdim = detect_time_dim(ds)
+    t2m = ds["2m_temperature"].sel(
+        {tdim: slice(str(start_date), str(end_date))},
+    )
+    six_hourly = t2m[tdim].dt.hour.isin([0, 6, 12, 18])
+    t2m = t2m.sel({tdim: six_hourly}).sortby("latitude")
+
+    if float(t2m.longitude.max()) > 180:
+        t2m = t2m.assign_coords(
+            longitude=(t2m.longitude.values + 180) % 360 - 180,
+        ).sortby("longitude")
+
+    pot_lon_min_era = pot_lon_min
+    pot_lon_max_era = pot_lon_max
+    if pot_lon_min_era > 180:
+        pot_lon_min_era -= 360
+    if pot_lon_max_era > 180:
+        pot_lon_max_era -= 360
+
+    t2m = t2m.sel(
+        latitude=slice(pot_lat_min, pot_lat_max),
+        longitude=slice(
+            min(pot_lon_min_era, pot_lon_max_era),
+            max(pot_lon_min_era, pot_lon_max_era),
+        ),
+    )
+
+    if t2m.latitude.size == 0 or t2m.longitude.size == 0:
+        logger.warning(
+            "  Case %d: empty spatial selection — skipping",
+            single_case.case_id_number,
+        )
+        return None
+
+    if quantile is None:
+        quantile = 0.85 if is_heatwave else 0.15
+    if op_str is None:
+        op_str = ">" if is_heatwave else "<"
+    cmp = resolve_op(op_str)
+
+    clim = defaults.get_climatology(quantile).sortby("latitude")
+
+    if float(clim.longitude.max()) > 180:
+        clim = clim.assign_coords(
+            longitude=(clim.longitude.values + 180) % 360 - 180,
+        ).sortby("longitude")
+
+    doy = t2m[tdim].dt.dayofyear
+    hour = t2m[tdim].dt.hour
+    max_clim_doy = int(clim.dayofyear.max())
+    doy_capped = doy.clip(max=max_clim_doy)
+
+    clim_aligned = clim.sel(
+        dayofyear=doy_capped,
+        hour=hour,
+    ).reindex_like(t2m, method="nearest")
+
+    exc_6h = cmp(t2m, clim_aligned)
+    daily_all_pass = exc_6h.resample({tdim: "1D"}).min().astype(bool)
+
+    land_mask = build_land_mask(daily_all_pass.longitude, daily_all_pass.latitude)
+
+    exc_mask = daily_all_pass & land_mask
+
+    logger.info("  Computing exceedance mask...")
+    mask_np = exc_mask.compute().values.astype(bool)
+
+    all_lats = daily_all_pass.latitude.values
+    all_lons = daily_all_pass.longitude.values
+    grid_res = np.abs(np.diff(all_lats[:2]))[0] if len(all_lats) > 1 else 0.25
+    band_pts = max(1, int(round(EXPANSION_DEGREES / grid_res)))
+
+    box_lon_min_era = _to_plot_lon(box_lon_min)
+    box_lon_max_era = _to_plot_lon(box_lon_max)
+
+    idx_s0 = _nearest_1d_index(all_lats, box_lat_min)
+    idx_n0 = _nearest_1d_index(all_lats, box_lat_max)
+    idx_w0 = _nearest_1d_index(all_lons, box_lon_min_era)
+    idx_e0 = _nearest_1d_index(all_lons, box_lon_max_era)
+
+    land_mask_np = land_mask.compute().values.astype(bool)
+    box_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
+    n_land_pts = int(box_land.sum())
+    if n_land_pts == 0:
+        logger.warning(
+            "  Case %d: no land points in initial box — skipping",
+            single_case.case_id_number,
+        )
+        return None
+
+    n_days_total = min(mask_np.shape[0], MAX_TEMPORAL_DAYS)
+    final_t = n_days_total
+    established = False
+
+    for t in range(n_days_total):
+        if t < 2:
+            continue
+
+        # "Currently active" = last 3 days all exceed threshold.
+        box_sl = (
+            slice(idx_s0, idx_n0 + 1),
+            slice(idx_w0, idx_e0 + 1),
+        )
+        currently_active = (
+            mask_np[t][box_sl] & mask_np[t - 1][box_sl] & mask_np[t - 2][box_sl]
+        )
+        active_land = int((currently_active & box_land).sum())
+        frac = active_land / n_land_pts
+
+        if t == 2 and frac < EDGE_VALIDITY_THRESHOLD:
+            logger.warning(
+                "  Day 3: only %.1f%% of land points have 3 consecutive days (< 50%%)",
+                frac * 100,
+            )
+
+        if not established:
+            if frac >= EDGE_VALIDITY_THRESHOLD:
+                established = True
+                logger.info(
+                    "  Event established at day %d (%.1f%% of land points active)",
+                    t,
+                    frac * 100,
+                )
+        else:
+            if frac < EDGE_VALIDITY_THRESHOLD:
+                final_t = t + 1
+                logger.info(
+                    "  Temporal stop at day %d (%.1f%% < 50%% of land points)",
+                    t,
+                    frac * 100,
+                )
+                break
+
+        if t == 9:
+            logger.warning(
+                "  Exceeded 10 days (frac %.1f%%), continuing...",
+                frac * 100,
+            )
+
+    if not established:
+        logger.warning(
+            "  Case %d: event never reached 50%% of land points — using all %d days",
+            single_case.case_id_number,
+            final_t,
+        )
+
+    logger.info(
+        "  Using %d of %d available days",
+        final_t,
+        mask_np.shape[0],
+    )
+
+    mask_np = mask_np[:final_t]
+    filtered = _consecutive_filter_np(mask_np, MIN_CONSECUTIVE_DAYS, 1)
+
+    # Spatial expansion starts from the peak-footprint day.
+    daily_counts = filtered.sum(axis=(1, 2))
+    peak_day = _peak_day_index(daily_counts)
+    peak_mask = filtered[peak_day]
+    logger.info(
+        "  Peak footprint on day %d (%d active grid points, %d tied days)",
+        peak_day,
+        int(daily_counts[peak_day]),
+        int((daily_counts == daily_counts.max()).sum()),
+    )
+
+    idx_s = idx_s0
+    idx_n = idx_n0
+    idx_w = idx_w0
+    idx_e = idx_e0
+
+    init_region_land = land_mask_np[idx_s0 : idx_n0 + 1, idx_w0 : idx_e0 + 1]
+    edges_active = _initial_edges_active(init_region_land, band_pts)
+    for edge, active in edges_active.items():
+        if not active:
+            logger.info("  %s edge disabled (< 25%% land)", edge.capitalize())
+    n_iter = 0
+
+    for iteration in range(MAX_SPATIAL_ITERATIONS):
+        n_iter = iteration + 1
+        region = peak_mask[idx_s : idx_n + 1, idx_w : idx_e + 1]
+        land_region = land_mask_np[idx_s : idx_n + 1, idx_w : idx_e + 1]
+
+        all_done = True
+        for edge in list(edges_active.keys()):
+            if not edges_active[edge]:
+                continue
+
+            frac = _edge_valid_fraction(region, land_region, edge, band_pts)
+            if frac < EDGE_VALIDITY_THRESHOLD:
+                edges_active[edge] = False
+            else:
+                all_done = False
+                if edge == "north":
+                    idx_n = min(
+                        len(all_lats) - 1,
+                        idx_n + band_pts,
+                    )
+                elif edge == "south":
+                    idx_s = max(0, idx_s - band_pts)
+                elif edge == "east":
+                    idx_e = min(
+                        len(all_lons) - 1,
+                        idx_e + band_pts,
+                    )
+                elif edge == "west":
+                    idx_w = max(0, idx_w - band_pts)
+
+        if all_done:
+            logger.info(
+                "  Spatial expansion converged at iteration %d",
+                n_iter,
+            )
+            break
+    else:
+        logger.info(
+            "  Reached max spatial iterations (%d)",
+            MAX_SPATIAL_ITERATIONS,
+        )
+
+    fin_lats = all_lats[idx_s : idx_n + 1]
+    fin_lons = all_lons[idx_w : idx_e + 1]
+    fin_filtered = filtered[:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+    consec = max_consecutive_days(fin_filtered)
+    peak_gridpoints = int((consec >= MIN_CONSECUTIVE_DAYS).sum())
+
+    result = {
+        "case_id": single_case.case_id_number,
+        "title": single_case.title,
+        "event_type": single_case.event_type,
+        "start_date": str(single_case.start_date),
+        "end_date": str(single_case.end_date),
+        "latitude_min": float(all_lats[idx_s]),
+        "latitude_max": float(all_lats[idx_n]),
+        "longitude_min": float(all_lons[idx_w]),
+        "longitude_max": float(all_lons[idx_e]),
+        "n_iterations": n_iter,
+        "final_days": final_t,
+        "_consec": consec,
+        "_lats": fin_lats,
+        "_lons": fin_lons,
+        "_peak_gridpoints": peak_gridpoints,
+    }
+    logger.info(
+        "  Final bounds: lat [%.2f, %.2f], lon [%.2f, %.2f] (%d days, %d iterations)",
+        result["latitude_min"],
+        result["latitude_max"],
+        result["longitude_min"],
+        result["longitude_max"],
+        final_t,
+        n_iter,
+    )
+
+    kind = "heatwave" if is_heatwave else "freeze"
+    start = result["start_date"][:10]
+    end = result["end_date"][:10]
+    out_png = str(out_dir / f"case_{result['case_id']}_consecutive_{kind}_days.png")
+    plot_consecutive_map(
+        consec,
+        fin_lats,
+        fin_lons,
+        cast(Literal["heat_wave", "cold_snap"], single_case.event_type),
+        title=(
+            f"Consecutive {kind.capitalize()} Days"
+            f" — {result['title']}\n{start} to {end}"
+        ),
+        output_path=out_png,
+    )
+    logger.info("  Saved plot: %s", out_png)
+
+    initial_box = (box_lat_min, box_lat_max, box_lon_min, box_lon_max)
+    final_box = (
+        result["latitude_min"],
+        result["latitude_max"],
+        result["longitude_min"],
+        result["longitude_max"],
+    )
+    peak_png = str(out_dir / f"case_{result['case_id']}_peak_day_{kind}.png")
+    plot_peak_day_with_bounds(
+        peak_mask,
+        all_lats,
+        all_lons,
+        initial_box,
+        final_box,
+        single_case.event_type,
+        title=(
+            f"Peak Footprint (day {peak_day}) — {result['title']}\n{start} to {end}"
+        ),
+        output_path=peak_png,
+    )
+
+    return result
+
+
+@nb.njit(cache=True)
+def _count_overlaps_nb(
+    labels_a: np.ndarray,
+    labels_b: np.ndarray,
+    n_a: int,
+    n_b: int,
+) -> np.ndarray:
+    """Return an (n_a, n_b) pixel-count overlap matrix between two grids.
+
+    ``mat[i, j]`` is the number of pixels where ``labels_a == i + 1``
+    and ``labels_b == j + 1``.
+    """
+    mat = np.zeros((n_a, n_b), dtype=np.int32)
+    rows, cols = labels_a.shape
+    for r in range(rows):
+        for c in range(cols):
+            a = labels_a[r, c]
+            b = labels_b[r, c]
+            if a > 0 and b > 0:
+                mat[a - 1, b - 1] += 1
+    return mat
+
+
+def _resolve_event(
+    oid: int,
+    overlap_mat: Optional[np.ndarray],
+    prev_map: Dict[int, int],
+    events: Dict[int, Dict],
+    cur_map: Dict[int, int],
+) -> Optional[int]:
+    """Find and merge prior-day events overlapping with blob ``oid``.
+
+    Returns the surviving event ID after any merges, or None if no
+    prior event overlaps the current blob.
+
+    Args:
+        oid: Current-day blob label (1-indexed).
+        overlap_mat: Pixel-count overlap matrix, or None.
+        prev_map: Mapping from previous-day blob label to event ID.
+        events: Live events keyed by event ID (mutated).
+        cur_map: Mapping from current-day blob label to event ID
+            (mutated).
+    """
+    if overlap_mat is None:
+        return None
+    col = overlap_mat[:, oid - 1]
+    prev_blob_ids = np.where(col > 0)[0] + 1
+    eids = {prev_map[p] for p in prev_blob_ids if p in prev_map}
+    alive = {e for e in eids if e in events and not events[e]["done"]}
+    if not alive:
+        return None
+
+    eid = min(alive)
+    for other in alive - {eid}:
+        merged = events.pop(other)
+        tgt = events[eid]
+        tgt["lat_min"] = min(tgt["lat_min"], merged["lat_min"])
+        tgt["lat_max"] = max(tgt["lat_max"], merged["lat_max"])
+        tgt["lon_min"], tgt["lon_max"] = _merge_lon_extent(
+            tgt["lon_min"],
+            tgt["lon_max"],
+            merged["lon_min"],
+            merged["lon_max"],
+        )
+        tgt["peak"] = max(tgt["peak"], merged["peak"])
+        tgt["peak_area_km2"] = max(tgt["peak_area_km2"], merged["peak_area_km2"])
+        tgt["start"] = min(tgt["start"], merged["start"])
+        for k, v in list(cur_map.items()):
+            if v == other:
+                cur_map[k] = eid
+    return eid
+
+
+def _terminate_declined_events(
+    events: Dict[int, Dict],
+    cur_map: Dict[int, int],
+) -> None:
+    """Mark events as done if absent today or below 50% of peak area."""
+    active = set(cur_map.values())
+    for eid, ev in events.items():
+        if ev["done"]:
+            continue
+        if eid not in active:
+            ev["done"] = True
+        elif ev["area"] < AREA_DECLINE_FRACTION * ev["peak"]:
+            ev["done"] = True
+
+
+def _bbox_from_blob(
+    li: np.ndarray,
+    lo: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+) -> tuple[float, float, float, float]:
+    """Return ``(lat_min, lat_max, lon_min, lon_max)`` for one blob.
+
+    When the blob touches both column 0 and column ``len(lons)-1``
+    it is assumed to cross the 0°/360° seam, and the bbox is emitted
+    using the wrap convention ``lon_min > lon_max``.
+    """
+    lat_min = float(lats[li].min())
+    lat_max = float(lats[li].max())
+    cols = np.unique(lo)
+    if cols[0] == 0 and cols[-1] == len(lons) - 1:
+        # Largest gap between columns marks the seam; cols on either
+        # side of the gap are the western (low-lon) and eastern
+        # (high-lon) clusters of the blob.
+        gap_idx = int(np.diff(cols).argmax())
+        west_max = int(cols[gap_idx])
+        east_min = int(cols[gap_idx + 1])
+        return lat_min, lat_max, float(lons[east_min]), float(lons[west_max])
+    return lat_min, lat_max, float(lons[lo].min()), float(lons[lo].max())
+
+
+def _arc_contains_point(start: float, end: float, p: float) -> bool:
+    """True if longitude ``p`` lies on the eastward arc start→end."""
+    if end >= start:
+        return start <= p <= end
+    return p >= start or p <= end
+
+
+def _arc_span(start: float, end: float) -> float:
+    """Eastward arc length from ``start`` to ``end`` in degrees."""
+    return (end - start) % 360.0
+
+
+def _merge_lon_extent(
+    cur_min: float, cur_max: float, new_min: float, new_max: float
+) -> tuple[float, float]:
+    """Union two longitude extents on a 360°-periodic axis.
+
+    Each input pair may use the wrap convention ``lon_min > lon_max``
+    (eastward arc that crosses the 0°/360° seam). Returns the
+    smallest enclosing eastward arc as ``(lon_min, lon_max)``.
+    """
+    arcs = ((cur_min, cur_max), (new_min, new_max))
+    pts = sorted({cur_min, cur_max, new_min, new_max})
+    best_span = float("inf")
+    best: tuple[float, float] = (cur_min, cur_max)
+    for s in pts:
+        for e in pts:
+            span = _arc_span(s, e)
+            if span >= best_span:
+                continue
+            if not all(
+                _arc_contains_point(s, e, a)
+                and _arc_contains_point(s, e, b)
+                and _arc_span(s, e) >= _arc_span(a, b)
+                for a, b in arcs
+            ):
+                continue
+            best_span = span
+            best = (s, e)
+    return best
+
+
+def detect_events(
+    filtered_mask: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    event_type: str,
+    area_grid: Optional[np.ndarray] = None,
+    min_seed_area_km2: float = 0.0,
+) -> List[Dict]:
+    """Track spatiotemporal events from a filtered boolean mask.
+
+    A new event is seeded only when the current-day blob AND the prior
+    ``MIN_CONSECUTIVE_DAYS - 1`` days share a single contiguous region
+    of at least ``min_seed_area_km2``. The joint intersection of the
+    blob footprint with all prior filtered masks is computed; the
+    largest connected component must meet the area threshold. Blobs
+    that fail the seed test but overlap an established event still
+    extend it.
+
+    Args:
+        filtered_mask: Boolean array of shape (time, lat, lon) with
+            consecutive-day filtering already applied.
+        dates: 1-D array of date labels aligned with axis 0.
+        lats: 1-D latitude array aligned with axis 1.
+        lons: 1-D longitude array aligned with axis 2.
+        event_type: Label string stored in each returned event dict.
+        area_grid: Optional 2-D array (lat, lon) of grid-cell areas
+            in km².
+        min_seed_area_km2: Minimum contiguous area in km² for the
+            seed test. Default 0.0 (all blobs seed).
+    """
+    filt_np = filtered_mask
+
+    n_days = filt_np.shape[0]
+    events: Dict[int, Dict] = {}
+    next_id = 1
+    prev_labels: Optional[np.ndarray] = None
+    prev_n_obj: int = 0
+    prev_map: Dict[int, int] = {}
+
+    for di in range(n_days):
+        day = filt_np[di]
+
+        if not day.any():
+            for ev in events.values():
+                if not ev["done"]:
+                    ev["done"] = True
+            prev_labels = None
+            prev_n_obj = 0
+            prev_map = {}
+            continue
+
+        labels, n_obj = ndimage.label(day)
+        labels = labels.astype(np.int32)
+        cur_map: Dict[int, int] = {}
+
+        overlap_mat: Optional[np.ndarray] = None
+        if prev_labels is not None and n_obj > 0 and prev_n_obj > 0:
+            overlap_mat = _count_overlaps_nb(
+                prev_labels,
+                labels,
+                prev_n_obj,
+                n_obj,
+            )
+
+        for oid in range(1, n_obj + 1):
+            om = labels == oid
+            area = int(om.sum())
+            li, lo = np.where(om)
+            blob_area_km2 = (
+                float(area_grid[li, lo].sum()) if area_grid is not None else 0.0
+            )
+
+            eid = _resolve_event(
+                oid,
+                overlap_mat,
+                prev_map,
+                events,
+                cur_map,
+            )
+
+            if eid is None:
+                if blob_area_km2 < min_seed_area_km2:
+                    continue
+                # Seed test: the blob footprint must have been
+                # >= min_seed_area_km2 on each of the prior
+                # MIN_CONSECUTIVE_DAYS - 1 days as a single
+                # contiguous region. Skip when history is too short.
+                if min_seed_area_km2 > 0:
+                    if di < MIN_CONSECUTIVE_DAYS - 1:
+                        continue
+                    joint_mask = om.copy()
+                    for k in range(1, MIN_CONSECUTIVE_DAYS):
+                        joint_mask &= filt_np[di - k]
+                    if not joint_mask.any():
+                        continue
+                    j_lbl, j_n = ndimage.label(joint_mask)
+                    if j_n == 0:
+                        continue
+                    best_area = max(
+                        float(area_grid[j_lbl == c].sum())
+                        if area_grid is not None
+                        else float((j_lbl == c).sum())
+                        for c in range(1, j_n + 1)
+                    )
+                    if best_area < min_seed_area_km2:
+                        continue
+                eid = next_id
+                next_id += 1
+                _lat_min, _lat_max, _lon_min, _lon_max = _bbox_from_blob(
+                    li, lo, lats, lons
+                )
+                events[eid] = {
+                    "type": event_type,
+                    "start": dates[di],
+                    "end": dates[di],
+                    "lat_min": _lat_min,
+                    "lat_max": _lat_max,
+                    "lon_min": _lon_min,
+                    "lon_max": _lon_max,
+                    "peak": area,
+                    "peak_area_km2": blob_area_km2,
+                    "area": area,
+                    "done": False,
+                    "initial_bbox": (
+                        _lat_min,
+                        _lat_max,
+                        _lon_min,
+                        _lon_max,
+                    ),
+                }
+            else:
+                ev = events[eid]
+                ev["end"] = dates[di]
+                ev["peak_area_km2"] = max(ev["peak_area_km2"], blob_area_km2)
+                blob_lat_min, blob_lat_max, blob_lon_min, blob_lon_max = (
+                    _bbox_from_blob(li, lo, lats, lons)
+                )
+                ev["lat_min"] = min(ev["lat_min"], blob_lat_min)
+                ev["lat_max"] = max(ev["lat_max"], blob_lat_max)
+                ev["lon_min"], ev["lon_max"] = _merge_lon_extent(
+                    ev["lon_min"], ev["lon_max"], blob_lon_min, blob_lon_max
+                )
+                ev["peak"] = max(ev["peak"], area)
+                ev["area"] = area
+
+            cur_map[oid] = eid
+
+        _terminate_declined_events(events, cur_map)
+        prev_labels = labels
+        prev_n_obj = n_obj
+        prev_map = cur_map
+
+    for ev in events.values():
+        ev["done"] = True
+
+    return list(events.values())
+
+
+def include_temps_with_events(
+    events: List[Dict],
+    t2m_daily_np: np.ndarray,
+    exc_filt: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+) -> List[Dict]:
+    """Add mean and minimum temperature (°C) to each event dict.
+
+    Uses a pre-computed global daily-mean temperature array so all
+    events share a single ERA5 fetch. Statistics are computed over
+    active (exceedant) grid point-days within each event's expanded
+    bounding box and time window.
+
+    Args:
+        events: Event dicts with lat_min/max, lon_min/max, start, end.
+        t2m_daily_np: Float32 array (days, lat, lon) of daily-mean
+            2m temperature in Celsius, aligned with dates/lats/lons.
+        exc_filt: Boolean array (time, lat, lon) from
+            ``_consecutive_filter_np``, aligned with dates/lats/lons.
+        dates: 1-D datetime64 array aligned with axis 0.
+        lats: 1-D latitude array aligned with axis 1.
+        lons: 1-D longitude array aligned with axis 2.
+
+    Returns:
+        The same event list with ``mean_temp_c`` and ``min_temp_c``
+        added (NaN when no active points exist).
+    """
+
+    for ev in events:
+        start_date = np.datetime64(ev["start"])
+        end_date = np.datetime64(ev["end"])
+        t_indices = np.where((dates >= start_date) & (dates <= end_date))[0]
+
+        idx_s = _nearest_1d_index(lats, ev["lat_min"])
+        idx_n = _nearest_1d_index(lats, ev["lat_max"])
+        idx_w = _nearest_1d_index(lons, ev["lon_min"])
+        idx_e = _nearest_1d_index(lons, ev["lon_max"])
+
+        exc_ev = exc_filt[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+        t2m_ev = t2m_daily_np[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+
+        n_days = min(t2m_ev.shape[0], exc_ev.shape[0])
+        active_temps = t2m_ev[:n_days][exc_ev[:n_days]]
+        if active_temps.size > 0:
+            ev["mean_temp_c"] = round(float(active_temps.mean()), 2)
+            ev["min_temp_c"] = round(float(active_temps.min()), 2)
+        else:
+            ev["mean_temp_c"] = float("nan")
+            ev["min_temp_c"] = float("nan")
+
+    return events
+
+
+def events_to_dataframe(
+    events: List[Dict],
+    min_gridpoints: int = MIN_GRIDPOINTS,
+    min_area_km2: float = MIN_AREA_KM2,
+) -> pd.DataFrame:
+    """Filter raw events and return a labelled DataFrame.
+
+    Drops events below ``min_gridpoints`` peak cells, below
+    ``min_area_km2`` peak area, or with fewer than
+    ``MIN_CONSECUTIVE_DAYS`` consecutive days.
+    """
+    columns = [
+        "label",
+        "event_type",
+        "start_date",
+        "end_date",
+        "latitude_min",
+        "latitude_max",
+        "longitude_min",
+        "longitude_max",
+        "max_consecutive_days",
+        "mean_consecutive_days",
+        "mean_temp_c",
+        "min_temp_c",
+    ]
+    if not events:
+        return pd.DataFrame(columns=columns)
+
+    n_before = len(events)
+    events = [
+        e
+        for e in events
+        if e["peak"] >= min_gridpoints
+        and e.get("peak_area_km2", 0.0) >= min_area_km2
+        and e.get("max_consecutive_days", 0) >= MIN_CONSECUTIVE_DAYS
+    ]
+    logger.info(
+        "  Filtered %d events (< %d pts, < %.0f km², or < %d consec days); %d remain",
+        n_before - len(events),
+        min_gridpoints,
+        min_area_km2,
+        MIN_CONSECUTIVE_DAYS,
+        len(events),
+    )
+
+    if not events:
+        return pd.DataFrame(columns=columns)
+
+    rows = [
+        {
+            "event_type": e["type"],
+            "start_date": str(e["start"]),
+            "end_date": str(e["end"]),
+            "latitude_min": e["lat_min"],
+            "latitude_max": e["lat_max"],
+            "longitude_min": e["lon_min"],
+            "longitude_max": e["lon_max"],
+            "max_consecutive_days": e.get("max_consecutive_days", 0),
+            "mean_consecutive_days": e.get("mean_consecutive_days", 0.0),
+            "mean_temp_c": e.get("mean_temp_c", float("nan")),
+            "min_temp_c": e.get("min_temp_c", float("nan")),
+        }
+        for e in events
+    ]
+    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
+    df.insert(0, "label", range(1, len(df) + 1))
+    return df
+
+
+def load_case(case_id_number: int) -> cases.IndividualCase:
+    """Load a single case from events.yaml by ``case_id_number``.
+
+    Raises:
+        ValueError: If no case with the given ID exists.
+    """
+    all_cases = cases.load_ewb_events_yaml_into_case_list()
+    for c in all_cases:
+        if c.case_id_number == case_id_number:
+            return c
+    hw_ids = ", ".join(
+        str(c.case_id_number) for c in all_cases if c.event_type == "heat_wave"
+    )
+    cs_ids = ", ".join(
+        str(c.case_id_number) for c in all_cases if c.event_type == "cold_snap"
+    )
+    raise ValueError(
+        f"Case {case_id_number} not found.\n"
+        f"  heat_wave ids: {hw_ids}\n"
+        f"  cold_snap ids:    {cs_ids}"
+    )
+
+
+def compute_consecutive_field(
+    single_case: cases.IndividualCase,
+    quantile: float | None = None,
+    op_str: str | None = None,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Return ``(consec, lats, lons)`` for a case's exceedance mask.
+
+    ``consec`` is an int32 (lat, lon) array of the max consecutive
+    event days over the case's window; lats and lons match the grid
+    of the case's bounding box.
+
+    Args:
+        single_case: The case to compute the field for.
+        quantile: Climatology quantile. Defaults to 0.85 for
+            heat_wave or 0.15 for freeze.
+        op_str: Comparison operator. Defaults to ``>=`` for
+            heat_wave or ``<=`` for freeze.
+    """
+    is_heatwave = single_case.event_type == "heat_wave"
+    if quantile is None:
+        quantile = 0.85 if is_heatwave else 0.15
+    if op_str is None:
+        op_str = ">" if is_heatwave else "<"
+    cmp = resolve_op(op_str)
+
+    start = str(single_case.start_date.date())
+    end = str(single_case.end_date.date())
+    bounds = single_case.location.as_geopandas().total_bounds
+    lon_min, lat_min, lon_max, lat_max = bounds
+
+    if lon_min < 0 and lon_max > 0:
+        lon_min += 360
+        if lon_max > 0:
+            lon_max += 360
+    if lon_max < 0:
+        lon_max += 360
+
+    logger.info("Opening ERA5 for %s to %s ...", start, end)
+    t2m = open_era5_t2m(start, end)
+    t2m = t2m.drop_indexes("longitude").set_xindex(
+        "longitude", index_cls=PeriodicBoundaryIndex, period=360
+    )
+
+    t2m = t2m.sel(
+        latitude=slice(lat_min, lat_max),
+        longitude=slice(lon_min, lon_max),
+    )
+
+    logger.info("  Subset: %s", dict(t2m.sizes))
+
+    tdim = detect_time_dim(t2m)
+
+    logger.info(
+        "Loading q=%.2f climatology (op=%s)...",
+        quantile,
+        op_str,
+    )
+    clim = defaults.get_climatology(quantile).sortby("latitude")
+    clim = clim.drop_indexes("longitude").set_xindex(
+        "longitude", index_cls=PeriodicBoundaryIndex, period=360
+    )
+    clim = clim.sel(
+        latitude=t2m.latitude,
+        longitude=t2m.longitude,
+        method="nearest",
+    )
+    doy = t2m[tdim].dt.dayofyear
+    hour = t2m[tdim].dt.hour
+    max_clim_doy = int(clim.dayofyear.max())
+    doy_capped = doy.clip(max=max_clim_doy)
+
+    clim_aligned = clim.sel(
+        dayofyear=doy_capped,
+        hour=hour,
+    )
+
+    logger.info("Computing 6-hourly exceedance...")
+    exc_6h = cmp(t2m, clim_aligned)
+    daily_all_pass = exc_6h.resample({tdim: "1D"}).min().astype(bool)
+
+    logger.info("Building land mask...")
+    land_mask = build_land_mask(daily_all_pass.longitude, daily_all_pass.latitude)
+
+    exc = daily_all_pass & land_mask
+    mask_np = exc.compute().values.astype(bool)
+    logger.info("Computing max consecutive days...")
+    consec = max_consecutive_days(mask_np)
+    return consec, daily_all_pass.latitude.values, daily_all_pass.longitude.values
+
+
+def results_to_dataframe(results: List[Dict]) -> pd.DataFrame:
+    """Convert ``process_event`` result dicts to a labelled DataFrame.
+
+    Events below ``MIN_GRIDPOINTS`` peak grid points are dropped.
+    None entries (skipped cases) are ignored. Rows are sorted by
+    ``start_date``.
+    """
+    columns = [
+        "label",
+        "case_id",
+        "title",
+        "event_type",
+        "start_date",
+        "end_date",
+        "latitude_min",
+        "latitude_max",
+        "longitude_min",
+        "longitude_max",
+    ]
+    if not results:
+        return pd.DataFrame(columns=columns)
+
+    valid = [r for r in results if r is not None]
+    n_before = len(valid)
+    valid = [r for r in valid if r["_peak_gridpoints"] >= MIN_GRIDPOINTS]
+    logger.info(
+        "  Filtered %d events below %d gridpoints; %d remain",
+        n_before - len(valid),
+        MIN_GRIDPOINTS,
+        len(valid),
+    )
+
+    rows = [
+        {
+            "event_type": r["event_type"],
+            "start_date": r["start_date"],
+            "end_date": r["end_date"],
+            "latitude_min": r["latitude_min"],
+            "latitude_max": r["latitude_max"],
+            "longitude_min": r["longitude_min"],
+            "longitude_max": r["longitude_max"],
+        }
+        for r in valid
+    ]
+    df = pd.DataFrame(rows).sort_values("start_date").reset_index(drop=True)
+    df.insert(0, "label", range(1, len(df) + 1))
+    return df
+
+
+def write_bounds_to_yaml(
+    results: List[Dict],
+    yaml_path: pathlib.Path,
+) -> None:
+    """Write refreshed bounded_region parameters back into events.yaml.
+
+    Uses ruamel.yaml round-trip mode so indentation, quoting style,
+    key order, blank lines, and comments are preserved. Only cases
+    present in ``results`` with a non-None value are modified.
+    """
+    result_map = {r["case_id"]: r for r in results if r is not None}
+    if not result_map:
+        logger.warning("write_bounds_to_yaml: no valid results, skipping.")
+        return
+
+    yaml = YAML(typ="rt")
+    yaml.preserve_quotes = True
+
+    with yaml_path.open("r") as fh:
+        data = yaml.load(fh)
+
+    updated = 0
+    for entry in data:
+        cid = entry.get("case_id_number")
+        if cid not in result_map:
+            continue
+        r = result_map[cid]
+        params = entry["location"]["parameters"]
+        params["latitude_min"] = round(r["latitude_min"], 2)
+        params["latitude_max"] = round(r["latitude_max"], 2)
+        params["longitude_min"] = round(r["longitude_min"], 2)
+        params["longitude_max"] = round(r["longitude_max"], 2)
+        updated += 1
+
+    with yaml_path.open("w") as fh:
+        yaml.dump(data, fh)
+
+    logger.info(
+        "write_bounds_to_yaml: updated %d cases in %s",
+        updated,
+        yaml_path,
+    )
+
+
+def _load_base_temp_events() -> list[cases.IndividualCase]:
+    """Load all cases from the bundled base_temp_events.yaml."""
+    import extremeweatherbench.data
+
+    old_yaml = importlib.resources.files(
+        extremeweatherbench.data,
+    ).joinpath("base_temp_events.yaml")
+    with importlib.resources.as_file(old_yaml) as f:
+        raw = cases.read_incoming_yaml(f)
+    return cases.load_individual_cases(raw)
+
+
+def _add_map_features(ax) -> None:
+    """Add coastlines, borders, states, lakes, rivers to a GeoAxes."""
+    ax.coastlines(linewidth=0.5, zorder=10)
+    ax.add_feature(
+        cfeature.BORDERS,
+        linewidth=0.6,
+        edgecolor="grey",
+    )
+    ax.add_feature(
+        cfeature.STATES,
+        linewidth=0.4,
+        edgecolor="lightgrey",
+    )
+    ax.add_feature(
+        cfeature.LAKES,
+        linewidth=0.3,
+        edgecolor="none",
+        facecolor="none",
+    )
+    ax.add_feature(
+        cfeature.RIVERS,
+        linewidth=0.2,
+        edgecolor="none",
+    )
+
+
+def plot_consecutive_map(
+    consec: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    event_type: Literal["heat_wave", "cold_snap"],
+    title: str,
+    output_path: str | None = None,
+    extent: tuple[float, float, float, float] | None = None,
+    ax=None,
+    add_colorbar: bool = True,
+    vmax: int | None = None,
+):
+    """Plot a max-consecutive-days field using the standard style.
+
+    When ``ax`` is provided the plot is drawn into that axis and
+    nothing is saved; ``output_path`` is ignored. When ``ax`` is
+    None a new figure is created and saved to ``output_path``.
+
+    Args:
+        consec: Int32 (nlat, nlon) array of max consecutive days.
+        lats: 1-D latitude array matching ``consec`` rows.
+        lons: 1-D longitude array (0-360) matching ``consec`` cols.
+        event_type: ``"heat_wave"`` (Reds) or ``"cold_snap"`` (Blues).
+        title: Two-line figure title (left-aligned).
+        output_path: Destination PNG path. Required when ``ax`` is
+            None.
+        extent: (lon_min, lon_max, lat_min, lat_max) in -180..180.
+            Derived from lats/lons when None.
+        ax: Existing cartopy GeoAxes to draw into. When provided no
+            figure is created or saved.
+        add_colorbar: If False, skip the per-axes colorbar so the
+            caller can supply a single shared colorbar.
+        vmax: Override the colormap upper bound (useful for sharing
+            one normalization across multiple panels).
+
+    Returns:
+        The pcolormesh mappable, so callers can build a shared
+        colorbar.
+    """
+    plot_data = consec.astype(float)
+    plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
+
+    if vmax is None:
+        valid = plot_data[~np.isnan(plot_data)]
+        vmax = int(valid.max()) if valid.size > 0 else MIN_CONSECUTIVE_DAYS
+    else:
+        vmax = int(vmax)
+
+    cmap_name = "Reds" if event_type == "heat_wave" else "Blues"
+    n_levels = vmax - MIN_CONSECUTIVE_DAYS + 1
+    cmap = plt.colormaps.get_cmap(cmap_name).resampled(n_levels)
+    bin_edges = np.arange(MIN_CONSECUTIVE_DAYS - 0.5, vmax + 1.5, 1)
+    norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
+
+    plot_lons = np.where(lons > 180.0, lons - 360.0, lons)
+
+    if extent is None:
+        lon_min, lon_max = float(plot_lons.min()), float(plot_lons.max())
+        lat_min, lat_max = float(lats.min()), float(lats.max())
+    else:
+        lon_min, lon_max, lat_min, lat_max = extent
+
+    own_fig = ax is None
+    if own_fig:
+        fig, ax = plt.subplots(
+            subplot_kw={"projection": ccrs.PlateCarree()},
+            figsize=(10, 8),
+        )
+    else:
+        fig = ax.get_figure()
+
+    ax.set_extent(
+        [lon_min, lon_max, lat_min, lat_max],
+        crs=ccrs.PlateCarree(),
+    )
+    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
+
+    im = ax.pcolormesh(
+        plot_lons,
+        lats,
+        plot_data,
+        cmap=cmap,
+        norm=norm,
+        transform=ccrs.PlateCarree(),
+        shading="auto",
+    )
+    _add_map_features(ax)
+
+    if add_colorbar:
+        divider = make_axes_locatable(ax)
+        cax = divider.append_axes(
+            "right",
+            size="3.75%",
+            pad=0.1,
+            axes_class=plt.Axes,
+        )
+        cbar = fig.colorbar(im, cax=cax)
+        cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
+        cbar.ax.set_ylabel(
+            "Consecutive Days",
+            rotation=270,
+            labelpad=8,
+            va="center",
+            fontsize=12,
+        )
+
+    ax.set_title(title, loc="left", fontsize=13)
+
+    if own_fig:
+        if output_path is None:
+            raise ValueError("output_path is required when ax is not provided.")
+        fig.savefig(output_path, dpi=200, bbox_inches="tight")
+        plt.close(fig)
+        logger.info("Saved consecutive-days plot to %s", output_path)
+
+    return im
+
+
+def plot_peak_day_with_bounds(
+    peak_mask: np.ndarray,
+    all_lats: np.ndarray,
+    all_lons: np.ndarray,
+    initial_box: tuple,
+    final_box: tuple,
+    event_type: str,
+    title: str,
+    output_path: str,
+) -> None:
+    """Plot the peak-footprint day with initial and final boxes.
+
+    Args:
+        peak_mask: 2-D boolean array (lat, lon) for the peak day.
+        all_lats: 1-D latitude array matching rows.
+        all_lons: 1-D longitude array (0-360) matching columns.
+        initial_box: (lat_min, lat_max, lon_min, lon_max) of the
+            initial centered region.
+        final_box: (lat_min, lat_max, lon_min, lon_max) after
+            spatial expansion.
+        event_type: ``"heat_wave"`` or ``"freeze"``.
+        title: Figure title.
+        output_path: Destination PNG path.
+    """
+    plot_lons = np.where(all_lons > 180.0, all_lons - 360.0, all_lons)
+    lon_min = float(plot_lons.min()) - 1
+    lon_max = float(plot_lons.max()) + 1
+    lat_min = float(all_lats.min()) - 1
+    lat_max = float(all_lats.max()) + 1
+
+    cmap = mcolors.ListedColormap(
+        ["white", "firebrick" if event_type == "heat_wave" else "steelblue"]
+    )
+    norm = mcolors.BoundaryNorm([0, 0.5, 1], cmap.N)
+
+    fig, ax = plt.subplots(
+        subplot_kw={"projection": ccrs.PlateCarree()},
+        figsize=(10, 8),
+    )
+    ax.set_extent(
+        [lon_min, lon_max, lat_min, lat_max],
+        crs=ccrs.PlateCarree(),
+    )
+    ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
+
+    ax.pcolormesh(
+        plot_lons,
+        all_lats,
+        peak_mask.astype(float),
+        cmap=cmap,
+        norm=norm,
+        transform=ccrs.PlateCarree(),
+        shading="auto",
+    )
+    ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
+    ax.add_feature(cfeature.BORDERS, linewidth=0.3)
+
+    def _add_rect(box, color, label, ls="-"):
+        blat_min, blat_max, blon_min, blon_max = box
+        blon_min = _to_plot_lon(blon_min)
+        blon_max = _to_plot_lon(blon_max)
+        rect = mpatches.Rectangle(
+            (blon_min, blat_min),
+            blon_max - blon_min,
+            blat_max - blat_min,
+            linewidth=2,
+            edgecolor=color,
+            facecolor="none",
+            linestyle=ls,
+            transform=ccrs.PlateCarree(),
+            label=label,
+        )
+        ax.add_patch(rect)
+
+    _add_rect(initial_box, "blue", "Initial box", ls="--")
+    _add_rect(final_box, "green", "Final bounds", ls="-")
+
+    ax.legend(loc="lower left", fontsize=9)
+    ax.set_title(title, loc="left", fontsize=12)
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("  Saved peak-day plot: %s", output_path)
+
+
+def _add_lon_rect(
+    ax: plt.Axes,
+    lon_min_0360: float,
+    lon_max_0360: float,
+    lat_min: float,
+    lat_max: float,
+    color: tuple,
+    linewidth: float = 0.8,
+    zorder: int = 4,
+) -> None:
+    """Add a lat/lon rectangle, splitting at the antimeridian if needed.
+
+    Inputs are 0-360 longitude. When the event wraps across the
+    0°/360° boundary the box is drawn as two rectangles so matplotlib
+    does not produce an inverted or near-zero-width patch.
+    """
+
+    lmin = _to_plot_lon(lon_min_0360)
+    lmax = _to_plot_lon(lon_max_0360)
+
+    def _rect(x0: float, x1: float) -> mpatches.Rectangle:
+        return mpatches.Rectangle(
+            (x0, lat_min),
+            x1 - x0,
+            lat_max - lat_min,
+            linewidth=linewidth,
+            edgecolor=color,
+            facecolor=(*color[:3], 0.25),
+            transform=ccrs.PlateCarree(),
+            zorder=zorder,
+        )
+
+    if lmin <= lmax:
+        ax.add_patch(_rect(lmin, lmax))
+    else:
+        ax.add_patch(_rect(lmin, 180.0))
+        ax.add_patch(_rect(-180.0, lmax))
+
+
+def plot_events_global(
+    df: pd.DataFrame,
+    event_type: str,
+    title: str,
+    output_path: str,
+) -> None:
+    """Plot detected events as bounding boxes on a global Robinson map.
+
+    Boxes are coloured by start date using ``Reds`` for heat waves or
+    ``Blues`` for cold snaps.
+    """
+    cmap_name = "Reds" if event_type == "heat_wave" else "Blues"
+    cmap = plt.colormaps.get_cmap(cmap_name)
+
+    fig = plt.figure(figsize=(18, 9))
+    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
+    ax.set_global()
+    ax.add_feature(cfeature.OCEAN, facecolor="lightcyan", zorder=0)
+    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=1)
+    ax.coastlines(linewidth=0.5, zorder=3)
+    ax.add_feature(cfeature.BORDERS, linewidth=0.4, edgecolor="grey", zorder=3)
+    ax.gridlines(draw_labels=False, linewidth=0.3, color="grey", alpha=0.5, zorder=2)
+
+    if df.empty:
+        ax.set_title(f"{title}\n(no events)", fontsize=11, loc="left")
+        fig.savefig(output_path, dpi=150, bbox_inches="tight")
+        plt.close(fig)
+        logger.info("Events plot saved to %s (no events)", output_path)
+        return
+
+    start_dates = pd.to_datetime(df["start_date"])
+    date_min = start_dates.min()
+    date_max = start_dates.max()
+    span = max((date_max - date_min).days, 1)
+    norm = mcolors.Normalize(vmin=0, vmax=span)
+
+    for _, row in df.iterrows():
+        days = (pd.to_datetime(row["start_date"]) - date_min).days
+        color = cmap(norm(days))
+        _add_lon_rect(
+            ax,
+            row["longitude_min"],
+            row["longitude_max"],
+            row["latitude_min"],
+            row["latitude_max"],
+            color,
+        )
+
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])
+    cbar = fig.colorbar(sm, ax=ax, orientation="vertical", fraction=0.02, pad=0.02)
+    tick_days = np.linspace(0, span, min(7, span + 1))
+    cbar.set_ticks(tick_days)
+    cbar.set_ticklabels(
+        [(date_min + pd.Timedelta(days=int(d))).strftime("%Y-%m-%d") for d in tick_days]
+    )
+    cbar.set_label("Event start date", fontsize=9)
+    ax.set_title(f"{title}\n{len(df)} events", fontsize=11, loc="left")
+    fig.savefig(output_path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("Events plot saved to %s", output_path)
+
+
+def plot_events_high_consec(
+    df: pd.DataFrame,
+    title: str,
+    output_path: str,
+    min_consec: int = 5,
+) -> None:
+    """Plot events with >= ``min_consec`` days, coloured by run length.
+
+    Uses the ``plasma_r`` colormap (neutral, not event-type specific).
+    """
+    sub = df[df["max_consecutive_days"] >= min_consec].copy()
+
+    fig = plt.figure(figsize=(18, 9))
+    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
+    ax.set_global()
+    ax.add_feature(cfeature.OCEAN, facecolor="lightcyan", zorder=0)
+    ax.add_feature(cfeature.LAND, facecolor="whitesmoke", zorder=1)
+    ax.coastlines(linewidth=0.5, zorder=3)
+    ax.add_feature(cfeature.BORDERS, linewidth=0.4, edgecolor="grey", zorder=3)
+    ax.gridlines(draw_labels=False, linewidth=0.3, color="grey", alpha=0.5, zorder=2)
+
+    if sub.empty:
+        ax.set_title(
+            f"{title}\n(no events with >= {min_consec} consecutive days)",
+            fontsize=11,
+            loc="left",
+        )
+        fig.savefig(output_path, dpi=150, bbox_inches="tight")
+        plt.close(fig)
+        logger.info(
+            "High-consec plot saved to %s (no qualifying events)",
+            output_path,
+        )
+        return
+
+    vmin = min_consec
+    vmax = int(sub["max_consecutive_days"].max())
+    n_levels = vmax - vmin + 1
+    cmap = plt.colormaps.get_cmap("plasma_r").resampled(n_levels)
+    bin_edges = np.arange(vmin - 0.5, vmax + 1.5, 1)
+    norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
+
+    for _, row in sub.iterrows():
+        consec_val = int(row["max_consecutive_days"])
+        color = cmap(norm(consec_val))
+        _add_lon_rect(
+            ax,
+            row["longitude_min"],
+            row["longitude_max"],
+            row["latitude_min"],
+            row["latitude_max"],
+            color,
+        )
+
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])
+    cbar = fig.colorbar(sm, ax=ax, orientation="vertical", fraction=0.02, pad=0.02)
+    cbar.set_ticks(range(vmin, vmax + 1))
+    cbar.set_label("Max Consecutive Days", fontsize=9)
+    ax.set_title(f"{title}\n{len(sub)} events", fontsize=11, loc="left")
+    fig.savefig(output_path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("High-consec plot saved to %s", output_path)
+
+
+def plot_event_consec_maps(
+    df: pd.DataFrame,
+    exc_filt: np.ndarray,
+    dates: np.ndarray,
+    lats: np.ndarray,
+    lons: np.ndarray,
+    stem: str,
+    min_consec: int = 3,
+) -> None:
+    """Save a per-event consecutive-days map for each qualifying event.
+
+    For each row in ``df`` with ``max_consecutive_days >= min_consec``,
+    slices ``exc_filt`` to the event's bounding box and time window,
+    computes per-grid-point max consecutive days, and writes a
+    ``plasma_r`` pcolormesh to
+    ``{stem}_event_{label:04d}_consec.png``.
+
+    Args:
+        df: DataFrame from ``events_to_dataframe``.
+        exc_filt: Boolean array (time, lat, lon) from
+            ``_consecutive_filter_np``, aligned with
+            dates/lats/lons.
+        dates: 1-D datetime64 array aligned with axis 0.
+        lats: 1-D latitude array aligned with axis 1.
+        lons: 1-D longitude array (0-360) aligned with axis 2.
+        stem: Output path stem.
+        min_consec: Minimum max_consecutive_days to plot. Default 3.
+    """
+    qualifying = df[df["max_consecutive_days"] >= min_consec]
+    if qualifying.empty:
+        logger.info(
+            "No events with >= %d consecutive days to plot individually.",
+            min_consec,
+        )
+        return
+
+    logger.info(
+        "Plotting %d per-event consecutive-days maps...",
+        len(qualifying),
+    )
+
+    plot_lons = np.where(lons > 180, lons - 360.0, lons)
+
+    for _, row in qualifying.iterrows():
+        label = int(row["label"])
+        start_date = np.datetime64(row["start_date"])
+        end_date = np.datetime64(row["end_date"])
+        t_indices = np.where((dates >= start_date) & (dates <= end_date))[0]
+
+        idx_s = _nearest_1d_index(lats, row["latitude_min"])
+        idx_n = _nearest_1d_index(lats, row["latitude_max"])
+        idx_w = _nearest_1d_index(lons, row["longitude_min"])
+        idx_e = _nearest_1d_index(lons, row["longitude_max"])
+
+        ev_filt = exc_filt[t_indices][:, idx_s : idx_n + 1, idx_w : idx_e + 1]
+        if ev_filt.size == 0:
+            continue
+        consec = max_consecutive_days(ev_filt)
+
+        ev_lats = lats[idx_s : idx_n + 1]
+        ev_lons = plot_lons[idx_w : idx_e + 1]
+
+        plot_data = consec.astype(float)
+        plot_data[consec < MIN_CONSECUTIVE_DAYS] = np.nan
+        valid = plot_data[~np.isnan(plot_data)]
+        if valid.size == 0:
+            continue
+
+        vmax = int(valid.max())
+        n_levels = vmax - MIN_CONSECUTIVE_DAYS + 1
+        cmap = plt.colormaps.get_cmap("plasma_r").resampled(n_levels)
+        bin_edges = np.arange(MIN_CONSECUTIVE_DAYS - 0.5, vmax + 1.5, 1)
+        norm = mcolors.BoundaryNorm(bin_edges, ncolors=n_levels)
+
+        fig, ax = plt.subplots(
+            subplot_kw={"projection": ccrs.PlateCarree()},
+            figsize=(10, 8),
+        )
+        ax.set_extent(
+            [
+                float(ev_lons.min()) - 1,
+                float(ev_lons.max()) + 1,
+                float(ev_lats.min()) - 1,
+                float(ev_lats.max()) + 1,
+            ],
+            crs=ccrs.PlateCarree(),
+        )
+        ax.add_feature(cfeature.OCEAN, facecolor="lightblue", zorder=0)
+        im = ax.pcolormesh(
+            ev_lons,
+            ev_lats,
+            plot_data,
+            cmap=cmap,
+            norm=norm,
+            transform=ccrs.PlateCarree(),
+            shading="auto",
+        )
+        ax.coastlines(linewidth=0.5, zorder=10)
+        ax.add_feature(cfeature.BORDERS, linewidth=0.6, edgecolor="grey")
+        ax.add_feature(cfeature.STATES, linewidth=0.4, edgecolor="lightgrey")
+
+        cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+        cbar.set_ticks(range(MIN_CONSECUTIVE_DAYS, vmax + 1))
+        cbar.set_label("Consecutive Days", fontsize=12)
+
+        start_str = str(row["start_date"])[:10]
+        end_str = str(row["end_date"])[:10]
+        ax.set_title(
+            f"Event {label}: Consecutive Exceedance Days\n{start_str} to {end_str}",
+            loc="left",
+            fontsize=13,
+        )
+        out_path = f"{stem}_event_{label:04d}_consec.png"
+        fig.savefig(out_path, dpi=200, bbox_inches="tight")
+        plt.close(fig)
+        logger.info("  Event %04d saved to %s", label, out_path)
+
+
+def _default_output(case_id_number: int, event_type: str) -> str:
+    kind = "heatwave" if event_type == "heat_wave" else "cold_snap"
+    return f"case_{case_id_number}_consecutive_{kind}_days.png"
+
+
+def _add_quantile_operator_args(parser: argparse.ArgumentParser) -> None:
+    """Add --quantile and --operator to a subparser (plot and case)."""
+    parser.add_argument(
+        "--quantile",
+        type=float,
+        default=None,
+        help=(
+            f"Climatology quantile (one of {VALID_QUANTILES}). "
+            "Defaults to 0.85 for heat_wave or 0.15 for freeze."
+        ),
+    )
+    parser.add_argument(
+        "--operator",
+        default=None,
+        help=(
+            "Comparison operator string (>, >=, <, <=, ==). "
+            "Defaults to > for heat_wave or < for freeze."
+        ),
+    )
+
+
+def _add_plot_args(parser: argparse.ArgumentParser) -> None:
+    """Populate the ``plot`` subparser."""
+    parser.add_argument(
+        "--case-id-number",
+        type=int,
+        required=True,
+        help="case_id_number from events.yaml",
+    )
+    parser.add_argument(
+        "--output",
+        default=None,
+        help=(
+            "Output PNG path "
+            "(default: case_N_consecutive_{heatwave|cold_snap}_days.png)"
+        ),
+    )
+    _add_quantile_operator_args(parser)
+
+
+def _add_case_args(parser: argparse.ArgumentParser) -> None:
+    """Populate the ``case`` subparser."""
+    parser.add_argument(
+        "--output",
+        default="heat_cold_yaml.csv",
+        help="Output CSV path",
+    )
+    parser.add_argument(
+        "--n-workers",
+        type=int,
+        default=4,
+        help="Number of parallel workers (joblib)",
+    )
+    parser.add_argument(
+        "--case-min",
+        type=int,
+        default=None,
+        help="Minimum case_id_number to process (inclusive)",
+    )
+    parser.add_argument(
+        "--case-max",
+        type=int,
+        default=None,
+        help="Maximum case_id_number to process (inclusive)",
+    )
+    _add_quantile_operator_args(parser)
+
+
+def _add_global_args(parser: argparse.ArgumentParser) -> None:
+    """Populate the ``global`` subparser."""
+    parser.add_argument(
+        "--start-date",
+        required=True,
+        help="Start date YYYY-MM-DD",
+    )
+    parser.add_argument(
+        "--end-date",
+        required=True,
+        help="End date YYYY-MM-DD",
+    )
+    parser.add_argument(
+        "--output",
+        default="events_global.csv",
+        help="Output CSV path stem (actual file will be {stem}_events.csv)",
+    )
+    parser.add_argument(
+        "--n-workers",
+        type=int,
+        default=4,
+        help="Number of dask workers",
+    )
+    parser.add_argument(
+        "--dask-batch",
+        type=int,
+        default=1,
+        help=(
+            "Number of monthly chunks to submit to Dask at once. "
+            "Default 1 (one month per batch). Dask still parallelizes "
+            "daily time steps within each month across workers. Larger "
+            "values increase cross-month parallelism but cause "
+            "rechunk-merge bottlenecks when months span many zarr "
+            "chunks."
+        ),
+    )
+    parser.add_argument(
+        "--quantile-lower",
+        type=float,
+        default=None,
+        help=(
+            f"Lower-bound climatology quantile ({VALID_QUANTILES}). "
+            "At least one of --quantile-lower or --quantile-upper required."
+        ),
+    )
+    parser.add_argument(
+        "--operator-lower",
+        default=">",
+        help="Comparison operator for the lower bound (default: >)",
+    )
+    parser.add_argument(
+        "--quantile-upper",
+        type=float,
+        default=None,
+        help=(
+            f"Upper-bound climatology quantile ({VALID_QUANTILES}). "
+            "Combine with --quantile-lower to define a band."
+        ),
+    )
+    parser.add_argument(
+        "--operator-upper",
+        default="<",
+        help="Comparison operator for the upper bound (default: <)",
+    )
+    parser.add_argument(
+        "--lat-min",
+        type=float,
+        default=-60.0,
+        help=(
+            "Minimum latitude to include in detection. Default -60.0 "
+            "(excludes Antarctica, whose polar grid geometry produces "
+            "full-circumference ring blobs that trivially meet the area "
+            "threshold). Pass -90.0 explicitly to include Antarctica."
+        ),
+    )
+    parser.add_argument(
+        "--lat-max",
+        type=float,
+        default=90.0,
+        help="Maximum latitude to include in detection. Default 90.0",
+    )
+    parser.add_argument(
+        "--chunk-months",
+        type=int,
+        default=1,
+        help=(
+            "Process the exceedance mask this many months at a time "
+            "to stay within Dask worker memory limits. Set to 0 to "
+            "compute the full date range at once. Default is 1."
+        ),
+    )
+    parser.add_argument(
+        "--min-area-km2",
+        type=float,
+        default=200_000.0,
+        help=(
+            "Minimum contiguous blob area in km² required to seed a "
+            "new event AND on each of the prior MIN_CONSECUTIVE_DAYS-1 "
+            "days (joint-area check). Always applied, including in "
+            "sensitivity mode. Default 200 000."
+        ),
+    )
+    parser.add_argument(
+        "--sensitivity-thresholds",
+        type=float,
+        nargs="+",
+        default=None,
+        metavar="KM2",
+        help=(
+            "Count surviving events at each supplied area threshold "
+            "(km²) and write a summary CSV. Events are seeded at "
+            "--min-area-km2 first; thresholds below that value will "
+            "equal the seed-filter count. Example: "
+            "--sensitivity-thresholds 200000 300000 500000 1000000"
+        ),
+    )
+
+
+def _run_plot(args: argparse.Namespace) -> None:
+    """Run the ``plot`` subcommand."""
+    t0 = time_module.time()
+    single_case = load_case(args.case_id_number)
+    logger.info(
+        "Case %d: %s (%s)",
+        single_case.case_id_number,
+        single_case.title,
+        single_case.event_type,
+    )
+
+    if args.output is None:
+        args.output = _default_output(
+            args.case_id_number,
+            single_case.event_type,
+        )
+
+    consec, lats, lons = compute_consecutive_field(
+        single_case,
+        quantile=args.quantile,
+        op_str=args.operator,
+    )
+
+    kind = "Heat Wave" if single_case.event_type == "heat_wave" else "Cold Snap"
+    start_str = str(single_case.start_date.date())
+    end_str = str(single_case.end_date.date())
+
+    plot_consecutive_map(
+        consec,
+        lats,
+        lons,
+        cast(Literal["heat_wave", "cold_snap"], single_case.event_type),
+        title=f"Consecutive {kind} Days\n{start_str} to {end_str}",
+        output_path=args.output,
+    )
+    logger.info("Done in %.1f s", time_module.time() - t0)
+
+
+def _run_case(args: argparse.Namespace) -> None:
+    """Run the ``case`` subcommand."""
+    wall_start = time_module.time()
+
+    client = Client(LocalCluster(n_workers=args.n_workers))
+    logger.info("Dask dashboard: %s", client.dashboard_link)
+
+    old_cases = _load_base_temp_events()
+    hw_fz = [
+        e
+        for e in old_cases
+        if e.event_type in ("heat_wave", "freeze")
+        and (args.case_min is None or e.case_id_number >= args.case_min)
+        and (args.case_max is None or e.case_id_number <= args.case_max)
+    ]
+    logger.info(
+        "Found %d heat_wave / freeze events in base_temp_events.yaml",
+        len(hw_fz),
+    )
+
+    out_dir = pathlib.Path(args.output).parent
+
+    results = joblib.Parallel(n_jobs=args.n_workers)(
+        joblib.delayed(process_event)(
+            c,
+            out_dir,
+            quantile=args.quantile,
+            op_str=args.operator,
+        )
+        for c in hw_fz
+    )
+
+    df = results_to_dataframe(results)
+    out_path = pathlib.Path(args.output)
+    if out_path.exists():
+        df.to_csv(out_path, index=False, mode="a", header=False)
+    else:
+        df.to_csv(out_path, index=False)
+
+    yaml_path = (
+        pathlib.Path(__file__).parent.parent
+        / "src"
+        / "extremeweatherbench"
+        / "data"
+        / "events.yaml"
+    )
+    write_bounds_to_yaml(results, yaml_path)
+
+    elapsed = time_module.time() - wall_start
+    logger.info(
+        "Done: %d events -> %s (%.1f s)",
+        len(df),
+        args.output,
+        elapsed,
+    )
+    client.close()
+
+
+def _run_global(
+    args: argparse.Namespace,
+    parser: argparse.ArgumentParser,
+) -> None:
+    """Run the ``global`` subcommand."""
+    if args.quantile_lower is None and args.quantile_upper is None:
+        parser.error(
+            "At least one of --quantile-lower or --quantile-upper must be provided."
+        )
+
+    ql, qu = args.quantile_lower, args.quantile_upper
+    if ql is not None and qu is not None:
+        event_type = f"q{ql:.2f}-q{qu:.2f}"
+    elif ql is not None:
+        event_type = f"q{ql:.2f}+"
+    else:
+        event_type = f"q{qu:.2f}-"
+
+    if ql is not None and ql >= 0.5:
+        plot_event_type = "heat_wave"
+    elif qu is not None and qu <= 0.5:
+        plot_event_type = "cold_snap"
+    else:
+        plot_event_type = "heat_wave"
+
+    is_sensitivity = args.sensitivity_thresholds is not None
+    if is_sensitivity:
+        below = [t for t in args.sensitivity_thresholds if t < args.min_area_km2]
+        if below:
+            logger.warning(
+                "Sensitivity thresholds %s are below the seed filter "
+                "(%.0f km²); those counts will equal the seed-filter total.",
+                below,
+                args.min_area_km2,
+            )
+
+    wall_start = time_module.time()
+    client = Client(LocalCluster(n_workers=args.n_workers))
+    logger.info("Dask dashboard: %s", client.dashboard_link)
+
+    logger.info("Pre-warming numba JIT...")
+    _tiny = np.zeros((2, 2), dtype=np.int32)
+    _tiny[0, 0] = 1
+    _tiny[1, 1] = 1
+    _count_overlaps_nb(_tiny, _tiny, 1, 1)
+    logger.info("  numba ready")
+
+    logger.info("Opening ERA5 data...")
+    t2m = open_era5_t2m(args.start_date, args.end_date)
+    if args.lat_min != -90.0 or args.lat_max != 90.0:
+        t2m = t2m.sel(latitude=slice(args.lat_min, args.lat_max))
+        logger.info(
+            "  Latitude filtered to [%.1f, %.1f]",
+            args.lat_min,
+            args.lat_max,
+        )
+    logger.info("  sizes=%s", dict(t2m.sizes))
+
+    logger.info(
+        "Loading climatology bounds (lower=%s, upper=%s)...",
+        args.quantile_lower,
+        args.quantile_upper,
+    )
+    clim_lower, clim_upper = get_climatology_bounds(
+        q_lower=args.quantile_lower,
+        q_upper=args.quantile_upper,
+    )
+
+    logger.info("Building land mask and grid-cell area array...")
+    land_mask = build_land_mask(t2m.longitude, t2m.latitude)
+    land_mask_np = land_mask.values.astype(bool)
+    area_grid = compute_grid_cell_area(t2m.latitude.values, t2m.longitude.values)
+
+    tdim = detect_time_dim(t2m)
+
+    # Build the exceedance mask and daily temperature together so
+    # both passes read the same ERA5 data once. Lazy month-chunks are
+    # handed to a single dask.compute() per batch so Dask can schedule
+    # months in parallel.
+    t0 = time_module.time()
+    if args.chunk_months > 0:
+        logger.info(
+            "Building lazy graphs for exceedance mask + temperature "
+            "(%d-month chunks, parallel)...",
+            args.chunk_months,
+        )
+        month_starts = pd.date_range(args.start_date, args.end_date, freq="MS")
+        chunk_labels = []
+        exc_lazy_list = []
+        t2m_lazy_list = []
+        for i in range(0, len(month_starts), args.chunk_months):
+            ms = month_starts[i]
+            me = month_starts[min(i + args.chunk_months, len(month_starts)) - 1]
+            me = me + pd.offsets.MonthEnd(1)
+            chunk_labels.append(f"{ms.strftime('%Y-%m')} - {me.strftime('%Y-%m')}")
+            t2m_chunk = t2m.sel({tdim: slice(str(ms.date()), str(me.date()))})
+            exc_lazy_list.append(
+                build_exceedance_mask(
+                    t2m_chunk,
+                    clim_lower,
+                    clim_upper,
+                    land_mask,
+                    op_lower=args.operator_lower,
+                    op_upper=args.operator_upper,
+                )
+            )
+            t2m_lazy_list.append(t2m_chunk.resample({tdim: "1D"}).mean())
+
+        n_chunks = len(exc_lazy_list)
+        batch = args.dask_batch
+        logger.info(
+            "  Computing %d chunks, %d per batch, via Dask...",
+            n_chunks,
+            batch,
+        )
+        exc_parts: list = []
+        t2m_parts: list = []
+        for b0 in range(0, n_chunks, batch):
+            b1 = min(b0 + batch, n_chunks)
+            b_exc = exc_lazy_list[b0:b1]
+            b_t2m = t2m_lazy_list[b0:b1]
+            b_sz = b1 - b0
+            b_results = dask.compute(*b_exc, *b_t2m)
+            exc_parts.extend(b_results[:b_sz])
+            t2m_parts.extend(b_results[b_sz:])
+            for label in chunk_labels[b0:b1]:
+                logger.info("  chunk %s done", label)
+
+        exc_da = xr.concat(exc_parts, dim=tdim)
+        t2m_daily_da = xr.concat(t2m_parts, dim=tdim)
+        del exc_parts, t2m_parts
+    else:
+        logger.info(
+            "Building lazy graphs (all-at-once) for exceedance mask + temperature..."
+        )
+        exc_lazy = build_exceedance_mask(
+            t2m,
+            clim_lower,
+            clim_upper,
+            land_mask,
+            op_lower=args.operator_lower,
+            op_upper=args.operator_upper,
+        )
+        t2m_lazy = t2m.resample({tdim: "1D"}).mean()
+        exc_da, t2m_daily_da = dask.compute(exc_lazy, t2m_lazy)
+
+    logger.info(
+        "  exceedance mask + temperature done in %.1f s",
+        time_module.time() - t0,
+    )
+
+    dates = exc_da[tdim].values
+    lats = exc_da.latitude.values
+    lons = exc_da.longitude.values
+    exc_np = exc_da.values.astype(bool)
+    del exc_da
+
+    t2m_daily_np = (t2m_daily_da.values - 273.15).astype(np.float32)
+    del t2m_daily_da
+
+    logger.info(
+        "Applying %d-day consecutive filter...",
+        MIN_CONSECUTIVE_DAYS,
+    )
+    exc_filt = _consecutive_filter_np(exc_np, MIN_CONSECUTIVE_DAYS, 1)
+    logger.info(
+        "  %d -> %d True cells",
+        exc_np.sum(),
+        exc_filt.sum(),
+    )
+    del exc_np
+
+    logger.info(
+        "Detecting events (min_seed_area_km2=%.0f)...",
+        args.min_area_km2,
+    )
+    t0 = time_module.time()
+    events = detect_events(
+        exc_filt,
+        dates,
+        lats,
+        lons,
+        event_type,
+        area_grid=area_grid,
+        min_seed_area_km2=args.min_area_km2,
+    )
+    logger.info("  %d raw events (%.1f s)", len(events), time_module.time() - t0)
+
+    logger.info(
+        "Expanding event bounds (%d-deg steps)...",
+        EXPANSION_DEGREES,
+    )
+    t0 = time_module.time()
+    events = joblib.Parallel(n_jobs=-1, prefer="threads")(
+        joblib.delayed(expand_event_bounds)(
+            ev, exc_filt, dates, lats, lons, land_mask_np
+        )
+        for ev in events
+    )
+    logger.info("  done in %.1f s", time_module.time() - t0)
+
+    logger.info("Computing per-event temperature statistics...")
+    t0 = time_module.time()
+    events = include_temps_with_events(
+        events, t2m_daily_np, exc_filt, dates, lats, lons
+    )
+    del t2m_daily_np
+    logger.info("  done in %.1f s", time_module.time() - t0)
+
+    stem = str(pathlib.Path(args.output).with_suffix(""))
+
+    df = events_to_dataframe(events, min_area_km2=args.min_area_km2)
+    events_csv = f"{stem}_events.csv"
+    df.to_csv(events_csv, index=False)
+    logger.info("%d events written to %s", len(df), events_csv)
+
+    logger.info("Computing max-consecutive-days plot...")
+    consec = max_consecutive_days(exc_filt)
+    plot_consecutive_map(
+        consec,
+        lats,
+        lons,
+        cast(Literal["heat_wave", "cold_snap"], plot_event_type),
+        title=(
+            f"Consecutive Exceedance Days ({event_type})"
+            f"\n{args.start_date} to {args.end_date}"
+        ),
+        output_path=f"{stem}_consec.png",
+    )
+    del consec
+
+    plot_events_global(
+        df,
+        event_type=plot_event_type,
+        title=(
+            f"Detected events ({event_type.replace('_', ' ')})"
+            f"\n{args.start_date} to {args.end_date}"
+        ),
+        output_path=f"{stem}_events.png",
+    )
+    plot_events_high_consec(
+        df,
+        title=(
+            f"Events \u22656 consecutive days ({event_type.replace('_', ' ')})"
+            f"\n{args.start_date} to {args.end_date}"
+        ),
+        output_path=f"{stem}_high_consec.png",
+    )
+
+    plot_event_consec_maps(df, exc_filt, dates, lats, lons, stem)
+
+    del exc_filt
+
+    if is_sensitivity:
+        thresholds = sorted(args.sensitivity_thresholds)
+        logger.info(
+            "Sensitivity test: counting events at %d thresholds...",
+            len(thresholds),
+        )
+        rows = []
+        for thresh in thresholds:
+            df_thresh = events_to_dataframe(events, min_area_km2=thresh)
+            rows.append({"area_threshold_km2": thresh, "n_events": len(df_thresh)})
+        sens_df = pd.DataFrame(rows)
+        print(sens_df.to_string(index=False))
+        sens_path = f"{stem}_sensitivity.csv"
+        sens_df.to_csv(sens_path, index=False)
+        logger.info("Sensitivity table saved to %s", sens_path)
+
+    elapsed = time_module.time() - wall_start
+    logger.info("Done in %.1f s", elapsed)
+    client.close()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        prog="temperature_events",
+        description=(
+            "ERA5 temperature-event tooling: plot a single case,"
+            " re-expand curated cases, or scan a date range globally."
+        ),
+    )
+    sub = parser.add_subparsers(dest="cmd", required=True)
+
+    plot_p = sub.add_parser(
+        "plot",
+        help="Plot one case from events.yaml.",
+        description=(
+            "Render a consecutive-exceedance-days map for one case"
+            " identified by case_id_number."
+        ),
+    )
+    _add_plot_args(plot_p)
+
+    case_p = sub.add_parser(
+        "case",
+        help="Re-expand curated heat_wave / freeze cases.",
+        description=(
+            "Iterate every centered_region case in base_temp_events.yaml,"
+            " expand its time and spatial bounds, and write the"
+            " refreshed bounds back to events.yaml."
+        ),
+    )
+    _add_case_args(case_p)
+
+    global_p = sub.add_parser(
+        "global",
+        help="Detect temperature events over a date range.",
+        description=(
+            "Scan ERA5 2m temperature over a date range, detect events"
+            " that stay within a climatology-quantile band for at least"
+            " MIN_CONSECUTIVE_DAYS days over land, and write a CSV plus"
+            " plots."
+        ),
+    )
+    _add_global_args(global_p)
+
+    args = parser.parse_args()
+    if args.cmd == "plot":
+        _run_plot(args)
+    elif args.cmd == "case":
+        _run_case(args)
+    elif args.cmd == "global":
+        _run_global(args, global_p)
+    else:  # pragma: no cover -- argparse enforces this
+        parser.error(f"Unknown subcommand: {args.cmd!r}")
+
+
+if __name__ == "__main__":
+    main()

From 446cff9719765097029f7f68a03bb72920d2db58 Mon Sep 17 00:00:00 2001
From: Daniel Rothenberg <daniel@brightband.com>
Date: Fri, 24 Apr 2026 12:54:12 -0600
Subject: [PATCH 71/72] fix: seed sample_calc_dataset fixture to eliminate
 flaky pressure test (#367)

np.random.normal(500, 200) for orographic heights occasionally draws
extreme negative values (e.g. -320 m below sea level), which push the
barometric-formula surface pressure above the test's 105 000 Pa bound.
The failure was latent but surfaced on Python 3.13 due to a different
RNG initialisation state.

Replace all np.random.* calls in the fixture with a local seeded
np.random.default_rng(42) instance so generated data is bit-reproducible
across Python versions and the pressure range assertion holds reliably.
---
 tests/conftest.py | 25 ++++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/tests/conftest.py b/tests/conftest.py
index 37bd91c5..f8a7d3df 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -391,7 +391,14 @@ def sample_calc_dataset():
     lon = np.linspace(-120, -80, 21)
     level = [1000, 850, 700, 500, 300, 200]
 
-    # Create realistic meteorological data
+    # Create realistic meteorological data.
+    # Use a seeded generator so results are bit-reproducible across Python
+    # versions; the global numpy RNG state can differ between interpreters and
+    # caused flaky failures when extreme draws produced physically impossible
+    # surface heights (negative elevation far below sea level) that pushed
+    # barometric-formula surface pressures above the 105 000 Pa test bound.
+    rng = np.random.default_rng(42)
+
     data_shape_3d = (len(time), len(lat), len(lon))
     data_shape_4d = (len(time), len(level), len(lat), len(lon))
 
@@ -399,35 +406,35 @@ def sample_calc_dataset():
         {
             "air_pressure_at_mean_sea_level": (
                 ["time", "latitude", "longitude"],
-                np.random.normal(101325, 1000, data_shape_3d),
+                rng.normal(101325, 1000, data_shape_3d),
             ),
             "surface_eastward_wind": (
                 ["time", "latitude", "longitude"],
-                np.random.normal(0, 10, data_shape_3d),
+                rng.normal(0, 10, data_shape_3d),
             ),
             "surface_northward_wind": (
                 ["time", "latitude", "longitude"],
-                np.random.normal(0, 10, data_shape_3d),
+                rng.normal(0, 10, data_shape_3d),
             ),
             "geopotential": (
                 ["time", "level", "latitude", "longitude"],
-                np.random.normal(5000, 1000, data_shape_4d) * calc.g0,
+                rng.normal(5000, 1000, data_shape_4d) * calc.g0,
             ),
             "geopotential_at_surface": (
                 ["time", "latitude", "longitude"],
-                np.random.normal(500, 200, data_shape_3d) * calc.g0,
+                rng.normal(500, 200, data_shape_3d) * calc.g0,
             ),
             "eastward_wind": (
                 ["time", "level", "latitude", "longitude"],
-                np.random.normal(0, 15, data_shape_4d),
+                rng.normal(0, 15, data_shape_4d),
             ),
             "northward_wind": (
                 ["time", "level", "latitude", "longitude"],
-                np.random.normal(0, 15, data_shape_4d),
+                rng.normal(0, 15, data_shape_4d),
             ),
             "specific_humidity": (
                 ["time", "level", "latitude", "longitude"],
-                np.random.uniform(0.001, 0.02, data_shape_4d),
+                rng.uniform(0.001, 0.02, data_shape_4d),
             ),
         },
         coords={

From 1e08fac69d78d63be558670bc901f1145c754d85 Mon Sep 17 00:00:00 2001
From: Daniel Rothenberg <daniel@brightband.com>
Date: Fri, 24 Apr 2026 13:01:05 -0600
Subject: [PATCH 72/72] fix: correct three CIN bugs in mixed-layer CAPE/CIN
 computation (#366)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* fix: correct three CIN bugs in mixed-layer CAPE/CIN computation

Three bugs in _cape.py caused CIN to be unreliable (sign wrong, magnitude
wrong, and false-LFC suppression). Discovered by benchmarking against xcape
and atmos on 6,400 ERA5 profiles.

Bug 1 — Sign convention: compute_buoyancy_energy_inline returns negative
values below the LFC; the raw accumulation was returned without negation,
producing negative CIN where callers expect a positive inhibition energy
(matching MetPy and xcape conventions). Fixed by negating cin at both return
sites.

Bug 2 — Integration scope: the LFC branch accumulated all buoyancy energy
unconditionally (including positively-buoyant layers between surface and LFC),
reducing CIN when a parcel had brief near-surface positive buoyancy before
encountering a cap. Added `if energy < 0` guard to match the no-LFC branch
and the behaviour of reference implementations.

Bug 3 — Spurious LCL buoyancy (critical): insert_lcl_level set the inserted
level's environment temperature to t_lcl (the parcel's saturation temperature)
instead of interpolating the actual atmospheric temperature in log-pressure.
This made env_tv ≈ parcel_tv at the LCL, injecting a +1-2 K buoyancy spike
that was detected as an LFC, hiding the real capping inversion above and
driving CIN to near-zero for profiles with low LCLs and strong cap inversions.

Adds TestCINSignConvention, TestCINIntegrationScope, and
TestLCLTemperatureInterpolation test classes with profiles that specifically
exercise each bug path and will catch regressions.

* fix: address Bugbot review comments on CIN convention consistency

- Document CIN sign convention in compute_ml_cape_cin_from_profile docstring:
  returned as a non-negative inhibition magnitude matching MetPy/xcape.
- Negate MetPy's negative-signed CIN in the reference data generator so
  stored cin_reference values match the implementation's positive convention;
  update both era5_reference.npz and pathological_profiles.npz accordingly.
- Rename expected_cin_range to expected_cin_magnitude_range in TestKnownProfile
  to make the positive convention explicit at the call site.
- Strengthen TestCINIntegrationScope: replace the weak cin >= 0 assertion with
  a quantitative lower bound (>50 J/kg) that would catch the original bug
  where positive near-surface buoyancy cancelled cap-layer CIN.

* fix: remove pytest.mark.flaky (pytest-rerunfailures not in deps)
---
 src/extremeweatherbench/_cape.py           |  37 ++-
 tests/data/era5_reference.npz              | Bin 72956 -> 72956 bytes
 tests/data/generate_cape_reference_data.py |   7 +-
 tests/data/pathological_profiles.npz       | Bin 5904 -> 5904 bytes
 tests/test_cape.py                         | 321 ++++++++++++++++++++-
 5 files changed, 346 insertions(+), 19 deletions(-)

diff --git a/src/extremeweatherbench/_cape.py b/src/extremeweatherbench/_cape.py
index bbb73713..8ee6839f 100644
--- a/src/extremeweatherbench/_cape.py
+++ b/src/extremeweatherbench/_cape.py
@@ -286,17 +286,23 @@ def insert_lcl_level(
         new_dewpoint[i] = dewpoint[i]
         new_geopotential[i] = geopotential[i]
 
-    # Insert LCL level
+    # Insert LCL level — interpolate all environmental fields in log-pressure.
+    # The temperature at the LCL level must be the *environmental* temperature
+    # interpolated from the surrounding levels, NOT the parcel's saturation
+    # temperature t_lcl.  Using t_lcl here makes env_tv ≈ parcel_tv at the LCL,
+    # injecting spurious positive buoyancy that creates a false LFC and hides
+    # the real capping inversion above.
     new_pressure[insert_idx] = p_lcl
-    new_temperature[insert_idx] = t_lcl
 
-    # Interpolate dewpoint and geopotential at LCL
     if insert_idx > 0 and insert_idx < n:
         log_p_below = np.log(pressure[insert_idx - 1])
         log_p_above = np.log(pressure[insert_idx])
         log_p_lcl = np.log(p_lcl)
 
         frac = (log_p_lcl - log_p_below) / (log_p_above - log_p_below)
+        new_temperature[insert_idx] = temperature[insert_idx - 1] + frac * (
+            temperature[insert_idx] - temperature[insert_idx - 1]
+        )
         new_dewpoint[insert_idx] = dewpoint[insert_idx - 1] + frac * (
             dewpoint[insert_idx] - dewpoint[insert_idx - 1]
         )
@@ -304,9 +310,11 @@ def insert_lcl_level(
             geopotential[insert_idx] - geopotential[insert_idx - 1]
         )
     elif insert_idx == 0:
+        new_temperature[insert_idx] = temperature[0]
         new_dewpoint[insert_idx] = dewpoint[0]
         new_geopotential[insert_idx] = geopotential[0]
     else:
+        new_temperature[insert_idx] = temperature[-1]
         new_dewpoint[insert_idx] = dewpoint[-1]
         new_geopotential[insert_idx] = geopotential[-1]
 
@@ -381,9 +389,6 @@ def compute_ml_cape_cin_from_profile(
     variety of inlined helper functions to ensure that the computation is as fast
     as possible.
 
-    WARNING: The CIN computation is not yet implemented correctly and may give
-    erroneous results.
-
     Args:
         pressure: The pressure in hPa.
         temperature: The temperature in Kelvin.
@@ -392,7 +397,10 @@ def compute_ml_cape_cin_from_profile(
         depth: The depth of the mixed layer in hPa.
 
     Returns:
-        The CAPE and CIN in J/kg.
+        A tuple (cape, cin) in J/kg.  Both values are non-negative.  CIN is
+        returned as a positive inhibition magnitude (e.g. 50 J/kg means the
+        parcel must overcome 50 J/kg of negative buoyancy to reach the LFC),
+        matching the convention used by MetPy and xcape.
     """
     n_levels = len(pressure)
 
@@ -553,9 +561,12 @@ def compute_ml_cape_cin_from_profile(
             if energy < 0:
                 cin += energy
 
-        return 0.0, cin
+        return 0.0, -cin
 
-    # Integrate CIN from surface to LFC
+    # Integrate CIN from surface to LFC — only negatively buoyant layers.
+    # Skipping positively buoyant layers is important: a parcel that is briefly
+    # positively buoyant near the surface before encountering a cap inversion
+    # should not have that positive energy cancel its CIN.
     for i in range(n_levels - 1):
         if i >= lfc_level_idx:
             if i == lfc_level_idx:
@@ -563,14 +574,16 @@ def compute_ml_cape_cin_from_profile(
                 tv_avg = (env_tv[i] + env_tv[i + 1]) * 0.5
                 parcel_avg = (parcel_tv[i] + parcel_tv[i + 1]) * 0.5
                 energy = compute_buoyancy_energy_inline(parcel_avg, tv_avg, dz)
-                cin += energy
+                if energy < 0:
+                    cin += energy
             break
 
         dz = heights[i + 1] - heights[i]
         tv_avg = (env_tv[i] + env_tv[i + 1]) * 0.5
         parcel_avg = (parcel_tv[i] + parcel_tv[i + 1]) * 0.5
         energy = compute_buoyancy_energy_inline(parcel_avg, tv_avg, dz)
-        cin += energy
+        if energy < 0:
+            cin += energy
 
     # Integrate CAPE from LFC to EL (or top)
     if el_pressure > 0:
@@ -617,7 +630,7 @@ def compute_ml_cape_cin_from_profile(
             if energy > 0:
                 cape += energy
 
-    return cape, cin
+    return cape, -cin
 
 
 # ============================================================================
diff --git a/tests/data/era5_reference.npz b/tests/data/era5_reference.npz
index 2faa45b1e47a8cd32c2294f827f4dde345a93f23..096ec8b4259ccd916dd9edaac1734665889c5a4a 100644
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diff --git a/tests/data/generate_cape_reference_data.py b/tests/data/generate_cape_reference_data.py
index 12de4daa..5d86a256 100644
--- a/tests/data/generate_cape_reference_data.py
+++ b/tests/data/generate_cape_reference_data.py
@@ -64,7 +64,10 @@ def compute_metpy_reference(
         try:
             cape, cin = mixed_layer_cape_cin(p, t, td)
             cape_ref[i] = cape.magnitude
-            cin_ref[i] = cin.magnitude
+            # MetPy returns CIN with a negative sign; negate to store as a
+            # non-negative inhibition magnitude matching our implementation's
+            # sign convention.
+            cin_ref[i] = -cin.magnitude
         except Exception:
             cape_ref[i] = np.nan
             cin_ref[i] = np.nan
@@ -287,7 +290,7 @@ def create_pathological_profiles():
         "dewpoint": td3,
         "geopotential": z3,
         "expected_cape": 500.0,  # Moderate
-        "expected_cin": -100.0,  # Significant CIN
+        "expected_cin": 100.0,  # Significant CIN (non-negative inhibition magnitude)
         "description": "Elevated convection with significant CIN",
     }
 
diff --git a/tests/data/pathological_profiles.npz b/tests/data/pathological_profiles.npz
index fb9dab47995e6c28c6a8c34b906e2997f6d39ed0..c225308de7f34c89f65ac551e5de5ddbe74671f9 100644
GIT binary patch
delta 64
ucmbQBH$iVh3LlHHfV<4(489$T4Ga(fr6V0s`3w!49r*>=A?icLb^`#69t%ML

delta 41
tcmbQBH$iVh3Lnd*?1w)lXYlQqe1nmnDe}N%YbLqP{`^er5Q$K+-2i555bFQ{

diff --git a/tests/test_cape.py b/tests/test_cape.py
index bec880d4..7a6ef138 100644
--- a/tests/test_cape.py
+++ b/tests/test_cape.py
@@ -151,7 +151,7 @@ def test_minimum_levels(self):
         cape, cin = compute_ml_cape_cin_from_profile(p, t, td, z)
 
         assert cape >= 0, "CAPE should be non-negative"
-        assert cin <= 0, "CIN should be non-positive"
+        assert cin >= 0, "CIN should be non-negative (positive convention)"
 
     def test_many_levels(self):
         """Test with many levels (100)."""
@@ -165,7 +165,7 @@ def test_many_levels(self):
         cape, cin = compute_ml_cape_cin_from_profile(p, t, td, z)
 
         assert cape >= 0, "CAPE should be non-negative"
-        assert cin <= 0, "CIN should be non-positive"
+        assert cin >= 0, "CIN should be non-negative (positive convention)"
 
     def test_supersaturated_levels(self):
         """Test with supersaturated levels (dewpoint > temperature)."""
@@ -350,7 +350,8 @@ def known_results(self):
                 "geopotential": 29.3 * 273 * np.log(1000 / p),
                 "mixed_layer_depth": 50,
                 "expected_cape_range": (200, 800),
-                "expected_cin_range": (-20, 20),
+                # CIN is returned as a non-negative inhibition magnitude.
+                "expected_cin_magnitude_range": (0, 50),
             }
         }
 
@@ -367,7 +368,7 @@ def test_known_profile_results(self, known_results):
             )
 
             cape_min, cape_max = profile["expected_cape_range"]
-            cin_min, cin_max = profile["expected_cin_range"]
+            cin_min, cin_max = profile["expected_cin_magnitude_range"]
 
             assert cape_min <= cape <= cape_max, (
                 f"{name}: CAPE {cape:.2f} outside expected range [{cape_min}, {cape_max}]"
@@ -407,7 +408,6 @@ def test_single_profile_performance(self, era5_reference):
             f"Single profile computation too slow: {time_per_call:.2f} μs"
         )
 
-    @pytest.mark.flaky(reruns=3)
     def test_batch_performance(self, era5_reference):
         """Test that batch processing is reasonably fast."""
         import time
@@ -532,3 +532,314 @@ def test_pathological(pathological_profiles, profile_name, threshold, sign):
         )
     else:
         raise ValueError(f"Invalid sign: {sign}")
+
+
+# ---------------------------------------------------------------------------
+# Regression tests for the three CIN bugs fixed in fix/improved-cape-cin
+# ---------------------------------------------------------------------------
+
+# Standard pressure / geopotential grid used by several tests below.
+_PRES = np.array(
+    [
+        1000.0,
+        925.0,
+        850.0,
+        700.0,
+        600.0,
+        500.0,
+        400.0,
+        300.0,
+        250.0,
+        200.0,
+        150.0,
+        100.0,
+        50.0,
+    ],
+    dtype=np.float64,
+)
+_GEOPOT = (
+    np.array(
+        [
+            0.0,
+            700.0,
+            1500.0,
+            3000.0,
+            4200.0,
+            5600.0,
+            7200.0,
+            9200.0,
+            10400.0,
+            11800.0,
+            13500.0,
+            16000.0,
+            20500.0,
+        ],
+        dtype=np.float64,
+    )
+    * 9.80665
+)  # convert m → J/kg (geopotential)
+
+
+class TestCINSignConvention:
+    """CIN must be returned as a non-negative magnitude (positive convention).
+
+    compute_buoyancy_energy_inline returns negative values below the LFC;
+    CIN must be negated before returning so callers receive a positive
+    inhibition energy (matching MetPy and xcape conventions).
+    """
+
+    def test_no_lfc_profile_returns_nonnegative_cin(self):
+        """Stable capped profile with no LFC: CAPE=0, CIN >= 0."""
+        t = np.array(
+            [
+                273.0,
+                270.0,
+                268.0,
+                260.0,
+                255.0,
+                250.0,
+                240.0,
+                230.0,
+                225.0,
+                220.0,
+                215.0,
+                210.0,
+                205.0,
+            ],
+            dtype=np.float64,
+        )
+        td = t - 20.0
+        cape, cin = compute_ml_cape_cin_from_profile(_PRES, t, td, _GEOPOT)
+        assert cape == 0.0, f"Stable profile should have CAPE=0, got {cape:.2f}"
+        assert cin >= 0.0, f"CIN should be non-negative, got {cin:.2f}"
+
+    def test_capped_convective_profile_returns_nonnegative_cin(self):
+        """Profile with LFC and cap inversion: CAPE > 0, CIN > 0."""
+        # Warm moist surface, strong cap at 850 hPa, then conditionally unstable above.
+        t = np.array(
+            [
+                303.0,
+                299.0,
+                296.0,
+                278.0,
+                270.0,
+                260.0,
+                245.0,
+                230.0,
+                225.0,
+                220.0,
+                215.0,
+                210.0,
+                205.0,
+            ],
+            dtype=np.float64,
+        )
+        td = np.array(
+            [
+                298.0,
+                292.0,
+                285.0,
+                268.0,
+                255.0,
+                245.0,
+                230.0,
+                215.0,
+                210.0,
+                205.0,
+                200.0,
+                195.0,
+                190.0,
+            ],
+            dtype=np.float64,
+        )
+        cape, cin = compute_ml_cape_cin_from_profile(_PRES, t, td, _GEOPOT)
+        assert cape > 0.0, f"Convective profile should have CAPE > 0, got {cape:.2f}"
+        assert cin >= 0.0, f"CIN should be non-negative, got {cin:.2f}"
+
+    def test_surface_buoyant_zero_cin(self):
+        """Surface-buoyant profile (parcel immediately positive): CIN should be ~0."""
+        # Very warm, very moist surface with no inversion.
+        t = np.array(
+            [
+                308.0,
+                300.0,
+                292.0,
+                278.0,
+                270.0,
+                260.0,
+                245.0,
+                230.0,
+                225.0,
+                220.0,
+                215.0,
+                210.0,
+                205.0,
+            ],
+            dtype=np.float64,
+        )
+        td = np.array(
+            [
+                305.0,
+                296.0,
+                288.0,
+                270.0,
+                258.0,
+                248.0,
+                232.0,
+                216.0,
+                210.0,
+                205.0,
+                200.0,
+                195.0,
+                190.0,
+            ],
+            dtype=np.float64,
+        )
+        cape, cin = compute_ml_cape_cin_from_profile(_PRES, t, td, _GEOPOT)
+        assert cape > 100.0, f"Should have significant CAPE, got {cape:.2f}"
+        assert cin < 10.0, (
+            f"Surface-buoyant profile should have near-zero CIN, got {cin:.2f}"
+        )
+
+
+class TestCINIntegrationScope:
+    """CIN must accumulate only negatively-buoyant layers between surface and LFC.
+
+    Before the fix, the LFC branch accumulated all buoyancy (positive and
+    negative) unconditionally.  A profile with a brief positive-buoyancy
+    layer near the surface followed by a cap should have CIN that reflects
+    only the cap, not a cancellation against the near-surface positive layer.
+    """
+
+    def test_cin_only_from_negative_buoyancy_layers(self):
+        """CIN should not be reduced by positively-buoyant sub-LFC layers."""
+        # Profile: parcel briefly buoyant near surface, then encounters a cap
+        # (stable layer 925-850 hPa), then free convection above.
+        t = np.array(
+            [
+                300.0,
+                297.0,
+                296.0,
+                278.0,
+                270.0,
+                260.0,
+                245.0,
+                230.0,
+                225.0,
+                220.0,
+                215.0,
+                210.0,
+                205.0,
+            ],
+            dtype=np.float64,
+        )
+        td = np.array(
+            [
+                297.0,
+                290.0,
+                280.0,
+                265.0,
+                252.0,
+                242.0,
+                228.0,
+                214.0,
+                208.0,
+                203.0,
+                198.0,
+                193.0,
+                188.0,
+            ],
+            dtype=np.float64,
+        )
+        # A second, drier version of the same profile (Td -12 K everywhere).
+        # With a drier ML parcel there is no near-surface positive buoyancy, so
+        # the LFC branch only ever sees negative buoyancy and CIN is unchanged.
+        # Comparing the two isolates the effect of the integration-scope bug:
+        # the buggy code would let the moist profile's brief positive buoyancy
+        # near the surface cancel some of the cap's negative buoyancy, giving a
+        # lower CIN than the drier profile despite having a stronger cap.
+        # After the fix both profiles accumulate only negative layers and the
+        # moist profile's CIN reflects the cap alone (≥50 J/kg).
+        t_dry = t.copy()
+        td_dry = td - 12.0
+
+        cape, cin = compute_ml_cape_cin_from_profile(_PRES, t, td, _GEOPOT)
+        cape_dry, cin_dry = compute_ml_cape_cin_from_profile(
+            _PRES, t_dry, td_dry, _GEOPOT
+        )
+
+        assert cape > 0.0, "Moist profile should have CAPE"
+        # CIN for the moist profile must be a substantial positive magnitude —
+        # if it were near-zero the sign-only check would pass even with the bug.
+        assert cin > 50.0, (
+            f"CIN should reflect the cap inversion (>50 J/kg), got {cin:.2f}. "
+            "A near-zero value suggests positive near-surface buoyancy is cancelling "
+            "the cap — the integration-scope bug may have been reintroduced."
+        )
+
+
+class TestLCLTemperatureInterpolation:
+    """The environment temperature at the inserted LCL level must be interpolated
+    from the surrounding profile, not set to the parcel's saturation temperature.
+
+    The original bug set new_temperature[insert_idx] = t_lcl (the parcel's LCL
+    temperature), making env_tv ≈ parcel_tv at the LCL and injecting a spurious
+    positive-buoyancy spike that generated a false LFC.  Profiles with a low LCL
+    and a strong capping inversion above were most affected: the false LFC hid the
+    real cap, driving CIN to near-zero when it should be substantial.
+    """
+
+    def test_capped_profile_cin_not_suppressed_by_false_lfc(self):
+        """A profile with a low LCL and cap inversion must have measurable CIN.
+
+        Before the fix, the spurious LCL buoyancy created a false LFC immediately
+        above the surface, making the integrator believe there was no cap and
+        returning CIN ≈ 0 even when the real cap required hundreds of J/kg to breach.
+        """
+        # Moist surface → low LCL.  Strong stable layer 925-850 hPa acts as cap.
+        t = np.array(
+            [
+                298.0,
+                297.0,
+                296.0,
+                278.0,
+                271.0,
+                262.0,
+                247.0,
+                232.0,
+                227.0,
+                222.0,
+                217.0,
+                212.0,
+                207.0,
+            ],
+            dtype=np.float64,
+        )
+        td = np.array(
+            [
+                296.5,
+                293.0,
+                286.0,
+                268.0,
+                256.0,
+                246.0,
+                232.0,
+                217.0,
+                211.0,
+                206.0,
+                201.0,
+                196.0,
+                191.0,
+            ],
+            dtype=np.float64,
+        )
+        cape, cin = compute_ml_cape_cin_from_profile(_PRES, t, td, _GEOPOT)
+        # If the false-LFC bug were present, CIN would be ~0 despite the clear cap.
+        # After the fix, the cap inversion is detected and CIN is substantial.
+        assert cape > 0.0, f"Profile should have CAPE above the cap, got {cape:.2f}"
+        assert cin > 10.0, (
+            f"Capped profile should have measurable CIN (>10 J/kg), got {cin:.2f}. "
+            "If CIN is near zero, the false-LFC bug may have been reintroduced: "
+            "check that insert_lcl_level interpolates the environment temperature "
+            "rather than using t_lcl."
+        )