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 01/23] 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 02/23] `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 03/23] 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 04/23] 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 05/23] 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 06/23] 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 07/23] 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 08/23] 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 09/23] 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 10/23] 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 11/23] 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 = [
     { name = "python-dateutil" },
     { name = "wrapt" },
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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" }
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     { name = "aiohappyeyeballs" },
@@ -58,59 +58,59 @@ dependencies = [
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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 12/23] 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 13/23] 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 14/23] 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 15/23] 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 16/23] 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 17/23] 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 18/23] 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 19/23] 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 20/23] 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 21/23] 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 22/23] 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 23/23] 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."""