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12 changes: 8 additions & 4 deletions scripts/compute_derived_variables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,6 +151,14 @@ def main(argv: list[str]) -> None:

source_dataset, source_chunks = xbeam.open_zarr(INPUT_PATH.value)

for var_name in PREEXISTING_VARIABLES_TO_REMOVE.value:
if var_name in source_dataset:
del source_dataset[var_name]
source_chunks = {
# Removing variables may remove some dims.
k: v for k, v in source_chunks.items() if k in source_dataset.dims
}

# Validate and clean-up the source datset.
if RENAME_RAW_TP_NAME.value:
source_dataset = source_dataset.rename(
Expand All @@ -168,10 +176,6 @@ def main(argv: list[str]) -> None:
rename_variables.get(k, k): v for k, v in source_chunks.items()
}

for var_name in PREEXISTING_VARIABLES_TO_REMOVE.value:
if var_name in source_dataset:
del source_dataset[var_name]

for var_name, dv in derived_variables.items():
if var_name in source_dataset:
raise ValueError(
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7 changes: 5 additions & 2 deletions weatherbench2/derived_variables.py
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Expand Up @@ -472,8 +472,11 @@ def compute(self, dataset: xr.Dataset) -> xr.DataArray:
class PrecipitationAccumulation(DerivedVariable):
"""Compute precipitation accumulation from hourly accumulations.

Accumulation is computed for the time period leading up to the lead_time.
E.g. 24h accumulation at lead_time=24h indicates 0-24h accumulation.
Accumulation is computed for the time period leading up to and including the
lead_time. E.g. 24h accumulation at lead_time=24h indicates accumulation
from lead_time=0 to lead_time=24. This is equal to the values of
`total_precipitation_name` at 24, minus the value at 0.

Caution: Small negative values sometimes appear in model output.
Here, we set them to zero.

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13 changes: 13 additions & 0 deletions weatherbench2/derived_variables_test.py
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Expand Up @@ -139,6 +139,19 @@ def testPrecipitationAccumulation6hr(self):
accumulation_hours=6,
)
result = derived_variable.compute(dataset)

# Test a few specific times for example's sake.
sel = lambda ds, hr: ds.sel(prediction_timedelta=f'{hr}hr')
np.testing.assert_array_equal(
(sel(dataset, 24) - sel(dataset, 24 - 6)).total_precipitation.data,
sel(result, 24),
)
np.testing.assert_array_equal(
(sel(dataset, 18) - sel(dataset, 18 - 6)).total_precipitation.data,
sel(result, 18),
)

# Test every timedelta.
expected = xr.DataArray(
[np.nan, 5, 10, 0, 6, 10, 0],
dims=['prediction_timedelta'],
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