From 7370756874cc9f85f7969243f15937f2662fc556 Mon Sep 17 00:00:00 2001
From: Aaron Spring <aaron.spring@mpimet.mpg.de>
Date: Mon, 17 May 2021 11:47:58 +0000
Subject: [PATCH] Update scoring_script.py
---
scoring/scoring_script.py | 13 +++++++------
1 file changed, 7 insertions(+), 6 deletions(-)
diff --git a/scoring/scoring_script.py b/scoring/scoring_script.py
index 1306d14dd..ad8bfb10e 100644
--- a/scoring/scoring_script.py
+++ b/scoring/scoring_script.py
@@ -15,27 +15,28 @@ if __name__ == "__main__":
observations_terciled = xr.open_dataset(observations_terciled_fin, engine='zarr').sel(forecast_reference_time='2020')
a=observations_terciled['2t'].sel(time=(observations_terciled.tp.forecast_reference_time + observations_terciled.tp.lead_time[0]))
b=observations_terciled['2t'].sel(time=(observations_terciled.tp.forecast_reference_time + observations_terciled.tp.lead_time[1]))
- observations_terciled['2t']=xr.concat([a.drop('time'),b.drop('time')],'lead_time')
+ observations_terciled['2t']=xr.concat([a.drop('time'),b.drop('time')],'lead_time').rename({'category_edge':'category'})
observations_terciled = observations_terciled.drop('time')
benchmark_forecasts_terciled = xr.open_dataset(benchmark_forecasts_terciled_fin, chunks={})
- benchmark_forecasts_terciled = benchmark_forecasts_terciled.rename({'category':'category_edge'})
+ # benchmark_forecasts_terciled = benchmark_forecasts_terciled.rename({'category':'category_edge'})
ML_predictions_terciled = xr.open_dataset(args.prediction)
ML_predictions_terciled = xr.concat([
ML_predictions_terciled.isel(step=[0,1]).mean('step'),
ML_predictions_terciled.isel(step=[2,3]).mean('step')
], 'lead_time').assign_coords(lead_time=observations_terciled.lead_time)
- ML_predictions_terciled = ML_predictions_terciled.assign_coords(category_edge=observations_terciled.category_edge)
+ ML_predictions_terciled = ML_predictions_terciled.assign_coords(category=observations_terciled.category)
for v in ['2t', 'tp']:
- assert v in ML_predictions_terciled.data_vars
+ if v not in ML_predictions_terciled.data_vars:
+ raise ValueError(f'Expected both variables in ML_predictions, didnt find {v} in {ML_predictions_terciled.data_vars}')
# same number of dimensions
assert set(ML_predictions_terciled.dims) == set(benchmark_forecasts_terciled.dims)
- rps_ML = xs.rps(observations_terciled, ML_predictions_terciled, category_edges=None, dim=['forecast_reference_time']).compute()
- rps_benchmark = xs.rps(observations_terciled, benchmark_forecasts_terciled, category_edges=None, dim=['forecast_reference_time']).compute()
+ rps_ML = xs.rps(observations_terciled, ML_predictions_terciled, category_edges=None, dim=['forecast_reference_time'], input_distributions='c').compute()
+ rps_benchmark = xs.rps(observations_terciled, benchmark_forecasts_terciled, category_edges=None, dim=['forecast_reference_time'], input_distributions='c').compute()
rpss = 1 - rps_ML / rps_benchmark # positive means ML better than ECMWF benchmark
# check for -inf grid cells
--
GitLab