climpred: weather and climate forecast verification in python

Abstract:

Predicting extreme events and variations in weather and climate yields numerous benefits for economic, social, and environmental decision-making. Institutions verify the forecast quality of their models by initializing large sets of retrospective forecasts to predict past variations and phenomena in “hindcast” studies. “Perfect model” simulations are configured to investigate the theoretical bounds of predictability through the sole use of models, which are not designed to be compared to observations.

Quantifying prediction skill for multi-dimensional geospatial model output is computationally expensive and a difficult coding challenge. The large datasets require parallel and out-of-memory computing to be analyzed efficiently. Further, aligning the many forecast initializations with differing observational products is a straight-forward, but exhausting and error-prone exercise for researchers.

To simplify and standardize forecast verification across scales from hourly weather to decadal climate forecasts, we built climpred: a python package for computationally efficient and methodologically consistent verification of ensemble prediction models. We rely on the python software ecosystem developed by the open pangeo geoscience community. We leverage NetCDF metadata using xarray and out-of-core computation parallelized with dask to scale analyses from a laptop to supercomputer.

With climpred, researchers can assess forecast quality from a large set of metrics in just a single line of code: hindcast.verify(metric=’rmse’, comparison=’m2o’, alignment=’same_verif’). This simplified and standardized process frees up resources to tackle the large process-based unknowns in predictability research. Here, we demonstrate a multi-model comparison of DCPP output using pangeo’s CMIP6 data from the cloud.

Documentation: climpred.readthedocs.io