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Abstract

Predicting extreme events and variations in weather and climate provides crucial informationfor economic, social, and environmental decision-making (Merryfield et al., 2020). However,quantifying prediction skill for multi-dimensional geospatial model output is computationallyexpensive and a difficult coding challenge. The large datasets (order gigabytes to terabytes)require parallel and out-of-memory computing to be analyzed efficiently. Further, aligning themany forecast initializations with differing observational products is a straight-forward, butexhausting and error-prone exercise for researchers.To simplify and standardize forecast verification across scales from hourly weather to decadalclimate forecasts, we builtclimpred: a community-driven python package for computationallyefficient and methodologically consistent verification of ensemble prediction models. The codebase is maintained through open-source development. It leveragesxarray(Hoyer & Hamman,2017) to anticipate core prediction ensemble dimensions (ensemblemember,initializationdate andleadtime) anddask(Dask Development Team, 2016;Rocklin, 2015) to performout-of-memory and parallelized computations on large datasets.climpredaims to offer a comprehensive set of analysis tools for assessing the quality ofdynamical forecasts relative to verification products (e.g., observations, reanalysis products,control simulations). The package includes a suite of deterministic and probabilistic verifica-tion metrics that are constantly expanded by the community and are generally organized inour companion package,xskillscore