climpred aims to be the primary package used to analyze output from initialized dynamical forecast models, ranging from short-term weather forecasts to decadal climate forecasts. The code base will be driven entirely by the geoscientific prediction community through open source development. It leverages
xarray to keep track of core prediction ensemble dimensions (e.g., ensemble member, initialization date, and lead time) and
dask to perform out-of-memory computations on large datasets.
The primary goal of climpred is to offer a comprehensive set of analysis tools for assessing the forecasts relative to references (e.g., observations, reanalysis products, control runs, baseline forecasts). This will range from simple deterministic and probabilistic verification metrics—such as mean absolute error and various skill scores—to more advanced analysis methods, such as relative entropy and mutual information.
climpred expects users to handle their domain-specific post-processing of model output, so that the package can focus on the actual analysis of forecasts.
climpred documentation will serve as a repository of unified analysis methods through jupyter notebook examples, and will also collect relevant references and literature.