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.