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 is driven 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 a validation product (e.g., observations,
reanalysis products, control simulations, baseline forecasts). This ranges from simple
deterministic and probabilistic verification metrics — such as, e.g.
mean absolute error or rank histogram — to more advanced contingency table-derived
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 collects relevant references and literature.