Overview: Why climpred?¶
There are many packages out there related to computing metrics on initialized geoscience predictions. However, we didn’t find any one package that unified all our needs.
Output from earth system prediction hindcast (also called re-forecast) experiments is
difficult to work with. A typical output file could contain the dimensions
climpred leverages the labeled dimensions of
xarray to handle the
headache of bookkeeping for you. We offer
objects that carry products to verify against (e.g., control runs,
reconstructions, uninitialized ensembles) along with your decadal prediction output.
When computing lead-dependent skill scores,
climpred handles all of the
lag-correlating for you, properly aligning the multiple time dimensions between
the hindcast and verification datasets. We offer a suite of vectorized
and probabilistic metrics that can be applied to time
series and grids. It’s as easy as adding your decadal prediction output to an object and
HindcastEnsemble.verify(metric='rmse', comparison='e2o', dim='init', alignment='maximize').