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
initialization, lead time, ensemble member, latitude, longitude,
depth. climpred leverages the labeled dimensions of xarray to handle the
headache of bookkeeping for you. We offer HindcastEnsemble and
PerfectModelEnsemble objects that carry products to verify against (e.g.,
control runs, reconstructions, uninitialized ensembles) along with your initialized
prediction output.
When computing lead-dependent skill scores, climpred handles all of the
init+lead-valid_time-matching for you, properly aligning the multiple
time dimensions between the hindcast and verification datasets.
We offer a suite of vectorized deterministic
and probabilistic metrics that can be applied to time
series and grids. It’s as easy as concatenating your initialized prediction output into
one xarray.Dataset and running the HindcastEnsemble.verify()
command:
>>> HindcastEnsemble.verify(
... metric="rmse", comparison="e2o", dim="init", alignment="maximize"
... )