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 decadal climate prediction 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 references
(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. We offer a suite of vectorized deterministic 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
running compute: HindcastEnsemble.compute_metric(metric='rmse')
.