climpred.metrics._crpss_es¶
- climpred.metrics._crpss_es(forecast: xarray.Dataset, verif: xarray.Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) xarray.Dataset [source]¶
Continuous Ranked Probability Skill Score Ensemble Spread.
If the ensemble variance is smaller than the observed
mse
, the ensemble is said to be under-dispersive (or overconfident). An ensemble with variance larger than the verification data indicates one that is over-dispersive (underconfident).- Parameters
forecast – Forecast with
member
dim.verif – Verification data without
member
dim.dim – Dimension to apply metric over. Expects at least
member
. Other dimensions are passed toxskillscore
and averaged.metric_kwargs – see
xskillscore.crps_ensemble()
andxskillscore.mse()
Notes
minimum
-∞
maximum
0.0
perfect
0.0
orientation
positive
under-dispersive
> 0.0
over-dispersive
< 0.0
References
Kadow et al. [2016]
Example
>>> HindcastEnsemble.verify( ... metric="crpss_es", ... comparison="m2o", ... alignment="same_verifs", ... dim="member", ... ) <xarray.Dataset> Dimensions: (init: 52, lead: 10) Coordinates: * init (init) object 1964-01-01 00:00:00 ... 2015-01-01 00:00:00 * lead (lead) int32 1 2 3 4 5 6 7 8 9 10 valid_time (init) object 1964-01-01 00:00:00 ... 2015-01-01 00:00:00 skill <U11 'initialized' Data variables: SST (lead, init) float64 -0.01121 -0.05575 ... -0.1263 -0.007483 Attributes: prediction_skill_software: climpred https://climpred.readthedocs.io/ skill_calculated_by_function: HindcastEnsemble.verify() number_of_members: 10 alignment: same_verifs metric: crpss_es comparison: m2o dim: member reference: []