climpred.metrics._spread#
- climpred.metrics._spread(forecast: Dataset, verif: Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) Dataset [source]#
Ensemble spread taking the standard deviation over the member dimension.
- Parameters:
forecast – Forecast.
verif – Verification data (not used).
dim – Dimension(s) to perform metric over.
metric_kwargs – see
std()
Notes
minimum
0.0
maximum
∞
perfect
obs.std()
orientation
negative
Example
>>> HindcastEnsemble.verify( ... metric="spread", ... comparison="m2o", ... alignment="same_verifs", ... dim=["member", "init"], ... ) <xarray.Dataset> Dimensions: (lead: 10) Coordinates: * lead (lead) int32 1 2 3 4 5 6 7 8 9 10 skill <U11 'initialized' Data variables: SST (lead) float64 0.1468 0.1738 0.1922 0.2096 ... 0.2142 0.2178 0.2098 Attributes: prediction_skill_software: climpred https://climpred.readthedocs.io/ skill_calculated_by_function: HindcastEnsemble.verify() number_of_initializations: 64 number_of_members: 10 alignment: same_verifs metric: spread comparison: m2o dim: ['member', 'init'] reference: []