climpred.metrics._unconditional_bias#
- climpred.metrics._unconditional_bias(forecast: Dataset, verif: Dataset, dim: str | List[str] | None = None, **metric_kwargs: Any) Dataset [source]#
Unconditional additive bias.
- Parameters:
forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over
metric_kwargs – see xarray.mean
Notes
minimum
-∞
maximum
∞
perfect
0.0
orientation
negative
References
Example
>>> HindcastEnsemble.verify( ... metric="unconditional_bias", ... comparison="e2o", ... alignment="same_verifs", ... dim="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 -0.01158 -0.02512 -0.0408 ... -0.1322 -0.1445 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: unconditional_bias comparison: e2o dim: init reference: []
Conditional bias is removed by
HindcastEnsemble.remove_bias()
.>>> HindcastEnsemble = HindcastEnsemble.remove_bias(alignment="same_verifs") >>> HindcastEnsemble.verify( ... metric="unconditional_bias", ... comparison="e2o", ... alignment="same_verifs", ... dim="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 3.203e-18 -1.068e-18 ... 2.882e-17 -2.776e-17 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: unconditional_bias comparison: e2o dim: init reference: []