climpred.metrics._mse#
- climpred.metrics._mse(forecast: Dataset, verif: Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) Dataset [source]#
Mean Sqaure Error (MSE).
The average of the squared difference between forecasts and verification data. This incorporates both the variance and bias of the estimator. Because the error is squared, it is more sensitive to large forecast errors than
mae
, and thus a more conservative metric. For example, a single error of 2°C counts the same as two 1°C errors when usingmae
. On the other hand, the 2°C error counts double formse
. See Jolliffe and Stephenson, 2011.- Parameters:
forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over.
metric_kwargs – see
xskillscore.mse()
Notes
minimum
0.0
maximum
∞
perfect
0.0
orientation
negative
See also
References
Jolliffe and Stephenson [2011]
Example
>>> HindcastEnsemble.verify( ... metric="mse", 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 0.006202 0.006536 0.007771 ... 0.02417 0.02769 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: mse comparison: e2o dim: init reference: []