climpred.metrics._mse¶
- climpred.metrics._mse(forecast, verif, dim=None, **metric_kwargs)[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
- Details:
minimum
0.0
maximum
∞
perfect
0.0
orientation
negative
See also
- Reference:
Ian T. Jolliffe and David B. Stephenson. Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, Ltd, Chichester, UK, December 2011. ISBN 978-1-119-96000-3 978-0-470-66071-3. URL: http://doi.wiley.com/10.1002/9781119960003.
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