climpred.metrics._mae
climpred.metrics._mae#
- climpred.metrics._mae(forecast: xarray.Dataset, verif: xarray.Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) xarray.Dataset [source]#
Mean Absolute Error (MAE).
The average of the absolute differences between forecasts and verification data. A more robust measure of forecast accuracy than
mse
which is sensitive to large outlier forecast errors.- Parameters
forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over.
metric_kwargs – see
xskillscore.mae()
Notes
minimum
0.0
maximum
∞
perfect
0.0
orientation
negative
See also
References
Jolliffe and Stephenson [2011]
Example
>>> HindcastEnsemble.verify( ... metric="mae", 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 0.06484 0.06684 0.07407 ... 0.1193 0.1361 0.1462 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: mae comparison: e2o dim: init reference: []