climpred.metrics._mae¶

climpred.metrics._mae(forecast, verif, dim=None, **metric_kwargs)[source]¶

Mean Absolute Error (MAE).

$MAE = \overline{|f - o|}$

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 (xarray object) – Forecast.
verif (xarray object) – Verification data.
dim (str) – Dimension(s) to perform metric over.
metric_kwargs (dict) – see mae()

Details:

minimum	0.0
maximum	∞
perfect	0.0
orientation	negative

See also

mae()

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='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