climpred.metrics._rmse¶

climpred.metrics._rmse(forecast: xarray.Dataset, verif: xarray.Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) → xarray.Dataset[source]¶

Root Mean Sqaure Error (RMSE).

$RMSE = \sqrt{\overline{(f - o)^{2}}}$

The square root of the average of the squared differences between forecasts and verification data.

Parameters

forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over.
metric_kwargs – see xskillscore.rmse()

Notes

minimum	0.0
maximum	∞
perfect	0.0
orientation	negative

See also

xskillscore.rmse()

Example

>>> HindcastEnsemble.verify(
...     metric="rmse", 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 0.07875 0.08085 0.08815 ... 0.1371 0.1555 0.1664
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:                        rmse
    comparison:                    e2o
    dim:                           init
    reference:                     []

climpred documentation

climpred.metrics._rmse¶