climpred.metrics._nmse#

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

Compte Normalized MSE (NMSE), also known as Normalized Ensemble Variance (NEV).

Mean Square Error (mse) normalized by the variance of the verification data.

NMSE = NEV = \frac{MSE}{\sigma^2_{o}\cdot fac} = \frac{\overline{(f - o)^{2}}}{\sigma^2_{o} \cdot fac},

where fac is 1 when using comparisons involving the ensemble mean (m2e, e2c, e2o) and 2 when using comparisons involving individual ensemble members (m2c, m2m, m2o). See _get_norm_factor().

Note

climpred uses a single-valued internal reference forecast for the NMSE, in the terminology of Murphy [1988]. I.e., we use a single climatological variance of the verification data within the experimental window for normalizing MSE.

Parameters:

  • forecast – Forecast.

  • verif – Verification data.

  • dim – Dimension(s) to perform metric over.

  • comparison – Name comparison needed for normalization factor fac, see _get_norm_factor() (Handled internally by the compute functions)

  • metric_kwargs – see xskillscore.mse()

Notes

minimum

0.0

maximum

∞

perfect

0.0

orientation

negative

better than climatology

0.0 - 1.0

worse than climatology

> 1.0

References

Griffies and Bryan [1997]

Example

>>> HindcastEnsemble.verify(
...     metric="nmse", 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 0.1732 0.1825 0.217 0.2309 ... 0.5247 0.6749 0.7732
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:                        nmse
    comparison:                    e2o
    dim:                           init
    reference:                     []