climpred.metrics._bias_slope

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climpred.metrics._bias_slope#

climpred.metrics._bias_slope(forecast: Dataset, verif: Dataset, dim: str | List[str] | None = None, **metric_kwargs: Any) → Dataset | DataArray[source]#

Bias slope between verification data and forecast standard deviations.

\[\text{bias slope} = \frac{s_{o}}{s_{f}} \cdot r_{fo},\]

where \(r_{fo}\) is the Pearson product-moment correlation between the forecast and the verification data and \(s_{o}\) and \(s_{f}\) are the standard deviations of the verification data and forecast over the experimental period, respectively.

Parameters:

  • forecast – Forecast.

  • verif – Verification data.

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

  • metric_kwargs – see xskillscore.pearson_r() and std()

Notes

minimum

0.0

maximum

∞

perfect

1.0

orientation

negative

References

https://www-miklip.dkrz.de/about/murcss/

Example

>>> HindcastEnsemble.verify(
...     metric="bias_slope",
...     comparison="e2o",
...     alignment="same_verifs",
...     dim="init",
... )
<xarray.Dataset> Size: 164B
Dimensions:  (lead: 10)
Coordinates:
  * lead     (lead) int32 40B 1 2 3 4 5 6 7 8 9 10
    skill    <U11 44B 'initialized'
Data variables:
    SST      (lead) float64 80B 0.7016 0.8049 0.8877 ... 1.002 1.004 0.961
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:                        bias_slope
    comparison:                    e2o
    dim:                           init
    reference:                     []
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