climpred.metrics._bias_slope#

climpred.metrics._bias_slope(forecast: xarray.Dataset, verif: xarray.Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) xarray.Dataset[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>
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 0.7016 0.8049 0.8877 0.9836 ... 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:                     []