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:                     []

climpred documentation

climpred.metrics._bias_slope

climpred.metrics._bias_slope#