climpred.metrics._bias_slope¶

climpred.metrics._bias_slope(forecast, verif, dim=None, **metric_kwargs)[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 (xarray object) – Forecast.
verif (xarray object) – Verification data.
dim (str) – Dimension(s) to perform metric over.
metric_kwargs (dict) – see pearson_r() and

:param std():

Details:

minimum	0.0
maximum	∞
perfect	1.0
orientation	negative

Reference:

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