climpred.metrics._spearman_r_p_value¶
- climpred.metrics._spearman_r_p_value(forecast, verif, dim=None, **metric_kwargs)[source]¶
Probability that forecast and verification data are monotonically uncorrelated.
Two-tailed p value associated with the Spearman’s rank correlation coefficient (
spearman_r
), assuming that all samples are independent. Usespearman_r_eff_p_value
to account for autocorrelation in the forecast and verification data.- Parameters
forecast (xarray object) – Forecast.
verif (xarray object) – Verification data.
dim (str) – Dimension(s) to perform metric over.
metric_kwargs (dict) – see
spearman_r_p_value()
- Details:
minimum
0.0
maximum
1.0
perfect
1.0
orientation
negative
Example
>>> HindcastEnsemble.verify(metric='spearman_r_p_value', 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 6.248e-24 1.515e-23 ... 4.288e-24 8.254e-24