- climpred.metrics._spearman_r(forecast: Dataset, verif: Dataset, dim: str | List[str] | None = None, **metric_kwargs: Any) Dataset #
Spearman’s rank correlation coefficient.
This correlation coefficient is nonparametric and assesses how well the relationship between the forecast and verification data can be described using a monotonic function. It is computed by first ranking the forecasts and verification data, and then correlating those ranks using the
This is also known as the anomaly correlation coefficient (ACC) when comparing anomalies, although the Pearson product-moment correlation coefficient
_pearson_r()is typically used when computing the ACC.
_spearman_r_eff_p_value`()to get the corresponding p value.
forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over.
metric_kwargs – see
>>> HindcastEnsemble.verify( ... metric="spearman_r", ... 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 0.9336 0.9311 0.9293 0.9474 ... 0.9465 0.9346 0.9328 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: spearman_r comparison: e2o dim: init reference: