- climpred.metrics._pearson_r_eff_p_value(forecast, verif, dim=None, **metric_kwargs)¶
Probability that forecast and verification data are linearly uncorrelated, accounting for autocorrelation.
Weights are not included here due to the dependence on temporal autocorrelation.
This metric can only be used for hindcast-type simulations.
The effective p value is computed by replacing the sample size in the t-statistic with the effective sample size, . The same Pearson product-moment correlation coefficient is used as when computing the standard p value.
where is computed via the autocorrelation in the forecast and verification data.
where and are the lag-1 autocorrelation coefficients for the forecast and verification data.
>>> HindcastEnsemble.verify(metric='pearson_r_eff_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 0.02333 0.08552 0.2679 ... 0.2369 0.2588 0.2703
Bretherton, Christopher S., et al. “The effective number of spatial degrees of freedom of a time-varying field.” Journal of climate 12.7 (1999): 1990-2009.