- climpred.metrics._effective_sample_size(forecast, verif, dim=None, **metric_kwargs)¶
Effective sample size for temporally correlated data.
Weights are not included here due to the dependence on temporal autocorrelation.
This metric can only be used for hindcast-type simulations.
The effective sample size extracts the number of independent samples between two time series being correlated. This is derived by assessing the magnitude of the lag-1 autocorrelation coefficient in each of the time series being correlated. A higher autocorrelation induces a lower effective sample size which raises the correlation coefficient for a given p value.
The effective sample size is used in computing the effective p value. See
where and are the lag-1 autocorrelation coefficients for the forecast and verification data.
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.
>>> HindcastEnsemble.verify(metric='effective_sample_size', 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 5.0 4.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0