climpred.metrics._effective_sample_size¶
-
climpred.metrics.
_effective_sample_size
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Effective sample size for temporally correlated data.
Note
Weights are not included here due to the dependence on temporal autocorrelation.
Note
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
pearson_r_eff_p_value
andspearman_r_eff_p_value
.where and are the lag-1 autocorrelation coefficients for 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
effective_sample_size()
- Details:
minimum
0.0
maximum
∞
perfect
N/A
orientation
positive
- Reference:
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.