climpred.metrics._pearson_r_p_value

climpred.metrics._pearson_r_p_value(forecast, verif, dim=None, **metric_kwargs)

Probability that forecast and verification data are linearly uncorrelated.

Two-tailed p value associated with the Pearson product-moment correlation coefficient (pearson_r), assuming that all samples are independent. Use pearson_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 xskillscore.pearson_r_p_value

Details:

minimum	0.0
maximum	1.0
perfect	1.0
orientation	negative

See also

• pearson_r()

• pearson_r_p_value()

• _pearson_r()

• _pearson_r_eff_p_value()

Example

>>> HindcastEnsemble.verify(metric='pearson_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 5.779e-23 2.753e-21 4.477e-21 ... 8.7e-22 6.781e-21