climpred.metrics._pearson_r_p_value

climpred.metrics._pearson_r_p_value(forecast: xarray.Dataset, verif: xarray.Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) → xarray.Dataset

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 – Forecast.

• verif – Verification data.

• dim – Dimension(s) to perform metric over.

• metric_kwargs – see xskillscore.pearson_r_p_value()

Notes

minimum	0.0
maximum	1.0
perfect	1.0
orientation	negative

See also

• xskillscore.pearson_r()

• xskillscore.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
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:                        pearson_r_p_value
    comparison:                    e2o
    dim:                           init
    reference:                     []