pysteps.verification.detcontscores.det_cont_fct¶

pysteps.verification.detcontscores.det_cont_fct(pred, obs, scores='', axis=None, conditioning=None)¶

Calculate simple and skill scores for deterministic continuous forecasts.

Parameters:

pred : array_like

Array of predictions. NaNs are ignored.

obs : array_like

Array of verifying observations. NaNs are ignored.

scores : {string, list of strings}, optional

The name(s) of the scores. The default, scores=”“, will compute all available scores. The available score names are:

Name	Description
beta	linear regression slope (conditional bias)
corr_p	pearson’s correleation coefficien (linear correlation)
corr_s*	spearman’s correlation coefficient (rank correlation)
DRMSE	debiased root mean squared error
MAE	mean absolute error
ME	mean error or bias
MSE	mean squared error
RMSE	root mean squared error
RV	reduction of variance (Brier Score, Nash-Sutcliffe Efficiency)
scatter*	half the distance between the 16% and 84% percentiles of the weighted cumulative error distribution, where error = dB(pred/obs), as in Germann et al. (2006)

axis : {int, tuple of int, None}, optional

Axis or axes along which a score is integrated. The default, axis=None, will integrate all of the elements of the input arrays.

If axis is -1 (or any negative integer), the integration is not performed and scores are computed on all of the elements in the input arrays.

If axis is a tuple of ints, the integration is performed on all of the axes specified in the tuple.

conditioning : {None, ‘single’, ‘double’}, optional

The type of conditioning on zeros used for the verification. The default, conditioning=None, includes zero pairs. With conditioning=’single’, only pairs with either pred or obs > 0 are included. With conditioning=’double’, only pairs with both pred and obs > 0 are included.

Returns:

result : dict: Dictionary containing the verification results.