pysteps.blending.skill_scores.lt_dependent_cor_nwp

pysteps.blending.skill_scores.lt_dependent_cor_nwp#

pysteps.blending.skill_scores.lt_dependent_cor_nwp(lt, correlations, outdir_path, n_model=0, skill_kwargs=None)#

Determine the correlation of a model field for lead time lt and cascade k, by assuming that the correlation determined at t=0 regresses towards the climatological values.

Parameters:
  • lt (int) – The lead time of the forecast in minutes.

  • correlations (array-like) – Array of shape [n_cascade_levels] containing per cascade_level the correlation between the normalized cascade of the observed (radar) rainfall field and the normalized cascade of the model field.

  • outdir_path (string) – Path to folder where the historical skill are stored. Defaults to path_workdir from rcparams.

  • n_model (int, optional) – The index number of the (NWP) model when the climatological skill of multiple (NWP) models is stored. For calculations with one model, or when n_model is not provided, n_model = 0.

  • skill_kwargs (dict, optional) – Dictionary containing e.g. the outdir_path, nmodels and window_length parameters.

Returns:

rho – Array of shape [n_cascade_levels] containing, for lead time lt, per cascade_level the correlation between the normalized cascade of the observed (radar) rainfall field and the normalized cascade of the model field.

Return type:

array-like

References

[BPS04] [BPS06]