pysteps.verification.ensscores.rankhist(X_f, X_o, X_min=None, normalize=True)#

Compute a rank histogram counts and optionally normalize the histogram.

  • X_f (array-like) – Array of shape (k,m,n,…) containing the values from an ensemble forecast of k members with shape (m,n,…).

  • X_o (array_like) – Array of shape (m,n,…) containing the observed values corresponding to the forecast.

  • X_min ({float,None}) – Threshold for minimum intensity. Forecast-observation pairs, where all ensemble members and verifying observations are below X_min, are not counted in the rank histogram. If set to None, thresholding is not used.

  • normalize ({bool, True}) – If True, normalize the rank histogram so that the bin counts sum to one.