pyPDAF.PDAF.diag_histogram

pyPDAF.PDAF.diag_histogram()

Computing the rank histogram of an ensemble.

A rank histogram is used to diagnose the reliability of the ensemble [1]. A perfectly reliable ensemble should have a uniform rank histogram.

The function can be called in the pre/poststep routine of PDAF both before and after the analysis step to collect the histogram information.

References

[1]

Hamill, T. M. (2001). Interpretation of rank histograms for verifying ensemble forecasts. Monthly Weather Review, 129(3), 550-560.

Parameters:

• ncall (int) – The number of calls used to increment the histogram and is needed to compute the delta-measure that describes the deviation from the ideal histogram.

• element (int) – Element of vector used for histogram. If element=0, all elements are used

• state (ndarray[tuple[dim], np.float64]) –

  Assumed truth

  The array dimension dim is State dimension

• ens (ndarray[tuple[dim, dim_ens], np.float64]) –

  Ensemble

  The 1st-th dimension dim is State dimension The 2nd-th dimension dim_ens is Ensemble size

• hist (ndarray[tuple[dim_ens+1], np.intc]) – Histogram about the state

Returns:

• hist (ndarray[tuple[dim_ens+1], np.intc]) – Histogram about the state

• delta (float) – deviation measure from flat histogram. It must be initialised to be 0

• status (int) – Status flag (0=success)