pyPDAF.PDAF.diag_CRPS_nompi

pyPDAF.PDAF.diag_CRPS_nompi()

Obtain a continuous rank probability score for an ensemble without using MPI parallelisation. The implementation is based on This function follows follows Hersbach, H., 2000: Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems. Wea. Forecasting, 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2

Parameters:

• element (int) – ID of element to be used If element=0, mean values over all elements are computed

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

  State ensemble

  The 1st-th dimension dim is PE-local state dimension The 2nd-th dimension dim_ens is Ensemble size

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

  State ensemble

  The array dimension dim is PE-local state dimension

Returns:

• CRPS (float) – CRPS

• reli (float) – Reliability

• resol (float) – resolution

• uncert (float) – uncertainty

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