pysteps.postprocessing

Methods for post-processing of forecasts.

pysteps.postprocessing.ensemblestats

Methods for the computation of ensemble statistics.

mean(X[, ignore_nan, X_thr])	Compute the mean value from a forecast ensemble field.
excprob(X, X_thr[, ignore_nan])	For a given forecast ensemble field, compute exceedance probabilities for the given intensity thresholds.
banddepth(X[, thr, norm])	Compute the modified band depth (Lopez-Pintado and Romo, 2009) for a k-member ensemble data set.

pysteps.postprocessing.probmatching

Methods for matching the probability distribution of two data sets.

compute_empirical_cdf(bin_edges, hist)	Compute an empirical cumulative distribution function from the given histogram.
nonparam_match_empirical_cdf(initial_array, ...)	Matches the empirical CDF of the initial array with the empirical CDF of a target array.
pmm_init(bin_edges_1, cdf_1, bin_edges_2, cdf_2)	Initialize a probability matching method (PMM) object from binned cumulative distribution functions (CDF).
pmm_compute(pmm, x)	For a given PMM object and x-coordinate, compute the probability matched value (i.e. the x-coordinate for which the target CDF has the same value as the source CDF).
shift_scale(R, f, rain_fraction_trg, ...)	Find shift and scale that is needed to return the required second_moment and rain area.
resample_distributions(first_array, ...[, ...])	Merges two distributions (e.g., from the extrapolation nowcast and NWP in the blending module) to effectively combine two distributions for probability matching without losing extremes.