pysteps.verification#

Methods for verification of deterministic, probabilistic and ensemble forecasts.

pysteps.verification.interface#

Interface for the verification module.

get_method(name[, type])

Return a callable function for the method corresponding to the given verification score.

Forecast evaluation and skill scores for deterministic categorial (dichotomous) forecasts.

`det_cat_fct`(pred, obs, thr[, scores, axis])	Calculate simple and skill scores for deterministic categorical (dichotomous) forecasts.
`det_cat_fct_init`(thr[, axis])	Initialize a contingency table object.
`det_cat_fct_accum`(contab, pred, obs)	Accumulate the frequency of "yes" and "no" forecasts and observations in the contingency table.
`det_cat_fct_merge`(contab_1, contab_2)	Merge two contingency table objects.
`det_cat_fct_compute`(contab[, scores])	Compute simple and skill scores for deterministic categorical (dichotomous) forecasts from a contingency table object.

Forecast evaluation and skill scores for deterministic continuous forecasts.

`det_cont_fct`(pred, obs[, scores, axis, ...])	Calculate simple and skill scores for deterministic continuous forecasts.
`det_cont_fct_init`([axis, conditioning, thr])	Initialize a verification error object.
`det_cont_fct_accum`(err, pred, obs)	Accumulate the forecast error in the verification error object.
`det_cont_fct_merge`(err_1, err_2)	Merge two verification error objects.
`det_cont_fct_compute`(err[, scores])	Compute simple and skill scores for deterministic continuous forecasts from a verification error object.

Evaluation and skill scores for ensemble forecasts.

`ensemble_skill`(X_f, X_o, metric, **kwargs)	Compute mean ensemble skill for a given skill metric.
`ensemble_spread`(X_f, metric, **kwargs)	Compute mean ensemble spread for a given skill metric.
`rankhist`(X_f, X_o[, X_min, normalize])	Compute a rank histogram counts and optionally normalize the histogram.
`rankhist_init`(num_ens_members[, X_min])	Initialize a rank histogram object.
`rankhist_accum`(rankhist, X_f, X_o)	Accumulate forecast-observation pairs to the given rank histogram.
`rankhist_compute`(rankhist[, normalize])	Return the rank histogram counts and optionally normalize the histogram.

Estimation of precipitation lifetime from a decaying verification score function (e.g. autocorrelation function).

`lifetime`(X_s, X_t[, rule])	Compute the average lifetime by integrating the correlation function as a function of lead time.
`lifetime_init`([rule])	Initialize a lifetime object.
`lifetime_accum`(lifetime, X_s, X_t)	Compute the lifetime by integrating the correlation function and accumulate the result into the given lifetime object.
`lifetime_compute`(lifetime)	Compute the average value from the lifetime object.

Methods for plotting verification results.

`plot_intensityscale`(intscale[, fig, ...])	Plot a intensity-scale verification table with a color bar and axis labels.
`plot_rankhist`(rankhist[, ax])	Plot a rank histogram.
`plot_reldiag`(reldiag[, ax])	Plot a reliability diagram.
`plot_ROC`(ROC[, ax, opt_prob_thr])	Plot a ROC curve.

Evaluation and skill scores for probabilistic forecasts.

`CRPS`(X_f, X_o)	Compute the continuous ranked probability score (CRPS).
`CRPS_init`()	Initialize a CRPS object.
`CRPS_accum`(CRPS, X_f, X_o)	Compute the average continuous ranked probability score (CRPS) for a set of forecast ensembles and the corresponding observations and accumulate the result to the given CRPS object.
`CRPS_compute`(CRPS)	Compute the averaged values from the given CRPS object.
`reldiag`(P_f, X_o, X_min[, n_bins, min_count])	Compute the x- and y- coordinates of the points in the reliability diagram.
`reldiag_init`(X_min[, n_bins, min_count])	Initialize a reliability diagram object.
`reldiag_accum`(reldiag, P_f, X_o)	Accumulate the given probability-observation pairs into the reliability diagram.
`reldiag_compute`(reldiag)	Compute the x- and y- coordinates of the points in the reliability diagram.
`ROC_curve`(P_f, X_o, X_min[, n_prob_thrs, ...])	Compute the ROC curve and its area from the given ROC object.
`ROC_curve_init`(X_min[, n_prob_thrs])	Initialize a ROC curve object.
`ROC_curve_accum`(ROC, P_f, X_o)	Accumulate the given probability-observation pairs into the given ROC object.
`ROC_curve_compute`(ROC[, compute_area])	Compute the ROC curve and its area from the given ROC object.

The Spatial-Amplitude-Location (SAL) score by [WPHF08].

`sal`(prediction, observation[, thr_factor, ...])	Compute the Structure-Amplitude-Location (SAL) spatial verification metric.
`sal_structure`(prediction, observation[, ...])	Compute the structure component for SAL based on [WPHF08].
`sal_amplitude`(prediction, observation)	Compute the amplitude component for SAL based on [WPHF08].
`sal_location`(prediction, observation[, ...])	Compute the first parameter of location component for SAL based on [WPHF08].

Skill scores for spatial forecasts.

`intensity_scale`(X_f, X_o, name, thrs[, ...])	Compute an intensity-scale verification score.
`intensity_scale_init`(name, thrs[, scales, ...])	Initialize an intensity-scale verification object.
`intensity_scale_accum`(intscale, X_f, X_o)	Compute and update the intensity-scale verification scores.
`intensity_scale_merge`(intscale_1, intscale_2)	Merge two intensity-scale verification objects.
`intensity_scale_compute`(intscale)	Return the intensity scale matrix.
`binary_mse`(X_f, X_o, thr[, wavelet, ...])	Compute the MSE of the binary error as a function of spatial scale.
`binary_mse_init`(thr[, wavelet])	Initialize a binary MSE (BMSE) verification object.
`binary_mse_accum`(bmse, X_f, X_o)	Accumulate forecast-observation pairs to an BMSE object.
`binary_mse_merge`(bmse_1, bmse_2)	Merge two BMSE objects.
`binary_mse_compute`(bmse[, return_scales])	Compute the BMSE.
`fss`(X_f, X_o, thr, scale)	Compute the fractions skill score (FSS) for a deterministic forecast field and the corresponding observation field.
`fss_init`(thr, scale)	Initialize a fractions skill score (FSS) verification object.
`fss_accum`(fss, X_f, X_o)	Accumulate forecast-observation pairs to an FSS object.
`fss_merge`(fss_1, fss_2)	Merge two FSS objects.
`fss_compute`(fss)	Compute the FSS.