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. |
pysteps.verification.detcatscores¶
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. |
pysteps.verification.detcontscores¶
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. |
pysteps.verification.ensscores¶
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. |
pysteps.verification.lifetime¶
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. |
pysteps.verification.plots¶
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. |
pysteps.verification.probscores¶
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. |
pysteps.verification.spatialscores¶
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. |