pysteps.verification.interface.get_method¶
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pysteps.verification.interface.get_method(name, type='deterministic')¶ Return a callable function for the method corresponding to the given verification score.
Parameters: - name : str
Name of the verification method. The available options are:
type: deterministic
Name Description ACC accuracy (proportion correct) BIAS frequency bias CSI critical success index (threat score) F1 the harmonic mean of precision and sensitivity FA false alarm rate (prob. of false detection, fall-out, false positive rate) FAR false alarm ratio (false discovery rate) GSS Gilbert skill score (equitable threat score) HK Hanssen-Kuipers discriminant (Pierce skill score) HSS Heidke skill score MCC Matthews correlation coefficient POD probability of detection (hit rate, sensitivity, recall, true positive rate) SEDI symmetric extremal dependency index beta1 linear regression slope (type 1 conditional bias) beta2 linear regression slope (type 2 conditional bias) corr_p pearson’s correleation coefficien (linear correlation) corr_s* spearman’s correlation coefficient (rank correlation) DRMSE debiased root mean squared error MAE mean absolute error of residuals ME mean error or bias of residuals MSE mean squared error NMSE normalized mean squared error RMSE root mean squared error RV reduction of variance (Brier Score, Nash-Sutcliffe Efficiency) scatter* half the distance between the 16% and 84% percentiles of the weighted cumulative error distribution, where error = dB(pred/obs), as in Germann et al. (2006) binary_mse binary MSE FSS fractions skill score type: ensemble
Name Description ens_skill mean ensemble skill ens_spread mean ensemble spread rankhist rank histogram type: probabilistic
Name Description CRPS continuous ranked probability score reldiag reliability diagram ROC ROC curve - type : {‘deterministic’, ‘ensemble’, ‘probabilistic’}, optional
Type of the verification method.
Notes
Multiplicative scores can be computed by passing log-tranformed values. Note that “scatter” is the only score that will be computed in dB units of the multiplicative error, i.e.: 10*log10(pred/obs).
beta1 measures the degree of conditional bias of the observations given the forecasts (type 1).
beta2 measures the degree of conditional bias of the forecasts given the observations (type 2).
The normalized MSE is computed as NMSE = E[(pred - obs)^2]/E[(pred + obs)^2].
The debiased RMSE is computed as DRMSE = sqrt(RMSE - ME^2).
The reduction of variance score is computed as RV = 1 - MSE/Var(obs).
Score names denoted by * can only be computed offline, meaning that the these cannot be computed using _init, _accum and _compute methods of this module.
References
Germann, U. , Galli, G. , Boscacci, M. and Bolliger, M. (2006), Radar precipitation measurement in a mountainous region. Q.J.R. Meteorol. Soc., 132: 1669-1692. doi:10.1256/qj.05.190
Potts, J. (2012), Chapter 2 - Basic concepts. Forecast verification: a practitioner’s guide in atmospheric sciences, I. T. Jolliffe, and D. B. Stephenson, Eds., Wiley-Blackwell, 11–29.