pysteps.verification.interface.get_method¶

pysteps.verification.interface.get_method(name, type='deterministic')¶

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

Parameters

namestr

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
SAL	Structure-Amplitude-Location 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.