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pysteps.verification.interface.get_method

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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:

  • 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

    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.

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