Description Usage Arguments Details References See Also Examples

Compute the spread to error ratio (`SPR`

) for probabilistic forecasts -
not unlike the functions in SpecsVerification. `SPR > 1`

indicates
overdispersion (underconfidence), whereas `SPR < 1`

indicates
overconfidence in the forecasts.

1 | ```
FairSprErr(ens, obs)
``` |

`ens` |
n x k matrix of n forecasts for k ensemble members |

`obs` |
vector with n verifying observations |

Here we define the spread-error rate as the square root of the ratio of mean ensemble variance to the mean squared error of the ensemble mean with the verifying observations. We inflate the intra ensemble sample variance to account for the finite ensemble size as in Weigel (2011).

Weigel, A.P. (2012). Ensemble forecasts. Forecast Verification: A Practitioner's Guide in Atmospheric Science, Second Edition, 141-166.

1 2 3 4 5 6 7 8 | ```
tm <- toymodel()
FairSprErr(tm$fcst, tm$obs)
## compute spread to error ratio using veriApply
veriApply('FairSprErr', fcst=tm$fcst, obs=tm$obs)
## compare with 'unfair' spread to error ratio
veriApply("EnsSprErr", fcst=tm$fcst, obs=tm$obs)
``` |

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