R: Fair Spread to Error Ratio

FairSprErr {easyVerification} | R Documentation

## Fair Spread to Error Ratio

### Description

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.

### Usage

    FairSprErr(ens, obs)

### Arguments

`ens` | n x k matrix of n forecasts for k ensemble members
`obs` | vector with n verifying observations

### Details

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).

### References

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

### See Also

`veriApply`, `FairSprErr`

### Examples

    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)

[Package easyVerification version 0.4.4 Index]