EnsErrorss: Ensemble Mean Error Skill scores

In easyVerification: Ensemble Forecast Verification for Large Data Sets

Ensemble Mean Error Skill scores

Description

Computes various ensemble mean error skill scores. EnsMaess computes the mean absolute error, EnsMsess the mean squared error, and EnsRmsess the square root of the mean squared error.

Usage

EnsErrorss(ens, ens.ref, obs, type)

EnsMaess(ens, ens.ref, obs)

EnsMsess(ens, ens.ref, obs)

EnsRmsess(ens, ens.ref, obs)

Arguments

ens	n x k matrix of n forecasts from k ensemble members
ens.ref	n x l matrix of m reference forecasts from l ensemble members
obs	n verifying observations
type	specifying what error metric to compute, one of [me, mae, mse, rmse]

See Also

veriApply, EnsError

Examples

tm <- toymodel()

## compute RMSE skill score against reference forecast with a bias of +2
EnsErrorss(ens = tm$fcst, ens.ref = tm$fcst + 2, obs = tm$obs, type = "rmse")

## compute skill score using veriApply
veriApply("EnsRmsess", fcst = tm$fcst, obs = tm$obs, fcst.ref = tm$fcst + 2)

easyVerification documentation built on Aug. 15, 2023, 9:06 a.m.