easyVerification (version 0.1.8)

EnsErrorss: Compute various ensemble mean error skill scores

Description

EnsMess computes the mean error, EnsMaess the mean absolute error, EnsMsess the mean squared error, and EnsRmsess the square root of the mean squared error (for consistency with the veri package).

Usage

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