smooth_mul: Multiplicative De-biasing With Smoothed Climatologies

In jonasbhend/biascorrection: Bias Correct Ensemble Forecasts

Multiplicative De-biasing With Smoothed Climatologies

Description

Computes multiplicative de-biasing with loess smoothing

Usage

smooth_mul(fcst, obs, fcst.out = fcst, span = min(1, 31/nrow(fcst)), ...)

Arguments

fcst	n x m x k array of n lead times, m forecasts, of k ensemble members
obs	n x m matrix of veryfing observations
fcst.out	array of forecast values to which bias correction should be applied (defaults to fcst)
span	the parameter which controls the degree of smoothing (see loess)
...	additional arguments for compatibility with other bias correction methods

Details

The bias corrected forecast is scaled by the lead-time dependent ratio of observed to forecast climatology, where the observed and the forecast climatologies are smoothed using a loess smoothing.

See Also

smoothobs_mul smooth mul

Examples

## initialise forcast observation pairs
signal <- outer(1.5 + sin(seq(0,4,length=215)), rnorm(30)**2, '*')
fcst <- array(rnorm(length(signal)*15)**2, c(dim(signal), 15)) * c(signal)
obs <- rnorm(length(signal), mean=1.4)**2 * signal 
fcst.debias <- biascorrection:::smooth_mul(fcst[,1:20,], obs[,1:20], fcst.out=fcst, span=0.5)

jonasbhend/biascorrection documentation built on Nov. 11, 2023, 1:16 a.m.