BiasCorrection {CSTools}R Documentation

Bias Correction based on the mean and standard deviation adjustment

Description

This function applies the simple bias adjustment technique described in Torralba et al. (2017). The adjusted forecasts have an equivalent standard deviation and mean to that of the reference dataset.

Usage

BiasCorrection(exp, obs, exp_cor = NULL, na.rm = FALSE)

Arguments

exp

a multidimensional array with named dimensions containing the seasonal forecast experiment data with at least 'member' and 'sdate' dimensions.

obs

a multidimensional array with named dimensions containing the observed data with at least 'sdate' dimension.

exp_cor

a multidimensional array with named dimensions containing the seasonl forecast experiment to be corrected. If it is NULL, the 'exp' forecast will be corrected.

na.rm

a logical value indicating whether missing values should be stripped before the computation proceeds, by default it is set to FALSE.

Value

an object of class s2dv_cube containing the bias corrected forecasts in the element called $data with the same dimensions of the experimental data.

Author(s)

VerĂ³nica Torralba, veronica.torralba@bsc.es

References

Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS)

Examples


# Example
# Creation of sample s2dverification objects. These are not complete
# s2dverification objects though. The Load function returns complete objects.
mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7)
dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7)
obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7)
dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7)
a <- BiasCorrection(exp = mod1, obs = obs1)
str(a)

[Package CSTools version 4.0.1 Index]