% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CST_BiasCorrection.R
\name{CST_BiasCorrection}
\alias{CST_BiasCorrection}
\title{Bias Correction based on the mean and standard deviation adjustment}
\usage{
CST_BiasCorrection(data)
}
\arguments{
\item{data}{CSTools object (an s2dverification object as output by the \code{Load} function from the s2dverification package).}
}
\value{
a CSTools object (s2dverification object) with the bias corrected forecasts a element called \code{data$biascalibration}.
}
\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.
}
\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)
lon <- seq(0, 30, 5)
lat <- seq(0, 25, 5)
data1 <- list(mod = mod1, obs = obs1, lat = lat, lon = lon)
a <- CST_BiasCorrection(data1)
str(a)
}
\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)
}
\author{
Verónica Torralba, \email{veronica.torralba@bsc.es}
}