% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CST_BiasCorrection.R
\name{BiasCorrection}
\alias{BiasCorrection}
\title{Bias Correction based on the mean and standard deviation adjustment}
\usage{
BiasCorrection(
  exp,
  obs,
  exp_cor = NULL,
  na.rm = FALSE,
  memb_dim = "member",
  sdate_dim = "sdate",
  dat_dim = NULL,
  ncores = NULL
)
}
\arguments{
\item{exp}{A multidimensional array with named dimensions containing the 
seasonal forecast experiment data with at least time and member dimensions.}

\item{obs}{A multidimensional array with named dimensions containing the 
observed data with at least time dimension.}

\item{exp_cor}{A multidimensional array with named dimensions containing the 
seasonal forecast experiment to be corrected with at least time and member 
dimension. If it is NULL, the 'exp' forecast will be corrected. The 
dimensions must be the same as 'exp' except dataset dimension. If there is 
only one corrected dataset, it should not have dataset dimension. If there 
is a corresponding corrected dataset for each 'exp' forecast, the dataset 
dimension must have the same length as in 'exp'. The default value is NULL.}

\item{na.rm}{A logical value indicating whether missing values should be 
stripped before the computation proceeds, by default it is set to FALSE.}

\item{memb_dim}{A character string indicating the name of the member 
dimension. By default, it is set to 'member'.}

\item{sdate_dim}{A character string indicating the name of the start date 
dimension. By default, it is set to 'sdate'.}

\item{dat_dim}{A character string indicating the name of dataset dimension. 
The length of this dimension can be different between 'exp' and 'obs'. 
The default value is NULL.}

\item{ncores}{An integer that indicates the number of cores for parallel 
computations using multiApply function. The default value is NULL.}
}
\value{
An array containing the bias corrected forecasts with the same 
dimensions of the experimental data.
}
\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{
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)
}
\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}
}