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% 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}
CST_BiasCorrection(
exp,
obs,
exp_cor = NULL,
na.rm = FALSE,
memb_dim = "member",
sdate_dim = "sdate",
dat_dim = NULL,
ncores = NULL
\item{exp}{An object of class \code{s2dv_cube} as returned by \code{CST_Load}
function, containing the seasonal forecast experiment data in the element
named \code{$data} with at least time and member dimensions.}
\item{obs}{An object of class \code{s2dv_cube} as returned by \code{CST_Load}
function, containing the observed data in the element named \code{$data}
with at least time dimension.}
\item{exp_cor}{An object of class \code{s2dv_cube} as returned by
\code{CST_Load} function, containing the seasonal forecast experiment to be
corrected with at least time dimension. If it is NULL, the 'exp' forecast
will be corrected. 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.}
An object of class \code{s2dv_cube} containing the bias corrected
forecasts with the same dimensions of the experimental data.
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)
lon <- seq(0, 30, 5)
lat <- seq(0, 25, 5)
exp <- list(data = mod1, lat = lat, lon = lon)
obs <- list(data = obs1, lat = lat, lon = lon)
attr(exp, 'class') <- 's2dv_cube'
attr(obs, 'class') <- 's2dv_cube'
a <- CST_BiasCorrection(exp = exp, obs = obs)
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}
}