CST_BiasCorrection.Rd

% 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(
  exp,
  obs,
  exp_cor = NULL,
  na.rm = FALSE,
  memb_dim = "member",
  sdate_dim = "sdate",
  ncores = NULL
)
}
\arguments{
\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}}

\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}.}

\item{exp_cor}{An object of class \code{s2dv_cube} as returned by 
\code{CST_Load} function, containing the seasonl forecast experiment to be 
corrected. If it is NULL, the 'exp' forecast will be corrected.}

\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{ncores}{An integer that indicates the number of cores for parallel 
computations using multiApply function. The default value is NULL.}
}
\value{
An object of class \code{s2dv_cube} 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)
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)
}
\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}
}