% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CST_Calibration.R
\name{CST_Calibration}
\alias{CST_Calibration}
\title{Forecast Calibration based on the ensemble inflation}
\usage{
CST_Calibration(data)
}
\arguments{
\item{data}{a CSTools object (an s2dverification object as output by the \code{Load} function from the s2dverification package).}
}
\value{
a CSTools object (s2dverification object) with the calibrated forecasts in a element called \code{data$calibration}.
}
\description{
This function applies a variance inflation technique described in Doblas-Reyes et al. (2005) in leave-one-out cross-validation. This bias adjustment method produces calibrated forecasts with equivalent mean and variance to that of the reference dataset, but at the same time preserve reliability.
}
\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_Calibration(data1)
str(a)

}
\references{
Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting—II calibration and combination. Tellus A. 2005;57:234–252. doi:10.1111/j.1600-0870.2005.00104.x
}
\author{
Verónica Torralba, \email{veronica.torralba@bsc.es}
}
