% 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}
\usage{
CST_Calibration(exp, obs, cal.method = "mse_min",
  eval.method = "leave-one-out", multi.model = F)
}
\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{cal.method}{is the calibration method used, can be either \code{bias}, \code{evmos}, \code{mse_min} or \code{crps_min}. Default value is \code{mse_min}.}

\item{eval.method}{is the sampling method used, can be either \code{in-sample} or \code{leave-one-out}. Default value is the \code{leave-one-out} cross validation.}

\item{multi.model}{is a boolean that is used only for the \code{mse_min} method. If multi-model ensembles or ensembles of different sizes are used, it must be set to \code{TRUE}. By default it is \code{FALSE}. Differences between the two approaches are generally small but may become large when using small ensemble sizes. Using multi.model when the calibration method is \code{bias}, \code{evmos} or \code{crps_min} will not affect the result.}
}
\value{
an object of class \code{s2dv_cube} containing the calibrated forecasts in the element \code{$data} with the same dimensions of the experimental data.
}
\description{
Four types of member-by-member bias correction can be performed. The \code{bias} method corrects the bias only, the \code{evmos} method applies a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011). The ensemble calibration methods \code{"mse_min"} and \code{"crps_min"} correct the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005) and Van Schaeybroeck and Vannitsem (2015), respectively. While the \code{"mse_min"} method minimizes a constrained mean-squared error using three parameters, the \code{"crps_min"} method features four parameters and minimizes the Continuous Ranked Probability Score (CRPS).

Both in-sample or our out-of-sample (leave-one-out cross validation) calibration are possible.
}
\author{
Verónica Torralba, \email{veronica.torralba@bsc.es}

Bert Van Schaeybroeck, \email{bertvs@meteo.be}
}
\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

Van Schaeybroeck, B., & Vannitsem, S. (2011). Post-processing through linear regression. Nonlinear Processes in Geophysics, 18(2), 147. doi:10.5194/npg-18-147-2011

Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble post‐processing using member‐by‐member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818.  doi:10.1002/qj.2397
}
\seealso{
\code{\link{CST_Load}}

# 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)
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_Calibration(exp = exp, obs = obs, cal.method = "mse_min", eval.method = "in-sample")
str(a)
}
\encoding{UTF-8}

