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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DynBiasCorrection.R
\name{DynBiasCorrection}
\alias{DynBiasCorrection}
\title{Performing a Bias Correction conditioned by the dynamical
properties of the data.}
\usage{
DynBiasCorrection(
pred.dim.obs,
pred.theta.obs,
pred.dim.mod,
pred.theta.mod,
ncores = NULL
)
}
\arguments{
\item{pred.dim.obs}{same than pred.dim.obs but for the observations}
\item{pred.theta.obs}{same than pred.theta.obs but for the observations}
\item{pred.dim.mod}{output of CST_Predictability or a list of two lists 'qdim' and 'pos.d'. The 'qdim' list
contains values of local dimension 'dim' divided by terciles:
d1: lower tercile and more predictability,
d2: middle tercile,
d3: higher tercile and less predictability
The 'pos.d' list contains the position of each tercile in parameter 'dim'}
\item{pred.theta.mod}{output of CST_Predictability or a list of two lists 'qtheta' and 'pos.t'.
The 'qtheta' list contains values of the inverse of persistence 'theta'
divided by terciles:
th1: lower tercile and more predictability,
th2: middle tercile,
th3: higher tercile and less predictability
The 'pos.t' list contains the position of each tercile in parameter 'theta'}
\item{ncores}{The number of cores to use in parallel computation}
}
\value{
dim.bias an s2dvcube object with a bias correction performed
conditioned by local dimension 'dim'
theta.bias an s2dvcube object with a bias correction performed
conditioned by the inverse of the persistence 'theta'
}
\description{
This function perform a bias correction conditioned by the
dynamical properties of the dataset. This function used the functions
'CST_Predictability' to divide in terciles the two dynamical proxies
computed with CST_ProxiesAttractor or ProxiesAttractor. A bias correction
between the model and the observations is performed using the division into
terciles of the local dimension 'dim' and inverse of the persistence 'theta'.
For instance, model values with lower 'dim' will be corrected with observed
values with lower 'dim', and the same for theta. The function gives two options
of bias correction: one for 'dim' and one for 'theta'
}
\examples{
# example 1: simple data s2dvcube style
expL <- rnorm(1:200)
dim(expL) <- c(member=10,lat = 4, lon = 5)
obsL <- c(rnorm(1:180),expL[1,,]*1.2)
dim(obsL) <- c(time = 10,lat = 4, lon = 5)
time_obsL <- paste(rep("01", 50), rep("01", 50), 1953 : 2003, sep = "-")
time_expL <- time_obsL[1]
lon <- seq(-1,5,1.5)
lat <- seq(30,35,1.5)
expL <- s2dv_cube(data = expL, lat = lat, lon = lon,
Dates = list(start = time_expL, end = time_expL))
obsL <- s2dv_cube(data = obsL, lat = lat, lon = lon,
attractor <- CST_ProxiesAttractor(data=obsL,quanti=qm)
predynObs = Predictability(dim=attractor$dim,theta=attractor$theta)
attractorMod<- CST_ProxiesAttractor(data=expL,quanti=qm)
predynMod = CST_Predictability(dim=attractorMod$dim,theta=attractorMod$theta)
dynbias= DynBiasCorrection(pred.dim.obs=predynObs$pred.dim,pred.theta.obs=predynMod$pred.theta,
}
\references{
Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D.,
and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large
scale atmospheric predictability.Nature Communications, 10(1), 1316.
DOI = https://doi.org/10.1038/s41467-019-09305-8 "
Faranda, D., Gabriele Messori and Pascal Yiou. (2017).
Dynamical proxies of North Atlantic predictability and extremes.
Scientific Reports, 7-41278, 2017.
}
\author{
Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
}