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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CST_ProxiesAttractor.R, R/ProxiesAttractor.R
\name{ProxiesAttractor}
\alias{ProxiesAttractor}
\title{Computing two dinamical proxies of the attractor.}
\usage{
ProxiesAttractor(data, quanti, iplot = FALSE, ncores = NULL)
ProxiesAttractor(data, quanti, iplot = FALSE, ncores = NULL)
}
\arguments{
\item{data}{data to create the attractor. Must be a matrix with the timesteps in nrow
and the grids in ncol(dat(time,grids)}
\item{quanti}{list of arbitrary length of secondary grids. Each secondary grid is to
be provided as a list of length 2 with longitudes and latitudes}
\item{iplot}{FALSE by default. If TRUE the function returns a simple plot
with three pannels on the top left a plot for local dimension 'd', on the top
right a plot for the inverse of the persistence 'theta', on the bottom a plot
for the 'attractor' plotted with the two properties 'd' (x axis) and 'theta'
(y axis)}
\item{ncores}{The number of cores to use in parallel computation}
}
\value{
dim and theta
dim and theta
}
\description{
This function computes two dinamical proxies of the attractor:
The local dimension (d) and the inverse of the persistence (theta).
These two parameters will be used as a condition for the computation of dynamical
scores to measure predictability and to compute bias correction conditioned by
the dynamics with the function DynBiasCorrection.
Funtion based on the matlab code (davide.faranda@lsce.ipsl.fr) used in:
This function computes two dinamical proxies of the attractor:
The local dimension (d) and the inverse of the persistence (theta).
These two parameters will be used as a condition for the computation of dynamical
scores to measure predictability and to compute bias correction conditioned by
the dynamics with the function DynBiasCorrection.
Funtion based on the matlab code (davide.faranda@lsce.ipsl.fr) used in:
}
\examples{
# Example 1: Computing the attractor using simple data
# Creating an example of matrix data(time,grids):
mat <- array(rnorm(36 * 40), c(time = 36, grid = 40))
qm <- 0.90 # imposing a threshold
Attractor <- ProxiesAttractor(data = mat, quanti = qm)
# Example 1: Computing the attractor using simple data
# Creating an example of matrix data(time,grids):
tm=2*6*3 # time
gm=4*10 # grids
m=matrix(rand(1,tm*gm),nrow=tm,ncol=gm)
qm=0.90 # imposing a threshold
Attractor=ProxiesAttractor(data=m,quanti=qm,iplot=FALSE)
}
\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.
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}
Maria M. Chaves-Montero, \email{mdm.chaves-montero@cmcc.it}
Veronica Torralba, \email{veronica.torralba@cmcc.it}
Davide Faranda, \email{davide.faranda@lsce.ipsl.fr}
Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
Maria M. Chaves-Montero, \email{mdm.chaves-montero@cmcc.it}
Veronica Torralba, \email{veronica.torralba@cmcc.it}
Davide Faranda, \email{davide.faranda@lsce.ipsl.fr}
}