| CST_ProxiesAttractor {CSTools} | R Documentation |
Computing two dinamical proxies of the attractor in s2dv_cube.
Description
This function computes two dinamical proxies of the attractor: The local dimension (d) and the inverse of the persistence (theta) for an 's2dv_cube' object. 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 Function based on the matlab code (davide.faranda@lsce.ipsl.fr) used in
Usage
CST_ProxiesAttractor(data, quanti, ncores = NULL)
Arguments
data |
An s2dv_cube object with the data to create the attractor. Must be a matrix with the timesteps in nrow and the grids in ncol(dat(time,grids) |
quanti |
A number lower than 1 indicating the quantile to perform the computation of local dimension and theta. |
ncores |
The number of cores to use in parallel computation. |
Value
dim and theta
Author(s)
Carmen Alvarez-Castro, carmen.alvarez-castro@cmcc.it
Maria M. Chaves-Montero, mdm.chaves-montero@cmcc.it
Veronica Torralba, veronica.torralba@cmcc.it
Davide Faranda, davide.faranda@lsce.ipsl.fr
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: 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.
Examples
# Example 1: Computing the attractor using simple s2dv data
obs <- rnorm(2 * 3 * 4 * 8 * 8)
dim(obs) <- c(dataset = 1, member = 2, sdate = 3, ftime = 4, lat = 8, lon = 8)
lon <- seq(10, 13.5, 0.5)
lat <- seq(40, 43.5, 0.5)
coords <- list(lon = lon, lat = lat)
data <- list(data = obs, coords = coords)
class(data) <- "s2dv_cube"
attractor <- CST_ProxiesAttractor(data = data, quanti = 0.6)