| Predictability {CSTools} | R Documentation |
Computing scores of predictability using two dynamical proxies based on dynamical systems theory.
Description
This function divides in terciles the two dynamical proxies computed with CST_ProxiesAttractor or ProxiesAttractor. These terciles will be used to measure the predictability of the system in dyn_scores. When the local dimension 'dim' is small and the inverse of persistence 'theta' is small the predictability is high, and viceversa.
Usage
Predictability(dim, theta, ncores = NULL)
Arguments
dim |
An array of N named dimensions containing the local dimension as the output of CST_ProxiesAttractor or ProxiesAttractor. |
theta |
An array of N named dimensions containing the inverse of the persistence 'theta' as the output of CST_ProxiesAttractor or ProxiesAttractor. |
ncores |
The number of cores to use in parallel computation. |
Value
A list of length 2:
'pred.dim', a list of two lists 'qdim' and 'pos.d'. The 'qdim' list contains values of local dimension 'dim' divided by terciles: d1: lower tercile (high predictability), d2: middle tercile, d3: higher tercile (low predictability) The 'pos.d' list contains the position of each tercile in parameter 'dim'.
'pred.theta', 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 (high predictability), th2: middle tercile, th3: higher tercile (low predictability) The 'pos.t' list contains the position of each tercile in parameter 'theta'.
dyn_scores values from 0 to 1. A dyn_score of 1 indicates the highest predictability.
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
# Creating an example of matrix dat(time,grids):
m <- matrix(rnorm(2000) * 10, nrow = 50, ncol = 40)
names(dim(m)) <- c('time', 'grid')
# imposing a threshold
quanti <- 0.90
# computing dyn_scores from parameters dim and theta of the attractor
attractor <- ProxiesAttractor(dat = m, quanti = 0.60)
predyn <- Predictability(dim = attractor$dim, theta = attractor$theta)