Predictability {CSTools}R Documentation

Computing scores of predictability using two dynamical proxies based on dynamical systems theory.


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.


Predictability(dim, theta, ncores = NULL)



An array of N named dimensions containing the local dimension as the output of CST_ProxiesAttractor or ProxiesAttractor.


An array of N named dimensions containing the inverse of the persistence 'theta' as the output of CST_ProxiesAttractor or ProxiesAttractor.


The number of cores to use in parallel computation


A list of length 2:

dyn_scores values from 0 to 1. A dyn_score of 1 indicates the highest predictability.


Carmen Alvarez-Castro,

Maria M. Chaves-Montero,

Veronica Torralba,

Davide Faranda,


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 = "

Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. Scientific Reports, 7-41278, 2017.


# 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)

[Package CSTools version 4.0.1 Index]