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 = 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.

### 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)
```

[Package CSTools version 4.0.1 Index]