BEI_ProbsWeighting {CSTools}

Computing the weighted tercile probabilities for SFSs.

Description

This function implements the computation to obtain the tercile probabilities for a weighted variable for SFSs using a normalized weights array,

Usage

BEI_ProbsWeighting(
  var_exp,
  aweights,
  terciles,
  time_dim_name = "time",
  memb_dim = "member"
)

Arguments

var_exp

Variable (e.g. precipitation, temperature, NAO index) array from a SFS with at least dimensions (time, member) for a spatially aggregated variable or dimensions (time, member, lat, lon) for a spatial variable, as 'time' the spatial dimension by default.

aweights

Normalized weights array with at least dimensions (time, member), when 'time' is the temporal dimension as default.

terciles

A numeric array with at least one dimension 'tercil' equal to 2, the first element is the lower tercil for a hindcast period, and the second element is the upper tercile.

time_dim_name

A character string indicating the name of the temporal dimension, by default 'time'.

memb_dim

A character string indicating the name of the member dimension, by default 'member'.

Value

BEI_ProbsWeighting() returns an array with at least two or four dimensions depending if the variable is a spatially aggregated variable (as e.g. NAO index)(time, tercil) or it is spatial variable (as e.g. precipitation or temperature)(time, tercile, lat, lon), containing the terciles probabilities computing with weighted members. The first tercil is the lower tercile, the second is the normal tercile and the third is the upper tercile.

Author(s)

Eroteida Sanchez-Garcia - AEMET, [e-mail]

References

Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, doi: 10.5194/asr-16-165-2019

Examples

# Example 1 
var_exp <- 1 : (2 * 4)
dim(var_exp) <- c(time = 2, member = 4)
aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.4, 0.3)
dim(aweights) <- c(time = 2, member = 4)
terciles <- c(2.5,5)
dim(terciles) <- c(tercil = 2)
res <- BEI_ProbsWeighting(var_exp, aweights, terciles)

# Example 2 
var_exp <- rnorm(48, 50, 9)
dim(var_exp) <- c(time = 2, member = 4, lat = 2, lon = 3)
aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.4, 0.3)
dim(aweights) <- c(time = 2, member = 4)
terciles <- rep(c(48,50), 2*3)
dim(terciles) <- c(tercil = 2, lat = 2, lon = 3)
res <- BEI_ProbsWeighting(var_exp, aweights, terciles)