# Computing the weighted tercile probabilities for SFSs. This function implements the computation to obtain the tercile probabilities for a weighted variable for SFSs using a normalized weights array, ```r 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'. ## Returns 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. ## Examples ```r # 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) ``` ## References Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, tools:::Rd_expr_doi("10.5194/asr-16-165-2019") ## Author(s) Eroteida Sanchez-Garcia - AEMET, esanchezg@aemet.es

BEI_ProbsWeighting function