1d:T7be,# Computing the weighted ensemble means for SFSs. This function implements the computation to obtain the weighted ensemble means for SFSs using a normalized weights array, ```r BEI_EMWeighting(var_exp, aweights, 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. - `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_EMWeighting() returns an array with at least one or three dimensions depending if the variable is spatially aggregated variable (as e.g. NAO index)(time) or it is spatial variable (as e.g. precipitation or temperature) (time, lat, lon), containing the ensemble means computing with weighted members. ## Examples ```r # Example 1 var_exp <- 1 : (2 * 3 * 4) dim(var_exp) <- c(time = 2, dataset = 3, member = 4) aweights <- runif(24, min = 0.001, max = 0.999) dim(aweights) <- c(time = 2, dataset = 3, member = 4) res <- BEI_EMWeighting(var_exp, aweights) # Example 2 var_exp <- 1 : (2 * 4 * 2 * 3) 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) res <- BEI_EMWeighting(var_exp, aweights) ``` ## 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_EMWeighting function