From ad8ad06c7f3997d52bbd3df38e2179f95d770d39 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Wed, 28 Aug 2019 07:11:05 +0000 Subject: [PATCH 01/40] New functions for AEMET-BestNaoWeighting --- R/CST_EMBestIndexWeighting.R | 203 ++++++++++++++++++++ R/CST_PDFBest.R | 198 +++++++++++++++++++ R/CST_PDFIndexFcst.R | 304 ++++++++++++++++++++++++++++++ R/CST_PDFIndexHind.R | 259 +++++++++++++++++++++++++ R/CST_ProbBestIndexWeighting.R | 278 +++++++++++++++++++++++++++ R/CST_WeightIndex.R | 193 +++++++++++++++++++ man/CST_EMBestIndexWeighting.Rd | 61 ++++++ man/CST_PDFBest.Rd | 68 +++++++ man/CST_PDFIndexFcst.Rd | 84 +++++++++ man/CST_PDFIndexHind.Rd | 69 +++++++ man/CST_ProbBestIndexWeighting.Rd | 66 +++++++ man/CST_WeightIndex.Rd | 57 ++++++ man/EMBestIndexWeighting.Rd | 64 +++++++ man/PDFBest.Rd | 53 ++++++ man/PDFIndexFcst.Rd | 62 ++++++ man/PDFIndexHind.Rd | 56 ++++++ man/ProbBestIndexWeighting.Rd | 67 +++++++ man/WeightIndex.Rd | 48 +++++ 18 files changed, 2190 insertions(+) create mode 100644 R/CST_EMBestIndexWeighting.R create mode 100644 R/CST_PDFBest.R create mode 100644 R/CST_PDFIndexFcst.R create mode 100644 R/CST_PDFIndexHind.R create mode 100644 R/CST_ProbBestIndexWeighting.R create mode 100644 R/CST_WeightIndex.R create mode 100644 man/CST_EMBestIndexWeighting.Rd create mode 100644 man/CST_PDFBest.Rd create mode 100644 man/CST_PDFIndexFcst.Rd create mode 100644 man/CST_PDFIndexHind.Rd create mode 100644 man/CST_ProbBestIndexWeighting.Rd create mode 100644 man/CST_WeightIndex.Rd create mode 100644 man/EMBestIndexWeighting.Rd create mode 100644 man/PDFBest.Rd create mode 100644 man/PDFIndexFcst.Rd create mode 100644 man/PDFIndexHind.Rd create mode 100644 man/ProbBestIndexWeighting.Rd create mode 100644 man/WeightIndex.Rd diff --git a/R/CST_EMBestIndexWeighting.R b/R/CST_EMBestIndexWeighting.R new file mode 100644 index 00000000..40eef1bd --- /dev/null +++ b/R/CST_EMBestIndexWeighting.R @@ -0,0 +1,203 @@ +#' @rdname CST_EMBestIndexWeighting +#' @title Computing the weighted ensemble means for SFSs of a CSTools object. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the weighted +#' ensemble means for SFSs through Best estimation of one index or driver of +#' climate variability (as e.g., the North Atlantic Oscillation (NAO)) and +#' accepts a CSTools object (an object of the class 's2dv_cube') as input. +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param var_exp An object of the class 's2dv_cube' containing the variable +#' (e.g. precipitation, temperature, NAO index) array. +#' The var_exp object is expected to have an element named \code{$data} with +#' at least two dimensions (time, member). +#' If the variable is spatial the dimensions would be (time, member, lat, lon). +#' If we want to compute for several SFSs, the array could have another +#' dimension (time, dataset, member, lat, lon). +#' @param weights_index_exp An object of the class 's2dv_cube' containing +#' the normalized weights array. +#' The weights_index_exp object is expected to have an element named +#' \code{$data} with at least two dimensions (time, member). +#' If we want to compute for several SFSs, the array could have another +#' dimension (time, dataset, member). +#' @return CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the +#' class 's2dv_cube'). +#' This object has at least an element named \code{$data} +#' with three or five dimensions depending if the variable is zonal +#' (as e.g. NAO index)(time, dataset, season) or it is spatial variable +#' (as e.g. precipitation or temperature) (time, dataset, season, lat, lon), +#' containing the ensemble means computing with weighted members. +#' @examples +#' var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) +#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) +#' weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) +#' dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) +#' var_exp <- list(data = var_exp) +#' weights_index_exp <- list(data = weights_index_exp) +#' class(var_exp) <- 's2dv_cube' +#' class(weights_index_exp) <- 's2dv_cube' +#' res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) +#' dim(res_CST$data) +#' # time dataset season lat lon +#' # 4 2 2 3 2 +CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { + if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { + stop("Parameter 'var_exp' and 'weights_index_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + } + if (!any(names(dim(var_exp$data)) == 'time')){ + stop("The component 'data' of the parameter 'var_exp' must have the ", + "dimension 'time'") + } + if (!any(names(dim(weights_index_exp$data)) == 'time')){ + stop("The component 'data' of the parameter 'weights_index_exp' must have ", + "the dimension 'time'") + } + if (!any(names(dim(var_exp$data)) == 'member')){ + stop("The component 'data' of the parameter 'var_exp' must have the ", + "dimension 'member'") + } + if (!any(names(dim(weights_index_exp$data)) == 'member')){ + stop("The component 'data' of the parameter 'weights_index_exp' must have ", + "the dimension 'member'") + } + if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ + stop("The length of the dimension 'time' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'time' of the parameter 'weights_index_exp' in the component 'data'") + } + if (dim(var_exp$data)['member'] != dim(weights_index_exp$data)['member']){ + stop("The length of the dimension 'member' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'member' of the parameter 'weights_index_exp' in the component 'data'") + } + if (any(names(dim(weights_index_exp$data)) == 'lat') & + dim(weights_index_exp$data)['lat'] != 1) { + stop("The length of the dimension 'lat' of the parameter 'weights_index_exp'", + " in the component 'data' must be equal to 1") + } + if (any(names(dim(weights_index_exp$data)) == 'lon') & + dim(weights_index_exp$data)['lon'] != 1) { + stop("The length of the dimension 'lon' of the parameter 'weights_index_exp'", + " in the component 'data' must be equal to 1") + } + if (any(names(dim(weights_index_exp$data)) == 'lon')) { + Dim <- dim(weights_index_exp$data) + dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] + } + if (any(names(dim(weights_index_exp$data)) == 'lat')) { + Dim <- dim(weights_index_exp$data) + dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lat')] + } + if (any(names(dim(weights_index_exp$data)) == 'lon')) { + Dim <- dim(weights_index_exp$data) + dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] + } + if (any(names(dim(var_exp$data)) == 'dataset') & + !any(names(dim(weights_index_exp$data)) == 'dataset')) { + stop("The component 'data' of the parameter 'weights_index_exp' must have'", + " the dimension 'dataset'") + } + if (!any(names(dim(var_exp$data)) == 'dataset') & + any(names(dim(weights_index_exp$data)) == 'dataset')) { + stop("The component 'data' of the parameter 'var_exp' must have'", + " the dimension 'dataset'") + } + if (any(names(dim(var_exp$data)) == 'dataset') & + any(names(dim(weights_index_exp$data)) == 'dataset') & + dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']) { + stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") + } + + em <- EMBestIndexWeighting(var_exp$data, weights_index_exp$data) + em_var_exp <- list() + em_var_exp$data <- em + em_var_exp$Datasets <- var_exp$Datasets + em_var_exp$source_files <- var_exp$source_files + em_var_exp$Dates <- var_exp$Dates + return(em_var_exp) +} + +#' @rdname EMBestIndexWeighting +#' @title Computing the weighted ensemble means for SFSs. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the weighted +#' ensemble means for SFSs through Best estimation of one index or driver of +#' climate variability (as e.g., the North Atlantic Oscillation (NAO)). +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with at least two dimensions (time, member). +#' If the variable is spatial the dimensions would be (time, member, lat, lon). +#' If we want to compute for several SFSs, the array could have another +#' dimension (time, dataset, member, lat, lon). +#' @param weights_index_exp Normalized weights array with at least two +#' dimensions (time, member). If we want to compute for several SFSs, +#' the array could have another dimension (time, dataset, member). +#' @return EMBestIndexWeighting() returns an array with two or four dimensions +#' depending if the variable is zonal (as e.g. NAO index)(time, dataset) +#' or it is spatial variable (as e.g. precipitation or temperature) +#' (time, dataset, lat, lon), containing the ensemble means computing +#' with weighted members. +#' @import multiApply +#' @export +#' @examples +#' # Example 1 for the EMBestIndexWeighting function +#' var_exp <- 1 : (2 * 3 * 4) +#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4) +#' weights_index_exp<- runif(24, min=0.001, max=0.999) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' res <- EMBestIndexWeighting(var_exp, weights_index_exp) +#' dim(res) +#' # time dataset +#' # 2 3 +#' str(res) +#' # num [1:2, 1:3] 36.2 28.8 38.3 31.9 31 ... +#' # Example 2 for the EMBestIndexWeighting function +#' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) +#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) +#' weights_index_exp<- runif(24, min=0.001, max=0.999) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' res <- EMBestIndexWeighting(var_exp, weights_index_exp) +#' dim(res) +#' # time dataset lat lon +#' # 2 3 2 3 +#' str(res) +#' # num [1:2, 1:3, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... +#' +EMBestIndexWeighting <- function(var_exp, weights_index_exp) { + return(Apply(list(var_exp, weights_index_exp), + target_dims = list(c('time','member'), c('time','member')), + fun = .EMBestIndexWeighting)$output1) +} + +#' Atomic EMBestIndexWeighting +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with dimensions (time, member). +#' @param weights_index_exp Normalized weights array with dimensions +#' (time, member). +#' @return .EMBestIndexWeighting returns an array of with dimensions (time), +#' the ensemble means computing with weighted members. +#' @examples +#' # Example for the Atomic EMBestIndexWeighting function +#' var_exp <- 1 : 6 +#' dim(var_exp) <- c(time = 2, member = 3) +#' weights_index_exp <- c(0.28, 0.15, 0.69, 0.64, 0.42, 0.17) +#' dim(weights_index_exp) <- c(time = 2, member = 3) +#' res <- .EMBestIndexWeighting(var_exp, weights_index_exp) +#' str(res) +#' # num [1:2(1d)] 4.45 3.88 +#' dim(res) +#' # time +#' # 2 +#' @noRd +.EMBestIndexWeighting <- function(var_exp, weights_index_exp) { + posTime <- match('time', names(dim(var_exp))) + var_exp_em <- apply(var_exp * weights_index_exp, posTime, sum) + dim(var_exp_em) <- c(time = length(var_exp_em)) + return(var_exp_em) +} diff --git a/R/CST_PDFBest.R b/R/CST_PDFBest.R new file mode 100644 index 00000000..6ce7f89b --- /dev/null +++ b/R/CST_PDFBest.R @@ -0,0 +1,198 @@ +#' @rdname CST_PDFBest +#' @title Computing the Best Index PDFs combining Index PDFs from two SFSs +#' of a CSTools object. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs +#' and accepts a CSTools object (an object of the class 's2dv_cube' as input. +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param pdf_exp An object of the class 's2dv_cube' +#' containing the parameters of the Index PDFs from two SFSs for a period +#' to combine and obtain the Best estimation. +#' The index_hind object is expected to have an element named \code{$data} +#' with dimensions (time, dataset = 2, member = 1, statistic = 4, season). +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDFs for the first SFS or dataset has been computed +#' with bias corrected. +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDFs for the second SFS or dataset has been computed +#' with bias corrected. +#' @return CST_PDFBest() returns a CSTools object (i.e., of the +#' class 's2dv_cube'). +#' This object has at least two elements: i) an element named \code{$data} +#' with dimensions (time, statistic = 2, season) and ii) an element +#' named \code{$statistic} with the names of 'statistic' dimension +#' of the first element ("mean", "sigma"). +#' The firt statistic is the parameter 'mean' of the PDF +#' The second statistic is the parameter 'standard deviation' of the PDF +#' @examples +#' pdf_exp <- c(1: (10 * 2* 4 * 1)) +#' dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, season = 1) +#' pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), +#' season = c('DJF'), Dates = list(start = 1997:2006)) +#' class(pdf_exp) <- 's2dv_cube' +#' res_CST <- CST_PDFBest(pdf_exp) +#' str(res_CST) +#' List of 5 +#' # $ data : num [1:10, 1:2, 1] 45.2 46.3 47.3 48.3 49.3 ... +#' # $ Datasets : chr [1:2] "ECMWF-S5" "UKMO-S13" +#' # $ season : chr "DJF" +#' # $ Dates :List of 1 +#' # ..$ start: int [1:10] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 +#' # $ statistic: chr [1:2] "mean" "sigma" +#' # - attr(*, "class")= chr "s2dv_cube" +#' dim(res_CST$data) +#' # time statistic season +#' # 10 2 1 +#' str(res_CST) +CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { + if (!inherits(pdf_exp, 's2dv_cube')) { + stop("Parameter 'pdf_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + } + if (!any(names(dim(pdf_exp$data)) == 'statistic')){ + stop("The dimension 'statistic' must be exist in the component 'data' ", + "of the parameter 'pdf_exp'") + } + if (!any(names(dim(pdf_exp$data)) == 'dataset')){ + stop("The dimension 'dataset' must be exist in the component 'data' ", + "of the parameter 'pdf_exp'") + } + if (dim(pdf_exp$data)['member'] != 1) { + stop("The length of the dimension 'member' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 1.") + } + if (dim(pdf_exp$data)['dataset'] != 2){ + stop("The length of the dimension 'dataset' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 2.") + } + if (dim(pdf_exp$data)['statistic'] != 4){ + stop("The length of the dimension 'statistic' in the component 'data' of the ", + "parameter 'pdf_exp' must be equal to 4'") + } + + if (dim(pdf_exp$data)['member'] == 1) { + Dim <- dim(pdf_exp$data) + dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] + } + + PDFstatistics <- PDFBest(pdf_exp$data, bc_dataset1 = bc_dataset1, + bc_dataset2 = bc_dataset2) + pdf_exp$data <- PDFstatistics + pdf_exp$statistic <- c("mean","sigma") + return(pdf_exp) +} + +#' @rdname PDFBest +#' @title Computing the Best Index PDFs combining two SFSs Index PDFs. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) obtained to combining the Index PDFs for two SFSs, +#' the Best Index estimation. +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param pdf_exp Statistics array for the SFSs Index PDFs for a period of years +#' with dimensions (time, dataset = 2, statistic = 4, season). +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDFs for the first SFS or dataset has been computed +#' with bias corrected. +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDFs for the second SFS or dataset has been computed +#' with bias corrected. +#' @return PDFBest() returns an array +#' with dimensions (time, statistic = 2, season). +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDFs. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDFs. +#' @import multiApply +#' @export +#' @examples +#' # Example for the PDFBest function +#' pdf_exp <- c(1: (10 * 2* 4 * 1)) +#' dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) +#' res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +#' dim(res) +#' # time statistic season +#' # 10 2 1 +#' str(res) +#' # num [1:10, 1:2, 1] 17.2 18.3 19.5 20.6 21.7 ... +#' +PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { + if (!any(names(dim(pdf_exp)) == 'statistic')){ + stop("The parameter 'pdf_exp' must have the dimension 'statistic'") + } + if(dim(pdf_exp)['statistic'] != 4){ + stop("The length of the dimension 'statistic' in the parameter 'pdf1'", + " must be equal to 4") + } + if(dim(pdf_exp)['dataset'] != 2){ + stop("The length of the dimension 'statistic' in the parameter 'pdf_exp'", + " must be equal to 2") + } + pdf_best <- Apply(list(pdf_exp), + target_dims = list(c('dataset','statistic'), + c('dataset','statistic')), + fun = .PDFBest, bc_dataset1, bc_dataset2) + pos <- match(c('time','statistic','season'), names(dim(pdf_best$output1))) + return(aperm(pdf_best$output1,pos)) +} + +#' Atomic PDFBest +#' @param pdf_exp Statistics array for two SFS PDFs with dimensions +#' (dataset = 2, statistic = 4). +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDF for the first SFS or dataset has been computed +#' with bias corrected. +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDF for the second SFS or dataset has been computed +#' with bias corrected. +#' @return .PDFBest returns an array with dimensions (statistic = 2). +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDF. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDF. +#' @examples +#' # Example for the Atomic PDFBest function +#' pdf_exp <- c(1.1,0.6,1.6,0.9,1,0.5,1.5,0.8) +#' dim(pdf_exp) <- c(dataset = 2, statistic = 4) +#' res <- .PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +#' str(res) +#' # num [1:2(1d)] 0.706 0.772 +#' dim(res) +#' # statistic +#' # 2 +#' @noRd +.PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { + pos <- match(c('dataset','statistic'), names(dim(pdf_exp))) + pdf_exp <- aperm(pdf_exp,pos) + if(bc_dataset1){ + # apply bias correction to model 1 + sigma_1 <- pdf_exp[1,4] + mean_1 <- pdf_exp[1,3] + } else{ + # not bias correction to model 1 + sigma_1 <- pdf_exp[1,2] + mean_1 <- pdf_exp[1,1] + } + if(bc_dataset2){ + # apply bias correction to model 2 + sigma_2 <- pdf_exp[2,4] + mean_2 <- pdf_exp[2,3] + } else { + # not bias correction to model 2 + sigma_2 <- pdf_exp[2,2] + mean_2 <- pdf_exp[2,1] + } + a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) + a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) + pdf_mean <- a_1*mean_1 + a_2*mean_2 + pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) + data <- c(pdf_mean, pdf_sigma) + #dim(data) <- c(time=1, statistic = 2) + dim(data) <- c(statistic = 2) + return(data) +} diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R new file mode 100644 index 00000000..c48a00e3 --- /dev/null +++ b/R/CST_PDFIndexFcst.R @@ -0,0 +1,304 @@ +#' @rdname CST_PDFIndexFcst +#' @title Computing the Index PDFs for a forecast year of a SFSs dataset +#' of a CSTools object. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs +#' and accepts a CSTools object (an object of the class 's2dv_cube') as input. +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param index_hind An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate for a hindcast period +#' from SFSs for improving. +#' The index_hind object is expected to have an element named \code{$data} +#' with dimensions (time, dataset, member, season). +#' @param index_obs An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate from observations or +#' reanalysis. +#' The index_obs object is expected to have an element named \code{$data} +#' with at least three dimensions (time, dataset, season). +#' It could have one more dimension (member) equal to 1. +#' @param index_fcst An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate for a forecast year +#' from SFSs for improving. +#' The index_fcst object is expected to have an element named \code{$data} +#' with dimensions (time = 1, dataset, member, season). +#' @param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#' @param na.rm Logical (default = FALSE). Should missing values be removed? +#' @return CST_PDFIndexFcst() returns a CSTools object (i.e., of the +#' class 's2dv_cube'). +#' This object has at least two elements: i) an element named \code{$data} +#' with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element +#' named \code{$statistic} with the names of 'statistic' dimension +#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @examples +#' index_hind <- 1 : (3 * 5 * 4 * 7) +#' dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) +#' index_hind <- list(data = index_hind) +#' class(index_hind) <- 's2dv_cube' +#' index_obs <- 1 : (3 * 5 * 4) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) +#' index_obs <- list(data = index_obs) +#' class(index_obs) <- 's2dv_cube' +#' index_fcst <- 1 : (3 * 4 * 7) +#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) +#' index_fcst <- list(data = index_fcst) +#' class(index_fcst) <- 's2dv_cube' +#' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') +#' str(res_CST) +#' dim(res_CST$data) +#' #time dataset statistic season +#' # 1 3 4 4 +CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method = 'ME', na.rm = FALSE) { + if (!inherits(index_hind, 's2dv_cube') || + !inherits(index_obs, 's2dv_cube') || + !inherits(index_fcst, 's2dv_cube')) { + stop("Parameter 'index_fcst', 'index_hind' + and 'index_obs' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + } + if (dim(index_obs$data)['member'] != 1) { + stop("The length of the dimension 'member' in the component 'data' ", + "of the parameter 'index_obs' must be equal to 1.") + } + if (dim(index_fcst$data)['member'] == 1 & + !any(names(dim(index_fcst$data)) == 'statistic')){ + stop("The dimension 'statistic' must be exist in the component 'data' ", + "of the parameter 'index_fcst' if the length of the dimension ", + "'member' is equal to 1") + } + if (dim(index_fcst$data)['time'] != 1){ + stop("The dimension 'time' must be equal to 1") + } + if (dim(index_hind$data)['member'] == 1 & + !any(names(dim(index_hind$data)) == 'statistic')){ + stop("The dimension 'statistic' must be exist in the component 'data' ", + "of the parameter 'index_hind' if the length of the dimension ", + "'member' is equal to 1") + } + if (dim(index_fcst$data)['member'] > 1 & + any(names(dim(index_fcst$data)) == 'statistic')){ + stop("The length of the dimension 'member' in the component 'data' of the ", + "parameter 'index_fcst' must be equal to 1 if the component 'data' ", + "of the parameter 'index_fcst' has the dimension 'statistic'") + } + if (dim(index_hind$data)['member'] > 1 & + any(names(dim(index_hind$data)) == 'statistic')){ + stop("The length of the dimension 'member' in the component 'data' of the ", + "parameter 'index_hind' must be equal to 1 if the component 'data' ", + "of the parameter 'index_hind' has the dimension 'statistic'") + } + if (dim(index_fcst$data)['member'] == 1) { + Dim <- dim(index_fcst$data) + dim(index_fcst$data) <- Dim[-which(names(Dim) == 'member')] + } + if (dim(index_hind$data)['member'] == 1) { + Dim <- dim(index_hind$data) + dim(index_hind$data) <- Dim[-which(names(Dim) == 'member')] + } + dimnames_fcst <- names(dim(index_fcst$data)) + dimnames_hind <- names(dim(index_hind$data)) + if(sum(dimnames_fcst == dimnames_hind) != length(dimnames_fcst)){ + stop("The dimensions of the component 'data' of the parameter ", + "'index_fcst' must be equal to the dimensions of the component ", + "'data' of the parameter 'index_hind'") + } + dimnames <- dimnames_fcst + if (any(names(dim(index_fcst$data)) == 'member')){ + dimnames[dimnames == 'member'] <- 'statistic' + } + PDFstatistics <- PDFIndexFcst(index_hind$data, index_obs$data, + index_fcst$data, method = method, na.rm = na.rm) + pos <- match(dimnames, names(dim(PDFstatistics))) + PDFstatistics <- aperm(PDFstatistics, pos) + names(dim(PDFstatistics)) <- dimnames + index_fcst$data <- PDFstatistics + index_fcst$Datasets <- c(index_hind$Datasets, index_obs$Datasets, index_fcst$Datasets) + index_fcst$source_files <- c(index_hind$source_files, index_obs$source_files, + index_fcst$source_files) + index_fcst$statistic <- c("mean_notbc","sigma_notbc","mean_bc","sigma_bc") + return(index_fcst) +} + +#' @rdname PDFIndexFcst +#' @title Computing the Index PDFs for a dataset of SFSs for a forecast year. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from a dataset of SFSs +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param index_hind Index (e.g. NAO index) array from SFSs with dimensions +#' (time, dataset, member) for a hindcast period. +#' @param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with dimension (time, dataset, member = 1) or (time, dataset). +#' @param index_fcst Index (e.g. NAO index) array from SFSs. +#' with dimensions (time = 1, dataset, member) for a forecast year. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param na.rm Logical (default = FALSE). Should missing values be removed? +#' @return PDFIndexFcst() returns an array +#' with dimensions (time = 1, statistic = 4, dataset) for the forecast year. +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @export +#' @examples +#' # Example for the PDFIndexFcst function +#' index_fcst <- 1 : (3 * 4) +#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 4) +#' index_obs<- 1 : (5 * 3) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1) +#' index_hind <- 1 : (5 * 3 * 4) +#' dim(index_hind) <- c(time = 5, dataset = 3, member = 4) +#' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic dataset +#' # 1 4 3 +#' str(res) +#' # num [1, 1:4, 1:3] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... +#' +PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', + na.rm = FALSE) { + if (any(names(dim(index_obs)) == 'member')) { + Dim <- dim(index_obs) + dim(index_obs) <- Dim[-which(names(Dim) == 'member')] + } + if (any(names(dim(index_fcst)) == 'member')){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c('time', 'member'), 'time', + c('time','member')), + fun = .PDFIndexFcst, method, na.rm) + } else if (any(names(dim(index_fcst)) == 'statistic')){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c('time', 'statistic'), 'time', + c('time','statistic')), + fun = .PDFIndexFcst, method, na.rm) + } + return(PDFstatistics$output1) +} + +#' Atomic PDFIndexFcst +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) for a hindcast period. +#' @param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with dimension (time). +#' @param index_fcst Index (e.g. NAO index) array from a SFS +#' with dimensions (time=1, member) for a forecast year. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexFcst returns an array with dimensions (time=1, statistic=4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the forecast year. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the forecast year. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the forecast year. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the forecast year. +#' @import multiApply +#' @import verification +#' @examples +#' # Example for the Atomic PDFIndexFcst function +#' index_fcst <- 1 : 3 +#' dim(index_fcst) <- c(time = 1, member = 3) +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_hind <- 1 : (10 * 3) +#' dim(index_hind) <- c(time = 10, member = 3) +#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) +#' str(res) +#' # num [1, 1:4] 2 10 -8 0 +#' dim(res) +#' # time statistic +#' # 1 4 +#' @noRd +.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method = 'ME', na.rm = TRUE) { + if (any(names(dim(index_fcst)) == 'member')) { + exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', + fun = mean)$output1 + exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', + fun = sd)$output1 + } else { + pos <- match(c('time','statistic'), names(dim(index_fcst))) + index_fcst <- aperm(index_fcst,pos) + exp_fcst_ens_mean <- index_fcst[,1] + exp_fcst_ens_sd <- index_fcst[,2] + } + data_hind <- .PDFIndexHind(index_hind, index_obs, method, na.rm) + exp_hind_ens_mean <- data_hind[,1] + pdfnotbc_mean <- exp_fcst_ens_mean + pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs) + if (method == 'ME') { + pdfbc <- MEBC(exp_hind_ens_mean, index_obs) + pdfbc_mean <- exp_fcst_ens_mean - pdfbc + } else if (method == 'LMEV') { + # linear model error-variance + exp_fcst_ens_var <- exp_fcst_ens_sd^2 + coeff <- LMEV(index_hind, index_obs) + pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) + } else { + stop("Error: Parameter 'method' is wrong") + } + pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs) + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + + +# Auxiliar function to compute the mean squared error between 'exp' and 'obs' +Sigma_notBC <- function(exp, obs) { + return(sqrt(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$MSE)) +} + +# Auxiliar function to compute the bias between 'exp' and 'obs' +MEBC <- function(exp, obs) { + return(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$ME) +} + +# Auxiliar function to compute the standard deviation of errors +# between 'obs' and 'exp' +Sigma_BC <- function(exp, obs, na.rm = FALSE) { + return(sd(obs - exp, na.rm = na.rm)) +} + +# Auxiliar function to compute the linear model used in the method +# called 'LMEV' to correct the bias +LMEV <- function(exp, obs, na.rm = FALSE) { + if (any(names(dim(exp)) == 'member')) { + exp_ens_mean <- Apply(list(exp), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + exp_ens_sd <- Apply(list(exp), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else { + pos <- match(c('time','statistic'), names(dim(exp))) + exp <- aperm(exp,pos) + exp_ens_mean <- exp[,1] + exp_ens_sd <- exp[,2] + } + error <- exp_ens_mean - obs + exp_ens_var <- exp_ens_sd^2 + statModel <- lm(error ~ exp_ens_var) + return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) +} diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R new file mode 100644 index 00000000..71ebd599 --- /dev/null +++ b/R/CST_PDFIndexHind.R @@ -0,0 +1,259 @@ +#' @rdname CST_PDFIndexHind +#' @title Computing the Index PDFs for the hindcast of a SFSs dataset +#' of a CSTools object +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from the hindcast of SFSs +#' and accepts a CSTools object (an object of the class 's2dv_cube') as input. +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param index_exp An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. +#' The index_exp object is expected to have an element named \code{$data} +#' with dimensions (time, dataset, member, season). +#' @param index_obs An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate from observations or +#' reanalysis. +#' The index_obs object is expected to have an element named \code{$data} +#' with at least three dimensions (time, dataset, season). +#' It could have one more dimension (member) equal to 1. +#' @param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#' @param na.rm Logical (default = FALSE). Should missing values be removed? +#' @return CST_PDFIndexHind() returns a CSTools object (i.e., of the +#' class 's2dv_cube'). +#' This object has at least two elements: i) an element named \code{$data} +#' with dimensions (time, dataset, statistic = 4, season) and ii) an element +#' named \code{$statistics} with the names of 'statistic' dimension +#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @examples +#' index_obs <- 1 : (3 * 5 * 4) +# 'dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) +#' index_exp <- 1 : (3 * 5 * 4 * 7) +#' dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) +#' index_exp <- list(data = index_exp) +#' index_obs <- list(data = index_obs) +#' class(index_exp) <- 's2dv_cube' +#' class(index_obs) <- 's2dv_cube' +#' res_CST <- CST_PDFIndexHind(index_exp, index_obs) +#' dim(res_CST$data) +#' #time dataset statistic season +#' # 5 3 4 4 +CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) { + if (!inherits(index_exp, 's2dv_cube') || !inherits(index_obs, 's2dv_cube')) { + stop("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + } + if (dim(index_obs$data)['member'] != 1) { + stop("The length of the dimension 'member' in the component 'data' ", + "of the parameter 'index_obs' must be equal to 1.") + } + if (dim(index_exp$data)['member'] == 1 & + !any(names(dim(index_exp$data)) == 'statistic')){ + stop("The dimension 'statistic' must be exist in the component 'data' ", + "of the parameter 'index_exp' if the length of the dimension ", + "'member' is equal to 1") + } + if (dim(index_exp$data)['member'] > 1 & + any(names(dim(index_exp$data)) == 'statistic')){ + stop("The length of the dimension 'member' in the component 'data' of the ", + "parameter 'index_hind' must be equal to 1 if the component 'data' ", + "of the parameter 'index_hind' has the dimension 'statistic'") + } + + if (dim(index_exp$data)['member'] == 1) { + Dim <- dim(index_exp$data) + dim(index_exp$data) <- Dim[-which(names(Dim) == 'member')] + } + dimnames <- names(dim(index_exp$data)) + if (any(names(dim(index_exp$data)) == 'member')){ + dimnames[dimnames == 'member'] <- 'statistic' + } + PDFstatistics <- PDFIndexHind(index_exp$data, index_obs$data, method = method, + na.rm = na.rm) + pos <- match(dimnames, names(dim(PDFstatistics))) + PDFstatistics <- aperm(PDFstatistics, pos) + names(dim(PDFstatistics)) <- dimnames + index_exp$data <- PDFstatistics + index_exp$Datasets <- c(index_exp$Datasets, index_obs$Datasets) + index_exp$source_files <- c(index_exp$source_files, index_obs$source_files) + index_exp$statistic <- c("mean_notbc","sigma_notbc","mean_bc","sigma_bc") + return(index_exp) +} + +#' @rdname PDFIndexHind +#' @title Computing the Index PDFs for a dataset of SFSs for a hindcats period. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from a dataset of SFSs +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param index_exp Index (e.g. NAO index) array from SFSs +#' with dimensions (time, dataset, member). +#' @param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset). +#' @param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#' @param na.rm Logical (default = FALSE). Should missing values be removed? +#' @return PDFIndexHind() returns an array +#' with dimensions (time, statistic = 4, dataset). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @export +#' @examples +#' # Example for the PDFIndexHind function +#' index_obs <- 1 : (3 * 5) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1) +#' index_exp <- 1 : (3 * 5 * 4) +#' dim(index_exp) <- c(time = 5, dataset = 3, member = 4) +#' res <- PDFIndexHind(index_exp, index_obs) +#' str(res) +#' # num [1:5, 1:4, 1:3] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... +#' dim(res) +#' # time statistic dataset +#' # 5 4 3 +#' + +PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { + if (any(names(dim(index_obs)) == 'member')) { + Dim <- dim(index_obs) + dim(index_obs) <- Dim[-which(names(Dim) == 'member')] + } + if (any(names(dim(index_exp)) == 'member')){ + PDFstatistics <- Apply(list(index_exp, index_obs), + target_dims = list(c('time', 'member'), 'time'), + fun = .PDFIndexHind, method, na.rm) + } else if (any(names(dim(index_exp)) == 'statistic')){ + PDFstatistics <- Apply(list(index_exp, index_obs), + target_dims = list(c('time', 'statistic'), 'time'), + fun = .PDFIndexHind, method, na.rm) + } + return(PDFstatistics$output1) +} + +#' Atomic PDFIndexHind +#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) for a hindcast period. +#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' or reanalysis with dimension (time). +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the hindcast period. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the hindcast period. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the hindcast period. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the hindcast period. +#' @import multiApply +#' @import verification +#' @examples +#' # Example for the Atomic PDFIndexHind function +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_exp <- 1 : (10 * 3) +#' dim(index_exp) <- c(time = 10, member = 3) +#' res <- .PDFIndexHind(index_exp, index_obs) +#' str(res) +#' # num [1:10, 1:4] 11 12 13 14 15 16 17 18 19 20 ... +#' dim(res) +#' # time statistic +#' # 10 4 +#' @noRd +.PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) { + if (any(names(dim(index_exp)) == 'member')) { + exp_ens_mean <- Apply(list(index_exp), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + exp_ens_sd <- Apply(list(index_exp), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else { + pos <- match(c('time','statistic'), names(dim(index_exp))) + index_exp <- aperm(index_exp,pos) + exp_ens_mean <- index_exp[,1] + exp_ens_sd <- index_exp[,2] + } + error <- exp_ens_mean - index_obs + pdfnotbc_mean <- exp_ens_mean + ind <- 1 : length(index_obs) + pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(exp_ens_mean[-x], + index_obs[-x])})) + + if (method == 'ME') { + pdfbc <- unlist(lapply(ind, function(x) {MEBC(exp_ens_mean[-x], + index_obs[-x])})) + pdfbc_mean <- exp_ens_mean - pdfbc + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x])})) + } else if (method == 'LMEV') { + # linear model error-variance + exp_ens_var <- exp_ens_sd^2 + coeff <- sapply(ind, function(x) {LMEV(index_exp[-x,], index_obs[-x])}) + pdfbc_mean <- exp_ens_mean - (unlist(coeff['coeff1',]) + + unlist(coeff['coeff2',])*exp_ens_var) + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x])})) + } else { + stop("Error: Parameter 'method' is wrong") + } + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + + + +# Auxiliar function to compute the mean squared error between 'exp' and 'obs' +Sigma_notBC <- function(exp, obs) { + return(sqrt(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$MSE)) +} + +# Auxiliar function to compute the bias between 'exp' and 'obs' +MEBC <- function(exp, obs) { + return(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$ME) +} + +# Auxiliar function to compute the standard deviation of errors +# between 'obs' and 'exp' +Sigma_BC <- function(exp, obs, na.rm = FALSE) { + return(sd(obs - exp, na.rm = na.rm)) +} + +# Auxiliar function to compute the linear model used in the method +# called 'LMEV' to correct the bias +LMEV <- function(exp, obs, na.rm = FALSE) { + if (any(names(dim(exp)) == 'member')) { + exp_ens_mean <- Apply(list(exp), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + exp_ens_sd <- Apply(list(exp), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else { + pos <- match(c('time','statistic'), names(dim(exp))) + exp <- aperm(exp,pos) + exp_ens_mean <- exp[,1] + exp_ens_sd <- exp[,2] + } + error <- exp_ens_mean - obs + exp_ens_var <- exp_ens_sd^2 + statModel <- lm(error ~ exp_ens_var) + return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) +} diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R new file mode 100644 index 00000000..947bd297 --- /dev/null +++ b/R/CST_ProbBestIndexWeighting.R @@ -0,0 +1,278 @@ +#' @rdname CST_ProbBestIndexWeighting +#' @title Computing the weighted tercile probabilities for SFSs of a CSTools +#' object. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the tercile +#' probabilities for a weighted variable from SFSs through Best estimation of +#' one index or driver of climate variability (as e.g., the North Atlantic +#' Oscillation (NAO)) and accepts a CSTools object (an object of the class +#' 's2dv_cube') as input. +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param var_exp An object of the class 's2dv_cube' containing the variable +#' (e.g. precipitation, temperature, NAO index) array. +#' The var_exp object is expected to have an element named \code{$data} with +#' at least two dimensions (time, member). +#' If the variable is spatial the dimensions would be (time, member, lat, lon). +#' If we want to compute for several SFSs, the array could have another +#' dimension (time, dataset, member, lat, lon). +#' @param weights_index_exp An object of the class 's2dv_cube' containing +#' the normalized weights array. +#' The weights_index_exp object is expected to have an element named +#' \code{$data} with at least two dimensions (time, member). +#' If we want to compute for several SFSs, the array could have another +#' dimension (time, dataset, member). +#' @return CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the +#' class 's2dv_cube'). +#' This object has at least an element named \code{$data} +#' with four or six dimensions depending if the variable is zonal +#' (as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable +#' (as e.g. precipitation or temperature) +#' (time, tercil, dataset, season, lat, lon), +#' containing the terciles probabilities computing with weighted members. +#' The firt tercil is the lower tercile, the second is the normal tercile and +#' the third is the upper tercile. +#' @examples +#' var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) +#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) +#' weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) +#' dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) +#' var_exp <- list(data = var_exp) +#' weights_index_exp <- list(data = weights_index_exp) +#' class(var_exp) <- 's2dv_cube' +#' class(weights_index_exp) <- 's2dv_cube' +#' res_CST <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) +#' dim(res_CST$data) +#' # time tercil dataset season lat lon +#' # 4 3 2 2 3 2 +CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { + if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { + stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + } + if (!any(names(dim(var_exp$data)) == 'time')){ + stop("The component 'data' of the parameter 'var_exp' must have the ", + "dimension 'time'") + } + if (!any(names(dim(weights_index_exp$data)) == 'time')){ + stop("The component 'data' of the parameter 'weights_index_exp' must have ", + "the dimension 'time'") + } + if (!any(names(dim(var_exp$data)) == 'member')){ + stop("The component 'data' of the parameter 'var_exp' must have the ", + "dimension 'member'") + } + if (!any(names(dim(weights_index_exp$data)) == 'member')){ + stop("The component 'data' of the parameter 'weights_index_exp' must have ", + "the dimension 'member'") + } + if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ + stop("The length of the dimension 'time' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'time' of the parameter 'weights_index_exp' in the component 'data'") + } + if (dim(var_exp$data)['member'] != dim(weights_index_exp$data)['member']){ + stop("The length of the dimension 'member' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'member' of the parameter 'weights_index_exp' in the component 'data'") + } + if (any(names(dim(weights_index_exp$data)) == 'lat') & + dim(weights_index_exp$data)['lat'] != 1) { + stop("The length of the dimension 'lat' of the parameter 'weights_index_exp'", + " in the component 'data' must be equal to 1") + } + if (any(names(dim(weights_index_exp$data)) == 'lon') & + dim(weights_index_exp$data)['lon'] != 1) { + stop("The length of the dimension 'lon' of the parameter 'weights_index_exp'", + " in the component 'data' must be equal to 1") + } + if (any(names(dim(weights_index_exp$data)) == 'lon')) { + Dim <- dim(weights_index_exp$data) + dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] + } + if (any(names(dim(weights_index_exp$data)) == 'lat')) { + Dim <- dim(weights_index_exp$data) + dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lat')] + } + if (any(names(dim(weights_index_exp$data)) == 'lon')) { + Dim <- dim(weights_index_exp$data) + dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] + } + if (any(names(dim(var_exp$data)) == 'dataset') & + !any(names(dim(weights_index_exp$data)) == 'dataset')) { + stop("The component 'data' of the parameter 'weights_index_exp' must have'", + " the dimension 'dataset'") + } + if (!any(names(dim(var_exp$data)) == 'dataset') & + any(names(dim(weights_index_exp$data)) == 'dataset')) { + stop("The component 'data' of the parameter 'var_exp' must have'", + " the dimension 'dataset'") + } + if (any(names(dim(var_exp$data)) == 'dataset') & + any(names(dim(weights_index_exp$data)) == 'dataset') & + dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']) { + stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") + } + + prob <- ProbBestIndexWeighting(var_exp$data, weights_index_exp$data) + prob_var_exp <- list() + prob_var_exp$data <- prob + prob_var_exp$Datasets <- var_exp$Datasets + prob_var_exp$source_files <- var_exp$source_files + prob_var_exp$Dates <- var_exp$Dates + return(prob_var_exp) +} + +#' @rdname ProbBestIndexWeighting +#' @title Computing the weighted tercile probabilities for SFSs. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the tercile +#' probabilities for a weighted variable from SFSs through Best estimation of +#' one index or driver of climate variability (as e.g., the North Atlantic +#' Oscillation (NAO)). +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with at least two dimensions (time, member). +#' If the variable is spatial the dimensions would be (time, member, lat, lon). +#' If we want to compute for several SFSs, the array could have another +#' dimension (time, dataset, member, lat, lon). +#' @param weights_index_exp Normalized weights array with at least two +#' dimensions (time, member). If we want to compute for several SFSs, +#' the array could have another dimension (time, dataset, member). +#' @return ProbBestIndexWeighting() returns an array with three or five dimensions +#' depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) +#' or it is spatial variable (as e.g. precipitation or temperature) +#' (time, tercil, dataset, lat, lon), containing the terciles probabilities +#' computing with weighted members. +#' The firt tercil is the lower tercile, the second is the normal tercile and +#' the third is the upper tercile. +#' @import multiApply +#' @export +#' @examples +#' # Example 1 for the ProbBestIndexWeighting function +#' var_exp <- 1 : (2 * 3 * 4) +#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4) +#' weights_index_exp<- runif(24, min=0.001, max=0.999) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) +#' dim(res) +#' # time tercil dataset +#' # 2 3 3 +#' str(res) +#' # num [1:2, 1:3, 1:3] 0.6773 0 0 0.0413 1.8419 ... +#' # Example 2 for the ProbBestIndexWeighting function +#' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) +#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) +#' weights_index_exp<- runif(24, min=0.001, max=0.999) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) +#' dim(res) +#' # time tercil dataset lat lon +#' # 2 3 3 2 3 +#' str(res) +#' # num [1:2, 1:3, 1:3, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... +#' +ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { + return(Apply(list(var_exp, weights_index_exp), + target_dims = list(c('time','member'), c('time','member')), + fun = .ProbBestIndexWeighting)$output1) +} + +#' Atomic ProbBestIndexWeighting +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with dimensions (time, member). +#' @param weights_index_exp Normalized weights array with dimensions +#' (time, member). +#' @return .ProbBestIndexWeighting returns an array of with dimensions +#' (time, tercil = 3), the terciles probabilities computing with weighted +#' members. +#' The firt tercil is the lower tercile, the second is the normal tercile and +#' the third is the upper tercile. +#' @examples +#' # Example for the Atomic EMBestIndexWeighting function +#' var_exp <- 1 : 8 +#' dim(var_exp) <- c(time = 2, member = 4) +#' weights_index_exp <- c(0.28, 0.15, 0.69, 0.64, 0.42, 0.17, 0.14, 0.22) +#' dim(weights_index_exp) <- c(time = 2, member = 4) +#' res <- .ProbBestIndexWeighting(var_exp, weights_index_exp) +#' str(res) +#' # num [1:2, 1:3] 0.28 0.15 0.69 0 0.56 1.03 +#' dim(res) +#' # time tercil +#' # 2 3 +#' @noRd +.ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { + # computing terciles + terciles_exp <- WeightTerciles(var_exp, weights_index_exp) + lowerTercile <- terciles_exp$lowerTercile + upperTercile <- terciles_exp$upperTercile + + # Probabilities + aTerciles <- Apply(list(var_exp), target_dims = list('member'), + fun = Data2Tercil, lowerTercile, upperTercile)$output1 + + pos <- match(names(dim(aTerciles)), c('time','member')) + aTerciles <- aperm(aTerciles,pos) + + pos <- match(names(dim(weights_index_exp)), c('time','member')) + weights_index_exp <- aperm(weights_index_exp, pos) + + probTercile <- array(NA, dim = c(dim(var_exp)['time'], tercil = 3)) + for (idTercil in 1:3){ + probTercile[,idTercil] <- Apply(list(aTerciles, weights_index_exp), + target_dims = list('member','member'), + fun = WeightTercil2Prob, idTercil)$output1 + } + return(probTercile) +} + +# Auxiliar function to compute in which tercile is a data value +Data2Tercil <- function(x,lt,ut) { + y <- x + y[x <= lt] <- 1 + y[x >= ut] <-3 + y[(x > lt) & (x < ut)] <- 2 + return (y) +} + +# Auxiliar function to convers weighted terciles to probabilities +WeightTercil2Prob <- function(aTerciles, aWeights, idTercil) { + return(sum(aWeights[which(aTerciles == idTercil)])) +} + +# Auxiliar function to compute weighted terciles +WeightTerciles <- function(data, weights) { + vectorData <- as.vector(data) + vectorWeights <- as.vector(weights/dim(weights)['time']) # normalized + lSortData <- sort(vectorData,index.return=TRUE) + indSort <- lSortData$ix # index asociated to weight + # corresponding for this data + dataSort <- lSortData$x + # Adding normalized weights. When 1/3 is reached, the data value + # is lower tercile and when 2/3 is reached, it is the upper tercile. + sumWeights <- 0 + ilowerTercile <- 0 + while ((sumWeights < 1/3) & (ilowerTercile < (dim(weights)['member'] * + dim(weights)['time']))){ + ilowerTercile<- ilowerTercile +1 + sumWeights <- sumWeights + vectorWeights[indSort[ilowerTercile]] + } + lowerTercile <- (dataSort[ilowerTercile]+ + dataSort[ilowerTercile-1])/2 + sumWeights <- 0 + iupperTercile <- 0 + while ((sumWeights < 2/3) & (iupperTercile < (dim(weights)['member'] * + dim(weights)['time']))){ + iupperTercile<- iupperTercile +1 + sumWeights <- sumWeights + vectorWeights[indSort[iupperTercile]] + } + upperTercile <- (dataSort[iupperTercile]+ + dataSort[iupperTercile-1])/2 + return(list(lowerTercile = lowerTercile, upperTercile = upperTercile)) +} + diff --git a/R/CST_WeightIndex.R b/R/CST_WeightIndex.R new file mode 100644 index 00000000..6d4daadb --- /dev/null +++ b/R/CST_WeightIndex.R @@ -0,0 +1,193 @@ +#' @rdname CST_WeightIndex +#' @title Computing the weights for SFSs using the Best Index PDFs +#' of a CSTools object +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the +#' normalized weights for SFS members using the Best Index estimation and +#' accepts a CSTools object (an object of the class 's2dv_cube') as input. +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param index_exp An object of the class 's2dv_cube' containing the indices +#' or drivers of climate variability (as e.g., the North Atlantic Oscillation +#' (NAO)) estimate from SFSs. +#' The index_exp object is expected to have an element named \code{$data} +#' with dimensions (time, dataset, member, season). +#' @param pdf_exp An object of the class 's2dv_cube' containing the statistics +#' which define a Gaussian PDFs for weighting the values estimate for the SFS +#' ensemble members. +#' The pdf_exp object is expected to have an element named \code{$data} +#' with at least three dimensions (time, statistic = 2, season). +#' It could have one or two dimension (dataset and/or member) equal to 1. +#' The firt statistic is the parameter 'mean' of the PDFs and +#' the second statistic is the parameter 'standard deviation' of the PDFs. +#' @return CST_WeightIndex() returns a CSTools object (i.e., of the +#' class 's2dv_cube'). +#' This object has at least an element named \code{$data} +#' with dimensions (time, dataset, member, season). +#' @examples +#' index_obs <- 1 : (3 * 5 * 4) +# 'dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) +#' index_exp <- 1 : (3 * 5 * 4 * 7) +#' dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) +#' index_exp <- list(data = index_exp) +#' index_obs <- list(data = index_obs) +#' class(index_exp) <- 's2dv_cube' +#' class(index_obs) <- 's2dv_cube' +#' res_CST <- CST_PDFIndexHind(index_exp, index_obs) +#' dim(res_CST$data) +#' #time dataset statistic season +#' # 5 3 4 4 +CST_WeightIndex <- function(index_exp, pdf_exp) { + if (!inherits(index_exp, 's2dv_cube') || !inherits(pdf_exp, 's2dv_cube')) { + stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + } + if (!any(names(dim(pdf_exp$data)) == 'statistic')){ + stop("The component 'data' of the parameter 'pdf_exp' must be the ", + "dimension 'statistic'") + } + if (dim(pdf_exp$data)['statistic'] != 2) { + stop("The length of the dimension 'statistic' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 2.") + } + if (any(names(dim(pdf_exp$data)) == 'member') & + dim(pdf_exp$data)['member'] != 1) { + stop("The length of the dimension 'member' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 1.") + } + if (any(names(dim(pdf_exp$data)) == 'dataset') & + dim(pdf_exp$data)['dataset'] != 1) { + stop("The length of the dimension 'dataset' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 1.") + } + if (!any(names(dim(index_exp$data)) == 'time')){ + stop("The component 'data' of the parameter 'index_exp' must be the ", + "dimension 'time'") + } + if (!any(names(dim(pdf_exp$data)) == 'time')){ + stop("The component 'data' of the parameter 'pdf_exp' must be the ", + "dimension 'time'") + } + if (dim(index_exp$data)['time'] != dim(pdf_exp$data)['time']){ + stop("The length of the dimension 'time' of the parameter 'index_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'time' of the parameter 'pdf_exp' in the component 'data'") + } + if (dim(index_exp$data)['time'] != dim(pdf_exp$data)['time']){ + stop("The length of the dimension 'time' of the parameter 'index_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'time' of the parameter 'pdf_exp' in the component 'data'") + } + if (dim(index_exp$data)['season'] != dim(pdf_exp$data)['season']){ + stop("The length of the dimension 'season' of the parameter 'index_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'season' of the parameter 'pdf_exp' in the component 'data'") + } + if (!any(names(dim(index_exp$data)) == 'member')){ + stop("The component 'data' of the parameter 'index_exp' must be the ", + "dimension 'member'") + } + if (any(names(dim(pdf_exp$data)) == 'member')) { + Dim <- dim(pdf_exp$data) + dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] + } + if (any(names(dim(pdf_exp$data)) == 'dataset')) { + Dim <- dim(pdf_exp$data) + dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'dataset')] + } + dimnames <- names(dim(index_exp$data)) + aWeigths <- WeightIndex(index_exp$data, pdf_exp$data) + pos <- match(dimnames, names(dim(aWeigths))) + aWeigths <- aperm(aWeigths, pos) + names(dim(aWeigths)) <- dimnames + weights_index_exp <- list() + weights_index_exp$data <- aWeigths + weights_index_exp$Datasets <- list(exp = index_exp$Datasets, + pdf = pdf_exp$Datasets) + weights_index_exp$source_files <- list(exp = index_exp$source_files, + pdf = pdf_exp$source_files) + weights_index_exp$Dates <- index_exp$Dates + class(weights_index_exp) <- 's2dv_cube' + return(weights_index_exp) +} + +#' @rdname WeightIndex +#' @title Computing the weights for SFSs using the Best Index PDFs. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the +#' normalized weights for each member of each SFS or dataset using the +#' Best Index estimation. +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' @param index_exp Index (e.g. NAO index) array, from a dataset of SFSs +#' for a period of years, with dimensions (time, dataset, member). +#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions +#' (time, statistic = 2) for a period of years. +#' The firt statistic is the parameter 'mean' of the PDF and +#' the second statistic is the parameter 'standard deviation' of the PDF. +#' @return WeightIndex() returns a normalized weights array with dimensions +#' (time, dataset, member). +#' @import multiApply +#' @export +#' @examples +#' # Example for the WeightIndex function +#' index_exp <- 1 : (10 * 3 * 5) +#' dim(index_exp) <- c(time = 10, dataset = 3, member = 5) +#' pdf_exp <- 1 : (10 * 2) +#' dim(pdf_exp) <- c(time = 10, statistic = 2) +#' res <- WeightIndex(index_exp, pdf_exp) +#' dim(res) +#' # time dataset member +#' # 10 3 5 +#' str(res) +#' # num [1:10, 1:3, 1:5] 0.976 0.958 0.935 0.908 0.881 ... +#' +WeightIndex <- function(index_exp, pdf_exp) { + aweights <- Apply(list(index_exp, pdf_exp), + target_dims = list('member', 'statistic'), + fun = .WeightIndex) + aweights <- aweights$output1 + dimnames <- names(dim(index_exp)) + pos <- match(dimnames, names(dim(aweights))) + aweights <- aperm(aweights, pos) + return(aweights) + +} + +#' Atomic WeightIndex +#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions +#' (member) +#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions +#' (statistic = 2). +#' The firt statistic is the parameter 'mean' of the PDF and +#' the second statistic is the parameter 'standard deviation' of the PDF. +#' @return .WeightIndex returns an array of with dimensions (member), +#' the normalized weights for each member of a SFS using a Best NAO PDF. +#' @examples +#' # Example for the Atomic WeightIndex function +#' index_exp <- c(1.3,3,-1) +#' dim(index_exp) <- c(member = 3) +#' pdf_exp <- c(1.5,0.8) +#' dim(pdf_exp) <- c(statistic = 2) +#' res <- .WeightIndex(index_exp, pdf_exp) +#' str(res) +#' # num [1:3(1d)] 0.84338 0.15003 0.00659 +#' dim(res) +#' # member +#' # 3 +#' @noRd +.WeightIndex <- function(index_exp, pdf_exp) { + aweights <- apply(index_exp, 1, dnorm, mean = pdf_exp[1], sd = pdf_exp[2]) + dim(aweights) <- dim(index_exp) + sumWeights <- sum(aweights) + aweightsNorm <- apply(aweights, 1, NormWeight, sumWeights) + dim(aweightsNorm) <- dim(index_exp) + return(aweightsNorm) +} + +# Auxiliar function to normalize a weight value +NormWeight <- function(weight, sumWeights) { + return(weight/sumWeights) +} diff --git a/man/CST_EMBestIndexWeighting.Rd b/man/CST_EMBestIndexWeighting.Rd new file mode 100644 index 00000000..9304d6aa --- /dev/null +++ b/man/CST_EMBestIndexWeighting.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_EMBestIndexWeighting.R +\name{CST_EMBestIndexWeighting} +\alias{CST_EMBestIndexWeighting} +\title{Computing the weighted ensemble means for SFSs of a CSTools object.} +\usage{ +CST_EMBestIndexWeighting(var_exp, weights_index_exp) +} +\arguments{ +\item{var_exp}{An object of the class 's2dv_cube' containing the variable +(e.g. precipitation, temperature, NAO index) array. +The var_exp object is expected to have an element named \code{$data} with +at least two dimensions (time, member). +If the variable is spatial the dimensions would be (time, member, lat, lon). +If we want to compute for several SFSs, the array could have another +dimension (time, dataset, member, lat, lon).} + +\item{weights_index_exp}{An object of the class 's2dv_cube' containing +the normalized weights array. +The weights_index_exp object is expected to have an element named +\code{$data} with at least two dimensions (time, member). +If we want to compute for several SFSs, the array could have another +dimension (time, dataset, member).} +} +\value{ +CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the +class 's2dv_cube'). +This object has at least an element named \code{$data} +with three or five dimensions depending if the variable is zonal +(as e.g. NAO index)(time, dataset, season) or it is spatial variable +(as e.g. precipitation or temperature) (time, dataset, season, lat, lon), +containing the ensemble means computing with weighted members. +} +\description{ +This function implements the computation to obtain the weighted +ensemble means for SFSs through Best estimation of one index or driver of +climate variability (as e.g., the North Atlantic Oscillation (NAO)) and +accepts a CSTools object (an object of the class 's2dv_cube') as input. +} +\examples{ +var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) +dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) +weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) +dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) +var_exp <- list(data = var_exp) +weights_index_exp <- list(data = weights_index_exp) +class(var_exp) <- 's2dv_cube' +class(weights_index_exp) <- 's2dv_cube' +res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) +dim(res_CST$data) +# time dataset season lat lon +# 4 2 2 3 2 +} +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/CST_PDFBest.Rd b/man/CST_PDFBest.Rd new file mode 100644 index 00000000..b5df5dc2 --- /dev/null +++ b/man/CST_PDFBest.Rd @@ -0,0 +1,68 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_PDFBest.R +\name{CST_PDFBest} +\alias{CST_PDFBest} +\title{Computing the Best Index PDFs combining Index PDFs from two SFSs +of a CSTools object.} +\usage{ +CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) +} +\arguments{ +\item{pdf_exp}{An object of the class 's2dv_cube' +containing the parameters of the Index PDFs from two SFSs for a period +to combine and obtain the Best estimation. +The index_hind object is expected to have an element named \code{$data} +with dimensions (time, dataset = 2, member = 1, statistic = 4, season).} + +\item{bc_dataset1}{Logical (default = TRUE). +If TRUE the Index PDFs for the first SFS or dataset has been computed +with bias corrected.} + +\item{bc_dataset2}{Logical (default = TRUE). +If TRUE the Index PDFs for the second SFS or dataset has been computed +with bias corrected.} +} +\value{ +CST_PDFBest() returns a CSTools object (i.e., of the +class 's2dv_cube'). +This object has at least two elements: i) an element named \code{$data} +with dimensions (time, statistic = 2, season) and ii) an element +named \code{$statistic} with the names of 'statistic' dimension +of the first element ("mean", "sigma"). +The firt statistic is the parameter 'mean' of the PDF +The second statistic is the parameter 'standard deviation' of the PDF +} +\description{ +This function implements the computation to obtain the index PDFs +(e.g. NAO index) to improve the index estimate from the forecast of SFSs +and accepts a CSTools object (an object of the class 's2dv_cube' as input. +} +\examples{ +pdf_exp <- c(1: (10 * 2* 4 * 1)) +dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, season = 1) +pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), + season = c('DJF'), Dates = list(start = 1997:2006)) +class(pdf_exp) <- 's2dv_cube' +res_CST <- CST_PDFBest(pdf_exp) +str(res_CST) +List of 5 +# $ data : num [1:10, 1:2, 1] 45.2 46.3 47.3 48.3 49.3 ... +# $ Datasets : chr [1:2] "ECMWF-S5" "UKMO-S13" +# $ season : chr "DJF" +# $ Dates :List of 1 +# ..$ start: int [1:10] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 +# $ statistic: chr [1:2] "mean" "sigma" +# - attr(*, "class")= chr "s2dv_cube" +dim(res_CST$data) +# time statistic season +# 10 2 1 +str(res_CST) +} +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/CST_PDFIndexFcst.Rd b/man/CST_PDFIndexFcst.Rd new file mode 100644 index 00000000..1067ad2b --- /dev/null +++ b/man/CST_PDFIndexFcst.Rd @@ -0,0 +1,84 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_PDFIndexFcst.R +\name{CST_PDFIndexFcst} +\alias{CST_PDFIndexFcst} +\title{Computing the Index PDFs for a forecast year of a SFSs dataset +of a CSTools object.} +\usage{ +CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = "ME", + na.rm = FALSE) +} +\arguments{ +\item{index_hind}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate for a hindcast period +from SFSs for improving. +The index_hind object is expected to have an element named \code{$data} +with dimensions (time, dataset, member, season).} + +\item{index_obs}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate from observations or +reanalysis. +The index_obs object is expected to have an element named \code{$data} +with at least three dimensions (time, dataset, season). +It could have one more dimension (member) equal to 1.} + +\item{index_fcst}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate for a forecast year +from SFSs for improving. +The index_fcst object is expected to have an element named \code{$data} +with dimensions (time = 1, dataset, member, season).} + +\item{method}{A character string indicating which methodology is applied +to compute the PDFs. One of "ME" (default) or "LMEV".} + +\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} +} +\value{ +CST_PDFIndexFcst() returns a CSTools object (i.e., of the +class 's2dv_cube'). +This object has at least two elements: i) an element named \code{$data} +with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element +named \code{$statistic} with the names of 'statistic' dimension +of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +The firt statistic is the parameter 'mean' of the PDF with not bias corrected +The second statistic is the parameter 'standard deviation' of the PDF +with not bias corrected +The third statistic is the parameter 'mean' of the PDF with bias corrected +The fourth statistic is the parameter 'standard deviation' of the PDF +with bias corrected +} +\description{ +This function implements the computation to obtain the index PDFs +(e.g. NAO index) to improve the index estimate from the forecast of SFSs +and accepts a CSTools object (an object of the class 's2dv_cube') as input. +} +\examples{ +index_hind <- 1 : (3 * 5 * 4 * 7) +dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) +index_hind <- list(data = index_hind) +class(index_hind) <- 's2dv_cube' +index_obs <- 1 : (3 * 5 * 4) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) +index_obs <- list(data = index_obs) +class(index_obs) <- 's2dv_cube' +index_fcst <- 1 : (3 * 4 * 7) +dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) +index_fcst <- list(data = index_fcst) +class(index_fcst) <- 's2dv_cube' +res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') +str(res_CST) +dim(res_CST$data) +#time dataset statistic season +# 1 3 4 4 +} +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/CST_PDFIndexHind.Rd b/man/CST_PDFIndexHind.Rd new file mode 100644 index 00000000..be0ca2fe --- /dev/null +++ b/man/CST_PDFIndexHind.Rd @@ -0,0 +1,69 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_PDFIndexHind.R +\name{CST_PDFIndexHind} +\alias{CST_PDFIndexHind} +\title{Computing the Index PDFs for the hindcast of a SFSs dataset +of a CSTools object} +\usage{ +CST_PDFIndexHind(index_exp, index_obs, method = "ME", na.rm = FALSE) +} +\arguments{ +\item{index_exp}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. +The index_exp object is expected to have an element named \code{$data} +with dimensions (time, dataset, member, season).} + +\item{index_obs}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate from observations or +reanalysis. +The index_obs object is expected to have an element named \code{$data} +with at least three dimensions (time, dataset, season). +It could have one more dimension (member) equal to 1.} + +\item{method}{A character string indicating which methodology is applied +to compute the PDFs. One of "ME" (default) or "LMEV".} + +\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} +} +\value{ +CST_PDFIndexHind() returns a CSTools object (i.e., of the +class 's2dv_cube'). +This object has at least two elements: i) an element named \code{$data} +with dimensions (time, dataset, statistic = 4, season) and ii) an element +named \code{$statistics} with the names of 'statistic' dimension +of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +The firt statistic is the parameter 'mean' of the PDF with not bias corrected +The second statistic is the parameter 'standard deviation' of the PDF +with not bias corrected +The third statistic is the parameter 'mean' of the PDF with bias corrected +The fourth statistic is the parameter 'standard deviation' of the PDF +with bias corrected +} +\description{ +This function implements the computation to obtain the index PDFs +(e.g. NAO index) to improve the index estimate from the hindcast of SFSs +and accepts a CSTools object (an object of the class 's2dv_cube') as input. +} +\examples{ +index_obs <- 1 : (3 * 5 * 4) +index_exp <- 1 : (3 * 5 * 4 * 7) +dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) +index_exp <- list(data = index_exp) +index_obs <- list(data = index_obs) +class(index_exp) <- 's2dv_cube' +class(index_obs) <- 's2dv_cube' +res_CST <- CST_PDFIndexHind(index_exp, index_obs) +dim(res_CST$data) +#time dataset statistic season +# 5 3 4 4 +} +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/CST_ProbBestIndexWeighting.Rd b/man/CST_ProbBestIndexWeighting.Rd new file mode 100644 index 00000000..a0e78c4e --- /dev/null +++ b/man/CST_ProbBestIndexWeighting.Rd @@ -0,0 +1,66 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_ProbBestIndexWeighting.R +\name{CST_ProbBestIndexWeighting} +\alias{CST_ProbBestIndexWeighting} +\title{Computing the weighted tercile probabilities for SFSs of a CSTools +object.} +\usage{ +CST_ProbBestIndexWeighting(var_exp, weights_index_exp) +} +\arguments{ +\item{var_exp}{An object of the class 's2dv_cube' containing the variable +(e.g. precipitation, temperature, NAO index) array. +The var_exp object is expected to have an element named \code{$data} with +at least two dimensions (time, member). +If the variable is spatial the dimensions would be (time, member, lat, lon). +If we want to compute for several SFSs, the array could have another +dimension (time, dataset, member, lat, lon).} + +\item{weights_index_exp}{An object of the class 's2dv_cube' containing +the normalized weights array. +The weights_index_exp object is expected to have an element named +\code{$data} with at least two dimensions (time, member). +If we want to compute for several SFSs, the array could have another +dimension (time, dataset, member).} +} +\value{ +CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the +class 's2dv_cube'). +This object has at least an element named \code{$data} +with four or six dimensions depending if the variable is zonal +(as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable +(as e.g. precipitation or temperature) +(time, tercil, dataset, season, lat, lon), +containing the terciles probabilities computing with weighted members. +The firt tercil is the lower tercile, the second is the normal tercile and +the third is the upper tercile. +} +\description{ +This function implements the computation to obtain the tercile +probabilities for a weighted variable from SFSs through Best estimation of +one index or driver of climate variability (as e.g., the North Atlantic +Oscillation (NAO)) and accepts a CSTools object (an object of the class +'s2dv_cube') as input. +} +\examples{ +var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) +dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) +weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) +dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) +var_exp <- list(data = var_exp) +weights_index_exp <- list(data = weights_index_exp) +class(var_exp) <- 's2dv_cube' +class(weights_index_exp) <- 's2dv_cube' +res_CST <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) +dim(res_CST$data) +# time tercil dataset season lat lon +# 4 3 2 2 3 2 +} +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/CST_WeightIndex.Rd b/man/CST_WeightIndex.Rd new file mode 100644 index 00000000..da76b8d9 --- /dev/null +++ b/man/CST_WeightIndex.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_WeightIndex.R +\name{CST_WeightIndex} +\alias{CST_WeightIndex} +\title{Computing the weights for SFSs using the Best Index PDFs +of a CSTools object} +\usage{ +CST_WeightIndex(index_exp, pdf_exp) +} +\arguments{ +\item{index_exp}{An object of the class 's2dv_cube' containing the indices +or drivers of climate variability (as e.g., the North Atlantic Oscillation +(NAO)) estimate from SFSs. +The index_exp object is expected to have an element named \code{$data} +with dimensions (time, dataset, member, season).} + +\item{pdf_exp}{An object of the class 's2dv_cube' containing the statistics +which define a Gaussian PDFs for weighting the values estimate for the SFS +ensemble members. +The pdf_exp object is expected to have an element named \code{$data} +with at least three dimensions (time, statistic = 2, season). +It could have one or two dimension (dataset and/or member) equal to 1. +The firt statistic is the parameter 'mean' of the PDFs and +the second statistic is the parameter 'standard deviation' of the PDFs.} +} +\value{ +CST_WeightIndex() returns a CSTools object (i.e., of the +class 's2dv_cube'). +This object has at least an element named \code{$data} +with dimensions (time, dataset, member, season). +} +\description{ +This function implements the computation to obtain the +normalized weights for SFS members using the Best Index estimation and +accepts a CSTools object (an object of the class 's2dv_cube') as input. +} +\examples{ +index_obs <- 1 : (3 * 5 * 4) +index_exp <- 1 : (3 * 5 * 4 * 7) +dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) +index_exp <- list(data = index_exp) +index_obs <- list(data = index_obs) +class(index_exp) <- 's2dv_cube' +class(index_obs) <- 's2dv_cube' +res_CST <- CST_PDFIndexHind(index_exp, index_obs) +dim(res_CST$data) +#time dataset statistic season +# 5 3 4 4 +} +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/EMBestIndexWeighting.Rd b/man/EMBestIndexWeighting.Rd new file mode 100644 index 00000000..253517b0 --- /dev/null +++ b/man/EMBestIndexWeighting.Rd @@ -0,0 +1,64 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_EMBestIndexWeighting.R +\name{EMBestIndexWeighting} +\alias{EMBestIndexWeighting} +\title{Computing the weighted ensemble means for SFSs.} +\usage{ +EMBestIndexWeighting(var_exp, weights_index_exp) +} +\arguments{ +\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) +array from a SFS with at least two dimensions (time, member). +If the variable is spatial the dimensions would be (time, member, lat, lon). +If we want to compute for several SFSs, the array could have another +dimension (time, dataset, member, lat, lon).} + +\item{weights_index_exp}{Normalized weights array with at least two +dimensions (time, member). If we want to compute for several SFSs, +the array could have another dimension (time, dataset, member).} +} +\value{ +EMBestIndexWeighting() returns an array with two or four dimensions +depending if the variable is zonal (as e.g. NAO index)(time, dataset) +or it is spatial variable (as e.g. precipitation or temperature) +(time, dataset, lat, lon), containing the ensemble means computing +with weighted members. +} +\description{ +This function implements the computation to obtain the weighted +ensemble means for SFSs through Best estimation of one index or driver of +climate variability (as e.g., the North Atlantic Oscillation (NAO)). +} +\examples{ +# Example 1 for the EMBestIndexWeighting function +var_exp <- 1 : (2 * 3 * 4) +dim(var_exp) <- c(time = 2, dataset = 3, member = 4) +weights_index_exp<- runif(24, min=0.001, max=0.999) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +res <- EMBestIndexWeighting(var_exp, weights_index_exp) +dim(res) +# time dataset +# 2 3 +str(res) +# num [1:2, 1:3] 36.2 28.8 38.3 31.9 31 ... +# Example 2 for the EMBestIndexWeighting function +var_exp <- 1 : (2 * 3 * 4 * 2 * 3) +dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) +weights_index_exp<- runif(24, min=0.001, max=0.999) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +res <- EMBestIndexWeighting(var_exp, weights_index_exp) +dim(res) +# time dataset lat lon +# 2 3 2 3 +str(res) +# num [1:2, 1:3, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... + +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/PDFBest.Rd b/man/PDFBest.Rd new file mode 100644 index 00000000..a46b0c8a --- /dev/null +++ b/man/PDFBest.Rd @@ -0,0 +1,53 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_PDFBest.R +\name{PDFBest} +\alias{PDFBest} +\title{Computing the Best Index PDFs combining two SFSs Index PDFs.} +\usage{ +PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) +} +\arguments{ +\item{pdf_exp}{Statistics array for the SFSs Index PDFs for a period of years +with dimensions (time, dataset = 2, statistic = 4, season).} + +\item{bc_dataset1}{Logical (default = TRUE). +If TRUE the Index PDFs for the first SFS or dataset has been computed +with bias corrected.} + +\item{bc_dataset2}{Logical (default = TRUE). +If TRUE the Index PDFs for the second SFS or dataset has been computed +with bias corrected.} +} +\value{ +PDFBest() returns an array +with dimensions (time, statistic = 2, season). +The firt statistic is the parameter 'mean' of the PDF for the best estimation +combining the two SFSs PDFs. +The second statistic is the parameter 'standard deviation' of the PDF for +the best estimation combining the two SFSs PDFs. +} +\description{ +This function implements the computation to obtain the index PDFs +(e.g. NAO index) obtained to combining the Index PDFs for two SFSs, +the Best Index estimation. +} +\examples{ +# Example for the PDFBest function +pdf_exp <- c(1: (10 * 2* 4 * 1)) +dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) +res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +dim(res) +# time statistic season +# 10 2 1 +str(res) +# num [1:10, 1:2, 1] 17.2 18.3 19.5 20.6 21.7 ... + +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/PDFIndexFcst.Rd b/man/PDFIndexFcst.Rd new file mode 100644 index 00000000..2926709e --- /dev/null +++ b/man/PDFIndexFcst.Rd @@ -0,0 +1,62 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_PDFIndexFcst.R +\name{PDFIndexFcst} +\alias{PDFIndexFcst} +\title{Computing the Index PDFs for a dataset of SFSs for a forecast year.} +\usage{ +PDFIndexFcst(index_hind, index_obs, index_fcst, method = "ME", + na.rm = FALSE) +} +\arguments{ +\item{index_hind}{Index (e.g. NAO index) array from SFSs with dimensions +(time, dataset, member) for a hindcast period.} + +\item{index_obs}{Index (e.g. NAO index) array from an observational database +or reanalysis with dimension (time, dataset, member = 1) or (time, dataset).} + +\item{index_fcst}{Index (e.g. NAO index) array from SFSs. +with dimensions (time = 1, dataset, member) for a forecast year.} + +\item{method}{A character string indicating which methodology is applied +to compute the PDF. One of "ME" (default) or "LMEV".} + +\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} +} +\value{ +PDFIndexFcst() returns an array +with dimensions (time = 1, statistic = 4, dataset) for the forecast year. +The firt statistic is the parameter 'mean' of the PDF with not bias corrected +The second statistic is the parameter 'standard deviation' of the PDF +with not bias corrected +The third statistic is the parameter 'mean' of the PDF with bias corrected +The fourth statistic is the parameter 'standard deviation' of the PDF +with bias corrected +} +\description{ +This function implements the computation to obtain the index PDFs +(e.g. NAO index) to improve the index estimate from a dataset of SFSs +} +\examples{ +# Example for the PDFIndexFcst function +index_fcst <- 1 : (3 * 4) +dim(index_fcst) <- c(time = 1, dataset = 3, member = 4) +index_obs<- 1 : (5 * 3) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1) +index_hind <- 1 : (5 * 3 * 4) +dim(index_hind) <- c(time = 5, dataset = 3, member = 4) +res <- PDFIndexFcst(index_hind, index_obs, index_fcst) +dim(res) +# time statistic dataset +# 1 4 3 +str(res) +# num [1, 1:4, 1:3] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... + +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/PDFIndexHind.Rd b/man/PDFIndexHind.Rd new file mode 100644 index 00000000..0836ace4 --- /dev/null +++ b/man/PDFIndexHind.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_PDFIndexHind.R +\name{PDFIndexHind} +\alias{PDFIndexHind} +\title{Computing the Index PDFs for a dataset of SFSs for a hindcats period.} +\usage{ +PDFIndexHind(index_exp, index_obs, method = "ME", na.rm = FALSE) +} +\arguments{ +\item{index_exp}{Index (e.g. NAO index) array from SFSs +with dimensions (time, dataset, member).} + +\item{index_obs}{Index (e.g. NAO index) array from an observational database +or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset).} + +\item{method}{A character string indicating which methodology is applied +to compute the PDFs. One of "ME" (default) or "LMEV".} + +\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} +} +\value{ +PDFIndexHind() returns an array +with dimensions (time, statistic = 4, dataset). +The firt statistic is the parameter 'mean' of the PDF with not bias corrected +The second statistic is the parameter 'standard deviation' of the PDF +with not bias corrected +The third statistic is the parameter 'mean' of the PDF with bias corrected +The fourth statistic is the parameter 'standard deviation' of the PDF +with bias corrected +} +\description{ +This function implements the computation to obtain the index PDFs +(e.g. NAO index) to improve the index estimate from a dataset of SFSs +} +\examples{ +# Example for the PDFIndexHind function +index_obs <- 1 : (3 * 5) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1) +index_exp <- 1 : (3 * 5 * 4) +dim(index_exp) <- c(time = 5, dataset = 3, member = 4) +res <- PDFIndexHind(index_exp, index_obs) +str(res) +# num [1:5, 1:4, 1:3] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... +dim(res) +# time statistic dataset +# 5 4 3 + +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/ProbBestIndexWeighting.Rd b/man/ProbBestIndexWeighting.Rd new file mode 100644 index 00000000..12fb42e9 --- /dev/null +++ b/man/ProbBestIndexWeighting.Rd @@ -0,0 +1,67 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_ProbBestIndexWeighting.R +\name{ProbBestIndexWeighting} +\alias{ProbBestIndexWeighting} +\title{Computing the weighted tercile probabilities for SFSs.} +\usage{ +ProbBestIndexWeighting(var_exp, weights_index_exp) +} +\arguments{ +\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) +array from a SFS with at least two dimensions (time, member). +If the variable is spatial the dimensions would be (time, member, lat, lon). +If we want to compute for several SFSs, the array could have another +dimension (time, dataset, member, lat, lon).} + +\item{weights_index_exp}{Normalized weights array with at least two +dimensions (time, member). If we want to compute for several SFSs, +the array could have another dimension (time, dataset, member).} +} +\value{ +ProbBestIndexWeighting() returns an array with three or five dimensions +depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) +or it is spatial variable (as e.g. precipitation or temperature) +(time, tercil, dataset, lat, lon), containing the terciles probabilities +computing with weighted members. +The firt tercil is the lower tercile, the second is the normal tercile and +the third is the upper tercile. +} +\description{ +This function implements the computation to obtain the tercile +probabilities for a weighted variable from SFSs through Best estimation of +one index or driver of climate variability (as e.g., the North Atlantic +Oscillation (NAO)). +} +\examples{ +# Example 1 for the ProbBestIndexWeighting function +var_exp <- 1 : (2 * 3 * 4) +dim(var_exp) <- c(time = 2, dataset = 3, member = 4) +weights_index_exp<- runif(24, min=0.001, max=0.999) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +res <- ProbBestIndexWeighting(var_exp, weights_index_exp) +dim(res) +# time tercil dataset +# 2 3 3 +str(res) +# num [1:2, 1:3, 1:3] 0.6773 0 0 0.0413 1.8419 ... +# Example 2 for the ProbBestIndexWeighting function +var_exp <- 1 : (2 * 3 * 4 * 2 * 3) +dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) +weights_index_exp<- runif(24, min=0.001, max=0.999) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +res <- ProbBestIndexWeighting(var_exp, weights_index_exp) +dim(res) +# time tercil dataset lat lon +# 2 3 3 2 3 +str(res) +# num [1:2, 1:3, 1:3, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... + +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/WeightIndex.Rd b/man/WeightIndex.Rd new file mode 100644 index 00000000..90aed8d5 --- /dev/null +++ b/man/WeightIndex.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_WeightIndex.R +\name{WeightIndex} +\alias{WeightIndex} +\title{Computing the weights for SFSs using the Best Index PDFs.} +\usage{ +WeightIndex(index_exp, pdf_exp) +} +\arguments{ +\item{index_exp}{Index (e.g. NAO index) array, from a dataset of SFSs +for a period of years, with dimensions (time, dataset, member).} + +\item{pdf_exp}{Statistics array to define a Gaussian PDF with dimensions +(time, statistic = 2) for a period of years. +The firt statistic is the parameter 'mean' of the PDF and +the second statistic is the parameter 'standard deviation' of the PDF.} +} +\value{ +WeightIndex() returns a normalized weights array with dimensions +(time, dataset, member). +} +\description{ +This function implements the computation to obtain the +normalized weights for each member of each SFS or dataset using the +Best Index estimation. +} +\examples{ +# Example for the WeightIndex function +index_exp <- 1 : (10 * 3 * 5) +dim(index_exp) <- c(time = 10, dataset = 3, member = 5) +pdf_exp <- 1 : (10 * 2) +dim(pdf_exp) <- c(time = 10, statistic = 2) +res <- WeightIndex(index_exp, pdf_exp) +dim(res) +# time dataset member +# 10 3 5 +str(res) +# num [1:10, 1:3, 1:5] 0.976 0.958 0.935 0.908 0.881 ... + +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} -- GitLab From 04b629250bedd4260e68de1a8d98f46cce3c2087 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Wed, 28 Aug 2019 10:40:23 +0000 Subject: [PATCH 02/40] Update function files and add zzz.R file --- R/CST_EMBestIndexWeighting.R | 107 ++++++++++--------- R/CST_PDFBest.R | 95 +++++++++-------- R/CST_PDFIndexFcst.R | 172 ++++++++++++------------------ R/CST_PDFIndexHind.R | 153 ++++++++++---------------- R/CST_ProbBestIndexWeighting.R | 151 +++++++++++++------------- R/CST_WeightIndex.R | 99 ++++++++--------- R/zzz.R | 40 +++++++ man/CST_EMBestIndexWeighting.Rd | 40 +++---- man/CST_PDFBest.Rd | 30 +++--- man/CST_PDFIndexFcst.Rd | 52 ++++----- man/CST_PDFIndexHind.Rd | 42 ++++---- man/CST_ProbBestIndexWeighting.Rd | 48 ++++----- man/CST_WeightIndex.Rd | 34 +++--- man/EMBestIndexWeighting.Rd | 34 +++--- man/PDFBest.Rd | 26 ++--- man/PDFIndexFcst.Rd | 24 ++--- man/PDFIndexHind.Rd | 20 ++-- man/ProbBestIndexWeighting.Rd | 40 +++---- man/WeightIndex.Rd | 20 ++-- 19 files changed, 597 insertions(+), 630 deletions(-) diff --git a/R/CST_EMBestIndexWeighting.R b/R/CST_EMBestIndexWeighting.R index 40eef1bd..db7802d4 100644 --- a/R/CST_EMBestIndexWeighting.R +++ b/R/CST_EMBestIndexWeighting.R @@ -1,33 +1,34 @@ #' @rdname CST_EMBestIndexWeighting #' @title Computing the weighted ensemble means for SFSs of a CSTools object. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the weighted -#' ensemble means for SFSs through Best estimation of one index or driver of -#' climate variability (as e.g., the North Atlantic Oscillation (NAO)) and +#' @description This function implements the computation to obtain the weighted +#' ensemble means for SFSs through Best estimation of one index or driver of +#' climate variability (as e.g., the North Atlantic Oscillation (NAO)) and #' accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp An object of the class 's2dv_cube' containing the variable +#' @param var_exp An object of the class 's2dv_cube' containing the variable #' (e.g. precipitation, temperature, NAO index) array. -#' The var_exp object is expected to have an element named \code{$data} with -#' at least two dimensions (time, member). +#' The var_exp object is expected to have an element named \code{$data} with +#' at least two dimensions (time, member). #' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another +#' If we want to compute for several SFSs, the array could have another #' dimension (time, dataset, member, lat, lon). -#' @param weights_index_exp An object of the class 's2dv_cube' containing +#' @param weights_index_exp An object of the class 's2dv_cube' containing #' the normalized weights array. -#' The weights_index_exp object is expected to have an element named -#' \code{$data} with at least two dimensions (time, member). -#' If we want to compute for several SFSs, the array could have another +#' The weights_index_exp object is expected to have an element named +#' \code{$data} with at least two dimensions (time, member). +#' If we want to compute for several SFSs, the array could have another #' dimension (time, dataset, member). -#' @return CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the +#' @return CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the #' class 's2dv_cube'). -#' This object has at least an element named \code{$data} -#' with three or five dimensions depending if the variable is zonal -#' (as e.g. NAO index)(time, dataset, season) or it is spatial variable -#' (as e.g. precipitation or temperature) (time, dataset, season, lat, lon), +#' This object has at least an element named \code{$data} +#' with three or five dimensions depending if the variable is zonal +#' (as e.g. NAO index)(time, dataset, season) or it is spatial variable +#' (as e.g. precipitation or temperature) (time, dataset, season, lat, lon), #' containing the ensemble means computing with weighted members. +#' @export #' @examples #' var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) #' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) @@ -39,8 +40,8 @@ #' class(weights_index_exp) <- 's2dv_cube' #' res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) #' dim(res_CST$data) -#' # time dataset season lat lon -#' # 4 2 2 3 2 +#' # time dataset season lat lon +#' # 4 2 2 3 2 CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { stop("Parameter 'var_exp' and 'weights_index_exp' must be of the class 's2dv_cube', ", @@ -72,46 +73,46 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { "in the component 'data' must be equal to the length of the dimension", " 'member' of the parameter 'weights_index_exp' in the component 'data'") } - if (any(names(dim(weights_index_exp$data)) == 'lat') & + if (any(names(dim(weights_index_exp$data)) == 'lat') & dim(weights_index_exp$data)['lat'] != 1) { stop("The length of the dimension 'lat' of the parameter 'weights_index_exp'", " in the component 'data' must be equal to 1") } - if (any(names(dim(weights_index_exp$data)) == 'lon') & + if (any(names(dim(weights_index_exp$data)) == 'lon') & dim(weights_index_exp$data)['lon'] != 1) { stop("The length of the dimension 'lon' of the parameter 'weights_index_exp'", " in the component 'data' must be equal to 1") } if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) + Dim <- dim(weights_index_exp$data) dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] } if (any(names(dim(weights_index_exp$data)) == 'lat')) { - Dim <- dim(weights_index_exp$data) + Dim <- dim(weights_index_exp$data) dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lat')] } if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) + Dim <- dim(weights_index_exp$data) dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] } - if (any(names(dim(var_exp$data)) == 'dataset') & + if (any(names(dim(var_exp$data)) == 'dataset') & !any(names(dim(weights_index_exp$data)) == 'dataset')) { stop("The component 'data' of the parameter 'weights_index_exp' must have'", " the dimension 'dataset'") } - if (!any(names(dim(var_exp$data)) == 'dataset') & + if (!any(names(dim(var_exp$data)) == 'dataset') & any(names(dim(weights_index_exp$data)) == 'dataset')) { stop("The component 'data' of the parameter 'var_exp' must have'", " the dimension 'dataset'") } - if (any(names(dim(var_exp$data)) == 'dataset') & + if (any(names(dim(var_exp$data)) == 'dataset') & any(names(dim(weights_index_exp$data)) == 'dataset') & dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']) { stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", "in the component 'data' must be equal to the length of the dimension", " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") } - + em <- EMBestIndexWeighting(var_exp$data, weights_index_exp$data) em_var_exp <- list() em_var_exp$data <- em @@ -124,25 +125,25 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' @rdname EMBestIndexWeighting #' @title Computing the weighted ensemble means for SFSs. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the weighted -#' ensemble means for SFSs through Best estimation of one index or driver of +#' @description This function implements the computation to obtain the weighted +#' ensemble means for SFSs through Best estimation of one index or driver of #' climate variability (as e.g., the North Atlantic Oscillation (NAO)). -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with at least two dimensions (time, member). +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with at least two dimensions (time, member). #' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another +#' If we want to compute for several SFSs, the array could have another #' dimension (time, dataset, member, lat, lon). -#' @param weights_index_exp Normalized weights array with at least two -#' dimensions (time, member). If we want to compute for several SFSs, +#' @param weights_index_exp Normalized weights array with at least two +#' dimensions (time, member). If we want to compute for several SFSs, #' the array could have another dimension (time, dataset, member). -#' @return EMBestIndexWeighting() returns an array with two or four dimensions -#' depending if the variable is zonal (as e.g. NAO index)(time, dataset) -#' or it is spatial variable (as e.g. precipitation or temperature) -#' (time, dataset, lat, lon), containing the ensemble means computing -#' with weighted members. +#' @return EMBestIndexWeighting() returns an array with two or four dimensions +#' depending if the variable is zonal (as e.g. NAO index)(time, dataset) +#' or it is spatial variable (as e.g. precipitation or temperature) +#' (time, dataset, lat, lon), containing the ensemble means computing +#' with weighted members. #' @import multiApply #' @export #' @examples @@ -153,8 +154,8 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) #' res <- EMBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time dataset -#' # 2 3 +#' # time dataset +#' # 2 3 #' str(res) #' # num [1:2, 1:3] 36.2 28.8 38.3 31.9 31 ... #' # Example 2 for the EMBestIndexWeighting function @@ -164,24 +165,24 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) #' res <- EMBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time dataset lat lon +#' # time dataset lat lon #' # 2 3 2 3 #' str(res) #' # num [1:2, 1:3, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... -#' +#' EMBestIndexWeighting <- function(var_exp, weights_index_exp) { - return(Apply(list(var_exp, weights_index_exp), + return(Apply(list(var_exp, weights_index_exp), target_dims = list(c('time','member'), c('time','member')), fun = .EMBestIndexWeighting)$output1) } #' Atomic EMBestIndexWeighting -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) #' array from a SFS with dimensions (time, member). -#' @param weights_index_exp Normalized weights array with dimensions +#' @param weights_index_exp Normalized weights array with dimensions #' (time, member). -#' @return .EMBestIndexWeighting returns an array of with dimensions (time), -#' the ensemble means computing with weighted members. +#' @return .EMBestIndexWeighting returns an array of with dimensions (time), +#' the ensemble means computing with weighted members. #' @examples #' # Example for the Atomic EMBestIndexWeighting function #' var_exp <- 1 : 6 @@ -194,7 +195,7 @@ EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(res) #' # time #' # 2 -#' @noRd +#' @noRd .EMBestIndexWeighting <- function(var_exp, weights_index_exp) { posTime <- match('time', names(dim(var_exp))) var_exp_em <- apply(var_exp * weights_index_exp, posTime, sum) diff --git a/R/CST_PDFBest.R b/R/CST_PDFBest.R index 6ce7f89b..e5dc0704 100644 --- a/R/CST_PDFBest.R +++ b/R/CST_PDFBest.R @@ -3,30 +3,31 @@ #' of a CSTools object. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs +#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs #' and accepts a CSTools object (an object of the class 's2dv_cube' as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param pdf_exp An object of the class 's2dv_cube' -#' containing the parameters of the Index PDFs from two SFSs for a period +#' @param pdf_exp An object of the class 's2dv_cube' +#' containing the parameters of the Index PDFs from two SFSs for a period #' to combine and obtain the Best estimation. -#' The index_hind object is expected to have an element named \code{$data} +#' The index_hind object is expected to have an element named \code{$data} #' with dimensions (time, dataset = 2, member = 1, statistic = 4, season). -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDFs for the first SFS or dataset has been computed +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDFs for the first SFS or dataset has been computed #' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDFs for the second SFS or dataset has been computed +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDFs for the second SFS or dataset has been computed #' with bias corrected. -#' @return CST_PDFBest() returns a CSTools object (i.e., of the +#' @return CST_PDFBest() returns a CSTools object (i.e., of the #' class 's2dv_cube'). -#' This object has at least two elements: i) an element named \code{$data} -#' with dimensions (time, statistic = 2, season) and ii) an element -#' named \code{$statistic} with the names of 'statistic' dimension +#' This object has at least two elements: i) an element named \code{$data} +#' with dimensions (time, statistic = 2, season) and ii) an element +#' named \code{$statistic} with the names of 'statistic' dimension #' of the first element ("mean", "sigma"). #' The firt statistic is the parameter 'mean' of the PDF #' The second statistic is the parameter 'standard deviation' of the PDF +#' @export #' @examples #' pdf_exp <- c(1: (10 * 2* 4 * 1)) #' dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, season = 1) @@ -45,7 +46,7 @@ #' # - attr(*, "class")= chr "s2dv_cube" #' dim(res_CST$data) #' # time statistic season -#' # 10 2 1 +#' # 10 2 1 #' str(res_CST) CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { if (!inherits(pdf_exp, 's2dv_cube')) { @@ -72,14 +73,14 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { stop("The length of the dimension 'statistic' in the component 'data' of the ", "parameter 'pdf_exp' must be equal to 4'") } - + if (dim(pdf_exp$data)['member'] == 1) { - Dim <- dim(pdf_exp$data) + Dim <- dim(pdf_exp$data) dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] } - - PDFstatistics <- PDFBest(pdf_exp$data, bc_dataset1 = bc_dataset1, - bc_dataset2 = bc_dataset2) + + PDFstatistics <- PDFBest(pdf_exp$data, bc_dataset1 = bc_dataset1, + bc_dataset2 = bc_dataset2) pdf_exp$data <- PDFstatistics pdf_exp$statistic <- c("mean","sigma") return(pdf_exp) @@ -89,24 +90,24 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { #' @title Computing the Best Index PDFs combining two SFSs Index PDFs. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) obtained to combining the Index PDFs for two SFSs, +#' (e.g. NAO index) obtained to combining the Index PDFs for two SFSs, #' the Best Index estimation. -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param pdf_exp Statistics array for the SFSs Index PDFs for a period of years +#' @param pdf_exp Statistics array for the SFSs Index PDFs for a period of years #' with dimensions (time, dataset = 2, statistic = 4, season). -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDFs for the first SFS or dataset has been computed +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDFs for the first SFS or dataset has been computed #' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDFs for the second SFS or dataset has been computed +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDFs for the second SFS or dataset has been computed #' with bias corrected. -#' @return PDFBest() returns an array +#' @return PDFBest() returns an array #' with dimensions (time, statistic = 2, season). #' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDFs. -#' The second statistic is the parameter 'standard deviation' of the PDF for +#' combining the two SFSs PDFs. +#' The second statistic is the parameter 'standard deviation' of the PDF for #' the best estimation combining the two SFSs PDFs. #' @import multiApply #' @export @@ -116,11 +117,11 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { #' dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) #' res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) #' dim(res) -#' # time statistic season -#' # 10 2 1 +#' # time statistic season +#' # 10 2 1 #' str(res) #' # num [1:10, 1:2, 1] 17.2 18.3 19.5 20.6 21.7 ... -#' +#' PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { if (!any(names(dim(pdf_exp)) == 'statistic')){ stop("The parameter 'pdf_exp' must have the dimension 'statistic'") @@ -132,9 +133,9 @@ PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { if(dim(pdf_exp)['dataset'] != 2){ stop("The length of the dimension 'statistic' in the parameter 'pdf_exp'", " must be equal to 2") - } - pdf_best <- Apply(list(pdf_exp), - target_dims = list(c('dataset','statistic'), + } + pdf_best <- Apply(list(pdf_exp), + target_dims = list(c('dataset','statistic'), c('dataset','statistic')), fun = .PDFBest, bc_dataset1, bc_dataset2) pos <- match(c('time','statistic','season'), names(dim(pdf_best$output1))) @@ -142,18 +143,18 @@ PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { } #' Atomic PDFBest -#' @param pdf_exp Statistics array for two SFS PDFs with dimensions +#' @param pdf_exp Statistics array for two SFS PDFs with dimensions #' (dataset = 2, statistic = 4). -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDF for the first SFS or dataset has been computed +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDF for the first SFS or dataset has been computed #' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDF for the second SFS or dataset has been computed +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDF for the second SFS or dataset has been computed #' with bias corrected. -#' @return .PDFBest returns an array with dimensions (statistic = 2). +#' @return .PDFBest returns an array with dimensions (statistic = 2). #' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDF. -#' The second statistic is the parameter 'standard deviation' of the PDF for +#' combining the two SFSs PDF. +#' The second statistic is the parameter 'standard deviation' of the PDF for #' the best estimation combining the two SFSs PDF. #' @examples #' # Example for the Atomic PDFBest function @@ -163,8 +164,8 @@ PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { #' str(res) #' # num [1:2(1d)] 0.706 0.772 #' dim(res) -#' # statistic -#' # 2 +#' # statistic +#' # 2 #' @noRd .PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { pos <- match(c('dataset','statistic'), names(dim(pdf_exp))) diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R index c48a00e3..c0cf8863 100644 --- a/R/CST_PDFIndexFcst.R +++ b/R/CST_PDFIndexFcst.R @@ -1,47 +1,48 @@ #' @rdname CST_PDFIndexFcst -#' @title Computing the Index PDFs for a forecast year of a SFSs dataset +#' @title Computing the Index PDFs for a forecast year of a SFSs dataset #' of a CSTools object. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs +#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs #' and accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_hind An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate for a hindcast period +#' @param index_hind An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate for a hindcast period #' from SFSs for improving. -#' The index_hind object is expected to have an element named \code{$data} +#' The index_hind object is expected to have an element named \code{$data} #' with dimensions (time, dataset, member, season). -#' @param index_obs An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate from observations or +#' @param index_obs An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate from observations or #' reanalysis. -#' The index_obs object is expected to have an element named \code{$data} -#' with at least three dimensions (time, dataset, season). +#' The index_obs object is expected to have an element named \code{$data} +#' with at least three dimensions (time, dataset, season). #' It could have one more dimension (member) equal to 1. -#' @param index_fcst An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate for a forecast year +#' @param index_fcst An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate for a forecast year #' from SFSs for improving. -#' The index_fcst object is expected to have an element named \code{$data} -#' with dimensions (time = 1, dataset, member, season). -#' @param method A character string indicating which methodology is applied +#' The index_fcst object is expected to have an element named \code{$data} +#' with dimensions (time = 1, dataset, member, season). +#' @param method A character string indicating which methodology is applied #' to compute the PDFs. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return CST_PDFIndexFcst() returns a CSTools object (i.e., of the +#' @return CST_PDFIndexFcst() returns a CSTools object (i.e., of the #' class 's2dv_cube'). -#' This object has at least two elements: i) an element named \code{$data} -#' with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element -#' named \code{$statistic} with the names of 'statistic' dimension -#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +#' This object has at least two elements: i) an element named \code{$data} +#' with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element +#' named \code{$statistic} with the names of 'statistic' dimension +#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") #' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected #' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected +#' @export #' @examples #' index_hind <- 1 : (3 * 5 * 4 * 7) #' dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) @@ -58,14 +59,14 @@ #' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') #' str(res_CST) #' dim(res_CST$data) -#' #time dataset statistic season -#' # 1 3 4 4 -CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, +#' #time dataset statistic season +#' # 1 3 4 4 +CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method = 'ME', na.rm = FALSE) { - if (!inherits(index_hind, 's2dv_cube') || + if (!inherits(index_hind, 's2dv_cube') || !inherits(index_obs, 's2dv_cube') || !inherits(index_fcst, 's2dv_cube')) { - stop("Parameter 'index_fcst', 'index_hind' + stop("Parameter 'index_fcst', 'index_hind' and 'index_obs' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } @@ -73,7 +74,7 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, stop("The length of the dimension 'member' in the component 'data' ", "of the parameter 'index_obs' must be equal to 1.") } - if (dim(index_fcst$data)['member'] == 1 & + if (dim(index_fcst$data)['member'] == 1 & !any(names(dim(index_fcst$data)) == 'statistic')){ stop("The dimension 'statistic' must be exist in the component 'data' ", "of the parameter 'index_fcst' if the length of the dimension ", @@ -82,30 +83,30 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, if (dim(index_fcst$data)['time'] != 1){ stop("The dimension 'time' must be equal to 1") } - if (dim(index_hind$data)['member'] == 1 & + if (dim(index_hind$data)['member'] == 1 & !any(names(dim(index_hind$data)) == 'statistic')){ stop("The dimension 'statistic' must be exist in the component 'data' ", "of the parameter 'index_hind' if the length of the dimension ", "'member' is equal to 1") } - if (dim(index_fcst$data)['member'] > 1 & + if (dim(index_fcst$data)['member'] > 1 & any(names(dim(index_fcst$data)) == 'statistic')){ stop("The length of the dimension 'member' in the component 'data' of the ", "parameter 'index_fcst' must be equal to 1 if the component 'data' ", "of the parameter 'index_fcst' has the dimension 'statistic'") } - if (dim(index_hind$data)['member'] > 1 & + if (dim(index_hind$data)['member'] > 1 & any(names(dim(index_hind$data)) == 'statistic')){ stop("The length of the dimension 'member' in the component 'data' of the ", "parameter 'index_hind' must be equal to 1 if the component 'data' ", "of the parameter 'index_hind' has the dimension 'statistic'") } if (dim(index_fcst$data)['member'] == 1) { - Dim <- dim(index_fcst$data) + Dim <- dim(index_fcst$data) dim(index_fcst$data) <- Dim[-which(names(Dim) == 'member')] } if (dim(index_hind$data)['member'] == 1) { - Dim <- dim(index_hind$data) + Dim <- dim(index_hind$data) dim(index_hind$data) <- Dim[-which(names(Dim) == 'member')] } dimnames_fcst <- names(dim(index_fcst$data)) @@ -119,11 +120,11 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, if (any(names(dim(index_fcst$data)) == 'member')){ dimnames[dimnames == 'member'] <- 'statistic' } - PDFstatistics <- PDFIndexFcst(index_hind$data, index_obs$data, - index_fcst$data, method = method, na.rm = na.rm) + PDFstatistics <- PDFIndexFcst(index_hind$data, index_obs$data, + index_fcst$data, method = method, na.rm = na.rm) pos <- match(dimnames, names(dim(PDFstatistics))) PDFstatistics <- aperm(PDFstatistics, pos) - names(dim(PDFstatistics)) <- dimnames + names(dim(PDFstatistics)) <- dimnames index_fcst$data <- PDFstatistics index_fcst$Datasets <- c(index_hind$Datasets, index_obs$Datasets, index_fcst$Datasets) index_fcst$source_files <- c(index_hind$source_files, index_obs$source_files, @@ -137,25 +138,25 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the index PDFs #' (e.g. NAO index) to improve the index estimate from a dataset of SFSs -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_hind Index (e.g. NAO index) array from SFSs with dimensions +#' @param index_hind Index (e.g. NAO index) array from SFSs with dimensions #' (time, dataset, member) for a hindcast period. -#' @param index_obs Index (e.g. NAO index) array from an observational database +#' @param index_obs Index (e.g. NAO index) array from an observational database #' or reanalysis with dimension (time, dataset, member = 1) or (time, dataset). -#' @param index_fcst Index (e.g. NAO index) array from SFSs. +#' @param index_fcst Index (e.g. NAO index) array from SFSs. #' with dimensions (time = 1, dataset, member) for a forecast year. -#' @param method A character string indicating which methodology is applied +#' @param method A character string indicating which methodology is applied #' to compute the PDF. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return PDFIndexFcst() returns an array -#' with dimensions (time = 1, statistic = 4, dataset) for the forecast year. +#' @return PDFIndexFcst() returns an array +#' with dimensions (time = 1, statistic = 4, dataset) for the forecast year. #' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected #' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected #' @import multiApply #' @export @@ -169,24 +170,24 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, #' dim(index_hind) <- c(time = 5, dataset = 3, member = 4) #' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) #' dim(res) -#' # time statistic dataset -#' # 1 4 3 +#' # time statistic dataset +#' # 1 4 3 #' str(res) #' # num [1, 1:4, 1:3] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... -#' +#' PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', na.rm = FALSE) { if (any(names(dim(index_obs)) == 'member')) { - Dim <- dim(index_obs) + Dim <- dim(index_obs) dim(index_obs) <- Dim[-which(names(Dim) == 'member')] } if (any(names(dim(index_fcst)) == 'member')){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), - target_dims = list(c('time', 'member'), 'time', + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c('time', 'member'), 'time', c('time','member')), fun = .PDFIndexFcst, method, na.rm) } else if (any(names(dim(index_fcst)) == 'statistic')){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), target_dims = list(c('time', 'statistic'), 'time', c('time','statistic')), fun = .PDFIndexFcst, method, na.rm) @@ -195,23 +196,23 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', } #' Atomic PDFIndexFcst -#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions #' (time, member) for a hindcast period. -#' @param index_obs Index (e.g. NAO index) array from an observational database +#' @param index_obs Index (e.g. NAO index) array from an observational database #' or reanalysis with dimension (time). -#' @param index_fcst Index (e.g. NAO index) array from a SFS +#' @param index_fcst Index (e.g. NAO index) array from a SFS #' with dimensions (time=1, member) for a forecast year. -#' @param method A character string indicating which methodology is applied +#' @param method A character string indicating which methodology is applied #' to compute the PDF. One of "ME" (default) or "LMEV". #' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexFcst returns an array with dimensions (time=1, statistic=4). +#' @return .PDFIndexFcst returns an array with dimensions (time=1, statistic=4). #' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' for the forecast year. #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected for the forecast year. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected #' for the forecast year. -#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected for the forecast year. #' @import multiApply #' @import verification @@ -227,10 +228,10 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', #' str(res) #' # num [1, 1:4] 2 10 -8 0 #' dim(res) -#' # time statistic -#' # 1 4 -#' @noRd -.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, +#' # time statistic +#' # 1 4 +#' @noRd +.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method = 'ME', na.rm = TRUE) { if (any(names(dim(index_fcst)) == 'member')) { exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', @@ -263,42 +264,3 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', names(dim(data)) <- c(names(dim(index_obs)), 'statistic') return(data) } - - -# Auxiliar function to compute the mean squared error between 'exp' and 'obs' -Sigma_notBC <- function(exp, obs) { - return(sqrt(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$MSE)) -} - -# Auxiliar function to compute the bias between 'exp' and 'obs' -MEBC <- function(exp, obs) { - return(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$ME) -} - -# Auxiliar function to compute the standard deviation of errors -# between 'obs' and 'exp' -Sigma_BC <- function(exp, obs, na.rm = FALSE) { - return(sd(obs - exp, na.rm = na.rm)) -} - -# Auxiliar function to compute the linear model used in the method -# called 'LMEV' to correct the bias -LMEV <- function(exp, obs, na.rm = FALSE) { - if (any(names(dim(exp)) == 'member')) { - exp_ens_mean <- Apply(list(exp), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - exp_ens_sd <- Apply(list(exp), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else { - pos <- match(c('time','statistic'), names(dim(exp))) - exp <- aperm(exp,pos) - exp_ens_mean <- exp[,1] - exp_ens_sd <- exp[,2] - } - error <- exp_ens_mean - obs - exp_ens_var <- exp_ens_sd^2 - statModel <- lm(error ~ exp_ens_var) - return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) -} diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R index 71ebd599..6d948dce 100644 --- a/R/CST_PDFIndexHind.R +++ b/R/CST_PDFIndexHind.R @@ -1,40 +1,41 @@ #' @rdname CST_PDFIndexHind -#' @title Computing the Index PDFs for the hindcast of a SFSs dataset +#' @title Computing the Index PDFs for the hindcast of a SFSs dataset #' of a CSTools object #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from the hindcast of SFSs +#' (e.g. NAO index) to improve the index estimate from the hindcast of SFSs #' and accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_exp An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., +#' @param index_exp An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. -#' The index_exp object is expected to have an element named \code{$data} +#' The index_exp object is expected to have an element named \code{$data} #' with dimensions (time, dataset, member, season). -#' @param index_obs An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate from observations or +#' @param index_obs An object of the class 's2dv_cube' +#' containing the indices or drivers of climate variability (as e.g., +#' the North Atlantic Oscillation (NAO)) estimate from observations or #' reanalysis. -#' The index_obs object is expected to have an element named \code{$data} -#' with at least three dimensions (time, dataset, season). +#' The index_obs object is expected to have an element named \code{$data} +#' with at least three dimensions (time, dataset, season). #' It could have one more dimension (member) equal to 1. -#' @param method A character string indicating which methodology is applied +#' @param method A character string indicating which methodology is applied #' to compute the PDFs. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return CST_PDFIndexHind() returns a CSTools object (i.e., of the +#' @return CST_PDFIndexHind() returns a CSTools object (i.e., of the #' class 's2dv_cube'). -#' This object has at least two elements: i) an element named \code{$data} -#' with dimensions (time, dataset, statistic = 4, season) and ii) an element -#' named \code{$statistics} with the names of 'statistic' dimension -#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +#' This object has at least two elements: i) an element named \code{$data} +#' with dimensions (time, dataset, statistic = 4, season) and ii) an element +#' named \code{$statistics} with the names of 'statistic' dimension +#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") #' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected #' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected +#' @export #' @examples #' index_obs <- 1 : (3 * 5 * 4) # 'dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) @@ -46,8 +47,8 @@ #' class(index_obs) <- 's2dv_cube' #' res_CST <- CST_PDFIndexHind(index_exp, index_obs) #' dim(res_CST$data) -#' #time dataset statistic season -#' # 5 3 4 4 +#' #time dataset statistic season +#' # 5 3 4 4 CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) { if (!inherits(index_exp, 's2dv_cube') || !inherits(index_obs, 's2dv_cube')) { stop("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", @@ -57,21 +58,21 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) stop("The length of the dimension 'member' in the component 'data' ", "of the parameter 'index_obs' must be equal to 1.") } - if (dim(index_exp$data)['member'] == 1 & + if (dim(index_exp$data)['member'] == 1 & !any(names(dim(index_exp$data)) == 'statistic')){ stop("The dimension 'statistic' must be exist in the component 'data' ", "of the parameter 'index_exp' if the length of the dimension ", "'member' is equal to 1") } - if (dim(index_exp$data)['member'] > 1 & + if (dim(index_exp$data)['member'] > 1 & any(names(dim(index_exp$data)) == 'statistic')){ stop("The length of the dimension 'member' in the component 'data' of the ", "parameter 'index_hind' must be equal to 1 if the component 'data' ", "of the parameter 'index_hind' has the dimension 'statistic'") } - + if (dim(index_exp$data)['member'] == 1) { - Dim <- dim(index_exp$data) + Dim <- dim(index_exp$data) dim(index_exp$data) <- Dim[-which(names(Dim) == 'member')] } dimnames <- names(dim(index_exp$data)) @@ -79,10 +80,10 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) dimnames[dimnames == 'member'] <- 'statistic' } PDFstatistics <- PDFIndexHind(index_exp$data, index_obs$data, method = method, - na.rm = na.rm) + na.rm = na.rm) pos <- match(dimnames, names(dim(PDFstatistics))) PDFstatistics <- aperm(PDFstatistics, pos) - names(dim(PDFstatistics)) <- dimnames + names(dim(PDFstatistics)) <- dimnames index_exp$data <- PDFstatistics index_exp$Datasets <- c(index_exp$Datasets, index_obs$Datasets) index_exp$source_files <- c(index_exp$source_files, index_obs$source_files) @@ -95,23 +96,23 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the index PDFs #' (e.g. NAO index) to improve the index estimate from a dataset of SFSs -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' @param index_exp Index (e.g. NAO index) array from SFSs #' with dimensions (time, dataset, member). -#' @param index_obs Index (e.g. NAO index) array from an observational database +#' @param index_obs Index (e.g. NAO index) array from an observational database #' or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset). -#' @param method A character string indicating which methodology is applied +#' @param method A character string indicating which methodology is applied #' to compute the PDFs. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return PDFIndexHind() returns an array -#' with dimensions (time, statistic = 4, dataset). +#' @return PDFIndexHind() returns an array +#' with dimensions (time, statistic = 4, dataset). #' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected #' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected #' @import multiApply #' @export @@ -125,21 +126,21 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) #' str(res) #' # num [1:5, 1:4, 1:3] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... #' dim(res) -#' # time statistic dataset -#' # 5 4 3 -#' +#' # time statistic dataset +#' # 5 4 3 +#' PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { if (any(names(dim(index_obs)) == 'member')) { - Dim <- dim(index_obs) + Dim <- dim(index_obs) dim(index_obs) <- Dim[-which(names(Dim) == 'member')] } if (any(names(dim(index_exp)) == 'member')){ - PDFstatistics <- Apply(list(index_exp, index_obs), + PDFstatistics <- Apply(list(index_exp, index_obs), target_dims = list(c('time', 'member'), 'time'), fun = .PDFIndexHind, method, na.rm) } else if (any(names(dim(index_exp)) == 'statistic')){ - PDFstatistics <- Apply(list(index_exp, index_obs), + PDFstatistics <- Apply(list(index_exp, index_obs), target_dims = list(c('time', 'statistic'), 'time'), fun = .PDFIndexHind, method, na.rm) } @@ -147,21 +148,21 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { } #' Atomic PDFIndexHind -#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions +#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions #' (time, member) for a hindcast period. -#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' @param index_obs Index (e.g. NAO index) array from an observational dataset #' or reanalysis with dimension (time). -#' @param method A character string indicating which methodology is applied +#' @param method A character string indicating which methodology is applied #' to compute the PDF. One of "ME" (default) or "LMEV". #' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' for the hindcast period. #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected for the hindcast period. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected #' for the hindcast period. -#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected for the hindcast period. #' @import multiApply #' @import verification @@ -175,9 +176,9 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { #' str(res) #' # num [1:10, 1:4] 11 12 13 14 15 16 17 18 19 20 ... #' dim(res) -#' # time statistic -#' # 10 4 -#' @noRd +#' # time statistic +#' # 10 4 +#' @noRd .PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) { if (any(names(dim(index_exp)) == 'member')) { exp_ens_mean <- Apply(list(index_exp), target_dims = 'member', @@ -193,22 +194,22 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { error <- exp_ens_mean - index_obs pdfnotbc_mean <- exp_ens_mean ind <- 1 : length(index_obs) - pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(exp_ens_mean[-x], + pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(exp_ens_mean[-x], index_obs[-x])})) - + if (method == 'ME') { - pdfbc <- unlist(lapply(ind, function(x) {MEBC(exp_ens_mean[-x], + pdfbc <- unlist(lapply(ind, function(x) {MEBC(exp_ens_mean[-x], index_obs[-x])})) pdfbc_mean <- exp_ens_mean - pdfbc - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], index_obs[-x])})) } else if (method == 'LMEV') { # linear model error-variance exp_ens_var <- exp_ens_sd^2 coeff <- sapply(ind, function(x) {LMEV(index_exp[-x,], index_obs[-x])}) - pdfbc_mean <- exp_ens_mean - (unlist(coeff['coeff1',]) + + pdfbc_mean <- exp_ens_mean - (unlist(coeff['coeff1',]) + unlist(coeff['coeff2',])*exp_ens_var) - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], index_obs[-x])})) } else { stop("Error: Parameter 'method' is wrong") @@ -217,43 +218,3 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { names(dim(data)) <- c(names(dim(index_obs)), 'statistic') return(data) } - - - -# Auxiliar function to compute the mean squared error between 'exp' and 'obs' -Sigma_notBC <- function(exp, obs) { - return(sqrt(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$MSE)) -} - -# Auxiliar function to compute the bias between 'exp' and 'obs' -MEBC <- function(exp, obs) { - return(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$ME) -} - -# Auxiliar function to compute the standard deviation of errors -# between 'obs' and 'exp' -Sigma_BC <- function(exp, obs, na.rm = FALSE) { - return(sd(obs - exp, na.rm = na.rm)) -} - -# Auxiliar function to compute the linear model used in the method -# called 'LMEV' to correct the bias -LMEV <- function(exp, obs, na.rm = FALSE) { - if (any(names(dim(exp)) == 'member')) { - exp_ens_mean <- Apply(list(exp), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - exp_ens_sd <- Apply(list(exp), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else { - pos <- match(c('time','statistic'), names(dim(exp))) - exp <- aperm(exp,pos) - exp_ens_mean <- exp[,1] - exp_ens_sd <- exp[,2] - } - error <- exp_ens_mean - obs - exp_ens_var <- exp_ens_sd^2 - statModel <- lm(error ~ exp_ens_var) - return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) -} diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index 947bd297..fe944ddd 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -1,38 +1,39 @@ #' @rdname CST_ProbBestIndexWeighting -#' @title Computing the weighted tercile probabilities for SFSs of a CSTools +#' @title Computing the weighted tercile probabilities for SFSs of a CSTools #' object. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the tercile -#' probabilities for a weighted variable from SFSs through Best estimation of -#' one index or driver of climate variability (as e.g., the North Atlantic -#' Oscillation (NAO)) and accepts a CSTools object (an object of the class +#' @description This function implements the computation to obtain the tercile +#' probabilities for a weighted variable from SFSs through Best estimation of +#' one index or driver of climate variability (as e.g., the North Atlantic +#' Oscillation (NAO)) and accepts a CSTools object (an object of the class #' 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp An object of the class 's2dv_cube' containing the variable +#' @param var_exp An object of the class 's2dv_cube' containing the variable #' (e.g. precipitation, temperature, NAO index) array. -#' The var_exp object is expected to have an element named \code{$data} with -#' at least two dimensions (time, member). +#' The var_exp object is expected to have an element named \code{$data} with +#' at least two dimensions (time, member). #' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another +#' If we want to compute for several SFSs, the array could have another #' dimension (time, dataset, member, lat, lon). -#' @param weights_index_exp An object of the class 's2dv_cube' containing +#' @param weights_index_exp An object of the class 's2dv_cube' containing #' the normalized weights array. -#' The weights_index_exp object is expected to have an element named -#' \code{$data} with at least two dimensions (time, member). -#' If we want to compute for several SFSs, the array could have another +#' The weights_index_exp object is expected to have an element named +#' \code{$data} with at least two dimensions (time, member). +#' If we want to compute for several SFSs, the array could have another #' dimension (time, dataset, member). -#' @return CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the +#' @return CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the #' class 's2dv_cube'). -#' This object has at least an element named \code{$data} -#' with four or six dimensions depending if the variable is zonal -#' (as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable -#' (as e.g. precipitation or temperature) -#' (time, tercil, dataset, season, lat, lon), -#' containing the terciles probabilities computing with weighted members. +#' This object has at least an element named \code{$data} +#' with four or six dimensions depending if the variable is zonal +#' (as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable +#' (as e.g. precipitation or temperature) +#' (time, tercil, dataset, season, lat, lon), +#' containing the terciles probabilities computing with weighted members. #' The firt tercil is the lower tercile, the second is the normal tercile and #' the third is the upper tercile. +#' @export #' @examples #' var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) #' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) @@ -44,8 +45,8 @@ #' class(weights_index_exp) <- 's2dv_cube' #' res_CST <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) #' dim(res_CST$data) -#' # time tercil dataset season lat lon -#' # 4 3 2 2 3 2 +#' # time tercil dataset season lat lon +#' # 4 3 2 2 3 2 CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", @@ -77,46 +78,46 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { "in the component 'data' must be equal to the length of the dimension", " 'member' of the parameter 'weights_index_exp' in the component 'data'") } - if (any(names(dim(weights_index_exp$data)) == 'lat') & + if (any(names(dim(weights_index_exp$data)) == 'lat') & dim(weights_index_exp$data)['lat'] != 1) { stop("The length of the dimension 'lat' of the parameter 'weights_index_exp'", " in the component 'data' must be equal to 1") } - if (any(names(dim(weights_index_exp$data)) == 'lon') & + if (any(names(dim(weights_index_exp$data)) == 'lon') & dim(weights_index_exp$data)['lon'] != 1) { stop("The length of the dimension 'lon' of the parameter 'weights_index_exp'", " in the component 'data' must be equal to 1") } if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) + Dim <- dim(weights_index_exp$data) dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] } if (any(names(dim(weights_index_exp$data)) == 'lat')) { - Dim <- dim(weights_index_exp$data) + Dim <- dim(weights_index_exp$data) dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lat')] } if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) + Dim <- dim(weights_index_exp$data) dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] } - if (any(names(dim(var_exp$data)) == 'dataset') & + if (any(names(dim(var_exp$data)) == 'dataset') & !any(names(dim(weights_index_exp$data)) == 'dataset')) { stop("The component 'data' of the parameter 'weights_index_exp' must have'", " the dimension 'dataset'") } - if (!any(names(dim(var_exp$data)) == 'dataset') & + if (!any(names(dim(var_exp$data)) == 'dataset') & any(names(dim(weights_index_exp$data)) == 'dataset')) { stop("The component 'data' of the parameter 'var_exp' must have'", " the dimension 'dataset'") } - if (any(names(dim(var_exp$data)) == 'dataset') & + if (any(names(dim(var_exp$data)) == 'dataset') & any(names(dim(weights_index_exp$data)) == 'dataset') & dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']) { stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", "in the component 'data' must be equal to the length of the dimension", " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") } - + prob <- ProbBestIndexWeighting(var_exp$data, weights_index_exp$data) prob_var_exp <- list() prob_var_exp$data <- prob @@ -129,28 +130,28 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' @rdname ProbBestIndexWeighting #' @title Computing the weighted tercile probabilities for SFSs. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the tercile -#' probabilities for a weighted variable from SFSs through Best estimation of -#' one index or driver of climate variability (as e.g., the North Atlantic +#' @description This function implements the computation to obtain the tercile +#' probabilities for a weighted variable from SFSs through Best estimation of +#' one index or driver of climate variability (as e.g., the North Atlantic #' Oscillation (NAO)). -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with at least two dimensions (time, member). +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with at least two dimensions (time, member). #' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another +#' If we want to compute for several SFSs, the array could have another #' dimension (time, dataset, member, lat, lon). -#' @param weights_index_exp Normalized weights array with at least two -#' dimensions (time, member). If we want to compute for several SFSs, +#' @param weights_index_exp Normalized weights array with at least two +#' dimensions (time, member). If we want to compute for several SFSs, #' the array could have another dimension (time, dataset, member). -#' @return ProbBestIndexWeighting() returns an array with three or five dimensions -#' depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) -#' or it is spatial variable (as e.g. precipitation or temperature) -#' (time, tercil, dataset, lat, lon), containing the terciles probabilities -#' computing with weighted members. +#' @return ProbBestIndexWeighting() returns an array with three or five dimensions +#' depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) +#' or it is spatial variable (as e.g. precipitation or temperature) +#' (time, tercil, dataset, lat, lon), containing the terciles probabilities +#' computing with weighted members. #' The firt tercil is the lower tercile, the second is the normal tercile and -#' the third is the upper tercile. +#' the third is the upper tercile. #' @import multiApply #' @export #' @examples @@ -161,8 +162,8 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) #' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time tercil dataset -#' # 2 3 3 +#' # time tercil dataset +#' # 2 3 3 #' str(res) #' # num [1:2, 1:3, 1:3] 0.6773 0 0 0.0413 1.8419 ... #' # Example 2 for the ProbBestIndexWeighting function @@ -172,25 +173,25 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) #' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time tercil dataset lat lon -#' # 2 3 3 2 3 +#' # time tercil dataset lat lon +#' # 2 3 3 2 3 #' str(res) #' # num [1:2, 1:3, 1:3, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... -#' +#' ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { - return(Apply(list(var_exp, weights_index_exp), + return(Apply(list(var_exp, weights_index_exp), target_dims = list(c('time','member'), c('time','member')), fun = .ProbBestIndexWeighting)$output1) } #' Atomic ProbBestIndexWeighting -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) #' array from a SFS with dimensions (time, member). -#' @param weights_index_exp Normalized weights array with dimensions +#' @param weights_index_exp Normalized weights array with dimensions #' (time, member). -#' @return .ProbBestIndexWeighting returns an array of with dimensions -#' (time, tercil = 3), the terciles probabilities computing with weighted -#' members. +#' @return .ProbBestIndexWeighting returns an array of with dimensions +#' (time, tercil = 3), the terciles probabilities computing with weighted +#' members. #' The firt tercil is the lower tercile, the second is the normal tercile and #' the third is the upper tercile. #' @examples @@ -203,29 +204,29 @@ ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' str(res) #' # num [1:2, 1:3] 0.28 0.15 0.69 0 0.56 1.03 #' dim(res) -#' # time tercil -#' # 2 3 -#' @noRd +#' # time tercil +#' # 2 3 +#' @noRd .ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { # computing terciles terciles_exp <- WeightTerciles(var_exp, weights_index_exp) lowerTercile <- terciles_exp$lowerTercile upperTercile <- terciles_exp$upperTercile - + # Probabilities - aTerciles <- Apply(list(var_exp), target_dims = list('member'), + aTerciles <- Apply(list(var_exp), target_dims = list('member'), fun = Data2Tercil, lowerTercile, upperTercile)$output1 - + pos <- match(names(dim(aTerciles)), c('time','member')) aTerciles <- aperm(aTerciles,pos) - + pos <- match(names(dim(weights_index_exp)), c('time','member')) weights_index_exp <- aperm(weights_index_exp, pos) - + probTercile <- array(NA, dim = c(dim(var_exp)['time'], tercil = 3)) for (idTercil in 1:3){ - probTercile[,idTercil] <- Apply(list(aTerciles, weights_index_exp), - target_dims = list('member','member'), + probTercile[,idTercil] <- Apply(list(aTerciles, weights_index_exp), + target_dims = list('member','member'), fun = WeightTercil2Prob, idTercil)$output1 } return(probTercile) @@ -247,17 +248,17 @@ WeightTercil2Prob <- function(aTerciles, aWeights, idTercil) { # Auxiliar function to compute weighted terciles WeightTerciles <- function(data, weights) { - vectorData <- as.vector(data) + vectorData <- as.vector(data) vectorWeights <- as.vector(weights/dim(weights)['time']) # normalized lSortData <- sort(vectorData,index.return=TRUE) - indSort <- lSortData$ix # index asociated to weight + indSort <- lSortData$ix # index asociated to weight # corresponding for this data dataSort <- lSortData$x - # Adding normalized weights. When 1/3 is reached, the data value + # Adding normalized weights. When 1/3 is reached, the data value # is lower tercile and when 2/3 is reached, it is the upper tercile. sumWeights <- 0 ilowerTercile <- 0 - while ((sumWeights < 1/3) & (ilowerTercile < (dim(weights)['member'] * + while ((sumWeights < 1/3) & (ilowerTercile < (dim(weights)['member'] * dim(weights)['time']))){ ilowerTercile<- ilowerTercile +1 sumWeights <- sumWeights + vectorWeights[indSort[ilowerTercile]] @@ -266,7 +267,7 @@ WeightTerciles <- function(data, weights) { dataSort[ilowerTercile-1])/2 sumWeights <- 0 iupperTercile <- 0 - while ((sumWeights < 2/3) & (iupperTercile < (dim(weights)['member'] * + while ((sumWeights < 2/3) & (iupperTercile < (dim(weights)['member'] * dim(weights)['time']))){ iupperTercile<- iupperTercile +1 sumWeights <- sumWeights + vectorWeights[indSort[iupperTercile]] diff --git a/R/CST_WeightIndex.R b/R/CST_WeightIndex.R index 6d4daadb..7a42710e 100644 --- a/R/CST_WeightIndex.R +++ b/R/CST_WeightIndex.R @@ -1,30 +1,31 @@ #' @rdname CST_WeightIndex -#' @title Computing the weights for SFSs using the Best Index PDFs +#' @title Computing the weights for SFSs using the Best Index PDFs #' of a CSTools object #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the -#' normalized weights for SFS members using the Best Index estimation and +#' @description This function implements the computation to obtain the +#' normalized weights for SFS members using the Best Index estimation and #' accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_exp An object of the class 's2dv_cube' containing the indices -#' or drivers of climate variability (as e.g., the North Atlantic Oscillation +#' @param index_exp An object of the class 's2dv_cube' containing the indices +#' or drivers of climate variability (as e.g., the North Atlantic Oscillation #' (NAO)) estimate from SFSs. -#' The index_exp object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member, season). -#' @param pdf_exp An object of the class 's2dv_cube' containing the statistics -#' which define a Gaussian PDFs for weighting the values estimate for the SFS +#' The index_exp object is expected to have an element named \code{$data} +#' with dimensions (time, dataset, member, season). +#' @param pdf_exp An object of the class 's2dv_cube' containing the statistics +#' which define a Gaussian PDFs for weighting the values estimate for the SFS #' ensemble members. -#' The pdf_exp object is expected to have an element named \code{$data} -#' with at least three dimensions (time, statistic = 2, season). +#' The pdf_exp object is expected to have an element named \code{$data} +#' with at least three dimensions (time, statistic = 2, season). #' It could have one or two dimension (dataset and/or member) equal to 1. -#' The firt statistic is the parameter 'mean' of the PDFs and +#' The firt statistic is the parameter 'mean' of the PDFs and #' the second statistic is the parameter 'standard deviation' of the PDFs. -#' @return CST_WeightIndex() returns a CSTools object (i.e., of the +#' @return CST_WeightIndex() returns a CSTools object (i.e., of the #' class 's2dv_cube'). -#' This object has at least an element named \code{$data} +#' This object has at least an element named \code{$data} #' with dimensions (time, dataset, member, season). +#' @export #' @examples #' index_obs <- 1 : (3 * 5 * 4) # 'dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) @@ -36,8 +37,8 @@ #' class(index_obs) <- 's2dv_cube' #' res_CST <- CST_PDFIndexHind(index_exp, index_obs) #' dim(res_CST$data) -#' #time dataset statistic season -#' # 5 3 4 4 +#' #time dataset statistic season +#' # 5 3 4 4 CST_WeightIndex <- function(index_exp, pdf_exp) { if (!inherits(index_exp, 's2dv_cube') || !inherits(pdf_exp, 's2dv_cube')) { stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", @@ -51,12 +52,12 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { stop("The length of the dimension 'statistic' in the component 'data' ", "of the parameter 'pdf_exp' must be equal to 2.") } - if (any(names(dim(pdf_exp$data)) == 'member') & + if (any(names(dim(pdf_exp$data)) == 'member') & dim(pdf_exp$data)['member'] != 1) { stop("The length of the dimension 'member' in the component 'data' ", "of the parameter 'pdf_exp' must be equal to 1.") } - if (any(names(dim(pdf_exp$data)) == 'dataset') & + if (any(names(dim(pdf_exp$data)) == 'dataset') & dim(pdf_exp$data)['dataset'] != 1) { stop("The length of the dimension 'dataset' in the component 'data' ", "of the parameter 'pdf_exp' must be equal to 1.") @@ -89,45 +90,45 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { "dimension 'member'") } if (any(names(dim(pdf_exp$data)) == 'member')) { - Dim <- dim(pdf_exp$data) + Dim <- dim(pdf_exp$data) dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] } if (any(names(dim(pdf_exp$data)) == 'dataset')) { - Dim <- dim(pdf_exp$data) + Dim <- dim(pdf_exp$data) dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'dataset')] } dimnames <- names(dim(index_exp$data)) - aWeigths <- WeightIndex(index_exp$data, pdf_exp$data) + aWeigths <- WeightIndex(index_exp$data, pdf_exp$data) pos <- match(dimnames, names(dim(aWeigths))) aWeigths <- aperm(aWeigths, pos) - names(dim(aWeigths)) <- dimnames + names(dim(aWeigths)) <- dimnames weights_index_exp <- list() weights_index_exp$data <- aWeigths - weights_index_exp$Datasets <- list(exp = index_exp$Datasets, + weights_index_exp$Datasets <- list(exp = index_exp$Datasets, pdf = pdf_exp$Datasets) - weights_index_exp$source_files <- list(exp = index_exp$source_files, + weights_index_exp$source_files <- list(exp = index_exp$source_files, pdf = pdf_exp$source_files) weights_index_exp$Dates <- index_exp$Dates - class(weights_index_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' return(weights_index_exp) } #' @rdname WeightIndex #' @title Computing the weights for SFSs using the Best Index PDFs. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the -#' normalized weights for each member of each SFS or dataset using the +#' @description This function implements the computation to obtain the +#' normalized weights for each member of each SFS or dataset using the #' Best Index estimation. -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_exp Index (e.g. NAO index) array, from a dataset of SFSs +#' @param index_exp Index (e.g. NAO index) array, from a dataset of SFSs #' for a period of years, with dimensions (time, dataset, member). -#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions +#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions #' (time, statistic = 2) for a period of years. -#' The firt statistic is the parameter 'mean' of the PDF and +#' The firt statistic is the parameter 'mean' of the PDF and #' the second statistic is the parameter 'standard deviation' of the PDF. -#' @return WeightIndex() returns a normalized weights array with dimensions +#' @return WeightIndex() returns a normalized weights array with dimensions #' (time, dataset, member). #' @import multiApply #' @export @@ -139,32 +140,32 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { #' dim(pdf_exp) <- c(time = 10, statistic = 2) #' res <- WeightIndex(index_exp, pdf_exp) #' dim(res) -#' # time dataset member -#' # 10 3 5 +#' # time dataset member +#' # 10 3 5 #' str(res) #' # num [1:10, 1:3, 1:5] 0.976 0.958 0.935 0.908 0.881 ... -#' +#' WeightIndex <- function(index_exp, pdf_exp) { - aweights <- Apply(list(index_exp, pdf_exp), - target_dims = list('member', 'statistic'), + aweights <- Apply(list(index_exp, pdf_exp), + target_dims = list('member', 'statistic'), fun = .WeightIndex) aweights <- aweights$output1 dimnames <- names(dim(index_exp)) pos <- match(dimnames, names(dim(aweights))) aweights <- aperm(aweights, pos) return(aweights) - + } #' Atomic WeightIndex -#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions +#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions #' (member) -#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions +#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions #' (statistic = 2). -#' The firt statistic is the parameter 'mean' of the PDF and +#' The firt statistic is the parameter 'mean' of the PDF and #' the second statistic is the parameter 'standard deviation' of the PDF. -#' @return .WeightIndex returns an array of with dimensions (member), -#' the normalized weights for each member of a SFS using a Best NAO PDF. +#' @return .WeightIndex returns an array of with dimensions (member), +#' the normalized weights for each member of a SFS using a Best NAO PDF. #' @examples #' # Example for the Atomic WeightIndex function #' index_exp <- c(1.3,3,-1) @@ -175,9 +176,9 @@ WeightIndex <- function(index_exp, pdf_exp) { #' str(res) #' # num [1:3(1d)] 0.84338 0.15003 0.00659 #' dim(res) -#' # member -#' # 3 -#' @noRd +#' # member +#' # 3 +#' @noRd .WeightIndex <- function(index_exp, pdf_exp) { aweights <- apply(index_exp, 1, dnorm, mean = pdf_exp[1], sd = pdf_exp[2]) dim(aweights) <- dim(index_exp) diff --git a/R/zzz.R b/R/zzz.R index 662624d9..46ecf0db 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -16,3 +16,43 @@ attributes(x) <- c(attributes(x), attr_bk) x } + +# Auxiliar function to compute the mean squared error between 'exp' and 'obs' +Sigma_notBC <- function(exp, obs) { + return(sqrt(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$MSE)) +} + +# Auxiliar function to compute the bias between 'exp' and 'obs' +MEBC <- function(exp, obs) { + return(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$ME) +} + +# Auxiliar function to compute the standard deviation of errors +# between 'obs' and 'exp' +Sigma_BC <- function(exp, obs, na.rm = FALSE) { + return(sd(obs - exp, na.rm = na.rm)) +} + +# Auxiliar function to compute the linear model used in the method +# called 'LMEV' to correct the bias in the Best NAO weighting +# methogology +LMEV <- function(exp, obs, na.rm = FALSE) { + if (any(names(dim(exp)) == 'member')) { + exp_ens_mean <- Apply(list(exp), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + exp_ens_sd <- Apply(list(exp), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else { + pos <- match(c('time','statistic'), names(dim(exp))) + exp <- aperm(exp,pos) + exp_ens_mean <- exp[,1] + exp_ens_sd <- exp[,2] + } + error <- exp_ens_mean - obs + exp_ens_var <- exp_ens_sd^2 + statModel <- lm(error ~ exp_ens_var) + return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) +} + diff --git a/man/CST_EMBestIndexWeighting.Rd b/man/CST_EMBestIndexWeighting.Rd index 9304d6aa..94035cca 100644 --- a/man/CST_EMBestIndexWeighting.Rd +++ b/man/CST_EMBestIndexWeighting.Rd @@ -7,34 +7,34 @@ CST_EMBestIndexWeighting(var_exp, weights_index_exp) } \arguments{ -\item{var_exp}{An object of the class 's2dv_cube' containing the variable +\item{var_exp}{An object of the class 's2dv_cube' containing the variable (e.g. precipitation, temperature, NAO index) array. -The var_exp object is expected to have an element named \code{$data} with -at least two dimensions (time, member). +The var_exp object is expected to have an element named \code{$data} with +at least two dimensions (time, member). If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another +If we want to compute for several SFSs, the array could have another dimension (time, dataset, member, lat, lon).} -\item{weights_index_exp}{An object of the class 's2dv_cube' containing +\item{weights_index_exp}{An object of the class 's2dv_cube' containing the normalized weights array. -The weights_index_exp object is expected to have an element named -\code{$data} with at least two dimensions (time, member). -If we want to compute for several SFSs, the array could have another +The weights_index_exp object is expected to have an element named +\code{$data} with at least two dimensions (time, member). +If we want to compute for several SFSs, the array could have another dimension (time, dataset, member).} } \value{ -CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the +CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the class 's2dv_cube'). -This object has at least an element named \code{$data} -with three or five dimensions depending if the variable is zonal -(as e.g. NAO index)(time, dataset, season) or it is spatial variable -(as e.g. precipitation or temperature) (time, dataset, season, lat, lon), +This object has at least an element named \code{$data} +with three or five dimensions depending if the variable is zonal +(as e.g. NAO index)(time, dataset, season) or it is spatial variable +(as e.g. precipitation or temperature) (time, dataset, season, lat, lon), containing the ensemble means computing with weighted members. } \description{ -This function implements the computation to obtain the weighted -ensemble means for SFSs through Best estimation of one index or driver of -climate variability (as e.g., the North Atlantic Oscillation (NAO)) and +This function implements the computation to obtain the weighted +ensemble means for SFSs through Best estimation of one index or driver of +climate variability (as e.g., the North Atlantic Oscillation (NAO)) and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ @@ -48,12 +48,12 @@ class(var_exp) <- 's2dv_cube' class(weights_index_exp) <- 's2dv_cube' res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) dim(res_CST$data) -# time dataset season lat lon -# 4 2 2 3 2 +# time dataset season lat lon +# 4 2 2 3 2 } \references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/CST_PDFBest.Rd b/man/CST_PDFBest.Rd index b5df5dc2..a87aecda 100644 --- a/man/CST_PDFBest.Rd +++ b/man/CST_PDFBest.Rd @@ -8,33 +8,33 @@ of a CSTools object.} CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) } \arguments{ -\item{pdf_exp}{An object of the class 's2dv_cube' -containing the parameters of the Index PDFs from two SFSs for a period +\item{pdf_exp}{An object of the class 's2dv_cube' +containing the parameters of the Index PDFs from two SFSs for a period to combine and obtain the Best estimation. -The index_hind object is expected to have an element named \code{$data} +The index_hind object is expected to have an element named \code{$data} with dimensions (time, dataset = 2, member = 1, statistic = 4, season).} -\item{bc_dataset1}{Logical (default = TRUE). -If TRUE the Index PDFs for the first SFS or dataset has been computed +\item{bc_dataset1}{Logical (default = TRUE). +If TRUE the Index PDFs for the first SFS or dataset has been computed with bias corrected.} -\item{bc_dataset2}{Logical (default = TRUE). -If TRUE the Index PDFs for the second SFS or dataset has been computed +\item{bc_dataset2}{Logical (default = TRUE). +If TRUE the Index PDFs for the second SFS or dataset has been computed with bias corrected.} } \value{ -CST_PDFBest() returns a CSTools object (i.e., of the +CST_PDFBest() returns a CSTools object (i.e., of the class 's2dv_cube'). -This object has at least two elements: i) an element named \code{$data} -with dimensions (time, statistic = 2, season) and ii) an element -named \code{$statistic} with the names of 'statistic' dimension +This object has at least two elements: i) an element named \code{$data} +with dimensions (time, statistic = 2, season) and ii) an element +named \code{$statistic} with the names of 'statistic' dimension of the first element ("mean", "sigma"). The firt statistic is the parameter 'mean' of the PDF The second statistic is the parameter 'standard deviation' of the PDF } \description{ This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from the forecast of SFSs +(e.g. NAO index) to improve the index estimate from the forecast of SFSs and accepts a CSTools object (an object of the class 's2dv_cube' as input. } \examples{ @@ -55,12 +55,12 @@ List of 5 # - attr(*, "class")= chr "s2dv_cube" dim(res_CST$data) # time statistic season -# 10 2 1 +# 10 2 1 str(res_CST) } \references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/CST_PDFIndexFcst.Rd b/man/CST_PDFIndexFcst.Rd index 1067ad2b..eb4ac3d9 100644 --- a/man/CST_PDFIndexFcst.Rd +++ b/man/CST_PDFIndexFcst.Rd @@ -2,57 +2,57 @@ % Please edit documentation in R/CST_PDFIndexFcst.R \name{CST_PDFIndexFcst} \alias{CST_PDFIndexFcst} -\title{Computing the Index PDFs for a forecast year of a SFSs dataset +\title{Computing the Index PDFs for a forecast year of a SFSs dataset of a CSTools object.} \usage{ CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = "ME", na.rm = FALSE) } \arguments{ -\item{index_hind}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate for a hindcast period +\item{index_hind}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate for a hindcast period from SFSs for improving. -The index_hind object is expected to have an element named \code{$data} +The index_hind object is expected to have an element named \code{$data} with dimensions (time, dataset, member, season).} -\item{index_obs}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate from observations or +\item{index_obs}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate from observations or reanalysis. -The index_obs object is expected to have an element named \code{$data} -with at least three dimensions (time, dataset, season). +The index_obs object is expected to have an element named \code{$data} +with at least three dimensions (time, dataset, season). It could have one more dimension (member) equal to 1.} -\item{index_fcst}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate for a forecast year +\item{index_fcst}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate for a forecast year from SFSs for improving. -The index_fcst object is expected to have an element named \code{$data} +The index_fcst object is expected to have an element named \code{$data} with dimensions (time = 1, dataset, member, season).} -\item{method}{A character string indicating which methodology is applied +\item{method}{A character string indicating which methodology is applied to compute the PDFs. One of "ME" (default) or "LMEV".} \item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ -CST_PDFIndexFcst() returns a CSTools object (i.e., of the +CST_PDFIndexFcst() returns a CSTools object (i.e., of the class 's2dv_cube'). -This object has at least two elements: i) an element named \code{$data} -with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element -named \code{$statistic} with the names of 'statistic' dimension -of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +This object has at least two elements: i) an element named \code{$data} +with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element +named \code{$statistic} with the names of 'statistic' dimension +of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") The firt statistic is the parameter 'mean' of the PDF with not bias corrected The second statistic is the parameter 'standard deviation' of the PDF with not bias corrected The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF +The fourth statistic is the parameter 'standard deviation' of the PDF with bias corrected } \description{ This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from the forecast of SFSs +(e.g. NAO index) to improve the index estimate from the forecast of SFSs and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ @@ -71,12 +71,12 @@ class(index_fcst) <- 's2dv_cube' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') str(res_CST) dim(res_CST$data) -#time dataset statistic season -# 1 3 4 4 +#time dataset statistic season +# 1 3 4 4 } \references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/CST_PDFIndexHind.Rd b/man/CST_PDFIndexHind.Rd index be0ca2fe..2444312a 100644 --- a/man/CST_PDFIndexHind.Rd +++ b/man/CST_PDFIndexHind.Rd @@ -2,48 +2,48 @@ % Please edit documentation in R/CST_PDFIndexHind.R \name{CST_PDFIndexHind} \alias{CST_PDFIndexHind} -\title{Computing the Index PDFs for the hindcast of a SFSs dataset +\title{Computing the Index PDFs for the hindcast of a SFSs dataset of a CSTools object} \usage{ CST_PDFIndexHind(index_exp, index_obs, method = "ME", na.rm = FALSE) } \arguments{ -\item{index_exp}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., +\item{index_exp}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. -The index_exp object is expected to have an element named \code{$data} +The index_exp object is expected to have an element named \code{$data} with dimensions (time, dataset, member, season).} -\item{index_obs}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate from observations or +\item{index_obs}{An object of the class 's2dv_cube' +containing the indices or drivers of climate variability (as e.g., +the North Atlantic Oscillation (NAO)) estimate from observations or reanalysis. -The index_obs object is expected to have an element named \code{$data} -with at least three dimensions (time, dataset, season). +The index_obs object is expected to have an element named \code{$data} +with at least three dimensions (time, dataset, season). It could have one more dimension (member) equal to 1.} -\item{method}{A character string indicating which methodology is applied +\item{method}{A character string indicating which methodology is applied to compute the PDFs. One of "ME" (default) or "LMEV".} \item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ -CST_PDFIndexHind() returns a CSTools object (i.e., of the +CST_PDFIndexHind() returns a CSTools object (i.e., of the class 's2dv_cube'). -This object has at least two elements: i) an element named \code{$data} -with dimensions (time, dataset, statistic = 4, season) and ii) an element -named \code{$statistics} with the names of 'statistic' dimension -of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") +This object has at least two elements: i) an element named \code{$data} +with dimensions (time, dataset, statistic = 4, season) and ii) an element +named \code{$statistics} with the names of 'statistic' dimension +of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") The firt statistic is the parameter 'mean' of the PDF with not bias corrected The second statistic is the parameter 'standard deviation' of the PDF with not bias corrected The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF +The fourth statistic is the parameter 'standard deviation' of the PDF with bias corrected } \description{ This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from the hindcast of SFSs +(e.g. NAO index) to improve the index estimate from the hindcast of SFSs and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ @@ -56,12 +56,12 @@ class(index_exp) <- 's2dv_cube' class(index_obs) <- 's2dv_cube' res_CST <- CST_PDFIndexHind(index_exp, index_obs) dim(res_CST$data) -#time dataset statistic season -# 5 3 4 4 +#time dataset statistic season +# 5 3 4 4 } \references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/CST_ProbBestIndexWeighting.Rd b/man/CST_ProbBestIndexWeighting.Rd index a0e78c4e..c9d9748b 100644 --- a/man/CST_ProbBestIndexWeighting.Rd +++ b/man/CST_ProbBestIndexWeighting.Rd @@ -2,44 +2,44 @@ % Please edit documentation in R/CST_ProbBestIndexWeighting.R \name{CST_ProbBestIndexWeighting} \alias{CST_ProbBestIndexWeighting} -\title{Computing the weighted tercile probabilities for SFSs of a CSTools +\title{Computing the weighted tercile probabilities for SFSs of a CSTools object.} \usage{ CST_ProbBestIndexWeighting(var_exp, weights_index_exp) } \arguments{ -\item{var_exp}{An object of the class 's2dv_cube' containing the variable +\item{var_exp}{An object of the class 's2dv_cube' containing the variable (e.g. precipitation, temperature, NAO index) array. -The var_exp object is expected to have an element named \code{$data} with -at least two dimensions (time, member). +The var_exp object is expected to have an element named \code{$data} with +at least two dimensions (time, member). If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another +If we want to compute for several SFSs, the array could have another dimension (time, dataset, member, lat, lon).} -\item{weights_index_exp}{An object of the class 's2dv_cube' containing +\item{weights_index_exp}{An object of the class 's2dv_cube' containing the normalized weights array. -The weights_index_exp object is expected to have an element named -\code{$data} with at least two dimensions (time, member). -If we want to compute for several SFSs, the array could have another +The weights_index_exp object is expected to have an element named +\code{$data} with at least two dimensions (time, member). +If we want to compute for several SFSs, the array could have another dimension (time, dataset, member).} } \value{ -CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the +CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the class 's2dv_cube'). -This object has at least an element named \code{$data} -with four or six dimensions depending if the variable is zonal -(as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable -(as e.g. precipitation or temperature) -(time, tercil, dataset, season, lat, lon), -containing the terciles probabilities computing with weighted members. +This object has at least an element named \code{$data} +with four or six dimensions depending if the variable is zonal +(as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable +(as e.g. precipitation or temperature) +(time, tercil, dataset, season, lat, lon), +containing the terciles probabilities computing with weighted members. The firt tercil is the lower tercile, the second is the normal tercile and the third is the upper tercile. } \description{ -This function implements the computation to obtain the tercile -probabilities for a weighted variable from SFSs through Best estimation of -one index or driver of climate variability (as e.g., the North Atlantic -Oscillation (NAO)) and accepts a CSTools object (an object of the class +This function implements the computation to obtain the tercile +probabilities for a weighted variable from SFSs through Best estimation of +one index or driver of climate variability (as e.g., the North Atlantic +Oscillation (NAO)) and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ @@ -53,12 +53,12 @@ class(var_exp) <- 's2dv_cube' class(weights_index_exp) <- 's2dv_cube' res_CST <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) dim(res_CST$data) -# time tercil dataset season lat lon -# 4 3 2 2 3 2 +# time tercil dataset season lat lon +# 4 3 2 2 3 2 } \references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/CST_WeightIndex.Rd b/man/CST_WeightIndex.Rd index da76b8d9..cd42bc8a 100644 --- a/man/CST_WeightIndex.Rd +++ b/man/CST_WeightIndex.Rd @@ -2,36 +2,36 @@ % Please edit documentation in R/CST_WeightIndex.R \name{CST_WeightIndex} \alias{CST_WeightIndex} -\title{Computing the weights for SFSs using the Best Index PDFs +\title{Computing the weights for SFSs using the Best Index PDFs of a CSTools object} \usage{ CST_WeightIndex(index_exp, pdf_exp) } \arguments{ -\item{index_exp}{An object of the class 's2dv_cube' containing the indices -or drivers of climate variability (as e.g., the North Atlantic Oscillation +\item{index_exp}{An object of the class 's2dv_cube' containing the indices +or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate from SFSs. -The index_exp object is expected to have an element named \code{$data} +The index_exp object is expected to have an element named \code{$data} with dimensions (time, dataset, member, season).} -\item{pdf_exp}{An object of the class 's2dv_cube' containing the statistics -which define a Gaussian PDFs for weighting the values estimate for the SFS +\item{pdf_exp}{An object of the class 's2dv_cube' containing the statistics +which define a Gaussian PDFs for weighting the values estimate for the SFS ensemble members. -The pdf_exp object is expected to have an element named \code{$data} -with at least three dimensions (time, statistic = 2, season). +The pdf_exp object is expected to have an element named \code{$data} +with at least three dimensions (time, statistic = 2, season). It could have one or two dimension (dataset and/or member) equal to 1. -The firt statistic is the parameter 'mean' of the PDFs and +The firt statistic is the parameter 'mean' of the PDFs and the second statistic is the parameter 'standard deviation' of the PDFs.} } \value{ -CST_WeightIndex() returns a CSTools object (i.e., of the +CST_WeightIndex() returns a CSTools object (i.e., of the class 's2dv_cube'). -This object has at least an element named \code{$data} +This object has at least an element named \code{$data} with dimensions (time, dataset, member, season). } \description{ -This function implements the computation to obtain the -normalized weights for SFS members using the Best Index estimation and +This function implements the computation to obtain the +normalized weights for SFS members using the Best Index estimation and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ @@ -44,12 +44,12 @@ class(index_exp) <- 's2dv_cube' class(index_obs) <- 's2dv_cube' res_CST <- CST_PDFIndexHind(index_exp, index_obs) dim(res_CST$data) -#time dataset statistic season -# 5 3 4 4 +#time dataset statistic season +# 5 3 4 4 } \references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/EMBestIndexWeighting.Rd b/man/EMBestIndexWeighting.Rd index 253517b0..62997637 100644 --- a/man/EMBestIndexWeighting.Rd +++ b/man/EMBestIndexWeighting.Rd @@ -7,26 +7,26 @@ EMBestIndexWeighting(var_exp, weights_index_exp) } \arguments{ -\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with at least two dimensions (time, member). +\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) +array from a SFS with at least two dimensions (time, member). If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another +If we want to compute for several SFSs, the array could have another dimension (time, dataset, member, lat, lon).} -\item{weights_index_exp}{Normalized weights array with at least two -dimensions (time, member). If we want to compute for several SFSs, +\item{weights_index_exp}{Normalized weights array with at least two +dimensions (time, member). If we want to compute for several SFSs, the array could have another dimension (time, dataset, member).} } \value{ -EMBestIndexWeighting() returns an array with two or four dimensions -depending if the variable is zonal (as e.g. NAO index)(time, dataset) -or it is spatial variable (as e.g. precipitation or temperature) -(time, dataset, lat, lon), containing the ensemble means computing +EMBestIndexWeighting() returns an array with two or four dimensions +depending if the variable is zonal (as e.g. NAO index)(time, dataset) +or it is spatial variable (as e.g. precipitation or temperature) +(time, dataset, lat, lon), containing the ensemble means computing with weighted members. } \description{ -This function implements the computation to obtain the weighted -ensemble means for SFSs through Best estimation of one index or driver of +This function implements the computation to obtain the weighted +ensemble means for SFSs through Best estimation of one index or driver of climate variability (as e.g., the North Atlantic Oscillation (NAO)). } \examples{ @@ -37,8 +37,8 @@ weights_index_exp<- runif(24, min=0.001, max=0.999) dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) res <- EMBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time dataset -# 2 3 +# time dataset +# 2 3 str(res) # num [1:2, 1:3] 36.2 28.8 38.3 31.9 31 ... # Example 2 for the EMBestIndexWeighting function @@ -48,15 +48,15 @@ weights_index_exp<- runif(24, min=0.001, max=0.999) dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) res <- EMBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time dataset lat lon +# time dataset lat lon # 2 3 2 3 str(res) # num [1:2, 1:3, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... - + } \references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/PDFBest.Rd b/man/PDFBest.Rd index a46b0c8a..d20753ac 100644 --- a/man/PDFBest.Rd +++ b/man/PDFBest.Rd @@ -7,28 +7,28 @@ PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) } \arguments{ -\item{pdf_exp}{Statistics array for the SFSs Index PDFs for a period of years +\item{pdf_exp}{Statistics array for the SFSs Index PDFs for a period of years with dimensions (time, dataset = 2, statistic = 4, season).} -\item{bc_dataset1}{Logical (default = TRUE). -If TRUE the Index PDFs for the first SFS or dataset has been computed +\item{bc_dataset1}{Logical (default = TRUE). +If TRUE the Index PDFs for the first SFS or dataset has been computed with bias corrected.} -\item{bc_dataset2}{Logical (default = TRUE). -If TRUE the Index PDFs for the second SFS or dataset has been computed +\item{bc_dataset2}{Logical (default = TRUE). +If TRUE the Index PDFs for the second SFS or dataset has been computed with bias corrected.} } \value{ -PDFBest() returns an array +PDFBest() returns an array with dimensions (time, statistic = 2, season). The firt statistic is the parameter 'mean' of the PDF for the best estimation -combining the two SFSs PDFs. -The second statistic is the parameter 'standard deviation' of the PDF for +combining the two SFSs PDFs. +The second statistic is the parameter 'standard deviation' of the PDF for the best estimation combining the two SFSs PDFs. } \description{ This function implements the computation to obtain the index PDFs -(e.g. NAO index) obtained to combining the Index PDFs for two SFSs, +(e.g. NAO index) obtained to combining the Index PDFs for two SFSs, the Best Index estimation. } \examples{ @@ -37,15 +37,15 @@ pdf_exp <- c(1: (10 * 2* 4 * 1)) dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) dim(res) -# time statistic season -# 10 2 1 +# time statistic season +# 10 2 1 str(res) # num [1:10, 1:2, 1] 17.2 18.3 19.5 20.6 21.7 ... } \references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/PDFIndexFcst.Rd b/man/PDFIndexFcst.Rd index 2926709e..779e5517 100644 --- a/man/PDFIndexFcst.Rd +++ b/man/PDFIndexFcst.Rd @@ -8,28 +8,28 @@ PDFIndexFcst(index_hind, index_obs, index_fcst, method = "ME", na.rm = FALSE) } \arguments{ -\item{index_hind}{Index (e.g. NAO index) array from SFSs with dimensions +\item{index_hind}{Index (e.g. NAO index) array from SFSs with dimensions (time, dataset, member) for a hindcast period.} -\item{index_obs}{Index (e.g. NAO index) array from an observational database +\item{index_obs}{Index (e.g. NAO index) array from an observational database or reanalysis with dimension (time, dataset, member = 1) or (time, dataset).} -\item{index_fcst}{Index (e.g. NAO index) array from SFSs. +\item{index_fcst}{Index (e.g. NAO index) array from SFSs. with dimensions (time = 1, dataset, member) for a forecast year.} -\item{method}{A character string indicating which methodology is applied +\item{method}{A character string indicating which methodology is applied to compute the PDF. One of "ME" (default) or "LMEV".} \item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ -PDFIndexFcst() returns an array -with dimensions (time = 1, statistic = 4, dataset) for the forecast year. +PDFIndexFcst() returns an array +with dimensions (time = 1, statistic = 4, dataset) for the forecast year. The firt statistic is the parameter 'mean' of the PDF with not bias corrected The second statistic is the parameter 'standard deviation' of the PDF with not bias corrected The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF +The fourth statistic is the parameter 'standard deviation' of the PDF with bias corrected } \description{ @@ -46,15 +46,15 @@ index_hind <- 1 : (5 * 3 * 4) dim(index_hind) <- c(time = 5, dataset = 3, member = 4) res <- PDFIndexFcst(index_hind, index_obs, index_fcst) dim(res) -# time statistic dataset -# 1 4 3 +# time statistic dataset +# 1 4 3 str(res) # num [1, 1:4, 1:3] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... - + } \references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/PDFIndexHind.Rd b/man/PDFIndexHind.Rd index 0836ace4..fa174761 100644 --- a/man/PDFIndexHind.Rd +++ b/man/PDFIndexHind.Rd @@ -10,22 +10,22 @@ PDFIndexHind(index_exp, index_obs, method = "ME", na.rm = FALSE) \item{index_exp}{Index (e.g. NAO index) array from SFSs with dimensions (time, dataset, member).} -\item{index_obs}{Index (e.g. NAO index) array from an observational database +\item{index_obs}{Index (e.g. NAO index) array from an observational database or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset).} -\item{method}{A character string indicating which methodology is applied +\item{method}{A character string indicating which methodology is applied to compute the PDFs. One of "ME" (default) or "LMEV".} \item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ -PDFIndexHind() returns an array -with dimensions (time, statistic = 4, dataset). +PDFIndexHind() returns an array +with dimensions (time, statistic = 4, dataset). The firt statistic is the parameter 'mean' of the PDF with not bias corrected The second statistic is the parameter 'standard deviation' of the PDF with not bias corrected The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF +The fourth statistic is the parameter 'standard deviation' of the PDF with bias corrected } \description{ @@ -42,13 +42,13 @@ res <- PDFIndexHind(index_exp, index_obs) str(res) # num [1:5, 1:4, 1:3] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... dim(res) -# time statistic dataset -# 5 4 3 - +# time statistic dataset +# 5 4 3 + } \references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/ProbBestIndexWeighting.Rd b/man/ProbBestIndexWeighting.Rd index 12fb42e9..cbb86e3b 100644 --- a/man/ProbBestIndexWeighting.Rd +++ b/man/ProbBestIndexWeighting.Rd @@ -7,29 +7,29 @@ ProbBestIndexWeighting(var_exp, weights_index_exp) } \arguments{ -\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with at least two dimensions (time, member). +\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) +array from a SFS with at least two dimensions (time, member). If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another +If we want to compute for several SFSs, the array could have another dimension (time, dataset, member, lat, lon).} -\item{weights_index_exp}{Normalized weights array with at least two -dimensions (time, member). If we want to compute for several SFSs, +\item{weights_index_exp}{Normalized weights array with at least two +dimensions (time, member). If we want to compute for several SFSs, the array could have another dimension (time, dataset, member).} } \value{ -ProbBestIndexWeighting() returns an array with three or five dimensions -depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) -or it is spatial variable (as e.g. precipitation or temperature) -(time, tercil, dataset, lat, lon), containing the terciles probabilities -computing with weighted members. +ProbBestIndexWeighting() returns an array with three or five dimensions +depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) +or it is spatial variable (as e.g. precipitation or temperature) +(time, tercil, dataset, lat, lon), containing the terciles probabilities +computing with weighted members. The firt tercil is the lower tercile, the second is the normal tercile and the third is the upper tercile. } \description{ -This function implements the computation to obtain the tercile -probabilities for a weighted variable from SFSs through Best estimation of -one index or driver of climate variability (as e.g., the North Atlantic +This function implements the computation to obtain the tercile +probabilities for a weighted variable from SFSs through Best estimation of +one index or driver of climate variability (as e.g., the North Atlantic Oscillation (NAO)). } \examples{ @@ -40,8 +40,8 @@ weights_index_exp<- runif(24, min=0.001, max=0.999) dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) res <- ProbBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time tercil dataset -# 2 3 3 +# time tercil dataset +# 2 3 3 str(res) # num [1:2, 1:3, 1:3] 0.6773 0 0 0.0413 1.8419 ... # Example 2 for the ProbBestIndexWeighting function @@ -51,15 +51,15 @@ weights_index_exp<- runif(24, min=0.001, max=0.999) dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) res <- ProbBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time tercil dataset lat lon -# 2 3 3 2 3 +# time tercil dataset lat lon +# 2 3 3 2 3 str(res) # num [1:2, 1:3, 1:3, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... - + } \references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ diff --git a/man/WeightIndex.Rd b/man/WeightIndex.Rd index 90aed8d5..a464004d 100644 --- a/man/WeightIndex.Rd +++ b/man/WeightIndex.Rd @@ -7,21 +7,21 @@ WeightIndex(index_exp, pdf_exp) } \arguments{ -\item{index_exp}{Index (e.g. NAO index) array, from a dataset of SFSs +\item{index_exp}{Index (e.g. NAO index) array, from a dataset of SFSs for a period of years, with dimensions (time, dataset, member).} -\item{pdf_exp}{Statistics array to define a Gaussian PDF with dimensions +\item{pdf_exp}{Statistics array to define a Gaussian PDF with dimensions (time, statistic = 2) for a period of years. -The firt statistic is the parameter 'mean' of the PDF and +The firt statistic is the parameter 'mean' of the PDF and the second statistic is the parameter 'standard deviation' of the PDF.} } \value{ -WeightIndex() returns a normalized weights array with dimensions +WeightIndex() returns a normalized weights array with dimensions (time, dataset, member). } \description{ -This function implements the computation to obtain the -normalized weights for each member of each SFS or dataset using the +This function implements the computation to obtain the +normalized weights for each member of each SFS or dataset using the Best Index estimation. } \examples{ @@ -32,15 +32,15 @@ pdf_exp <- 1 : (10 * 2) dim(pdf_exp) <- c(time = 10, statistic = 2) res <- WeightIndex(index_exp, pdf_exp) dim(res) -# time dataset member -# 10 3 5 +# time dataset member +# 10 3 5 str(res) # num [1:10, 1:3, 1:5] 0.976 0.958 0.935 0.908 0.881 ... } \references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ -- GitLab From 2ac4e9a4ba68a53849a2affe6f15b63193782b2c Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Wed, 28 Aug 2019 10:56:27 +0000 Subject: [PATCH 03/40] Update NAMESPACE file --- DESCRIPTION | 2 +- NAMESPACE | 13 +++++++++++++ man/CST_Anomaly.Rd | 7 +++---- man/CST_BiasCorrection.Rd | 7 +++---- man/CST_Calibration.Rd | 7 +++---- man/CST_Load.Rd | 1 - man/CST_MultiMetric.Rd | 9 ++++----- man/CST_MultivarRMSE.Rd | 7 +++---- man/CST_RFSlope.Rd | 1 - man/CST_RFWeights.Rd | 7 +++---- man/CST_RainFARM.Rd | 7 +++---- man/PlotCombinedMap.Rd | 7 +++---- man/PlotForecastPDF.Rd | 1 - man/PlotMostLikelyQuantileMap.Rd | 7 +++---- man/RFSlope.Rd | 1 - man/RainFARM.Rd | 1 - man/areave_data.Rd | 1 - man/lonlat_data.Rd | 1 - man/lonlat_prec.Rd | 1 - 19 files changed, 42 insertions(+), 46 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 23fafc4c..dfc56570 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -53,4 +53,4 @@ VignetteBuilder: knitr License: Apache License 2.0 Encoding: UTF-8 LazyData: true -RoxygenNote: 5.0.0 +RoxygenNote: 6.0.1 diff --git a/NAMESPACE b/NAMESPACE index ef0bfd71..9cd1f764 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,23 +3,36 @@ export(CST_Anomaly) export(CST_BiasCorrection) export(CST_Calibration) +export(CST_EMBestIndexWeighting) export(CST_Load) export(CST_MultiMetric) export(CST_MultivarRMSE) +export(CST_PDFBest) +export(CST_PDFIndexFcst) +export(CST_PDFIndexHind) +export(CST_ProbBestIndexWeighting) export(CST_RFSlope) export(CST_RFWeights) export(CST_RainFARM) +export(CST_WeightIndex) +export(EMBestIndexWeighting) +export(PDFBest) +export(PDFIndexFcst) +export(PDFIndexHind) export(PlotCombinedMap) export(PlotForecastPDF) export(PlotMostLikelyQuantileMap) +export(ProbBestIndexWeighting) export(RFSlope) export(RainFARM) +export(WeightIndex) import(ggplot2) import(multiApply) import(ncdf4) import(rainfarmr) import(s2dverification) import(stats) +import(verification) importFrom(data.table,CJ) importFrom(data.table,data.table) importFrom(data.table,setkey) diff --git a/man/CST_Anomaly.Rd b/man/CST_Anomaly.Rd index e1c31f0c..b214a31a 100644 --- a/man/CST_Anomaly.Rd +++ b/man/CST_Anomaly.Rd @@ -53,13 +53,12 @@ str(anom3) anom4 <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = FALSE) str(anom4) +} +\seealso{ +\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} } \author{ Perez-Zanon Nuria, \email{nuria.perez@bsc.es} Pena Jesus, \email{jesus.pena@bsc.es} } -\seealso{ -\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} -} - diff --git a/man/CST_BiasCorrection.Rd b/man/CST_BiasCorrection.Rd index 485199ea..a1b415fb 100644 --- a/man/CST_BiasCorrection.Rd +++ b/man/CST_BiasCorrection.Rd @@ -35,10 +35,9 @@ attr(obs, 'class') <- 's2dv_cube' a <- CST_BiasCorrection(exp = exp, obs = obs) str(a) } -\author{ -Verónica Torralba, \email{veronica.torralba@bsc.es} -} \references{ Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) } -\encoding{UTF-8} +\author{ +Verónica Torralba, \email{veronica.torralba@bsc.es} +} diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd index c8419076..58b12617 100644 --- a/man/CST_Calibration.Rd +++ b/man/CST_Calibration.Rd @@ -25,13 +25,12 @@ c(exp, obs) \%<-\% areave_data exp_calibrated <- CST_Calibration(exp = exp, obs = obs) str(exp_calibrated) } -\author{ -Verónica Torralba, \email{veronica.torralba@bsc.es} -} \references{ Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x } \seealso{ \code{\link{CST_Load}} } -\encoding{UTF-8} +\author{ +Verónica Torralba, \email{veronica.torralba@bsc.es} +} diff --git a/man/CST_Load.Rd b/man/CST_Load.Rd index 1fee022c..bf03ba42 100644 --- a/man/CST_Load.Rd +++ b/man/CST_Load.Rd @@ -47,4 +47,3 @@ obs <- CSTools::lonlat_data$obs \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} } - diff --git a/man/CST_MultiMetric.Rd b/man/CST_MultiMetric.Rd index 079a5588..8e3ce593 100644 --- a/man/CST_MultiMetric.Rd +++ b/man/CST_MultiMetric.Rd @@ -37,15 +37,14 @@ c(ano_exp, ano_obs) \%<-\% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb a <- CST_MultiMetric(exp = ano_exp, obs = ano_obs) str(a) } -\author{ -Mishra Niti, \email{niti.mishra@bsc.es} - -Perez-Zanon Nuria, \email{nuria.perez@bsc.es} -} \references{ Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dverification]{Corr}}, \code{\link[s2dverification]{RMS}}, \code{\link[s2dverification]{RMSSS}} and \code{\link{CST_Load}} } +\author{ +Mishra Niti, \email{niti.mishra@bsc.es} +Perez-Zanon Nuria, \email{nuria.perez@bsc.es} +} diff --git a/man/CST_MultivarRMSE.Rd b/man/CST_MultivarRMSE.Rd index 685eaf77..24af608c 100644 --- a/man/CST_MultivarRMSE.Rd +++ b/man/CST_MultivarRMSE.Rd @@ -56,10 +56,9 @@ weight <- c(1, 2) a <- CST_MultivarRMSE(exp = ano_exp, obs = ano_obs, weight = weight) str(a) } -\author{ -Deborah Verfaillie, \email{deborah.verfaillie@bsc.es} -} \seealso{ \code{\link[s2dverification]{RMS}} and \code{\link{CST_Load}} } - +\author{ +Deborah Verfaillie, \email{deborah.verfaillie@bsc.es} +} diff --git a/man/CST_RFSlope.Rd b/man/CST_RFSlope.Rd index d2b5aec0..0c4e1671 100644 --- a/man/CST_RFSlope.Rd +++ b/man/CST_RFSlope.Rd @@ -50,4 +50,3 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } - diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd index 08a7b850..ef5ebe4d 100644 --- a/man/CST_RFWeights.Rd +++ b/man/CST_RFWeights.Rd @@ -47,9 +47,6 @@ nf <- 8 ww <- CST_RFWeights("./worldclim.nc", nf, lon, lat, fsmooth = TRUE) } } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} \references{ Terzago, S., Palazzi, E., & von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: @@ -57,4 +54,6 @@ A simple method to reproduce a realistic fine-scale climatology. Natural Hazards and Earth System Sciences, 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 . } - +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd index ca32e2d8..8df8f1f4 100644 --- a/man/CST_RainFARM.Rd +++ b/man/CST_RainFARM.Rd @@ -92,12 +92,11 @@ dim(res$data) # dataset member sdate ftime lat lon realization # 1 2 3 4 64 64 3 } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} \references{ Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 ; D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724. } - +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd index 6857c64d..ec47d72e 100644 --- a/man/PlotCombinedMap.Rd +++ b/man/PlotCombinedMap.Rd @@ -67,12 +67,11 @@ PlotCombinedMap(list(a, b, c), lons, lats, bar_titles = paste('\% of belonging to', c('a', 'b', 'c')), brks = 20, width = 10, height = 8) } +\seealso{ +\code{PlotCombinedMap} and \code{PlotEquiMap} +} \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} Veronica Torralba, \email{veronica.torralba@bsc.es} } -\seealso{ -\code{PlotCombinedMap} and \code{PlotEquiMap} -} - diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd index bed0bd31..a74ab710 100644 --- a/man/PlotForecastPDF.Rd +++ b/man/PlotForecastPDF.Rd @@ -49,4 +49,3 @@ PlotForecastPDF(fcsts2, c(-0.66, 0.66), extreme.limits = c(-1.2, 1.2), \author{ Llorenç Lledó \email{llledo@bsc.es} } -\encoding{UTF-8} diff --git a/man/PlotMostLikelyQuantileMap.Rd b/man/PlotMostLikelyQuantileMap.Rd index 6c92850e..0d984ede 100644 --- a/man/PlotMostLikelyQuantileMap.Rd +++ b/man/PlotMostLikelyQuantileMap.Rd @@ -109,11 +109,10 @@ PlotMostLikelyQuantileMap(bins, lons, lats, mask = 1 - (w1 + w2 / max(c(w1, w2))), brks = 20, width = 10, height = 8) -} -\author{ -Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es} } \seealso{ \code{PlotCombinedMap} and \code{PlotEquiMap} } - +\author{ +Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es} +} diff --git a/man/RFSlope.Rd b/man/RFSlope.Rd index 09a24ff5..5308ef8c 100644 --- a/man/RFSlope.Rd +++ b/man/RFSlope.Rd @@ -60,4 +60,3 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } - diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd index 3ef2a2f6..e447e162 100644 --- a/man/RainFARM.Rd +++ b/man/RainFARM.Rd @@ -113,4 +113,3 @@ dim(res$data) \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } - diff --git a/man/areave_data.Rd b/man/areave_data.Rd index cc79c85c..a772220a 100644 --- a/man/areave_data.Rd +++ b/man/areave_data.Rd @@ -41,4 +41,3 @@ areave_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} - diff --git a/man/lonlat_data.Rd b/man/lonlat_data.Rd index eca7abac..0c6ee30f 100644 --- a/man/lonlat_data.Rd +++ b/man/lonlat_data.Rd @@ -41,4 +41,3 @@ lonlat_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} - diff --git a/man/lonlat_prec.Rd b/man/lonlat_prec.Rd index 69cb94e8..345e3cab 100644 --- a/man/lonlat_prec.Rd +++ b/man/lonlat_prec.Rd @@ -29,4 +29,3 @@ lonlat_prec <- CST_Load('prlr', exp = list(infile), obs = NULL, Jost von Hardenberg \email{j.vonhardenberg@isac.cnr.it} } \keyword{data} - -- GitLab From d2e729183f6ae7793475701a9f126c659234d4f0 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Wed, 28 Aug 2019 12:09:38 +0000 Subject: [PATCH 04/40] Update files CST_PDFIndexHind.R and CST_PDFIndexFcst.R --- NAMESPACE | 2 +- R/CST_PDFIndexFcst.R | 2 +- R/CST_PDFIndexHind.R | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 9cd1f764..123e5a9b 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -32,7 +32,6 @@ import(ncdf4) import(rainfarmr) import(s2dverification) import(stats) -import(verification) importFrom(data.table,CJ) importFrom(data.table,data.table) importFrom(data.table,setkey) @@ -60,3 +59,4 @@ importFrom(plyr,.) importFrom(plyr,dlply) importFrom(reshape2,melt) importFrom(utils,glob2rx) +importFrom(verification,verify) diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R index c0cf8863..3e4bb23e 100644 --- a/R/CST_PDFIndexFcst.R +++ b/R/CST_PDFIndexFcst.R @@ -215,7 +215,7 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', #' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected for the forecast year. #' @import multiApply -#' @import verification +#' @importFrom verification verify #' @examples #' # Example for the Atomic PDFIndexFcst function #' index_fcst <- 1 : 3 diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R index 6d948dce..0da0d1e1 100644 --- a/R/CST_PDFIndexHind.R +++ b/R/CST_PDFIndexHind.R @@ -165,7 +165,7 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { #' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected for the hindcast period. #' @import multiApply -#' @import verification +#' @importFrom verification verify #' @examples #' # Example for the Atomic PDFIndexHind function #' index_obs <- 1 : 10 -- GitLab From 6195ddbe51e90205e52bff9267b8663fc8ac8ddc Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 29 Aug 2019 06:35:16 +0000 Subject: [PATCH 05/40] Update DESCRIPTION file --- DESCRIPTION | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index dfc56570..bc9a1952 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -14,7 +14,8 @@ Authors@R: c( person("Deborah", "Verfaillie", , "deborah.verfaillie@bsc.es", role = "aut"), person("Lauriane", "Batte", , "lauriane.batte@meteo.fr", role = "ctb"), person("Jesus", "Peña", , "jesus.pena@bsc.es", role = "ctb"), - person("Bert", "van Schaeybroeck", , "bertvs@meteo.be", role = "ctb")) + person("Bert", "van Schaeybroeck", , "bertvs@meteo.be", role = "ctb"), + person("Eroteida", "Sanchez-Garcia", , "esanchezg@aemet.es", role = "ctb")) Description: Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, @@ -43,7 +44,8 @@ Imports: graphics, grDevices, stats, - utils + utils, + verification Suggests: zeallot, testthat, -- GitLab From 298a072f7c85768843b52912720ef4d82ba83106 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 29 Aug 2019 06:50:05 +0000 Subject: [PATCH 06/40] Update CST_PDFBest.R file --- R/CST_PDFBest.R | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/R/CST_PDFBest.R b/R/CST_PDFBest.R index e5dc0704..bc877913 100644 --- a/R/CST_PDFBest.R +++ b/R/CST_PDFBest.R @@ -36,7 +36,7 @@ #' class(pdf_exp) <- 's2dv_cube' #' res_CST <- CST_PDFBest(pdf_exp) #' str(res_CST) -#' List of 5 +#' # List of 5 #' # $ data : num [1:10, 1:2, 1] 45.2 46.3 47.3 48.3 49.3 ... #' # $ Datasets : chr [1:2] "ECMWF-S5" "UKMO-S13" #' # $ season : chr "DJF" @@ -47,7 +47,6 @@ #' dim(res_CST$data) #' # time statistic season #' # 10 2 1 -#' str(res_CST) CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { if (!inherits(pdf_exp, 's2dv_cube')) { stop("Parameter 'pdf_exp' must be of the class 's2dv_cube', ", -- GitLab From bae4803d75bfc7bd44570b1e4cf63d1feab285cd Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 29 Aug 2019 07:16:02 +0000 Subject: [PATCH 07/40] Update CST_PDFBest.Rd file --- man/CST_PDFBest.Rd | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/man/CST_PDFBest.Rd b/man/CST_PDFBest.Rd index a87aecda..f2bbb0e5 100644 --- a/man/CST_PDFBest.Rd +++ b/man/CST_PDFBest.Rd @@ -45,7 +45,7 @@ pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), class(pdf_exp) <- 's2dv_cube' res_CST <- CST_PDFBest(pdf_exp) str(res_CST) -List of 5 +# List of 5 # $ data : num [1:10, 1:2, 1] 45.2 46.3 47.3 48.3 49.3 ... # $ Datasets : chr [1:2] "ECMWF-S5" "UKMO-S13" # $ season : chr "DJF" @@ -56,7 +56,6 @@ List of 5 dim(res_CST$data) # time statistic season # 10 2 1 -str(res_CST) } \references{ Regionally improved seasonal forecast of precipitation through -- GitLab From 123d0f6a96724c63c53533e4596e0ec57ced5a2d Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 29 Aug 2019 07:59:56 +0000 Subject: [PATCH 08/40] Delete examples in CST_PDFIndexFcst.R file --- R/CST_PDFIndexFcst.R | 18 ------------------ man/CST_PDFIndexFcst.Rd | 19 ------------------- 2 files changed, 37 deletions(-) diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R index 3e4bb23e..90e05aa8 100644 --- a/R/CST_PDFIndexFcst.R +++ b/R/CST_PDFIndexFcst.R @@ -43,24 +43,6 @@ #' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected #' @export -#' @examples -#' index_hind <- 1 : (3 * 5 * 4 * 7) -#' dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) -#' index_hind <- list(data = index_hind) -#' class(index_hind) <- 's2dv_cube' -#' index_obs <- 1 : (3 * 5 * 4) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -#' index_obs <- list(data = index_obs) -#' class(index_obs) <- 's2dv_cube' -#' index_fcst <- 1 : (3 * 4 * 7) -#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) -#' index_fcst <- list(data = index_fcst) -#' class(index_fcst) <- 's2dv_cube' -#' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') -#' str(res_CST) -#' dim(res_CST$data) -#' #time dataset statistic season -#' # 1 3 4 4 CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method = 'ME', na.rm = FALSE) { if (!inherits(index_hind, 's2dv_cube') || diff --git a/man/CST_PDFIndexFcst.Rd b/man/CST_PDFIndexFcst.Rd index eb4ac3d9..b3a87640 100644 --- a/man/CST_PDFIndexFcst.Rd +++ b/man/CST_PDFIndexFcst.Rd @@ -55,25 +55,6 @@ This function implements the computation to obtain the index PDFs (e.g. NAO index) to improve the index estimate from the forecast of SFSs and accepts a CSTools object (an object of the class 's2dv_cube') as input. } -\examples{ -index_hind <- 1 : (3 * 5 * 4 * 7) -dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) -index_hind <- list(data = index_hind) -class(index_hind) <- 's2dv_cube' -index_obs <- 1 : (3 * 5 * 4) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -index_obs <- list(data = index_obs) -class(index_obs) <- 's2dv_cube' -index_fcst <- 1 : (3 * 4 * 7) -dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) -index_fcst <- list(data = index_fcst) -class(index_fcst) <- 's2dv_cube' -res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') -str(res_CST) -dim(res_CST$data) -#time dataset statistic season -# 1 3 4 4 -} \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -- GitLab From 3a30566438cf4d547a8cbc893dfc0237c1f4c7bf Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 29 Aug 2019 13:11:06 +0000 Subject: [PATCH 09/40] Add vignette, data and examples in CST_PDFIndexFcst.R --- R/CST_PDFIndexFcst.R | 18 ++ R/CST_PDFIndexHind.R | 2 +- data/lNAOs_CST.RData | Bin 0 -> 5317 bytes data/pr_hindcast_ecmf-s5_CST.RData | Bin 0 -> 2433332 bytes man/CST_PDFIndexFcst.Rd | 19 ++ man/CST_PDFIndexHind.Rd | 1 + vignettes/BestNaoWeighting_vignette.Rmd | 220 ++++++++++++++++++++++++ 7 files changed, 259 insertions(+), 1 deletion(-) create mode 100644 data/lNAOs_CST.RData create mode 100644 data/pr_hindcast_ecmf-s5_CST.RData create mode 100644 vignettes/BestNaoWeighting_vignette.Rmd diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R index 90e05aa8..3e4bb23e 100644 --- a/R/CST_PDFIndexFcst.R +++ b/R/CST_PDFIndexFcst.R @@ -43,6 +43,24 @@ #' The fourth statistic is the parameter 'standard deviation' of the PDF #' with bias corrected #' @export +#' @examples +#' index_hind <- 1 : (3 * 5 * 4 * 7) +#' dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) +#' index_hind <- list(data = index_hind) +#' class(index_hind) <- 's2dv_cube' +#' index_obs <- 1 : (3 * 5 * 4) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) +#' index_obs <- list(data = index_obs) +#' class(index_obs) <- 's2dv_cube' +#' index_fcst <- 1 : (3 * 4 * 7) +#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) +#' index_fcst <- list(data = index_fcst) +#' class(index_fcst) <- 's2dv_cube' +#' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') +#' str(res_CST) +#' dim(res_CST$data) +#' #time dataset statistic season +#' # 1 3 4 4 CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method = 'ME', na.rm = FALSE) { if (!inherits(index_hind, 's2dv_cube') || diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R index 0da0d1e1..10053364 100644 --- a/R/CST_PDFIndexHind.R +++ b/R/CST_PDFIndexHind.R @@ -38,7 +38,7 @@ #' @export #' @examples #' index_obs <- 1 : (3 * 5 * 4) -# 'dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) 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a/vignettes/BestNaoWeighting_vignette.Rmd +++ b/vignettes/BestNaoWeighting_vignette.Rmd @@ -198,7 +198,7 @@ PlotEquiMap(t(probData_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[lengt ``` -![](./output/probLower_weight_CST.png){width=70%} +![](./vignettes/Figures/probLower_weight_CST.png){width=70%} In a similar way we can plot the map with the probability that the total precipitation from November 2012 to March 2013, for example, will be in the lower tercile from ECMWF Seasonal Forecast System 5 (raw) to compare results running the code: @@ -217,4 +217,4 @@ PlotEquiMap(t(probDataRaw_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[le ``` -![](./output/probLower_raw_CST.png){width=70%} \ No newline at end of file +![](./vignettes/Figures/probLower_raw_CST.png){width=70%} -- GitLab From 677b8777b289313cf8c578537bf94eab943d5b0e Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 5 Sep 2019 09:44:23 +0000 Subject: [PATCH 12/40] Updating vignette --- vignettes/BestNaoWeighting_vignette.Rmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/vignettes/BestNaoWeighting_vignette.Rmd b/vignettes/BestNaoWeighting_vignette.Rmd index 2b65d0e4..f7a7c655 100644 --- a/vignettes/BestNaoWeighting_vignette.Rmd +++ b/vignettes/BestNaoWeighting_vignette.Rmd @@ -189,7 +189,7 @@ probData_CST <- prob_var_exp$data[which(var_exp$Dates$start == 2012),1,1,,,1] brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") -ofile <- "./output/probLower_weight_CST.png" +ofile <- "./Figures/probLower_weight_CST.png" PlotEquiMap(t(probData_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, fileout =ofile, brks=brks, cols = cols, toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on the best NAO pdf estimate"), @@ -198,7 +198,7 @@ PlotEquiMap(t(probData_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[lengt ``` -![](./vignettes/Figures/probLower_weight_CST.png){width=70%} +![](./Figures/probLower_weight_CST.png){width=70%} In a similar way we can plot the map with the probability that the total precipitation from November 2012 to March 2013, for example, will be in the lower tercile from ECMWF Seasonal Forecast System 5 (raw) to compare results running the code: @@ -208,7 +208,7 @@ probDataRaw_CST <- prob_var_exp_raw$data[which(var_exp$Dates$start == 2012),1,1, brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") -ofile <- "./output/probLower_raw_CST.png" +ofile <- "./Figures/probLower_raw_CST.png" PlotEquiMap(t(probDataRaw_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, fileout =ofile, brks=brks, cols = cols, toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on ECMWF-S5 (raw)"), @@ -217,4 +217,4 @@ PlotEquiMap(t(probDataRaw_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[le ``` -![](./vignettes/Figures/probLower_raw_CST.png){width=70%} +![](./Figures/probLower_raw_CST.png){width=70%} -- GitLab From 209b579cd84325d2cc34cec3630923cc9f142711 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 5 Sep 2019 12:36:34 +0000 Subject: [PATCH 13/40] Delete ggplot2 library in the vignette --- vignettes/BestNaoWeighting_vignette.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/vignettes/BestNaoWeighting_vignette.Rmd b/vignettes/BestNaoWeighting_vignette.Rmd index f7a7c655..6cea9d78 100644 --- a/vignettes/BestNaoWeighting_vignette.Rmd +++ b/vignettes/BestNaoWeighting_vignette.Rmd @@ -18,7 +18,6 @@ In our R session, we will need to: library(multiApply) library(verification) library(s2dverification) -library(ggplot2) ``` Data files used in the following code will be in `./data_CST` directory. -- GitLab From c1a7d1c9ee89697bb9a3c9b8b0fe1c1ceae3c73e Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Fri, 6 Sep 2019 17:20:36 +0000 Subject: [PATCH 14/40] Change example in CST_WeightIndex.R and update documentation --- DESCRIPTION | 2 +- R/CST_WeightIndex.R | 16 ++++++++-------- man/CST_WeightIndex.Rd | 15 ++++++++------- man/PlotCombinedMap.Rd | 8 ++++---- man/PlotForecastPDF.Rd | 9 +++++---- man/RainFARM.Rd | 7 ++++--- 6 files changed, 30 insertions(+), 27 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index bc9a1952..4e04364e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -55,4 +55,4 @@ VignetteBuilder: knitr License: Apache License 2.0 Encoding: UTF-8 LazyData: true -RoxygenNote: 6.0.1 +RoxygenNote: 6.1.1 diff --git a/R/CST_WeightIndex.R b/R/CST_WeightIndex.R index 7a42710e..93eb01f1 100644 --- a/R/CST_WeightIndex.R +++ b/R/CST_WeightIndex.R @@ -27,18 +27,18 @@ #' with dimensions (time, dataset, member, season). #' @export #' @examples -#' index_obs <- 1 : (3 * 5 * 4) -# 'dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -#' index_exp <- 1 : (3 * 5 * 4 * 7) +#' index_exp <- 1 : (5 * 3 * 7 * 4) #' dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) #' index_exp <- list(data = index_exp) -#' index_obs <- list(data = index_obs) +#' pdf_exp <- 1 : (5 * 2 * 4) +#' dim(pdf_exp) <- c(time = 5, statistic = 2, season = 4) +#' pdf_exp <- list(data = pdf_exp) #' class(index_exp) <- 's2dv_cube' -#' class(index_obs) <- 's2dv_cube' -#' res_CST <- CST_PDFIndexHind(index_exp, index_obs) +#' class(pdf_exp) <- 's2dv_cube' +#' res_CST <- CST_WeightIndex(index_exp, pdf_exp) #' dim(res_CST$data) -#' #time dataset statistic season -#' # 5 3 4 4 +#' #time dataset member season +#' # 5 3 7 4 CST_WeightIndex <- function(index_exp, pdf_exp) { if (!inherits(index_exp, 's2dv_cube') || !inherits(pdf_exp, 's2dv_cube')) { stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", diff --git a/man/CST_WeightIndex.Rd b/man/CST_WeightIndex.Rd index cd42bc8a..8d3fa23d 100644 --- a/man/CST_WeightIndex.Rd +++ b/man/CST_WeightIndex.Rd @@ -35,17 +35,18 @@ normalized weights for SFS members using the Best Index estimation and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ -index_obs <- 1 : (3 * 5 * 4) -index_exp <- 1 : (3 * 5 * 4 * 7) +index_exp <- 1 : (5 * 3 * 7 * 4) dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) index_exp <- list(data = index_exp) -index_obs <- list(data = index_obs) +pdf_exp <- 1 : (5 * 2 * 4) +dim(pdf_exp) <- c(time = 5, statistic = 2, season = 4) +pdf_exp <- list(data = pdf_exp) class(index_exp) <- 's2dv_cube' -class(index_obs) <- 's2dv_cube' -res_CST <- CST_PDFIndexHind(index_exp, index_obs) +class(pdf_exp) <- 's2dv_cube' +res_CST <- CST_WeightIndex(index_exp, pdf_exp) dim(res_CST$data) -#time dataset statistic season -# 5 3 4 4 +#time dataset member season +# 5 3 7 4 } \references{ Regionally improved seasonal forecast of precipitation through diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd index ec47d72e..62e26904 100644 --- a/man/PlotCombinedMap.Rd +++ b/man/PlotCombinedMap.Rd @@ -5,10 +5,10 @@ \title{Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function} \usage{ PlotCombinedMap(maps, lon, lat, map_select_fun, display_range, - map_dim = "map", brks = NULL, cols = NULL, col_unknown_map = "white", - mask = NULL, col_mask = "grey", bar_titles = NULL, legend_scale = 1, - fileout = NULL, width = 8, height = 5, size_units = "in", res = 100, - ...) + map_dim = "map", brks = NULL, cols = NULL, + col_unknown_map = "white", mask = NULL, col_mask = "grey", + bar_titles = NULL, legend_scale = 1, fileout = NULL, width = 8, + height = 5, size_units = "in", res = 100, ...) } \arguments{ \item{maps}{List of matrices to plot, each with (longitude, latitude) dimensions, or 3-dimensional array with the dimensions (longitude, latitude, map). Dimension names are required.} diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd index a74ab710..c1959ee8 100644 --- a/man/PlotForecastPDF.Rd +++ b/man/PlotForecastPDF.Rd @@ -4,10 +4,11 @@ \alias{PlotForecastPDF} \title{Plot one or multiple ensemble forecast pdfs for the same event} \usage{ -PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, obs = NULL, - plotfile = NULL, title = "Set a title", var.name = "Varname (units)", - fcst.names = NULL, add.ensmemb = c("above", "below", "no"), - color.set = c("ggplot", "s2s4e", "hydro")) +PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, + obs = NULL, plotfile = NULL, title = "Set a title", + var.name = "Varname (units)", fcst.names = NULL, + add.ensmemb = c("above", "below", "no"), color.set = c("ggplot", + "s2s4e", "hydro")) } \arguments{ \item{fcst}{a dataframe or array containing all the ensember members for each frecast. If \code{'fcst'} is an array, it should have two labelled dimensions, and one of them should be \code{'members'}. If \code{'fcsts'} is a data.frame, each column shoul be a separate forecast, with the rows beeing the different ensemble members.} diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd index e447e162..35e7c2a5 100644 --- a/man/RainFARM.Rd +++ b/man/RainFARM.Rd @@ -4,9 +4,10 @@ \alias{RainFARM} \title{RainFARM stochastic precipitation downscaling (reduced version)} \usage{ -RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, kmin = 1, - fglob = FALSE, fsmooth = TRUE, time_dim = NULL, lon_dim = "lon", - lat_dim = "lat", drop_realization_dim = FALSE, verbose = FALSE) +RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, + kmin = 1, fglob = FALSE, fsmooth = TRUE, time_dim = NULL, + lon_dim = "lon", lat_dim = "lat", drop_realization_dim = FALSE, + verbose = FALSE) } \arguments{ \item{data}{Precipitation array to downscale. -- GitLab From 60bf2bc6b21696ffc60362a565e76e114093e46e Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Mon, 16 Sep 2019 07:05:24 +0000 Subject: [PATCH 15/40] Update vignette header --- vignettes/BestNaoWeighting_vignette.Rmd | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/vignettes/BestNaoWeighting_vignette.Rmd b/vignettes/BestNaoWeighting_vignette.Rmd index 6cea9d78..404baa1f 100644 --- a/vignettes/BestNaoWeighting_vignette.Rmd +++ b/vignettes/BestNaoWeighting_vignette.Rmd @@ -1,10 +1,19 @@ --- -title: "Achiving BestNAOWeighting in CSTools" -author: "Eroteida Sánchez García" -date: "29 Aug 2019" -output: html_document +author: "Eroteida Sánchez-García" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteEngine{knitr::knitr} + %\VignetteIndexEntry{Achiving BestNAOWeighting in CSTools} + %\usepackage[utf8]{inputenc} --- +Achiving BestNAOWeighting in CSTools +----------------------------------------- + +**reference**: Sánchez-García, E., Voces-Aboy, J., Navascués, N., & Rodríguez-Camino, E. (2019). Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Adv. Sci. Res., 16, 165174, + + ## Before start System requeriment: CDO -- GitLab From 178150e10f11dcadc2f3e5484500e3c2972608e9 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 19 Sep 2019 11:14:16 +0000 Subject: [PATCH 16/40] Fixing some errors using R v3.2.0 in all functions --- R/CST_EMBestIndexWeighting.R | 184 ++++++++++++----------- R/CST_PDFBest.R | 60 ++++---- R/CST_PDFIndexFcst.R | 157 +++++++++++++++++--- R/CST_PDFIndexHind.R | 122 ++++++++++++--- R/CST_ProbBestIndexWeighting.R | 190 +++++++++++++----------- R/CST_WeightIndex.R | 96 ++++++------ man/CST_EMBestIndexWeighting.Rd | 22 ++- man/CST_PDFIndexFcst.Rd | 25 +++- man/CST_PDFIndexHind.Rd | 18 ++- man/CST_ProbBestIndexWeighting.Rd | 16 +- man/CST_WeightIndex.Rd | 1 - man/EMBestIndexWeighting.Rd | 36 +++-- man/PDFIndexFcst.Rd | 7 +- man/PDFIndexHind.Rd | 6 +- man/ProbBestIndexWeighting.Rd | 40 +++-- man/WeightIndex.Rd | 8 +- vignettes/BestNaoWeighting_vignette.Rmd | 14 +- 17 files changed, 621 insertions(+), 381 deletions(-) diff --git a/R/CST_EMBestIndexWeighting.R b/R/CST_EMBestIndexWeighting.R index db7802d4..c23666e8 100644 --- a/R/CST_EMBestIndexWeighting.R +++ b/R/CST_EMBestIndexWeighting.R @@ -11,16 +11,12 @@ #' @param var_exp An object of the class 's2dv_cube' containing the variable #' (e.g. precipitation, temperature, NAO index) array. #' The var_exp object is expected to have an element named \code{$data} with -#' at least two dimensions (time, member). -#' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another -#' dimension (time, dataset, member, lat, lon). +#' three dimensions (time, dataset, member) for a zonal variable +#' or five dimensions (time, dataset, member, lat, lon) for a spatial variable. #' @param weights_index_exp An object of the class 's2dv_cube' containing #' the normalized weights array. #' The weights_index_exp object is expected to have an element named -#' \code{$data} with at least two dimensions (time, member). -#' If we want to compute for several SFSs, the array could have another -#' dimension (time, dataset, member). +#' \code{$data} with dimensions (time, dataset, member, season). #' @return CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the #' class 's2dv_cube'). #' This object has at least an element named \code{$data} @@ -30,89 +26,98 @@ #' containing the ensemble means computing with weighted members. #' @export #' @examples -#' var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) -#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) -#' weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) -#' dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) +#' var_exp <- 1 : (4 * 2 * 3 * 3 * 2) +#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) +#' weights_index_exp <- runif(4 * 2 * 3, 0.0001, 0.9999) +#' dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 1) #' var_exp <- list(data = var_exp) #' weights_index_exp <- list(data = weights_index_exp) #' class(var_exp) <- 's2dv_cube' #' class(weights_index_exp) <- 's2dv_cube' #' res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) #' dim(res_CST$data) -#' # time dataset season lat lon -#' # 4 2 2 3 2 +#' # time dataset season lat lon +#' # 4 2 1 3 2 CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { stop("Parameter 'var_exp' and 'weights_index_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } - if (!any(names(dim(var_exp$data)) == 'time')){ - stop("The component 'data' of the parameter 'var_exp' must have the ", - "dimension 'time'") + if(!length(dim(var_exp$data)) %in% c(3,5)){ + stop("The number of dimensions in the component 'data' ", + "of the parameter 'index_exp' must be 3 (zonal variable) or 5 (spatial ", + "variable") } - if (!any(names(dim(weights_index_exp$data)) == 'time')){ - stop("The component 'data' of the parameter 'weights_index_exp' must have ", - "the dimension 'time'") + if (!any(names(dim(var_exp$data)) == 'time') || + names(dim(var_exp$data)[1]) != 'time'){ + stop("The first dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'time'") } - if (!any(names(dim(var_exp$data)) == 'member')){ - stop("The component 'data' of the parameter 'var_exp' must have the ", - "dimension 'member'") + if (!any(names(dim(var_exp$data)) == 'dataset') || + names(dim(var_exp$data)[2]) != 'dataset'){ + stop("The second dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'dataset'") } - if (!any(names(dim(weights_index_exp$data)) == 'member')){ - stop("The component 'data' of the parameter 'weights_index_exp' must have ", - "the dimension 'member'") + if (!any(names(dim(var_exp$data)) == 'member') || + names(dim(var_exp$data)[3]) != 'member'){ + stop("The third dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'member'") + } + if (length(dim(var_exp$data)) == 5){ + if (!any(names(dim(var_exp$data)) == 'lat') || + names(dim(var_exp$data)[4]) != 'lat'){ + stop("The fifth dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'lat'") + } + if (!any(names(dim(var_exp$data)) == 'lon') || + names(dim(var_exp$data)[5]) != 'lon'){ + stop("The sixth dimension in the component 'data' ", + "of the parameter 'lon' must be 'season'") + } + } + + if (length(dim(weights_index_exp$data)) == 4){ + if (!any(names(dim(weights_index_exp$data)) == 'time') || + names(dim(weights_index_exp$data)[1]) != 'time'){ + stop("The first dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'time'") + } + if (!any(names(dim(weights_index_exp$data)) == 'dataset') || + names(dim(weights_index_exp$data)[2]) != 'dataset'){ + stop("The second dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'dataset'") + } + if (!any(names(dim(weights_index_exp$data)) == 'member') || + names(dim(weights_index_exp$data)[3]) != 'member'){ + stop("The third dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'member'") + } + if (!any(names(dim(weights_index_exp$data)) == 'season') || + names(dim(weights_index_exp$data)[4]) != 'season'){ + stop("The fourth dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'season'") + } + } else { + stop("The number of dimensions in the component 'data' ", + "of the parameter 'weights_index_exp' must be equal to 4 ", + "(time, dataset, member, season)") } if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ stop("The length of the dimension 'time' of the parameter 'var_exp' ", "in the component 'data' must be equal to the length of the dimension", " 'time' of the parameter 'weights_index_exp' in the component 'data'") } + if (dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']){ + stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") + } if (dim(var_exp$data)['member'] != dim(weights_index_exp$data)['member']){ stop("The length of the dimension 'member' of the parameter 'var_exp' ", "in the component 'data' must be equal to the length of the dimension", " 'member' of the parameter 'weights_index_exp' in the component 'data'") } - if (any(names(dim(weights_index_exp$data)) == 'lat') & - dim(weights_index_exp$data)['lat'] != 1) { - stop("The length of the dimension 'lat' of the parameter 'weights_index_exp'", - " in the component 'data' must be equal to 1") - } - if (any(names(dim(weights_index_exp$data)) == 'lon') & - dim(weights_index_exp$data)['lon'] != 1) { - stop("The length of the dimension 'lon' of the parameter 'weights_index_exp'", - " in the component 'data' must be equal to 1") - } - if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) - dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] - } - if (any(names(dim(weights_index_exp$data)) == 'lat')) { - Dim <- dim(weights_index_exp$data) - dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lat')] - } - if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) - dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] - } - if (any(names(dim(var_exp$data)) == 'dataset') & - !any(names(dim(weights_index_exp$data)) == 'dataset')) { - stop("The component 'data' of the parameter 'weights_index_exp' must have'", - " the dimension 'dataset'") - } - if (!any(names(dim(var_exp$data)) == 'dataset') & - any(names(dim(weights_index_exp$data)) == 'dataset')) { - stop("The component 'data' of the parameter 'var_exp' must have'", - " the dimension 'dataset'") - } - if (any(names(dim(var_exp$data)) == 'dataset') & - any(names(dim(weights_index_exp$data)) == 'dataset') & - dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']) { - stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") - } - + em <- EMBestIndexWeighting(var_exp$data, weights_index_exp$data) em_var_exp <- list() em_var_exp$data <- em @@ -132,17 +137,15 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with at least two dimensions (time, member). -#' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another -#' dimension (time, dataset, member, lat, lon). -#' @param weights_index_exp Normalized weights array with at least two -#' dimensions (time, member). If we want to compute for several SFSs, -#' the array could have another dimension (time, dataset, member). -#' @return EMBestIndexWeighting() returns an array with two or four dimensions -#' depending if the variable is zonal (as e.g. NAO index)(time, dataset) +#' array from a SFS with three dimensions (time, dataset, member) for a zonal +#' variable or dimensions (time, dataset, member, lat, lon) for a spatial +#' variable. +#' @param weights_index_exp Normalized weights array with dimensions +#' (time, dataset, member, season). +#' @return EMBestIndexWeighting() returns an array with three or five dimensions +#' depending if the variable is zonal (as e.g. NAO index)(time, dataset, season) #' or it is spatial variable (as e.g. precipitation or temperature) -#' (time, dataset, lat, lon), containing the ensemble means computing +#' (time, dataset, season, lat, lon), containing the ensemble means computing #' with weighted members. #' @import multiApply #' @export @@ -151,29 +154,35 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' var_exp <- 1 : (2 * 3 * 4) #' dim(var_exp) <- c(time = 2, dataset = 3, member = 4) #' weights_index_exp<- runif(24, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) #' res <- EMBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time dataset -#' # 2 3 +#' # time dataset season +#' # 2 3 1 #' str(res) -#' # num [1:2, 1:3] 36.2 28.8 38.3 31.9 31 ... +#' # num [1:2, 1:3, 1] 36.2 28.8 38.3 31.9 31 ... #' # Example 2 for the EMBestIndexWeighting function #' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) #' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) -#' weights_index_exp<- runif(24, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' weights_index_exp<- runif(48, min=0.001, max=0.999) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 2) #' res <- EMBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time dataset lat lon -#' # 2 3 2 3 +#' # time dataset season lat lon +#' # 2 3 2 2 3 #' str(res) -#' # num [1:2, 1:3, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... +#' # num [1:2, 1:3, 1:2, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... #' EMBestIndexWeighting <- function(var_exp, weights_index_exp) { - return(Apply(list(var_exp, weights_index_exp), + res <- Apply(list(var_exp, weights_index_exp), target_dims = list(c('time','member'), c('time','member')), - fun = .EMBestIndexWeighting)$output1) + fun = .EMBestIndexWeighting)$output1 + Dim <- names(dim(var_exp)) + Dim[Dim == 'member'] <- 'season' + pos <- match(Dim, names(dim(res))) + res <- aperm(res, pos) + names(dim(res)) <- Dim + return(res) } #' Atomic EMBestIndexWeighting @@ -197,8 +206,9 @@ EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' # 2 #' @noRd .EMBestIndexWeighting <- function(var_exp, weights_index_exp) { - posTime <- match('time', names(dim(var_exp))) + posTime <- 1 var_exp_em <- apply(var_exp * weights_index_exp, posTime, sum) dim(var_exp_em) <- c(time = length(var_exp_em)) return(var_exp_em) } + diff --git a/R/CST_PDFBest.R b/R/CST_PDFBest.R index bc877913..2b53824d 100644 --- a/R/CST_PDFBest.R +++ b/R/CST_PDFBest.R @@ -52,31 +52,46 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { stop("Parameter 'pdf_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } - if (!any(names(dim(pdf_exp$data)) == 'statistic')){ - stop("The dimension 'statistic' must be exist in the component 'data' ", - "of the parameter 'pdf_exp'") + if (!any(names(dim(pdf_exp$data)) == 'time') || + names(dim(pdf_exp$data)[1]) != 'time'){ + stop("The first dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'time'") } - if (!any(names(dim(pdf_exp$data)) == 'dataset')){ - stop("The dimension 'dataset' must be exist in the component 'data' ", - "of the parameter 'pdf_exp'") + if (!any(names(dim(pdf_exp$data)) == 'dataset') || + names(dim(pdf_exp$data)[2]) != 'dataset'){ + stop("The second dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'dataset'") } - if (dim(pdf_exp$data)['member'] != 1) { - stop("The length of the dimension 'member' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 1.") + if (!any(names(dim(pdf_exp$data)) == 'member') || + names(dim(pdf_exp$data)[3]) != 'member'){ + stop("The third dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'member'") + } + if (!any(names(dim(pdf_exp$data)) == 'statistic') || + names(dim(pdf_exp$data)[4]) != 'statistic'){ + stop("The fourth dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'statistic'") + } + if (!any(names(dim(pdf_exp$data)) == 'season') || + names(dim(pdf_exp$data)[5]) != 'season'){ + stop("The fifth dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'season'") } if (dim(pdf_exp$data)['dataset'] != 2){ stop("The length of the dimension 'dataset' in the component 'data' ", "of the parameter 'pdf_exp' must be equal to 2.") } + if (dim(pdf_exp$data)['member'] != 1) { + stop("The length of the dimension 'member' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 1.") + } if (dim(pdf_exp$data)['statistic'] != 4){ stop("The length of the dimension 'statistic' in the component 'data' of the ", "parameter 'pdf_exp' must be equal to 4'") } - if (dim(pdf_exp$data)['member'] == 1) { - Dim <- dim(pdf_exp$data) - dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] - } + Dim <- dim(pdf_exp$data) + dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] PDFstatistics <- PDFBest(pdf_exp$data, bc_dataset1 = bc_dataset1, bc_dataset2 = bc_dataset2) @@ -122,23 +137,14 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { #' # num [1:10, 1:2, 1] 17.2 18.3 19.5 20.6 21.7 ... #' PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { - if (!any(names(dim(pdf_exp)) == 'statistic')){ - stop("The parameter 'pdf_exp' must have the dimension 'statistic'") - } - if(dim(pdf_exp)['statistic'] != 4){ - stop("The length of the dimension 'statistic' in the parameter 'pdf1'", - " must be equal to 4") - } - if(dim(pdf_exp)['dataset'] != 2){ - stop("The length of the dimension 'statistic' in the parameter 'pdf_exp'", - " must be equal to 2") - } pdf_best <- Apply(list(pdf_exp), target_dims = list(c('dataset','statistic'), c('dataset','statistic')), fun = .PDFBest, bc_dataset1, bc_dataset2) pos <- match(c('time','statistic','season'), names(dim(pdf_best$output1))) - return(aperm(pdf_best$output1,pos)) + res <- aperm(pdf_best$output1,pos) + names(dim(res)) <- c('time','statistic','season') + return(res) } #' Atomic PDFBest @@ -167,8 +173,7 @@ PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { #' # 2 #' @noRd .PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { - pos <- match(c('dataset','statistic'), names(dim(pdf_exp))) - pdf_exp <- aperm(pdf_exp,pos) + #names(dim(pdf_exp)) <- c('dataset','statistic') if(bc_dataset1){ # apply bias correction to model 1 sigma_1 <- pdf_exp[1,4] @@ -192,7 +197,6 @@ PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { pdf_mean <- a_1*mean_1 + a_2*mean_2 pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) data <- c(pdf_mean, pdf_sigma) - #dim(data) <- c(time=1, statistic = 2) dim(data) <- c(statistic = 2) return(data) } diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R index 3e4bb23e..d16b8a08 100644 --- a/R/CST_PDFIndexFcst.R +++ b/R/CST_PDFIndexFcst.R @@ -13,20 +13,28 @@ #' the North Atlantic Oscillation (NAO)) estimate for a hindcast period #' from SFSs for improving. #' The index_hind object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member, season). +#' with dimensions (time, dataset, member, season) or +#' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must +#' be greater than 1. When dimension 'statistic' doesn't exist, the dimension +#' 'member' must be greater than 1. #' @param index_obs An object of the class 's2dv_cube' #' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate from observations or #' reanalysis. #' The index_obs object is expected to have an element named \code{$data} -#' with at least three dimensions (time, dataset, season). +#' with at least three dimensions (time, dataset, season). The dimensions 'time' +#' must be greater than 1. #' It could have one more dimension (member) equal to 1. #' @param index_fcst An object of the class 's2dv_cube' #' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate for a forecast year #' from SFSs for improving. #' The index_fcst object is expected to have an element named \code{$data} -#' with dimensions (time = 1, dataset, member, season). +#' with dimensions (time = 1, dataset, member, season) or +#' (time = 1, dataset, member = 1, statistic = 2, season). When dimension +#' 'statistic' doesn't exist, the dimension 'member' must be greater than 1. +#' @param type_exp A character string ('ens' or 'sta') to identify if the +#' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied #' to compute the PDFs. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? @@ -44,15 +52,15 @@ #' with bias corrected #' @export #' @examples -#' index_hind <- 1 : (3 * 5 * 4 * 7) +#' index_hind <- 1 : (5 * 3 * 7 * 4) #' dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) #' index_hind <- list(data = index_hind) #' class(index_hind) <- 's2dv_cube' -#' index_obs <- 1 : (3 * 5 * 4) +#' index_obs <- 1 : (5 * 3 * 4) #' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) #' index_obs <- list(data = index_obs) #' class(index_obs) <- 's2dv_cube' -#' index_fcst <- 1 : (3 * 4 * 7) +#' index_fcst <- 1 : (3 * 7 * 4) #' dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) #' index_fcst <- list(data = index_fcst) #' class(index_fcst) <- 's2dv_cube' @@ -61,8 +69,8 @@ #' dim(res_CST$data) #' #time dataset statistic season #' # 1 3 4 4 -CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, - method = 'ME', na.rm = FALSE) { +CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + type_exp = 'ens', method = 'ME', na.rm = FALSE) { if (!inherits(index_hind, 's2dv_cube') || !inherits(index_obs, 's2dv_cube') || !inherits(index_fcst, 's2dv_cube')) { @@ -70,10 +78,65 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, and 'index_obs' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } + if(!(method %in% c('ME', 'LMEV'))){ + stop("The parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("The parameter 'na.rm' must be a logical value.") + } + if(!(type_exp %in% c('ens', 'sta'))){ + stop("The parameter 'type_exp' must be equal to 'ens' or 'sta'") + } + if (is.null(names(dim(index_hind$data))) + || is.null(names(dim(index_obs$data))) + || is.null(names(dim(index_fcst$data)))) { + stop("Element 'data' from parameters 'index_hind', 'index_obs' and 'index_fcst'", + " should have dimmension names.") + } + if (!all(names(dim(index_obs$data)) %in% c('time', 'dataset', 'member', 'season'))) { + stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") + } + if (!all(names(dim(index_hind$data)) %in% c('time', 'dataset', 'member', 'statistic', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") + } + if (!all(names(dim(index_fcst$data)) %in% c('time', 'dataset', 'member', 'statistic', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") + } + if (!all(c('time', 'dataset', 'season') %in% names(dim(index_obs$data)))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") + } + if (!all(c('time', 'dataset', 'member', 'season') %in% names(dim(index_hind$data)))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") + } + if (!all(c('time', 'dataset', 'member', 'season') %in% names(dim(index_fcst$data)))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") + } + if (dim(index_obs$data)['time'] != dim(index_hind$data)['time']) { + stop("The length of the dimensions 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_hind$data)['season'] != dim(index_fcst$data)['season']) { + stop("The length of the dimensions 'season' in the component 'data' ", + "of the parameter 'index_hind' and 'index_fcst' must be equals.") + } + if (dim(index_obs$data)['season'] != dim(index_hind$data)['season']) { + stop("The length of the dimensions 'season' in the component 'data' ", + "of the parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_obs$data)['time'] == 1) { + stop("The length of the dimension 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be greater than 1.") + } if (dim(index_obs$data)['member'] != 1) { stop("The length of the dimension 'member' in the component 'data' ", "of the parameter 'index_obs' must be equal to 1.") } + if (dim(index_hind$data)['member'] == 1 & + !any(names(dim(index_hind$data)) == 'statistic')){ + stop("The dimension 'statistic' must be exist in the component 'data' ", + "of the parameter 'index_hind' if the length of the dimension ", + "'member' is equal to 1") + } if (dim(index_fcst$data)['member'] == 1 & !any(names(dim(index_fcst$data)) == 'statistic')){ stop("The dimension 'statistic' must be exist in the component 'data' ", @@ -101,6 +164,40 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, "parameter 'index_hind' must be equal to 1 if the component 'data' ", "of the parameter 'index_hind' has the dimension 'statistic'") } + if (dim(index_hind$data)['member'] == 1 & + dim(index_hind$data)['statistic'] != 2){ + stop("The length of the dimension 'statistic' in the component 'data' of the ", + "parameter 'index_hind' must be equal to 2 if the component 'data' ", + "of the parameter 'index_hind' has the dimension 'member' equal to 1.") + } + if (dim(index_fcst$data)['member'] == 1 & + dim(index_fcst$data)['statistic'] != 2){ + stop("The length of the dimension 'statistic' in the component 'data' of the ", + "parameter 'index_fcst' must be equal to 2 if the component 'data' ", + "of the parameter 'index_fcst' has the dimension 'member' equal to 1.") + } + if (any(names(dim(index_fcst)) == 'statistic') & type_exp != 'sta'){ + stop("The parameter 'type_exp' must be equal to 'sta' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_fcst' exits") + } + if (any(names(dim(index_fcst)) == 'member') & + !any(names(dim(index_fcst)) == 'statistic') & type_exp != 'ens'){ + stop("The parameter 'type_exp' must be equal to 'ens' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_fcst' doesn't exit") + } + if (any(names(dim(index_hind)) == 'statistic') & type_exp != 'sta'){ + stop("The parameter 'type_exp' must be equal to 'sta' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_hind' exits") + } + if (any(names(dim(index_hind)) == 'member') & + !any(names(dim(index_hind)) == 'statistic') & type_exp != 'ens'){ + stop("The parameter 'type_exp' must be equal to 'ens' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_hind' doesn't exit") + } if (dim(index_fcst$data)['member'] == 1) { Dim <- dim(index_fcst$data) dim(index_fcst$data) <- Dim[-which(names(Dim) == 'member')] @@ -121,7 +218,8 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, dimnames[dimnames == 'member'] <- 'statistic' } PDFstatistics <- PDFIndexFcst(index_hind$data, index_obs$data, - index_fcst$data, method = method, na.rm = na.rm) + index_fcst$data, type_exp = type_exp, + method = method, na.rm = na.rm) pos <- match(dimnames, names(dim(PDFstatistics))) PDFstatistics <- aperm(PDFstatistics, pos) names(dim(PDFstatistics)) <- dimnames @@ -147,6 +245,8 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, #' or reanalysis with dimension (time, dataset, member = 1) or (time, dataset). #' @param index_fcst Index (e.g. NAO index) array from SFSs. #' with dimensions (time = 1, dataset, member) for a forecast year. +#' @param type_exp A character string ('ens' or 'sta') to identify if the +#' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied #' to compute the PDF. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? @@ -175,22 +275,22 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, #' str(res) #' # num [1, 1:4, 1:3] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... #' -PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', - na.rm = FALSE) { +PDFIndexFcst <- function(index_hind, index_obs, index_fcst, type_exp = 'ens', + method ='ME', na.rm = FALSE) { if (any(names(dim(index_obs)) == 'member')) { Dim <- dim(index_obs) dim(index_obs) <- Dim[-which(names(Dim) == 'member')] } - if (any(names(dim(index_fcst)) == 'member')){ + if (type_exp == 'ens'){ PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), target_dims = list(c('time', 'member'), 'time', c('time','member')), - fun = .PDFIndexFcst, method, na.rm) - } else if (any(names(dim(index_fcst)) == 'statistic')){ + fun = .PDFIndexFcst, type_exp, method, na.rm) + } else if (type_exp == 'sta'){ PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), target_dims = list(c('time', 'statistic'), 'time', c('time','statistic')), - fun = .PDFIndexFcst, method, na.rm) + fun = .PDFIndexFcst, type_exp, method, na.rm) } return(PDFstatistics$output1) } @@ -202,6 +302,8 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', #' or reanalysis with dimension (time). #' @param index_fcst Index (e.g. NAO index) array from a SFS #' with dimensions (time=1, member) for a forecast year. +#' @param type_exp A character string ('ens' or 'sta') to identify if the +#' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied #' to compute the PDF. One of "ME" (default) or "LMEV". #' @param na.rm Logical. Should missing values be removed? @@ -231,35 +333,44 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, method ='ME', #' # time statistic #' # 1 4 #' @noRd -.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, - method = 'ME', na.rm = TRUE) { - if (any(names(dim(index_fcst)) == 'member')) { +.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, type_exp = 'ens', + method = 'ME', na.rm = FALSE) { + names(dim(index_obs)) <- c('time') + if (type_exp == 'ens') { + names(dim(index_hind)) <- c('time', 'member') + names(dim(index_fcst)) <- c('time', 'member') exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', fun = mean)$output1 exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', fun = sd)$output1 + data_hind <- .PDFIndexHind(index_hind, index_obs, type_exp, method, na.rm) } else { + names(dim(index_hind)) <- c('time', 'statistic') + names(dim(index_fcst)) <- c('time', 'statistic') pos <- match(c('time','statistic'), names(dim(index_fcst))) index_fcst <- aperm(index_fcst,pos) exp_fcst_ens_mean <- index_fcst[,1] exp_fcst_ens_sd <- index_fcst[,2] + data_hind <- .PDFIndexHind(index_hind, index_obs, type_exp, method, na.rm) } - data_hind <- .PDFIndexHind(index_hind, index_obs, method, na.rm) + exp_hind_ens_mean <- data_hind[,1] pdfnotbc_mean <- exp_fcst_ens_mean - pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs) + pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs, na.rm) if (method == 'ME') { - pdfbc <- MEBC(exp_hind_ens_mean, index_obs) + pdfbc <- MEBC(exp_hind_ens_mean, index_obs, na.rm) pdfbc_mean <- exp_fcst_ens_mean - pdfbc } else if (method == 'LMEV') { # linear model error-variance exp_fcst_ens_var <- exp_fcst_ens_sd^2 - coeff <- LMEV(index_hind, index_obs) + dim_hind <- names(dim(index_hind)) + dim_obs <- names(dim(index_obs)) + coeff <- LMEV(index_hind, index_obs, dim_hind, dim_obs, na.rm) pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) } else { stop("Error: Parameter 'method' is wrong") } - pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs) + pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs, na.rm) data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) names(dim(data)) <- c(names(dim(index_obs)), 'statistic') return(data) diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R index 10053364..aa5cf85a 100644 --- a/R/CST_PDFIndexHind.R +++ b/R/CST_PDFIndexHind.R @@ -12,14 +12,20 @@ #' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. #' The index_exp object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member, season). +#' with dimensions (time, dataset, member, season) or +#' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must be +#' greater than 1. When dimension 'statistic' doesn't exist, the dimension +#' 'member' must be greater than 1. #' @param index_obs An object of the class 's2dv_cube' #' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate from observations or #' reanalysis. #' The index_obs object is expected to have an element named \code{$data} -#' with at least three dimensions (time, dataset, season). +#' with dimensions (time, dataset, season). The dimensions 'time' must be +#' greater than 1. #' It could have one more dimension (member) equal to 1. +#' @param type_exp A character string ('ens' or 'sta') to identify if the +#' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied #' to compute the PDFs. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? @@ -37,9 +43,9 @@ #' with bias corrected #' @export #' @examples -#' index_obs <- 1 : (3 * 5 * 4) +#' index_obs <- 1 : (5 * 3 * 4) #' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -#' index_exp <- 1 : (3 * 5 * 4 * 7) +#' index_exp <- 1 : (5 * 3 * 7 * 4) #' dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) #' index_exp <- list(data = index_exp) #' index_obs <- list(data = index_obs) @@ -49,11 +55,49 @@ #' dim(res_CST$data) #' #time dataset statistic season #' # 5 3 4 4 -CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) { +CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'ME', na.rm = FALSE) { if (!inherits(index_exp, 's2dv_cube') || !inherits(index_obs, 's2dv_cube')) { stop("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } + if(!(method %in% c('ME', 'LMEV'))){ + stop("The parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("The parameter 'na.rm' must be a logical value.") + } + if(!(type_exp %in% c('ens', 'sta'))){ + stop("The parameter 'type_exp' must be equal to 'ens' or 'sta'") + } + if (is.null(names(dim(index_exp$data))) || is.null(names(dim(index_obs$data)))) { + stop("Element 'data' from parameters 'index_exp' and 'index_obs'", + " should have dimmension names.") + } + if (!all(names(dim(index_obs$data)) %in% c('time', 'dataset', 'member', 'season'))) { + stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") + } + if (!all(names(dim(index_exp$data)) %in% c('time', 'dataset', 'member', 'statistic', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") + } + + if (!all(c('time', 'dataset', 'season') %in% names(dim(index_obs$data)))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") + } + if (!all(c('time', 'dataset', 'member', 'season') %in% names(dim(index_exp$data)))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") + } + if (dim(index_obs$data)['time'] != dim(index_exp$data)['time']) { + stop("The length of the dimensions 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be equals.") + } + if (dim(index_obs$data)['season'] != dim(index_exp$data)['season']) { + stop("The length of the dimensions 'season' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be equals.") + } + if (dim(index_obs$data)['time'] == 1) { + stop("The length of the dimension 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be greater than 1.") + } if (dim(index_obs$data)['member'] != 1) { stop("The length of the dimension 'member' in the component 'data' ", "of the parameter 'index_obs' must be equal to 1.") @@ -67,10 +111,28 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) if (dim(index_exp$data)['member'] > 1 & any(names(dim(index_exp$data)) == 'statistic')){ stop("The length of the dimension 'member' in the component 'data' of the ", - "parameter 'index_hind' must be equal to 1 if the component 'data' ", - "of the parameter 'index_hind' has the dimension 'statistic'") + "parameter 'index_exp' must be equal to 1 if the component 'data' ", + "of the parameter 'index_exp' has the dimension 'statistic'") } - + + if (dim(index_exp$data)['member'] == 1 & + dim(index_exp$data)['statistic'] != 2){ + stop("The length of the dimension 'statistic' in the component 'data' of the ", + "parameter 'index_exp' must be equal to 2 if the component 'data' ", + "of the parameter 'index_exp' has the dimension 'member' equal to 1.") + } + if (any(names(dim(index_exp)) == 'statistic') & type_exp != 'sta'){ + stop("The parameter 'type_exp' must be equal to 'sta' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_exp' exits") + } + if (any(names(dim(index_exp)) == 'member') & + !any(names(dim(index_exp)) == 'statistic') & type_exp != 'ens'){ + stop("The parameter 'type_exp' must be equal to 'ens' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_exp' doesn't exit") + } + if (dim(index_exp$data)['member'] == 1) { Dim <- dim(index_exp$data) dim(index_exp$data) <- Dim[-which(names(Dim) == 'member')] @@ -79,7 +141,8 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) if (any(names(dim(index_exp$data)) == 'member')){ dimnames[dimnames == 'member'] <- 'statistic' } - PDFstatistics <- PDFIndexHind(index_exp$data, index_obs$data, method = method, + PDFstatistics <- PDFIndexHind(index_exp$data, index_obs$data, + type_exp = type_exp, method = method, na.rm = na.rm) pos <- match(dimnames, names(dim(PDFstatistics))) PDFstatistics <- aperm(PDFstatistics, pos) @@ -103,6 +166,8 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) #' with dimensions (time, dataset, member). #' @param index_obs Index (e.g. NAO index) array from an observational database #' or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset). +#' @param type_exp A character string ('ens' or 'sta') to identify if the +#' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied #' to compute the PDFs. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? @@ -130,19 +195,20 @@ CST_PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) #' # 5 4 3 #' -PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { +PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method ='ME', + na.rm = FALSE) { if (any(names(dim(index_obs)) == 'member')) { Dim <- dim(index_obs) dim(index_obs) <- Dim[-which(names(Dim) == 'member')] } - if (any(names(dim(index_exp)) == 'member')){ + if (type_exp == 'ens'){ PDFstatistics <- Apply(list(index_exp, index_obs), target_dims = list(c('time', 'member'), 'time'), - fun = .PDFIndexHind, method, na.rm) - } else if (any(names(dim(index_exp)) == 'statistic')){ + fun = .PDFIndexHind, type_exp, method, na.rm) + } else if (type_exp == 'sta'){ PDFstatistics <- Apply(list(index_exp, index_obs), target_dims = list(c('time', 'statistic'), 'time'), - fun = .PDFIndexHind, method, na.rm) + fun = .PDFIndexHind, type_exp, method, na.rm) } return(PDFstatistics$output1) } @@ -152,6 +218,8 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { #' (time, member) for a hindcast period. #' @param index_obs Index (e.g. NAO index) array from an observational dataset #' or reanalysis with dimension (time). +#' @param type_exp A character string ('ens' or 'sta') to identify if the +#' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied #' to compute the PDF. One of "ME" (default) or "LMEV". #' @param na.rm Logical. Should missing values be removed? @@ -179,13 +247,17 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { #' # time statistic #' # 10 4 #' @noRd -.PDFIndexHind <- function(index_exp, index_obs, method = 'ME', na.rm = FALSE) { - if (any(names(dim(index_exp)) == 'member')) { +.PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'ME', + na.rm = FALSE) { + names(dim(index_obs)) <- c('time') + if (type_exp == 'ens') { + names(dim(index_exp)) <- c('time', 'member') exp_ens_mean <- Apply(list(index_exp), target_dims = 'member', fun = mean, na.rm = na.rm)$output1 exp_ens_sd <- Apply(list(index_exp), target_dims = 'member', fun = sd, na.rm = na.rm)$output1 - } else { + } else if (type_exp == 'sta'){ + names(dim(index_exp)) <- c('time', 'statistic') pos <- match(c('time','statistic'), names(dim(index_exp))) index_exp <- aperm(index_exp,pos) exp_ens_mean <- index_exp[,1] @@ -195,22 +267,28 @@ PDFIndexHind <- function(index_exp, index_obs, method ='ME', na.rm = FALSE) { pdfnotbc_mean <- exp_ens_mean ind <- 1 : length(index_obs) pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(exp_ens_mean[-x], - index_obs[-x])})) + index_obs[-x], + na.rm)})) if (method == 'ME') { pdfbc <- unlist(lapply(ind, function(x) {MEBC(exp_ens_mean[-x], - index_obs[-x])})) + index_obs[-x], na.rm)})) pdfbc_mean <- exp_ens_mean - pdfbc pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x])})) + index_obs[-x], + na.rm)})) } else if (method == 'LMEV') { # linear model error-variance exp_ens_var <- exp_ens_sd^2 - coeff <- sapply(ind, function(x) {LMEV(index_exp[-x,], index_obs[-x])}) + dim_exp <- names(dim(index_exp)) + dim_obs <- names(dim(index_obs)) + coeff <- sapply(ind, function(x) {LMEV(index_exp[-x,], index_obs[-x], + dim_exp, dim_obs, na.rm)}) pdfbc_mean <- exp_ens_mean - (unlist(coeff['coeff1',]) + unlist(coeff['coeff2',])*exp_ens_var) pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x])})) + index_obs[-x], + na.rm)})) } else { stop("Error: Parameter 'method' is wrong") } diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index fe944ddd..7b6cda37 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -13,16 +13,12 @@ #' @param var_exp An object of the class 's2dv_cube' containing the variable #' (e.g. precipitation, temperature, NAO index) array. #' The var_exp object is expected to have an element named \code{$data} with -#' at least two dimensions (time, member). -#' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another -#' dimension (time, dataset, member, lat, lon). +#' three dimensions (time, dataset, member) for a zonal variable +#' or five dimensions (time, dataset, member, lat, lon) for a spatial variable. #' @param weights_index_exp An object of the class 's2dv_cube' containing #' the normalized weights array. #' The weights_index_exp object is expected to have an element named -#' \code{$data} with at least two dimensions (time, member). -#' If we want to compute for several SFSs, the array could have another -#' dimension (time, dataset, member). +#' \code{$data} with dimensions (time, dataset, member, season). #' @return CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the #' class 's2dv_cube'). #' This object has at least an element named \code{$data} @@ -31,12 +27,12 @@ #' (as e.g. precipitation or temperature) #' (time, tercil, dataset, season, lat, lon), #' containing the terciles probabilities computing with weighted members. -#' The firt tercil is the lower tercile, the second is the normal tercile and +#' The first tercil is the lower tercile, the second is the normal tercile and #' the third is the upper tercile. #' @export #' @examples -#' var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) -#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) +#' var_exp <- 1 : (4 * 2 * 3 * 3 * 2) +#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) #' weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) #' dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) #' var_exp <- list(data = var_exp) @@ -52,72 +48,80 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } - if (!any(names(dim(var_exp$data)) == 'time')){ - stop("The component 'data' of the parameter 'var_exp' must have the ", - "dimension 'time'") + if(!length(dim(var_exp$data)) %in% c(3,5)){ + stop("The number of dimensions in the component 'data' ", + "of the parameter 'index_exp' must be 3 (zonal variable) or 5 (spatial ", + "variable") + } + if (!any(names(dim(var_exp$data)) == 'time') || + names(dim(var_exp$data)[1]) != 'time'){ + stop("The first dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'time'") + } + if (!any(names(dim(var_exp$data)) == 'dataset') || + names(dim(var_exp$data)[2]) != 'dataset'){ + stop("The second dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'dataset'") } - if (!any(names(dim(weights_index_exp$data)) == 'time')){ - stop("The component 'data' of the parameter 'weights_index_exp' must have ", - "the dimension 'time'") + if (!any(names(dim(var_exp$data)) == 'member') || + names(dim(var_exp$data)[3]) != 'member'){ + stop("The third dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'member'") } - if (!any(names(dim(var_exp$data)) == 'member')){ - stop("The component 'data' of the parameter 'var_exp' must have the ", - "dimension 'member'") + if (length(dim(var_exp$data)) == 5){ + if (!any(names(dim(var_exp$data)) == 'lat') || + names(dim(var_exp$data)[4]) != 'lat'){ + stop("The fifth dimension in the component 'data' ", + "of the parameter 'var_exp' must be 'lat'") + } + if (!any(names(dim(var_exp$data)) == 'lon') || + names(dim(var_exp$data)[5]) != 'lon'){ + stop("The sixth dimension in the component 'data' ", + "of the parameter 'lon' must be 'season'") + } } - if (!any(names(dim(weights_index_exp$data)) == 'member')){ - stop("The component 'data' of the parameter 'weights_index_exp' must have ", - "the dimension 'member'") + if (length(dim(weights_index_exp$data)) == 4){ + if (!any(names(dim(weights_index_exp$data)) == 'time') || + names(dim(weights_index_exp$data)[1]) != 'time'){ + stop("The first dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'time'") + } + if (!any(names(dim(weights_index_exp$data)) == 'dataset') || + names(dim(weights_index_exp$data)[2]) != 'dataset'){ + stop("The second dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'dataset'") + } + if (!any(names(dim(weights_index_exp$data)) == 'member') || + names(dim(weights_index_exp$data)[3]) != 'member'){ + stop("The third dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'member'") + } + if (!any(names(dim(weights_index_exp$data)) == 'season') || + names(dim(weights_index_exp$data)[4]) != 'season'){ + stop("The fourth dimension in the component 'data' ", + "of the parameter 'weights_index_exp' must be 'season'") + } + } else { + stop("The number of dimensions in the component 'data' ", + "of the parameter 'weights_index_exp' must be equal to 4 ", + "(time, dataset, member, season)") } if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ stop("The length of the dimension 'time' of the parameter 'var_exp' ", "in the component 'data' must be equal to the length of the dimension", " 'time' of the parameter 'weights_index_exp' in the component 'data'") } + if (dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']){ + stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") + } if (dim(var_exp$data)['member'] != dim(weights_index_exp$data)['member']){ stop("The length of the dimension 'member' of the parameter 'var_exp' ", "in the component 'data' must be equal to the length of the dimension", " 'member' of the parameter 'weights_index_exp' in the component 'data'") } - if (any(names(dim(weights_index_exp$data)) == 'lat') & - dim(weights_index_exp$data)['lat'] != 1) { - stop("The length of the dimension 'lat' of the parameter 'weights_index_exp'", - " in the component 'data' must be equal to 1") - } - if (any(names(dim(weights_index_exp$data)) == 'lon') & - dim(weights_index_exp$data)['lon'] != 1) { - stop("The length of the dimension 'lon' of the parameter 'weights_index_exp'", - " in the component 'data' must be equal to 1") - } - if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) - dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] - } - if (any(names(dim(weights_index_exp$data)) == 'lat')) { - Dim <- dim(weights_index_exp$data) - dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lat')] - } - if (any(names(dim(weights_index_exp$data)) == 'lon')) { - Dim <- dim(weights_index_exp$data) - dim(weights_index_exp$data) <- Dim[-which(names(Dim) == 'lon')] - } - if (any(names(dim(var_exp$data)) == 'dataset') & - !any(names(dim(weights_index_exp$data)) == 'dataset')) { - stop("The component 'data' of the parameter 'weights_index_exp' must have'", - " the dimension 'dataset'") - } - if (!any(names(dim(var_exp$data)) == 'dataset') & - any(names(dim(weights_index_exp$data)) == 'dataset')) { - stop("The component 'data' of the parameter 'var_exp' must have'", - " the dimension 'dataset'") - } - if (any(names(dim(var_exp$data)) == 'dataset') & - any(names(dim(weights_index_exp$data)) == 'dataset') & - dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']) { - stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") - } - + prob <- ProbBestIndexWeighting(var_exp$data, weights_index_exp$data) prob_var_exp <- list() prob_var_exp$data <- prob @@ -138,19 +142,17 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with at least two dimensions (time, member). -#' If the variable is spatial the dimensions would be (time, member, lat, lon). -#' If we want to compute for several SFSs, the array could have another -#' dimension (time, dataset, member, lat, lon). -#' @param weights_index_exp Normalized weights array with at least two -#' dimensions (time, member). If we want to compute for several SFSs, -#' the array could have another dimension (time, dataset, member). -#' @return ProbBestIndexWeighting() returns an array with three or five dimensions -#' depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) -#' or it is spatial variable (as e.g. precipitation or temperature) -#' (time, tercil, dataset, lat, lon), containing the terciles probabilities -#' computing with weighted members. -#' The firt tercil is the lower tercile, the second is the normal tercile and +#' array from a SFS with three dimensions (time, dataset, member) for a zonal +#' variable or dimensions (time, dataset, member, lat, lon) for a spatial +#' variable. +#' @param weights_index_exp Normalized weights array with dimensions +#' (time, dataset, member, season). +#' @return ProbBestIndexWeighting() returns an array with four or six dimensions +#' depending if the variable is zonal (as e.g. NAO index) +#' (time, tercil, dataset, season) or it is spatial variable (as e.g. +#' precipitation or temperature) (time, tercil, dataset, season, 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. #' @import multiApply #' @export @@ -159,29 +161,40 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' var_exp <- 1 : (2 * 3 * 4) #' dim(var_exp) <- c(time = 2, dataset = 3, member = 4) #' weights_index_exp<- runif(24, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) #' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time tercil dataset -#' # 2 3 3 +#' # time tercil dataset season +#' # 2 3 3 1 #' str(res) -#' # num [1:2, 1:3, 1:3] 0.6773 0 0 0.0413 1.8419 ... +#' # num [1:2, 1:3, 1:3 1] 0.6773 0 0 0.0413 1.8419 ... #' # Example 2 for the ProbBestIndexWeighting function #' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) #' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) #' weights_index_exp<- runif(24, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) #' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) #' dim(res) -#' # time tercil dataset lat lon -#' # 2 3 3 2 3 +#' # time tercil dataset season lat lon +#' # 2 3 3 1 2 3 #' str(res) -#' # num [1:2, 1:3, 1:3, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... +#' # num [1:2, 1:3, 1:3, 1, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... #' ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { - return(Apply(list(var_exp, weights_index_exp), + res <- Apply(list(var_exp, weights_index_exp), target_dims = list(c('time','member'), c('time','member')), - fun = .ProbBestIndexWeighting)$output1) + fun = .ProbBestIndexWeighting)$output1 + if (length(dim(var_exp) == 5)){ + Dim <- c('time', 'tercil', 'dataset', 'season', 'lat', 'lon') + } else if(length(dim(var_exp) == 3)){ + Dim <- c('time', 'tercil', 'dataset', 'season') + } else { + stop("Wrong dimension") + } + pos <- match(Dim, names(dim(res))) + res <- aperm(res, pos) + names(dim(res)) <- Dim + return(res) } #' Atomic ProbBestIndexWeighting @@ -209,6 +222,8 @@ ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' @noRd .ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { # computing terciles + names(dim(var_exp)) <- c('time','member') + names(dim(weights_index_exp)) <- c('time','member') terciles_exp <- WeightTerciles(var_exp, weights_index_exp) lowerTercile <- terciles_exp$lowerTercile upperTercile <- terciles_exp$upperTercile @@ -219,9 +234,7 @@ ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { pos <- match(names(dim(aTerciles)), c('time','member')) aTerciles <- aperm(aTerciles,pos) - - pos <- match(names(dim(weights_index_exp)), c('time','member')) - weights_index_exp <- aperm(weights_index_exp, pos) + names(dim(aTerciles)) <- c('time','member') probTercile <- array(NA, dim = c(dim(var_exp)['time'], tercil = 3)) for (idTercil in 1:3){ @@ -238,6 +251,7 @@ Data2Tercil <- function(x,lt,ut) { y[x <= lt] <- 1 y[x >= ut] <-3 y[(x > lt) & (x < ut)] <- 2 + dim(y) <- c(member = length(x)) return (y) } diff --git a/R/CST_WeightIndex.R b/R/CST_WeightIndex.R index 93eb01f1..eafc561e 100644 --- a/R/CST_WeightIndex.R +++ b/R/CST_WeightIndex.R @@ -18,7 +18,6 @@ #' ensemble members. #' The pdf_exp object is expected to have an element named \code{$data} #' with at least three dimensions (time, statistic = 2, season). -#' It could have one or two dimension (dataset and/or member) equal to 1. #' The firt statistic is the parameter 'mean' of the PDFs and #' the second statistic is the parameter 'standard deviation' of the PDFs. #' @return CST_WeightIndex() returns a CSTools object (i.e., of the @@ -44,36 +43,52 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } - if (!any(names(dim(pdf_exp$data)) == 'statistic')){ - stop("The component 'data' of the parameter 'pdf_exp' must be the ", - "dimension 'statistic'") + if(length(dim(index_exp$data)) != 4){ + stop("The number of dimensions in the component 'data' ", + "of the parameter 'index_exp' must be 4") } - if (dim(pdf_exp$data)['statistic'] != 2) { - stop("The length of the dimension 'statistic' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 2.") + if (!any(names(dim(index_exp$data)) == 'time') || + names(dim(index_exp$data)[1]) != 'time'){ + stop("The first dimension in the component 'data' ", + "of the parameter 'index_exp' must be 'time'") } - if (any(names(dim(pdf_exp$data)) == 'member') & - dim(pdf_exp$data)['member'] != 1) { - stop("The length of the dimension 'member' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 1.") + if (!any(names(dim(index_exp$data)) == 'dataset') || + names(dim(index_exp$data)[2]) != 'dataset'){ + stop("The second dimension in the component 'data' ", + "of the parameter 'index_exp' must be 'dataset'") } - if (any(names(dim(pdf_exp$data)) == 'dataset') & - dim(pdf_exp$data)['dataset'] != 1) { - stop("The length of the dimension 'dataset' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 1.") + if (!any(names(dim(index_exp$data)) == 'member') || + names(dim(index_exp$data)[3]) != 'member'){ + stop("The third dimension in the component 'data' ", + "of the parameter 'index_exp' must be 'member'") } - if (!any(names(dim(index_exp$data)) == 'time')){ - stop("The component 'data' of the parameter 'index_exp' must be the ", - "dimension 'time'") + if (!any(names(dim(index_exp$data)) == 'season') || + names(dim(index_exp$data)[4]) != 'season'){ + stop("The fourth dimension in the component 'data' ", + "of the parameter 'index_exp' must be 'season'") } - if (!any(names(dim(pdf_exp$data)) == 'time')){ - stop("The component 'data' of the parameter 'pdf_exp' must be the ", - "dimension 'time'") + if(length(dim(pdf_exp$data)) != 3){ + stop("The number of dimensions in the component 'data' ", + "of the parameter 'pdf_exp' must be 3") } - if (dim(index_exp$data)['time'] != dim(pdf_exp$data)['time']){ - stop("The length of the dimension 'time' of the parameter 'index_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'time' of the parameter 'pdf_exp' in the component 'data'") + if (!any(names(dim(pdf_exp$data)) == 'time') || + names(dim(pdf_exp$data)[1]) != 'time'){ + stop("The first dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'time'") + } + if (!any(names(dim(pdf_exp$data)) == 'statistic') || + names(dim(pdf_exp$data)[2]) != 'statistic'){ + stop("The second dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'statistic'") + } + if (!any(names(dim(pdf_exp$data)) == 'season') || + names(dim(pdf_exp$data)[3]) != 'season'){ + stop("The third dimension in the component 'data' ", + "of the parameter 'pdf_exp' must be 'season'") + } + if (dim(pdf_exp$data)['statistic'] != 2) { + stop("The length of the dimension 'statistic' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 2.") } if (dim(index_exp$data)['time'] != dim(pdf_exp$data)['time']){ stop("The length of the dimension 'time' of the parameter 'index_exp' ", @@ -85,22 +100,15 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { "in the component 'data' must be equal to the length of the dimension", " 'season' of the parameter 'pdf_exp' in the component 'data'") } - if (!any(names(dim(index_exp$data)) == 'member')){ - stop("The component 'data' of the parameter 'index_exp' must be the ", - "dimension 'member'") - } - if (any(names(dim(pdf_exp$data)) == 'member')) { - Dim <- dim(pdf_exp$data) - dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] - } - if (any(names(dim(pdf_exp$data)) == 'dataset')) { - Dim <- dim(pdf_exp$data) - dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'dataset')] - } + dimnames <- names(dim(index_exp$data)) aWeigths <- WeightIndex(index_exp$data, pdf_exp$data) - pos <- match(dimnames, names(dim(aWeigths))) - aWeigths <- aperm(aWeigths, pos) + if((dim(aWeigths)[1] != dim(index_exp$data)[1]) || + (dim(aWeigths)[2] != dim(index_exp$data)[2]) || + (dim(aWeigths)[3] != dim(index_exp$data)[3]) || + (dim(aWeigths)[4] != dim(index_exp$data)[4])){ + stop("Wrong dimensions of variable 'aWeigths'") + } names(dim(aWeigths)) <- dimnames weights_index_exp <- list() weights_index_exp$data <- aWeigths @@ -134,10 +142,10 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { #' @export #' @examples #' # Example for the WeightIndex function -#' index_exp <- 1 : (10 * 3 * 5) -#' dim(index_exp) <- c(time = 10, dataset = 3, member = 5) -#' pdf_exp <- 1 : (10 * 2) -#' dim(pdf_exp) <- c(time = 10, statistic = 2) +#' index_exp <- 1 : (10 * 3 * 5 * 1) +#' dim(index_exp) <- c(time = 10, dataset = 3, member = 5, season = 1) +#' pdf_exp <- 1 : (10 * 2 * 1) +#' dim(pdf_exp) <- c(time = 10, statistic = 2, season =1) #' res <- WeightIndex(index_exp, pdf_exp) #' dim(res) #' # time dataset member @@ -151,7 +159,7 @@ WeightIndex <- function(index_exp, pdf_exp) { fun = .WeightIndex) aweights <- aweights$output1 dimnames <- names(dim(index_exp)) - pos <- match(dimnames, names(dim(aweights))) + pos <- match(dimnames, c('member', 'time', 'dataset', 'season')) aweights <- aperm(aweights, pos) return(aweights) diff --git a/man/CST_EMBestIndexWeighting.Rd b/man/CST_EMBestIndexWeighting.Rd index 94035cca..faf64c06 100644 --- a/man/CST_EMBestIndexWeighting.Rd +++ b/man/CST_EMBestIndexWeighting.Rd @@ -10,17 +10,13 @@ CST_EMBestIndexWeighting(var_exp, weights_index_exp) \item{var_exp}{An object of the class 's2dv_cube' containing the variable (e.g. precipitation, temperature, NAO index) array. The var_exp object is expected to have an element named \code{$data} with -at least two dimensions (time, member). -If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another -dimension (time, dataset, member, lat, lon).} +three dimensions (time, dataset, member) for a zonal variable +or five dimensions (time, dataset, member, lat, lon) for a spatial variable.} \item{weights_index_exp}{An object of the class 's2dv_cube' containing the normalized weights array. The weights_index_exp object is expected to have an element named -\code{$data} with at least two dimensions (time, member). -If we want to compute for several SFSs, the array could have another -dimension (time, dataset, member).} +\code{$data} with dimensions (time, dataset, member, season).} } \value{ CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the @@ -38,18 +34,18 @@ climate variability (as e.g., the North Atlantic Oscillation (NAO)) and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ -var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) -dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) -weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) -dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) +var_exp <- 1 : (4 * 2 * 3 * 3 * 2) +dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) +weights_index_exp <- runif(4 * 2 * 3, 0.0001, 0.9999) +dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 1) var_exp <- list(data = var_exp) weights_index_exp <- list(data = weights_index_exp) class(var_exp) <- 's2dv_cube' class(weights_index_exp) <- 's2dv_cube' res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) dim(res_CST$data) -# time dataset season lat lon -# 4 2 2 3 2 +# time dataset season lat lon +# 4 2 1 3 2 } \references{ Regionally improved seasonal forecast of precipitation through diff --git a/man/CST_PDFIndexFcst.Rd b/man/CST_PDFIndexFcst.Rd index eb4ac3d9..4616bc8e 100644 --- a/man/CST_PDFIndexFcst.Rd +++ b/man/CST_PDFIndexFcst.Rd @@ -5,8 +5,8 @@ \title{Computing the Index PDFs for a forecast year of a SFSs dataset of a CSTools object.} \usage{ -CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = "ME", - na.rm = FALSE) +CST_PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = "ens", + method = "ME", na.rm = FALSE) } \arguments{ \item{index_hind}{An object of the class 's2dv_cube' @@ -14,14 +14,18 @@ containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate for a hindcast period from SFSs for improving. The index_hind object is expected to have an element named \code{$data} -with dimensions (time, dataset, member, season).} +with dimensions (time, dataset, member, season) or +(time, dataset, member=1, statistic=2, season). The dimensions 'time' must +be greater than 1. When dimension 'statistic' doesn't exist, the dimension +'member' must be greater than 1.} \item{index_obs}{An object of the class 's2dv_cube' containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate from observations or reanalysis. The index_obs object is expected to have an element named \code{$data} -with at least three dimensions (time, dataset, season). +with at least three dimensions (time, dataset, season). The dimensions 'time' +must be greater than 1. It could have one more dimension (member) equal to 1.} \item{index_fcst}{An object of the class 's2dv_cube' @@ -29,7 +33,12 @@ containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate for a forecast year from SFSs for improving. The index_fcst object is expected to have an element named \code{$data} -with dimensions (time = 1, dataset, member, season).} +with dimensions (time = 1, dataset, member, season) or +(time = 1, dataset, member = 1, statistic = 2, season). When dimension +'statistic' doesn't exist, the dimension 'member' must be greater than 1.} + +\item{type_exp}{A character string ('ens' or 'sta') to identify if the +experiment output is an ensemble members or statistics.} \item{method}{A character string indicating which methodology is applied to compute the PDFs. One of "ME" (default) or "LMEV".} @@ -56,15 +65,15 @@ This function implements the computation to obtain the index PDFs and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ -index_hind <- 1 : (3 * 5 * 4 * 7) +index_hind <- 1 : (5 * 3 * 7 * 4) dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) index_hind <- list(data = index_hind) class(index_hind) <- 's2dv_cube' -index_obs <- 1 : (3 * 5 * 4) +index_obs <- 1 : (5 * 3 * 4) dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) index_obs <- list(data = index_obs) class(index_obs) <- 's2dv_cube' -index_fcst <- 1 : (3 * 4 * 7) +index_fcst <- 1 : (3 * 7 * 4) dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) index_fcst <- list(data = index_fcst) class(index_fcst) <- 's2dv_cube' diff --git a/man/CST_PDFIndexHind.Rd b/man/CST_PDFIndexHind.Rd index 216e0bb5..aee829f1 100644 --- a/man/CST_PDFIndexHind.Rd +++ b/man/CST_PDFIndexHind.Rd @@ -5,23 +5,31 @@ \title{Computing the Index PDFs for the hindcast of a SFSs dataset of a CSTools object} \usage{ -CST_PDFIndexHind(index_exp, index_obs, method = "ME", na.rm = FALSE) +CST_PDFIndexHind(index_exp, index_obs, type_exp = "ens", method = "ME", + na.rm = FALSE) } \arguments{ \item{index_exp}{An object of the class 's2dv_cube' containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. The index_exp object is expected to have an element named \code{$data} -with dimensions (time, dataset, member, season).} +with dimensions (time, dataset, member, season) or +(time, dataset, member=1, statistic=2, season). The dimensions 'time' must be +greater than 1. When dimension 'statistic' doesn't exist, the dimension +'member' must be greater than 1.} \item{index_obs}{An object of the class 's2dv_cube' containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate from observations or reanalysis. The index_obs object is expected to have an element named \code{$data} -with at least three dimensions (time, dataset, season). +with dimensions (time, dataset, season). The dimensions 'time' must be +greater than 1. It could have one more dimension (member) equal to 1.} +\item{type_exp}{A character string ('ens' or 'sta') to identify if the +experiment output is an ensemble members or statistics.} + \item{method}{A character string indicating which methodology is applied to compute the PDFs. One of "ME" (default) or "LMEV".} @@ -47,9 +55,9 @@ This function implements the computation to obtain the index PDFs and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ -index_obs <- 1 : (3 * 5 * 4) +index_obs <- 1 : (5 * 3 * 4) dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -index_exp <- 1 : (3 * 5 * 4 * 7) +index_exp <- 1 : (5 * 3 * 7 * 4) dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) index_exp <- list(data = index_exp) index_obs <- list(data = index_obs) diff --git a/man/CST_ProbBestIndexWeighting.Rd b/man/CST_ProbBestIndexWeighting.Rd index c9d9748b..3725cad4 100644 --- a/man/CST_ProbBestIndexWeighting.Rd +++ b/man/CST_ProbBestIndexWeighting.Rd @@ -11,17 +11,13 @@ CST_ProbBestIndexWeighting(var_exp, weights_index_exp) \item{var_exp}{An object of the class 's2dv_cube' containing the variable (e.g. precipitation, temperature, NAO index) array. The var_exp object is expected to have an element named \code{$data} with -at least two dimensions (time, member). -If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another -dimension (time, dataset, member, lat, lon).} +three dimensions (time, dataset, member) for a zonal variable +or five dimensions (time, dataset, member, lat, lon) for a spatial variable.} \item{weights_index_exp}{An object of the class 's2dv_cube' containing the normalized weights array. The weights_index_exp object is expected to have an element named -\code{$data} with at least two dimensions (time, member). -If we want to compute for several SFSs, the array could have another -dimension (time, dataset, member).} +\code{$data} with dimensions (time, dataset, member, season).} } \value{ CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the @@ -32,7 +28,7 @@ with four or six dimensions depending if the variable is zonal (as e.g. precipitation or temperature) (time, tercil, dataset, season, lat, lon), containing the terciles probabilities computing with weighted members. -The firt tercil is the lower tercile, the second is the normal tercile and +The first tercil is the lower tercile, the second is the normal tercile and the third is the upper tercile. } \description{ @@ -43,8 +39,8 @@ Oscillation (NAO)) and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ -var_exp <- 1 : (4 * 2 * 3 * 2 * 3 * 2) -dim(var_exp) <- c(time = 4, dataset = 2, member = 3, season = 2, lat = 3, lon = 2) +var_exp <- 1 : (4 * 2 * 3 * 3 * 2) +dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) var_exp <- list(data = var_exp) diff --git a/man/CST_WeightIndex.Rd b/man/CST_WeightIndex.Rd index 8d3fa23d..03d2639e 100644 --- a/man/CST_WeightIndex.Rd +++ b/man/CST_WeightIndex.Rd @@ -19,7 +19,6 @@ which define a Gaussian PDFs for weighting the values estimate for the SFS ensemble members. The pdf_exp object is expected to have an element named \code{$data} with at least three dimensions (time, statistic = 2, season). -It could have one or two dimension (dataset and/or member) equal to 1. The firt statistic is the parameter 'mean' of the PDFs and the second statistic is the parameter 'standard deviation' of the PDFs.} } diff --git a/man/EMBestIndexWeighting.Rd b/man/EMBestIndexWeighting.Rd index 62997637..a45b3ddc 100644 --- a/man/EMBestIndexWeighting.Rd +++ b/man/EMBestIndexWeighting.Rd @@ -8,20 +8,18 @@ EMBestIndexWeighting(var_exp, weights_index_exp) } \arguments{ \item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with at least two dimensions (time, member). -If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another -dimension (time, dataset, member, lat, lon).} +array from a SFS with three dimensions (time, dataset, member) for a zonal +variable or dimensions (time, dataset, member, lat, lon) for a spatial +variable.} -\item{weights_index_exp}{Normalized weights array with at least two -dimensions (time, member). If we want to compute for several SFSs, -the array could have another dimension (time, dataset, member).} +\item{weights_index_exp}{Normalized weights array with dimensions +(time, dataset, member, season).} } \value{ -EMBestIndexWeighting() returns an array with two or four dimensions -depending if the variable is zonal (as e.g. NAO index)(time, dataset) +EMBestIndexWeighting() returns an array with three or five dimensions +depending if the variable is zonal (as e.g. NAO index)(time, dataset, season) or it is spatial variable (as e.g. precipitation or temperature) -(time, dataset, lat, lon), containing the ensemble means computing +(time, dataset, season, lat, lon), containing the ensemble means computing with weighted members. } \description{ @@ -34,24 +32,24 @@ climate variability (as e.g., the North Atlantic Oscillation (NAO)). var_exp <- 1 : (2 * 3 * 4) dim(var_exp) <- c(time = 2, dataset = 3, member = 4) weights_index_exp<- runif(24, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) res <- EMBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time dataset -# 2 3 +# time dataset season +# 2 3 1 str(res) -# num [1:2, 1:3] 36.2 28.8 38.3 31.9 31 ... +# num [1:2, 1:3, 1] 36.2 28.8 38.3 31.9 31 ... # Example 2 for the EMBestIndexWeighting function var_exp <- 1 : (2 * 3 * 4 * 2 * 3) dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) -weights_index_exp<- runif(24, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +weights_index_exp<- runif(48, min=0.001, max=0.999) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 2) res <- EMBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time dataset lat lon -# 2 3 2 3 +# time dataset season lat lon +# 2 3 2 2 3 str(res) -# num [1:2, 1:3, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... +# num [1:2, 1:3, 1:2, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... } \references{ diff --git a/man/PDFIndexFcst.Rd b/man/PDFIndexFcst.Rd index 779e5517..98f68521 100644 --- a/man/PDFIndexFcst.Rd +++ b/man/PDFIndexFcst.Rd @@ -4,8 +4,8 @@ \alias{PDFIndexFcst} \title{Computing the Index PDFs for a dataset of SFSs for a forecast year.} \usage{ -PDFIndexFcst(index_hind, index_obs, index_fcst, method = "ME", - na.rm = FALSE) +PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = "ens", + method = "ME", na.rm = FALSE) } \arguments{ \item{index_hind}{Index (e.g. NAO index) array from SFSs with dimensions @@ -17,6 +17,9 @@ or reanalysis with dimension (time, dataset, member = 1) or (time, dataset).} \item{index_fcst}{Index (e.g. NAO index) array from SFSs. with dimensions (time = 1, dataset, member) for a forecast year.} +\item{type_exp}{A character string ('ens' or 'sta') to identify if the +experiment output is an ensemble members or statistics.} + \item{method}{A character string indicating which methodology is applied to compute the PDF. One of "ME" (default) or "LMEV".} diff --git a/man/PDFIndexHind.Rd b/man/PDFIndexHind.Rd index fa174761..0b7bafda 100644 --- a/man/PDFIndexHind.Rd +++ b/man/PDFIndexHind.Rd @@ -4,7 +4,8 @@ \alias{PDFIndexHind} \title{Computing the Index PDFs for a dataset of SFSs for a hindcats period.} \usage{ -PDFIndexHind(index_exp, index_obs, method = "ME", na.rm = FALSE) +PDFIndexHind(index_exp, index_obs, type_exp = "ens", method = "ME", + na.rm = FALSE) } \arguments{ \item{index_exp}{Index (e.g. NAO index) array from SFSs @@ -13,6 +14,9 @@ with dimensions (time, dataset, member).} \item{index_obs}{Index (e.g. NAO index) array from an observational database or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset).} +\item{type_exp}{A character string ('ens' or 'sta') to identify if the +experiment output is an ensemble members or statistics.} + \item{method}{A character string indicating which methodology is applied to compute the PDFs. One of "ME" (default) or "LMEV".} diff --git a/man/ProbBestIndexWeighting.Rd b/man/ProbBestIndexWeighting.Rd index cbb86e3b..2a3d6de3 100644 --- a/man/ProbBestIndexWeighting.Rd +++ b/man/ProbBestIndexWeighting.Rd @@ -8,22 +8,20 @@ ProbBestIndexWeighting(var_exp, weights_index_exp) } \arguments{ \item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with at least two dimensions (time, member). -If the variable is spatial the dimensions would be (time, member, lat, lon). -If we want to compute for several SFSs, the array could have another -dimension (time, dataset, member, lat, lon).} +array from a SFS with three dimensions (time, dataset, member) for a zonal +variable or dimensions (time, dataset, member, lat, lon) for a spatial +variable.} -\item{weights_index_exp}{Normalized weights array with at least two -dimensions (time, member). If we want to compute for several SFSs, -the array could have another dimension (time, dataset, member).} +\item{weights_index_exp}{Normalized weights array with dimensions +(time, dataset, member, season).} } \value{ -ProbBestIndexWeighting() returns an array with three or five dimensions -depending if the variable is zonal (as e.g. NAO index)(time, tercil, dataset) -or it is spatial variable (as e.g. precipitation or temperature) -(time, tercil, dataset, lat, lon), containing the terciles probabilities -computing with weighted members. -The firt tercil is the lower tercile, the second is the normal tercile and +ProbBestIndexWeighting() returns an array with four or six dimensions +depending if the variable is zonal (as e.g. NAO index) +(time, tercil, dataset, season) or it is spatial variable (as e.g. +precipitation or temperature) (time, tercil, dataset, season, 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. } \description{ @@ -37,24 +35,24 @@ Oscillation (NAO)). var_exp <- 1 : (2 * 3 * 4) dim(var_exp) <- c(time = 2, dataset = 3, member = 4) weights_index_exp<- runif(24, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) res <- ProbBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time tercil dataset -# 2 3 3 +# time tercil dataset season +# 2 3 3 1 str(res) -# num [1:2, 1:3, 1:3] 0.6773 0 0 0.0413 1.8419 ... +# num [1:2, 1:3, 1:3 1] 0.6773 0 0 0.0413 1.8419 ... # Example 2 for the ProbBestIndexWeighting function var_exp <- 1 : (2 * 3 * 4 * 2 * 3) dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) weights_index_exp<- runif(24, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4) +dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) res <- ProbBestIndexWeighting(var_exp, weights_index_exp) dim(res) -# time tercil dataset lat lon -# 2 3 3 2 3 +# time tercil dataset season lat lon +# 2 3 3 1 2 3 str(res) -# num [1:2, 1:3, 1:3, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... +# num [1:2, 1:3, 1:3, 1, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... } \references{ diff --git a/man/WeightIndex.Rd b/man/WeightIndex.Rd index a464004d..b64ab199 100644 --- a/man/WeightIndex.Rd +++ b/man/WeightIndex.Rd @@ -26,10 +26,10 @@ Best Index estimation. } \examples{ # Example for the WeightIndex function -index_exp <- 1 : (10 * 3 * 5) -dim(index_exp) <- c(time = 10, dataset = 3, member = 5) -pdf_exp <- 1 : (10 * 2) -dim(pdf_exp) <- c(time = 10, statistic = 2) +index_exp <- 1 : (10 * 3 * 5 * 1) +dim(index_exp) <- c(time = 10, dataset = 3, member = 5, season = 1) +pdf_exp <- 1 : (10 * 2 * 1) +dim(pdf_exp) <- c(time = 10, statistic = 2, season =1) res <- WeightIndex(index_exp, pdf_exp) dim(res) # time dataset member diff --git a/vignettes/BestNaoWeighting_vignette.Rmd b/vignettes/BestNaoWeighting_vignette.Rmd index 404baa1f..438e33b7 100644 --- a/vignettes/BestNaoWeighting_vignette.Rmd +++ b/vignettes/BestNaoWeighting_vignette.Rmd @@ -71,6 +71,7 @@ we combine them to obtain the Best NAO PDF estimate by runnning: # forecast period using the named 'LMEV' methodology or scheme pdf_hind_m1_CST <- CST_PDFIndexHind(lNAOs_CST$index_hind1, lNAOs_CST$index_obs, + type_exp = 'ens', method = 'LMEV', na.rm = FALSE) str(pdf_hind_m1_CST) @@ -78,7 +79,8 @@ dim(pdf_hind_m1_CST$data) pdf_fcst_m1_CST <- CST_PDFIndexFcst(lNAOs_CST$index_hind1, lNAOs_CST$index_obs, - lNAOs_CST$index_fcst1, + lNAOs_CST$index_fcst1, + type_exp = 'ens', method = 'LMEV', na.rm = FALSE) str(pdf_fcst_m1_CST) @@ -87,7 +89,8 @@ dim(pdf_fcst_m1_CST$data) # Improving the S-ClimWaRe (M2) NAO index DPFs estimate for the hindcast and # forecast period using the named 'ME' methodology or scheme pdf_hind_m2_CST <- CST_PDFIndexHind(lNAOs_CST$index_hind2, - lNAOs_CST$index_obs, + lNAOs_CST$index_obs, + type_exp = 'sta', method = 'ME', na.rm = FALSE) str(pdf_hind_m2_CST) @@ -95,7 +98,8 @@ dim(pdf_hind_m2_CST$data) pdf_fcst_m2_CST <- CST_PDFIndexFcst(lNAOs_CST$index_hind2, lNAOs_CST$index_obs, - lNAOs_CST$index_fcst2, + lNAOs_CST$index_fcst2, + type_exp = 'sta', method = 'ME', na.rm = FALSE) str(pdf_fcst_m2_CST) @@ -193,7 +197,7 @@ estimate is obtained running the code: coordVAR <- list(lonmin = -10, lonmax = 5, latmin = 35, latmax = 45) lonPlot <- seq(coordVAR$lonmin,coordVAR$lonmax,0.50) latPlot <- seq(coordVAR$latmax,coordVAR$latmin,-0.50) -probData_CST <- prob_var_exp$data[which(var_exp$Dates$start == 2012),1,1,,,1] +probData_CST <- prob_var_exp$data[which(var_exp$Dates$start == 2012),1,1,1,,] brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") @@ -212,7 +216,7 @@ In a similar way we can plot the map with the probability that the total precipi March 2013, for example, will be in the lower tercile from ECMWF Seasonal Forecast System 5 (raw) to compare results running the code: ```{r message=FALSE} -probDataRaw_CST <- prob_var_exp_raw$data[which(var_exp$Dates$start == 2012),1,1,,,1] +probDataRaw_CST <- prob_var_exp_raw$data[which(var_exp$Dates$start == 2012),1,1,1,,] brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") -- GitLab From 00769876c66f17517c2193b5a5c9f8c8ebeaad86 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 19 Sep 2019 12:16:35 +0000 Subject: [PATCH 17/40] Fixing error na.rm of vignette in file zzz.R --- R/zzz.R | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/R/zzz.R b/R/zzz.R index 46ecf0db..b9328692 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -18,13 +18,27 @@ } # Auxiliar function to compute the mean squared error between 'exp' and 'obs' -Sigma_notBC <- function(exp, obs) { +Sigma_notBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } return(sqrt(verify(obs, exp, frcst.type = "cont", obs.type = "cont")$MSE)) } # Auxiliar function to compute the bias between 'exp' and 'obs' -MEBC <- function(exp, obs) { +MEBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } return(verify(obs, exp, frcst.type = "cont", obs.type = "cont")$ME) } @@ -38,13 +52,19 @@ Sigma_BC <- function(exp, obs, na.rm = FALSE) { # Auxiliar function to compute the linear model used in the method # called 'LMEV' to correct the bias in the Best NAO weighting # methogology -LMEV <- function(exp, obs, na.rm = FALSE) { +LMEV <- function(exp, obs, dim_exp, + dim_obs, na.rm = FALSE) { + names(dim(exp)) <- dim_exp + names(dim(obs)) <- dim_obs if (any(names(dim(exp)) == 'member')) { exp_ens_mean <- Apply(list(exp), target_dims = 'member', fun = mean, na.rm = na.rm)$output1 exp_ens_sd <- Apply(list(exp), target_dims = 'member', fun = sd, na.rm = na.rm)$output1 } else { + if(na.rm & any(is.na(index_exp))){ + print("Some value are NA") + } pos <- match(c('time','statistic'), names(dim(exp))) exp <- aperm(exp,pos) exp_ens_mean <- exp[,1] -- GitLab From 22f0bdcf58a2f730b1f06edf89eee1ea8474c0ca Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Fri, 20 Sep 2019 09:34:52 +0000 Subject: [PATCH 18/40] Fixing error in function 'ProbBestIndexWeighting' --- R/CST_EMBestIndexWeighting.R | 4 ---- R/CST_PDFBest.R | 3 ++- R/CST_ProbBestIndexWeighting.R | 8 ++------ R/CST_WeightIndex.R | 19 ++++++++++--------- man/CST_PDFBest.Rd | 3 ++- man/EMBestIndexWeighting.Rd | 4 ---- man/ProbBestIndexWeighting.Rd | 4 ---- man/WeightIndex.Rd | 14 +++++++------- 8 files changed, 23 insertions(+), 36 deletions(-) diff --git a/R/CST_EMBestIndexWeighting.R b/R/CST_EMBestIndexWeighting.R index c23666e8..98822a09 100644 --- a/R/CST_EMBestIndexWeighting.R +++ b/R/CST_EMBestIndexWeighting.R @@ -159,8 +159,6 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(res) #' # time dataset season #' # 2 3 1 -#' str(res) -#' # num [1:2, 1:3, 1] 36.2 28.8 38.3 31.9 31 ... #' # Example 2 for the EMBestIndexWeighting function #' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) #' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) @@ -170,8 +168,6 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(res) #' # time dataset season lat lon #' # 2 3 2 2 3 -#' str(res) -#' # num [1:2, 1:3, 1:2, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... #' EMBestIndexWeighting <- function(var_exp, weights_index_exp) { res <- Apply(list(var_exp, weights_index_exp), diff --git a/R/CST_PDFBest.R b/R/CST_PDFBest.R index 2b53824d..9430f317 100644 --- a/R/CST_PDFBest.R +++ b/R/CST_PDFBest.R @@ -30,7 +30,8 @@ #' @export #' @examples #' pdf_exp <- c(1: (10 * 2* 4 * 1)) -#' dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, season = 1) +#' dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, +#' season = 1) #' pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), #' season = c('DJF'), Dates = list(start = 1997:2006)) #' class(pdf_exp) <- 's2dv_cube' diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index 7b6cda37..efc0f387 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -166,8 +166,6 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(res) #' # time tercil dataset season #' # 2 3 3 1 -#' str(res) -#' # num [1:2, 1:3, 1:3 1] 0.6773 0 0 0.0413 1.8419 ... #' # Example 2 for the ProbBestIndexWeighting function #' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) #' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) @@ -177,16 +175,14 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { #' dim(res) #' # time tercil dataset season lat lon #' # 2 3 3 1 2 3 -#' str(res) -#' # num [1:2, 1:3, 1:3, 1, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... #' ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { res <- Apply(list(var_exp, weights_index_exp), target_dims = list(c('time','member'), c('time','member')), fun = .ProbBestIndexWeighting)$output1 - if (length(dim(var_exp) == 5)){ + if (length(dim(var_exp)) == 5){ Dim <- c('time', 'tercil', 'dataset', 'season', 'lat', 'lon') - } else if(length(dim(var_exp) == 3)){ + } else if(length(dim(var_exp)) == 3){ Dim <- c('time', 'tercil', 'dataset', 'season') } else { stop("Wrong dimension") diff --git a/R/CST_WeightIndex.R b/R/CST_WeightIndex.R index eafc561e..b541fd7e 100644 --- a/R/CST_WeightIndex.R +++ b/R/CST_WeightIndex.R @@ -131,13 +131,13 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { #' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' @param index_exp Index (e.g. NAO index) array, from a dataset of SFSs -#' for a period of years, with dimensions (time, dataset, member). +#' for a period of years, with dimensions (time, dataset, member, season). #' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions -#' (time, statistic = 2) for a period of years. +#' (time, statistic = 2, season) for a period of years. #' The firt statistic is the parameter 'mean' of the PDF and #' the second statistic is the parameter 'standard deviation' of the PDF. #' @return WeightIndex() returns a normalized weights array with dimensions -#' (time, dataset, member). +#' (time, dataset, member, season). #' @import multiApply #' @export #' @examples @@ -145,22 +145,23 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { #' index_exp <- 1 : (10 * 3 * 5 * 1) #' dim(index_exp) <- c(time = 10, dataset = 3, member = 5, season = 1) #' pdf_exp <- 1 : (10 * 2 * 1) -#' dim(pdf_exp) <- c(time = 10, statistic = 2, season =1) +#' dim(pdf_exp) <- c(time = 10, statistic = 2, season = 1) #' res <- WeightIndex(index_exp, pdf_exp) #' dim(res) -#' # time dataset member -#' # 10 3 5 +#' # time dataset member season +#' # 10 3 5 1 #' str(res) -#' # num [1:10, 1:3, 1:5] 0.976 0.958 0.935 0.908 0.881 ... +#' # num [1:10, 1:3, 1:5, 1] 0.976 0.958 0.935 0.908 0.881 ... #' WeightIndex <- function(index_exp, pdf_exp) { aweights <- Apply(list(index_exp, pdf_exp), target_dims = list('member', 'statistic'), - fun = .WeightIndex) - aweights <- aweights$output1 + fun = .WeightIndex)$output1 + dimnames <- names(dim(index_exp)) pos <- match(dimnames, c('member', 'time', 'dataset', 'season')) aweights <- aperm(aweights, pos) + names(dim(aweights)) <- dimnames return(aweights) } diff --git a/man/CST_PDFBest.Rd b/man/CST_PDFBest.Rd index f2bbb0e5..ec767e91 100644 --- a/man/CST_PDFBest.Rd +++ b/man/CST_PDFBest.Rd @@ -39,7 +39,8 @@ and accepts a CSTools object (an object of the class 's2dv_cube' as input. } \examples{ pdf_exp <- c(1: (10 * 2* 4 * 1)) -dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, season = 1) +dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, +season = 1) pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), season = c('DJF'), Dates = list(start = 1997:2006)) class(pdf_exp) <- 's2dv_cube' diff --git a/man/EMBestIndexWeighting.Rd b/man/EMBestIndexWeighting.Rd index a45b3ddc..dbd5b971 100644 --- a/man/EMBestIndexWeighting.Rd +++ b/man/EMBestIndexWeighting.Rd @@ -37,8 +37,6 @@ res <- EMBestIndexWeighting(var_exp, weights_index_exp) dim(res) # time dataset season # 2 3 1 -str(res) -# num [1:2, 1:3, 1] 36.2 28.8 38.3 31.9 31 ... # Example 2 for the EMBestIndexWeighting function var_exp <- 1 : (2 * 3 * 4 * 2 * 3) dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) @@ -48,8 +46,6 @@ res <- EMBestIndexWeighting(var_exp, weights_index_exp) dim(res) # time dataset season lat lon # 2 3 2 2 3 -str(res) -# num [1:2, 1:3, 1:2, 1:2, 1:3] 30.7 10.6 30.8 19.7 35.1 ... } \references{ diff --git a/man/ProbBestIndexWeighting.Rd b/man/ProbBestIndexWeighting.Rd index 2a3d6de3..be6dea12 100644 --- a/man/ProbBestIndexWeighting.Rd +++ b/man/ProbBestIndexWeighting.Rd @@ -40,8 +40,6 @@ res <- ProbBestIndexWeighting(var_exp, weights_index_exp) dim(res) # time tercil dataset season # 2 3 3 1 -str(res) -# num [1:2, 1:3, 1:3 1] 0.6773 0 0 0.0413 1.8419 ... # Example 2 for the ProbBestIndexWeighting function var_exp <- 1 : (2 * 3 * 4 * 2 * 3) dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) @@ -51,8 +49,6 @@ res <- ProbBestIndexWeighting(var_exp, weights_index_exp) dim(res) # time tercil dataset season lat lon # 2 3 3 1 2 3 -str(res) -# num [1:2, 1:3, 1:3, 1, 1:2, 1:3] 0.703 0 0 0.236 1.87 ... } \references{ diff --git a/man/WeightIndex.Rd b/man/WeightIndex.Rd index b64ab199..7917b65b 100644 --- a/man/WeightIndex.Rd +++ b/man/WeightIndex.Rd @@ -8,16 +8,16 @@ WeightIndex(index_exp, pdf_exp) } \arguments{ \item{index_exp}{Index (e.g. NAO index) array, from a dataset of SFSs -for a period of years, with dimensions (time, dataset, member).} +for a period of years, with dimensions (time, dataset, member, season).} \item{pdf_exp}{Statistics array to define a Gaussian PDF with dimensions -(time, statistic = 2) for a period of years. +(time, statistic = 2, season) for a period of years. The firt statistic is the parameter 'mean' of the PDF and the second statistic is the parameter 'standard deviation' of the PDF.} } \value{ WeightIndex() returns a normalized weights array with dimensions -(time, dataset, member). +(time, dataset, member, season). } \description{ This function implements the computation to obtain the @@ -29,13 +29,13 @@ Best Index estimation. index_exp <- 1 : (10 * 3 * 5 * 1) dim(index_exp) <- c(time = 10, dataset = 3, member = 5, season = 1) pdf_exp <- 1 : (10 * 2 * 1) -dim(pdf_exp) <- c(time = 10, statistic = 2, season =1) +dim(pdf_exp) <- c(time = 10, statistic = 2, season = 1) res <- WeightIndex(index_exp, pdf_exp) dim(res) -# time dataset member -# 10 3 5 +# time dataset member season +# 10 3 5 1 str(res) -# num [1:10, 1:3, 1:5] 0.976 0.958 0.935 0.908 0.881 ... +# num [1:10, 1:3, 1:5, 1] 0.976 0.958 0.935 0.908 0.881 ... } \references{ -- GitLab From 6c75fe8116611d83d08d47b483a3b2f43bdb676b Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Fri, 20 Sep 2019 10:08:33 +0000 Subject: [PATCH 19/40] Fixing an error in auxiliar function 'Data2Tercil' --- R/CST_ProbBestIndexWeighting.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index efc0f387..835c7621 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -246,7 +246,7 @@ Data2Tercil <- function(x,lt,ut) { y <- x y[x <= lt] <- 1 y[x >= ut] <-3 - y[(x > lt) & (x < ut)] <- 2 + y[(x > rep(lt,length(x))) & (x < rep(lt,length(x)))] <- 2 dim(y) <- c(member = length(x)) return (y) } -- GitLab From c6fe82355f0a98b4ab0b2e4b73bb10f8b4bc97fe Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Mon, 23 Sep 2019 09:03:38 +0000 Subject: [PATCH 20/40] Adding 'test-CST_PDFIndexHind.R' and fixing some errors in 'CST_PDFIndexHind.R' function --- R/CST_PDFIndexHind.R | 107 ++++---- man/CST_PDFIndexHind.Rd | 9 +- man/PDFIndexHind.Rd | 36 ++- tests/testthat/test-CST_PDFIndexHind.R | 341 +++++++++++++++++++++++++ 4 files changed, 435 insertions(+), 58 deletions(-) create mode 100644 tests/testthat/test-CST_PDFIndexHind.R diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R index aa5cf85a..8dde9bbb 100644 --- a/R/CST_PDFIndexHind.R +++ b/R/CST_PDFIndexHind.R @@ -14,16 +14,15 @@ #' The index_exp object is expected to have an element named \code{$data} #' with dimensions (time, dataset, member, season) or #' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must be -#' greater than 1. When dimension 'statistic' doesn't exist, the dimension +#' greater than 2. When dimension 'statistic' doesn't exist, the dimension #' 'member' must be greater than 1. #' @param index_obs An object of the class 's2dv_cube' #' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate from observations or #' reanalysis. #' The index_obs object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, season). The dimensions 'time' must be -#' greater than 1. -#' It could have one more dimension (member) equal to 1. +#' with dimensions (time, dataset, member = 1, season). The dimension 'time' +#' must be greater than 2. #' @param type_exp A character string ('ens' or 'sta') to identify if the #' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied @@ -43,7 +42,7 @@ #' with bias corrected #' @export #' @examples -#' index_obs <- 1 : (5 * 3 * 4) +#' index_obs <- 1 : (5 * 3 * 1 * 4) #' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) #' index_exp <- 1 : (5 * 3 * 7 * 4) #' dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) @@ -73,30 +72,37 @@ CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'M stop("Element 'data' from parameters 'index_exp' and 'index_obs'", " should have dimmension names.") } - if (!all(names(dim(index_obs$data)) %in% c('time', 'dataset', 'member', 'season'))) { + if(length(dim(index_obs$data)) != 4){ stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") - } - if (!all(names(dim(index_exp$data)) %in% c('time', 'dataset', 'member', 'statistic', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") - } - - if (!all(c('time', 'dataset', 'season') %in% names(dim(index_obs$data)))){ + }else if (!all(names(dim(index_obs$data)) == c('time', 'dataset', 'member', 'season'))) { stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") } - if (!all(c('time', 'dataset', 'member', 'season') %in% names(dim(index_exp$data)))){ + if(length(dim(index_exp$data)) == 4 ){ + if (!all(names(dim(index_exp$data)) == c('time', 'dataset', 'member', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") + } + } else if (length(dim(index_exp$data)) == 5 ){ + if (!all(names(dim(index_exp$data)) == c('time', 'dataset', 'member', 'statistic', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") + } + } else { stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") } if (dim(index_obs$data)['time'] != dim(index_exp$data)['time']) { stop("The length of the dimensions 'time' in the component 'data' ", "of the parameter 'index_obs' and 'index_exp' must be equals.") } + if (dim(index_obs$data)['dataset'] != dim(index_exp$data)['dataset']) { + stop("The length of the dimensions 'dataset' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be equals.") + } if (dim(index_obs$data)['season'] != dim(index_exp$data)['season']) { stop("The length of the dimensions 'season' in the component 'data' ", "of the parameter 'index_obs' and 'index_exp' must be equals.") } - if (dim(index_obs$data)['time'] == 1) { + if (dim(index_obs$data)['time'] <= 2) { stop("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be greater than 1.") + "of the parameter 'index_obs' and 'index_exp' must be greater than 2.") } if (dim(index_obs$data)['member'] != 1) { stop("The length of the dimension 'member' in the component 'data' ", @@ -114,25 +120,21 @@ CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'M "parameter 'index_exp' must be equal to 1 if the component 'data' ", "of the parameter 'index_exp' has the dimension 'statistic'") } - if (dim(index_exp$data)['member'] == 1 & dim(index_exp$data)['statistic'] != 2){ stop("The length of the dimension 'statistic' in the component 'data' of the ", "parameter 'index_exp' must be equal to 2 if the component 'data' ", "of the parameter 'index_exp' has the dimension 'member' equal to 1.") } - if (any(names(dim(index_exp)) == 'statistic') & type_exp != 'sta'){ + if (any(names(dim(index_exp$data)) == 'statistic') & type_exp != 'sta'){ stop("The parameter 'type_exp' must be equal to 'sta' because the ", "dimension 'statistic' in the component 'data' of the ", "parameter 'index_exp' exits") - } - if (any(names(dim(index_exp)) == 'member') & - !any(names(dim(index_exp)) == 'statistic') & type_exp != 'ens'){ - stop("The parameter 'type_exp' must be equal to 'ens' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_exp' doesn't exit") - } + } + PDFstatistics <- PDFIndexHind(index_exp$data, index_obs$data, + type_exp = type_exp, method = method, + na.rm = na.rm) if (dim(index_exp$data)['member'] == 1) { Dim <- dim(index_exp$data) dim(index_exp$data) <- Dim[-which(names(Dim) == 'member')] @@ -141,9 +143,7 @@ CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'M if (any(names(dim(index_exp$data)) == 'member')){ dimnames[dimnames == 'member'] <- 'statistic' } - PDFstatistics <- PDFIndexHind(index_exp$data, index_obs$data, - type_exp = type_exp, method = method, - na.rm = na.rm) + pos <- match(dimnames, names(dim(PDFstatistics))) PDFstatistics <- aperm(PDFstatistics, pos) names(dim(PDFstatistics)) <- dimnames @@ -163,16 +163,20 @@ CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'M #' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' @param index_exp Index (e.g. NAO index) array from SFSs -#' with dimensions (time, dataset, member). +#' with dimensions (time, dataset, member, season) or +#' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must be +#' greater than 2. When dimension 'statistic' doesn't exist, the dimension +#' 'member' must be greater than 1. #' @param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset). +#' or reanalysis with dimensions (time, dataset, member = 1, season). The +#' dimension 'time' must be greater than 2. #' @param type_exp A character string ('ens' or 'sta') to identify if the #' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied #' to compute the PDFs. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? #' @return PDFIndexHind() returns an array -#' with dimensions (time, statistic = 4, dataset). +#' with dimensions (time, statistic = 4, dataset, season). #' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected @@ -183,24 +187,39 @@ CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'M #' @export #' @examples #' # Example for the PDFIndexHind function -#' index_obs <- 1 : (3 * 5) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1) -#' index_exp <- 1 : (3 * 5 * 4) -#' dim(index_exp) <- c(time = 5, dataset = 3, member = 4) +#' Example 1 +#' index_obs <- 1 : (5 * 3 * 1 * 1) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) +#' index_exp <- 1 : (5 * 3 * 4 * 1) +#' dim(index_exp) <- c(time = 5, dataset = 3, member = 4, season = 1) #' res <- PDFIndexHind(index_exp, index_obs) #' str(res) -#' # num [1:5, 1:4, 1:3] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... +#' # num [1:5, 1:4, 1:3, 1] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... +#' dim(res) +#' # time statistic dataset season +#' # 5 4 3 1 +#' Example 2 +#' index_obs <- 1 : (5 * 3 * 1 * 1) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) +#' index_exp <- 1 : (5 * 3 * 1* 2 * 1) +#' dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 2, season = 1) +#' res <- PDFIndexHind(index_exp, index_obs, type_exp = 'sta') +#' str(res) +#' # num [1:5, 1:4, 1:3, 1] 1 2 3 4 5 0 0 0 0 0 ... #' dim(res) -#' # time statistic dataset -#' # 5 4 3 +#' # time statistic dataset season +#' # 5 4 3 1 #' - PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method ='ME', na.rm = FALSE) { - if (any(names(dim(index_obs)) == 'member')) { - Dim <- dim(index_obs) - dim(index_obs) <- Dim[-which(names(Dim) == 'member')] + + if('statistic' %in% names(dim(index_exp)) & dim(index_exp)['member'] == 1){ + Dim <- dim(index_exp) + dim(index_exp) <- Dim[-which(names(Dim) == 'member')] } + Dim <- dim(index_obs) + dim(index_obs) <- Dim[-which(names(Dim) == 'member')] + if (type_exp == 'ens'){ PDFstatistics <- Apply(list(index_exp, index_obs), target_dims = list(c('time', 'member'), 'time'), @@ -215,9 +234,11 @@ PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method ='ME', #' Atomic PDFIndexHind #' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions -#' (time, member) for a hindcast period. +#' (time, member) for a hindcast period. The dimension 'time' must be greater +#' than 2. #' @param index_obs Index (e.g. NAO index) array from an observational dataset -#' or reanalysis with dimension (time). +#' or reanalysis with dimension (time). The dimension 'time' must be greater +#' than 2. #' @param type_exp A character string ('ens' or 'sta') to identify if the #' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied diff --git a/man/CST_PDFIndexHind.Rd b/man/CST_PDFIndexHind.Rd index aee829f1..5f44a22c 100644 --- a/man/CST_PDFIndexHind.Rd +++ b/man/CST_PDFIndexHind.Rd @@ -15,7 +15,7 @@ the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. The index_exp object is expected to have an element named \code{$data} with dimensions (time, dataset, member, season) or (time, dataset, member=1, statistic=2, season). The dimensions 'time' must be -greater than 1. When dimension 'statistic' doesn't exist, the dimension +greater than 2. When dimension 'statistic' doesn't exist, the dimension 'member' must be greater than 1.} \item{index_obs}{An object of the class 's2dv_cube' @@ -23,9 +23,8 @@ containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate from observations or reanalysis. The index_obs object is expected to have an element named \code{$data} -with dimensions (time, dataset, season). The dimensions 'time' must be -greater than 1. -It could have one more dimension (member) equal to 1.} +with dimensions (time, dataset, member = 1, season). The dimension 'time' +must be greater than 2.} \item{type_exp}{A character string ('ens' or 'sta') to identify if the experiment output is an ensemble members or statistics.} @@ -55,7 +54,7 @@ This function implements the computation to obtain the index PDFs and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ -index_obs <- 1 : (5 * 3 * 4) +index_obs <- 1 : (5 * 3 * 1 * 4) dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) index_exp <- 1 : (5 * 3 * 7 * 4) dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) diff --git a/man/PDFIndexHind.Rd b/man/PDFIndexHind.Rd index 0b7bafda..80e60965 100644 --- a/man/PDFIndexHind.Rd +++ b/man/PDFIndexHind.Rd @@ -9,10 +9,14 @@ PDFIndexHind(index_exp, index_obs, type_exp = "ens", method = "ME", } \arguments{ \item{index_exp}{Index (e.g. NAO index) array from SFSs -with dimensions (time, dataset, member).} +with dimensions (time, dataset, member, season) or +(time, dataset, member=1, statistic=2, season). The dimensions 'time' must be +greater than 2. When dimension 'statistic' doesn't exist, the dimension +'member' must be greater than 1.} \item{index_obs}{Index (e.g. NAO index) array from an observational database -or reanalysis with dimensions (time, dataset, member = 1) or (time, dataset).} +or reanalysis with dimensions (time, dataset, member = 1, season). The +dimension 'time' must be greater than 2.} \item{type_exp}{A character string ('ens' or 'sta') to identify if the experiment output is an ensemble members or statistics.} @@ -24,7 +28,7 @@ to compute the PDFs. One of "ME" (default) or "LMEV".} } \value{ PDFIndexHind() returns an array -with dimensions (time, statistic = 4, dataset). +with dimensions (time, statistic = 4, dataset, season). The firt statistic is the parameter 'mean' of the PDF with not bias corrected The second statistic is the parameter 'standard deviation' of the PDF with not bias corrected @@ -38,16 +42,28 @@ This function implements the computation to obtain the index PDFs } \examples{ # Example for the PDFIndexHind function -index_obs <- 1 : (3 * 5) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1) -index_exp <- 1 : (3 * 5 * 4) -dim(index_exp) <- c(time = 5, dataset = 3, member = 4) +Example 1 +index_obs <- 1 : (5 * 3 * 1 * 1) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) +index_exp <- 1 : (5 * 3 * 4 * 1) +dim(index_exp) <- c(time = 5, dataset = 3, member = 4, season = 1) res <- PDFIndexHind(index_exp, index_obs) str(res) -# num [1:5, 1:4, 1:3] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... +# num [1:5, 1:4, 1:3, 1] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... dim(res) -# time statistic dataset -# 5 4 3 +# time statistic dataset season +# 5 4 3 1 +Example 2 +index_obs <- 1 : (5 * 3 * 1 * 1) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) +index_exp <- 1 : (5 * 3 * 1* 2 * 1) +dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 2, season = 1) +res <- PDFIndexHind(index_exp, index_obs, type_exp = 'sta') +str(res) +# num [1:5, 1:4, 1:3, 1] 1 2 3 4 5 0 0 0 0 0 ... +dim(res) +# time statistic dataset season +# 5 4 3 1 } \references{ diff --git a/tests/testthat/test-CST_PDFIndexHind.R b/tests/testthat/test-CST_PDFIndexHind.R new file mode 100644 index 00000000..bc8937f3 --- /dev/null +++ b/tests/testthat/test-CST_PDFIndexHind.R @@ -0,0 +1,341 @@ +context("Generic tests") +test_that("basic use case", { + index_obs <- 1 : 5 + dim(index_obs) <- c(time = 5, dataset = 1, member = 1, season = 1) + index_exp <- 1 : (5 * 7) + dim(index_exp) <- c(time = 5, dataset = 1, member = 7, season = 1) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + result <- list(data = array(c(16:20,rep(15,5),rep(NA,10)), + dim = c(time = 5, dataset = 1, statistic = 4, + season = 1)), + statistic = c("mean_notbc", "sigma_notbc", "mean_bc", + "sigma_bc")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, + type_exp = 'ens', method = 'LMEV'), result) + + index_obs <- 1 : 5 + dim(index_obs) <- c(time = 5, dataset = 1, member = 1, season = 1) + index_exp <- 1 : (5 * 2) + dim(index_exp) <- c(time = 5, dataset = 1, member = 1, statistic = 2, season = 1) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + result <- list(data = array(c(1:5,rep(0,5),c(1:5), rep(0,5)), + dim = c(time = 5, dataset = 1, statistic = 4, + season = 1)), + statistic = c("mean_notbc", "sigma_notbc", "mean_bc", + "sigma_bc")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, + type_exp = 'sta'), result) + + index_obs <- 1 : (5 * 3 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + result <- list(data = array(c(46:60,rep(45,15),1:15,rep(0,15),151:165, + rep(135,15),16:30,rep(0,15),256:270,rep(225,15), + 31:45,rep(0,15),361:375,rep(315,15),46:60, + rep(0,15)), dim = c(time = 5, dataset = 3, + statistic = 4, season = 4)), + statistic = c("mean_notbc", "sigma_notbc", "mean_bc", + "sigma_bc")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + result) + + index_exp2 <- index_exp + index_exp2$data[1, 1, 1, 1] <- NA + CST_PDFIndexHind(index_exp = index_exp2, index_obs = index_obs, na.rm = FALSE) + result <- list(data = array(c(NA,47:60,45,rep(NA,4),rep(45,10),rep(NA,5),6:15, + rep(NA,5),rep(0,10),151:165, + rep(135,15),16:30,rep(0,15),256:270,rep(225,15), + 31:45,rep(0,15),361:375,rep(315,15),46:60, + rep(0,15)), dim = c(time = 5, dataset = 3, + statistic = 4, season = 4)), + statistic = c("mean_notbc", "sigma_notbc", "mean_bc", + "sigma_bc")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFIndexHind(index_exp = index_exp2, index_obs = index_obs, + na.rm = FALSE), result) + + +}) + +test_that("Sanity checks", { + expect_error( + CST_PDFIndexHind(index_exp = 1), + paste0("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.")) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + class(index_exp) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 1 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = 1), + paste0("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.")) + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, method = 'PEPE'), + "The parameter 'method' must be equal to 'ME' or 'LMEV'") + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, method = 2), + "The parameter 'method' must be equal to 'ME' or 'LMEV'") + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, method = NA), + "The parameter 'method' must be equal to 'ME' or 'LMEV'") + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, na.rm = 'PEPE'), + "The parameter 'na.rm' must be a logical value.") + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'PEPE'), + "The parameter 'type_exp' must be equal to 'ens' or 'sta'") + + index_obs <- 1 : (5 * 3 * 2 * 4) + dim(index_obs) <- c(5, 3, 2, 4) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Element 'data' from parameters 'index_exp' and 'index_obs'", + " should have dimmension names.")) + + index_obs <- 1 : (2 * 3 * 1 * 5) + dim(index_obs) <- c(season = 2, dataset = 3, member = 1, time = 5) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 2) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) + + index_obs <- 1 : (5 * 3 * 1 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_exp <- 1 : (3 * 5 * 2 * 7) + dim(index_exp) <- c(dataset = 3, time = 5, season = 2, member = 7) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) + + index_obs <- 1 : (1 * 5 * 3 * 2) + dim(index_obs) <- c(member = 1, time = 5, dataset = 3, season = 2) + index_exp <- 1 : (3 * 5 * 2 * 7) + dim(index_exp) <- c(dataset = 3, time = 5, season = 2, member = 7) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) + + index_obs <- 1 : (5 * 3 * 1) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) + + index_obs <- 1 : (5 * 3 * 1 * 1) + dim(index_obs) <- c(reftime = 5, dataset = 3, member = 1, season = 1) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) + + index_obs <- 1 : (5 * 3 * 1 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, seasonal = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) + + index_obs <- 1 : (5 * 3 * 1 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_exp <- 1 : (5 * 3 * 7) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) + + index_obs <- 1 : (5 * 3 * 1 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_exp <- 1 : (5 * 3 * 1 * 2 * 1) + dim(index_exp) <- c(time = 5, dataset = 3, member = 1, myStatistic = 2, season = 1) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) + + + index_obs <- 1 : (3 * 3 * 1 * 4) + dim(index_obs) <- c(time = 3, dataset = 3, member = 1, season = 4) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("The length of the dimensions 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be equals.")) + + index_obs <- 1 : (3 * 3 * 1 * 4) + dim(index_obs) <- c(time = 3, dataset = 3, member = 1, season = 4) + index_exp <- 1 : (3 * 4 * 7 * 4) + dim(index_exp) <- c(time = 3, dataset = 4, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("The length of the dimensions 'dataset' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be equals.")) + + index_obs <- 1 : (3 * 3 * 1 * 2) + dim(index_obs) <- c(time = 3, dataset = 3, member = 1, season = 2) + index_exp <- 1 : (3 * 3 * 7 * 4) + dim(index_exp) <- c(time = 3, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("The length of the dimensions 'season' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be equals.")) + + + index_obs <- 1 : (1 * 3 * 2 * 4) + dim(index_obs) <- c(time = 1, dataset = 3, member = 2, season = 4) + index_exp <- 1 : (1 * 3 * 7 * 4) + dim(index_exp) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("The length of the dimension 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_exp' must be greater than 2.")) + + index_obs <- 1 : (5 * 3 * 2 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 2, season = 4) + index_exp <- 1 : (5 * 3 * 7 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("The length of the dimension 'member' in the component 'data' ", + "of the parameter 'index_obs' must be equal to 1.")) + + index_obs <- 1 : (5 * 3 * 1 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_exp <- 1 : (5 * 3 * 1 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), + paste0("The dimension 'statistic' must be exist in the component 'data' ", + "of the parameter 'index_exp' if the length of the dimension ", + "'member' is equal to 1")) + + index_obs <- 1 : (5 * 3 * 1 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_exp <- 1 : (5 * 3 * 7 * 2 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, statistic = 2, + season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'sta'), + paste0("The length of the dimension 'member' in the component 'data' of the ", + "parameter 'index_exp' must be equal to 1 if the component 'data' ", + "of the parameter 'index_exp' has the dimension 'statistic'")) + + index_obs <- 1 : (5 * 3 * 1 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_exp <- 1 : (5 * 3 * 1 * 3 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 3, + season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'sta'), + paste0("The length of the dimension 'statistic' in the component 'data' of the ", + "parameter 'index_exp' must be equal to 2 if the component 'data' ", + "of the parameter 'index_exp' has the dimension 'member' equal to 1.")) + + index_obs <- 1 : (5 * 3 * 1 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_exp <- 1 : (5 * 3 * 1 * 2 * 4) + dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 2, + season = 4) + index_exp <- list(data = index_exp) + index_obs <- list(data = index_obs) + class(index_exp) <- 's2dv_cube' + class(index_obs) <- 's2dv_cube' + expect_error( + CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'ens'), + paste0("The parameter 'type_exp' must be equal to 'sta' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_exp' exits")) +}) -- GitLab From 6f1595149c8e7ae05d017efaae55eb8d5af59039 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Mon, 23 Sep 2019 09:20:41 +0000 Subject: [PATCH 21/40] Fixing some errors in 'CST_PDFIndexHind.R' function --- R/CST_PDFIndexHind.R | 4 ++-- man/PDFIndexHind.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R index 8dde9bbb..ee4ac657 100644 --- a/R/CST_PDFIndexHind.R +++ b/R/CST_PDFIndexHind.R @@ -187,7 +187,7 @@ CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'M #' @export #' @examples #' # Example for the PDFIndexHind function -#' Example 1 +#' # Example 1 #' index_obs <- 1 : (5 * 3 * 1 * 1) #' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) #' index_exp <- 1 : (5 * 3 * 4 * 1) @@ -198,7 +198,7 @@ CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'M #' dim(res) #' # time statistic dataset season #' # 5 4 3 1 -#' Example 2 +#' # Example 2 #' index_obs <- 1 : (5 * 3 * 1 * 1) #' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) #' index_exp <- 1 : (5 * 3 * 1* 2 * 1) diff --git a/man/PDFIndexHind.Rd b/man/PDFIndexHind.Rd index 80e60965..18f3ed63 100644 --- a/man/PDFIndexHind.Rd +++ b/man/PDFIndexHind.Rd @@ -42,7 +42,7 @@ This function implements the computation to obtain the index PDFs } \examples{ # Example for the PDFIndexHind function -Example 1 +# Example 1 index_obs <- 1 : (5 * 3 * 1 * 1) dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) index_exp <- 1 : (5 * 3 * 4 * 1) @@ -53,7 +53,7 @@ str(res) dim(res) # time statistic dataset season # 5 4 3 1 -Example 2 +# Example 2 index_obs <- 1 : (5 * 3 * 1 * 1) dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) index_exp <- 1 : (5 * 3 * 1* 2 * 1) -- GitLab From d65e50174757f9159dbf78d3ef5836f461f2ce3b Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Mon, 23 Sep 2019 09:37:29 +0000 Subject: [PATCH 22/40] Fixing some errors in 'test-CST_PDFIndexHind.R' --- tests/testthat/test-CST_PDFIndexHind.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test-CST_PDFIndexHind.R b/tests/testthat/test-CST_PDFIndexHind.R index bc8937f3..bed6442b 100644 --- a/tests/testthat/test-CST_PDFIndexHind.R +++ b/tests/testthat/test-CST_PDFIndexHind.R @@ -120,7 +120,7 @@ test_that("Sanity checks", { index_obs <- 1 : (2 * 3 * 1 * 5) dim(index_obs) <- c(season = 2, dataset = 3, member = 1, time = 5) index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 2) + dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) index_exp <- list(data = index_exp) index_obs <- list(data = index_obs) class(index_exp) <- 's2dv_cube' -- GitLab From 8d149440f2d1fa71f9b25f47fd649f804e9b1164 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Tue, 24 Sep 2019 14:08:29 +0000 Subject: [PATCH 23/40] Adding more tests and fixing some errors in my functions --- R/CST_EMBestIndexWeighting.R | 74 +--- R/CST_PDFBest.R | 48 +-- R/CST_PDFIndexFcst.R | 207 +++++----- R/CST_ProbBestIndexWeighting.R | 87 ++-- R/CST_WeightIndex.R | 64 +-- man/CST_PDFBest.Rd | 2 +- man/CST_PDFIndexFcst.Rd | 8 +- man/CST_WeightIndex.Rd | 2 +- man/PDFBest.Rd | 2 +- man/PDFIndexFcst.Rd | 27 +- .../testthat/test-CST_EMBestIndexWeighting.R | 144 +++++++ tests/testthat/test-CST_PDFBest.R | 102 +++++ tests/testthat/test-CST_PDFIndexFcst.R | 378 ++++++++++++++++++ .../test-CST_ProbBestIndexWeighting.R | 172 ++++++++ tests/testthat/test-CST_WeightIndex.R | 143 +++++++ 15 files changed, 1148 insertions(+), 312 deletions(-) create mode 100644 tests/testthat/test-CST_EMBestIndexWeighting.R create mode 100644 tests/testthat/test-CST_PDFBest.R create mode 100644 tests/testthat/test-CST_PDFIndexFcst.R create mode 100644 tests/testthat/test-CST_ProbBestIndexWeighting.R create mode 100644 tests/testthat/test-CST_WeightIndex.R diff --git a/R/CST_EMBestIndexWeighting.R b/R/CST_EMBestIndexWeighting.R index 98822a09..2f721c2f 100644 --- a/R/CST_EMBestIndexWeighting.R +++ b/R/CST_EMBestIndexWeighting.R @@ -43,64 +43,30 @@ CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { stop("Parameter 'var_exp' and 'weights_index_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } - if(!length(dim(var_exp$data)) %in% c(3,5)){ - stop("The number of dimensions in the component 'data' ", - "of the parameter 'index_exp' must be 3 (zonal variable) or 5 (spatial ", - "variable") + if (is.null(names(dim(var_exp$data))) + || is.null(names(dim(weights_index_exp$data)))) { + stop("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", + " should have dimmension names.") } - if (!any(names(dim(var_exp$data)) == 'time') || - names(dim(var_exp$data)[1]) != 'time'){ - stop("The first dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'time'") - } - if (!any(names(dim(var_exp$data)) == 'dataset') || - names(dim(var_exp$data)[2]) != 'dataset'){ - stop("The second dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'dataset'") - } - if (!any(names(dim(var_exp$data)) == 'member') || - names(dim(var_exp$data)[3]) != 'member'){ - stop("The third dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'member'") - } - if (length(dim(var_exp$data)) == 5){ - if (!any(names(dim(var_exp$data)) == 'lat') || - names(dim(var_exp$data)[4]) != 'lat'){ - stop("The fifth dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'lat'") - } - if (!any(names(dim(var_exp$data)) == 'lon') || - names(dim(var_exp$data)[5]) != 'lon'){ - stop("The sixth dimension in the component 'data' ", - "of the parameter 'lon' must be 'season'") + if(length(dim(var_exp$data)) == 3 ){ + if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member'))){ + stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") + } + } else if (length(dim(var_exp$data)) == 5 ){ + if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member', + 'lat', 'lon'))){ + stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") } + } else { + stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") } - - if (length(dim(weights_index_exp$data)) == 4){ - if (!any(names(dim(weights_index_exp$data)) == 'time') || - names(dim(weights_index_exp$data)[1]) != 'time'){ - stop("The first dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'time'") - } - if (!any(names(dim(weights_index_exp$data)) == 'dataset') || - names(dim(weights_index_exp$data)[2]) != 'dataset'){ - stop("The second dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'dataset'") - } - if (!any(names(dim(weights_index_exp$data)) == 'member') || - names(dim(weights_index_exp$data)[3]) != 'member'){ - stop("The third dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'member'") - } - if (!any(names(dim(weights_index_exp$data)) == 'season') || - names(dim(weights_index_exp$data)[4]) != 'season'){ - stop("The fourth dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'season'") - } + if(length(dim(weights_index_exp$data)) == 4 ){ + if (!all(names(dim(weights_index_exp$data)) == c('time', 'dataset', + 'member', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") + } } else { - stop("The number of dimensions in the component 'data' ", - "of the parameter 'weights_index_exp' must be equal to 4 ", - "(time, dataset, member, season)") + stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") } if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ stop("The length of the dimension 'time' of the parameter 'var_exp' ", diff --git a/R/CST_PDFBest.R b/R/CST_PDFBest.R index 9430f317..2967e6c2 100644 --- a/R/CST_PDFBest.R +++ b/R/CST_PDFBest.R @@ -29,7 +29,7 @@ #' The second statistic is the parameter 'standard deviation' of the PDF #' @export #' @examples -#' pdf_exp <- c(1: (10 * 2* 4 * 1)) +#' pdf_exp <- c(1: (10 * 2 * 1 * 4 * 1)) #' dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, #' season = 1) #' pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), @@ -53,32 +53,23 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { stop("Parameter 'pdf_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } - if (!any(names(dim(pdf_exp$data)) == 'time') || - names(dim(pdf_exp$data)[1]) != 'time'){ - stop("The first dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'time'") + if (!is.logical(bc_dataset1)) { + stop("The parameter 'bc_dataset1' must be a logical value.") } - if (!any(names(dim(pdf_exp$data)) == 'dataset') || - names(dim(pdf_exp$data)[2]) != 'dataset'){ - stop("The second dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'dataset'") + if (!is.logical(bc_dataset2)) { + stop("The parameter 'bc_dataset2' must be a logical value.") } - if (!any(names(dim(pdf_exp$data)) == 'member') || - names(dim(pdf_exp$data)[3]) != 'member'){ - stop("The third dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'member'") + if (is.null(names(dim(pdf_exp$data)))) { + stop("Element 'data' from parameters 'pdf_exp' should have dimmension ", + "names.") } - if (!any(names(dim(pdf_exp$data)) == 'statistic') || - names(dim(pdf_exp$data)[4]) != 'statistic'){ - stop("The fourth dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'statistic'") + if(length(dim(pdf_exp$data)) != 5){ + stop("Wrong dimensions of the component 'data' of parameter 'pdf_exp'") + } else if (!all(names(dim(pdf_exp$data)) == c('time', 'dataset', 'member', + 'statistic', 'season'))) { + stop("Wrong dimensions of the component 'data' of parameter 'pdf_exp'") } - if (!any(names(dim(pdf_exp$data)) == 'season') || - names(dim(pdf_exp$data)[5]) != 'season'){ - stop("The fifth dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'season'") - } - if (dim(pdf_exp$data)['dataset'] != 2){ + if (dim(pdf_exp$data)['dataset'] != 2) { stop("The length of the dimension 'dataset' in the component 'data' ", "of the parameter 'pdf_exp' must be equal to 2.") } @@ -86,11 +77,12 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { stop("The length of the dimension 'member' in the component 'data' ", "of the parameter 'pdf_exp' must be equal to 1.") } - if (dim(pdf_exp$data)['statistic'] != 4){ - stop("The length of the dimension 'statistic' in the component 'data' of the ", - "parameter 'pdf_exp' must be equal to 4'") + if (dim(pdf_exp$data)['statistic'] != 4) { + stop("The length of the dimension 'statistic' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 4.") } - + + Dim <- dim(pdf_exp$data) dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] @@ -128,7 +120,7 @@ CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { #' @export #' @examples #' # Example for the PDFBest function -#' pdf_exp <- c(1: (10 * 2* 4 * 1)) +#' pdf_exp <- c(1: (10 * 2 * 4 * 1)) #' dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) #' res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) #' dim(res) diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R index d16b8a08..096c7787 100644 --- a/R/CST_PDFIndexFcst.R +++ b/R/CST_PDFIndexFcst.R @@ -15,16 +15,15 @@ #' The index_hind object is expected to have an element named \code{$data} #' with dimensions (time, dataset, member, season) or #' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must -#' be greater than 1. When dimension 'statistic' doesn't exist, the dimension +#' be greater than 2. When dimension 'statistic' doesn't exist, the dimension #' 'member' must be greater than 1. #' @param index_obs An object of the class 's2dv_cube' #' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate from observations or #' reanalysis. #' The index_obs object is expected to have an element named \code{$data} -#' with at least three dimensions (time, dataset, season). The dimensions 'time' -#' must be greater than 1. -#' It could have one more dimension (member) equal to 1. +#' with dimensions (time, dataset, member = 1, season). The dimensions 'time' +#' must be greater than 2. #' @param index_fcst An object of the class 's2dv_cube' #' containing the indices or drivers of climate variability (as e.g., #' the North Atlantic Oscillation (NAO)) estimate for a forecast year @@ -65,7 +64,6 @@ #' index_fcst <- list(data = index_fcst) #' class(index_fcst) <- 's2dv_cube' #' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') -#' str(res_CST) #' dim(res_CST$data) #' #time dataset statistic season #' # 1 3 4 4 @@ -74,8 +72,8 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, if (!inherits(index_hind, 's2dv_cube') || !inherits(index_obs, 's2dv_cube') || !inherits(index_fcst, 's2dv_cube')) { - stop("Parameter 'index_fcst', 'index_hind' - and 'index_obs' must be of the class 's2dv_cube', ", + stop("Parameter 'index_fcst', 'index_hind' ", + "and 'index_obs' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } if(!(method %in% c('ME', 'LMEV'))){ @@ -93,111 +91,87 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, stop("Element 'data' from parameters 'index_hind', 'index_obs' and 'index_fcst'", " should have dimmension names.") } - if (!all(names(dim(index_obs$data)) %in% c('time', 'dataset', 'member', 'season'))) { - stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") - } - if (!all(names(dim(index_hind$data)) %in% c('time', 'dataset', 'member', 'statistic', 'season'))){ + if((length(names(dim(index_hind$data))) != length(names(dim(index_fcst$data)))) + || (!all(names(dim(index_hind$data)) == names(dim(index_fcst$data))))){ + stop("The dimension names of the component 'data' of the parameter 'index_hind' ", + "and 'index_fcst' must be equals.") + } + if(length(dim(index_hind$data)) == 4 ){ + if (!all(names(dim(index_hind$data)) == c('time', 'dataset', 'member', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") + } + } else if (length(dim(index_hind$data)) == 5 ){ + if (!all(names(dim(index_hind$data)) == c('time', 'dataset', 'member', 'statistic', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") + } + } else { stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") } - if (!all(names(dim(index_fcst$data)) %in% c('time', 'dataset', 'member', 'statistic', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") - } - if (!all(c('time', 'dataset', 'season') %in% names(dim(index_obs$data)))){ + if(length(dim(index_obs$data)) != 4){ + stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") + }else if (!all(names(dim(index_obs$data)) == c('time', 'dataset', 'member', 'season'))) { stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") } - if (!all(c('time', 'dataset', 'member', 'season') %in% names(dim(index_hind$data)))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") - } - if (!all(c('time', 'dataset', 'member', 'season') %in% names(dim(index_fcst$data)))){ + if(length(dim(index_fcst$data)) == 4 ){ + if (!all(names(dim(index_fcst$data)) == c('time', 'dataset', 'member', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") + } + } else if (length(dim(index_fcst$data)) == 5 ){ + if (!all(names(dim(index_fcst$data)) == c('time', 'dataset', 'member', 'statistic', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") + } + } else { stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") } - if (dim(index_obs$data)['time'] != dim(index_hind$data)['time']) { + if (dim(index_hind$data)['time'] != dim(index_obs$data)['time']) { stop("The length of the dimensions 'time' in the component 'data' ", "of the parameter 'index_obs' and 'index_hind' must be equals.") } - if (dim(index_hind$data)['season'] != dim(index_fcst$data)['season']) { - stop("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_hind' and 'index_fcst' must be equals.") + if ((dim(index_hind$data)['dataset'] != dim(index_obs$data)['dataset']) + | (dim(index_hind$data)['dataset'] != dim(index_fcst$data)['dataset'])) { + stop("The length of the dimensions 'dataset' in the component 'data' ", + "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", + "equals.") } - if (dim(index_obs$data)['season'] != dim(index_hind$data)['season']) { + if ((dim(index_hind$data)['season'] != dim(index_obs$data)['season']) + | (dim(index_hind$data)['season'] != dim(index_fcst$data)['season'])) { stop("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_obs' and 'index_hind' must be equals.") + "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", + "equals.") } - if (dim(index_obs$data)['time'] == 1) { + if (dim(index_obs$data)['time'] <= 2) { stop("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be greater than 1.") - } - if (dim(index_obs$data)['member'] != 1) { - stop("The length of the dimension 'member' in the component 'data' ", - "of the parameter 'index_obs' must be equal to 1.") - } - if (dim(index_hind$data)['member'] == 1 & - !any(names(dim(index_hind$data)) == 'statistic')){ - stop("The dimension 'statistic' must be exist in the component 'data' ", - "of the parameter 'index_hind' if the length of the dimension ", - "'member' is equal to 1") - } - if (dim(index_fcst$data)['member'] == 1 & - !any(names(dim(index_fcst$data)) == 'statistic')){ - stop("The dimension 'statistic' must be exist in the component 'data' ", - "of the parameter 'index_fcst' if the length of the dimension ", - "'member' is equal to 1") + "of the parameter 'index_obs' and 'index_hind' must be greater than 2.") } - if (dim(index_fcst$data)['time'] != 1){ - stop("The dimension 'time' must be equal to 1") - } - if (dim(index_hind$data)['member'] == 1 & - !any(names(dim(index_hind$data)) == 'statistic')){ - stop("The dimension 'statistic' must be exist in the component 'data' ", - "of the parameter 'index_hind' if the length of the dimension ", - "'member' is equal to 1") - } - if (dim(index_fcst$data)['member'] > 1 & - any(names(dim(index_fcst$data)) == 'statistic')){ - stop("The length of the dimension 'member' in the component 'data' of the ", - "parameter 'index_fcst' must be equal to 1 if the component 'data' ", - "of the parameter 'index_fcst' has the dimension 'statistic'") - } - if (dim(index_hind$data)['member'] > 1 & - any(names(dim(index_hind$data)) == 'statistic')){ - stop("The length of the dimension 'member' in the component 'data' of the ", - "parameter 'index_hind' must be equal to 1 if the component 'data' ", - "of the parameter 'index_hind' has the dimension 'statistic'") + if (dim(index_fcst$data)['time'] != 1) { + stop("The length of the dimension 'time' in the component 'data' ", + "of the parameter 'index_fcst' must be equal to 1.") } if (dim(index_hind$data)['member'] == 1 & dim(index_hind$data)['statistic'] != 2){ - stop("The length of the dimension 'statistic' in the component 'data' of the ", - "parameter 'index_hind' must be equal to 2 if the component 'data' ", - "of the parameter 'index_hind' has the dimension 'member' equal to 1.") + stop("The dimension 'statistic' must be equal to 2 in the component 'data' ", + "of the parameter 'index_hind' and 'index_fcst' if the length of the ", + "dimension 'member' is equal to 1") } - if (dim(index_fcst$data)['member'] == 1 & - dim(index_fcst$data)['statistic'] != 2){ - stop("The length of the dimension 'statistic' in the component 'data' of the ", - "parameter 'index_fcst' must be equal to 2 if the component 'data' ", - "of the parameter 'index_fcst' has the dimension 'member' equal to 1.") + if (dim(index_obs$data)['member'] != 1){ + stop("The dimension 'member' must be equal to 1 in the component 'data' ", + "of the parameter 'index_obs'.") } - if (any(names(dim(index_fcst)) == 'statistic') & type_exp != 'sta'){ - stop("The parameter 'type_exp' must be equal to 'sta' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_fcst' exits") - } - if (any(names(dim(index_fcst)) == 'member') & - !any(names(dim(index_fcst)) == 'statistic') & type_exp != 'ens'){ - stop("The parameter 'type_exp' must be equal to 'ens' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_fcst' doesn't exit") - } - if (any(names(dim(index_hind)) == 'statistic') & type_exp != 'sta'){ + if (any(names(dim(index_fcst$data)) == 'statistic') & type_exp != 'sta'){ stop("The parameter 'type_exp' must be equal to 'sta' because the ", "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_hind' exits") + "parameter 'index_fcst' exits") } - if (any(names(dim(index_hind)) == 'member') & - !any(names(dim(index_hind)) == 'statistic') & type_exp != 'ens'){ - stop("The parameter 'type_exp' must be equal to 'ens' because the ", + if (!any(names(dim(index_fcst$data)) == 'statistic') & type_exp == 'sta'){ + stop("The parameter 'type_exp' can't be equal to 'sta' because the ", "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_hind' doesn't exit") + "parameter 'index_fcst' doesn't exit.") } + + PDFstatistics <- PDFIndexFcst(index_hind$data, index_obs$data, + index_fcst$data, type_exp = type_exp, + method = method, na.rm = na.rm) + if (dim(index_fcst$data)['member'] == 1) { Dim <- dim(index_fcst$data) dim(index_fcst$data) <- Dim[-which(names(Dim) == 'member')] @@ -206,20 +180,12 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, Dim <- dim(index_hind$data) dim(index_hind$data) <- Dim[-which(names(Dim) == 'member')] } - dimnames_fcst <- names(dim(index_fcst$data)) - dimnames_hind <- names(dim(index_hind$data)) - if(sum(dimnames_fcst == dimnames_hind) != length(dimnames_fcst)){ - stop("The dimensions of the component 'data' of the parameter ", - "'index_fcst' must be equal to the dimensions of the component ", - "'data' of the parameter 'index_hind'") - } - dimnames <- dimnames_fcst + + dimnames <- names(dim(index_fcst$data)) if (any(names(dim(index_fcst$data)) == 'member')){ dimnames[dimnames == 'member'] <- 'statistic' } - PDFstatistics <- PDFIndexFcst(index_hind$data, index_obs$data, - index_fcst$data, type_exp = type_exp, - method = method, na.rm = na.rm) + pos <- match(dimnames, names(dim(PDFstatistics))) PDFstatistics <- aperm(PDFstatistics, pos) names(dim(PDFstatistics)) <- dimnames @@ -240,9 +206,13 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, #' estimation of winter NAO, Sanchez-Garcia, E. et al., #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' @param index_hind Index (e.g. NAO index) array from SFSs with dimensions -#' (time, dataset, member) for a hindcast period. +#' (time, dataset, member, season) or +#' (time, dataset, member=1, statistic=2, season) for a hindcast period. +#' The dimensions 'time' must be greater than 2. When dimension 'statistic' +#' doesn't exist, the dimension member' must be greater than 1. #' @param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with dimension (time, dataset, member = 1) or (time, dataset). +#' or reanalysis with dimension (time, dataset, member = 1, season). +#' The dimensions 'time' must be greater than 2. #' @param index_fcst Index (e.g. NAO index) array from SFSs. #' with dimensions (time = 1, dataset, member) for a forecast year. #' @param type_exp A character string ('ens' or 'sta') to identify if the @@ -250,8 +220,11 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, #' @param method A character string indicating which methodology is applied #' to compute the PDF. One of "ME" (default) or "LMEV". #' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return PDFIndexFcst() returns an array -#' with dimensions (time = 1, statistic = 4, dataset) for the forecast year. +#' @return PDFIndexFcst() returns an array with dimensions +#' (time = 1, dataset, member, season) or +#' (time = 1, dataset, member = 1, statistic = 2, season) for the forecast year. +#' When dimension statistic' doesn't exist, the dimension 'member' must be +#' greater than 1. #' The firt statistic is the parameter 'mean' of the PDF with not bias corrected #' The second statistic is the parameter 'standard deviation' of the PDF #' with not bias corrected @@ -263,24 +236,32 @@ CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, #' @examples #' # Example for the PDFIndexFcst function #' index_fcst <- 1 : (3 * 4) -#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 4) +#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 4, season = 1) #' index_obs<- 1 : (5 * 3) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1) +#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) #' index_hind <- 1 : (5 * 3 * 4) -#' dim(index_hind) <- c(time = 5, dataset = 3, member = 4) +#' dim(index_hind) <- c(time = 5, dataset = 3, member = 4, season = 1) #' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) #' dim(res) -#' # time statistic dataset -#' # 1 4 3 +#' # time statistic dataset season +#' # 1 4 3 1 #' str(res) -#' # num [1, 1:4, 1:3] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... +#' # num [1, 1:4, 1:3, 1] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... #' PDFIndexFcst <- function(index_hind, index_obs, index_fcst, type_exp = 'ens', method ='ME', na.rm = FALSE) { - if (any(names(dim(index_obs)) == 'member')) { - Dim <- dim(index_obs) - dim(index_obs) <- Dim[-which(names(Dim) == 'member')] + + if('statistic' %in% names(dim(index_hind)) & dim(index_hind)['member'] == 1){ + Dim <- dim(index_hind) + dim(index_hind) <- Dim[-which(names(Dim) == 'member')] } + if('statistic' %in% names(dim(index_fcst)) & dim(index_fcst)['member'] == 1){ + Dim <- dim(index_fcst) + dim(index_fcst) <- Dim[-which(names(Dim) == 'member')] + } + Dim <- dim(index_obs) + dim(index_obs) <- Dim[-which(names(Dim) == 'member')] + if (type_exp == 'ens'){ PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), target_dims = list(c('time', 'member'), 'time', @@ -301,7 +282,7 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, type_exp = 'ens', #' @param index_obs Index (e.g. NAO index) array from an observational database #' or reanalysis with dimension (time). #' @param index_fcst Index (e.g. NAO index) array from a SFS -#' with dimensions (time=1, member) for a forecast year. +#' with dimensions (time = 1, member) for a forecast year. #' @param type_exp A character string ('ens' or 'sta') to identify if the #' experiment output is an ensemble members or statistics. #' @param method A character string indicating which methodology is applied diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index 835c7621..3ee9d486 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -45,66 +45,33 @@ #' # 4 3 2 2 3 2 CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { - stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", + stop("Parameter 'var_exp' and 'weights_index_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } - if(!length(dim(var_exp$data)) %in% c(3,5)){ - stop("The number of dimensions in the component 'data' ", - "of the parameter 'index_exp' must be 3 (zonal variable) or 5 (spatial ", - "variable") + if (is.null(names(dim(var_exp$data))) + || is.null(names(dim(weights_index_exp$data)))) { + stop("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", + " should have dimmension names.") } - if (!any(names(dim(var_exp$data)) == 'time') || - names(dim(var_exp$data)[1]) != 'time'){ - stop("The first dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'time'") - } - if (!any(names(dim(var_exp$data)) == 'dataset') || - names(dim(var_exp$data)[2]) != 'dataset'){ - stop("The second dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'dataset'") - } - if (!any(names(dim(var_exp$data)) == 'member') || - names(dim(var_exp$data)[3]) != 'member'){ - stop("The third dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'member'") - } - if (length(dim(var_exp$data)) == 5){ - if (!any(names(dim(var_exp$data)) == 'lat') || - names(dim(var_exp$data)[4]) != 'lat'){ - stop("The fifth dimension in the component 'data' ", - "of the parameter 'var_exp' must be 'lat'") - } - if (!any(names(dim(var_exp$data)) == 'lon') || - names(dim(var_exp$data)[5]) != 'lon'){ - stop("The sixth dimension in the component 'data' ", - "of the parameter 'lon' must be 'season'") + if(length(dim(var_exp$data)) == 3 ){ + if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member'))){ + stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") + } + } else if (length(dim(var_exp$data)) == 5 ){ + if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member', + 'lat', 'lon'))){ + stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") } + } else { + stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") } - if (length(dim(weights_index_exp$data)) == 4){ - if (!any(names(dim(weights_index_exp$data)) == 'time') || - names(dim(weights_index_exp$data)[1]) != 'time'){ - stop("The first dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'time'") - } - if (!any(names(dim(weights_index_exp$data)) == 'dataset') || - names(dim(weights_index_exp$data)[2]) != 'dataset'){ - stop("The second dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'dataset'") - } - if (!any(names(dim(weights_index_exp$data)) == 'member') || - names(dim(weights_index_exp$data)[3]) != 'member'){ - stop("The third dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'member'") - } - if (!any(names(dim(weights_index_exp$data)) == 'season') || - names(dim(weights_index_exp$data)[4]) != 'season'){ - stop("The fourth dimension in the component 'data' ", - "of the parameter 'weights_index_exp' must be 'season'") - } + if(length(dim(weights_index_exp$data)) == 4 ){ + if (!all(names(dim(weights_index_exp$data)) == c('time', 'dataset', + 'member', 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") + } } else { - stop("The number of dimensions in the component 'data' ", - "of the parameter 'weights_index_exp' must be equal to 4 ", - "(time, dataset, member, season)") + stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") } if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ stop("The length of the dimension 'time' of the parameter 'var_exp' ", @@ -122,6 +89,18 @@ CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { " 'member' of the parameter 'weights_index_exp' in the component 'data'") } + if (sum(!is.na(as.vector(var_exp$data))) + != length(as.vector(var_exp$data))){ + stop("There are one or more elements of the component 'data' of the ", + "parameter 'var_exp' equal to NA.") + } + + if (sum(!is.na(as.vector(weights_index_exp$data))) + != length(as.vector(weights_index_exp$data))){ + stop("There are one or more elements of the component 'data' of the ", + "parameter 'weights_index_exp' equal to NA.") + } + prob <- ProbBestIndexWeighting(var_exp$data, weights_index_exp$data) prob_var_exp <- list() prob_var_exp$data <- prob diff --git a/R/CST_WeightIndex.R b/R/CST_WeightIndex.R index b541fd7e..9639a117 100644 --- a/R/CST_WeightIndex.R +++ b/R/CST_WeightIndex.R @@ -17,7 +17,7 @@ #' which define a Gaussian PDFs for weighting the values estimate for the SFS #' ensemble members. #' The pdf_exp object is expected to have an element named \code{$data} -#' with at least three dimensions (time, statistic = 2, season). +#' with dimensions (time, statistic = 2, season). #' The firt statistic is the parameter 'mean' of the PDFs and #' the second statistic is the parameter 'standard deviation' of the PDFs. #' @return CST_WeightIndex() returns a CSTools object (i.e., of the @@ -43,64 +43,38 @@ CST_WeightIndex <- function(index_exp, pdf_exp) { stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") } + if (is.null(names(dim(index_exp$data))) || is.null(names(dim(pdf_exp$data)))) { + stop("Element 'data' from parameters 'index_exp' and 'pdf_exp'", + " should have dimmension names.") + } if(length(dim(index_exp$data)) != 4){ stop("The number of dimensions in the component 'data' ", "of the parameter 'index_exp' must be 4") - } - if (!any(names(dim(index_exp$data)) == 'time') || - names(dim(index_exp$data)[1]) != 'time'){ - stop("The first dimension in the component 'data' ", - "of the parameter 'index_exp' must be 'time'") - } - if (!any(names(dim(index_exp$data)) == 'dataset') || - names(dim(index_exp$data)[2]) != 'dataset'){ - stop("The second dimension in the component 'data' ", - "of the parameter 'index_exp' must be 'dataset'") - } - if (!any(names(dim(index_exp$data)) == 'member') || - names(dim(index_exp$data)[3]) != 'member'){ - stop("The third dimension in the component 'data' ", - "of the parameter 'index_exp' must be 'member'") - } - if (!any(names(dim(index_exp$data)) == 'season') || - names(dim(index_exp$data)[4]) != 'season'){ - stop("The fourth dimension in the component 'data' ", - "of the parameter 'index_exp' must be 'season'") + } else if (!all(names(dim(index_exp$data)) == c('time', 'dataset', 'member', + 'season'))) { + stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") } if(length(dim(pdf_exp$data)) != 3){ stop("The number of dimensions in the component 'data' ", "of the parameter 'pdf_exp' must be 3") - } - if (!any(names(dim(pdf_exp$data)) == 'time') || - names(dim(pdf_exp$data)[1]) != 'time'){ - stop("The first dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'time'") - } - if (!any(names(dim(pdf_exp$data)) == 'statistic') || - names(dim(pdf_exp$data)[2]) != 'statistic'){ - stop("The second dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'statistic'") - } - if (!any(names(dim(pdf_exp$data)) == 'season') || - names(dim(pdf_exp$data)[3]) != 'season'){ - stop("The third dimension in the component 'data' ", - "of the parameter 'pdf_exp' must be 'season'") + } else if (!all(names(dim(pdf_exp$data)) == c('time', 'statistic', + 'season'))){ + stop("Wrong dimensions of the component 'data' of parameter 'pdf_exp'") } if (dim(pdf_exp$data)['statistic'] != 2) { stop("The length of the dimension 'statistic' in the component 'data' ", "of the parameter 'pdf_exp' must be equal to 2.") } - if (dim(index_exp$data)['time'] != dim(pdf_exp$data)['time']){ - stop("The length of the dimension 'time' of the parameter 'index_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'time' of the parameter 'pdf_exp' in the component 'data'") + if (dim(index_exp$data)['time'] != dim(pdf_exp$data)['time']) { + stop("The length of the dimensions 'time' in the component 'data' ", + "of the parameter 'index_exp' and 'pdf_exp' must be equals.") } - if (dim(index_exp$data)['season'] != dim(pdf_exp$data)['season']){ - stop("The length of the dimension 'season' of the parameter 'index_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'season' of the parameter 'pdf_exp' in the component 'data'") + if (dim(index_exp$data)['season'] != dim(pdf_exp$data)['season']) { + stop("The length of the dimensions 'season' in the component 'data' ", + "of the parameter 'index_exp' and 'pdf_exp' must be equals.") } - + + dimnames <- names(dim(index_exp$data)) aWeigths <- WeightIndex(index_exp$data, pdf_exp$data) if((dim(aWeigths)[1] != dim(index_exp$data)[1]) || diff --git a/man/CST_PDFBest.Rd b/man/CST_PDFBest.Rd index ec767e91..7318956d 100644 --- a/man/CST_PDFBest.Rd +++ b/man/CST_PDFBest.Rd @@ -38,7 +38,7 @@ This function implements the computation to obtain the index PDFs and accepts a CSTools object (an object of the class 's2dv_cube' as input. } \examples{ -pdf_exp <- c(1: (10 * 2* 4 * 1)) +pdf_exp <- c(1: (10 * 2 * 1 * 4 * 1)) dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, season = 1) pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), diff --git a/man/CST_PDFIndexFcst.Rd b/man/CST_PDFIndexFcst.Rd index 4616bc8e..3d5101ca 100644 --- a/man/CST_PDFIndexFcst.Rd +++ b/man/CST_PDFIndexFcst.Rd @@ -16,7 +16,7 @@ from SFSs for improving. The index_hind object is expected to have an element named \code{$data} with dimensions (time, dataset, member, season) or (time, dataset, member=1, statistic=2, season). The dimensions 'time' must -be greater than 1. When dimension 'statistic' doesn't exist, the dimension +be greater than 2. When dimension 'statistic' doesn't exist, the dimension 'member' must be greater than 1.} \item{index_obs}{An object of the class 's2dv_cube' @@ -24,9 +24,8 @@ containing the indices or drivers of climate variability (as e.g., the North Atlantic Oscillation (NAO)) estimate from observations or reanalysis. The index_obs object is expected to have an element named \code{$data} -with at least three dimensions (time, dataset, season). The dimensions 'time' -must be greater than 1. -It could have one more dimension (member) equal to 1.} +with dimensions (time, dataset, member = 1, season). The dimensions 'time' +must be greater than 2.} \item{index_fcst}{An object of the class 's2dv_cube' containing the indices or drivers of climate variability (as e.g., @@ -78,7 +77,6 @@ dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) index_fcst <- list(data = index_fcst) class(index_fcst) <- 's2dv_cube' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') -str(res_CST) dim(res_CST$data) #time dataset statistic season # 1 3 4 4 diff --git a/man/CST_WeightIndex.Rd b/man/CST_WeightIndex.Rd index 03d2639e..89cfe481 100644 --- a/man/CST_WeightIndex.Rd +++ b/man/CST_WeightIndex.Rd @@ -18,7 +18,7 @@ with dimensions (time, dataset, member, season).} which define a Gaussian PDFs for weighting the values estimate for the SFS ensemble members. The pdf_exp object is expected to have an element named \code{$data} -with at least three dimensions (time, statistic = 2, season). +with dimensions (time, statistic = 2, season). The firt statistic is the parameter 'mean' of the PDFs and the second statistic is the parameter 'standard deviation' of the PDFs.} } diff --git a/man/PDFBest.Rd b/man/PDFBest.Rd index d20753ac..e155d888 100644 --- a/man/PDFBest.Rd +++ b/man/PDFBest.Rd @@ -33,7 +33,7 @@ the Best Index estimation. } \examples{ # Example for the PDFBest function -pdf_exp <- c(1: (10 * 2* 4 * 1)) +pdf_exp <- c(1: (10 * 2 * 4 * 1)) dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) dim(res) diff --git a/man/PDFIndexFcst.Rd b/man/PDFIndexFcst.Rd index 98f68521..f46ccad3 100644 --- a/man/PDFIndexFcst.Rd +++ b/man/PDFIndexFcst.Rd @@ -9,10 +9,14 @@ PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = "ens", } \arguments{ \item{index_hind}{Index (e.g. NAO index) array from SFSs with dimensions -(time, dataset, member) for a hindcast period.} +(time, dataset, member, season) or +(time, dataset, member=1, statistic=2, season) for a hindcast period. +The dimensions 'time' must be greater than 2. When dimension 'statistic' +doesn't exist, the dimension member' must be greater than 1.} \item{index_obs}{Index (e.g. NAO index) array from an observational database -or reanalysis with dimension (time, dataset, member = 1) or (time, dataset).} +or reanalysis with dimension (time, dataset, member = 1, season). +The dimensions 'time' must be greater than 2.} \item{index_fcst}{Index (e.g. NAO index) array from SFSs. with dimensions (time = 1, dataset, member) for a forecast year.} @@ -26,8 +30,11 @@ to compute the PDF. One of "ME" (default) or "LMEV".} \item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ -PDFIndexFcst() returns an array -with dimensions (time = 1, statistic = 4, dataset) for the forecast year. +PDFIndexFcst() returns an array with dimensions +(time = 1, dataset, member, season) or +(time = 1, dataset, member = 1, statistic = 2, season) for the forecast year. +When dimension statistic' doesn't exist, the dimension 'member' must be +greater than 1. The firt statistic is the parameter 'mean' of the PDF with not bias corrected The second statistic is the parameter 'standard deviation' of the PDF with not bias corrected @@ -42,17 +49,17 @@ This function implements the computation to obtain the index PDFs \examples{ # Example for the PDFIndexFcst function index_fcst <- 1 : (3 * 4) -dim(index_fcst) <- c(time = 1, dataset = 3, member = 4) +dim(index_fcst) <- c(time = 1, dataset = 3, member = 4, season = 1) index_obs<- 1 : (5 * 3) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) index_hind <- 1 : (5 * 3 * 4) -dim(index_hind) <- c(time = 5, dataset = 3, member = 4) +dim(index_hind) <- c(time = 5, dataset = 3, member = 4, season = 1) res <- PDFIndexFcst(index_hind, index_obs, index_fcst) dim(res) -# time statistic dataset -# 1 4 3 +# time statistic dataset season +# 1 4 3 1 str(res) -# num [1, 1:4, 1:3] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... +# num [1, 1:4, 1:3, 1] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... } \references{ diff --git a/tests/testthat/test-CST_EMBestIndexWeighting.R b/tests/testthat/test-CST_EMBestIndexWeighting.R new file mode 100644 index 00000000..74e42cf7 --- /dev/null +++ b/tests/testthat/test-CST_EMBestIndexWeighting.R @@ -0,0 +1,144 @@ +context("Generic tests") +test_that("basic use case", { + var_exp <- 1 : (2 * 1 * 3 * 3 * 2) + dim(var_exp) <- c(time = 2, dataset = 1, member = 3, lat = 3, lon = 2) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + result <- array(c(5.669304, 5.831401, 16.03673, 13.97668, + 26.40415, 22.12196, 36.77157, 30.26724, + 47.13899, 38.41252, 57.50641, 46.55779), + dim = c(time = 2, dataset = 1, season = 1, + lat = 3, lon = 2)) + + expect_equal(CST_EMBestIndexWeighting(var_exp, weights_index_exp)$data, result) + +}) + +test_that("Sanity checks", { + expect_error( + CST_EMBestIndexWeighting(var_exp = 1), + paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", + "'s2dv_cube', as output by CSTools::CST_Load.")) + expect_error( + CST_EMBestIndexWeighting(var_exp = var_exp, weights_index_exp = 1), + paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", + "'s2dv_cube', as output by CSTools::CST_Load.")) + + var_exp <- 1 : (2 * 1) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + set.seed(23) + weights_index_exp <- runif(2 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + weights_index_exp <- list(data = weights_index_exp) + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", + " should have dimmension names.")) + + var_exp <- 1 : (2 * 1 * 3 * 3) + dim(var_exp) <- c(time = 2, dataset = 1, member = 3, lat = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) + + var_exp <- 1 : (1 * 2 * 3) + dim(var_exp) <- c(dataset = 1, time = 2, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) + + var_exp <- 1 : (2 * 1 * 3) + dim(var_exp) <- c(time = 2, dataset = 1, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) + + var_exp <- 1 : (2 * 1 * 3) + dim(var_exp) <- c(time = 2, dataset = 1, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(member = 3, season = 1, time = 2, dataset = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) + + + var_exp <- 1 : (3 * 1 * 3) + dim(var_exp) <- c(time = 3, dataset = 1, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("The length of the dimension 'time' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'time' of the parameter 'weights_index_exp' in the component 'data'")) + + var_exp <- 1 : (2 * 2 * 3) + dim(var_exp) <- c(time = 2, dataset = 2, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("The length of the dimension 'dataset' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'dataset' of the parameter 'weights_index_exp' in the component 'data'")) + + + var_exp <- 1 : (2 * 2 * 4) + dim(var_exp) <- c(time = 2, dataset = 2, member = 4) + set.seed(23) + weights_index_exp <- runif(2 * 2 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 2, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_EMBestIndexWeighting(var_exp, weights_index_exp), + paste0("The length of the dimension 'member' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'member' of the parameter 'weights_index_exp' in the component 'data'")) + +}) diff --git a/tests/testthat/test-CST_PDFBest.R b/tests/testthat/test-CST_PDFBest.R new file mode 100644 index 00000000..508d4b48 --- /dev/null +++ b/tests/testthat/test-CST_PDFBest.R @@ -0,0 +1,102 @@ +context("Generic tests") +test_that("basic use case", { + pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) + dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 4, season = 1) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + result <- list(data = array(c(14.28166, 15.29171, 16.30088, + 14.37964, 15.09211, 15.80411), + dim = c(time = 3, statistic = 2, + season = 1)), + statistic = c("mean", "sigma")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE), result) + + + result2 <- list(data = array(c(5.952278, 7.090211, 8.215385, + 8.849182, 9.638375, 10.418918), + dim = c(time = 3, statistic = 2, + season = 1)), + statistic = c("mean", "sigma")) + attr(result2, 'class') <- 's2dv_cube' + expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE), result2) + + +}) + +test_that("Sanity checks", { + pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) + dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 4, season = 1) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp = 1), + paste0("Parameter 'pdf_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.")) + expect_error( + CST_PDFBest(pdf_exp = pdf_exp, bc_dataset1 = 2, bc_dataset2 = 'PEPE'), + "The parameter 'bc_dataset1' must be a logical value.") + expect_error( + CST_PDFBest(pdf_exp = pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = 'PEPE'), + "The parameter 'bc_dataset2' must be a logical value.") + + pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp), + paste0("Element 'data' from parameters 'pdf_exp' should have dimmension ", + "names.")) + + pdf_exp <- c(1: (3 * 2 * 1 * 4)) + dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 4) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) + + pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) + dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, myStatistic = 4) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) + + pdf_exp <- c(1: (2 * 3 * 1 * 4 * 1)) + dim(pdf_exp) <- c(dataset = 2, time = 3, member = 1, statistic = 4, season = 1) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) + + pdf_exp <- c(1: (3 * 3 * 1 * 4 * 1)) + dim(pdf_exp) <- c(time = 3, dataset = 3, member = 1, statistic = 4, season = 1) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp), + paste0("The length of the dimension 'dataset' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 2.")) + + pdf_exp <- c(1: (3 * 2 * 3 * 4 * 1)) + dim(pdf_exp) <- c(time = 3, dataset = 2, member = 3, statistic = 4, season = 1) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp), + paste0("The length of the dimension 'member' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 1.")) + + pdf_exp <- c(1: (3 * 2 * 1 * 5 * 1)) + dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 5, season = 1) + pdf_exp <- list(data = pdf_exp) + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_PDFBest(pdf_exp), + paste0("The length of the dimension 'statistic' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 4.")) + +}) diff --git a/tests/testthat/test-CST_PDFIndexFcst.R b/tests/testthat/test-CST_PDFIndexFcst.R new file mode 100644 index 00000000..a6abefec --- /dev/null +++ b/tests/testthat/test-CST_PDFIndexFcst.R @@ -0,0 +1,378 @@ +context("Generic tests") +test_that("basic use case", { + index_hind <- 1 : (5 * 3 * 7 * 4) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + result <- list(data = array(c(10:12, rep(45,3), rep(NA,6), + 31:33, rep(135,3), rep(NA,6), + 52:54, rep(225,3), rep(NA,6), + 73:75, rep(315,3), rep(NA,6)), + dim = c(time = 1, dataset = 3, statistic = 4, + season = 4)), + statistic = c("mean_notbc", "sigma_notbc", "mean_bc", + "sigma_bc")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFIndexFcst(index_hind, index_obs, index_fcst, + type_exp = 'ens', method = 'LMEV', + na.rm = FALSE), result) + + index_hind <- 1 : (5 * 3 * 1* 2 * 2) + dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 2, + season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 2 * 2) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 2, + season = 2) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + result <- list(data = array(c(1:3, rep(0,3), 1:3, rep(0,3), + 7:9, rep(15,3), -8:-6, rep(0,3)), + dim = c(time = 1, dataset = 3, statistic = 4, + season = 2)), + statistic = c("mean_notbc", "sigma_notbc", "mean_bc", + "sigma_bc")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFIndexFcst(index_hind, index_obs, index_fcst, + type_exp = 'sta', na.rm = FALSE), result) + + + index_hind2 <- index_hind + index_hind2$data[1, 1, 1, 1, 1] <- NA + result <- list(data = array(c(1:3, rep(0,3), 1:3, NA, rep(0,2), + 7:9, rep(15,3), -8:-6, rep(0,3)), + dim = c(time = 1, dataset = 3, statistic = 4, + season = 2)), + statistic = c("mean_notbc", "sigma_notbc", "mean_bc", + "sigma_bc")) + attr(result, 'class') <- 's2dv_cube' + expect_equal(CST_PDFIndexFcst(index_hind = index_hind2, index_obs = index_obs, + index_fcst = index_fcst, type_exp = 'sta', + na.rm = FALSE), result) +}) + +test_that("Sanity checks", { + index_hind <- 1 : (5 * 3 * 7 * 4) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind = 1), + paste0("Parameter 'index_fcst', 'index_hind' and 'index_obs' must be ", + "of the class 's2dv_cube', as output by CSTools::CST_Load.")) + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = 1), + paste0("Parameter 'index_fcst', 'index_hind' and 'index_obs' must be ", + "of the class 's2dv_cube', as output by CSTools::CST_Load.")) + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, + index_fcst = 1), + paste0("Parameter 'index_fcst', 'index_hind' and 'index_obs' must be ", + "of the class 's2dv_cube', as output by CSTools::CST_Load.")) + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, + index_fcst = index_fcst, method = 'PEPE'), + "The parameter 'method' must be equal to 'ME' or 'LMEV'") + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, + index_fcst = index_fcst, method = 2), + "The parameter 'method' must be equal to 'ME' or 'LMEV'") + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, + index_fcst = index_fcst, method = NA), + "The parameter 'method' must be equal to 'ME' or 'LMEV'") + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, + index_fcst = index_fcst, na.rm = 'PEPE'), + "The parameter 'na.rm' must be a logical value.") + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, + index_fcst = index_fcst, type_exp = 'PEPE'), + "The parameter 'type_exp' must be equal to 'ens' or 'sta'") + + index_hind <- 1 : (5 * 3 * 7 * 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, + index_fcst = index_fcst), + paste0("Element 'data' from parameters 'index_hind', 'index_obs' and 'index_fcst'", + " should have dimmension names.")) + + index_hind <- 1 : (3 * 5 * 7 * 4) + dim(index_hind) <- c(time = 3, dataset = 5, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (3 * 5 * 4) + dim(index_obs) <- c(time = 3, dataset = 5, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (5 * 2 * 4) + dim(index_fcst) <- c(time = 1, dataset = 5, member = 1, statistic = 2, + season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The dimension names of the component 'data' of the parameter ", + "'index_hind' and 'index_fcst' must be equals.")) + + index_hind <- 1 : (3 * 5 * 7 * 4) + dim(index_hind) <- c(dataset = 3, time = 5, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The dimension names of the component 'data' of the parameter 'index_hind' ", + "and 'index_fcst' must be equals.")) + + index_hind <- 1 : (5 * 3 * 7 * 4) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, season = 4, member = 1) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) + + index_hind <- 1 : (5 * 3 * 7 * 4) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 4) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (7 * 4 * 3) + dim(index_fcst) <- c(time = 1, member = 7, season = 4, dataset = 3) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The dimension names of the component 'data' of the parameter 'index_hind' ", + "and 'index_fcst' must be equals.")) + + index_hind <- 1 : (5 * 3 * 4) + dim(index_hind) <- c(time = 5, dataset = 3, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 4) + dim(index_obs) <- c(time = 5, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The dimension names of the component 'data' of the parameter 'index_hind' ", + "and 'index_fcst' must be equals.")) + + index_hind <- 1 : (5 * 3 * 7 * 4) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (4 * 3 * 4) + dim(index_obs) <- c(time = 4, dataset = 3, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The length of the dimensions 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_hind' must be equals.")) + + index_hind <- 1 : (5 * 3 * 7 * 4) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 2 * 4) + dim(index_obs) <- c(time = 5, dataset = 2, member = 1, season = 4) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (4* 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 4, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The length of the dimensions 'dataset' in the component 'data' ", + "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", + "equals.")) + + index_hind <- 1 : (5 * 3 * 7 * 2) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 5) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 5) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 4) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The length of the dimensions 'season' in the component 'data' ", + "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", + "equals.")) + + index_hind <- 1 : (2 * 3 * 7 * 2) + dim(index_hind) <- c(time = 2, dataset = 3, member = 7, season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (2 * 3 * 2) + dim(index_obs) <- c(time = 2, dataset = 3, member = 1, season = 2) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 7 * 2) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 2) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The length of the dimension 'time' in the component 'data' ", + "of the parameter 'index_obs' and 'index_hind' must be greater than 2.")) + + index_hind <- 1 : (5 * 3 * 7 * 2) + dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (2 * 3 * 7 * 2) + dim(index_fcst) <- c(time = 2, dataset = 3, member = 7, season = 2) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The length of the dimension 'time' in the component 'data' ", + "of the parameter 'index_fcst' must be equal to 1.")) + + index_hind <- 1 : (5 * 3 * 3 * 2) + dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 3, + season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (1 * 3 * 3 * 2) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 3, + season = 2) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The dimension 'statistic' must be equal to 2 in the component 'data' ", + "of the parameter 'index_hind' and 'index_fcst' if the length of the ", + "dimension 'member' is equal to 1")) + + index_hind <- 1 : (5 * 3 * 2 * 2) + dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 2, + season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 2 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 2, season = 2) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (1 * 3 * 2 * 2) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 2, + season = 2) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst), + paste0("The dimension 'member' must be equal to 1 in the component 'data' ", + "of the parameter 'index_obs'.")) + + index_hind <- 1 : (5 * 3 * 1 * 2 * 2) + dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 2, + season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 2 * 2) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 2, + season = 2) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = 'ens'), + paste0("The parameter 'type_exp' must be equal to 'sta' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_fcst' exits")) + + index_hind <- 1 : (5 * 3 * 8 * 2) + dim(index_hind) <- c(time = 5, dataset = 3, member = 8, season = 2) + index_hind <- list(data = index_hind) + class(index_hind) <- 's2dv_cube' + index_obs <- 1 : (5 * 3 * 2) + dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) + index_obs <- list(data = index_obs) + class(index_obs) <- 's2dv_cube' + index_fcst <- 1 : (3 * 8 * 2) + dim(index_fcst) <- c(time = 1, dataset = 3, member = 8, season = 2) + index_fcst <- list(data = index_fcst) + class(index_fcst) <- 's2dv_cube' + expect_error( + CST_PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = 'sta'), + paste0("The parameter 'type_exp' can't be equal to 'sta' because the ", + "dimension 'statistic' in the component 'data' of the ", + "parameter 'index_fcst' doesn't exit.")) +}) diff --git a/tests/testthat/test-CST_ProbBestIndexWeighting.R b/tests/testthat/test-CST_ProbBestIndexWeighting.R new file mode 100644 index 00000000..9fef5062 --- /dev/null +++ b/tests/testthat/test-CST_ProbBestIndexWeighting.R @@ -0,0 +1,172 @@ +context("Generic tests") +test_that("basic use case", { + var_exp <- 1 : (1 * 1 * 2 * 1 * 1) + dim(var_exp) <- c(time = 1, dataset = 1, member = 2, lat = 1, lon = 1) + weights_index_exp <- c(0.34, 0.66) + dim(weights_index_exp) <- c(time = 1, dataset = 1, member = 2, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + result <-array(c(0.34, 0, 0.66), + dim = c(time = 1, tercil = 3, dataset = 1, + season = 1, lat = 1, lon = 1)) + expect_equal(CST_ProbBestIndexWeighting(var_exp, weights_index_exp)$data, result) +}) + +test_that("Sanity checks", { + expect_error( + CST_ProbBestIndexWeighting(var_exp = 1), + paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", + "'s2dv_cube', as output by CSTools::CST_Load.")) + expect_error( + CST_ProbBestIndexWeighting(var_exp = var_exp, weights_index_exp = 1), + paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", + "'s2dv_cube', as output by CSTools::CST_Load.")) + + var_exp <- 1 : (2 * 1) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + set.seed(23) + weights_index_exp <- runif(2 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + weights_index_exp <- list(data = weights_index_exp) + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", + " should have dimmension names.")) + + var_exp <- 1 : (2 * 1 * 3 * 3) + dim(var_exp) <- c(time = 2, dataset = 1, member = 3, lat = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) + + var_exp <- 1 : (1 * 2 * 3) + dim(var_exp) <- c(dataset = 1, time = 2, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) + + var_exp <- 1 : (2 * 1 * 3) + dim(var_exp) <- c(time = 2, dataset = 1, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) + + var_exp <- 1 : (2 * 1 * 3) + dim(var_exp) <- c(time = 2, dataset = 1, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(member = 3, season = 1, time = 2, dataset = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) + + + var_exp <- 1 : (3 * 1 * 3) + dim(var_exp) <- c(time = 3, dataset = 1, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("The length of the dimension 'time' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'time' of the parameter 'weights_index_exp' in the component 'data'")) + + var_exp <- 1 : (2 * 2 * 3) + dim(var_exp) <- c(time = 2, dataset = 2, member = 3) + set.seed(23) + weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("The length of the dimension 'dataset' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'dataset' of the parameter 'weights_index_exp' in the component 'data'")) + + + var_exp <- 1 : (2 * 2 * 4) + dim(var_exp) <- c(time = 2, dataset = 2, member = 4) + set.seed(23) + weights_index_exp <- runif(2 * 2 * 3, 0.0001, 0.9999) + dim(weights_index_exp) <- c(time = 2, dataset = 2, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("The length of the dimension 'member' of the parameter 'var_exp' ", + "in the component 'data' must be equal to the length of the dimension", + " 'member' of the parameter 'weights_index_exp' in the component 'data'")) + + var_exp <- c(NA, 2 : (3 * 1 * 3 * 3 * 2)) + dim(var_exp) <- c(time = 3, dataset = 1, member = 3, lat = 3, lon = 2) + weights_index_exp <- c(0.2, 0.3482908, 0.3422055, + 0.1, 0.3504734, 0.3472929, + 0.6, 0.3012359, 0.3105016) + dim(weights_index_exp) <- c(time = 3, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("There are one or more elements of the component 'data' of the ", + "parameter 'var_exp' equal to NA.")) + + + var_exp <- 1 : (3 * 1 * 3 * 3 * 2) + dim(var_exp) <- c(time = 3, dataset = 1, member = 3, lat = 3, lon = 2) + weights_index_exp <- c(NA, 0.3482908, 0.3422055, + NA, 0.3504734, 0.3472929, + NA, 0.3012359, 0.3105016) + dim(weights_index_exp) <- c(time = 3, dataset = 1, member = 3, season = 1) + var_exp <- list(data = var_exp) + weights_index_exp <- list(data = weights_index_exp) + class(var_exp) <- 's2dv_cube' + class(weights_index_exp) <- 's2dv_cube' + expect_error( + CST_ProbBestIndexWeighting(var_exp, weights_index_exp), + paste0("There are one or more elements of the component 'data' of the ", + "parameter 'weights_index_exp' equal to NA.")) + + + +}) diff --git a/tests/testthat/test-CST_WeightIndex.R b/tests/testthat/test-CST_WeightIndex.R new file mode 100644 index 00000000..40e52ede --- /dev/null +++ b/tests/testthat/test-CST_WeightIndex.R @@ -0,0 +1,143 @@ +context("Generic tests") +test_that("basic use case", { + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 2 * 1) + dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + result <- array(c(0.4521103, 0.4017633, 0.3620216, 0.3545549, + 0.1858681, 0.2436818), + dim = c(time = 2, dataset = 1, member = 3, + season = 1)) + + expect_equal(CST_WeightIndex(index_exp, pdf_exp)$data, result) + +}) + +test_that("Sanity checks", { + expect_error( + CST_WeightIndex(index_exp = 1), + paste0("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.")) + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = 1), + paste0("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 2 * 1) + dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("Element 'data' from parameters 'index_exp' and 'pdf_exp'", + " should have dimmension names.")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 2 * 1) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("Element 'data' from parameters 'index_exp' and 'pdf_exp'", + " should have dimmension names.")) + + index_exp <- 1 : (2 * 1 * 3 * 1 * 2) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1, + statistic = 2) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 2 * 1) + dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("The number of dimensions in the component 'data' ", + "of the parameter 'index_exp' must be 4")) + + index_exp <- 1 : (3 * 2 * 1 * 1) + dim(index_exp) <- c(member = 3, time = 2, dataset = 1, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 2 * 1) + dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 2) + dim(pdf_exp) <- c(time = 2, statistic = 2) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("The number of dimensions in the component 'data' ", + "of the parameter 'pdf_exp' must be 3")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 1 * 2) + dim(pdf_exp) <- c(statistic = 2, season = 1, time = 2) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 4 * 1) + dim(pdf_exp) <- c(time = 2, statistic = 4, season = 1) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("The length of the dimension 'statistic' in the component 'data' ", + "of the parameter 'pdf_exp' must be equal to 2.")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (5 * 2 * 1) + dim(pdf_exp) <- c(time = 5, statistic = 2, season = 1) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("The length of the dimensions 'time' in the component 'data' ", + "of the parameter 'index_exp' and 'pdf_exp' must be equals.")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + pdf_exp <- 1 : (2 * 2 * 3) + dim(pdf_exp) <- c(time = 2, statistic = 2, season = 3) + pdf_exp <- list(data = pdf_exp) + class(index_exp) <- 's2dv_cube' + class(pdf_exp) <- 's2dv_cube' + expect_error( + CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), + paste0("The length of the dimensions 'season' in the component 'data' ", + "of the parameter 'index_exp' and 'pdf_exp' must be equals.")) +}) -- GitLab From cd0b19ac011ff48a2abb7e81a65b203c5ab2e196 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Fri, 18 Oct 2019 07:13:54 +0000 Subject: [PATCH 24/40] Adding parameter tolerance in tests and fixing axiliar function in CST_ProbBestIndexWeighting.R --- R/CST_ProbBestIndexWeighting.R | 2 +- tests/testthat/test-CST_EMBestIndexWeighting.R | 2 +- .../{test-CST_PDFIndexFcst.R => test-CST_IndexFcst.R} | 6 +++--- .../{test-CST_PDFIndexHind.R => test-CST_IndexHind.R} | 8 ++++---- tests/testthat/test-CST_PDFBest.R | 4 ++-- tests/testthat/test-CST_ProbBestIndexWeighting.R | 2 +- tests/testthat/test-CST_WeightIndex.R | 2 +- 7 files changed, 13 insertions(+), 13 deletions(-) rename tests/testthat/{test-CST_PDFIndexFcst.R => test-CST_IndexFcst.R} (99%) rename tests/testthat/{test-CST_PDFIndexHind.R => test-CST_IndexHind.R} (98%) diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index 3ee9d486..016e2a1e 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -225,7 +225,7 @@ Data2Tercil <- function(x,lt,ut) { y <- x y[x <= lt] <- 1 y[x >= ut] <-3 - y[(x > rep(lt,length(x))) & (x < rep(lt,length(x)))] <- 2 + y[(x > rep(lt,length(x))) & (x < rep(ut,length(x)))] <- 2 dim(y) <- c(member = length(x)) return (y) } diff --git a/tests/testthat/test-CST_EMBestIndexWeighting.R b/tests/testthat/test-CST_EMBestIndexWeighting.R index 74e42cf7..b4a824a3 100644 --- a/tests/testthat/test-CST_EMBestIndexWeighting.R +++ b/tests/testthat/test-CST_EMBestIndexWeighting.R @@ -15,7 +15,7 @@ test_that("basic use case", { dim = c(time = 2, dataset = 1, season = 1, lat = 3, lon = 2)) - expect_equal(CST_EMBestIndexWeighting(var_exp, weights_index_exp)$data, result) + expect_equal(CST_EMBestIndexWeighting(var_exp, weights_index_exp)$data, result, tolerance=1e-4) }) diff --git a/tests/testthat/test-CST_PDFIndexFcst.R b/tests/testthat/test-CST_IndexFcst.R similarity index 99% rename from tests/testthat/test-CST_PDFIndexFcst.R rename to tests/testthat/test-CST_IndexFcst.R index a6abefec..83d80f56 100644 --- a/tests/testthat/test-CST_PDFIndexFcst.R +++ b/tests/testthat/test-CST_IndexFcst.R @@ -23,7 +23,7 @@ test_that("basic use case", { attr(result, 'class') <- 's2dv_cube' expect_equal(CST_PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = 'ens', method = 'LMEV', - na.rm = FALSE), result) + na.rm = FALSE), result, tolerance=1e-4) index_hind <- 1 : (5 * 3 * 1* 2 * 2) dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 2, @@ -47,7 +47,7 @@ test_that("basic use case", { "sigma_bc")) attr(result, 'class') <- 's2dv_cube' expect_equal(CST_PDFIndexFcst(index_hind, index_obs, index_fcst, - type_exp = 'sta', na.rm = FALSE), result) + type_exp = 'sta', na.rm = FALSE), result, tolerance=1e-4) index_hind2 <- index_hind @@ -61,7 +61,7 @@ test_that("basic use case", { attr(result, 'class') <- 's2dv_cube' expect_equal(CST_PDFIndexFcst(index_hind = index_hind2, index_obs = index_obs, index_fcst = index_fcst, type_exp = 'sta', - na.rm = FALSE), result) + na.rm = FALSE), result, tolerance=1e-4) }) test_that("Sanity checks", { diff --git a/tests/testthat/test-CST_PDFIndexHind.R b/tests/testthat/test-CST_IndexHind.R similarity index 98% rename from tests/testthat/test-CST_PDFIndexHind.R rename to tests/testthat/test-CST_IndexHind.R index bed6442b..6f70f8b6 100644 --- a/tests/testthat/test-CST_PDFIndexHind.R +++ b/tests/testthat/test-CST_IndexHind.R @@ -15,7 +15,7 @@ test_that("basic use case", { "sigma_bc")) attr(result, 'class') <- 's2dv_cube' expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, - type_exp = 'ens', method = 'LMEV'), result) + type_exp = 'ens', method = 'LMEV'), result, tolerance=1e-4) index_obs <- 1 : 5 dim(index_obs) <- c(time = 5, dataset = 1, member = 1, season = 1) @@ -32,7 +32,7 @@ test_that("basic use case", { "sigma_bc")) attr(result, 'class') <- 's2dv_cube' expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, - type_exp = 'sta'), result) + type_exp = 'sta'), result, tolerance=1e-4) index_obs <- 1 : (5 * 3 * 4) dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) @@ -51,7 +51,7 @@ test_that("basic use case", { "sigma_bc")) attr(result, 'class') <- 's2dv_cube' expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - result) + result, tolerance=1e-4) index_exp2 <- index_exp index_exp2$data[1, 1, 1, 1] <- NA @@ -66,7 +66,7 @@ test_that("basic use case", { "sigma_bc")) attr(result, 'class') <- 's2dv_cube' expect_equal(CST_PDFIndexHind(index_exp = index_exp2, index_obs = index_obs, - na.rm = FALSE), result) + na.rm = FALSE), result, tolerance=1e-4) }) diff --git a/tests/testthat/test-CST_PDFBest.R b/tests/testthat/test-CST_PDFBest.R index 508d4b48..c1da44b6 100644 --- a/tests/testthat/test-CST_PDFBest.R +++ b/tests/testthat/test-CST_PDFBest.R @@ -10,7 +10,7 @@ test_that("basic use case", { season = 1)), statistic = c("mean", "sigma")) attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE), result) + expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE), result, tolerance=1e-4) result2 <- list(data = array(c(5.952278, 7.090211, 8.215385, @@ -19,7 +19,7 @@ test_that("basic use case", { season = 1)), statistic = c("mean", "sigma")) attr(result2, 'class') <- 's2dv_cube' - expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE), result2) + expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE), result2, tolerance=1e-4) }) diff --git a/tests/testthat/test-CST_ProbBestIndexWeighting.R b/tests/testthat/test-CST_ProbBestIndexWeighting.R index 9fef5062..f456c90b 100644 --- a/tests/testthat/test-CST_ProbBestIndexWeighting.R +++ b/tests/testthat/test-CST_ProbBestIndexWeighting.R @@ -11,7 +11,7 @@ test_that("basic use case", { result <-array(c(0.34, 0, 0.66), dim = c(time = 1, tercil = 3, dataset = 1, season = 1, lat = 1, lon = 1)) - expect_equal(CST_ProbBestIndexWeighting(var_exp, weights_index_exp)$data, result) + expect_equal(CST_ProbBestIndexWeighting(var_exp, weights_index_exp)$data, result, tolerance=1e-4) }) test_that("Sanity checks", { diff --git a/tests/testthat/test-CST_WeightIndex.R b/tests/testthat/test-CST_WeightIndex.R index 40e52ede..ccea838a 100644 --- a/tests/testthat/test-CST_WeightIndex.R +++ b/tests/testthat/test-CST_WeightIndex.R @@ -13,7 +13,7 @@ test_that("basic use case", { dim = c(time = 2, dataset = 1, member = 3, season = 1)) - expect_equal(CST_WeightIndex(index_exp, pdf_exp)$data, result) + expect_equal(CST_WeightIndex(index_exp, pdf_exp)$data, result, tolerance=1e-4) }) -- GitLab From 20d44fa60c8e542585575ee1a42d7a5d14c7e05b Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Fri, 18 Oct 2019 07:18:16 +0000 Subject: [PATCH 25/40] Adding parameter tolerance in tests and fixing axiliar function in CST_ProbBestIndexWeighting.R --- tests/testthat/{test-CST_IndexFcst.R => test-CST_PDFIndexFcst.R} | 0 tests/testthat/{test-CST_IndexHind.R => test-CST_PDFIndexHind.R} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename tests/testthat/{test-CST_IndexFcst.R => test-CST_PDFIndexFcst.R} (100%) rename tests/testthat/{test-CST_IndexHind.R => test-CST_PDFIndexHind.R} (100%) diff --git a/tests/testthat/test-CST_IndexFcst.R b/tests/testthat/test-CST_PDFIndexFcst.R similarity index 100% rename from tests/testthat/test-CST_IndexFcst.R rename to tests/testthat/test-CST_PDFIndexFcst.R diff --git a/tests/testthat/test-CST_IndexHind.R b/tests/testthat/test-CST_PDFIndexHind.R similarity index 100% rename from tests/testthat/test-CST_IndexHind.R rename to tests/testthat/test-CST_PDFIndexHind.R -- GitLab From 249d27ba37b32545593fcb1012b76ecf878e06ea Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Fri, 18 Oct 2019 07:44:29 +0000 Subject: [PATCH 26/40] Fixing test-CST_WeightIndex.R --- tests/testthat/test-CST_WeightIndex.R | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/testthat/test-CST_WeightIndex.R b/tests/testthat/test-CST_WeightIndex.R index ccea838a..e4a29c4f 100644 --- a/tests/testthat/test-CST_WeightIndex.R +++ b/tests/testthat/test-CST_WeightIndex.R @@ -22,6 +22,11 @@ test_that("Sanity checks", { CST_WeightIndex(index_exp = 1), paste0("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.")) + + index_exp <- 1 : (2 * 1 * 3 * 1) + dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) + index_exp <- list(data = index_exp) + class(index_exp) <- 's2dv_cube' expect_error( CST_WeightIndex(index_exp = index_exp, pdf_exp = 1), paste0("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", -- GitLab From 2fdb480225c53ebddb2dfafac1db02d6e7de14f5 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Fri, 18 Oct 2019 11:02:42 +0000 Subject: [PATCH 27/40] Fixing auxiliar function 'WeightTerciles' and 'test-CST_ProbBestIndexWeighting.R' --- R/CST_ProbBestIndexWeighting.R | 34 ++++--------------- .../test-CST_ProbBestIndexWeighting.R | 4 +-- 2 files changed, 9 insertions(+), 29 deletions(-) diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index 016e2a1e..feae9133 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -222,10 +222,12 @@ ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { # Auxiliar function to compute in which tercile is a data value Data2Tercil <- function(x,lt,ut) { - y <- x + #y <- x + y <- rep(2,length(x)) y[x <= lt] <- 1 y[x >= ut] <-3 - y[(x > rep(lt,length(x))) & (x < rep(ut,length(x)))] <- 2 + #y[(x > rep(lt,length(x))) & (x < rep(ut,length(x)))] <- 2 + #y[(x > lt) & (x < ut)] <- 2 dim(y) <- c(member = length(x)) return (y) } @@ -239,30 +241,8 @@ WeightTercil2Prob <- function(aTerciles, aWeights, idTercil) { WeightTerciles <- function(data, weights) { vectorData <- as.vector(data) vectorWeights <- as.vector(weights/dim(weights)['time']) # normalized - lSortData <- sort(vectorData,index.return=TRUE) - indSort <- lSortData$ix # index asociated to weight - # corresponding for this data - dataSort <- lSortData$x - # Adding normalized weights. When 1/3 is reached, the data value - # is lower tercile and when 2/3 is reached, it is the upper tercile. - sumWeights <- 0 - ilowerTercile <- 0 - while ((sumWeights < 1/3) & (ilowerTercile < (dim(weights)['member'] * - dim(weights)['time']))){ - ilowerTercile<- ilowerTercile +1 - sumWeights <- sumWeights + vectorWeights[indSort[ilowerTercile]] - } - lowerTercile <- (dataSort[ilowerTercile]+ - dataSort[ilowerTercile-1])/2 - sumWeights <- 0 - iupperTercile <- 0 - while ((sumWeights < 2/3) & (iupperTercile < (dim(weights)['member'] * - dim(weights)['time']))){ - iupperTercile<- iupperTercile +1 - sumWeights <- sumWeights + vectorWeights[indSort[iupperTercile]] - } - upperTercile <- (dataSort[iupperTercile]+ - dataSort[iupperTercile-1])/2 - return(list(lowerTercile = lowerTercile, upperTercile = upperTercile)) + auxVectorData <- c(rep(vectorData,round(vectorWeights*100,0))) + auxTerciles <- quantile(auxVectorData, probs = c(1/3,2/3)) + return(list(lowerTercile = auxTerciles[1], upperTercile = auxTerciles[2])) } diff --git a/tests/testthat/test-CST_ProbBestIndexWeighting.R b/tests/testthat/test-CST_ProbBestIndexWeighting.R index f456c90b..05f399cc 100644 --- a/tests/testthat/test-CST_ProbBestIndexWeighting.R +++ b/tests/testthat/test-CST_ProbBestIndexWeighting.R @@ -9,8 +9,8 @@ test_that("basic use case", { class(var_exp) <- 's2dv_cube' class(weights_index_exp) <- 's2dv_cube' result <-array(c(0.34, 0, 0.66), - dim = c(time = 1, tercil = 3, dataset = 1, - season = 1, lat = 1, lon = 1)) + dim = c(time = 1, tercil = 3, dataset = 1, + season = 1, lat = 1, lon = 1)) expect_equal(CST_ProbBestIndexWeighting(var_exp, weights_index_exp)$data, result, tolerance=1e-4) }) -- GitLab From 6d705b0dfe6d60552fbcc977e62084471dea223c Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Sun, 20 Oct 2019 21:06:45 +0000 Subject: [PATCH 28/40] Updating 'CST_ProbBestIndexWeighting.R' --- R/CST_ProbBestIndexWeighting.R | 41 ++++++++++++++++++++++++++++++---- 1 file changed, 37 insertions(+), 4 deletions(-) diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R index feae9133..2dd64f89 100644 --- a/R/CST_ProbBestIndexWeighting.R +++ b/R/CST_ProbBestIndexWeighting.R @@ -225,7 +225,10 @@ Data2Tercil <- function(x,lt,ut) { #y <- x y <- rep(2,length(x)) y[x <= lt] <- 1 - y[x >= ut] <-3 + y[x >= ut] <- 3 + if (lt == ut) { + warning("The upper and lower terciles are equals") + } #y[(x > rep(lt,length(x))) & (x < rep(ut,length(x)))] <- 2 #y[(x > lt) & (x < ut)] <- 2 dim(y) <- c(member = length(x)) @@ -241,8 +244,38 @@ WeightTercil2Prob <- function(aTerciles, aWeights, idTercil) { WeightTerciles <- function(data, weights) { vectorData <- as.vector(data) vectorWeights <- as.vector(weights/dim(weights)['time']) # normalized - auxVectorData <- c(rep(vectorData,round(vectorWeights*100,0))) - auxTerciles <- quantile(auxVectorData, probs = c(1/3,2/3)) - return(list(lowerTercile = auxTerciles[1], upperTercile = auxTerciles[2])) + lSortData <- sort(vectorData,index.return=TRUE) + indSort <- lSortData$ix # index asociated to weight + # corresponding for this data + dataSort <- lSortData$x + # Adding normalized weights. When 1/3 is reached, the data value + # is lower tercile and when 2/3 is reached, it is the upper tercile. + sumWeights <- 0 + ilowerTercile <- 0 + while ((sumWeights < 1/3) & (ilowerTercile < (dim(weights)['member'] * + dim(weights)['time']))){ + ilowerTercile<- ilowerTercile +1 + sumWeights <- sumWeights + vectorWeights[indSort[ilowerTercile]] + } + if (ilowerTercile == 1){ + lowerTercile <- dataSort[ilowerTercile] + } else { + lowerTercile <- (dataSort[ilowerTercile]+ + dataSort[ilowerTercile-1])/2 + } + sumWeights <- 0 + iupperTercile <- 0 + while ((sumWeights < 2/3) & (iupperTercile < (dim(weights)['member'] * + dim(weights)['time']))){ + iupperTercile<- iupperTercile +1 + sumWeights <- sumWeights + vectorWeights[indSort[iupperTercile]] + } + if (iupperTercile == 1) { + upperTercile <- dataSort[iupperTercile] + } else { + upperTercile <- (dataSort[iupperTercile]+ + dataSort[iupperTercile-1])/2 + } + return(list(lowerTercile = lowerTercile, upperTercile = upperTercile)) } -- GitLab From 0b41ade577afaf1a774ad8215a993da712f2d286 Mon Sep 17 00:00:00 2001 From: EROTEIDA SANCHEZ GARCIA Date: Wed, 23 Oct 2019 18:04:42 +0200 Subject: [PATCH 29/40] New version with 3 functions --- DESCRIPTION | 4 +- NAMESPACE | 13 +- R/CST_EMBestIndexWeighting.R | 176 ---- R/CST_PDFBest.R | 195 ---- R/CST_PDFIndexFcst.R | 358 ------- R/CST_PDFIndexHind.R | 319 ------- R/CST_ProbBestIndexWeighting.R | 281 ------ R/CST_WeightIndex.R | 177 ---- R/CST_Weighted.R | 454 +++++++++ R/PDFBest.R | 878 ++++++++++++++++++ R/WeightIndex.R | 104 +++ R/zzz.R | 4 +- man/CST_Anomaly.Rd | 7 +- man/CST_BiasCorrection.Rd | 7 +- man/CST_Calibration.Rd | 7 +- man/CST_EMBestIndexWeighting.Rd | 57 -- man/CST_Load.Rd | 1 + man/CST_MultiMetric.Rd | 9 +- man/CST_MultivarRMSE.Rd | 7 +- man/CST_PDFBest.Rd | 68 -- man/CST_PDFIndexFcst.Rd | 91 -- man/CST_PDFIndexHind.Rd | 77 -- man/CST_ProbBestIndexWeighting.Rd | 62 -- man/CST_RFSlope.Rd | 1 + man/CST_RFWeights.Rd | 7 +- man/CST_RainFARM.Rd | 7 +- man/CST_WeightIndex.Rd | 57 -- man/CST_Weighted.Rd | 71 ++ man/EMBestIndexWeighting.Rd | 58 -- man/EMWeighted.Rd | 61 ++ man/PDFBest.Rd | 112 ++- man/PDFIndexFcst.Rd | 72 -- man/PDFIndexHind.Rd | 76 -- man/PlotCombinedMap.Rd | 15 +- man/PlotForecastPDF.Rd | 10 +- man/PlotMostLikelyQuantileMap.Rd | 7 +- man/ProbBestIndexWeighting.Rd | 61 -- man/ProbsWeighted.Rd | 62 ++ man/RFSlope.Rd | 1 + man/RainFARM.Rd | 8 +- man/WeightIndex.Rd | 47 +- man/areave_data.Rd | 1 + man/lonlat_data.Rd | 1 + man/lonlat_prec.Rd | 1 + vignettes/BestNaoWeighting_vignette_new.Rmd | 159 ++++ vignettes/Figures/probLower_CST_weighted.png | Bin 0 -> 43107 bytes .../Figures/probLower_CST_weighted_raw.png | Bin 0 -> 43780 bytes 47 files changed, 1963 insertions(+), 2288 deletions(-) delete mode 100644 R/CST_EMBestIndexWeighting.R delete mode 100644 R/CST_PDFBest.R delete mode 100644 R/CST_PDFIndexFcst.R delete mode 100644 R/CST_PDFIndexHind.R delete mode 100644 R/CST_ProbBestIndexWeighting.R delete mode 100644 R/CST_WeightIndex.R create mode 100644 R/CST_Weighted.R create mode 100644 R/PDFBest.R create mode 100644 R/WeightIndex.R delete mode 100644 man/CST_EMBestIndexWeighting.Rd delete mode 100644 man/CST_PDFBest.Rd delete mode 100644 man/CST_PDFIndexFcst.Rd delete mode 100644 man/CST_PDFIndexHind.Rd delete mode 100644 man/CST_ProbBestIndexWeighting.Rd delete mode 100644 man/CST_WeightIndex.Rd create mode 100644 man/CST_Weighted.Rd delete mode 100644 man/EMBestIndexWeighting.Rd create mode 100644 man/EMWeighted.Rd delete mode 100644 man/PDFIndexFcst.Rd delete mode 100644 man/PDFIndexHind.Rd delete mode 100644 man/ProbBestIndexWeighting.Rd create mode 100644 man/ProbsWeighted.Rd create mode 100644 vignettes/BestNaoWeighting_vignette_new.Rmd create mode 100644 vignettes/Figures/probLower_CST_weighted.png create mode 100644 vignettes/Figures/probLower_CST_weighted_raw.png diff --git a/DESCRIPTION b/DESCRIPTION index 4e04364e..0ac6c809 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -21,7 +21,7 @@ Description: Exploits dynamical seasonal forecasts in order to provide contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain - tailored products. This package was developed in the context of the + tailored products. This package was developed in the context of the ERA4CS project MEDSCOPE. Doblas-Reyes et al. (2005) . Mishra et al. (2018) . @@ -55,4 +55,4 @@ VignetteBuilder: knitr License: Apache License 2.0 Encoding: UTF-8 LazyData: true -RoxygenNote: 6.1.1 +RoxygenNote: 5.0.0 diff --git a/NAMESPACE b/NAMESPACE index 123e5a9b..e9aa768d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,26 +3,19 @@ export(CST_Anomaly) export(CST_BiasCorrection) export(CST_Calibration) -export(CST_EMBestIndexWeighting) export(CST_Load) export(CST_MultiMetric) export(CST_MultivarRMSE) -export(CST_PDFBest) -export(CST_PDFIndexFcst) -export(CST_PDFIndexHind) -export(CST_ProbBestIndexWeighting) export(CST_RFSlope) export(CST_RFWeights) export(CST_RainFARM) -export(CST_WeightIndex) -export(EMBestIndexWeighting) +export(CST_Weighted) +export(EMWeighted) export(PDFBest) -export(PDFIndexFcst) -export(PDFIndexHind) export(PlotCombinedMap) export(PlotForecastPDF) export(PlotMostLikelyQuantileMap) -export(ProbBestIndexWeighting) +export(ProbsWeighted) export(RFSlope) export(RainFARM) export(WeightIndex) diff --git a/R/CST_EMBestIndexWeighting.R b/R/CST_EMBestIndexWeighting.R deleted file mode 100644 index 2f721c2f..00000000 --- a/R/CST_EMBestIndexWeighting.R +++ /dev/null @@ -1,176 +0,0 @@ -#' @rdname CST_EMBestIndexWeighting -#' @title Computing the weighted ensemble means for SFSs of a CSTools object. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the weighted -#' ensemble means for SFSs through Best estimation of one index or driver of -#' climate variability (as e.g., the North Atlantic Oscillation (NAO)) and -#' accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp An object of the class 's2dv_cube' containing the variable -#' (e.g. precipitation, temperature, NAO index) array. -#' The var_exp object is expected to have an element named \code{$data} with -#' three dimensions (time, dataset, member) for a zonal variable -#' or five dimensions (time, dataset, member, lat, lon) for a spatial variable. -#' @param weights_index_exp An object of the class 's2dv_cube' containing -#' the normalized weights array. -#' The weights_index_exp object is expected to have an element named -#' \code{$data} with dimensions (time, dataset, member, season). -#' @return CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the -#' class 's2dv_cube'). -#' This object has at least an element named \code{$data} -#' with three or five dimensions depending if the variable is zonal -#' (as e.g. NAO index)(time, dataset, season) or it is spatial variable -#' (as e.g. precipitation or temperature) (time, dataset, season, lat, lon), -#' containing the ensemble means computing with weighted members. -#' @export -#' @examples -#' var_exp <- 1 : (4 * 2 * 3 * 3 * 2) -#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) -#' weights_index_exp <- runif(4 * 2 * 3, 0.0001, 0.9999) -#' dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 1) -#' var_exp <- list(data = var_exp) -#' weights_index_exp <- list(data = weights_index_exp) -#' class(var_exp) <- 's2dv_cube' -#' class(weights_index_exp) <- 's2dv_cube' -#' res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) -#' dim(res_CST$data) -#' # time dataset season lat lon -#' # 4 2 1 3 2 -CST_EMBestIndexWeighting <- function(var_exp, weights_index_exp) { - if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { - stop("Parameter 'var_exp' and 'weights_index_exp' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") - } - if (is.null(names(dim(var_exp$data))) - || is.null(names(dim(weights_index_exp$data)))) { - stop("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", - " should have dimmension names.") - } - if(length(dim(var_exp$data)) == 3 ){ - if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member'))){ - stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") - } - } else if (length(dim(var_exp$data)) == 5 ){ - if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member', - 'lat', 'lon'))){ - stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") - } - } else { - stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") - } - if(length(dim(weights_index_exp$data)) == 4 ){ - if (!all(names(dim(weights_index_exp$data)) == c('time', 'dataset', - 'member', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") - } - } else { - stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") - } - if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ - stop("The length of the dimension 'time' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'time' of the parameter 'weights_index_exp' in the component 'data'") - } - if (dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']){ - stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") - } - if (dim(var_exp$data)['member'] != dim(weights_index_exp$data)['member']){ - stop("The length of the dimension 'member' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'member' of the parameter 'weights_index_exp' in the component 'data'") - } - - em <- EMBestIndexWeighting(var_exp$data, weights_index_exp$data) - em_var_exp <- list() - em_var_exp$data <- em - em_var_exp$Datasets <- var_exp$Datasets - em_var_exp$source_files <- var_exp$source_files - em_var_exp$Dates <- var_exp$Dates - return(em_var_exp) -} - -#' @rdname EMBestIndexWeighting -#' @title Computing the weighted ensemble means for SFSs. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the weighted -#' ensemble means for SFSs through Best estimation of one index or driver of -#' climate variability (as e.g., the North Atlantic Oscillation (NAO)). -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with three dimensions (time, dataset, member) for a zonal -#' variable or dimensions (time, dataset, member, lat, lon) for a spatial -#' variable. -#' @param weights_index_exp Normalized weights array with dimensions -#' (time, dataset, member, season). -#' @return EMBestIndexWeighting() returns an array with three or five dimensions -#' depending if the variable is zonal (as e.g. NAO index)(time, dataset, season) -#' or it is spatial variable (as e.g. precipitation or temperature) -#' (time, dataset, season, lat, lon), containing the ensemble means computing -#' with weighted members. -#' @import multiApply -#' @export -#' @examples -#' # Example 1 for the EMBestIndexWeighting function -#' var_exp <- 1 : (2 * 3 * 4) -#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4) -#' weights_index_exp<- runif(24, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) -#' res <- EMBestIndexWeighting(var_exp, weights_index_exp) -#' dim(res) -#' # time dataset season -#' # 2 3 1 -#' # Example 2 for the EMBestIndexWeighting function -#' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) -#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) -#' weights_index_exp<- runif(48, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 2) -#' res <- EMBestIndexWeighting(var_exp, weights_index_exp) -#' dim(res) -#' # time dataset season lat lon -#' # 2 3 2 2 3 -#' -EMBestIndexWeighting <- function(var_exp, weights_index_exp) { - res <- Apply(list(var_exp, weights_index_exp), - target_dims = list(c('time','member'), c('time','member')), - fun = .EMBestIndexWeighting)$output1 - Dim <- names(dim(var_exp)) - Dim[Dim == 'member'] <- 'season' - pos <- match(Dim, names(dim(res))) - res <- aperm(res, pos) - names(dim(res)) <- Dim - return(res) -} - -#' Atomic EMBestIndexWeighting -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with dimensions (time, member). -#' @param weights_index_exp Normalized weights array with dimensions -#' (time, member). -#' @return .EMBestIndexWeighting returns an array of with dimensions (time), -#' the ensemble means computing with weighted members. -#' @examples -#' # Example for the Atomic EMBestIndexWeighting function -#' var_exp <- 1 : 6 -#' dim(var_exp) <- c(time = 2, member = 3) -#' weights_index_exp <- c(0.28, 0.15, 0.69, 0.64, 0.42, 0.17) -#' dim(weights_index_exp) <- c(time = 2, member = 3) -#' res <- .EMBestIndexWeighting(var_exp, weights_index_exp) -#' str(res) -#' # num [1:2(1d)] 4.45 3.88 -#' dim(res) -#' # time -#' # 2 -#' @noRd -.EMBestIndexWeighting <- function(var_exp, weights_index_exp) { - posTime <- 1 - var_exp_em <- apply(var_exp * weights_index_exp, posTime, sum) - dim(var_exp_em) <- c(time = length(var_exp_em)) - return(var_exp_em) -} - diff --git a/R/CST_PDFBest.R b/R/CST_PDFBest.R deleted file mode 100644 index 2967e6c2..00000000 --- a/R/CST_PDFBest.R +++ /dev/null @@ -1,195 +0,0 @@ -#' @rdname CST_PDFBest -#' @title Computing the Best Index PDFs combining Index PDFs from two SFSs -#' of a CSTools object. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs -#' and accepts a CSTools object (an object of the class 's2dv_cube' as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param pdf_exp An object of the class 's2dv_cube' -#' containing the parameters of the Index PDFs from two SFSs for a period -#' to combine and obtain the Best estimation. -#' The index_hind object is expected to have an element named \code{$data} -#' with dimensions (time, dataset = 2, member = 1, statistic = 4, season). -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDFs for the first SFS or dataset has been computed -#' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDFs for the second SFS or dataset has been computed -#' with bias corrected. -#' @return CST_PDFBest() returns a CSTools object (i.e., of the -#' class 's2dv_cube'). -#' This object has at least two elements: i) an element named \code{$data} -#' with dimensions (time, statistic = 2, season) and ii) an element -#' named \code{$statistic} with the names of 'statistic' dimension -#' of the first element ("mean", "sigma"). -#' The firt statistic is the parameter 'mean' of the PDF -#' The second statistic is the parameter 'standard deviation' of the PDF -#' @export -#' @examples -#' pdf_exp <- c(1: (10 * 2 * 1 * 4 * 1)) -#' dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, -#' season = 1) -#' pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), -#' season = c('DJF'), Dates = list(start = 1997:2006)) -#' class(pdf_exp) <- 's2dv_cube' -#' res_CST <- CST_PDFBest(pdf_exp) -#' str(res_CST) -#' # List of 5 -#' # $ data : num [1:10, 1:2, 1] 45.2 46.3 47.3 48.3 49.3 ... -#' # $ Datasets : chr [1:2] "ECMWF-S5" "UKMO-S13" -#' # $ season : chr "DJF" -#' # $ Dates :List of 1 -#' # ..$ start: int [1:10] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 -#' # $ statistic: chr [1:2] "mean" "sigma" -#' # - attr(*, "class")= chr "s2dv_cube" -#' dim(res_CST$data) -#' # time statistic season -#' # 10 2 1 -CST_PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { - if (!inherits(pdf_exp, 's2dv_cube')) { - stop("Parameter 'pdf_exp' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") - } - if (!is.logical(bc_dataset1)) { - stop("The parameter 'bc_dataset1' must be a logical value.") - } - if (!is.logical(bc_dataset2)) { - stop("The parameter 'bc_dataset2' must be a logical value.") - } - if (is.null(names(dim(pdf_exp$data)))) { - stop("Element 'data' from parameters 'pdf_exp' should have dimmension ", - "names.") - } - if(length(dim(pdf_exp$data)) != 5){ - stop("Wrong dimensions of the component 'data' of parameter 'pdf_exp'") - } else if (!all(names(dim(pdf_exp$data)) == c('time', 'dataset', 'member', - 'statistic', 'season'))) { - stop("Wrong dimensions of the component 'data' of parameter 'pdf_exp'") - } - if (dim(pdf_exp$data)['dataset'] != 2) { - stop("The length of the dimension 'dataset' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 2.") - } - if (dim(pdf_exp$data)['member'] != 1) { - stop("The length of the dimension 'member' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 1.") - } - if (dim(pdf_exp$data)['statistic'] != 4) { - stop("The length of the dimension 'statistic' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 4.") - } - - - Dim <- dim(pdf_exp$data) - dim(pdf_exp$data) <- Dim[-which(names(Dim) == 'member')] - - PDFstatistics <- PDFBest(pdf_exp$data, bc_dataset1 = bc_dataset1, - bc_dataset2 = bc_dataset2) - pdf_exp$data <- PDFstatistics - pdf_exp$statistic <- c("mean","sigma") - return(pdf_exp) -} - -#' @rdname PDFBest -#' @title Computing the Best Index PDFs combining two SFSs Index PDFs. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) obtained to combining the Index PDFs for two SFSs, -#' the Best Index estimation. -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param pdf_exp Statistics array for the SFSs Index PDFs for a period of years -#' with dimensions (time, dataset = 2, statistic = 4, season). -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDFs for the first SFS or dataset has been computed -#' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDFs for the second SFS or dataset has been computed -#' with bias corrected. -#' @return PDFBest() returns an array -#' with dimensions (time, statistic = 2, season). -#' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDFs. -#' The second statistic is the parameter 'standard deviation' of the PDF for -#' the best estimation combining the two SFSs PDFs. -#' @import multiApply -#' @export -#' @examples -#' # Example for the PDFBest function -#' pdf_exp <- c(1: (10 * 2 * 4 * 1)) -#' dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) -#' res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) -#' dim(res) -#' # time statistic season -#' # 10 2 1 -#' str(res) -#' # num [1:10, 1:2, 1] 17.2 18.3 19.5 20.6 21.7 ... -#' -PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { - pdf_best <- Apply(list(pdf_exp), - target_dims = list(c('dataset','statistic'), - c('dataset','statistic')), - fun = .PDFBest, bc_dataset1, bc_dataset2) - pos <- match(c('time','statistic','season'), names(dim(pdf_best$output1))) - res <- aperm(pdf_best$output1,pos) - names(dim(res)) <- c('time','statistic','season') - return(res) -} - -#' Atomic PDFBest -#' @param pdf_exp Statistics array for two SFS PDFs with dimensions -#' (dataset = 2, statistic = 4). -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDF for the first SFS or dataset has been computed -#' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDF for the second SFS or dataset has been computed -#' with bias corrected. -#' @return .PDFBest returns an array with dimensions (statistic = 2). -#' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDF. -#' The second statistic is the parameter 'standard deviation' of the PDF for -#' the best estimation combining the two SFSs PDF. -#' @examples -#' # Example for the Atomic PDFBest function -#' pdf_exp <- c(1.1,0.6,1.6,0.9,1,0.5,1.5,0.8) -#' dim(pdf_exp) <- c(dataset = 2, statistic = 4) -#' res <- .PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) -#' str(res) -#' # num [1:2(1d)] 0.706 0.772 -#' dim(res) -#' # statistic -#' # 2 -#' @noRd -.PDFBest <- function(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { - #names(dim(pdf_exp)) <- c('dataset','statistic') - if(bc_dataset1){ - # apply bias correction to model 1 - sigma_1 <- pdf_exp[1,4] - mean_1 <- pdf_exp[1,3] - } else{ - # not bias correction to model 1 - sigma_1 <- pdf_exp[1,2] - mean_1 <- pdf_exp[1,1] - } - if(bc_dataset2){ - # apply bias correction to model 2 - sigma_2 <- pdf_exp[2,4] - mean_2 <- pdf_exp[2,3] - } else { - # not bias correction to model 2 - sigma_2 <- pdf_exp[2,2] - mean_2 <- pdf_exp[2,1] - } - a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) - a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) - pdf_mean <- a_1*mean_1 + a_2*mean_2 - pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) - data <- c(pdf_mean, pdf_sigma) - dim(data) <- c(statistic = 2) - return(data) -} diff --git a/R/CST_PDFIndexFcst.R b/R/CST_PDFIndexFcst.R deleted file mode 100644 index 096c7787..00000000 --- a/R/CST_PDFIndexFcst.R +++ /dev/null @@ -1,358 +0,0 @@ -#' @rdname CST_PDFIndexFcst -#' @title Computing the Index PDFs for a forecast year of a SFSs dataset -#' of a CSTools object. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from the forecast of SFSs -#' and accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_hind An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate for a hindcast period -#' from SFSs for improving. -#' The index_hind object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member, season) or -#' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must -#' be greater than 2. When dimension 'statistic' doesn't exist, the dimension -#' 'member' must be greater than 1. -#' @param index_obs An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate from observations or -#' reanalysis. -#' The index_obs object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member = 1, season). The dimensions 'time' -#' must be greater than 2. -#' @param index_fcst An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate for a forecast year -#' from SFSs for improving. -#' The index_fcst object is expected to have an element named \code{$data} -#' with dimensions (time = 1, dataset, member, season) or -#' (time = 1, dataset, member = 1, statistic = 2, season). When dimension -#' 'statistic' doesn't exist, the dimension 'member' must be greater than 1. -#' @param type_exp A character string ('ens' or 'sta') to identify if the -#' experiment output is an ensemble members or statistics. -#' @param method A character string indicating which methodology is applied -#' to compute the PDFs. One of "ME" (default) or "LMEV". -#' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return CST_PDFIndexFcst() returns a CSTools object (i.e., of the -#' class 's2dv_cube'). -#' This object has at least two elements: i) an element named \code{$data} -#' with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element -#' named \code{$statistic} with the names of 'statistic' dimension -#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @export -#' @examples -#' index_hind <- 1 : (5 * 3 * 7 * 4) -#' dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) -#' index_hind <- list(data = index_hind) -#' class(index_hind) <- 's2dv_cube' -#' index_obs <- 1 : (5 * 3 * 4) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -#' index_obs <- list(data = index_obs) -#' class(index_obs) <- 's2dv_cube' -#' index_fcst <- 1 : (3 * 7 * 4) -#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) -#' index_fcst <- list(data = index_fcst) -#' class(index_fcst) <- 's2dv_cube' -#' res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') -#' dim(res_CST$data) -#' #time dataset statistic season -#' # 1 3 4 4 -CST_PDFIndexFcst <- function(index_hind, index_obs, index_fcst, - type_exp = 'ens', method = 'ME', na.rm = FALSE) { - if (!inherits(index_hind, 's2dv_cube') || - !inherits(index_obs, 's2dv_cube') || - !inherits(index_fcst, 's2dv_cube')) { - stop("Parameter 'index_fcst', 'index_hind' ", - "and 'index_obs' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") - } - if(!(method %in% c('ME', 'LMEV'))){ - stop("The parameter 'method' must be equal to 'ME' or 'LMEV'") - } - if (!is.logical(na.rm)) { - stop("The parameter 'na.rm' must be a logical value.") - } - if(!(type_exp %in% c('ens', 'sta'))){ - stop("The parameter 'type_exp' must be equal to 'ens' or 'sta'") - } - if (is.null(names(dim(index_hind$data))) - || is.null(names(dim(index_obs$data))) - || is.null(names(dim(index_fcst$data)))) { - stop("Element 'data' from parameters 'index_hind', 'index_obs' and 'index_fcst'", - " should have dimmension names.") - } - if((length(names(dim(index_hind$data))) != length(names(dim(index_fcst$data)))) - || (!all(names(dim(index_hind$data)) == names(dim(index_fcst$data))))){ - stop("The dimension names of the component 'data' of the parameter 'index_hind' ", - "and 'index_fcst' must be equals.") - } - if(length(dim(index_hind$data)) == 4 ){ - if (!all(names(dim(index_hind$data)) == c('time', 'dataset', 'member', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") - } - } else if (length(dim(index_hind$data)) == 5 ){ - if (!all(names(dim(index_hind$data)) == c('time', 'dataset', 'member', 'statistic', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") - } - } else { - stop("Wrong dimensions of the component 'data' of parameter 'index_hind'") - } - if(length(dim(index_obs$data)) != 4){ - stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") - }else if (!all(names(dim(index_obs$data)) == c('time', 'dataset', 'member', 'season'))) { - stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") - } - if(length(dim(index_fcst$data)) == 4 ){ - if (!all(names(dim(index_fcst$data)) == c('time', 'dataset', 'member', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") - } - } else if (length(dim(index_fcst$data)) == 5 ){ - if (!all(names(dim(index_fcst$data)) == c('time', 'dataset', 'member', 'statistic', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") - } - } else { - stop("Wrong dimensions of the component 'data' of parameter 'index_fcst'") - } - if (dim(index_hind$data)['time'] != dim(index_obs$data)['time']) { - stop("The length of the dimensions 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_hind' must be equals.") - } - if ((dim(index_hind$data)['dataset'] != dim(index_obs$data)['dataset']) - | (dim(index_hind$data)['dataset'] != dim(index_fcst$data)['dataset'])) { - stop("The length of the dimensions 'dataset' in the component 'data' ", - "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", - "equals.") - } - if ((dim(index_hind$data)['season'] != dim(index_obs$data)['season']) - | (dim(index_hind$data)['season'] != dim(index_fcst$data)['season'])) { - stop("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", - "equals.") - } - if (dim(index_obs$data)['time'] <= 2) { - stop("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_hind' must be greater than 2.") - } - if (dim(index_fcst$data)['time'] != 1) { - stop("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_fcst' must be equal to 1.") - } - if (dim(index_hind$data)['member'] == 1 & - dim(index_hind$data)['statistic'] != 2){ - stop("The dimension 'statistic' must be equal to 2 in the component 'data' ", - "of the parameter 'index_hind' and 'index_fcst' if the length of the ", - "dimension 'member' is equal to 1") - } - if (dim(index_obs$data)['member'] != 1){ - stop("The dimension 'member' must be equal to 1 in the component 'data' ", - "of the parameter 'index_obs'.") - } - if (any(names(dim(index_fcst$data)) == 'statistic') & type_exp != 'sta'){ - stop("The parameter 'type_exp' must be equal to 'sta' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_fcst' exits") - } - if (!any(names(dim(index_fcst$data)) == 'statistic') & type_exp == 'sta'){ - stop("The parameter 'type_exp' can't be equal to 'sta' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_fcst' doesn't exit.") - } - - PDFstatistics <- PDFIndexFcst(index_hind$data, index_obs$data, - index_fcst$data, type_exp = type_exp, - method = method, na.rm = na.rm) - - if (dim(index_fcst$data)['member'] == 1) { - Dim <- dim(index_fcst$data) - dim(index_fcst$data) <- Dim[-which(names(Dim) == 'member')] - } - if (dim(index_hind$data)['member'] == 1) { - Dim <- dim(index_hind$data) - dim(index_hind$data) <- Dim[-which(names(Dim) == 'member')] - } - - dimnames <- names(dim(index_fcst$data)) - if (any(names(dim(index_fcst$data)) == 'member')){ - dimnames[dimnames == 'member'] <- 'statistic' - } - - pos <- match(dimnames, names(dim(PDFstatistics))) - PDFstatistics <- aperm(PDFstatistics, pos) - names(dim(PDFstatistics)) <- dimnames - index_fcst$data <- PDFstatistics - index_fcst$Datasets <- c(index_hind$Datasets, index_obs$Datasets, index_fcst$Datasets) - index_fcst$source_files <- c(index_hind$source_files, index_obs$source_files, - index_fcst$source_files) - index_fcst$statistic <- c("mean_notbc","sigma_notbc","mean_bc","sigma_bc") - return(index_fcst) -} - -#' @rdname PDFIndexFcst -#' @title Computing the Index PDFs for a dataset of SFSs for a forecast year. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from a dataset of SFSs -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_hind Index (e.g. NAO index) array from SFSs with dimensions -#' (time, dataset, member, season) or -#' (time, dataset, member=1, statistic=2, season) for a hindcast period. -#' The dimensions 'time' must be greater than 2. When dimension 'statistic' -#' doesn't exist, the dimension member' must be greater than 1. -#' @param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with dimension (time, dataset, member = 1, season). -#' The dimensions 'time' must be greater than 2. -#' @param index_fcst Index (e.g. NAO index) array from SFSs. -#' with dimensions (time = 1, dataset, member) for a forecast year. -#' @param type_exp A character string ('ens' or 'sta') to identify if the -#' experiment output is an ensemble members or statistics. -#' @param method A character string indicating which methodology is applied -#' to compute the PDF. One of "ME" (default) or "LMEV". -#' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return PDFIndexFcst() returns an array with dimensions -#' (time = 1, dataset, member, season) or -#' (time = 1, dataset, member = 1, statistic = 2, season) for the forecast year. -#' When dimension statistic' doesn't exist, the dimension 'member' must be -#' greater than 1. -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @import multiApply -#' @export -#' @examples -#' # Example for the PDFIndexFcst function -#' index_fcst <- 1 : (3 * 4) -#' dim(index_fcst) <- c(time = 1, dataset = 3, member = 4, season = 1) -#' index_obs<- 1 : (5 * 3) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) -#' index_hind <- 1 : (5 * 3 * 4) -#' dim(index_hind) <- c(time = 5, dataset = 3, member = 4, season = 1) -#' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) -#' dim(res) -#' # time statistic dataset season -#' # 1 4 3 1 -#' str(res) -#' # num [1, 1:4, 1:3, 1] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... -#' -PDFIndexFcst <- function(index_hind, index_obs, index_fcst, type_exp = 'ens', - method ='ME', na.rm = FALSE) { - - if('statistic' %in% names(dim(index_hind)) & dim(index_hind)['member'] == 1){ - Dim <- dim(index_hind) - dim(index_hind) <- Dim[-which(names(Dim) == 'member')] - } - if('statistic' %in% names(dim(index_fcst)) & dim(index_fcst)['member'] == 1){ - Dim <- dim(index_fcst) - dim(index_fcst) <- Dim[-which(names(Dim) == 'member')] - } - Dim <- dim(index_obs) - dim(index_obs) <- Dim[-which(names(Dim) == 'member')] - - if (type_exp == 'ens'){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), - target_dims = list(c('time', 'member'), 'time', - c('time','member')), - fun = .PDFIndexFcst, type_exp, method, na.rm) - } else if (type_exp == 'sta'){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), - target_dims = list(c('time', 'statistic'), 'time', - c('time','statistic')), - fun = .PDFIndexFcst, type_exp, method, na.rm) - } - return(PDFstatistics$output1) -} - -#' Atomic PDFIndexFcst -#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions -#' (time, member) for a hindcast period. -#' @param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with dimension (time). -#' @param index_fcst Index (e.g. NAO index) array from a SFS -#' with dimensions (time = 1, member) for a forecast year. -#' @param type_exp A character string ('ens' or 'sta') to identify if the -#' experiment output is an ensemble members or statistics. -#' @param method A character string indicating which methodology is applied -#' to compute the PDF. One of "ME" (default) or "LMEV". -#' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexFcst returns an array with dimensions (time=1, statistic=4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' for the forecast year. -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected for the forecast year. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' for the forecast year. -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected for the forecast year. -#' @import multiApply -#' @importFrom verification verify -#' @examples -#' # Example for the Atomic PDFIndexFcst function -#' index_fcst <- 1 : 3 -#' dim(index_fcst) <- c(time = 1, member = 3) -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = length(index_obs)) -#' index_hind <- 1 : (10 * 3) -#' dim(index_hind) <- c(time = 10, member = 3) -#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) -#' str(res) -#' # num [1, 1:4] 2 10 -8 0 -#' dim(res) -#' # time statistic -#' # 1 4 -#' @noRd -.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, type_exp = 'ens', - method = 'ME', na.rm = FALSE) { - names(dim(index_obs)) <- c('time') - if (type_exp == 'ens') { - names(dim(index_hind)) <- c('time', 'member') - names(dim(index_fcst)) <- c('time', 'member') - exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', - fun = mean)$output1 - exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', - fun = sd)$output1 - data_hind <- .PDFIndexHind(index_hind, index_obs, type_exp, method, na.rm) - } else { - names(dim(index_hind)) <- c('time', 'statistic') - names(dim(index_fcst)) <- c('time', 'statistic') - pos <- match(c('time','statistic'), names(dim(index_fcst))) - index_fcst <- aperm(index_fcst,pos) - exp_fcst_ens_mean <- index_fcst[,1] - exp_fcst_ens_sd <- index_fcst[,2] - data_hind <- .PDFIndexHind(index_hind, index_obs, type_exp, method, na.rm) - } - - exp_hind_ens_mean <- data_hind[,1] - pdfnotbc_mean <- exp_fcst_ens_mean - pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs, na.rm) - if (method == 'ME') { - pdfbc <- MEBC(exp_hind_ens_mean, index_obs, na.rm) - pdfbc_mean <- exp_fcst_ens_mean - pdfbc - } else if (method == 'LMEV') { - # linear model error-variance - exp_fcst_ens_var <- exp_fcst_ens_sd^2 - dim_hind <- names(dim(index_hind)) - dim_obs <- names(dim(index_obs)) - coeff <- LMEV(index_hind, index_obs, dim_hind, dim_obs, na.rm) - pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) - } else { - stop("Error: Parameter 'method' is wrong") - } - pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs, na.rm) - data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) - names(dim(data)) <- c(names(dim(index_obs)), 'statistic') - return(data) -} diff --git a/R/CST_PDFIndexHind.R b/R/CST_PDFIndexHind.R deleted file mode 100644 index ee4ac657..00000000 --- a/R/CST_PDFIndexHind.R +++ /dev/null @@ -1,319 +0,0 @@ -#' @rdname CST_PDFIndexHind -#' @title Computing the Index PDFs for the hindcast of a SFSs dataset -#' of a CSTools object -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from the hindcast of SFSs -#' and accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_exp An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. -#' The index_exp object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member, season) or -#' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must be -#' greater than 2. When dimension 'statistic' doesn't exist, the dimension -#' 'member' must be greater than 1. -#' @param index_obs An object of the class 's2dv_cube' -#' containing the indices or drivers of climate variability (as e.g., -#' the North Atlantic Oscillation (NAO)) estimate from observations or -#' reanalysis. -#' The index_obs object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member = 1, season). The dimension 'time' -#' must be greater than 2. -#' @param type_exp A character string ('ens' or 'sta') to identify if the -#' experiment output is an ensemble members or statistics. -#' @param method A character string indicating which methodology is applied -#' to compute the PDFs. One of "ME" (default) or "LMEV". -#' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return CST_PDFIndexHind() returns a CSTools object (i.e., of the -#' class 's2dv_cube'). -#' This object has at least two elements: i) an element named \code{$data} -#' with dimensions (time, dataset, statistic = 4, season) and ii) an element -#' named \code{$statistics} with the names of 'statistic' dimension -#' of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @export -#' @examples -#' index_obs <- 1 : (5 * 3 * 1 * 4) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -#' index_exp <- 1 : (5 * 3 * 7 * 4) -#' dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) -#' index_exp <- list(data = index_exp) -#' index_obs <- list(data = index_obs) -#' class(index_exp) <- 's2dv_cube' -#' class(index_obs) <- 's2dv_cube' -#' res_CST <- CST_PDFIndexHind(index_exp, index_obs) -#' dim(res_CST$data) -#' #time dataset statistic season -#' # 5 3 4 4 -CST_PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'ME', na.rm = FALSE) { - if (!inherits(index_exp, 's2dv_cube') || !inherits(index_obs, 's2dv_cube')) { - stop("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") - } - if(!(method %in% c('ME', 'LMEV'))){ - stop("The parameter 'method' must be equal to 'ME' or 'LMEV'") - } - if (!is.logical(na.rm)) { - stop("The parameter 'na.rm' must be a logical value.") - } - if(!(type_exp %in% c('ens', 'sta'))){ - stop("The parameter 'type_exp' must be equal to 'ens' or 'sta'") - } - if (is.null(names(dim(index_exp$data))) || is.null(names(dim(index_obs$data)))) { - stop("Element 'data' from parameters 'index_exp' and 'index_obs'", - " should have dimmension names.") - } - if(length(dim(index_obs$data)) != 4){ - stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") - }else if (!all(names(dim(index_obs$data)) == c('time', 'dataset', 'member', 'season'))) { - stop("Wrong dimensions of the component 'data' of parameter 'index_obs'") - } - if(length(dim(index_exp$data)) == 4 ){ - if (!all(names(dim(index_exp$data)) == c('time', 'dataset', 'member', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") - } - } else if (length(dim(index_exp$data)) == 5 ){ - if (!all(names(dim(index_exp$data)) == c('time', 'dataset', 'member', 'statistic', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") - } - } else { - stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") - } - if (dim(index_obs$data)['time'] != dim(index_exp$data)['time']) { - stop("The length of the dimensions 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be equals.") - } - if (dim(index_obs$data)['dataset'] != dim(index_exp$data)['dataset']) { - stop("The length of the dimensions 'dataset' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be equals.") - } - if (dim(index_obs$data)['season'] != dim(index_exp$data)['season']) { - stop("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be equals.") - } - if (dim(index_obs$data)['time'] <= 2) { - stop("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be greater than 2.") - } - if (dim(index_obs$data)['member'] != 1) { - stop("The length of the dimension 'member' in the component 'data' ", - "of the parameter 'index_obs' must be equal to 1.") - } - if (dim(index_exp$data)['member'] == 1 & - !any(names(dim(index_exp$data)) == 'statistic')){ - stop("The dimension 'statistic' must be exist in the component 'data' ", - "of the parameter 'index_exp' if the length of the dimension ", - "'member' is equal to 1") - } - if (dim(index_exp$data)['member'] > 1 & - any(names(dim(index_exp$data)) == 'statistic')){ - stop("The length of the dimension 'member' in the component 'data' of the ", - "parameter 'index_exp' must be equal to 1 if the component 'data' ", - "of the parameter 'index_exp' has the dimension 'statistic'") - } - if (dim(index_exp$data)['member'] == 1 & - dim(index_exp$data)['statistic'] != 2){ - stop("The length of the dimension 'statistic' in the component 'data' of the ", - "parameter 'index_exp' must be equal to 2 if the component 'data' ", - "of the parameter 'index_exp' has the dimension 'member' equal to 1.") - } - if (any(names(dim(index_exp$data)) == 'statistic') & type_exp != 'sta'){ - stop("The parameter 'type_exp' must be equal to 'sta' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_exp' exits") - } - - PDFstatistics <- PDFIndexHind(index_exp$data, index_obs$data, - type_exp = type_exp, method = method, - na.rm = na.rm) - if (dim(index_exp$data)['member'] == 1) { - Dim <- dim(index_exp$data) - dim(index_exp$data) <- Dim[-which(names(Dim) == 'member')] - } - dimnames <- names(dim(index_exp$data)) - if (any(names(dim(index_exp$data)) == 'member')){ - dimnames[dimnames == 'member'] <- 'statistic' - } - - pos <- match(dimnames, names(dim(PDFstatistics))) - PDFstatistics <- aperm(PDFstatistics, pos) - names(dim(PDFstatistics)) <- dimnames - index_exp$data <- PDFstatistics - index_exp$Datasets <- c(index_exp$Datasets, index_obs$Datasets) - index_exp$source_files <- c(index_exp$source_files, index_obs$source_files) - index_exp$statistic <- c("mean_notbc","sigma_notbc","mean_bc","sigma_bc") - return(index_exp) -} - -#' @rdname PDFIndexHind -#' @title Computing the Index PDFs for a dataset of SFSs for a hindcats period. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from a dataset of SFSs -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_exp Index (e.g. NAO index) array from SFSs -#' with dimensions (time, dataset, member, season) or -#' (time, dataset, member=1, statistic=2, season). The dimensions 'time' must be -#' greater than 2. When dimension 'statistic' doesn't exist, the dimension -#' 'member' must be greater than 1. -#' @param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with dimensions (time, dataset, member = 1, season). The -#' dimension 'time' must be greater than 2. -#' @param type_exp A character string ('ens' or 'sta') to identify if the -#' experiment output is an ensemble members or statistics. -#' @param method A character string indicating which methodology is applied -#' to compute the PDFs. One of "ME" (default) or "LMEV". -#' @param na.rm Logical (default = FALSE). Should missing values be removed? -#' @return PDFIndexHind() returns an array -#' with dimensions (time, statistic = 4, dataset, season). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @import multiApply -#' @export -#' @examples -#' # Example for the PDFIndexHind function -#' # Example 1 -#' index_obs <- 1 : (5 * 3 * 1 * 1) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) -#' index_exp <- 1 : (5 * 3 * 4 * 1) -#' dim(index_exp) <- c(time = 5, dataset = 3, member = 4, season = 1) -#' res <- PDFIndexHind(index_exp, index_obs) -#' str(res) -#' # num [1:5, 1:4, 1:3, 1] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... -#' dim(res) -#' # time statistic dataset season -#' # 5 4 3 1 -#' # Example 2 -#' index_obs <- 1 : (5 * 3 * 1 * 1) -#' dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) -#' index_exp <- 1 : (5 * 3 * 1* 2 * 1) -#' dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 2, season = 1) -#' res <- PDFIndexHind(index_exp, index_obs, type_exp = 'sta') -#' str(res) -#' # num [1:5, 1:4, 1:3, 1] 1 2 3 4 5 0 0 0 0 0 ... -#' dim(res) -#' # time statistic dataset season -#' # 5 4 3 1 -#' -PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method ='ME', - na.rm = FALSE) { - - if('statistic' %in% names(dim(index_exp)) & dim(index_exp)['member'] == 1){ - Dim <- dim(index_exp) - dim(index_exp) <- Dim[-which(names(Dim) == 'member')] - } - Dim <- dim(index_obs) - dim(index_obs) <- Dim[-which(names(Dim) == 'member')] - - if (type_exp == 'ens'){ - PDFstatistics <- Apply(list(index_exp, index_obs), - target_dims = list(c('time', 'member'), 'time'), - fun = .PDFIndexHind, type_exp, method, na.rm) - } else if (type_exp == 'sta'){ - PDFstatistics <- Apply(list(index_exp, index_obs), - target_dims = list(c('time', 'statistic'), 'time'), - fun = .PDFIndexHind, type_exp, method, na.rm) - } - return(PDFstatistics$output1) -} - -#' Atomic PDFIndexHind -#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions -#' (time, member) for a hindcast period. The dimension 'time' must be greater -#' than 2. -#' @param index_obs Index (e.g. NAO index) array from an observational dataset -#' or reanalysis with dimension (time). The dimension 'time' must be greater -#' than 2. -#' @param type_exp A character string ('ens' or 'sta') to identify if the -#' experiment output is an ensemble members or statistics. -#' @param method A character string indicating which methodology is applied -#' to compute the PDF. One of "ME" (default) or "LMEV". -#' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' for the hindcast period. -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected for the hindcast period. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' for the hindcast period. -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected for the hindcast period. -#' @import multiApply -#' @importFrom verification verify -#' @examples -#' # Example for the Atomic PDFIndexHind function -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = length(index_obs)) -#' index_exp <- 1 : (10 * 3) -#' dim(index_exp) <- c(time = 10, member = 3) -#' res <- .PDFIndexHind(index_exp, index_obs) -#' str(res) -#' # num [1:10, 1:4] 11 12 13 14 15 16 17 18 19 20 ... -#' dim(res) -#' # time statistic -#' # 10 4 -#' @noRd -.PDFIndexHind <- function(index_exp, index_obs, type_exp = 'ens', method = 'ME', - na.rm = FALSE) { - names(dim(index_obs)) <- c('time') - if (type_exp == 'ens') { - names(dim(index_exp)) <- c('time', 'member') - exp_ens_mean <- Apply(list(index_exp), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - exp_ens_sd <- Apply(list(index_exp), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else if (type_exp == 'sta'){ - names(dim(index_exp)) <- c('time', 'statistic') - pos <- match(c('time','statistic'), names(dim(index_exp))) - index_exp <- aperm(index_exp,pos) - exp_ens_mean <- index_exp[,1] - exp_ens_sd <- index_exp[,2] - } - error <- exp_ens_mean - index_obs - pdfnotbc_mean <- exp_ens_mean - ind <- 1 : length(index_obs) - pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(exp_ens_mean[-x], - index_obs[-x], - na.rm)})) - - if (method == 'ME') { - pdfbc <- unlist(lapply(ind, function(x) {MEBC(exp_ens_mean[-x], - index_obs[-x], na.rm)})) - pdfbc_mean <- exp_ens_mean - pdfbc - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x], - na.rm)})) - } else if (method == 'LMEV') { - # linear model error-variance - exp_ens_var <- exp_ens_sd^2 - dim_exp <- names(dim(index_exp)) - dim_obs <- names(dim(index_obs)) - coeff <- sapply(ind, function(x) {LMEV(index_exp[-x,], index_obs[-x], - dim_exp, dim_obs, na.rm)}) - pdfbc_mean <- exp_ens_mean - (unlist(coeff['coeff1',]) + - unlist(coeff['coeff2',])*exp_ens_var) - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x], - na.rm)})) - } else { - stop("Error: Parameter 'method' is wrong") - } - data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) - names(dim(data)) <- c(names(dim(index_obs)), 'statistic') - return(data) -} diff --git a/R/CST_ProbBestIndexWeighting.R b/R/CST_ProbBestIndexWeighting.R deleted file mode 100644 index 2dd64f89..00000000 --- a/R/CST_ProbBestIndexWeighting.R +++ /dev/null @@ -1,281 +0,0 @@ -#' @rdname CST_ProbBestIndexWeighting -#' @title Computing the weighted tercile probabilities for SFSs of a CSTools -#' object. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the tercile -#' probabilities for a weighted variable from SFSs through Best estimation of -#' one index or driver of climate variability (as e.g., the North Atlantic -#' Oscillation (NAO)) and accepts a CSTools object (an object of the class -#' 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp An object of the class 's2dv_cube' containing the variable -#' (e.g. precipitation, temperature, NAO index) array. -#' The var_exp object is expected to have an element named \code{$data} with -#' three dimensions (time, dataset, member) for a zonal variable -#' or five dimensions (time, dataset, member, lat, lon) for a spatial variable. -#' @param weights_index_exp An object of the class 's2dv_cube' containing -#' the normalized weights array. -#' The weights_index_exp object is expected to have an element named -#' \code{$data} with dimensions (time, dataset, member, season). -#' @return CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the -#' class 's2dv_cube'). -#' This object has at least an element named \code{$data} -#' with four or six dimensions depending if the variable is zonal -#' (as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable -#' (as e.g. precipitation or temperature) -#' (time, tercil, dataset, season, lat, lon), -#' containing the terciles probabilities computing with weighted members. -#' The first tercil is the lower tercile, the second is the normal tercile and -#' the third is the upper tercile. -#' @export -#' @examples -#' var_exp <- 1 : (4 * 2 * 3 * 3 * 2) -#' dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) -#' weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) -#' dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) -#' var_exp <- list(data = var_exp) -#' weights_index_exp <- list(data = weights_index_exp) -#' class(var_exp) <- 's2dv_cube' -#' class(weights_index_exp) <- 's2dv_cube' -#' res_CST <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) -#' dim(res_CST$data) -#' # time tercil dataset season lat lon -#' # 4 3 2 2 3 2 -CST_ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { - if (!inherits(var_exp, 's2dv_cube') || !inherits(weights_index_exp, 's2dv_cube')) { - stop("Parameter 'var_exp' and 'weights_index_exp' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") - } - if (is.null(names(dim(var_exp$data))) - || is.null(names(dim(weights_index_exp$data)))) { - stop("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", - " should have dimmension names.") - } - if(length(dim(var_exp$data)) == 3 ){ - if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member'))){ - stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") - } - } else if (length(dim(var_exp$data)) == 5 ){ - if (!all(names(dim(var_exp$data)) == c('time', 'dataset', 'member', - 'lat', 'lon'))){ - stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") - } - } else { - stop("Wrong dimensions of the component 'data' of parameter 'var_exp'") - } - if(length(dim(weights_index_exp$data)) == 4 ){ - if (!all(names(dim(weights_index_exp$data)) == c('time', 'dataset', - 'member', 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") - } - } else { - stop("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'") - } - if (dim(var_exp$data)['time'] != dim(weights_index_exp$data)['time']){ - stop("The length of the dimension 'time' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'time' of the parameter 'weights_index_exp' in the component 'data'") - } - if (dim(var_exp$data)['dataset'] != dim(weights_index_exp$data)['dataset']){ - stop("The length of the dimension 'dataset' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'dataset' of the parameter 'weights_index_exp' in the component 'data'") - } - if (dim(var_exp$data)['member'] != dim(weights_index_exp$data)['member']){ - stop("The length of the dimension 'member' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'member' of the parameter 'weights_index_exp' in the component 'data'") - } - - if (sum(!is.na(as.vector(var_exp$data))) - != length(as.vector(var_exp$data))){ - stop("There are one or more elements of the component 'data' of the ", - "parameter 'var_exp' equal to NA.") - } - - if (sum(!is.na(as.vector(weights_index_exp$data))) - != length(as.vector(weights_index_exp$data))){ - stop("There are one or more elements of the component 'data' of the ", - "parameter 'weights_index_exp' equal to NA.") - } - - prob <- ProbBestIndexWeighting(var_exp$data, weights_index_exp$data) - prob_var_exp <- list() - prob_var_exp$data <- prob - prob_var_exp$Datasets <- var_exp$Datasets - prob_var_exp$source_files <- var_exp$source_files - prob_var_exp$Dates <- var_exp$Dates - return(prob_var_exp) -} - -#' @rdname ProbBestIndexWeighting -#' @title Computing the weighted tercile probabilities for SFSs. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the tercile -#' probabilities for a weighted variable from SFSs through Best estimation of -#' one index or driver of climate variability (as e.g., the North Atlantic -#' Oscillation (NAO)). -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with three dimensions (time, dataset, member) for a zonal -#' variable or dimensions (time, dataset, member, lat, lon) for a spatial -#' variable. -#' @param weights_index_exp Normalized weights array with dimensions -#' (time, dataset, member, season). -#' @return ProbBestIndexWeighting() returns an array with four or six dimensions -#' depending if the variable is zonal (as e.g. NAO index) -#' (time, tercil, dataset, season) or it is spatial variable (as e.g. -#' precipitation or temperature) (time, tercil, dataset, season, 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. -#' @import multiApply -#' @export -#' @examples -#' # Example 1 for the ProbBestIndexWeighting function -#' var_exp <- 1 : (2 * 3 * 4) -#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4) -#' weights_index_exp<- runif(24, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) -#' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) -#' dim(res) -#' # time tercil dataset season -#' # 2 3 3 1 -#' # Example 2 for the ProbBestIndexWeighting function -#' var_exp <- 1 : (2 * 3 * 4 * 2 * 3) -#' dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) -#' weights_index_exp<- runif(24, min=0.001, max=0.999) -#' dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) -#' res <- ProbBestIndexWeighting(var_exp, weights_index_exp) -#' dim(res) -#' # time tercil dataset season lat lon -#' # 2 3 3 1 2 3 -#' -ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { - res <- Apply(list(var_exp, weights_index_exp), - target_dims = list(c('time','member'), c('time','member')), - fun = .ProbBestIndexWeighting)$output1 - if (length(dim(var_exp)) == 5){ - Dim <- c('time', 'tercil', 'dataset', 'season', 'lat', 'lon') - } else if(length(dim(var_exp)) == 3){ - Dim <- c('time', 'tercil', 'dataset', 'season') - } else { - stop("Wrong dimension") - } - pos <- match(Dim, names(dim(res))) - res <- aperm(res, pos) - names(dim(res)) <- Dim - return(res) -} - -#' Atomic ProbBestIndexWeighting -#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with dimensions (time, member). -#' @param weights_index_exp Normalized weights array with dimensions -#' (time, member). -#' @return .ProbBestIndexWeighting returns an array of with dimensions -#' (time, tercil = 3), the terciles probabilities computing with weighted -#' members. -#' The firt tercil is the lower tercile, the second is the normal tercile and -#' the third is the upper tercile. -#' @examples -#' # Example for the Atomic EMBestIndexWeighting function -#' var_exp <- 1 : 8 -#' dim(var_exp) <- c(time = 2, member = 4) -#' weights_index_exp <- c(0.28, 0.15, 0.69, 0.64, 0.42, 0.17, 0.14, 0.22) -#' dim(weights_index_exp) <- c(time = 2, member = 4) -#' res <- .ProbBestIndexWeighting(var_exp, weights_index_exp) -#' str(res) -#' # num [1:2, 1:3] 0.28 0.15 0.69 0 0.56 1.03 -#' dim(res) -#' # time tercil -#' # 2 3 -#' @noRd -.ProbBestIndexWeighting <- function(var_exp, weights_index_exp) { - # computing terciles - names(dim(var_exp)) <- c('time','member') - names(dim(weights_index_exp)) <- c('time','member') - terciles_exp <- WeightTerciles(var_exp, weights_index_exp) - lowerTercile <- terciles_exp$lowerTercile - upperTercile <- terciles_exp$upperTercile - - # Probabilities - aTerciles <- Apply(list(var_exp), target_dims = list('member'), - fun = Data2Tercil, lowerTercile, upperTercile)$output1 - - pos <- match(names(dim(aTerciles)), c('time','member')) - aTerciles <- aperm(aTerciles,pos) - names(dim(aTerciles)) <- c('time','member') - - probTercile <- array(NA, dim = c(dim(var_exp)['time'], tercil = 3)) - for (idTercil in 1:3){ - probTercile[,idTercil] <- Apply(list(aTerciles, weights_index_exp), - target_dims = list('member','member'), - fun = WeightTercil2Prob, idTercil)$output1 - } - return(probTercile) -} - -# Auxiliar function to compute in which tercile is a data value -Data2Tercil <- function(x,lt,ut) { - #y <- x - y <- rep(2,length(x)) - y[x <= lt] <- 1 - y[x >= ut] <- 3 - if (lt == ut) { - warning("The upper and lower terciles are equals") - } - #y[(x > rep(lt,length(x))) & (x < rep(ut,length(x)))] <- 2 - #y[(x > lt) & (x < ut)] <- 2 - dim(y) <- c(member = length(x)) - return (y) -} - -# Auxiliar function to convers weighted terciles to probabilities -WeightTercil2Prob <- function(aTerciles, aWeights, idTercil) { - return(sum(aWeights[which(aTerciles == idTercil)])) -} - -# Auxiliar function to compute weighted terciles -WeightTerciles <- function(data, weights) { - vectorData <- as.vector(data) - vectorWeights <- as.vector(weights/dim(weights)['time']) # normalized - lSortData <- sort(vectorData,index.return=TRUE) - indSort <- lSortData$ix # index asociated to weight - # corresponding for this data - dataSort <- lSortData$x - # Adding normalized weights. When 1/3 is reached, the data value - # is lower tercile and when 2/3 is reached, it is the upper tercile. - sumWeights <- 0 - ilowerTercile <- 0 - while ((sumWeights < 1/3) & (ilowerTercile < (dim(weights)['member'] * - dim(weights)['time']))){ - ilowerTercile<- ilowerTercile +1 - sumWeights <- sumWeights + vectorWeights[indSort[ilowerTercile]] - } - if (ilowerTercile == 1){ - lowerTercile <- dataSort[ilowerTercile] - } else { - lowerTercile <- (dataSort[ilowerTercile]+ - dataSort[ilowerTercile-1])/2 - } - sumWeights <- 0 - iupperTercile <- 0 - while ((sumWeights < 2/3) & (iupperTercile < (dim(weights)['member'] * - dim(weights)['time']))){ - iupperTercile<- iupperTercile +1 - sumWeights <- sumWeights + vectorWeights[indSort[iupperTercile]] - } - if (iupperTercile == 1) { - upperTercile <- dataSort[iupperTercile] - } else { - upperTercile <- (dataSort[iupperTercile]+ - dataSort[iupperTercile-1])/2 - } - return(list(lowerTercile = lowerTercile, upperTercile = upperTercile)) -} - diff --git a/R/CST_WeightIndex.R b/R/CST_WeightIndex.R deleted file mode 100644 index 9639a117..00000000 --- a/R/CST_WeightIndex.R +++ /dev/null @@ -1,177 +0,0 @@ -#' @rdname CST_WeightIndex -#' @title Computing the weights for SFSs using the Best Index PDFs -#' of a CSTools object -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the -#' normalized weights for SFS members using the Best Index estimation and -#' accepts a CSTools object (an object of the class 's2dv_cube') as input. -#' @references Regionally improved seasonal forecast of precipitation through -#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_exp An object of the class 's2dv_cube' containing the indices -#' or drivers of climate variability (as e.g., the North Atlantic Oscillation -#' (NAO)) estimate from SFSs. -#' The index_exp object is expected to have an element named \code{$data} -#' with dimensions (time, dataset, member, season). -#' @param pdf_exp An object of the class 's2dv_cube' containing the statistics -#' which define a Gaussian PDFs for weighting the values estimate for the SFS -#' ensemble members. -#' The pdf_exp object is expected to have an element named \code{$data} -#' with dimensions (time, statistic = 2, season). -#' The firt statistic is the parameter 'mean' of the PDFs and -#' the second statistic is the parameter 'standard deviation' of the PDFs. -#' @return CST_WeightIndex() returns a CSTools object (i.e., of the -#' class 's2dv_cube'). -#' This object has at least an element named \code{$data} -#' with dimensions (time, dataset, member, season). -#' @export -#' @examples -#' index_exp <- 1 : (5 * 3 * 7 * 4) -#' dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) -#' index_exp <- list(data = index_exp) -#' pdf_exp <- 1 : (5 * 2 * 4) -#' dim(pdf_exp) <- c(time = 5, statistic = 2, season = 4) -#' pdf_exp <- list(data = pdf_exp) -#' class(index_exp) <- 's2dv_cube' -#' class(pdf_exp) <- 's2dv_cube' -#' res_CST <- CST_WeightIndex(index_exp, pdf_exp) -#' dim(res_CST$data) -#' #time dataset member season -#' # 5 3 7 4 -CST_WeightIndex <- function(index_exp, pdf_exp) { - if (!inherits(index_exp, 's2dv_cube') || !inherits(pdf_exp, 's2dv_cube')) { - stop("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") - } - if (is.null(names(dim(index_exp$data))) || is.null(names(dim(pdf_exp$data)))) { - stop("Element 'data' from parameters 'index_exp' and 'pdf_exp'", - " should have dimmension names.") - } - if(length(dim(index_exp$data)) != 4){ - stop("The number of dimensions in the component 'data' ", - "of the parameter 'index_exp' must be 4") - } else if (!all(names(dim(index_exp$data)) == c('time', 'dataset', 'member', - 'season'))) { - stop("Wrong dimensions of the component 'data' of parameter 'index_exp'") - } - if(length(dim(pdf_exp$data)) != 3){ - stop("The number of dimensions in the component 'data' ", - "of the parameter 'pdf_exp' must be 3") - } else if (!all(names(dim(pdf_exp$data)) == c('time', 'statistic', - 'season'))){ - stop("Wrong dimensions of the component 'data' of parameter 'pdf_exp'") - } - if (dim(pdf_exp$data)['statistic'] != 2) { - stop("The length of the dimension 'statistic' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 2.") - } - if (dim(index_exp$data)['time'] != dim(pdf_exp$data)['time']) { - stop("The length of the dimensions 'time' in the component 'data' ", - "of the parameter 'index_exp' and 'pdf_exp' must be equals.") - } - if (dim(index_exp$data)['season'] != dim(pdf_exp$data)['season']) { - stop("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_exp' and 'pdf_exp' must be equals.") - } - - - dimnames <- names(dim(index_exp$data)) - aWeigths <- WeightIndex(index_exp$data, pdf_exp$data) - if((dim(aWeigths)[1] != dim(index_exp$data)[1]) || - (dim(aWeigths)[2] != dim(index_exp$data)[2]) || - (dim(aWeigths)[3] != dim(index_exp$data)[3]) || - (dim(aWeigths)[4] != dim(index_exp$data)[4])){ - stop("Wrong dimensions of variable 'aWeigths'") - } - names(dim(aWeigths)) <- dimnames - weights_index_exp <- list() - weights_index_exp$data <- aWeigths - weights_index_exp$Datasets <- list(exp = index_exp$Datasets, - pdf = pdf_exp$Datasets) - weights_index_exp$source_files <- list(exp = index_exp$source_files, - pdf = pdf_exp$source_files) - weights_index_exp$Dates <- index_exp$Dates - class(weights_index_exp) <- 's2dv_cube' - return(weights_index_exp) -} - -#' @rdname WeightIndex -#' @title Computing the weights for SFSs using the Best Index PDFs. -#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' @description This function implements the computation to obtain the -#' normalized weights for each member of each SFS or dataset using the -#' Best Index estimation. -#' @references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' @param index_exp Index (e.g. NAO index) array, from a dataset of SFSs -#' for a period of years, with dimensions (time, dataset, member, season). -#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions -#' (time, statistic = 2, season) for a period of years. -#' The firt statistic is the parameter 'mean' of the PDF and -#' the second statistic is the parameter 'standard deviation' of the PDF. -#' @return WeightIndex() returns a normalized weights array with dimensions -#' (time, dataset, member, season). -#' @import multiApply -#' @export -#' @examples -#' # Example for the WeightIndex function -#' index_exp <- 1 : (10 * 3 * 5 * 1) -#' dim(index_exp) <- c(time = 10, dataset = 3, member = 5, season = 1) -#' pdf_exp <- 1 : (10 * 2 * 1) -#' dim(pdf_exp) <- c(time = 10, statistic = 2, season = 1) -#' res <- WeightIndex(index_exp, pdf_exp) -#' dim(res) -#' # time dataset member season -#' # 10 3 5 1 -#' str(res) -#' # num [1:10, 1:3, 1:5, 1] 0.976 0.958 0.935 0.908 0.881 ... -#' -WeightIndex <- function(index_exp, pdf_exp) { - aweights <- Apply(list(index_exp, pdf_exp), - target_dims = list('member', 'statistic'), - fun = .WeightIndex)$output1 - - dimnames <- names(dim(index_exp)) - pos <- match(dimnames, c('member', 'time', 'dataset', 'season')) - aweights <- aperm(aweights, pos) - names(dim(aweights)) <- dimnames - return(aweights) - -} - -#' Atomic WeightIndex -#' @param index_exp Index (e.g. NAO index) array from a SFS with dimensions -#' (member) -#' @param pdf_exp Statistics array to define a Gaussian PDF with dimensions -#' (statistic = 2). -#' The firt statistic is the parameter 'mean' of the PDF and -#' the second statistic is the parameter 'standard deviation' of the PDF. -#' @return .WeightIndex returns an array of with dimensions (member), -#' the normalized weights for each member of a SFS using a Best NAO PDF. -#' @examples -#' # Example for the Atomic WeightIndex function -#' index_exp <- c(1.3,3,-1) -#' dim(index_exp) <- c(member = 3) -#' pdf_exp <- c(1.5,0.8) -#' dim(pdf_exp) <- c(statistic = 2) -#' res <- .WeightIndex(index_exp, pdf_exp) -#' str(res) -#' # num [1:3(1d)] 0.84338 0.15003 0.00659 -#' dim(res) -#' # member -#' # 3 -#' @noRd -.WeightIndex <- function(index_exp, pdf_exp) { - aweights <- apply(index_exp, 1, dnorm, mean = pdf_exp[1], sd = pdf_exp[2]) - dim(aweights) <- dim(index_exp) - sumWeights <- sum(aweights) - aweightsNorm <- apply(aweights, 1, NormWeight, sumWeights) - dim(aweightsNorm) <- dim(index_exp) - return(aweightsNorm) -} - -# Auxiliar function to normalize a weight value -NormWeight <- function(weight, sumWeights) { - return(weight/sumWeights) -} diff --git a/R/CST_Weighted.R b/R/CST_Weighted.R new file mode 100644 index 00000000..271d5dd3 --- /dev/null +++ b/R/CST_Weighted.R @@ -0,0 +1,454 @@ +#' Weighted SFSs of a CSTools object. +#' +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#' @description Function to apply weights to a 's2dv_cube' object. +#' It could return a weighted ensemble mean (deterministic output) or +#' the terciles probabilities (probabilistic output) for SFSs. +#' +#' @references Regionally improved seasonal forecast of precipitation through +#' Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#' @param var_exp An object of the class 's2dv_cube' containing the variable +#' (e.g. precipitation, temperature, NAO index) array. +#' The var_exp object is expected to have an element named \code{$data} with +#' at least a temporal dimension and a dimension named 'member'. +#' @param aweights Normalized weights array with at least dimensions +#' (time, member), when 'time' is the temporal dimension as default. +#' @param type A character string indicating the type of output. +#' If 'type' = 'probs', the function returns, in the element data from +#' 'var_exp' parameter, an array with at least two +#' or four dimensions depending if the variable is zonal (as e.g. NAO index), +#' dimension (time, tercil) or it is spatial variable (as e.g. precipitation +#' or temperature), dimension (time, tercile, lat, lon), containing the terciles +#' probabilities computing with weighted members. +#' The first tercil is the lower tercile, the second is the normal tercile and +#' the third is the upper tercile. +#' If 'type' = 'ensembleMean', the function returns, in the element data from +#' 'var_exp' parameter, an array with at least one or three dimensions +#' depending if the variable is zonal (as e.g. NAO index)(time) +#' or it is spatial variable (as e.g. precipitation or temperature) +#' (time, lat, lon), containing the ensemble means computing +#' with weighted members. +#' @param time_dim_name A character string indicating the name of the +#' temporal dimension, by default 'time'. +#' +#' @return CST_Weighted() returns a CSTools object (i.e., of the +#' class 's2dv_cube'). +#' This object has at least an element named \code{$data} +#' with at least a temporal dimension (and dimension 'tercil' when the output +#' are tercile probabilities), containing the ensemble means computing with +#' weighted members or probabilities of terciles. +#' +#' @examples +#' var_exp <- 1 : (2 * 4 * 3 * 2) +#' dim(var_exp) <- c(time = 2, member = 4, lat = 3, lon = 2) +#' aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.4, 0.3, 0.1, 0.2, 0.4, 0.4, 0.1, 0.2, 0.4, 0.2) +#' dim(aweights) <- c(time = 2, member = 4, dataset = 2) +#' var_exp <- list(data = var_exp) +#' class(var_exp) <- 's2dv_cube' +#' res_CST <- CST_Weighted(var_exp, aweights) +#' dim(res_CST$data) +#' # time lat lon dataset +#' # 2 3 2 2 +#' @export +CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', + time_dim_name = 'time') { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!is.character(type)) { + stop("Parameter 'type' must be a character string ('probs' or ", + "'ensembleMean'), indicating the type of output.") + } + if (length(type) > 1) { + warning("Parameter 'type' has length greater than 1 and ", + "only the first element will be used.") + type <- type[1] + } + if (!inherits(var_exp, 's2dv_cube')) { + stop("Parameter 'var_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + } + if (!is.array(aweights)) { + stop("Parameter 'aweights' must be an array.") + } + if (is.null(names(dim(var_exp$data))) || is.null(names(dim(aweights)))) { + stop("Element 'data' from parameter 'var_exp' and parameter 'aweights'", + " should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(var_exp$data)))) { + stop("Element 'data' from parameter 'var_exp' must have ", + "temporal dimension.") + } + if(!(time_dim_name %in% names(dim(aweights)))) { + stop("Parameter 'aweights' must have temporal dimension.") + } + if(!('member' %in% names(dim(var_exp$data)))) { + stop("Element 'data' from parameter 'var_exp' must have ", + "temporal dimension.") + } + if(!('member' %in% names(dim(aweights)))) { + stop("Parameter 'aweights' must have temporal dimension.") + } + if (dim(var_exp$data)[time_dim_name] != dim(aweights)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of element 'data' from parameter 'var_exp' and parameter ", + "'aweights' must be equals.") + } + if (dim(var_exp$data)['member'] != dim(aweights)['member']) { + stop("Length of dimension 'member' of element 'data' from ", + "parameter 'var_exp' and parameter 'aweights' must be equals.") + } + + if (type == 'ensembleMean'){ + em <- EMWeighted(var_exp$data, aweights, time_dim_name) + var_exp$data <- em + } else if (type == 'probs'){ + probs <- ProbsWeighted(var_exp$data, aweights, time_dim_name) + var_exp$data <- probs + } else { + stop("Parameter 'type' must be a character string ('probs' or ", + "'ensembleMean'), indicating the type of output.") + } + return(var_exp) +} + +#' @rdname EMWeighted +#' @title Computing the weighted ensemble means for SFSs. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' @description This function implements the computation to obtain the weighted +#' ensemble means for SFSs using a normalized weights array, +#' +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with at least dimensions (time, member) for a zonal +#' variable or dimensions (time, member, lat, lon) for a spatial +#' variable, as 'time' the spatial dimension by default. +#' @param aweights Normalized weights array with at least dimensions +#' (time, member), when 'time' is the temporal dimension as default. +#' @param time_dim_name A character string indicating the name of the +#' temporal dimension, by default 'time'. +#' +#' @return EMWeighted() returns an array with at least one or three dimensions +#' depending if the variable is zonal (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. +#' @import multiApply +#' +#' @examples +#' # 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 <- EMWeighted(var_exp, aweights) +#' dim(res) +#' # time dataset +#' # 2 3 +#' # 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 <- EMWeighted(var_exp, aweights) +#' dim(res) +#' # time lat lon +#' # 2 2 3 +#' +#' @export +EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!is.array(var_exp)) { + stop("Parameter 'var_exp' must be an array.") + } + if (!is.array(aweights)) { + stop("Parameter 'aweights' must be an array.") + } + if (is.null(names(dim(var_exp))) || is.null(names(dim(aweights)))) { + stop("Parameters 'var_exp' and 'aweights'", + " should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(var_exp)))) { + stop("Parameter 'var_exp' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(aweights)))) { + stop("Parameter 'aweights' must have temporal dimension.") + } + if(!('member' %in% names(dim(var_exp)))) { + stop("Parameter 'var_exp' must have temporal dimension.") + } + if(!('member' %in% names(dim(aweights)))) { + stop("Parameter 'aweights' must have temporal dimension.") + } + if (dim(var_exp)[time_dim_name] != dim(aweights)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'var_exp' and 'aweights' must be equals.") + } + if (dim(var_exp)['member'] != dim(aweights)['member']) { + stop("Length of dimension 'member' ", + "of parameter 'var_exp' and 'aweights' must be equals.") + } + + res <- Apply(list(var_exp, aweights), + target_dims = list(c(time_dim_name,'member'), + c(time_dim_name,'member')), + fun = .EMWeighted, time_dim_name)$output1 + return(res) +} + +#' Atomic EMWeighted +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with a temporal dimension, +#' by default 'time', and dimension 'member'. +#' @param aweights Normalized weights array with a temporal dimension, +#' by default 'time', and dimension 'member' +#' @param time_dim_name A character string indicating the name of the +#' temporal dimension, by default 'time'. +#' @return .EMWeighted returns an array of with a temporal dimension, +#' by default 'time', containing the weighted ensemble means. +#' @examples +#' # Example for the Atomic EMWeighted function +#' var_exp <- 1 : 6 +#' dim(var_exp) <- c(time = 2, member = 3) +#' aweights <- c(0.28, 0.15, 0.69, 0.64, 0.42, 0.17) +#' dim(aweights) <- c(time = 2, member = 3) +#' res <- .EMWeighted(var_exp, aweights) +#' str(res) +#' # num [1:2(1d)] 4.45 3.88 +#' dim(res) +#' # time +#' # 2 +#' @noRd +.EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { + + posTime <- match(time_dim_name, names(dim(var_exp))) + var_exp_em <- as.array(apply(var_exp * aweights, posTime, sum)) + names(dim(var_exp_em)) <- time_dim_name + return(var_exp_em) +} + + +#' Computing the weighted tercile probabilities for SFSs. +#' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#' @description This function implements the computation to obtain the tercile +#' probabilities for a weighted variable for SFSs using a normalized weights array, +#' +#' @references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with at least dimensions (time, member) for a zonal +#' variable or dimensions (time, member, lat, lon) for a spatial +#' variable, as 'time' the spatial dimension by default. +#' @param aweights Normalized weights array with at least dimensions +#' (time, member), when 'time' is the temporal dimension as default. +#' @param time_dim_name A character string indicating the name of the +#' temporal dimension, by default 'time'. +#' +#' @return ProbsWeighted() returns an array with at least two or four dimensions +#' depending if the variable is zonal (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. +#' +#' @import multiApply +#' +#' @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) +#' res <- ProbsWeighted(var_exp, aweights) +#' dim(res) +#' # time tercil +#' # 2 3 +#' # 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) +#' res <- ProbsWeighted(var_exp, aweights) +#' dim(res) +#' # time tercil lat lon +#' # 2 3 2 3 +#' @export +ProbsWeighted <- function(var_exp, aweights, time_dim_name = 'time') { + + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!is.array(var_exp)) { + stop("Parameter 'var_exp' must be an array.") + } + if (!is.array(aweights)) { + stop("Parameter 'aweights' must be an array.") + } + if (is.null(names(dim(var_exp))) || is.null(names(dim(aweights)))) { + stop("Parameters 'var_exp' and 'aweights'", + " should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(var_exp)))) { + stop("Parameter 'var_exp' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(aweights)))) { + stop("Parameter 'aweights' must have temporal dimension.") + } + if(!('member' %in% names(dim(var_exp)))) { + stop("Parameter 'var_exp' must have temporal dimension.") + } + if(!('member' %in% names(dim(aweights)))) { + stop("Parameter 'aweights' must have temporal dimension.") + } + if (dim(var_exp)[time_dim_name] != dim(aweights)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'var_exp' and 'aweights' must be equals.") + } + if (dim(var_exp)['member'] != dim(aweights)['member']) { + stop("Length of dimension 'member' ", + "of parameter 'var_exp' and 'aweights' must be equals.") + } + + res <- Apply(list(var_exp, aweights), + target_dims = list(c(time_dim_name,'member'), c(time_dim_name,'member')), + fun = .ProbsWeighted, time_dim_name)$output1 + return(res) +} + +#' Atomic ProbsWeighted +#' @param var_exp Variable (e.g. precipitation, temperature, NAO index) +#' array from a SFS with a temporal dimension, +#' by default 'time', and dimension 'member'. +#' @param aweights Normalized weights array with a temporal dimension, +#' by default 'time', and dimension 'member' +#' @param time_dim_name A character string indicating the name of the +#' temporal dimension, by default 'time'. +#' @return .ProbsWeighted returns an array of with a temporal dimension, +#' as default 'time', and 'tercil' dimension, containing the probabilities +#' for each tercile computing with weighted members. +#' The firt tercil is the lower tercile, the second is the normal tercile and +#' the third is the upper tercile. +#' @examples +#' # Example +#' var_exp <- 1 : 8 +#' dim(var_exp) <- c(stime = 2, member = 4) +#' aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.3, 0.3) +#' dim(aweights) <- c(stime = 2, member = 4) +#' res <- .ProbsWeighted(var_exp, aweights, time_dim_name = 'stime') +#' dim(res) +#' # stime tercil +#' # 2 3 +#' @noRd +.ProbsWeighted <- function(var_exp, aweights, time_dim_name = 'time') { + # computing terciles + #names(dim(var_exp)) <- c('time','member') + #names(dim(aweights)) <- c('time','member') + terciles_exp <- WeightTerciles(var_exp, aweights, time_dim_name) + lowerTercile <- terciles_exp$lowerTercile + upperTercile <- terciles_exp$upperTercile + + # Probabilities + aTerciles <- Apply(list(var_exp), target_dims = list('member'), + fun = Data2Tercil, lowerTercile, upperTercile)$output1 + + pos <- match(names(dim(aTerciles)), c(time_dim_name,'member')) + aTerciles <- aperm(aTerciles,pos) + names(dim(aTerciles)) <- c(time_dim_name,'member') + + probTercile <- array(NA, dim = c(dim(var_exp)[time_dim_name], tercil = 3)) + for (idTercil in 1:3){ + probTercile[,idTercil] <- Apply(list(aTerciles, aweights), + target_dims = list('member','member'), + fun = WeightTercil2Prob, idTercil)$output1 + } + return(probTercile) +} + +# Auxiliar function to compute in which tercile is a data value +Data2Tercil <- function(x,lt,ut) { + y <- rep(2,length(x)) + y[x <= lt] <- 1 + y[x >= ut] <- 3 + if (lt == ut) { + warning("The upper and lower terciles are equals") + } + dim(y) <- c(member = length(x)) + return (y) +} + +# Auxiliar function to convers weighted terciles to probabilities +WeightTercil2Prob <- function(aTerciles, aWeights, idTercil) { + return(sum(aWeights[which(aTerciles == idTercil)])) +} + +# Auxiliar function to compute weighted terciles +WeightTerciles <- function(data, aweights, time_dim_name = 'time') { + namesdimdata <- names(dim(data)) + namesdimaweights <- names(dim(aweights)) + pos <- match(namesdimdata, namesdimaweights) + aweights <- aperm(aweights, pos) + names(dim(aweights)) <- namesdimdata + vectorData <- as.vector(data) + vectorWeights <- as.vector(aweights/dim(aweights)[time_dim_name]) # normalized + lSortData <- sort(vectorData,index.return=TRUE) + indSort <- lSortData$ix # index asociated to weight + # corresponding for this data + dataSort <- lSortData$x + # Adding normalized weights. When 1/3 is reached, the data value + # is lower tercile and when 2/3 is reached, it is the upper tercile. + sumWeights <- 0 + ilowerTercile <- 0 + while ((sumWeights < 1/3) & (ilowerTercile < length(aweights))){ + ilowerTercile<- ilowerTercile +1 + sumWeights <- sumWeights + vectorWeights[indSort[ilowerTercile]] + } + if (ilowerTercile == 1){ + lowerTercile <- dataSort[ilowerTercile] + } else { + lowerTercile <- (dataSort[ilowerTercile]+ + dataSort[ilowerTercile-1])/2 + } + sumWeights <- 0 + iupperTercile <- 0 + while ((sumWeights < 2/3) & (iupperTercile < length(aweights))){ + iupperTercile<- iupperTercile +1 + sumWeights <- sumWeights + vectorWeights[indSort[iupperTercile]] + } + if (iupperTercile == 1) { + upperTercile <- dataSort[iupperTercile] + } else { + upperTercile <- (dataSort[iupperTercile]+ + dataSort[iupperTercile-1])/2 + } + return(list(lowerTercile = lowerTercile, upperTercile = upperTercile)) +} diff --git a/R/PDFBest.R b/R/PDFBest.R new file mode 100644 index 00000000..078692a0 --- /dev/null +++ b/R/PDFBest.R @@ -0,0 +1,878 @@ +#' Computing the Best Index PDFs combining Index PDFs from two SFSs +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) obtained to combining the Index PDFs for two SFSs, +#' the Best Index estimation. +#' +#'@references Regionally improved seasonal forecast of precipitation through +#'Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param index_hind1 Index (e.g. NAO index) array from a SFS (named SFS1) +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' at the same time. +#'@param index_hind2 Index (e.g. NAO index) array from a SFS (named SFS2) +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_fcst1 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS1 +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_fcst2 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS2 +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param method_BC A character vector of maximun length 2 indicating the bias correction +#'methodology to be applied on each SFS. If it is 'none' or any of its elements is 'none', +#'the bias correction won't be applied. Available methods developped are "ME" and "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#' @return PDFBest() returns an array +#' with at least a temporal dimension, by default 'time' and dimension 'statistic', +#' equal to 2. +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDFs. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDFs. +#' If index_fcst1 and/or index_fcst2 are null, returns the values for hindcast period. +#' Otherwise, it returns the values for a forecast year. +#'@import multiApply +#' +#'@examples +#' # Example 1 for the PDFBest function +#' index_obs<- rnorm(10, sd = 3) +#' dim(index_obs) <- c(time = 5, season = 2) +#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) +#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) +#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +#' method_BC <- 'ME' +#' res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, +#' index_fcst2, method_BC) +#' dim(res) +#' time statistic season +#' 1 2 2 +#' # Example 2 for the PDFBest function +#' index_obs<- rnorm(10, sd = 3) +#' dim(index_obs) <- c(time = 5, season = 2) +#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) +#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) +#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +#' method_BC <- c('LMEV', 'ME') +#' res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) +#' dim(res) +#'@export + +PDFBest <- function(index_obs, index_hind1, index_hind2, + index_fcst1 = NULL, index_fcst2 = NULL, method_BC = 'none', + time_dim_name = 'time', na.rm = FALSE) { + + if (!is.logical(na.rm)) { + stop("The parameter 'na.rm' must be a logical value.") + } + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!is.character(method_BC) || !is.vector(method_BC)){ + stop("Parameter 'method_BC' must be a character vector.") + } + if (!(length(method_BC) == 1 || length(method_BC) == 2)){ + stop("Length of parameter 'method_BC' must be 1 or 2.") + } + if(!all(method_BC %in% c('ME', 'LMEV', 'none'))){ + stop("Elements of parameter 'method_BC' must be equals to + 'none, 'ME' or 'LMEV'") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (!is.array(index_hind1)) { + stop("Parameter 'index_hind1' must be an array.") + } + if (!is.array(index_hind2)) { + stop("Parameter 'index_hind2' must be an array.") + } + if (is.null(names(dim(index_hind1))) || + is.null(names(dim(index_obs))) || + is.null(names(dim(index_hind2)))) { + stop("Parameters 'index_obs', 'index_hind1' and 'index_hind2' ", + "should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind1)))) { + stop("Parameter 'index_hind1' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind2)))) { + stop("Parameter 'index_hind2' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs', 'index_hind1' and 'index_hind2' ", + "must be greater than 2.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind1)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_obs' and 'index_hind1' must be equals.") + } + if (dim(index_hind1)[time_dim_name] != dim(index_hind2)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_hind1' and 'index_hind2' must be equals.") + } + if('member' %in% names(dim(index_hind1)) & + 'statistic' %in% names(dim(index_hind1))) { + stop("Parameter 'index_hind1' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if('member' %in% names(dim(index_hind2)) & + 'statistic' %in% names(dim(index_hind2))) { + stop("Parameter 'index_hind2' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if(!('member' %in% names(dim(index_hind1))) & + !('statistic' %in% names(dim(index_hind1)))) { + stop("Parameter 'index_hind1' must have dimension ", + "'member' or 'statistic'") + } + if(!('member' %in% names(dim(index_hind2))) & + !('statistic' %in% names(dim(index_hind2)))) { + stop("Parameter 'index_hind2' must have dimension ", + "'member' or 'statistic'") + } + if ('member' %in% names(dim(index_hind1))){ + if (dim(index_hind1)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind1' must be greater than 1.") + } + } + if ('member' %in% names(dim(index_hind2))){ + if (dim(index_hind2)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind2' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind1))){ + if (dim(index_hind1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind1' must be equal to 2.") + } + } + if ('statistic' %in% names(dim(index_hind2))){ + if (dim(index_hind2)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind2' must be equal to 2.") + } + } + if (!is.null(index_fcst1)){ + if (!is.array(index_fcst1)) { + stop("Parameter 'index_fcst1' must be an array.") + } + if (is.null(names(dim(index_fcst1)))){ + stop("Parameters 'index_fcst1' should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_fcst1)))) { + stop("Parameter 'index_fcst1' must have temporal dimension.") + } + if (dim(index_fcst1)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst1' must be equal to 1.") + } + if((length(names(dim(index_hind1))) != length(names(dim(index_fcst1)))) + || (!all(names(dim(index_hind1)) == names(dim(index_fcst1))))){ + stop("Dimension names of parameter 'index_hind1' and 'index_fcst1' ", + "must be equals.") + } + if ('member' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst1' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst1' must be equal to 2.") + } + } + } + if (!is.null(index_fcst2)){ + if (!is.array(index_fcst2)) { + stop("Parameter 'index_fcst2' must be an array.") + } + if (is.null(names(dim(index_fcst2)))){ + stop("Parameters 'index_fcst2' should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_fcst2)))) { + stop("Parameter 'index_fcst2' must have temporal dimension.") + } + if (dim(index_fcst2)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst2' must be equal to 1.") + } + if((length(names(dim(index_hind2))) != length(names(dim(index_fcst2)))) + || (!all(names(dim(index_hind2)) == names(dim(index_fcst2))))){ + stop("Dimension names of parameter 'index_hind2' and 'index_fcst2' ", + "must be equals.") + } + if ('member' %in% names(dim(index_fcst2))){ + if (dim(index_fcst2)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst2' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst1' must be equal to 2.") + } + } + } + + if (all(method_BC == 'none')){ + method_BC1 <- 'ME' + method_BC2 <- 'ME' + bc_dataset1 <- FALSE + bc_dataset2 <- FALSE + } else if (length(method_BC) == 1){ + method_BC1 <- method_BC + method_BC2 <- method_BC + bc_dataset1 <- TRUE + bc_dataset2 <- TRUE + } else { + if(method_BC[1] == 'none'){ + method_BC1 <- 'ME' + bc_dataset1 <- FALSE + } else { + method_BC1 <- method_BC[1] + bc_dataset1 <- TRUE + } + if(method_BC[2] == 'none'){ + method_BC2 <- 'ME' + bc_dataset2 <- FALSE + } else { + method_BC2 <- method_BC[2] + bc_dataset2 <- TRUE + } + } + + pdf_hind1 <- PDFIndexHind(index_hind1, index_obs, method = method_BC1, + time_dim_name = time_dim_name, na.rm = na.rm) + pdf_hind2 <- PDFIndexHind(index_hind2, index_obs, method = method_BC2, + time_dim_name = time_dim_name, na.rm = na.rm) + if (is.null(index_fcst1) || is.null(index_fcst2)){ + pdf_best <- Apply(list(pdf_hind1, pdf_hind2), + target_dims = list('statistic', + 'statistic'), + fun = .PDFBest, + bc_dataset1, bc_dataset2) + } else { + pdf_fcst1 <- PDFIndexFcst(index_hind1, index_obs, index_fcst1, + method_BC1, time_dim_name = time_dim_name, + na.rm = na.rm) + pdf_fcst2 <- PDFIndexFcst(index_hind2, index_obs, index_fcst2, + method_BC2, time_dim_name = time_dim_name, + na.rm = na.rm) + pdf_best <- Apply(list(pdf_fcst1, pdf_fcst2), + target_dims = list('statistic', + 'statistic'), + fun = .PDFBest, + bc_dataset1, bc_dataset2) + } + Dim <- names(dim(index_hind1)) + if('member' %in% Dim){ + pos_aux <- match('member', Dim) + Dim[pos_aux] <- 'statistic' + } + pos <- match(Dim, names(dim(pdf_best$output1))) + res <- aperm(pdf_best$output1,pos) + names(dim(res)) <- Dim + return(res) +} + +#' Atomic PDFBest +#'@param pdf_1 Statistics array for the first SFS PDF with one dimension +#' 'statistic' equal to 4. +#' @param pdf_2 Statistics array for the second SFS PDF with one dimension +#' 'statistic' equal to 4. +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDFs for the first SFS has been computed +#' with bias corrected. +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDFs for the second SFS has been computed +#' with bias corrected. +#' +#' @return .PDFBest returns an array with dimensions (statistic = 2). +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDF. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDF. +#' +#' @examples +#' # Example for the Atomic PDFBest function +#' pdf_1 <- c(1.1,0.6,1.6,0.9) +#' dim(pdf_1) <- c(statistic = 4) +#' pdf_2 <- c(1,0.5,1.5,0.8) +#' dim(pdf_2) <- c(statistic = 4) +#' res <- .PDFBest(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +#' str(res) +#' dim(res) +#' # statistic +#' # 2 +#' @noRd +.PDFBest <- function(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { + if(bc_dataset1){ + # apply bias correction to model 1 + mean_1 <- pdf_1[3] + sigma_1 <- pdf_1[4] + } else{ + # not bias correction to model 1 + mean_1 <- pdf_1[1] + sigma_1 <- pdf_1[2] + } + if(bc_dataset2){ + # apply bias correction to model 2 + mean_2 <- pdf_2[3] + sigma_2 <- pdf_2[4] + } else { + # not bias correction to model 2 + mean_2 <- pdf_2[1] + sigma_2 <- pdf_2[2] + } + a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) + a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) + pdf_mean <- a_1*mean_1 + a_2*mean_2 + pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) + data <- c(pdf_mean, pdf_sigma) + dim(data) <- c(statistic = 2) + return(data) +} + +#'Computing the Index PDFs for a dataset of SFSs for a hindcats period. +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from SFSs for a hindcast period. +#' +#'@references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_hind Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#'@return an array with at least two dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @examples +#' # Example for the PDFIndexHind function +#' # Example 1 +#' index_obs <- 1 : (5 * 3 ) +#' dim(index_obs) <- c(time = 5, season = 3) +#' index_hind <- 1 : (5 * 4 * 3) +#' dim(index_hind) <- c(time = 5, member = 4, season = 3) +#' res <- PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic season +#' # 5 4 3 +#' # Example 2 +#' index_obs <- 1 : (5 * 3) +#' dim(index_obs) <- c(time = 5, season = 3) +#' index_hind <- 1 : (5 * 2 * 3) +#' dim(index_hind) <- c(time = 5, statistic = 2, season = 3) +#' res <- PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic season +#' # 5 4 3 +#'@noRd +PDFIndexHind <- function(index_hind, index_obs, method ='ME', + time_dim_name = 'time', na.rm = FALSE) { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!(method %in% c('ME', 'LMEV'))){ + stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.array(index_hind)) { + stop("Parameter 'index_hind' must be an array.") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (is.null(names(dim(index_hind))) || is.null(names(dim(index_obs)))) { + stop("Parameters 'index_hind' and 'index_obs'", + " should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { + stop("Length of the temporal dimensions ", + "of the parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs' and 'index_hind' must be greater than 2.") + } + if('member' %in% names(dim(index_hind)) & + 'statistic' %in% names(dim(index_hind))) { + stop("Parameter 'index_hind' must have at least dimension ", + "'member' or 'statistic', not 'member' and 'statistic' together.") + } + + if(!('member' %in% names(dim(index_hind))) & + !('statistic' %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have dimension ", + "'member' or 'statistic'") + } + + if ('member' %in% names(dim(index_hind))){ + if (dim(index_hind)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind))){ + if (dim(index_hind)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind' must be equal to 2.") + } + } + + if ('member' %in% names(dim(index_hind))) { + PDFstatistics <- Apply(list(index_hind, index_obs), + target_dims = list(c(time_dim_name, 'member'), time_dim_name), + fun = .PDFIndexHind, method, time_dim_name, na.rm) + } else if ('statistic' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs), + target_dims = list(c(time_dim_name, 'statistic'), time_dim_name), + fun = .PDFIndexHind, method, time_dim_name, na.rm) + } + return(PDFstatistics$output1) +} + + + +#' Atomic PDFIndexHind +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) or (time, statistic) for a hindcast period. +#' The temporal dimension, by default 'time', must be greater +#' than 2. +#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' or reanalysis with dimension (time). The temporal dimension, +#' by default 'time', must be greater than 2. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the hindcast period. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the hindcast period. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the hindcast period. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the hindcast period. +#' @import multiApply +#' @importFrom verification verify +#' @examples +#' # Example for the Atomic PDFIndexHind function +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_hind <- 1 : (10 * 3) +#' dim(index_hind) <- c(time = 10, member = 3) +#' res <- .PDFIndexHind(index_hind, index_obs) +#' str(res) +#' # num [1:10, 1:4] 11 12 13 14 15 16 17 18 19 20 ... +#' dim(res) +#' # time statistic +#' # 10 4 +#' @noRd +.PDFIndexHind <- function(index_hind, index_obs, method = 'ME', + time_dim_name = 'time', na.rm = FALSE) { + dimnameshind <- names(dim(index_hind)) + + if ('member' %in% dimnameshind) { + pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) + index_hind <- aperm(index_hind, pos) + names(dim(index_hind)) <- c(time_dim_name, 'member') + hind_ens_mean <- Apply(list(index_hind), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + hind_ens_sd <- Apply(list(index_hind), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else if ('statistic' %in% dimnameshind){ + pos <- match(c(time_dim_name, 'statistic'), names(dim(index_hind))) + index_hind <- aperm(index_hind,pos) + names(dim(index_hind)) <- c(time_dim_name, 'statistic') + hind_ens_mean <- index_hind[,1] + hind_ens_sd <- index_hind[,2] + } else { + stop("Wrong dimension of parameter 'index_hind'.") + } + error <- hind_ens_mean - index_obs + pdfnotbc_mean <- hind_ens_mean + ind <- 1 : length(index_obs) + pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(hind_ens_mean[-x], + index_obs[-x], + na.rm)})) + + if (method == 'ME') { + pdfbc <- unlist(lapply(ind, function(x) {MEBC(hind_ens_mean[-x], + index_obs[-x], na.rm)})) + pdfbc_mean <- hind_ens_mean - pdfbc + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x], + na.rm)})) + } else if (method == 'LMEV') { + # linear model error-variance + hind_ens_var <- hind_ens_sd^2 + dim_hind <- names(dim(index_hind)) + dim_obs <- names(dim(index_obs)) + coeff <- sapply(ind, function(x) {LMEV(index_hind[-x,], index_obs[-x], + dim_hind, dim_obs, na.rm)}) + pdfbc_mean <- hind_ens_mean - (unlist(coeff['coeff1',]) + + unlist(coeff['coeff2',])*hind_ens_var) + + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x], + na.rm)})) + } else { + stop("Error: Parameter 'method' is wrong") + } + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + +#' Computing the Index PDFs for a dataset of SFSs for a forecast year. +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from SFSs for a forecast year. +#' +#'@references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_hind Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param index_fcst Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#'@return an array with at least two dimensions (time = 1, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @examples +#' # Example for the PDFIndexFcst function +#' index_fcst <- 1 : (8 * 4) +#' dim(index_fcst) <- c(time = 1, member = 8, season = 4) +#' index_obs<- 1 : (5 * 4) +#' dim(index_obs) <- c(time = 5, season = 4) +#' index_hind <- 1 : (5 * 4 * 4) +#' dim(index_hind) <- c(time = 5, member = 4, season = 4) +#' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic season +#' # 1 4 4 +#' +#'@noRd +PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method ='ME', time_dim_name = 'time', + na.rm = FALSE) { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if(!(method %in% c('ME', 'LMEV'))){ + stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.array(index_hind)) { + stop("Parameter 'index_hind' must be an array.") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (!is.array(index_fcst)) { + stop("Parameter 'index_fcst' must be an array.") + } + if (is.null(names(dim(index_hind))) || + is.null(names(dim(index_obs))) || + is.null(names(dim(index_fcst)))) { + stop("Parameters 'index_hind', 'index_obs' and 'index_fcst' ", + "should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_fcst)))) { + stop("Parameter 'index_fcst' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs' and 'index_hind' must be greater than 2.") + } + if (dim(index_fcst)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst' must be equal to 1.") + } + if((length(names(dim(index_hind))) != length(names(dim(index_fcst)))) + || (!all(names(dim(index_hind)) == names(dim(index_fcst))))){ + stop("Dimension names of parameter 'index_hind' and 'index_fcst' ", + "must be equals.") + } + if('member' %in% names(dim(index_hind)) & + 'statistic' %in% names(dim(index_hind))) { + stop("Parameter 'index_hind' and 'index_fcst' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if(!('member' %in% names(dim(index_hind))) & + !('statistic' %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' and 'index_fcst' must have dimension ", + "'member' or 'statistic'") + } + if ('member' %in% names(dim(index_hind))){ + if (dim(index_hind)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind' must be greater than 1.") + } + } + if ('member' %in% names(dim(index_fcst))){ + if (dim(index_fcst)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind))){ + if (dim(index_hind)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind' must be equal to 2.") + } + } + if ('statistic' %in% names(dim(index_fcst))){ + if (dim(index_fcst)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst' must be equal to 2.") + } + } + + if ('member' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c(time_dim_name, 'member'), + time_dim_name, + c(time_dim_name,'member')), + fun = .PDFIndexFcst, method, time_dim_name, na.rm) + } else if ('statistic' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c(time_dim_name, 'statistic'), + time_dim_name, + c(time_dim_name,'statistic')), + fun = .PDFIndexFcst, method, time_dim_name, na.rm) + } + return(PDFstatistics$output1) +} + +#' Atomic PDFIndexFcst +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) or (time, statistic) for a hindcast period. +#' The temporal dimension, by default 'time', must be greater +#' than 2. +#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' or reanalysis with dimension (time). The temporal dimension, +#' by default 'time', must be greater than 2. +#' @param index_fcst Index (e.g. NAO index) array from SFSs +#' with dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, +#' the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexFcst returns an array with dimensions (time = 1, statistic=4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the forecast year. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the forecast year. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the forecast year. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the forecast year. +#' @import multiApply +#' @importFrom verification verify +#' @examples +#' # Example 1 for the Atomic PDFIndexFcst function +#' index_fcst <- 1 : (1 * 6) +#' dim(index_fcst) <- c(time = 1, member = 6) +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_hind <- 1 : (10 * 3) +#' dim(index_hind) <- c(time = 10, member = 3) +#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic +#' # 1 4 +#' # Example 2 for the Atomic PDFIndexFcst function +#' index_fcst <- 1 : (1 * 2) +#' dim(index_fcst) <- c(time = 1, statistic = 2) +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = 10) +#' index_hind <- 1 : (10 * 2) +#' dim(index_hind) <- c(time = 10, statistic = 2) +#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic +#' # 1 4 +#' @noRd +.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method = 'ME', + time_dim_name = 'time', + na.rm = FALSE) { + dimnameshind <- names(dim(index_hind)) + if ('member' %in% dimnameshind) { + pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) + index_hind <- aperm(index_hind, pos) + names(dim(index_hind)) <- c(time_dim_name, 'member') + exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', + fun = mean)$output1 + exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', + fun = sd)$output1 + } else { + pos <- match(c(time_dim_name,'statistic'), names(dim(index_fcst))) + index_fcst <- aperm(index_fcst,pos) + names(dim(index_fcst)) <- c(time_dim_name, 'statistic') + exp_fcst_ens_mean <- index_fcst[,1] + exp_fcst_ens_sd <- index_fcst[,2] + } + data_hind <- .PDFIndexHind(index_hind, index_obs, method, time_dim_name, na.rm) + exp_hind_ens_mean <- data_hind[,1] + pdfnotbc_mean <- exp_fcst_ens_mean + pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs, na.rm) + if (method == 'ME') { + pdfbc <- MEBC(exp_hind_ens_mean, index_obs, na.rm) + pdfbc_mean <- exp_fcst_ens_mean - pdfbc + } else if (method == 'LMEV') { + # linear model error-variance + exp_fcst_ens_var <- exp_fcst_ens_sd^2 + dim_hind <- names(dim(index_hind)) + dim_obs <- names(dim(index_obs)) + coeff <- LMEV(index_hind, index_obs, dim_hind, dim_obs, time_dim_name, na.rm) + pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) + } else { + stop("Parameter 'method' must be 'ME' or 'LMEV'.") + } + pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs, na.rm) + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + diff --git a/R/WeightIndex.R b/R/WeightIndex.R new file mode 100644 index 00000000..261d286e --- /dev/null +++ b/R/WeightIndex.R @@ -0,0 +1,104 @@ +#'Computing the weights for SFSs using the Best Index PDFs. +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the +#' normalized weights for each member of each SFS or dataset using the +#' pdfs indicated by the parameter 'pdf_weight' ( for instance the Best Index +#' estimation obtained using the 'PDFBest' function. +#' +#'@references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_weight Index (e.g. NAO index) array, from a dataset of SFSs +#' for a period of years, with at least dimensions 'member'. +#' Additional dimensions, for instance, a temporal dimension as 'time', +#' must have the same lenght in both parameters, +#' 'index_weight' and 'pdf_weight'. +#' @param pdf_weight Statistics array to define a Gaussian PDF with at least +#' dimensions 'statistic'. +#' The firt statistic is the parameter 'mean' of the PDF and +#' the second statistic is the parameter 'standard deviation' of the PDF. +#' +#'@return WeightIndex() returns a normalized weights array with the same +#' dimensions that index_weight. +#' +#'@import multiApply +#' +#'@examples +#' # Example for the WeightIndex function +#' index_weight <- 1 : (10 * 3 * 5 * 1) +#' dim(index_weight) <- c(sdate = 5, dataset = 3, member = 5, season = 1) +#' pdf_weight <- 1 : (10 * 3 * 2 * 1) +#' dim(pdf_weight) <- c(sdate = 10, dataset = 3, statistic = 2, season = 1) +#' res <- WeightIndex(index_weight, pdf_weight) +#' dim(res) +#' # sdate dataset member season +#' # 10 3 5 1 +#' +#'@export +WeightIndex <- function(index_weight, pdf_weight) { + + if (!is.array(index_weight)) { + stop("Parameter 'index_weight' must be an array.") + } + if (!is.array(pdf_weight)) { + stop("Parameter 'pdf_weight' must be an array.") + } + if (is.null(names(dim(index_weight))) || is.null(names(dim(pdf_weight)))) { + stop("Parameters 'index_weight' and 'pdf_weight'", + " should have dimmension names.") + } + if (dim(pdf_weight)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of the parameter 'pdf_weight' must be equal to 2.") + } + if (dim(pdf_weight)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of the parameter 'pdf_weight' must be equal to 2.") + } + aweights <- Apply(list(index_weight, pdf_weight), + target_dims = list('member', 'statistic'), + fun = .WeightIndex)$output1 + + dimnames <- names(dim(index_weight)) + pos <- match(dimnames, names(dim(aweights))) + aweights <- aperm(aweights, pos) + names(dim(aweights)) <- dimnames + return(aweights) +} + +#' Atomic WeightIndex +#' @param index_weight Index (e.g. NAO index) array from a SFS with dimensions +#' (member) +#' @param pdf_weight Statistics array to define a Gaussian PDF with dimensions +#' (statistic = 2). +#' The firt statistic is the parameter 'mean' of the PDF and +#' the second statistic is the parameter 'standard deviation' of the PDF. +#' @return .WeightIndex returns an array of with dimensions (member), +#' the normalized weights for each member of a SFS using a Best NAO PDF. +#' @examples +#' # Example for the Atomic WeightIndex function +#' index_weight <- c(1.3,3,-1) +#' dim(index_weight) <- c(member = 3) +#' pdf_weight <- c(1.5,0.8) +#' dim(pdf_weight) <- c(statistic = 2) +#' res <- .WeightIndex(index_weight, pdf_weight) +#' dim(res) +#' # member +#' # 3 +#' @noRd +.WeightIndex <- function(index_weight, pdf_weight) { + aweights <- apply(index_weight, 1, dnorm, mean = pdf_weight[1], sd = pdf_weight[2]) + dim(aweights) <- dim(index_weight) + sumWeights <- sum(aweights) + aweightsNorm <- apply(aweights, 1, NormWeight, sumWeights) + dim(aweightsNorm) <- dim(index_weight) + return(aweightsNorm) +} + +# Auxiliar function to normalize a weight value +NormWeight <- function(weight, sumWeights) { + return(weight/sumWeights) +} diff --git a/R/zzz.R b/R/zzz.R index b9328692..f9aa6c1c 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -53,7 +53,7 @@ Sigma_BC <- function(exp, obs, na.rm = FALSE) { # called 'LMEV' to correct the bias in the Best NAO weighting # methogology LMEV <- function(exp, obs, dim_exp, - dim_obs, na.rm = FALSE) { + dim_obs, time_dim_name = 'time', na.rm = FALSE) { names(dim(exp)) <- dim_exp names(dim(obs)) <- dim_obs if (any(names(dim(exp)) == 'member')) { @@ -65,7 +65,7 @@ LMEV <- function(exp, obs, dim_exp, if(na.rm & any(is.na(index_exp))){ print("Some value are NA") } - pos <- match(c('time','statistic'), names(dim(exp))) + pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) exp <- aperm(exp,pos) exp_ens_mean <- exp[,1] exp_ens_sd <- exp[,2] diff --git a/man/CST_Anomaly.Rd b/man/CST_Anomaly.Rd index b214a31a..e1c31f0c 100644 --- a/man/CST_Anomaly.Rd +++ b/man/CST_Anomaly.Rd @@ -53,12 +53,13 @@ str(anom3) anom4 <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = FALSE) str(anom4) -} -\seealso{ -\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} } \author{ Perez-Zanon Nuria, \email{nuria.perez@bsc.es} Pena Jesus, \email{jesus.pena@bsc.es} } +\seealso{ +\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} +} + diff --git a/man/CST_BiasCorrection.Rd b/man/CST_BiasCorrection.Rd index a1b415fb..e8a82af0 100644 --- a/man/CST_BiasCorrection.Rd +++ b/man/CST_BiasCorrection.Rd @@ -35,9 +35,10 @@ attr(obs, 'class') <- 's2dv_cube' a <- CST_BiasCorrection(exp = exp, obs = obs) str(a) } -\references{ -Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) -} \author{ Verónica Torralba, \email{veronica.torralba@bsc.es} } +\references{ +Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) +} + diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd index 58b12617..396d87ca 100644 --- a/man/CST_Calibration.Rd +++ b/man/CST_Calibration.Rd @@ -25,12 +25,13 @@ c(exp, obs) \%<-\% areave_data exp_calibrated <- CST_Calibration(exp = exp, obs = obs) str(exp_calibrated) } +\author{ +Verónica Torralba, \email{veronica.torralba@bsc.es} +} \references{ Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x } \seealso{ \code{\link{CST_Load}} } -\author{ -Verónica Torralba, \email{veronica.torralba@bsc.es} -} + diff --git a/man/CST_EMBestIndexWeighting.Rd b/man/CST_EMBestIndexWeighting.Rd deleted file mode 100644 index faf64c06..00000000 --- a/man/CST_EMBestIndexWeighting.Rd +++ /dev/null @@ -1,57 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_EMBestIndexWeighting.R -\name{CST_EMBestIndexWeighting} -\alias{CST_EMBestIndexWeighting} -\title{Computing the weighted ensemble means for SFSs of a CSTools object.} -\usage{ -CST_EMBestIndexWeighting(var_exp, weights_index_exp) -} -\arguments{ -\item{var_exp}{An object of the class 's2dv_cube' containing the variable -(e.g. precipitation, temperature, NAO index) array. -The var_exp object is expected to have an element named \code{$data} with -three dimensions (time, dataset, member) for a zonal variable -or five dimensions (time, dataset, member, lat, lon) for a spatial variable.} - -\item{weights_index_exp}{An object of the class 's2dv_cube' containing -the normalized weights array. -The weights_index_exp object is expected to have an element named -\code{$data} with dimensions (time, dataset, member, season).} -} -\value{ -CST_EMBestIndexWeighting() returns a CSTools object (i.e., of the -class 's2dv_cube'). -This object has at least an element named \code{$data} -with three or five dimensions depending if the variable is zonal -(as e.g. NAO index)(time, dataset, season) or it is spatial variable -(as e.g. precipitation or temperature) (time, dataset, season, lat, lon), -containing the ensemble means computing with weighted members. -} -\description{ -This function implements the computation to obtain the weighted -ensemble means for SFSs through Best estimation of one index or driver of -climate variability (as e.g., the North Atlantic Oscillation (NAO)) and -accepts a CSTools object (an object of the class 's2dv_cube') as input. -} -\examples{ -var_exp <- 1 : (4 * 2 * 3 * 3 * 2) -dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) -weights_index_exp <- runif(4 * 2 * 3, 0.0001, 0.9999) -dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 1) -var_exp <- list(data = var_exp) -weights_index_exp <- list(data = weights_index_exp) -class(var_exp) <- 's2dv_cube' -class(weights_index_exp) <- 's2dv_cube' -res_CST <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) -dim(res_CST$data) -# time dataset season lat lon -# 4 2 1 3 2 -} -\references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/CST_Load.Rd b/man/CST_Load.Rd index bf03ba42..1fee022c 100644 --- a/man/CST_Load.Rd +++ b/man/CST_Load.Rd @@ -47,3 +47,4 @@ obs <- CSTools::lonlat_data$obs \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} } + diff --git a/man/CST_MultiMetric.Rd b/man/CST_MultiMetric.Rd index 8e3ce593..079a5588 100644 --- a/man/CST_MultiMetric.Rd +++ b/man/CST_MultiMetric.Rd @@ -37,14 +37,15 @@ c(ano_exp, ano_obs) \%<-\% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb a <- CST_MultiMetric(exp = ano_exp, obs = ano_obs) str(a) } +\author{ +Mishra Niti, \email{niti.mishra@bsc.es} + +Perez-Zanon Nuria, \email{nuria.perez@bsc.es} +} \references{ Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dverification]{Corr}}, \code{\link[s2dverification]{RMS}}, \code{\link[s2dverification]{RMSSS}} and \code{\link{CST_Load}} } -\author{ -Mishra Niti, \email{niti.mishra@bsc.es} -Perez-Zanon Nuria, \email{nuria.perez@bsc.es} -} diff --git a/man/CST_MultivarRMSE.Rd b/man/CST_MultivarRMSE.Rd index 24af608c..685eaf77 100644 --- a/man/CST_MultivarRMSE.Rd +++ b/man/CST_MultivarRMSE.Rd @@ -56,9 +56,10 @@ weight <- c(1, 2) a <- CST_MultivarRMSE(exp = ano_exp, obs = ano_obs, weight = weight) str(a) } -\seealso{ -\code{\link[s2dverification]{RMS}} and \code{\link{CST_Load}} -} \author{ Deborah Verfaillie, \email{deborah.verfaillie@bsc.es} } +\seealso{ +\code{\link[s2dverification]{RMS}} and \code{\link{CST_Load}} +} + diff --git a/man/CST_PDFBest.Rd b/man/CST_PDFBest.Rd deleted file mode 100644 index 7318956d..00000000 --- a/man/CST_PDFBest.Rd +++ /dev/null @@ -1,68 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_PDFBest.R -\name{CST_PDFBest} -\alias{CST_PDFBest} -\title{Computing the Best Index PDFs combining Index PDFs from two SFSs -of a CSTools object.} -\usage{ -CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) -} -\arguments{ -\item{pdf_exp}{An object of the class 's2dv_cube' -containing the parameters of the Index PDFs from two SFSs for a period -to combine and obtain the Best estimation. -The index_hind object is expected to have an element named \code{$data} -with dimensions (time, dataset = 2, member = 1, statistic = 4, season).} - -\item{bc_dataset1}{Logical (default = TRUE). -If TRUE the Index PDFs for the first SFS or dataset has been computed -with bias corrected.} - -\item{bc_dataset2}{Logical (default = TRUE). -If TRUE the Index PDFs for the second SFS or dataset has been computed -with bias corrected.} -} -\value{ -CST_PDFBest() returns a CSTools object (i.e., of the -class 's2dv_cube'). -This object has at least two elements: i) an element named \code{$data} -with dimensions (time, statistic = 2, season) and ii) an element -named \code{$statistic} with the names of 'statistic' dimension -of the first element ("mean", "sigma"). -The firt statistic is the parameter 'mean' of the PDF -The second statistic is the parameter 'standard deviation' of the PDF -} -\description{ -This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from the forecast of SFSs -and accepts a CSTools object (an object of the class 's2dv_cube' as input. -} -\examples{ -pdf_exp <- c(1: (10 * 2 * 1 * 4 * 1)) -dim(pdf_exp) <- c(time = 10, dataset = 2, member = 1, statistic = 4, -season = 1) -pdf_exp <- list(data = pdf_exp, Datasets = c("ECMWF-S5","UKMO-S13"), - season = c('DJF'), Dates = list(start = 1997:2006)) -class(pdf_exp) <- 's2dv_cube' -res_CST <- CST_PDFBest(pdf_exp) -str(res_CST) -# List of 5 -# $ data : num [1:10, 1:2, 1] 45.2 46.3 47.3 48.3 49.3 ... -# $ Datasets : chr [1:2] "ECMWF-S5" "UKMO-S13" -# $ season : chr "DJF" -# $ Dates :List of 1 -# ..$ start: int [1:10] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 -# $ statistic: chr [1:2] "mean" "sigma" -# - attr(*, "class")= chr "s2dv_cube" -dim(res_CST$data) -# time statistic season -# 10 2 1 -} -\references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/CST_PDFIndexFcst.Rd b/man/CST_PDFIndexFcst.Rd deleted file mode 100644 index 3d5101ca..00000000 --- a/man/CST_PDFIndexFcst.Rd +++ /dev/null @@ -1,91 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_PDFIndexFcst.R -\name{CST_PDFIndexFcst} -\alias{CST_PDFIndexFcst} -\title{Computing the Index PDFs for a forecast year of a SFSs dataset -of a CSTools object.} -\usage{ -CST_PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = "ens", - method = "ME", na.rm = FALSE) -} -\arguments{ -\item{index_hind}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate for a hindcast period -from SFSs for improving. -The index_hind object is expected to have an element named \code{$data} -with dimensions (time, dataset, member, season) or -(time, dataset, member=1, statistic=2, season). The dimensions 'time' must -be greater than 2. When dimension 'statistic' doesn't exist, the dimension -'member' must be greater than 1.} - -\item{index_obs}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate from observations or -reanalysis. -The index_obs object is expected to have an element named \code{$data} -with dimensions (time, dataset, member = 1, season). The dimensions 'time' -must be greater than 2.} - -\item{index_fcst}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate for a forecast year -from SFSs for improving. -The index_fcst object is expected to have an element named \code{$data} -with dimensions (time = 1, dataset, member, season) or -(time = 1, dataset, member = 1, statistic = 2, season). When dimension -'statistic' doesn't exist, the dimension 'member' must be greater than 1.} - -\item{type_exp}{A character string ('ens' or 'sta') to identify if the -experiment output is an ensemble members or statistics.} - -\item{method}{A character string indicating which methodology is applied -to compute the PDFs. One of "ME" (default) or "LMEV".} - -\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} -} -\value{ -CST_PDFIndexFcst() returns a CSTools object (i.e., of the -class 's2dv_cube'). -This object has at least two elements: i) an element named \code{$data} -with dimensions (time = 1, dataset, statistic = 4, season) and ii) an element -named \code{$statistic} with the names of 'statistic' dimension -of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") -The firt statistic is the parameter 'mean' of the PDF with not bias corrected -The second statistic is the parameter 'standard deviation' of the PDF -with not bias corrected -The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF -with bias corrected -} -\description{ -This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from the forecast of SFSs -and accepts a CSTools object (an object of the class 's2dv_cube') as input. -} -\examples{ -index_hind <- 1 : (5 * 3 * 7 * 4) -dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) -index_hind <- list(data = index_hind) -class(index_hind) <- 's2dv_cube' -index_obs <- 1 : (5 * 3 * 4) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -index_obs <- list(data = index_obs) -class(index_obs) <- 's2dv_cube' -index_fcst <- 1 : (3 * 7 * 4) -dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) -index_fcst <- list(data = index_fcst) -class(index_fcst) <- 's2dv_cube' -res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') -dim(res_CST$data) -#time dataset statistic season -# 1 3 4 4 -} -\references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/CST_PDFIndexHind.Rd b/man/CST_PDFIndexHind.Rd deleted file mode 100644 index 5f44a22c..00000000 --- a/man/CST_PDFIndexHind.Rd +++ /dev/null @@ -1,77 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_PDFIndexHind.R -\name{CST_PDFIndexHind} -\alias{CST_PDFIndexHind} -\title{Computing the Index PDFs for the hindcast of a SFSs dataset -of a CSTools object} -\usage{ -CST_PDFIndexHind(index_exp, index_obs, type_exp = "ens", method = "ME", - na.rm = FALSE) -} -\arguments{ -\item{index_exp}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate from SFSs for improving. -The index_exp object is expected to have an element named \code{$data} -with dimensions (time, dataset, member, season) or -(time, dataset, member=1, statistic=2, season). The dimensions 'time' must be -greater than 2. When dimension 'statistic' doesn't exist, the dimension -'member' must be greater than 1.} - -\item{index_obs}{An object of the class 's2dv_cube' -containing the indices or drivers of climate variability (as e.g., -the North Atlantic Oscillation (NAO)) estimate from observations or -reanalysis. -The index_obs object is expected to have an element named \code{$data} -with dimensions (time, dataset, member = 1, season). The dimension 'time' -must be greater than 2.} - -\item{type_exp}{A character string ('ens' or 'sta') to identify if the -experiment output is an ensemble members or statistics.} - -\item{method}{A character string indicating which methodology is applied -to compute the PDFs. One of "ME" (default) or "LMEV".} - -\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} -} -\value{ -CST_PDFIndexHind() returns a CSTools object (i.e., of the -class 's2dv_cube'). -This object has at least two elements: i) an element named \code{$data} -with dimensions (time, dataset, statistic = 4, season) and ii) an element -named \code{$statistics} with the names of 'statistic' dimension -of the first element ("mean_notbc", "sigma_notbc", "mean_bc", "sigma_bc") -The firt statistic is the parameter 'mean' of the PDF with not bias corrected -The second statistic is the parameter 'standard deviation' of the PDF -with not bias corrected -The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF -with bias corrected -} -\description{ -This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from the hindcast of SFSs -and accepts a CSTools object (an object of the class 's2dv_cube') as input. -} -\examples{ -index_obs <- 1 : (5 * 3 * 1 * 4) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) -index_exp <- 1 : (5 * 3 * 7 * 4) -dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) -index_exp <- list(data = index_exp) -index_obs <- list(data = index_obs) -class(index_exp) <- 's2dv_cube' -class(index_obs) <- 's2dv_cube' -res_CST <- CST_PDFIndexHind(index_exp, index_obs) -dim(res_CST$data) -#time dataset statistic season -# 5 3 4 4 -} -\references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/CST_ProbBestIndexWeighting.Rd b/man/CST_ProbBestIndexWeighting.Rd deleted file mode 100644 index 3725cad4..00000000 --- a/man/CST_ProbBestIndexWeighting.Rd +++ /dev/null @@ -1,62 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_ProbBestIndexWeighting.R -\name{CST_ProbBestIndexWeighting} -\alias{CST_ProbBestIndexWeighting} -\title{Computing the weighted tercile probabilities for SFSs of a CSTools -object.} -\usage{ -CST_ProbBestIndexWeighting(var_exp, weights_index_exp) -} -\arguments{ -\item{var_exp}{An object of the class 's2dv_cube' containing the variable -(e.g. precipitation, temperature, NAO index) array. -The var_exp object is expected to have an element named \code{$data} with -three dimensions (time, dataset, member) for a zonal variable -or five dimensions (time, dataset, member, lat, lon) for a spatial variable.} - -\item{weights_index_exp}{An object of the class 's2dv_cube' containing -the normalized weights array. -The weights_index_exp object is expected to have an element named -\code{$data} with dimensions (time, dataset, member, season).} -} -\value{ -CST_ProbBestIndexWeighting() returns a CSTools object (i.e., of the -class 's2dv_cube'). -This object has at least an element named \code{$data} -with four or six dimensions depending if the variable is zonal -(as e.g. NAO index)(time, tercil, dataset, season) or it is spatial variable -(as e.g. precipitation or temperature) -(time, tercil, dataset, season, lat, lon), -containing the terciles probabilities computing with weighted members. -The first tercil is the lower tercile, the second is the normal tercile and -the third is the upper tercile. -} -\description{ -This function implements the computation to obtain the tercile -probabilities for a weighted variable from SFSs through Best estimation of -one index or driver of climate variability (as e.g., the North Atlantic -Oscillation (NAO)) and accepts a CSTools object (an object of the class -'s2dv_cube') as input. -} -\examples{ -var_exp <- 1 : (4 * 2 * 3 * 3 * 2) -dim(var_exp) <- c(time = 4, dataset = 2, member = 3, lat = 3, lon = 2) -weights_index_exp <- runif(4 * 2 * 3 * 2, 0.0001, 0.9999) -dim(weights_index_exp) <- c(time = 4, dataset = 2, member = 3, season = 2) -var_exp <- list(data = var_exp) -weights_index_exp <- list(data = weights_index_exp) -class(var_exp) <- 's2dv_cube' -class(weights_index_exp) <- 's2dv_cube' -res_CST <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) -dim(res_CST$data) -# time tercil dataset season lat lon -# 4 3 2 2 3 2 -} -\references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/CST_RFSlope.Rd b/man/CST_RFSlope.Rd index 0c4e1671..d2b5aec0 100644 --- a/man/CST_RFSlope.Rd +++ b/man/CST_RFSlope.Rd @@ -50,3 +50,4 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } + diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd index ef5ebe4d..08a7b850 100644 --- a/man/CST_RFWeights.Rd +++ b/man/CST_RFWeights.Rd @@ -47,6 +47,9 @@ nf <- 8 ww <- CST_RFWeights("./worldclim.nc", nf, lon, lat, fsmooth = TRUE) } } +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} \references{ Terzago, S., Palazzi, E., & von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: @@ -54,6 +57,4 @@ A simple method to reproduce a realistic fine-scale climatology. Natural Hazards and Earth System Sciences, 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 . } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} + diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd index 8df8f1f4..ca32e2d8 100644 --- a/man/CST_RainFARM.Rd +++ b/man/CST_RainFARM.Rd @@ -92,11 +92,12 @@ dim(res$data) # dataset member sdate ftime lat lon realization # 1 2 3 4 64 64 3 } +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} \references{ Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 ; D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724. } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} + diff --git a/man/CST_WeightIndex.Rd b/man/CST_WeightIndex.Rd deleted file mode 100644 index 89cfe481..00000000 --- a/man/CST_WeightIndex.Rd +++ /dev/null @@ -1,57 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_WeightIndex.R -\name{CST_WeightIndex} -\alias{CST_WeightIndex} -\title{Computing the weights for SFSs using the Best Index PDFs -of a CSTools object} -\usage{ -CST_WeightIndex(index_exp, pdf_exp) -} -\arguments{ -\item{index_exp}{An object of the class 's2dv_cube' containing the indices -or drivers of climate variability (as e.g., the North Atlantic Oscillation -(NAO)) estimate from SFSs. -The index_exp object is expected to have an element named \code{$data} -with dimensions (time, dataset, member, season).} - -\item{pdf_exp}{An object of the class 's2dv_cube' containing the statistics -which define a Gaussian PDFs for weighting the values estimate for the SFS -ensemble members. -The pdf_exp object is expected to have an element named \code{$data} -with dimensions (time, statistic = 2, season). -The firt statistic is the parameter 'mean' of the PDFs and -the second statistic is the parameter 'standard deviation' of the PDFs.} -} -\value{ -CST_WeightIndex() returns a CSTools object (i.e., of the -class 's2dv_cube'). -This object has at least an element named \code{$data} -with dimensions (time, dataset, member, season). -} -\description{ -This function implements the computation to obtain the -normalized weights for SFS members using the Best Index estimation and -accepts a CSTools object (an object of the class 's2dv_cube') as input. -} -\examples{ -index_exp <- 1 : (5 * 3 * 7 * 4) -dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) -index_exp <- list(data = index_exp) -pdf_exp <- 1 : (5 * 2 * 4) -dim(pdf_exp) <- c(time = 5, statistic = 2, season = 4) -pdf_exp <- list(data = pdf_exp) -class(index_exp) <- 's2dv_cube' -class(pdf_exp) <- 's2dv_cube' -res_CST <- CST_WeightIndex(index_exp, pdf_exp) -dim(res_CST$data) -#time dataset member season -# 5 3 7 4 -} -\references{ -Regionally improved seasonal forecast of precipitation through -Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/CST_Weighted.Rd b/man/CST_Weighted.Rd new file mode 100644 index 00000000..a509119b --- /dev/null +++ b/man/CST_Weighted.Rd @@ -0,0 +1,71 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_Weighted.R +\name{CST_Weighted} +\alias{CST_Weighted} +\title{Weighted SFSs of a CSTools object.} +\usage{ +CST_Weighted(var_exp, aweights, type = "ensembleMean", + time_dim_name = "time") +} +\arguments{ +\item{var_exp}{An object of the class 's2dv_cube' containing the variable +(e.g. precipitation, temperature, NAO index) array. +The var_exp object is expected to have an element named \code{$data} with +at least a temporal dimension and a dimension named 'member'.} + +\item{aweights}{Normalized weights array with at least dimensions +(time, member), when 'time' is the temporal dimension as default.} + +\item{type}{A character string indicating the type of output. +If 'type' = 'probs', the function returns, in the element data from +'var_exp' parameter, an array with at least two +or four dimensions depending if the variable is zonal (as e.g. NAO index), +dimension (time, tercil) or it is spatial variable (as e.g. precipitation +or temperature), dimension (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. +If 'type' = 'ensembleMean', the function returns, in the element data from +'var_exp' parameter, an array with at least one or three dimensions +depending if the variable is zonal (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.} + +\item{time_dim_name}{A character string indicating the name of the +temporal dimension, by default 'time'.} +} +\value{ +CST_Weighted() returns a CSTools object (i.e., of the +class 's2dv_cube'). +This object has at least an element named \code{$data} +with at least a temporal dimension (and dimension 'tercil' when the output +are tercile probabilities), containing the ensemble means computing with +weighted members or probabilities of terciles. +} +\description{ +Function to apply weights to a 's2dv_cube' object. +It could return a weighted ensemble mean (deterministic output) or +the terciles probabilities (probabilistic output) for SFSs. +} +\examples{ +var_exp <- 1 : (2 * 4 * 3 * 2) +dim(var_exp) <- c(time = 2, member = 4, lat = 3, lon = 2) +aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.4, 0.3, 0.1, 0.2, 0.4, 0.4, 0.1, 0.2, 0.4, 0.2) +dim(aweights) <- c(time = 2, member = 4, dataset = 2) +var_exp <- list(data = var_exp) +class(var_exp) <- 's2dv_cube' +res_CST <- CST_Weighted(var_exp, aweights) +dim(res_CST$data) +# time lat lon dataset +# 2 3 2 2 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} + diff --git a/man/EMBestIndexWeighting.Rd b/man/EMBestIndexWeighting.Rd deleted file mode 100644 index dbd5b971..00000000 --- a/man/EMBestIndexWeighting.Rd +++ /dev/null @@ -1,58 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_EMBestIndexWeighting.R -\name{EMBestIndexWeighting} -\alias{EMBestIndexWeighting} -\title{Computing the weighted ensemble means for SFSs.} -\usage{ -EMBestIndexWeighting(var_exp, weights_index_exp) -} -\arguments{ -\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with three dimensions (time, dataset, member) for a zonal -variable or dimensions (time, dataset, member, lat, lon) for a spatial -variable.} - -\item{weights_index_exp}{Normalized weights array with dimensions -(time, dataset, member, season).} -} -\value{ -EMBestIndexWeighting() returns an array with three or five dimensions -depending if the variable is zonal (as e.g. NAO index)(time, dataset, season) -or it is spatial variable (as e.g. precipitation or temperature) -(time, dataset, season, lat, lon), containing the ensemble means computing -with weighted members. -} -\description{ -This function implements the computation to obtain the weighted -ensemble means for SFSs through Best estimation of one index or driver of -climate variability (as e.g., the North Atlantic Oscillation (NAO)). -} -\examples{ -# Example 1 for the EMBestIndexWeighting function -var_exp <- 1 : (2 * 3 * 4) -dim(var_exp) <- c(time = 2, dataset = 3, member = 4) -weights_index_exp<- runif(24, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) -res <- EMBestIndexWeighting(var_exp, weights_index_exp) -dim(res) -# time dataset season -# 2 3 1 -# Example 2 for the EMBestIndexWeighting function -var_exp <- 1 : (2 * 3 * 4 * 2 * 3) -dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) -weights_index_exp<- runif(48, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 2) -res <- EMBestIndexWeighting(var_exp, weights_index_exp) -dim(res) -# time dataset season lat lon -# 2 3 2 2 3 - -} -\references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/EMWeighted.Rd b/man/EMWeighted.Rd new file mode 100644 index 00000000..7eddba6a --- /dev/null +++ b/man/EMWeighted.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_Weighted.R +\name{EMWeighted} +\alias{EMWeighted} +\title{Computing the weighted ensemble means for SFSs.} +\usage{ +EMWeighted(var_exp, aweights, time_dim_name = "time") +} +\arguments{ +\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) +array from a SFS with at least dimensions (time, member) for a zonal +variable or dimensions (time, member, lat, lon) for a spatial +variable, as 'time' the spatial dimension by default.} + +\item{aweights}{Normalized weights array with at least dimensions +(time, member), when 'time' is the temporal dimension as default.} + +\item{time_dim_name}{A character string indicating the name of the +temporal dimension, by default 'time'.} +} +\value{ +EMWeighted() returns an array with at least one or three dimensions +depending if the variable is zonal (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. +} +\description{ +This function implements the computation to obtain the weighted +ensemble means for SFSs using a normalized weights array, +} +\examples{ +# 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 <- EMWeighted(var_exp, aweights) +dim(res) +# time dataset +# 2 3 +# 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 <- EMWeighted(var_exp, aweights) +dim(res) +# time lat lon +# 2 2 3 + +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} + diff --git a/man/PDFBest.Rd b/man/PDFBest.Rd index e155d888..76e8be1e 100644 --- a/man/PDFBest.Rd +++ b/man/PDFBest.Rd @@ -1,30 +1,68 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_PDFBest.R +% Please edit documentation in R/PDFBest.R \name{PDFBest} \alias{PDFBest} -\title{Computing the Best Index PDFs combining two SFSs Index PDFs.} +\title{Computing the Best Index PDFs combining Index PDFs from two SFSs} \usage{ -PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE) +PDFBest(index_obs, index_hind1, index_hind2, index_fcst1 = NULL, + index_fcst2 = NULL, method_BC = "none", time_dim_name = "time", + na.rm = FALSE) } \arguments{ -\item{pdf_exp}{Statistics array for the SFSs Index PDFs for a period of years -with dimensions (time, dataset = 2, statistic = 4, season).} +\item{index_obs}{Index (e.g. NAO index) array from an observational database +or reanalysis with at least a temporal dimension (by default 'time'), +which must be greater than 2.} -\item{bc_dataset1}{Logical (default = TRUE). -If TRUE the Index PDFs for the first SFS or dataset has been computed -with bias corrected.} +\item{index_hind1}{Index (e.g. NAO index) array from a SFS (named SFS1) +with at least two dimensions (time , member) or (time, statistic). +The temporal dimension, by default 'time', must be greater than 2. +The dimension 'member' must be greater than 1. +The dimension 'statistic' must be equal to 2, for containing the two paramenters of +a normal distribution (mean and sd) representing the ensemble of a SFS. +It is not possible to have the dimension 'member' and 'statistic' at the same time.} -\item{bc_dataset2}{Logical (default = TRUE). -If TRUE the Index PDFs for the second SFS or dataset has been computed -with bias corrected.} +\item{index_hind2}{Index (e.g. NAO index) array from a SFS (named SFS2) +with at least two dimensions (time , member) or (time, statistic). +The temporal dimension, by default 'time', must be greater than 2. +The dimension 'member' must be greater than 1. +The dimension 'statistic' must be equal to 2, for containing the two paramenters of +a normal distribution (mean and sd) representing the ensemble of a SFS. +It is not possible to have the dimension 'member' and 'statistic' together.} + +\item{index_fcst1}{(optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS1 +with at least two dimensions (time , member) or (time, statistic). +The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +The dimension 'member' must be greater than 1. +The dimension 'statistic' must be equal to 2, for containing the two paramenters of +a normal distribution (mean and sd) representing the ensemble of a SFS. +It is not possible to have the dimension 'member' and 'statistic' together.} + +\item{index_fcst2}{(optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS2 +with at least two dimensions (time , member) or (time, statistic). +The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +The dimension 'member' must be greater than 1. +The dimension 'statistic' must be equal to 2, for containing the two paramenters of +a normal distribution (mean and sd) representing the ensemble of a SFS. +It is not possible to have the dimension 'member' and 'statistic' together.} + +\item{method_BC}{A character vector of maximun length 2 indicating the bias correction +methodology to be applied on each SFS. If it is 'none' or any of its elements is 'none', +the bias correction won't be applied. Available methods developped are "ME" and "LMEV".} + +\item{time_dim_name}{A character string indicating the name of the temporal dimension, by default 'time'.} + +\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ PDFBest() returns an array -with dimensions (time, statistic = 2, season). +with at least a temporal dimension, by default 'time' and dimension 'statistic', +equal to 2. The firt statistic is the parameter 'mean' of the PDF for the best estimation combining the two SFSs PDFs. The second statistic is the parameter 'standard deviation' of the PDF for the best estimation combining the two SFSs PDFs. +If index_fcst1 and/or index_fcst2 are null, returns the values for hindcast period. +Otherwise, it returns the values for a forecast year. } \description{ This function implements the computation to obtain the index PDFs @@ -32,22 +70,44 @@ This function implements the computation to obtain the index PDFs the Best Index estimation. } \examples{ -# Example for the PDFBest function -pdf_exp <- c(1: (10 * 2 * 4 * 1)) -dim(pdf_exp) <- c(time = 10, dataset = 2, statistic = 4, season = 1) -res <- PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +# Example 1 for the PDFBest function +index_obs<- rnorm(10, sd = 3) +dim(index_obs) <- c(time = 5, season = 2) +index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +dim(index_hind1) <- c(time = 5, member = 4, season = 2) +index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +dim(index_hind2) <- c(time = 5, member = 6, season = 2) +index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +method_BC <- 'ME' +res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, +index_fcst2, method_BC) +dim(res) +time statistic season + 1 2 2 +# Example 2 for the PDFBest function +index_obs<- rnorm(10, sd = 3) +dim(index_obs) <- c(time = 5, season = 2) +index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +dim(index_hind1) <- c(time = 5, member = 4, season = 2) +index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +dim(index_hind2) <- c(time = 5, member = 6, season = 2) +index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +method_BC <- c('LMEV', 'ME') +res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) dim(res) -# time statistic season -# 10 2 1 -str(res) -# num [1:10, 1:2, 1] 17.2 18.3 19.5 20.6 21.7 ... - -} -\references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} } +\references{ +Regionally improved seasonal forecast of precipitation through +Best estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} + diff --git a/man/PDFIndexFcst.Rd b/man/PDFIndexFcst.Rd deleted file mode 100644 index f46ccad3..00000000 --- a/man/PDFIndexFcst.Rd +++ /dev/null @@ -1,72 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_PDFIndexFcst.R -\name{PDFIndexFcst} -\alias{PDFIndexFcst} -\title{Computing the Index PDFs for a dataset of SFSs for a forecast year.} -\usage{ -PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = "ens", - method = "ME", na.rm = FALSE) -} -\arguments{ -\item{index_hind}{Index (e.g. NAO index) array from SFSs with dimensions -(time, dataset, member, season) or -(time, dataset, member=1, statistic=2, season) for a hindcast period. -The dimensions 'time' must be greater than 2. When dimension 'statistic' -doesn't exist, the dimension member' must be greater than 1.} - -\item{index_obs}{Index (e.g. NAO index) array from an observational database -or reanalysis with dimension (time, dataset, member = 1, season). -The dimensions 'time' must be greater than 2.} - -\item{index_fcst}{Index (e.g. NAO index) array from SFSs. -with dimensions (time = 1, dataset, member) for a forecast year.} - -\item{type_exp}{A character string ('ens' or 'sta') to identify if the -experiment output is an ensemble members or statistics.} - -\item{method}{A character string indicating which methodology is applied -to compute the PDF. One of "ME" (default) or "LMEV".} - -\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} -} -\value{ -PDFIndexFcst() returns an array with dimensions -(time = 1, dataset, member, season) or -(time = 1, dataset, member = 1, statistic = 2, season) for the forecast year. -When dimension statistic' doesn't exist, the dimension 'member' must be -greater than 1. -The firt statistic is the parameter 'mean' of the PDF with not bias corrected -The second statistic is the parameter 'standard deviation' of the PDF -with not bias corrected -The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF -with bias corrected -} -\description{ -This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from a dataset of SFSs -} -\examples{ -# Example for the PDFIndexFcst function -index_fcst <- 1 : (3 * 4) -dim(index_fcst) <- c(time = 1, dataset = 3, member = 4, season = 1) -index_obs<- 1 : (5 * 3) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) -index_hind <- 1 : (5 * 3 * 4) -dim(index_hind) <- c(time = 5, dataset = 3, member = 4, season = 1) -res <- PDFIndexFcst(index_hind, index_obs, index_fcst) -dim(res) -# time statistic dataset season -# 1 4 3 1 -str(res) -# num [1, 1:4, 1:3, 1] 5.5 22.5 -17 0 6.5 22.5 -16 0 7.5 22.5 ... - -} -\references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/PDFIndexHind.Rd b/man/PDFIndexHind.Rd deleted file mode 100644 index 18f3ed63..00000000 --- a/man/PDFIndexHind.Rd +++ /dev/null @@ -1,76 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_PDFIndexHind.R -\name{PDFIndexHind} -\alias{PDFIndexHind} -\title{Computing the Index PDFs for a dataset of SFSs for a hindcats period.} -\usage{ -PDFIndexHind(index_exp, index_obs, type_exp = "ens", method = "ME", - na.rm = FALSE) -} -\arguments{ -\item{index_exp}{Index (e.g. NAO index) array from SFSs -with dimensions (time, dataset, member, season) or -(time, dataset, member=1, statistic=2, season). The dimensions 'time' must be -greater than 2. When dimension 'statistic' doesn't exist, the dimension -'member' must be greater than 1.} - -\item{index_obs}{Index (e.g. NAO index) array from an observational database -or reanalysis with dimensions (time, dataset, member = 1, season). The -dimension 'time' must be greater than 2.} - -\item{type_exp}{A character string ('ens' or 'sta') to identify if the -experiment output is an ensemble members or statistics.} - -\item{method}{A character string indicating which methodology is applied -to compute the PDFs. One of "ME" (default) or "LMEV".} - -\item{na.rm}{Logical (default = FALSE). Should missing values be removed?} -} -\value{ -PDFIndexHind() returns an array -with dimensions (time, statistic = 4, dataset, season). -The firt statistic is the parameter 'mean' of the PDF with not bias corrected -The second statistic is the parameter 'standard deviation' of the PDF -with not bias corrected -The third statistic is the parameter 'mean' of the PDF with bias corrected -The fourth statistic is the parameter 'standard deviation' of the PDF -with bias corrected -} -\description{ -This function implements the computation to obtain the index PDFs -(e.g. NAO index) to improve the index estimate from a dataset of SFSs -} -\examples{ -# Example for the PDFIndexHind function -# Example 1 -index_obs <- 1 : (5 * 3 * 1 * 1) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) -index_exp <- 1 : (5 * 3 * 4 * 1) -dim(index_exp) <- c(time = 5, dataset = 3, member = 4, season = 1) -res <- PDFIndexHind(index_exp, index_obs) -str(res) -# num [1:5, 1:4, 1:3, 1] 23.5 24.5 25.5 26.5 27.5 22.5 22.5 22.5 22.5 22.5 ... -dim(res) -# time statistic dataset season -# 5 4 3 1 -# Example 2 -index_obs <- 1 : (5 * 3 * 1 * 1) -dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 1) -index_exp <- 1 : (5 * 3 * 1* 2 * 1) -dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 2, season = 1) -res <- PDFIndexHind(index_exp, index_obs, type_exp = 'sta') -str(res) -# num [1:5, 1:4, 1:3, 1] 1 2 3 4 5 0 0 0 0 0 ... -dim(res) -# time statistic dataset season -# 5 4 3 1 - -} -\references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd index 62e26904..6857c64d 100644 --- a/man/PlotCombinedMap.Rd +++ b/man/PlotCombinedMap.Rd @@ -5,10 +5,10 @@ \title{Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function} \usage{ PlotCombinedMap(maps, lon, lat, map_select_fun, display_range, - map_dim = "map", brks = NULL, cols = NULL, - col_unknown_map = "white", mask = NULL, col_mask = "grey", - bar_titles = NULL, legend_scale = 1, fileout = NULL, width = 8, - height = 5, size_units = "in", res = 100, ...) + map_dim = "map", brks = NULL, cols = NULL, col_unknown_map = "white", + mask = NULL, col_mask = "grey", bar_titles = NULL, legend_scale = 1, + fileout = NULL, width = 8, height = 5, size_units = "in", res = 100, + ...) } \arguments{ \item{maps}{List of matrices to plot, each with (longitude, latitude) dimensions, or 3-dimensional array with the dimensions (longitude, latitude, map). Dimension names are required.} @@ -67,11 +67,12 @@ PlotCombinedMap(list(a, b, c), lons, lats, bar_titles = paste('\% of belonging to', c('a', 'b', 'c')), brks = 20, width = 10, height = 8) } -\seealso{ -\code{PlotCombinedMap} and \code{PlotEquiMap} -} \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} Veronica Torralba, \email{veronica.torralba@bsc.es} } +\seealso{ +\code{PlotCombinedMap} and \code{PlotEquiMap} +} + diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd index c1959ee8..3d1d65c3 100644 --- a/man/PlotForecastPDF.Rd +++ b/man/PlotForecastPDF.Rd @@ -4,11 +4,10 @@ \alias{PlotForecastPDF} \title{Plot one or multiple ensemble forecast pdfs for the same event} \usage{ -PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, - obs = NULL, plotfile = NULL, title = "Set a title", - var.name = "Varname (units)", fcst.names = NULL, - add.ensmemb = c("above", "below", "no"), color.set = c("ggplot", - "s2s4e", "hydro")) +PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, obs = NULL, + plotfile = NULL, title = "Set a title", var.name = "Varname (units)", + fcst.names = NULL, add.ensmemb = c("above", "below", "no"), + color.set = c("ggplot", "s2s4e", "hydro")) } \arguments{ \item{fcst}{a dataframe or array containing all the ensember members for each frecast. If \code{'fcst'} is an array, it should have two labelled dimensions, and one of them should be \code{'members'}. If \code{'fcsts'} is a data.frame, each column shoul be a separate forecast, with the rows beeing the different ensemble members.} @@ -50,3 +49,4 @@ PlotForecastPDF(fcsts2, c(-0.66, 0.66), extreme.limits = c(-1.2, 1.2), \author{ Llorenç Lledó \email{llledo@bsc.es} } + diff --git a/man/PlotMostLikelyQuantileMap.Rd b/man/PlotMostLikelyQuantileMap.Rd index 0d984ede..6c92850e 100644 --- a/man/PlotMostLikelyQuantileMap.Rd +++ b/man/PlotMostLikelyQuantileMap.Rd @@ -109,10 +109,11 @@ PlotMostLikelyQuantileMap(bins, lons, lats, mask = 1 - (w1 + w2 / max(c(w1, w2))), brks = 20, width = 10, height = 8) -} -\seealso{ -\code{PlotCombinedMap} and \code{PlotEquiMap} } \author{ Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es} } +\seealso{ +\code{PlotCombinedMap} and \code{PlotEquiMap} +} + diff --git a/man/ProbBestIndexWeighting.Rd b/man/ProbBestIndexWeighting.Rd deleted file mode 100644 index be6dea12..00000000 --- a/man/ProbBestIndexWeighting.Rd +++ /dev/null @@ -1,61 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_ProbBestIndexWeighting.R -\name{ProbBestIndexWeighting} -\alias{ProbBestIndexWeighting} -\title{Computing the weighted tercile probabilities for SFSs.} -\usage{ -ProbBestIndexWeighting(var_exp, weights_index_exp) -} -\arguments{ -\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with three dimensions (time, dataset, member) for a zonal -variable or dimensions (time, dataset, member, lat, lon) for a spatial -variable.} - -\item{weights_index_exp}{Normalized weights array with dimensions -(time, dataset, member, season).} -} -\value{ -ProbBestIndexWeighting() returns an array with four or six dimensions -depending if the variable is zonal (as e.g. NAO index) -(time, tercil, dataset, season) or it is spatial variable (as e.g. -precipitation or temperature) (time, tercil, dataset, season, 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. -} -\description{ -This function implements the computation to obtain the tercile -probabilities for a weighted variable from SFSs through Best estimation of -one index or driver of climate variability (as e.g., the North Atlantic -Oscillation (NAO)). -} -\examples{ -# Example 1 for the ProbBestIndexWeighting function -var_exp <- 1 : (2 * 3 * 4) -dim(var_exp) <- c(time = 2, dataset = 3, member = 4) -weights_index_exp<- runif(24, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) -res <- ProbBestIndexWeighting(var_exp, weights_index_exp) -dim(res) -# time tercil dataset season -# 2 3 3 1 -# Example 2 for the ProbBestIndexWeighting function -var_exp <- 1 : (2 * 3 * 4 * 2 * 3) -dim(var_exp) <- c(time = 2, dataset = 3, member = 4, lat = 2, lon = 3) -weights_index_exp<- runif(24, min=0.001, max=0.999) -dim(weights_index_exp) <- c(time = 2, dataset = 3, member = 4, season = 1) -res <- ProbBestIndexWeighting(var_exp, weights_index_exp) -dim(res) -# time tercil dataset season lat lon -# 2 3 3 1 2 3 - -} -\references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} diff --git a/man/ProbsWeighted.Rd b/man/ProbsWeighted.Rd new file mode 100644 index 00000000..b6336cf3 --- /dev/null +++ b/man/ProbsWeighted.Rd @@ -0,0 +1,62 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_Weighted.R +\name{ProbsWeighted} +\alias{ProbsWeighted} +\title{Computing the weighted tercile probabilities for SFSs.} +\usage{ +ProbsWeighted(var_exp, aweights, time_dim_name = "time") +} +\arguments{ +\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) +array from a SFS with at least dimensions (time, member) for a zonal +variable or dimensions (time, member, lat, lon) for a spatial +variable, as 'time' the spatial dimension by default.} + +\item{aweights}{Normalized weights array with at least dimensions +(time, member), when 'time' is the temporal dimension as default.} + +\item{time_dim_name}{A character string indicating the name of the +temporal dimension, by default 'time'.} +} +\value{ +ProbsWeighted() returns an array with at least two or four dimensions +depending if the variable is zonal (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. +} +\description{ +This function implements the computation to obtain the tercile +probabilities for a weighted variable for SFSs using a normalized weights array, +} +\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) +res <- ProbsWeighted(var_exp, aweights) +dim(res) +# time tercil +# 2 3 +# 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) +res <- ProbsWeighted(var_exp, aweights) +dim(res) +# time tercil lat lon +# 2 3 2 3 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} +\references{ +Regionally improved seasonal forecast of precipitation through Best +estimation of winter NAO, Sanchez-Garcia, E. et al., +Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +} + diff --git a/man/RFSlope.Rd b/man/RFSlope.Rd index 5308ef8c..09a24ff5 100644 --- a/man/RFSlope.Rd +++ b/man/RFSlope.Rd @@ -60,3 +60,4 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } + diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd index 35e7c2a5..3ef2a2f6 100644 --- a/man/RainFARM.Rd +++ b/man/RainFARM.Rd @@ -4,10 +4,9 @@ \alias{RainFARM} \title{RainFARM stochastic precipitation downscaling (reduced version)} \usage{ -RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, - kmin = 1, fglob = FALSE, fsmooth = TRUE, time_dim = NULL, - lon_dim = "lon", lat_dim = "lat", drop_realization_dim = FALSE, - verbose = FALSE) +RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, kmin = 1, + fglob = FALSE, fsmooth = TRUE, time_dim = NULL, lon_dim = "lon", + lat_dim = "lat", drop_realization_dim = FALSE, verbose = FALSE) } \arguments{ \item{data}{Precipitation array to downscale. @@ -114,3 +113,4 @@ dim(res$data) \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } + diff --git a/man/WeightIndex.Rd b/man/WeightIndex.Rd index 7917b65b..7eed0e70 100644 --- a/man/WeightIndex.Rd +++ b/man/WeightIndex.Rd @@ -1,48 +1,51 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_WeightIndex.R +% Please edit documentation in R/WeightIndex.R \name{WeightIndex} \alias{WeightIndex} \title{Computing the weights for SFSs using the Best Index PDFs.} \usage{ -WeightIndex(index_exp, pdf_exp) +WeightIndex(index_weight, pdf_weight) } \arguments{ -\item{index_exp}{Index (e.g. NAO index) array, from a dataset of SFSs -for a period of years, with dimensions (time, dataset, member, season).} +\item{index_weight}{Index (e.g. NAO index) array, from a dataset of SFSs +for a period of years, with at least dimensions 'member'. +Additional dimensions, for instance, a temporal dimension as 'time', +must have the same lenght in both parameters, +'index_weight' and 'pdf_weight'.} -\item{pdf_exp}{Statistics array to define a Gaussian PDF with dimensions -(time, statistic = 2, season) for a period of years. +\item{pdf_weight}{Statistics array to define a Gaussian PDF with at least +dimensions 'statistic'. The firt statistic is the parameter 'mean' of the PDF and the second statistic is the parameter 'standard deviation' of the PDF.} } \value{ -WeightIndex() returns a normalized weights array with dimensions -(time, dataset, member, season). +WeightIndex() returns a normalized weights array with the same +dimensions that index_weight. } \description{ This function implements the computation to obtain the -normalized weights for each member of each SFS or dataset using the -Best Index estimation. +normalized weights for each member of each SFS or dataset using the +pdfs indicated by the parameter 'pdf_weight' ( for instance the Best Index +estimation obtained using the 'PDFBest' function. } \examples{ # Example for the WeightIndex function -index_exp <- 1 : (10 * 3 * 5 * 1) -dim(index_exp) <- c(time = 10, dataset = 3, member = 5, season = 1) -pdf_exp <- 1 : (10 * 2 * 1) -dim(pdf_exp) <- c(time = 10, statistic = 2, season = 1) -res <- WeightIndex(index_exp, pdf_exp) +index_weight <- 1 : (10 * 3 * 5 * 1) +dim(index_weight) <- c(sdate = 5, dataset = 3, member = 5, season = 1) +pdf_weight <- 1 : (10 * 3 * 2 * 1) +dim(pdf_weight) <- c(sdate = 10, dataset = 3, statistic = 2, season = 1) +res <- WeightIndex(index_weight, pdf_weight) dim(res) -# time dataset member season -# 10 3 5 1 -str(res) -# num [1:10, 1:3, 1:5, 1] 0.976 0.958 0.935 0.908 0.881 ... +# sdate dataset member season +# 10 3 5 1 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} } \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} + diff --git a/man/areave_data.Rd b/man/areave_data.Rd index a772220a..cc79c85c 100644 --- a/man/areave_data.Rd +++ b/man/areave_data.Rd @@ -41,3 +41,4 @@ areave_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} + diff --git a/man/lonlat_data.Rd b/man/lonlat_data.Rd index 0c6ee30f..eca7abac 100644 --- a/man/lonlat_data.Rd +++ b/man/lonlat_data.Rd @@ -41,3 +41,4 @@ lonlat_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} + diff --git a/man/lonlat_prec.Rd b/man/lonlat_prec.Rd index 345e3cab..69cb94e8 100644 --- a/man/lonlat_prec.Rd +++ b/man/lonlat_prec.Rd @@ -29,3 +29,4 @@ lonlat_prec <- CST_Load('prlr', exp = list(infile), obs = NULL, Jost von Hardenberg \email{j.vonhardenberg@isac.cnr.it} } \keyword{data} + diff --git a/vignettes/BestNaoWeighting_vignette_new.Rmd b/vignettes/BestNaoWeighting_vignette_new.Rmd new file mode 100644 index 00000000..e4122aa1 --- /dev/null +++ b/vignettes/BestNaoWeighting_vignette_new.Rmd @@ -0,0 +1,159 @@ +--- +author: "Eroteida Sánchez-García" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteEngine{knitr::knitr} + %\VignetteIndexEntry{Achiving Best Estimate Index} + %\usepackage[utf8]{inputenc} +--- + +Achiving the Precipitation Best prediction giving the NAO index +------------------------------------------------------------------- + +The boreal winter precipitation forecast, acumulated from November to March, can be improved by considering the NAO index. The first step is to find the best estimation of winter NAO, given by two Seasonal Forecast System (SFS). The second step is to employ the enhanced NAO index pdf to produce weights for a SFS (it could be the same or a different SFS from the previous one). The third step is to apply this weights to a precipitation field. The methodology has been proved to improve the skill on precipitation forecast in the iberian peninsula given the relation between the winter precipitation and the NAO index in seasonal time scale (Sánchez-García, E., Voces-Aboy, J., Navascués, N., & Rodríguez-Camino, E. (2019). Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Adv. Sci. Res., 16, 165174, ). + +This document is aim to ilustrate a practical use of the functions included in CSTools applying this methodology. + + +## Loading packages and data + +Open an R sesion and load library CSTools: + +``` +library(CSTools) +``` +The required data to applied this methodology are: +- the winter index NAO for two (or three) different SFS in a reference period (hindcast) and in a future simulation (forecast), +- the observed (reconstructed) NAO index in the reference period and +- the acumulated precipitation field from a SFS that aims to be improved (hindcast or hindcast and forecast) + +Given the memory limitations, the following example uses synthetic data. + +The first SFS is a dynamical model containing 25 ensemble members and its output will be save in the object `NAO_hind1` for the 20 years reference period and `NAO_fcst1` for a next season forecast. +The second SFS is a empirical model, so, the `NAO_hind2` and `NAO_fcst2` are characterized by a mean and standard deviation saved in the 'statistics' dimension. + +The synthetic data is created by running the following lines: + + +``` +NAO_obs <- rnorm(20, sd=3) +dim(NAO_obs) <- c(time = 20) +NAO_hind1 <- rnorm(20 * 25, sd=3) +dim(NAO_hind1) <- c(time = 20, member = 25) +NAO_hind2_mean <- rnorm(20, sd=3) +NAO_hind2_sd <- rnorm(20, mean=5, sd=1) +NAO_hind2 <- cbind(NAO_hind2_mean,NAO_hind2_sd) +dim(NAO_hind2) <- c(time=20, statistic=2) + +NAO_fcst1 <- rnorm(1 * 51, sd=3) +dim(NAO_fcst1) <- c(time = 1, member = 51) +NAO_fcst2 <- c(1.55,4.02) +dim(NAO_fcst2) <- c(time=1, statistic=2) +``` + +The acumulated precipiation field (only march for memory limiations) is loaded by running: + +``` +lonlat_prec$data <- apply(lonlat_data$prec, c(1, 2, 3, 5, 6), sum) +``` + + +## 1- Best Estimation Index NAO + +The function `PDFBest` performes the next steps: + - improves the PDF NAO index for each SFS (it could use bias correction method) and + - does a statistical combination of these improved NAO indexes. +Its output is an array containing the parameters (mean and starndar deviation) of the PDF for the reference period. + + +``` +pdf_hind_best <- BEI_PDFBest(NAO_obs, NAO_hind1, NAO_hind2, index_fcst1 = NULL, + index_fcst2 = NULL, method_BC = 'none', + time_dim_name = 'time', na.rm = FALSE) +``` + + +## 2- Compute weights using the Best Estimation of Index NAO + + +An array of weights is calculated for a SFS. This SFS could be a different SFS than the ones in section 1. +The function WeightIndex computes these weights for each ensemble's member based on the best NAO PDF estimate. + + +``` +weigths_hind <- BEI_Weights(NAO_hind1, pdf_hind_best) +``` + +The expected dimensions of these weights are 'member' and temporal dimension. + + +## 3- Apply weights to a precipitation field + + +The function `CST_BEI_Weighting` computes the ensemble mean or the terciles probabilities for a climate variable. +The corrected precipitation field is obtained by running: + +``` +em_prec <- CST_BEI_Weighting(latlon_prec, weights_hind, type = 'ensembleMean') +prob_prec <- CST_BEI_Weighting(latlon_prec, weights_hind, type = 'probs') +``` + +## Comparison and visualization + +The original model output can be compared against the BEI corrected field. +To do this a equiprobability weights are created and they are applied to the original precipitation field: + + +``` +aweights_raw <- rep(1/25, 3 * 25) +dim(aweights_raw) <- dim(aweights) + +em_prec_raw <- CST_Weighted(latlon_prec, aweights_raw, type = 'ensembleMean') +prob_prec_raw <- CST_Weighted(latlon_prec, aweights_raw, type = 'probs') + +``` + +A map with the probability that the total precipitation will be in the lower tercile based on the Best Estimated Index NAO is obtained by running the code: + +``` +coordVAR <- list(lonmin = -10, lonmax = 5, latmin = 35, latmax = 45) +lonPlot <- seq(coordVAR$lonmin,coordVAR$lonmax,0.50) +latPlot <- seq(coordVAR$latmax,coordVAR$latmin,-0.50) +probData_CST <- prob_var_exp$data[which(var_exp$Dates$start == 2012),1,1,,] +brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) +cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") +names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") +ofile <- "./Figures/probLower_CST_weighted.png" +PlotEquiMap(t(probData_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, + fileout =ofile, brks=brks, cols = cols, + toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on the best NAO pdf estimate"), + title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) + +PlotEquiMap(prob_prec[,,1], lon = prob_prec$lon, lat = prob_prec$lat, filled_continents = FALSE, + fileout = ofile) +PlotMostLikelyQuantileMap( ) +``` + +![](./Figures/probLower_weight_CST.png){width=70%} + + + +In a similar way we can plot the map with the probability that the total precipitation from November 2012 to +March 2013, for example, will be in the lower tercile from ECMWF Seasonal Forecast System 5 (raw) to compare results running the code: + +```{r message=FALSE} +probDataRaw_CST <- prob_var_exp_raw$data[which(var_exp$Dates$start == 2012),1,1,,] +brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) +cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") +names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") +ofile <- "./Figures/probLower_CST_weighted_raw.png" +PlotEquiMap(t(probDataRaw_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, + fileout =ofile, brks=brks, cols = cols, + toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on ECMWF-S5 (raw)"), + title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) + + +``` + +![](./Figures/probLower_CST_weighted_raw.png){width=70%} diff --git a/vignettes/Figures/probLower_CST_weighted.png b/vignettes/Figures/probLower_CST_weighted.png new file mode 100644 index 0000000000000000000000000000000000000000..d9f76f59f76c595ad63ced58336f9979e0be8218 GIT binary patch literal 43107 zcmcG$bySsY*FCyP>5vp@q@|>k7NonoBm_xmX#@$87Eu~ekVXWQZUGSq=|&`^yW_0w z^L^utamMfbcg{V0frodq_r9-dU2DxX=Uh8VLroqZhYAOQK;SDXJkUZQ(C85elzl8z z_>B_d3=e$6c2zKRM<56-k^e*aD4B1MK+q!;AKcgRNnfA#HX&K2ySB}08P;%=$d2;h z1`bEI*0mvVj>pqzRk0#Cv3gv0SBoR{jOEC@$-aD~uQVVdC4PdT@%je>)iON!PhgNq z+OAa?Istd{(Z%j!^eu07iJD}W&CI%73(9bKdAOc@w7&N5hZTqHX5>3V&{L8c{QC`h 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z0GMt7+JNWtaY7BFON>4i0*}ySLVNtfcuwHuNi3SU)3-zFx~j)O*JrdOT=8`~1McVK z=<=&i356J#6P^6wn`A20*~@MYQ|Z97y4RqyAb!5$oWzenm#}*@U1Hdn5RT`Abj~~9 zK~z62QQ!Tg#I@Va+Z#rv=!2UtS_DbxT&s}do!TpNIY!V6Ixv(MW5Yrn^&HfR(2&8> zx@F72#dL?V4#@;$1E3uR)-td~SV0J3jC0HG2!*xv^??uQ^SQRicCbd5-P)#S)?X8>`8psoyLCaZ^KHHL3s29R6EKCd@KSQz(hzYVf z>whUgLrY6MI$9URPyDn#GhD49v4O(S`S^IyMwAT>IHBYd#RKyO2^n_`1q>g65}KNv zOFGcP&~BSOQ`1;o&A)^hoRYddDMJDJEr?F#s9c~04E!p(hU$G yW$UtoyMov@c2h~%ES&rv|8ueOZ?(%$r|%uz(&$~ZF}KCUPUG(C Date: Thu, 24 Oct 2019 16:32:36 +0200 Subject: [PATCH 30/40] version mejorada por Nuria --- R/{PDFBest.R => BEI_PDFBest.R} | 98 ++++- R/{WeightIndex.R => BEI_Weights.R} | 54 ++- R/{CST_Weighted.R => CST_BEI_Weighting.R} | 64 ++- R/zzz.R | 59 --- tests/testthat/test-BEI_PDFBest.R | 167 ++++++++ tests/testthat/test-BEI_Weights.R | 90 +++++ tests/testthat/test-CST_BEI_Weighting.R | 177 ++++++++ .../testthat/test-CST_EMBestIndexWeighting.R | 144 ------- tests/testthat/test-CST_PDFBest.R | 102 ----- tests/testthat/test-CST_PDFIndexFcst.R | 378 ------------------ tests/testthat/test-CST_PDFIndexHind.R | 341 ---------------- .../test-CST_ProbBestIndexWeighting.R | 172 -------- ...w.Rmd => BestEstimationIndex_vignette.Rmd} | 74 +--- vignettes/BestNaoWeighting_vignette.Rmd | 232 ----------- ...t_CST.png => BestEstimationIndex_fig1.png} | Bin .../Figures/BestEstimationIndex_fig2.png | Bin 0 -> 43513 bytes .../Figures/BestEstimationIndex_fig3.png | Bin 0 -> 6939 bytes vignettes/Figures/probLower_CST_weighted.png | Bin 43107 -> 0 bytes .../Figures/probLower_CST_weighted_raw.png | Bin 43780 -> 0 bytes vignettes/Figures/probLower_raw_CST.png | Bin 43825 -> 0 bytes 20 files changed, 607 insertions(+), 1545 deletions(-) rename R/{PDFBest.R => BEI_PDFBest.R} (92%) rename R/{WeightIndex.R => BEI_Weights.R} (64%) rename R/{CST_Weighted.R => CST_BEI_Weighting.R} (90%) create mode 100644 tests/testthat/test-BEI_PDFBest.R create mode 100644 tests/testthat/test-BEI_Weights.R create mode 100644 tests/testthat/test-CST_BEI_Weighting.R delete mode 100644 tests/testthat/test-CST_EMBestIndexWeighting.R delete mode 100644 tests/testthat/test-CST_PDFBest.R delete mode 100644 tests/testthat/test-CST_PDFIndexFcst.R delete mode 100644 tests/testthat/test-CST_PDFIndexHind.R delete mode 100644 tests/testthat/test-CST_ProbBestIndexWeighting.R rename vignettes/{BestNaoWeighting_vignette_new.Rmd => BestEstimationIndex_vignette.Rmd} (60%) delete mode 100644 vignettes/BestNaoWeighting_vignette.Rmd rename vignettes/Figures/{probLower_weight_CST.png => BestEstimationIndex_fig1.png} (100%) create mode 100644 vignettes/Figures/BestEstimationIndex_fig2.png create mode 100644 vignettes/Figures/BestEstimationIndex_fig3.png delete mode 100644 vignettes/Figures/probLower_CST_weighted.png delete mode 100644 vignettes/Figures/probLower_CST_weighted_raw.png delete mode 100644 vignettes/Figures/probLower_raw_CST.png diff --git a/R/PDFBest.R b/R/BEI_PDFBest.R similarity index 92% rename from R/PDFBest.R rename to R/BEI_PDFBest.R index 078692a0..bd96d7fc 100644 --- a/R/PDFBest.R +++ b/R/BEI_PDFBest.R @@ -47,7 +47,7 @@ #'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. #'@param na.rm Logical (default = FALSE). Should missing values be removed? #' -#' @return PDFBest() returns an array +#' @return BEI_PDFBest() returns an array #' with at least a temporal dimension, by default 'time' and dimension 'statistic', #' equal to 2. #' The firt statistic is the parameter 'mean' of the PDF for the best estimation @@ -59,7 +59,7 @@ #'@import multiApply #' #'@examples -#' # Example 1 for the PDFBest function +#' # Example 1 for the BEI_PDFBest function #' index_obs<- rnorm(10, sd = 3) #' dim(index_obs) <- c(time = 5, season = 2) #' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) @@ -71,12 +71,12 @@ #' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) #' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) #' method_BC <- 'ME' -#' res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, +#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, #' index_fcst2, method_BC) #' dim(res) #' time statistic season #' 1 2 2 -#' # Example 2 for the PDFBest function +#' # Example 2 for the BEI_PDFBest function #' index_obs<- rnorm(10, sd = 3) #' dim(index_obs) <- c(time = 5, season = 2) #' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) @@ -88,20 +88,22 @@ #' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) #' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) #' method_BC <- c('LMEV', 'ME') -#' res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) +#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) #' dim(res) +#' time statistic season +#' 1 2 2 #'@export -PDFBest <- function(index_obs, index_hind1, index_hind2, +BEI_PDFBest <- function(index_obs, index_hind1, index_hind2, index_fcst1 = NULL, index_fcst2 = NULL, method_BC = 'none', time_dim_name = 'time', na.rm = FALSE) { if (!is.logical(na.rm)) { - stop("The parameter 'na.rm' must be a logical value.") + stop("Parameter 'na.rm' must be a logical value.") } if (!is.character(time_dim_name)) { - stop("Parameter 'time_dim_name' must be a character string indicating", - " the name of the temporal dimension.") + stop("Parameter 'time_dim_name' must be a character string ", + "indicating the name of the temporal dimension.") } if (length(time_dim_name) > 1) { warning("Parameter 'time_dim_name' has length greater than 1 and ", @@ -115,8 +117,8 @@ PDFBest <- function(index_obs, index_hind1, index_hind2, stop("Length of parameter 'method_BC' must be 1 or 2.") } if(!all(method_BC %in% c('ME', 'LMEV', 'none'))){ - stop("Elements of parameter 'method_BC' must be equals to - 'none, 'ME' or 'LMEV'") + stop("Elements of parameter 'method_BC' must be equals to ", + "'none, 'ME' or 'LMEV'") } if (!is.array(index_obs)) { stop("Parameter 'index_obs' must be an array.") @@ -301,7 +303,7 @@ PDFBest <- function(index_obs, index_hind1, index_hind2, pdf_best <- Apply(list(pdf_hind1, pdf_hind2), target_dims = list('statistic', 'statistic'), - fun = .PDFBest, + fun = .BEI_PDFBest, bc_dataset1, bc_dataset2) } else { pdf_fcst1 <- PDFIndexFcst(index_hind1, index_obs, index_fcst1, @@ -313,7 +315,7 @@ PDFBest <- function(index_obs, index_hind1, index_hind2, pdf_best <- Apply(list(pdf_fcst1, pdf_fcst2), target_dims = list('statistic', 'statistic'), - fun = .PDFBest, + fun = .BEI_PDFBest, bc_dataset1, bc_dataset2) } Dim <- names(dim(index_hind1)) @@ -327,7 +329,7 @@ PDFBest <- function(index_obs, index_hind1, index_hind2, return(res) } -#' Atomic PDFBest +#' Atomic BEI_PDFBest #'@param pdf_1 Statistics array for the first SFS PDF with one dimension #' 'statistic' equal to 4. #' @param pdf_2 Statistics array for the second SFS PDF with one dimension @@ -339,25 +341,25 @@ PDFBest <- function(index_obs, index_hind1, index_hind2, #' If TRUE the Index PDFs for the second SFS has been computed #' with bias corrected. #' -#' @return .PDFBest returns an array with dimensions (statistic = 2). +#' @return .BEI_PDFBest returns an array with dimensions (statistic = 2). #' The firt statistic is the parameter 'mean' of the PDF for the best estimation #' combining the two SFSs PDF. #' The second statistic is the parameter 'standard deviation' of the PDF for #' the best estimation combining the two SFSs PDF. #' #' @examples -#' # Example for the Atomic PDFBest function +#' # Example for the Atomic BEI_PDFBest function #' pdf_1 <- c(1.1,0.6,1.6,0.9) #' dim(pdf_1) <- c(statistic = 4) #' pdf_2 <- c(1,0.5,1.5,0.8) #' dim(pdf_2) <- c(statistic = 4) -#' res <- .PDFBest(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +#' res <- .BEI_PDFBest(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = FALSE) #' str(res) #' dim(res) #' # statistic #' # 2 #' @noRd -.PDFBest <- function(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { +.BEI_PDFBest <- function(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { if(bc_dataset1){ # apply bias correction to model 1 mean_1 <- pdf_1[3] @@ -876,3 +878,63 @@ PDFIndexFcst <- function(index_hind, index_obs, index_fcst, return(data) } +# Auxiliar function to compute the mean squared error between 'exp' and 'obs' +Sigma_notBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } + return(sqrt(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$MSE)) +} + +# Auxiliar function to compute the bias between 'exp' and 'obs' +MEBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } + return(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$ME) +} + +# Auxiliar function to compute the standard deviation of errors +# between 'obs' and 'exp' +Sigma_BC <- function(exp, obs, na.rm = FALSE) { + return(sd(obs - exp, na.rm = na.rm)) +} + +# Auxiliar function to compute the linear model used in the method +# called 'LMEV' to correct the bias in the Best NAO weighting +# methogology +LMEV <- function(exp, obs, dim_exp, + dim_obs, time_dim_name = 'time', na.rm = FALSE) { + names(dim(exp)) <- dim_exp + names(dim(obs)) <- dim_obs + if (any(names(dim(exp)) == 'member')) { + exp_ens_mean <- Apply(list(exp), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + exp_ens_sd <- Apply(list(exp), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else { + if(na.rm & any(is.na(index_exp))){ + print("Some value are NA") + } + pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) + exp <- aperm(exp,pos) + exp_ens_mean <- exp[,1] + exp_ens_sd <- exp[,2] + } + error <- exp_ens_mean - obs + exp_ens_var <- exp_ens_sd^2 + statModel <- lm(error ~ exp_ens_var) + return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) +} + + diff --git a/R/WeightIndex.R b/R/BEI_Weights.R similarity index 64% rename from R/WeightIndex.R rename to R/BEI_Weights.R index 261d286e..32c270a0 100644 --- a/R/WeightIndex.R +++ b/R/BEI_Weights.R @@ -21,25 +21,34 @@ #' The firt statistic is the parameter 'mean' of the PDF and #' the second statistic is the parameter 'standard deviation' of the PDF. #' -#'@return WeightIndex() returns a normalized weights array with the same +#'@return BEI_Weights() returns a normalized weights array with the same #' dimensions that index_weight. #' #'@import multiApply #' #'@examples -#' # Example for the WeightIndex function +#' # Example for the BEI_Weights function #' index_weight <- 1 : (10 * 3 * 5 * 1) -#' dim(index_weight) <- c(sdate = 5, dataset = 3, member = 5, season = 1) +#' dim(index_weight) <- c(sdate = 10, dataset = 3, member = 5, season = 1) #' pdf_weight <- 1 : (10 * 3 * 2 * 1) #' dim(pdf_weight) <- c(sdate = 10, dataset = 3, statistic = 2, season = 1) -#' res <- WeightIndex(index_weight, pdf_weight) +#' res <- BEI_Weights(index_weight, pdf_weight) #' dim(res) #' # sdate dataset member season #' # 10 3 5 1 #' #'@export -WeightIndex <- function(index_weight, pdf_weight) { +BEI_Weights <- function(index_weight, pdf_weight, time_dim_name = 'time') { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string ", + "indicating the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } if (!is.array(index_weight)) { stop("Parameter 'index_weight' must be an array.") } @@ -50,17 +59,34 @@ WeightIndex <- function(index_weight, pdf_weight) { stop("Parameters 'index_weight' and 'pdf_weight'", " should have dimmension names.") } - if (dim(pdf_weight)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of the parameter 'pdf_weight' must be equal to 2.") + if(!('member' %in% names(dim(index_weight)))) { + stop("Parameter 'index_weight' must have dimension 'member'.") + } + if(!('statistic' %in% names(dim(pdf_weight)))) { + stop("Parameter 'pdf_weight' must have dimension 'statistic'.") + } + if(time_dim_name %in% names(dim(index_weight)) & + !time_dim_name %in% names(dim(pdf_weight))) { + stop("Parameter 'pdf_weight' must have temporal dimension.") + } + if(!time_dim_name %in% names(dim(index_weight)) & + time_dim_name %in% names(dim(pdf_weight))) { + stop("Parameter 'index_weight' must have temporal dimension.") + } + if(time_dim_name %in% names(dim(index_weight)) & + time_dim_name %in% names(dim(pdf_weight)) & + dim(index_weight)[time_dim_name] != dim(pdf_weight)[time_dim_name]){ + stop("Length of temporal dimension of parameters must be equal") } if (dim(pdf_weight)['statistic'] != 2) { stop("Length of dimension 'statistic' ", "of the parameter 'pdf_weight' must be equal to 2.") } + + aweights <- Apply(list(index_weight, pdf_weight), target_dims = list('member', 'statistic'), - fun = .WeightIndex)$output1 + fun = .BEI_Weights)$output1 dimnames <- names(dim(index_weight)) pos <- match(dimnames, names(dim(aweights))) @@ -69,27 +95,27 @@ WeightIndex <- function(index_weight, pdf_weight) { return(aweights) } -#' Atomic WeightIndex +#' Atomic BEI_Weights #' @param index_weight Index (e.g. NAO index) array from a SFS with dimensions #' (member) #' @param pdf_weight Statistics array to define a Gaussian PDF with dimensions #' (statistic = 2). #' The firt statistic is the parameter 'mean' of the PDF and #' the second statistic is the parameter 'standard deviation' of the PDF. -#' @return .WeightIndex returns an array of with dimensions (member), +#' @return .BEI_Weights returns an array of with dimensions (member), #' the normalized weights for each member of a SFS using a Best NAO PDF. #' @examples -#' # Example for the Atomic WeightIndex function +#' # Example for the Atomic BEI_Weights function #' index_weight <- c(1.3,3,-1) #' dim(index_weight) <- c(member = 3) #' pdf_weight <- c(1.5,0.8) #' dim(pdf_weight) <- c(statistic = 2) -#' res <- .WeightIndex(index_weight, pdf_weight) +#' res <- .BEI_Weights(index_weight, pdf_weight) #' dim(res) #' # member #' # 3 #' @noRd -.WeightIndex <- function(index_weight, pdf_weight) { +.BEI_Weights <- function(index_weight, pdf_weight) { aweights <- apply(index_weight, 1, dnorm, mean = pdf_weight[1], sd = pdf_weight[2]) dim(aweights) <- dim(index_weight) sumWeights <- sum(aweights) diff --git a/R/CST_Weighted.R b/R/CST_BEI_Weighting.R similarity index 90% rename from R/CST_Weighted.R rename to R/CST_BEI_Weighting.R index 271d5dd3..5bfccdff 100644 --- a/R/CST_Weighted.R +++ b/R/CST_BEI_Weighting.R @@ -1,4 +1,4 @@ -#' Weighted SFSs of a CSTools object. +#' Weighting SFSs of a CSTools object. #' #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @@ -34,7 +34,7 @@ #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. #' -#' @return CST_Weighted() returns a CSTools object (i.e., of the +#' @return CST_BEI_Weighting() returns a CSTools object (i.e., of the #' class 's2dv_cube'). #' This object has at least an element named \code{$data} #' with at least a temporal dimension (and dimension 'tercil' when the output @@ -48,12 +48,12 @@ #' dim(aweights) <- c(time = 2, member = 4, dataset = 2) #' var_exp <- list(data = var_exp) #' class(var_exp) <- 's2dv_cube' -#' res_CST <- CST_Weighted(var_exp, aweights) +#' res_CST <- CST_BEI_Weighting(var_exp, aweights) #' dim(res_CST$data) #' # time lat lon dataset #' # 2 3 2 2 #' @export -CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', +CST_BEI_Weighting <- function(var_exp, aweights, type = 'ensembleMean', time_dim_name = 'time') { if (!is.character(time_dim_name)) { stop("Parameter 'time_dim_name' must be a character string indicating", @@ -65,8 +65,8 @@ CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', time_dim_name <- time_dim_name[1] } if (!is.character(type)) { - stop("Parameter 'type' must be a character string ('probs' or ", - "'ensembleMean'), indicating the type of output.") + stop("Parameter 'type' must be a character string, 'probs' or ", + "'ensembleMean', indicating the type of output.") } if (length(type) > 1) { warning("Parameter 'type' has length greater than 1 and ", @@ -93,10 +93,10 @@ CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', } if(!('member' %in% names(dim(var_exp$data)))) { stop("Element 'data' from parameter 'var_exp' must have ", - "temporal dimension.") + "dimension 'member'.") } if(!('member' %in% names(dim(aweights)))) { - stop("Parameter 'aweights' must have temporal dimension.") + stop("Parameter 'aweights' must have dimension 'member'.") } if (dim(var_exp$data)[time_dim_name] != dim(aweights)[time_dim_name]) { stop("Length of temporal dimensions ", @@ -109,10 +109,10 @@ CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', } if (type == 'ensembleMean'){ - em <- EMWeighted(var_exp$data, aweights, time_dim_name) + em <- BEI_EMWeighting(var_exp$data, aweights, time_dim_name) var_exp$data <- em } else if (type == 'probs'){ - probs <- ProbsWeighted(var_exp$data, aweights, time_dim_name) + probs <- BEI_ProbsWeighting(var_exp$data, aweights, time_dim_name) var_exp$data <- probs } else { stop("Parameter 'type' must be a character string ('probs' or ", @@ -121,7 +121,7 @@ CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', return(var_exp) } -#' @rdname EMWeighted +#' @rdname BEI_EMWeighting #' @title Computing the weighted ensemble means for SFSs. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the weighted @@ -140,7 +140,7 @@ CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. #' -#' @return EMWeighted() returns an array with at least one or three dimensions +#' @return BEI_EMWeighting() returns an array with at least one or three dimensions #' depending if the variable is zonal (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 @@ -153,7 +153,7 @@ CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', #' 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 <- EMWeighted(var_exp, aweights) +#' res <- BEI_EMWeighting(var_exp, aweights) #' dim(res) #' # time dataset #' # 2 3 @@ -162,13 +162,13 @@ CST_Weighted <- function(var_exp, aweights, type = 'ensembleMean', #' 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 <- EMWeighted(var_exp, aweights) +#' res <- BEI_EMWeighting(var_exp, aweights) #' dim(res) #' # time lat lon #' # 2 2 3 #' #' @export -EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { +BEI_EMWeighting <- function(var_exp, aweights, time_dim_name = 'time') { if (!is.character(time_dim_name)) { stop("Parameter 'time_dim_name' must be a character string indicating", " the name of the temporal dimension.") @@ -212,11 +212,11 @@ EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { res <- Apply(list(var_exp, aweights), target_dims = list(c(time_dim_name,'member'), c(time_dim_name,'member')), - fun = .EMWeighted, time_dim_name)$output1 + fun = .BEI_EMWeighting, time_dim_name)$output1 return(res) } -#' Atomic EMWeighted +#' Atomic BEI_EMWeighting #' @param var_exp Variable (e.g. precipitation, temperature, NAO index) #' array from a SFS with a temporal dimension, #' by default 'time', and dimension 'member'. @@ -224,22 +224,22 @@ EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { #' by default 'time', and dimension 'member' #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. -#' @return .EMWeighted returns an array of with a temporal dimension, +#' @return .BEI_EMWeighting returns an array of with a temporal dimension, #' by default 'time', containing the weighted ensemble means. #' @examples -#' # Example for the Atomic EMWeighted function +#' # Example for the Atomic BEI_EMWeighting function #' var_exp <- 1 : 6 #' dim(var_exp) <- c(time = 2, member = 3) #' aweights <- c(0.28, 0.15, 0.69, 0.64, 0.42, 0.17) #' dim(aweights) <- c(time = 2, member = 3) -#' res <- .EMWeighted(var_exp, aweights) +#' res <- .BEI_EMWeighting(var_exp, aweights) #' str(res) #' # num [1:2(1d)] 4.45 3.88 #' dim(res) #' # time #' # 2 #' @noRd -.EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { +.BEI_EMWeighting <- function(var_exp, aweights, time_dim_name = 'time') { posTime <- match(time_dim_name, names(dim(var_exp))) var_exp_em <- as.array(apply(var_exp * aweights, posTime, sum)) @@ -267,7 +267,7 @@ EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. #' -#' @return ProbsWeighted() returns an array with at least two or four dimensions +#' @return BEI_ProbsWeighting() returns an array with at least two or four dimensions #' depending if the variable is zonal (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 @@ -283,7 +283,7 @@ EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { #' 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) -#' res <- ProbsWeighted(var_exp, aweights) +#' res <- BEI_ProbsWeighting(var_exp, aweights) #' dim(res) #' # time tercil #' # 2 3 @@ -292,12 +292,12 @@ EMWeighted <- function(var_exp, aweights, time_dim_name = 'time') { #' 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 <- ProbsWeighted(var_exp, aweights) +#' res <- BEI_ProbsWeighting(var_exp, aweights) #' dim(res) #' # time tercil lat lon #' # 2 3 2 3 -#' @export -ProbsWeighted <- function(var_exp, aweights, time_dim_name = 'time') { +#' @noRd +BEI_ProbsWeighting <- function(var_exp, aweights, time_dim_name = 'time') { if (!is.character(time_dim_name)) { stop("Parameter 'time_dim_name' must be a character string indicating", @@ -341,11 +341,11 @@ ProbsWeighted <- function(var_exp, aweights, time_dim_name = 'time') { res <- Apply(list(var_exp, aweights), target_dims = list(c(time_dim_name,'member'), c(time_dim_name,'member')), - fun = .ProbsWeighted, time_dim_name)$output1 + fun = .BEI_ProbsWeighting, time_dim_name)$output1 return(res) } -#' Atomic ProbsWeighted +#' Atomic BEI_ProbsWeighting #' @param var_exp Variable (e.g. precipitation, temperature, NAO index) #' array from a SFS with a temporal dimension, #' by default 'time', and dimension 'member'. @@ -353,7 +353,7 @@ ProbsWeighted <- function(var_exp, aweights, time_dim_name = 'time') { #' by default 'time', and dimension 'member' #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. -#' @return .ProbsWeighted returns an array of with a temporal dimension, +#' @return .BEI_ProbsWeighting returns an array of with a temporal dimension, #' as default 'time', and 'tercil' dimension, containing the probabilities #' for each tercile computing with weighted members. #' The firt tercil is the lower tercile, the second is the normal tercile and @@ -364,15 +364,13 @@ ProbsWeighted <- function(var_exp, aweights, time_dim_name = 'time') { #' dim(var_exp) <- c(stime = 2, member = 4) #' aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.3, 0.3) #' dim(aweights) <- c(stime = 2, member = 4) -#' res <- .ProbsWeighted(var_exp, aweights, time_dim_name = 'stime') +#' res <- .BEI_ProbsWeighting(var_exp, aweights, time_dim_name = 'stime') #' dim(res) #' # stime tercil #' # 2 3 #' @noRd -.ProbsWeighted <- function(var_exp, aweights, time_dim_name = 'time') { +.BEI_ProbsWeighting <- function(var_exp, aweights, time_dim_name = 'time') { # computing terciles - #names(dim(var_exp)) <- c('time','member') - #names(dim(aweights)) <- c('time','member') terciles_exp <- WeightTerciles(var_exp, aweights, time_dim_name) lowerTercile <- terciles_exp$lowerTercile upperTercile <- terciles_exp$upperTercile diff --git a/R/zzz.R b/R/zzz.R index f9aa6c1c..1414e949 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -17,62 +17,3 @@ x } -# Auxiliar function to compute the mean squared error between 'exp' and 'obs' -Sigma_notBC <- function(exp, obs, na.rm = FALSE) { - if (na.rm){ - indNA <- c(which(is.na(exp)),which(is.na(obs))) - if (length(indNA) > 0){ - exp <- exp[-indNA] - obs <- obs[-indNA] - } - } - return(sqrt(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$MSE)) -} - -# Auxiliar function to compute the bias between 'exp' and 'obs' -MEBC <- function(exp, obs, na.rm = FALSE) { - if (na.rm){ - indNA <- c(which(is.na(exp)),which(is.na(obs))) - if (length(indNA) > 0){ - exp <- exp[-indNA] - obs <- obs[-indNA] - } - } - return(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$ME) -} - -# Auxiliar function to compute the standard deviation of errors -# between 'obs' and 'exp' -Sigma_BC <- function(exp, obs, na.rm = FALSE) { - return(sd(obs - exp, na.rm = na.rm)) -} - -# Auxiliar function to compute the linear model used in the method -# called 'LMEV' to correct the bias in the Best NAO weighting -# methogology -LMEV <- function(exp, obs, dim_exp, - dim_obs, time_dim_name = 'time', na.rm = FALSE) { - names(dim(exp)) <- dim_exp - names(dim(obs)) <- dim_obs - if (any(names(dim(exp)) == 'member')) { - exp_ens_mean <- Apply(list(exp), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - exp_ens_sd <- Apply(list(exp), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else { - if(na.rm & any(is.na(index_exp))){ - print("Some value are NA") - } - pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) - exp <- aperm(exp,pos) - exp_ens_mean <- exp[,1] - exp_ens_sd <- exp[,2] - } - error <- exp_ens_mean - obs - exp_ens_var <- exp_ens_sd^2 - statModel <- lm(error ~ exp_ens_var) - return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) -} - diff --git a/tests/testthat/test-BEI_PDFBest.R b/tests/testthat/test-BEI_PDFBest.R new file mode 100644 index 00000000..f2415f2e --- /dev/null +++ b/tests/testthat/test-BEI_PDFBest.R @@ -0,0 +1,167 @@ +context("Generic tests") +test_that("basic use case", { + index_obs<- c(2, 3, 0.6) + dim(index_obs) <- c(time = 3) + index_hind1 <- c(-2.4, 3.6, -5, -1, -0.5, -0.2, 3, 0.6, 1, 0, -0.5, 2) + dim(index_hind1) <- c(time = 3, member = 4) + index_hind2 <- c(4, -10, 1.7, 1, -2, 3, -2, -1.5, 2.9) + dim(index_hind2) <- c(time = 3, member = 3) + index_fcst1 <- c(-2.6, -0.6, 1.4, -2.4, 8.1) + dim(index_fcst1) <- c(time = 1, member = 5) + index_fcst2 <- c(0, 4, -10, 2, -6, 3) + dim(index_fcst2) <- c(time = 1, member = 6) + + result <- array(c(2.574303, 0.8630711), + dim=c(time = 1, statistic = 2)) + + expect_equal(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = 'ME'), result, tolerance=1e-4) + + + result <- array(c(0.1282658, 0.4108197), + dim=c(time = 1, statistic = 2)) + + expect_equal(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('LMEV', 'ME')), result, + tolerance=1e-4) + + result <- array(c(0.4923944, 1.73306), + dim=c(time = 1, statistic = 2)) + + expect_equal(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = 'none'), result, + tolerance=1e-4) + + index_obs<- c(2, 3, 0.6) + dim(index_obs) <- c(time = 3) + index_hind1 <- c(-2.4, 3.6, -5, -1, -0.5, -0.2, 3, 0.6, 1, 0, -0.5, 2) + dim(index_hind1) <- c(time = 3, member = 4) + index_hind2 <- c(1.194390, 4.298768, -1.222586, 4.419386, 3.972776, 3.998928) + dim(index_hind2) <- c(time = 3, statistic=2) + index_fcst1 <- c(-2.6, -0.6, 1.4, -2.4, 8.1) + dim(index_fcst1) <- c(time = 1, member = 5) + index_fcst2 <- as.array(c(2.94, 4.08)) + dim(index_fcst2) <- c(time = 1, statistic = 2) + + result <- array(c(1.774329, 1.475816), + dim=c(time = 1, statistic = 2)) + + expect_equal(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none',"ME")), result, + tolerance=1e-4) + + result <- array(c(0.2415013, 4.2232416, -0.9925062, 1.5508469, 0.9097119, + 1.5486845), dim=c(time = 3, statistic = 2)) + + expect_equal(BEI_PDFBest(index_obs, index_hind1, index_hind2, + method_BC = c('none',"ME")), result, + tolerance=1e-4) + +}) + +test_that("Sanity checks", { + index_obs<- c(2, 3, 0.6) + dim(index_obs) <- c(time = 3) + index_hind1 <- c(-2.4, 3.6, -5, -1, -0.5, -0.2, 3, 0.6, 1, 0, -0.5, 2) + dim(index_hind1) <- c(time = 3, member = 4) + index_hind2 <- c(1.194390, 4.298768, -1.222586, 4.419386, 3.972776, 3.998928) + dim(index_hind2) <- c(time = 3, statistic=2) + index_fcst1 <- c(-2.6, -0.6, 1.4, -2.4, 8.1) + dim(index_fcst1) <- c(time = 1, member = 5) + index_fcst2 <- as.array(c(2.94, 4.08)) + dim(index_fcst2) <- c(time = 1, statistic = 2) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none',"ME"), na.rm = 2), + "Parameter 'na.rm' must be a logical value.") + + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none',"ME"), + time_dim_name = 2), + "Parameter 'time_dim_name' must be a character string indicating ", + "the name of the temporal dimension.") + + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = list('none',"ME")), + paste0("Parameter 'method_BC' must be a character vector.")) + + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME','ME')), + "Length of parameter 'method_BC' must be 1 or 2.") + + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','HME')), + "Elements of parameter 'method_BC' must be equals to ", + "'none, 'ME' or 'LMEV'") + + expect_error(BEI_PDFBest(index_obs = 2, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Parameter 'index_obs' must be an array.") + + expect_error(BEI_PDFBest(index_obs, index_hind1 = 3, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Parameter 'index_hind1' must be an array.") + + index_hind1 <- 1:15 + dim(index_hind1) <- c(3, 5) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Parameters 'index_obs', 'index_hind1' and 'index_hind2' ", + "should have dimmension names.") + + dim(index_hind1) <- c(pepe = 3, member = 5) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Parameter 'index_hind1' must have temporal dimension.") + + index_hind1 <- 1:8 + dim(index_hind1) <- c(time = 2, member = 4) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Length of temporal dimensions ", + "of parameter 'index_obs' and 'index_hind1' must be equals.") + + index_hind1 <- 1:12 + dim(index_hind1) <- c(time = 3, season = 4) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Parameter 'index_hind1' must have dimension 'member' or 'statistic'") + + dim(index_hind1) <- c(time = 3, member = 4, statistic = 1) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Parameter 'index_hind1' must have at least dimension 'member' ", + "or 'statistic', not 'member' and 'statistic' together.") + index_hind1 <- 1:9 + dim(index_hind1) <- c(time = 3, member = 1, season = 3) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Length of dimension 'member' ", + "of parameter 'index_hind1' must be greater than 1.") + + + index_hind1 <- 1:12 + dim(index_hind1) <- c(time = 3, member = 4) + index_hind2 <- 1:9 + dim(index_hind2) <- c(time = 3, statistic=3) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Length of dimension 'statistic' ", + "of parameter 'index_hind2' must be equal to 2.") + + index_hind2 <- 1:6 + dim(index_hind2) <- c(time = 3, statistic=2) + index_fcst1 <- 1:8 + dim(index_fcst1) <- c(time = 1, member = 8) + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1 = 2, + index_fcst2, method_BC = c('none','ME')), + "Parameter 'index_fcst1' must be an array.") + + index_fcst1 <- 1:16 + dim(index_fcst1) <- c(time = 2, member = 8) + + expect_error(BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, + index_fcst2, method_BC = c('none','ME')), + "Length of temporal dimension ", + "of parameter 'index_fcst1' must be equal to 1.") + +}) diff --git a/tests/testthat/test-BEI_Weights.R b/tests/testthat/test-BEI_Weights.R new file mode 100644 index 00000000..59b2fca1 --- /dev/null +++ b/tests/testthat/test-BEI_Weights.R @@ -0,0 +1,90 @@ +context("Generic tests") +test_that("basic use case", { + index_weight <- 1 : (3 * 3) + dim(index_weight) <- c(sdate = 3, member = 3) + pdf_weight <- 1 : (3 * 2) + dim(pdf_weight) <- c(sdate = 3, statistic = 2) + + result <- array(c(0.4808867, 0.4306591, 0.4017633, + 0.3629923, 0.3597167, 0.3545549, + 0.1561210, 0.2096243, 0.2436818), + dim = c(sdate = 3, member = 3)) + + expect_equal(BEI_Weights(index_weight, pdf_weight), result, tolerance=1e-4) + +}) + +test_that("Sanity checks", { + expect_error( + BEI_Weights(index_weight, pdf_weight, time_dim_name = 1), + "Parameter 'time_dim_name' must be a character string ", + "indicating the name of the temporal dimension.") + + expect_error( + BEI_Weights(index_weight = 1), + "Parameter 'index_weight' must be an array.") + + expect_error( + BEI_Weights(index_weight, pdf_weight = 1), + "Parameter 'pdf_weight' must be an array.") + + index_weight <- 1 : (3 * 3) + dim(index_weight) <- c(3, 3) + pdf_weight <- 1 : (3 * 2) + dim(pdf_weight) <- c(3, 2) + + expect_error( + BEI_Weights(index_weight, pdf_weight), + "Parameters 'index_weight' and 'pdf_weight'", + " should have dimmension names.") + + index_weight <- 1 : (3 * 3) + dim(index_weight) <- c(time= 3, season = 3) + pdf_weight <- 1 : (3 * 2) + dim(pdf_weight) <- c(time = 3, statistic = 2) + expect_error( + BEI_Weights(index_weight, pdf_weight), + "Parameter 'index_weight' must have dimension 'member'.") + + index_weight <- 1 : (3 * 3) + dim(index_weight) <- c(time= 3, member = 3) + pdf_weight <- 1 : (3 * 2) + dim(pdf_weight) <- c(time = 3, season = 2) + expect_error( + BEI_Weights(index_weight, pdf_weight), + "Parameter 'pdf_weight' must have dimension 'statistic'.") + + index_weight <- 1 : (3 * 3) + dim(index_weight) <- c(time= 3, member = 3) + pdf_weight <- 1 : 2 + dim(pdf_weight) <- c(statistic = 2) + expect_error( + BEI_Weights(index_weight, pdf_weight), + "Parameter 'pdf_weight' must have temporal dimension.") + + index_weight <- 1 : 3 + dim(index_weight) <- c(member = 3) + pdf_weight <- 1 : 4 + dim(pdf_weight) <- c(time = 2, statistic = 2) + expect_error( + BEI_Weights(index_weight, pdf_weight), + "Parameter 'index_weight' must have temporal dimension.") + + index_weight <- 1 : 12 + dim(index_weight) <- c(time= 4, member = 3) + pdf_weight <- 1 : 4 + dim(pdf_weight) <- c(time = 2, statistic = 2) + expect_error( + BEI_Weights(index_weight, pdf_weight), + "Length of temporal dimension of parameters must be equal") + + index_weight <- 1 : (3 * 3) + dim(index_weight) <- c(sdate = 3, member = 3) + pdf_weight <- 1 : (3 * 3) + dim(pdf_weight) <- c(sdate = 3, statistic = 3) + expect_error( + BEI_Weights(index_weight, pdf_weight), + "Length of dimension 'statistic' ", + "of the parameter 'pdf_weight' must be equal to 2.") + +}) diff --git a/tests/testthat/test-CST_BEI_Weighting.R b/tests/testthat/test-CST_BEI_Weighting.R new file mode 100644 index 00000000..2a6cc009 --- /dev/null +++ b/tests/testthat/test-CST_BEI_Weighting.R @@ -0,0 +1,177 @@ +context("Generic tests") +test_that("basic use case", { + + var_exp <- 1 : (2 * 4 * 3 * 2) + dim(var_exp) <- c(time = 2, member = 4, lat = 3, lon = 2) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.4, 0.3, 0.1, 0.2, 0.4, 0.4, 0.1, 0.2, 0.4, 0.2) + dim(aweights) <- c(time = 2, member = 4, dataset = 2) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + result <- array(c(4.4, 5.4, 12.4, 13.4, 20.4, 21.4, + 28.4, 29.4, 36.4, 37.4, 44.4, 45.4, + 4.6, 4.8, 12.6, 12.8, 20.6, 20.8, + 28.6, 28.8, 36.6, 36.8, 44.6, 44.8), + dim = c(time = 2, lat = 3, lon = 2, dataset =2)) + + expect_equal(CST_BEI_Weighting(var_exp, aweights, type = 'ensembleMean', + time_dim_name = 'time')$data, result, tolerance=1e-4) + + var_exp <- 1 : (2 * 3 * 1 * 2) + dim(var_exp) <- c(time = 2, member = 3, lat = 1, lon = 2) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(time = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + result <- array(c(0.5, 0.1, 0.0, 0.4, 0.5, 0.5, + 0.5, 0.1, 0.0, 0.4, 0.5, 0.5), + dim = c(time = 2, tercil = 3, lat = 1, lon = 2)) + + expect_equal(CST_BEI_Weighting(var_exp, aweights, type = 'probs', + time_dim_name = 'time')$data, result, tolerance=1e-4) + + var_exp <- 1 : (2 * 3 * 1 * 2) + dim(var_exp) <- c(sdate = 2, member = 3, lat = 1, lon = 2) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(sdate = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + result <- array(c(0.5, 0.1, 0.0, 0.4, 0.5, 0.5, + 0.5, 0.1, 0.0, 0.4, 0.5, 0.5), + dim = c(sdate = 2, tercil = 3, lat = 1, lon = 2)) + + expect_equal(CST_BEI_Weighting(var_exp, aweights, type = 'probs', + time_dim_name = 'sdate')$data, result, tolerance=1e-4) + + var_exp <- 1 : (2 * 3) + dim(var_exp) <- c(sdate = 2, member = 3) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(sdate = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + result <- array(c(0.5, 0.1, 0.0, 0.4, 0.5, 0.5), + dim = c(sdate = 2, tercil = 3)) + + expect_equal(CST_BEI_Weighting(var_exp, aweights, type = 'probs', + time_dim_name = 'sdate')$data, result, tolerance=1e-4) +}) + +test_that("Sanity checks", { + expect_error( + CST_BEI_Weighting(var_exp, aweights, type = 'probs', time_dim_name = 1), + "Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + + expect_warning( + CST_BEI_Weighting(var_exp, aweights, type = 'probs', time_dim_name = c('sdate', 'pepe')), + "Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + + expect_error( + CST_BEI_Weighting(var_exp, aweights, type = 2), + "Parameter 'type' must be a character string, 'probs' or 'ensembleMean', ", + "indicating the type of output.") + + expect_warning( + CST_BEI_Weighting(var_exp, aweights, type = c('probs', 'ensmeble'), + time_dim_name = c('sdate', 'pepe')), + "Parameter 'type' has length greater than 1 and ", + "only the first element will be used.") + + expect_error( + CST_BEI_Weighting(var_exp = 1, aweights), + "Parameter 'var_exp' must be of the class 's2dv_cube', ", + "as output by CSTools::CST_Load.") + + expect_error( + CST_BEI_Weighting(var_exp, aweights = 2), + "Parameter 'aweights' must be an array.") + + var_exp <- 1 : (2 * 3) + dim(var_exp) <- c(2, 3) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(2, 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + expect_error( + CST_BEI_Weighting(var_exp, aweights), + "Element 'data' from parameter 'var_exp' and parameter 'aweights'", + " should have dimmension names.") + + var_exp <- 1 : (2 * 3) + dim(var_exp) <- c(sdate = 2, member = 3) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(sdate = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + expect_error( + CST_BEI_Weighting(var_exp, aweights), + "Element 'data' from parameter 'var_exp' must have ", + "temporal dimension.") + + var_exp <- 1 : (2 * 3) + dim(var_exp) <- c(time = 2, member = 3) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(sdate = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + expect_error( + CST_BEI_Weighting(var_exp, aweights), + "Parameter 'aweights' must have temporal dimension.") + + var_exp <- 1 : (2 * 3) + dim(var_exp) <- c(time = 2, season = 3) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(time = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + expect_error( + CST_BEI_Weighting(var_exp, aweights), + "Element 'data' from parameter 'var_exp' must have ", + "dimension 'member'.") + + var_exp <- 1 : (2 * 3) + dim(var_exp) <- c(time = 2, member = 3) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(time = 2, season = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + expect_error( + CST_BEI_Weighting(var_exp, aweights), + "Parameter 'aweights' must have ", + "dimension 'member'.") + + var_exp <- 1 : (3 * 3) + dim(var_exp) <- c(time = 3, member = 3) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(time = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + expect_error( + CST_BEI_Weighting(var_exp, aweights), + "Length of temporal dimensions ", + "of element 'data' from parameter 'var_exp' and parameter ", + "'aweights' must be equals.") + + var_exp <- 1 : (3 * 4) + dim(var_exp) <- c(time = 3, member = 4) + aweights <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5) + dim(aweights) <- c(time = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' + + expect_error( + CST_BEI_Weighting(var_exp, aweights), + "Length of temporal dimensions of element 'data' from ", + "parameter 'var_exp' and parameter 'aweights' must be equals.") + +}) diff --git a/tests/testthat/test-CST_EMBestIndexWeighting.R b/tests/testthat/test-CST_EMBestIndexWeighting.R deleted file mode 100644 index b4a824a3..00000000 --- a/tests/testthat/test-CST_EMBestIndexWeighting.R +++ /dev/null @@ -1,144 +0,0 @@ -context("Generic tests") -test_that("basic use case", { - var_exp <- 1 : (2 * 1 * 3 * 3 * 2) - dim(var_exp) <- c(time = 2, dataset = 1, member = 3, lat = 3, lon = 2) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - result <- array(c(5.669304, 5.831401, 16.03673, 13.97668, - 26.40415, 22.12196, 36.77157, 30.26724, - 47.13899, 38.41252, 57.50641, 46.55779), - dim = c(time = 2, dataset = 1, season = 1, - lat = 3, lon = 2)) - - expect_equal(CST_EMBestIndexWeighting(var_exp, weights_index_exp)$data, result, tolerance=1e-4) - -}) - -test_that("Sanity checks", { - expect_error( - CST_EMBestIndexWeighting(var_exp = 1), - paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.")) - expect_error( - CST_EMBestIndexWeighting(var_exp = var_exp, weights_index_exp = 1), - paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.")) - - var_exp <- 1 : (2 * 1) - var_exp <- list(data = var_exp) - class(var_exp) <- 's2dv_cube' - set.seed(23) - weights_index_exp <- runif(2 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - weights_index_exp <- list(data = weights_index_exp) - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", - " should have dimmension names.")) - - var_exp <- 1 : (2 * 1 * 3 * 3) - dim(var_exp) <- c(time = 2, dataset = 1, member = 3, lat = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) - - var_exp <- 1 : (1 * 2 * 3) - dim(var_exp) <- c(dataset = 1, time = 2, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) - - var_exp <- 1 : (2 * 1 * 3) - dim(var_exp) <- c(time = 2, dataset = 1, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) - - var_exp <- 1 : (2 * 1 * 3) - dim(var_exp) <- c(time = 2, dataset = 1, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(member = 3, season = 1, time = 2, dataset = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) - - - var_exp <- 1 : (3 * 1 * 3) - dim(var_exp) <- c(time = 3, dataset = 1, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("The length of the dimension 'time' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'time' of the parameter 'weights_index_exp' in the component 'data'")) - - var_exp <- 1 : (2 * 2 * 3) - dim(var_exp) <- c(time = 2, dataset = 2, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("The length of the dimension 'dataset' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'dataset' of the parameter 'weights_index_exp' in the component 'data'")) - - - var_exp <- 1 : (2 * 2 * 4) - dim(var_exp) <- c(time = 2, dataset = 2, member = 4) - set.seed(23) - weights_index_exp <- runif(2 * 2 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 2, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_EMBestIndexWeighting(var_exp, weights_index_exp), - paste0("The length of the dimension 'member' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'member' of the parameter 'weights_index_exp' in the component 'data'")) - -}) diff --git a/tests/testthat/test-CST_PDFBest.R b/tests/testthat/test-CST_PDFBest.R deleted file mode 100644 index c1da44b6..00000000 --- a/tests/testthat/test-CST_PDFBest.R +++ /dev/null @@ -1,102 +0,0 @@ -context("Generic tests") -test_that("basic use case", { - pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) - dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 4, season = 1) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - result <- list(data = array(c(14.28166, 15.29171, 16.30088, - 14.37964, 15.09211, 15.80411), - dim = c(time = 3, statistic = 2, - season = 1)), - statistic = c("mean", "sigma")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = TRUE), result, tolerance=1e-4) - - - result2 <- list(data = array(c(5.952278, 7.090211, 8.215385, - 8.849182, 9.638375, 10.418918), - dim = c(time = 3, statistic = 2, - season = 1)), - statistic = c("mean", "sigma")) - attr(result2, 'class') <- 's2dv_cube' - expect_equal(CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE), result2, tolerance=1e-4) - - -}) - -test_that("Sanity checks", { - pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) - dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 4, season = 1) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp = 1), - paste0("Parameter 'pdf_exp' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.")) - expect_error( - CST_PDFBest(pdf_exp = pdf_exp, bc_dataset1 = 2, bc_dataset2 = 'PEPE'), - "The parameter 'bc_dataset1' must be a logical value.") - expect_error( - CST_PDFBest(pdf_exp = pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = 'PEPE'), - "The parameter 'bc_dataset2' must be a logical value.") - - pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp), - paste0("Element 'data' from parameters 'pdf_exp' should have dimmension ", - "names.")) - - pdf_exp <- c(1: (3 * 2 * 1 * 4)) - dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 4) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) - - pdf_exp <- c(1: (3 * 2 * 1 * 4 * 1)) - dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, myStatistic = 4) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) - - pdf_exp <- c(1: (2 * 3 * 1 * 4 * 1)) - dim(pdf_exp) <- c(dataset = 2, time = 3, member = 1, statistic = 4, season = 1) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) - - pdf_exp <- c(1: (3 * 3 * 1 * 4 * 1)) - dim(pdf_exp) <- c(time = 3, dataset = 3, member = 1, statistic = 4, season = 1) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp), - paste0("The length of the dimension 'dataset' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 2.")) - - pdf_exp <- c(1: (3 * 2 * 3 * 4 * 1)) - dim(pdf_exp) <- c(time = 3, dataset = 2, member = 3, statistic = 4, season = 1) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp), - paste0("The length of the dimension 'member' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 1.")) - - pdf_exp <- c(1: (3 * 2 * 1 * 5 * 1)) - dim(pdf_exp) <- c(time = 3, dataset = 2, member = 1, statistic = 5, season = 1) - pdf_exp <- list(data = pdf_exp) - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_PDFBest(pdf_exp), - paste0("The length of the dimension 'statistic' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 4.")) - -}) diff --git a/tests/testthat/test-CST_PDFIndexFcst.R b/tests/testthat/test-CST_PDFIndexFcst.R deleted file mode 100644 index 83d80f56..00000000 --- a/tests/testthat/test-CST_PDFIndexFcst.R +++ /dev/null @@ -1,378 +0,0 @@ -context("Generic tests") -test_that("basic use case", { - index_hind <- 1 : (5 * 3 * 7 * 4) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - result <- list(data = array(c(10:12, rep(45,3), rep(NA,6), - 31:33, rep(135,3), rep(NA,6), - 52:54, rep(225,3), rep(NA,6), - 73:75, rep(315,3), rep(NA,6)), - dim = c(time = 1, dataset = 3, statistic = 4, - season = 4)), - statistic = c("mean_notbc", "sigma_notbc", "mean_bc", - "sigma_bc")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFIndexFcst(index_hind, index_obs, index_fcst, - type_exp = 'ens', method = 'LMEV', - na.rm = FALSE), result, tolerance=1e-4) - - index_hind <- 1 : (5 * 3 * 1* 2 * 2) - dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 2, - season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 2 * 2) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 2, - season = 2) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - result <- list(data = array(c(1:3, rep(0,3), 1:3, rep(0,3), - 7:9, rep(15,3), -8:-6, rep(0,3)), - dim = c(time = 1, dataset = 3, statistic = 4, - season = 2)), - statistic = c("mean_notbc", "sigma_notbc", "mean_bc", - "sigma_bc")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFIndexFcst(index_hind, index_obs, index_fcst, - type_exp = 'sta', na.rm = FALSE), result, tolerance=1e-4) - - - index_hind2 <- index_hind - index_hind2$data[1, 1, 1, 1, 1] <- NA - result <- list(data = array(c(1:3, rep(0,3), 1:3, NA, rep(0,2), - 7:9, rep(15,3), -8:-6, rep(0,3)), - dim = c(time = 1, dataset = 3, statistic = 4, - season = 2)), - statistic = c("mean_notbc", "sigma_notbc", "mean_bc", - "sigma_bc")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFIndexFcst(index_hind = index_hind2, index_obs = index_obs, - index_fcst = index_fcst, type_exp = 'sta', - na.rm = FALSE), result, tolerance=1e-4) -}) - -test_that("Sanity checks", { - index_hind <- 1 : (5 * 3 * 7 * 4) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind = 1), - paste0("Parameter 'index_fcst', 'index_hind' and 'index_obs' must be ", - "of the class 's2dv_cube', as output by CSTools::CST_Load.")) - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = 1), - paste0("Parameter 'index_fcst', 'index_hind' and 'index_obs' must be ", - "of the class 's2dv_cube', as output by CSTools::CST_Load.")) - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, - index_fcst = 1), - paste0("Parameter 'index_fcst', 'index_hind' and 'index_obs' must be ", - "of the class 's2dv_cube', as output by CSTools::CST_Load.")) - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, - index_fcst = index_fcst, method = 'PEPE'), - "The parameter 'method' must be equal to 'ME' or 'LMEV'") - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, - index_fcst = index_fcst, method = 2), - "The parameter 'method' must be equal to 'ME' or 'LMEV'") - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, - index_fcst = index_fcst, method = NA), - "The parameter 'method' must be equal to 'ME' or 'LMEV'") - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, - index_fcst = index_fcst, na.rm = 'PEPE'), - "The parameter 'na.rm' must be a logical value.") - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, - index_fcst = index_fcst, type_exp = 'PEPE'), - "The parameter 'type_exp' must be equal to 'ens' or 'sta'") - - index_hind <- 1 : (5 * 3 * 7 * 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind = index_hind, index_obs = index_obs, - index_fcst = index_fcst), - paste0("Element 'data' from parameters 'index_hind', 'index_obs' and 'index_fcst'", - " should have dimmension names.")) - - index_hind <- 1 : (3 * 5 * 7 * 4) - dim(index_hind) <- c(time = 3, dataset = 5, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (3 * 5 * 4) - dim(index_obs) <- c(time = 3, dataset = 5, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (5 * 2 * 4) - dim(index_fcst) <- c(time = 1, dataset = 5, member = 1, statistic = 2, - season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The dimension names of the component 'data' of the parameter ", - "'index_hind' and 'index_fcst' must be equals.")) - - index_hind <- 1 : (3 * 5 * 7 * 4) - dim(index_hind) <- c(dataset = 3, time = 5, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The dimension names of the component 'data' of the parameter 'index_hind' ", - "and 'index_fcst' must be equals.")) - - index_hind <- 1 : (5 * 3 * 7 * 4) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, season = 4, member = 1) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) - - index_hind <- 1 : (5 * 3 * 7 * 4) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (7 * 4 * 3) - dim(index_fcst) <- c(time = 1, member = 7, season = 4, dataset = 3) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The dimension names of the component 'data' of the parameter 'index_hind' ", - "and 'index_fcst' must be equals.")) - - index_hind <- 1 : (5 * 3 * 4) - dim(index_hind) <- c(time = 5, dataset = 3, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 4) - dim(index_obs) <- c(time = 5, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The dimension names of the component 'data' of the parameter 'index_hind' ", - "and 'index_fcst' must be equals.")) - - index_hind <- 1 : (5 * 3 * 7 * 4) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (4 * 3 * 4) - dim(index_obs) <- c(time = 4, dataset = 3, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The length of the dimensions 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_hind' must be equals.")) - - index_hind <- 1 : (5 * 3 * 7 * 4) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 2 * 4) - dim(index_obs) <- c(time = 5, dataset = 2, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (4* 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 4, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The length of the dimensions 'dataset' in the component 'data' ", - "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", - "equals.")) - - index_hind <- 1 : (5 * 3 * 7 * 2) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 5) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 5) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 4) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_hind', 'index_obs' and 'index_fcst' must be ", - "equals.")) - - index_hind <- 1 : (2 * 3 * 7 * 2) - dim(index_hind) <- c(time = 2, dataset = 3, member = 7, season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (2 * 3 * 2) - dim(index_obs) <- c(time = 2, dataset = 3, member = 1, season = 2) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 7 * 2) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 2) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_hind' must be greater than 2.")) - - index_hind <- 1 : (5 * 3 * 7 * 2) - dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (2 * 3 * 7 * 2) - dim(index_fcst) <- c(time = 2, dataset = 3, member = 7, season = 2) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_fcst' must be equal to 1.")) - - index_hind <- 1 : (5 * 3 * 3 * 2) - dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 3, - season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (1 * 3 * 3 * 2) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 3, - season = 2) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The dimension 'statistic' must be equal to 2 in the component 'data' ", - "of the parameter 'index_hind' and 'index_fcst' if the length of the ", - "dimension 'member' is equal to 1")) - - index_hind <- 1 : (5 * 3 * 2 * 2) - dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 2, - season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 2 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 2, season = 2) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (1 * 3 * 2 * 2) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 2, - season = 2) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst), - paste0("The dimension 'member' must be equal to 1 in the component 'data' ", - "of the parameter 'index_obs'.")) - - index_hind <- 1 : (5 * 3 * 1 * 2 * 2) - dim(index_hind) <- c(time = 5, dataset = 3, member = 1, statistic = 2, - season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 2 * 2) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 1, statistic = 2, - season = 2) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = 'ens'), - paste0("The parameter 'type_exp' must be equal to 'sta' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_fcst' exits")) - - index_hind <- 1 : (5 * 3 * 8 * 2) - dim(index_hind) <- c(time = 5, dataset = 3, member = 8, season = 2) - index_hind <- list(data = index_hind) - class(index_hind) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - index_fcst <- 1 : (3 * 8 * 2) - dim(index_fcst) <- c(time = 1, dataset = 3, member = 8, season = 2) - index_fcst <- list(data = index_fcst) - class(index_fcst) <- 's2dv_cube' - expect_error( - CST_PDFIndexFcst(index_hind, index_obs, index_fcst, type_exp = 'sta'), - paste0("The parameter 'type_exp' can't be equal to 'sta' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_fcst' doesn't exit.")) -}) diff --git a/tests/testthat/test-CST_PDFIndexHind.R b/tests/testthat/test-CST_PDFIndexHind.R deleted file mode 100644 index 6f70f8b6..00000000 --- a/tests/testthat/test-CST_PDFIndexHind.R +++ /dev/null @@ -1,341 +0,0 @@ -context("Generic tests") -test_that("basic use case", { - index_obs <- 1 : 5 - dim(index_obs) <- c(time = 5, dataset = 1, member = 1, season = 1) - index_exp <- 1 : (5 * 7) - dim(index_exp) <- c(time = 5, dataset = 1, member = 7, season = 1) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - result <- list(data = array(c(16:20,rep(15,5),rep(NA,10)), - dim = c(time = 5, dataset = 1, statistic = 4, - season = 1)), - statistic = c("mean_notbc", "sigma_notbc", "mean_bc", - "sigma_bc")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, - type_exp = 'ens', method = 'LMEV'), result, tolerance=1e-4) - - index_obs <- 1 : 5 - dim(index_obs) <- c(time = 5, dataset = 1, member = 1, season = 1) - index_exp <- 1 : (5 * 2) - dim(index_exp) <- c(time = 5, dataset = 1, member = 1, statistic = 2, season = 1) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - result <- list(data = array(c(1:5,rep(0,5),c(1:5), rep(0,5)), - dim = c(time = 5, dataset = 1, statistic = 4, - season = 1)), - statistic = c("mean_notbc", "sigma_notbc", "mean_bc", - "sigma_bc")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, - type_exp = 'sta'), result, tolerance=1e-4) - - index_obs <- 1 : (5 * 3 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - result <- list(data = array(c(46:60,rep(45,15),1:15,rep(0,15),151:165, - rep(135,15),16:30,rep(0,15),256:270,rep(225,15), - 31:45,rep(0,15),361:375,rep(315,15),46:60, - rep(0,15)), dim = c(time = 5, dataset = 3, - statistic = 4, season = 4)), - statistic = c("mean_notbc", "sigma_notbc", "mean_bc", - "sigma_bc")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - result, tolerance=1e-4) - - index_exp2 <- index_exp - index_exp2$data[1, 1, 1, 1] <- NA - CST_PDFIndexHind(index_exp = index_exp2, index_obs = index_obs, na.rm = FALSE) - result <- list(data = array(c(NA,47:60,45,rep(NA,4),rep(45,10),rep(NA,5),6:15, - rep(NA,5),rep(0,10),151:165, - rep(135,15),16:30,rep(0,15),256:270,rep(225,15), - 31:45,rep(0,15),361:375,rep(315,15),46:60, - rep(0,15)), dim = c(time = 5, dataset = 3, - statistic = 4, season = 4)), - statistic = c("mean_notbc", "sigma_notbc", "mean_bc", - "sigma_bc")) - attr(result, 'class') <- 's2dv_cube' - expect_equal(CST_PDFIndexHind(index_exp = index_exp2, index_obs = index_obs, - na.rm = FALSE), result, tolerance=1e-4) - - -}) - -test_that("Sanity checks", { - expect_error( - CST_PDFIndexHind(index_exp = 1), - paste0("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.")) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - class(index_exp) <- 's2dv_cube' - index_obs <- 1 : (5 * 3 * 1 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_obs <- list(data = index_obs) - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = 1), - paste0("Parameter 'index_exp' and 'index_obs' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.")) - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, method = 'PEPE'), - "The parameter 'method' must be equal to 'ME' or 'LMEV'") - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, method = 2), - "The parameter 'method' must be equal to 'ME' or 'LMEV'") - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, method = NA), - "The parameter 'method' must be equal to 'ME' or 'LMEV'") - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, na.rm = 'PEPE'), - "The parameter 'na.rm' must be a logical value.") - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'PEPE'), - "The parameter 'type_exp' must be equal to 'ens' or 'sta'") - - index_obs <- 1 : (5 * 3 * 2 * 4) - dim(index_obs) <- c(5, 3, 2, 4) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Element 'data' from parameters 'index_exp' and 'index_obs'", - " should have dimmension names.")) - - index_obs <- 1 : (2 * 3 * 1 * 5) - dim(index_obs) <- c(season = 2, dataset = 3, member = 1, time = 5) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) - - index_obs <- 1 : (5 * 3 * 1 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_exp <- 1 : (3 * 5 * 2 * 7) - dim(index_exp) <- c(dataset = 3, time = 5, season = 2, member = 7) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) - - index_obs <- 1 : (1 * 5 * 3 * 2) - dim(index_obs) <- c(member = 1, time = 5, dataset = 3, season = 2) - index_exp <- 1 : (3 * 5 * 2 * 7) - dim(index_exp) <- c(dataset = 3, time = 5, season = 2, member = 7) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) - - index_obs <- 1 : (5 * 3 * 1) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) - - index_obs <- 1 : (5 * 3 * 1 * 1) - dim(index_obs) <- c(reftime = 5, dataset = 3, member = 1, season = 1) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_obs'")) - - index_obs <- 1 : (5 * 3 * 1 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, seasonal = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) - - index_obs <- 1 : (5 * 3 * 1 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_exp <- 1 : (5 * 3 * 7) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) - - index_obs <- 1 : (5 * 3 * 1 * 2) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 2) - index_exp <- 1 : (5 * 3 * 1 * 2 * 1) - dim(index_exp) <- c(time = 5, dataset = 3, member = 1, myStatistic = 2, season = 1) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) - - - index_obs <- 1 : (3 * 3 * 1 * 4) - dim(index_obs) <- c(time = 3, dataset = 3, member = 1, season = 4) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("The length of the dimensions 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be equals.")) - - index_obs <- 1 : (3 * 3 * 1 * 4) - dim(index_obs) <- c(time = 3, dataset = 3, member = 1, season = 4) - index_exp <- 1 : (3 * 4 * 7 * 4) - dim(index_exp) <- c(time = 3, dataset = 4, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("The length of the dimensions 'dataset' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be equals.")) - - index_obs <- 1 : (3 * 3 * 1 * 2) - dim(index_obs) <- c(time = 3, dataset = 3, member = 1, season = 2) - index_exp <- 1 : (3 * 3 * 7 * 4) - dim(index_exp) <- c(time = 3, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be equals.")) - - - index_obs <- 1 : (1 * 3 * 2 * 4) - dim(index_obs) <- c(time = 1, dataset = 3, member = 2, season = 4) - index_exp <- 1 : (1 * 3 * 7 * 4) - dim(index_exp) <- c(time = 1, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("The length of the dimension 'time' in the component 'data' ", - "of the parameter 'index_obs' and 'index_exp' must be greater than 2.")) - - index_obs <- 1 : (5 * 3 * 2 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 2, season = 4) - index_exp <- 1 : (5 * 3 * 7 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("The length of the dimension 'member' in the component 'data' ", - "of the parameter 'index_obs' must be equal to 1.")) - - index_obs <- 1 : (5 * 3 * 1 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_exp <- 1 : (5 * 3 * 1 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs), - paste0("The dimension 'statistic' must be exist in the component 'data' ", - "of the parameter 'index_exp' if the length of the dimension ", - "'member' is equal to 1")) - - index_obs <- 1 : (5 * 3 * 1 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_exp <- 1 : (5 * 3 * 7 * 2 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 7, statistic = 2, - season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'sta'), - paste0("The length of the dimension 'member' in the component 'data' of the ", - "parameter 'index_exp' must be equal to 1 if the component 'data' ", - "of the parameter 'index_exp' has the dimension 'statistic'")) - - index_obs <- 1 : (5 * 3 * 1 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_exp <- 1 : (5 * 3 * 1 * 3 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 3, - season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'sta'), - paste0("The length of the dimension 'statistic' in the component 'data' of the ", - "parameter 'index_exp' must be equal to 2 if the component 'data' ", - "of the parameter 'index_exp' has the dimension 'member' equal to 1.")) - - index_obs <- 1 : (5 * 3 * 1 * 4) - dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) - index_exp <- 1 : (5 * 3 * 1 * 2 * 4) - dim(index_exp) <- c(time = 5, dataset = 3, member = 1, statistic = 2, - season = 4) - index_exp <- list(data = index_exp) - index_obs <- list(data = index_obs) - class(index_exp) <- 's2dv_cube' - class(index_obs) <- 's2dv_cube' - expect_error( - CST_PDFIndexHind(index_exp = index_exp, index_obs = index_obs, type_exp = 'ens'), - paste0("The parameter 'type_exp' must be equal to 'sta' because the ", - "dimension 'statistic' in the component 'data' of the ", - "parameter 'index_exp' exits")) -}) diff --git a/tests/testthat/test-CST_ProbBestIndexWeighting.R b/tests/testthat/test-CST_ProbBestIndexWeighting.R deleted file mode 100644 index 05f399cc..00000000 --- a/tests/testthat/test-CST_ProbBestIndexWeighting.R +++ /dev/null @@ -1,172 +0,0 @@ -context("Generic tests") -test_that("basic use case", { - var_exp <- 1 : (1 * 1 * 2 * 1 * 1) - dim(var_exp) <- c(time = 1, dataset = 1, member = 2, lat = 1, lon = 1) - weights_index_exp <- c(0.34, 0.66) - dim(weights_index_exp) <- c(time = 1, dataset = 1, member = 2, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - result <-array(c(0.34, 0, 0.66), - dim = c(time = 1, tercil = 3, dataset = 1, - season = 1, lat = 1, lon = 1)) - expect_equal(CST_ProbBestIndexWeighting(var_exp, weights_index_exp)$data, result, tolerance=1e-4) -}) - -test_that("Sanity checks", { - expect_error( - CST_ProbBestIndexWeighting(var_exp = 1), - paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.")) - expect_error( - CST_ProbBestIndexWeighting(var_exp = var_exp, weights_index_exp = 1), - paste0("Parameter 'var_exp' and 'weights_index_exp' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.")) - - var_exp <- 1 : (2 * 1) - var_exp <- list(data = var_exp) - class(var_exp) <- 's2dv_cube' - set.seed(23) - weights_index_exp <- runif(2 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - weights_index_exp <- list(data = weights_index_exp) - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("Element 'data' from parameters 'var_exp' and 'weights_index_exp'", - " should have dimmension names.")) - - var_exp <- 1 : (2 * 1 * 3 * 3) - dim(var_exp) <- c(time = 2, dataset = 1, member = 3, lat = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) - - var_exp <- 1 : (1 * 2 * 3) - dim(var_exp) <- c(dataset = 1, time = 2, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'var_exp'")) - - var_exp <- 1 : (2 * 1 * 3) - dim(var_exp) <- c(time = 2, dataset = 1, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) - - var_exp <- 1 : (2 * 1 * 3) - dim(var_exp) <- c(time = 2, dataset = 1, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(member = 3, season = 1, time = 2, dataset = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'weights_index_exp'")) - - - var_exp <- 1 : (3 * 1 * 3) - dim(var_exp) <- c(time = 3, dataset = 1, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("The length of the dimension 'time' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'time' of the parameter 'weights_index_exp' in the component 'data'")) - - var_exp <- 1 : (2 * 2 * 3) - dim(var_exp) <- c(time = 2, dataset = 2, member = 3) - set.seed(23) - weights_index_exp <- runif(2 * 1 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("The length of the dimension 'dataset' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'dataset' of the parameter 'weights_index_exp' in the component 'data'")) - - - var_exp <- 1 : (2 * 2 * 4) - dim(var_exp) <- c(time = 2, dataset = 2, member = 4) - set.seed(23) - weights_index_exp <- runif(2 * 2 * 3, 0.0001, 0.9999) - dim(weights_index_exp) <- c(time = 2, dataset = 2, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("The length of the dimension 'member' of the parameter 'var_exp' ", - "in the component 'data' must be equal to the length of the dimension", - " 'member' of the parameter 'weights_index_exp' in the component 'data'")) - - var_exp <- c(NA, 2 : (3 * 1 * 3 * 3 * 2)) - dim(var_exp) <- c(time = 3, dataset = 1, member = 3, lat = 3, lon = 2) - weights_index_exp <- c(0.2, 0.3482908, 0.3422055, - 0.1, 0.3504734, 0.3472929, - 0.6, 0.3012359, 0.3105016) - dim(weights_index_exp) <- c(time = 3, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("There are one or more elements of the component 'data' of the ", - "parameter 'var_exp' equal to NA.")) - - - var_exp <- 1 : (3 * 1 * 3 * 3 * 2) - dim(var_exp) <- c(time = 3, dataset = 1, member = 3, lat = 3, lon = 2) - weights_index_exp <- c(NA, 0.3482908, 0.3422055, - NA, 0.3504734, 0.3472929, - NA, 0.3012359, 0.3105016) - dim(weights_index_exp) <- c(time = 3, dataset = 1, member = 3, season = 1) - var_exp <- list(data = var_exp) - weights_index_exp <- list(data = weights_index_exp) - class(var_exp) <- 's2dv_cube' - class(weights_index_exp) <- 's2dv_cube' - expect_error( - CST_ProbBestIndexWeighting(var_exp, weights_index_exp), - paste0("There are one or more elements of the component 'data' of the ", - "parameter 'weights_index_exp' equal to NA.")) - - - -}) diff --git a/vignettes/BestNaoWeighting_vignette_new.Rmd b/vignettes/BestEstimationIndex_vignette.Rmd similarity index 60% rename from vignettes/BestNaoWeighting_vignette_new.Rmd rename to vignettes/BestEstimationIndex_vignette.Rmd index e4122aa1..634434e5 100644 --- a/vignettes/BestNaoWeighting_vignette_new.Rmd +++ b/vignettes/BestEstimationIndex_vignette.Rmd @@ -37,25 +37,21 @@ The synthetic data is created by running the following lines: ``` -NAO_obs <- rnorm(20, sd=3) -dim(NAO_obs) <- c(time = 20) -NAO_hind1 <- rnorm(20 * 25, sd=3) -dim(NAO_hind1) <- c(time = 20, member = 25) -NAO_hind2_mean <- rnorm(20, sd=3) -NAO_hind2_sd <- rnorm(20, mean=5, sd=1) +NAO_obs <- rnorm(3, sd=3) +dim(NAO_obs) <- c(time = 3) +NAO_hind1 <- rnorm(3 * 6, sd=3) +dim(NAO_hind1) <- c(time = 3, member = 6) +NAO_hind2_mean <- rnorm(3, sd=3) +NAO_hind2_sd <- rnorm(3, mean=5, sd=1) NAO_hind2 <- cbind(NAO_hind2_mean,NAO_hind2_sd) -dim(NAO_hind2) <- c(time=20, statistic=2) +dim(NAO_hind2) <- c(time=3, statistic=2) -NAO_fcst1 <- rnorm(1 * 51, sd=3) -dim(NAO_fcst1) <- c(time = 1, member = 51) -NAO_fcst2 <- c(1.55,4.02) -dim(NAO_fcst2) <- c(time=1, statistic=2) ``` The acumulated precipiation field (only march for memory limiations) is loaded by running: ``` -lonlat_prec$data <- apply(lonlat_data$prec, c(1, 2, 3, 5, 6), sum) +lonlat_prec$data <- apply(lonlat_prec$data, c(1, 2, 3, 5, 6), sum) ``` @@ -82,7 +78,8 @@ The function WeightIndex computes these weights for each ensemble's member based ``` -weigths_hind <- BEI_Weights(NAO_hind1, pdf_hind_best) +weights_hind <- BEI_Weights(NAO_hind1, pdf_hind_best) +names(dim(weights_hind))[1] <- 'sdate' ``` The expected dimensions of these weights are 'member' and temporal dimension. @@ -95,8 +92,8 @@ The function `CST_BEI_Weighting` computes the ensemble mean or the terciles prob The corrected precipitation field is obtained by running: ``` -em_prec <- CST_BEI_Weighting(latlon_prec, weights_hind, type = 'ensembleMean') -prob_prec <- CST_BEI_Weighting(latlon_prec, weights_hind, type = 'probs') +em_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'ensembleMean', time_dim_name = 'sdate') +prob_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'probs', ) ``` ## Comparison and visualization @@ -106,54 +103,27 @@ To do this a equiprobability weights are created and they are applied to the ori ``` -aweights_raw <- rep(1/25, 3 * 25) -dim(aweights_raw) <- dim(aweights) +aweights_raw <- rep(1/6, 3 * 6) +dim(aweights_raw) <- dim(weights_hind) -em_prec_raw <- CST_Weighted(latlon_prec, aweights_raw, type = 'ensembleMean') -prob_prec_raw <- CST_Weighted(latlon_prec, aweights_raw, type = 'probs') +em_prec_raw <- CST_BEI_Weighting(lonlat_prec, aweights_raw, type = 'ensembleMean', time_dim_name = 'sdate') +prob_prec_raw <- CST_BEI_Weighting(lonlat_prec, aweights_raw, type = 'probs', time_dim_name = 'sdate') ``` -A map with the probability that the total precipitation will be in the lower tercile based on the Best Estimated Index NAO is obtained by running the code: +A map with the probability that the total precipitation will be in the lower/normal/upper tercile based on the Best Estimated Index NAO could be obtained using the 'PlotEquiMap' function or 'PlotMostLikelyQuantileMap' function from 'CSToools' package. + +The following figures show the results obtained for real data: winter precipitation (November to March) from 1997 to 2016 over Iberia Peninsula, combining the ECMWF-S5 dynamical model and the S-ClimWaRe empirical model from AEMET system to obtain the Best Estimation of Index NAO. -``` -coordVAR <- list(lonmin = -10, lonmax = 5, latmin = 35, latmax = 45) -lonPlot <- seq(coordVAR$lonmin,coordVAR$lonmax,0.50) -latPlot <- seq(coordVAR$latmax,coordVAR$latmin,-0.50) -probData_CST <- prob_var_exp$data[which(var_exp$Dates$start == 2012),1,1,,] -brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) -cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") -names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") -ofile <- "./Figures/probLower_CST_weighted.png" -PlotEquiMap(t(probData_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, - fileout =ofile, brks=brks, cols = cols, - toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on the best NAO pdf estimate"), - title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) - -PlotEquiMap(prob_prec[,,1], lon = prob_prec$lon, lat = prob_prec$lat, filled_continents = FALSE, - fileout = ofile) -PlotMostLikelyQuantileMap( ) -``` -![](./Figures/probLower_weight_CST.png){width=70%} + +![](./Figures/BestEstimationIndex_fig1.png){width=70%} In a similar way we can plot the map with the probability that the total precipitation from November 2012 to March 2013, for example, will be in the lower tercile from ECMWF Seasonal Forecast System 5 (raw) to compare results running the code: -```{r message=FALSE} -probDataRaw_CST <- prob_var_exp_raw$data[which(var_exp$Dates$start == 2012),1,1,,] -brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) -cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") -names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") -ofile <- "./Figures/probLower_CST_weighted_raw.png" -PlotEquiMap(t(probDataRaw_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, - fileout =ofile, brks=brks, cols = cols, - toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on ECMWF-S5 (raw)"), - title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) - -``` -![](./Figures/probLower_CST_weighted_raw.png){width=70%} +![](./Figures/BestEstimationIndex_fig2.png){width=70%} diff --git a/vignettes/BestNaoWeighting_vignette.Rmd b/vignettes/BestNaoWeighting_vignette.Rmd deleted file mode 100644 index 438e33b7..00000000 --- a/vignettes/BestNaoWeighting_vignette.Rmd +++ /dev/null @@ -1,232 +0,0 @@ ---- -author: "Eroteida Sánchez-García" -date: "`r Sys.Date()`" -output: rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::knitr} - %\VignetteIndexEntry{Achiving BestNAOWeighting in CSTools} - %\usepackage[utf8]{inputenc} ---- - -Achiving BestNAOWeighting in CSTools ------------------------------------------ - -**reference**: Sánchez-García, E., Voces-Aboy, J., Navascués, N., & Rodríguez-Camino, E. (2019). Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Adv. Sci. Res., 16, 165174, - - -## Before start - -System requeriment: CDO - -In our R session, we will need to: - -- define the working directory: `setwd("/home/...../AEMET_CSTOOLS")` -- install and load R packages - -```{r message=FALSE} -library(multiApply) -library(verification) -library(s2dverification) -``` - -Data files used in the following code will be in `./data_CST` directory. - - -## Workflow of develop-BestNAOWeighting branch in CSTools - -Loading functions: - -```{r} -source("../R/CST_PDFIndexHind.R") -source("../R/CST_PDFIndexFcst.R") -source("../R/CST_PDFBest.R") -source("../R/CST_WeightIndex.R") -source("../R/CST_EMBestIndexWeighting.R") -source("../R/CST_ProbBestIndexWeighting.R") -source("../R/zzz.R") -``` - -It is necessary to have calculated previosly the NAO index for models to combine -(for instance, a dynamical model as first guess combined with an empirical -model) in order to achieve the Best NAO estimation. - -The first step is to load from a RData file the list lNAOs_CST with the NAO -index as CST object for period December,January and February, obtained -previously for the models to combine: - - - -```{r message=FALSE} -fileNAOs_CST <- ("../data/lNAOs_CST.RData") -load(fileNAOs_CST) -``` - - -Improving the two seasonal forecats systems (modelsECMWF-S5 as M1 and S-ClimWaRe -as M2) NAO index DPFs estimate for the hindcast and forecast period, and ,then, -we combine them to obtain the Best NAO PDF estimate by runnning: - -```{r message=FALSE} -# Improving the ECMWF-S5 (M1) NAO index DPFs estimate for the hindcast and -# forecast period using the named 'LMEV' methodology or scheme -pdf_hind_m1_CST <- CST_PDFIndexHind(lNAOs_CST$index_hind1, - lNAOs_CST$index_obs, - type_exp = 'ens', - method = 'LMEV', - na.rm = FALSE) -str(pdf_hind_m1_CST) -dim(pdf_hind_m1_CST$data) - -pdf_fcst_m1_CST <- CST_PDFIndexFcst(lNAOs_CST$index_hind1, - lNAOs_CST$index_obs, - lNAOs_CST$index_fcst1, - type_exp = 'ens', - method = 'LMEV', - na.rm = FALSE) -str(pdf_fcst_m1_CST) -dim(pdf_fcst_m1_CST$data) - -# Improving the S-ClimWaRe (M2) NAO index DPFs estimate for the hindcast and -# forecast period using the named 'ME' methodology or scheme -pdf_hind_m2_CST <- CST_PDFIndexHind(lNAOs_CST$index_hind2, - lNAOs_CST$index_obs, - type_exp = 'sta', - method = 'ME', - na.rm = FALSE) -str(pdf_hind_m2_CST) -dim(pdf_hind_m2_CST$data) - -pdf_fcst_m2_CST <- CST_PDFIndexFcst(lNAOs_CST$index_hind2, - lNAOs_CST$index_obs, - lNAOs_CST$index_fcst2, - type_exp = 'sta', - method = 'ME', - na.rm = FALSE) -str(pdf_fcst_m2_CST) -dim(pdf_fcst_m2_CST$data) - -# Best NAO pdf estimate for hindcast period -pdf_aux <- array(NA, dim = c(dim(pdf_hind_m1_CST$data)['time'], - dataset = 2, member = 1, - statistic = 4, season = 1)) -pdf_aux[,1,,,] <- pdf_hind_m1_CST$data -pdf_aux[,2,,,] <- pdf_hind_m2_CST$data -pdf_exp <- list(data = pdf_aux, Datasets = c("ECMWF-S5","SCLIMWARE"), - season = c('DJF'), - Dates = list(start = pdf_hind_m2_CST$Dates$start)) -class(pdf_exp) <- 's2dv_cube' -pdf_hind_best_CST <- CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) - -# Best NAO pdf estimate for forecast period -pdf_aux <- array(NA, dim = c(time = 1, dataset = 2, member = 1, - statistic = 4, season = 1)) -pdf_aux[,1,,,] <- pdf_fcst_m1_CST$data -pdf_aux[,2,,,] <- pdf_fcst_m2_CST$data -pdf_exp <- list(data = pdf_aux, Datasets = c("ECMWF-S5","SCLIMWARE"), - season = c('DJF'), Dates = list(start = pdf_fcst_m2_CST$Dates$start)) -class(pdf_exp) <- 's2dv_cube' -pdf_fcst_best_CST <- CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) - -``` - - -The content of the list `pdf_hind_best_CST` can be reviewed visualizing the structure: -```{r} -str(pdf_hind_best_CST) -``` - - -Now, we can obtain an array with weights for each ensemble's member based on this Best NAO pdf estimate running the following code: - -```{r message=FALSE} -# For the hindcast period -# Computing the weights for ECMWF-S5 members using the Best NAO estimate -weigths_index_CST <- CST_WeightIndex(lNAOs_CST$index_hind1, pdf_hind_best_CST) -``` - - -The content of the weight's array can be reviewed visualizing the structure: - -```{r} -dim(weigths_index_CST$data) -str(weigths_index_CST) - -``` - - -Now, we need to load the total precipitation from November to March for hindcast -period over Iberian Peninsula. Then, we compute terciles and the probability -that the variable data will be in the lower (or upper) tercile based on the best -NAO pdf estimate running the code: - -```{r message=FALSE} -# Loading the precipitation data for November to March from ECMWS-S5 -# in a CSTools object -filePR_CST <- ("../data/pr_hindcast_ecmf-s5_CST.RData") -load(filePR_CST) - -# Computing terciles and probabilities after weighting the ensemble members -weights_index_exp <- weigths_index_CST -em_var_exp <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) -prob_var_exp <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) - -``` - - -If we want to compare with the raw output model: - -```{r message=FALSE} -# Computing terciles and probabilities with CST_function -weights_index_exp_raw <- weights_index_exp -weights_index_exp_raw$data <- rep(1/25, 20 * 25) -dim(weights_index_exp_raw$data) <- dim(weights_index_exp$data) - -em_var_exp_raw <- CST_EMBestIndexWeighting(var_exp, weights_index_exp_raw) -prob_var_exp_raw <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp_raw) - -``` - - -## Plotting probability maps: - -A map with the probability that the total precipitation from November 2012 to -March 2013, for example, will be in the lower tercile based on the best NAO pdf -estimate is obtained running the code: - -```{r message=FALSE} -coordVAR <- list(lonmin = -10, lonmax = 5, latmin = 35, latmax = 45) -lonPlot <- seq(coordVAR$lonmin,coordVAR$lonmax,0.50) -latPlot <- seq(coordVAR$latmax,coordVAR$latmin,-0.50) -probData_CST <- prob_var_exp$data[which(var_exp$Dates$start == 2012),1,1,1,,] -brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) -cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") -names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") -ofile <- "./Figures/probLower_weight_CST.png" -PlotEquiMap(t(probData_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, - fileout =ofile, brks=brks, cols = cols, - toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on the best NAO pdf estimate"), - title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) - - -``` - -![](./Figures/probLower_weight_CST.png){width=70%} - -In a similar way we can plot the map with the probability that the total precipitation from November 2012 to -March 2013, for example, will be in the lower tercile from ECMWF Seasonal Forecast System 5 (raw) to compare results running the code: - -```{r message=FALSE} -probDataRaw_CST <- prob_var_exp_raw$data[which(var_exp$Dates$start == 2012),1,1,1,,] -brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) -cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") -names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") -ofile <- "./Figures/probLower_raw_CST.png" -PlotEquiMap(t(probDataRaw_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, - fileout =ofile, brks=brks, cols = cols, - toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on ECMWF-S5 (raw)"), - title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) - - -``` - -![](./Figures/probLower_raw_CST.png){width=70%} diff --git a/vignettes/Figures/probLower_weight_CST.png b/vignettes/Figures/BestEstimationIndex_fig1.png similarity index 100% rename from vignettes/Figures/probLower_weight_CST.png rename to vignettes/Figures/BestEstimationIndex_fig1.png diff --git a/vignettes/Figures/BestEstimationIndex_fig2.png b/vignettes/Figures/BestEstimationIndex_fig2.png new file mode 100644 index 0000000000000000000000000000000000000000..e10333a577d1a2cba98d4efa702af3f6e8c14bab GIT binary patch literal 43513 zcmcG0Wmr{f6y`x16c7*uM7ksdL^`Adq(o^XMYXx!O>-vIdl>)KDWN6zu_XZ6OUF9hX zyVAzknA*{!!t4mYY&F)=F|GI7)@rIV&bH_s7HUtW?l6~$n@l8^NNgQ-R-ULlmfhL8 zCOjWWCAQ>nG#7KUH78vAW?KY9oWO@b0*fXfuPsLi5dgnt7GV&vA%CF>HS`Na{*>ZU zDEapu=C{}+$oCR-aJKyWnRS~{6Z!uV#BFG}$e+PR2?ofYSSr{5AD!g3pc8ZUO~x2= zL&H*o7QdO98JAe~=g%eM9RF^pS&&wgl^Jo~)~U8(&?hG&zkvh<1Lw=4z-Mo 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z{ph#i00tuBz(acm+E^rL^ucd*=L2oo_92 zxI8|-w}Q7jaWw62tb1IatW%%DpM&cH>h=gao1$FHB+ZFd+sz*lXuEI#@=Wl;5c>ju zM4`o=z|yG@yCXpcCpbq3#@^(_1fo9zo&!iW;71zwpk)M3Ofis{o0&oAkG(JokP#&4 zXvGEBp=JGdYl^NpC5K9rs-2sAoG^XWRRLE~_riwmSUD~~b0!iebIaDFmNJxfpU0lE zs0Z(X$z1=nwWTDn%yUi3b`R0*b10CFg6-t$N*d4ZwceQ3ax@yT815vO$B%%NLlWpb zkM<1oLf{oUfzFV&F1v7Je#2APLiP*M3+vU6KsVYg>dcS?w;Px;CNmX2j>%+tgP$EA zKMFWH}DllaG!h4Nd=V!nH};O)V<~D@T Date: Thu, 24 Oct 2019 16:36:47 +0200 Subject: [PATCH 31/40] version massss mejorada por Nuria --- NAMESPACE | 9 ++- man/{EMWeighted.Rd => BEI_EMWeighting.Rd} | 14 ++--- man/{PDFBest.Rd => BEI_PDFBest.Rd} | 20 +++--- man/{WeightIndex.Rd => BEI_Weights.Rd} | 16 ++--- man/{CST_Weighted.Rd => CST_BEI_Weighting.Rd} | 14 ++--- man/ProbsWeighted.Rd | 62 ------------------- 6 files changed, 37 insertions(+), 98 deletions(-) rename man/{EMWeighted.Rd => BEI_EMWeighting.Rd} (84%) rename man/{PDFBest.Rd => BEI_PDFBest.Rd} (91%) rename man/{WeightIndex.Rd => BEI_Weights.Rd} (80%) rename man/{CST_Weighted.Rd => CST_BEI_Weighting.Rd} (89%) delete mode 100644 man/ProbsWeighted.Rd diff --git a/NAMESPACE b/NAMESPACE index e9aa768d..aa7e3f31 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,6 +1,10 @@ # Generated by roxygen2: do not edit by hand +export(BEI_EMWeighting) +export(BEI_PDFBest) +export(BEI_Weights) export(CST_Anomaly) +export(CST_BEI_Weighting) export(CST_BiasCorrection) export(CST_Calibration) export(CST_Load) @@ -9,16 +13,11 @@ export(CST_MultivarRMSE) export(CST_RFSlope) export(CST_RFWeights) export(CST_RainFARM) -export(CST_Weighted) -export(EMWeighted) -export(PDFBest) export(PlotCombinedMap) export(PlotForecastPDF) export(PlotMostLikelyQuantileMap) -export(ProbsWeighted) export(RFSlope) export(RainFARM) -export(WeightIndex) import(ggplot2) import(multiApply) import(ncdf4) diff --git a/man/EMWeighted.Rd b/man/BEI_EMWeighting.Rd similarity index 84% rename from man/EMWeighted.Rd rename to man/BEI_EMWeighting.Rd index 7eddba6a..8dbaf632 100644 --- a/man/EMWeighted.Rd +++ b/man/BEI_EMWeighting.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_Weighted.R -\name{EMWeighted} -\alias{EMWeighted} +% Please edit documentation in R/CST_BEI_Weighting.R +\name{BEI_EMWeighting} +\alias{BEI_EMWeighting} \title{Computing the weighted ensemble means for SFSs.} \usage{ -EMWeighted(var_exp, aweights, time_dim_name = "time") +BEI_EMWeighting(var_exp, aweights, time_dim_name = "time") } \arguments{ \item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) @@ -19,7 +19,7 @@ variable, as 'time' the spatial dimension by default.} temporal dimension, by default 'time'.} } \value{ -EMWeighted() returns an array with at least one or three dimensions +BEI_EMWeighting() returns an array with at least one or three dimensions depending if the variable is zonal (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 @@ -35,7 +35,7 @@ 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 <- EMWeighted(var_exp, aweights) +res <- BEI_EMWeighting(var_exp, aweights) dim(res) # time dataset # 2 3 @@ -44,7 +44,7 @@ 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 <- EMWeighted(var_exp, aweights) +res <- BEI_EMWeighting(var_exp, aweights) dim(res) # time lat lon # 2 2 3 diff --git a/man/PDFBest.Rd b/man/BEI_PDFBest.Rd similarity index 91% rename from man/PDFBest.Rd rename to man/BEI_PDFBest.Rd index 76e8be1e..eb68a933 100644 --- a/man/PDFBest.Rd +++ b/man/BEI_PDFBest.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/PDFBest.R -\name{PDFBest} -\alias{PDFBest} +% Please edit documentation in R/BEI_PDFBest.R +\name{BEI_PDFBest} +\alias{BEI_PDFBest} \title{Computing the Best Index PDFs combining Index PDFs from two SFSs} \usage{ -PDFBest(index_obs, index_hind1, index_hind2, index_fcst1 = NULL, +BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1 = NULL, index_fcst2 = NULL, method_BC = "none", time_dim_name = "time", na.rm = FALSE) } @@ -54,7 +54,7 @@ the bias correction won't be applied. Available methods developped are "ME" and \item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ -PDFBest() returns an array +BEI_PDFBest() returns an array with at least a temporal dimension, by default 'time' and dimension 'statistic', equal to 2. The firt statistic is the parameter 'mean' of the PDF for the best estimation @@ -70,7 +70,7 @@ This function implements the computation to obtain the index PDFs the Best Index estimation. } \examples{ -# Example 1 for the PDFBest function +# Example 1 for the BEI_PDFBest function index_obs<- rnorm(10, sd = 3) dim(index_obs) <- c(time = 5, season = 2) index_hind1 <- rnorm(40, mean = 0.2, sd = 3) @@ -82,12 +82,12 @@ dim(index_fcst1) <- c(time = 1, member = 8, season = 2) index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) dim(index_fcst2) <- c(time = 1, member = 9, season = 2) method_BC <- 'ME' -res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, +res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) dim(res) time statistic season 1 2 2 -# Example 2 for the PDFBest function +# Example 2 for the BEI_PDFBest function index_obs<- rnorm(10, sd = 3) dim(index_obs) <- c(time = 5, season = 2) index_hind1 <- rnorm(40, mean = 0.2, sd = 3) @@ -99,8 +99,10 @@ dim(index_fcst1) <- c(time = 1, member = 8, season = 2) index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) dim(index_fcst2) <- c(time = 1, member = 9, season = 2) method_BC <- c('LMEV', 'ME') -res <- PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) +res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) dim(res) +time statistic season + 1 2 2 } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/WeightIndex.Rd b/man/BEI_Weights.Rd similarity index 80% rename from man/WeightIndex.Rd rename to man/BEI_Weights.Rd index 7eed0e70..84301b58 100644 --- a/man/WeightIndex.Rd +++ b/man/BEI_Weights.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/WeightIndex.R -\name{WeightIndex} -\alias{WeightIndex} +% Please edit documentation in R/BEI_Weights.R +\name{BEI_Weights} +\alias{BEI_Weights} \title{Computing the weights for SFSs using the Best Index PDFs.} \usage{ -WeightIndex(index_weight, pdf_weight) +BEI_Weights(index_weight, pdf_weight, time_dim_name = "time") } \arguments{ \item{index_weight}{Index (e.g. NAO index) array, from a dataset of SFSs @@ -19,7 +19,7 @@ The firt statistic is the parameter 'mean' of the PDF and the second statistic is the parameter 'standard deviation' of the PDF.} } \value{ -WeightIndex() returns a normalized weights array with the same +BEI_Weights() returns a normalized weights array with the same dimensions that index_weight. } \description{ @@ -29,12 +29,12 @@ pdfs indicated by the parameter 'pdf_weight' ( for instance the Best Index estimation obtained using the 'PDFBest' function. } \examples{ -# Example for the WeightIndex function +# Example for the BEI_Weights function index_weight <- 1 : (10 * 3 * 5 * 1) -dim(index_weight) <- c(sdate = 5, dataset = 3, member = 5, season = 1) +dim(index_weight) <- c(sdate = 10, dataset = 3, member = 5, season = 1) pdf_weight <- 1 : (10 * 3 * 2 * 1) dim(pdf_weight) <- c(sdate = 10, dataset = 3, statistic = 2, season = 1) -res <- WeightIndex(index_weight, pdf_weight) +res <- BEI_Weights(index_weight, pdf_weight) dim(res) # sdate dataset member season # 10 3 5 1 diff --git a/man/CST_Weighted.Rd b/man/CST_BEI_Weighting.Rd similarity index 89% rename from man/CST_Weighted.Rd rename to man/CST_BEI_Weighting.Rd index a509119b..fe4144f3 100644 --- a/man/CST_Weighted.Rd +++ b/man/CST_BEI_Weighting.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_Weighted.R -\name{CST_Weighted} -\alias{CST_Weighted} -\title{Weighted SFSs of a CSTools object.} +% Please edit documentation in R/CST_BEI_Weighting.R +\name{CST_BEI_Weighting} +\alias{CST_BEI_Weighting} +\title{Weighting SFSs of a CSTools object.} \usage{ -CST_Weighted(var_exp, aweights, type = "ensembleMean", +CST_BEI_Weighting(var_exp, aweights, type = "ensembleMean", time_dim_name = "time") } \arguments{ @@ -36,7 +36,7 @@ with weighted members.} temporal dimension, by default 'time'.} } \value{ -CST_Weighted() returns a CSTools object (i.e., of the +CST_BEI_Weighting() returns a CSTools object (i.e., of the class 's2dv_cube'). This object has at least an element named \code{$data} with at least a temporal dimension (and dimension 'tercil' when the output @@ -55,7 +55,7 @@ aweights <- c(0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.4, 0.3, 0.1, 0.2, 0.4, 0.4, 0.1, 0 dim(aweights) <- c(time = 2, member = 4, dataset = 2) var_exp <- list(data = var_exp) class(var_exp) <- 's2dv_cube' -res_CST <- CST_Weighted(var_exp, aweights) +res_CST <- CST_BEI_Weighting(var_exp, aweights) dim(res_CST$data) # time lat lon dataset # 2 3 2 2 diff --git a/man/ProbsWeighted.Rd b/man/ProbsWeighted.Rd deleted file mode 100644 index b6336cf3..00000000 --- a/man/ProbsWeighted.Rd +++ /dev/null @@ -1,62 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/CST_Weighted.R -\name{ProbsWeighted} -\alias{ProbsWeighted} -\title{Computing the weighted tercile probabilities for SFSs.} -\usage{ -ProbsWeighted(var_exp, aweights, time_dim_name = "time") -} -\arguments{ -\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with at least dimensions (time, member) for a zonal -variable or dimensions (time, member, lat, lon) for a spatial -variable, as 'time' the spatial dimension by default.} - -\item{aweights}{Normalized weights array with at least dimensions -(time, member), when 'time' is the temporal dimension as default.} - -\item{time_dim_name}{A character string indicating the name of the -temporal dimension, by default 'time'.} -} -\value{ -ProbsWeighted() returns an array with at least two or four dimensions -depending if the variable is zonal (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. -} -\description{ -This function implements the computation to obtain the tercile -probabilities for a weighted variable for SFSs using a normalized weights array, -} -\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) -res <- ProbsWeighted(var_exp, aweights) -dim(res) -# time tercil -# 2 3 -# 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) -res <- ProbsWeighted(var_exp, aweights) -dim(res) -# time tercil lat lon -# 2 3 2 3 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} -\references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} - -- GitLab From 54e7bea9aabd64c917e966362caf0efc5428c0d0 Mon Sep 17 00:00:00 2001 From: EROTEIDA SANCHEZ GARCIA Date: Thu, 24 Oct 2019 17:03:00 +0200 Subject: [PATCH 32/40] version mucho massss mejorada por Nuria --- NAMESPACE | 1 - R/BEI_PDFBest.R | 6 +- R/BEI_Weights.R | 1 + R/CST_BEI_Weighting.R | 4 +- data/lNAOs_CST.RData | Bin 5317 -> 0 bytes data/pr_hindcast_ecmf-s5_CST.RData | Bin 2433332 -> 0 bytes man/BEI_EMWeighting.Rd | 61 ---------- man/BEI_PDFBest.Rd | 4 +- man/BEI_Weights.Rd | 2 + tests/testthat/test-CST_BEI_Weighting.R | 1 - tests/testthat/test-CST_WeightIndex.R | 148 ------------------------ 11 files changed, 10 insertions(+), 218 deletions(-) delete mode 100644 data/lNAOs_CST.RData delete mode 100644 data/pr_hindcast_ecmf-s5_CST.RData delete mode 100644 man/BEI_EMWeighting.Rd delete mode 100644 tests/testthat/test-CST_WeightIndex.R diff --git a/NAMESPACE b/NAMESPACE index aa7e3f31..95b0961d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,6 +1,5 @@ # Generated by roxygen2: do not edit by hand -export(BEI_EMWeighting) export(BEI_PDFBest) export(BEI_Weights) export(CST_Anomaly) diff --git a/R/BEI_PDFBest.R b/R/BEI_PDFBest.R index bd96d7fc..b1c45fe4 100644 --- a/R/BEI_PDFBest.R +++ b/R/BEI_PDFBest.R @@ -74,8 +74,8 @@ #' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, #' index_fcst2, method_BC) #' dim(res) -#' time statistic season -#' 1 2 2 +#' # time statistic season +#' # 1 2 2 #' # Example 2 for the BEI_PDFBest function #' index_obs<- rnorm(10, sd = 3) #' dim(index_obs) <- c(time = 5, season = 2) @@ -923,7 +923,7 @@ LMEV <- function(exp, obs, dim_exp, exp_ens_sd <- Apply(list(exp), target_dims = 'member', fun = sd, na.rm = na.rm)$output1 } else { - if(na.rm & any(is.na(index_exp))){ + if(na.rm & any(is.na(exp))){ print("Some value are NA") } pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) diff --git a/R/BEI_Weights.R b/R/BEI_Weights.R index 32c270a0..55261984 100644 --- a/R/BEI_Weights.R +++ b/R/BEI_Weights.R @@ -20,6 +20,7 @@ #' dimensions 'statistic'. #' The firt statistic is the parameter 'mean' of the PDF and #' the second statistic is the parameter 'standard deviation' of the PDF. +#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. #' #'@return BEI_Weights() returns a normalized weights array with the same #' dimensions that index_weight. diff --git a/R/CST_BEI_Weighting.R b/R/CST_BEI_Weighting.R index 5bfccdff..6f6033b1 100644 --- a/R/CST_BEI_Weighting.R +++ b/R/CST_BEI_Weighting.R @@ -121,7 +121,7 @@ CST_BEI_Weighting <- function(var_exp, aweights, type = 'ensembleMean', return(var_exp) } -#' @rdname BEI_EMWeighting + #' @title Computing the weighted ensemble means for SFSs. #' @author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' @description This function implements the computation to obtain the weighted @@ -167,7 +167,7 @@ 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-\item{var_exp}{Variable (e.g. precipitation, temperature, NAO index) -array from a SFS with at least dimensions (time, member) for a zonal -variable or dimensions (time, member, lat, lon) for a spatial -variable, as 'time' the spatial dimension by default.} - -\item{aweights}{Normalized weights array with at least dimensions -(time, member), when 'time' is the temporal dimension as default.} - -\item{time_dim_name}{A character string indicating the name of the -temporal dimension, by default 'time'.} -} -\value{ -BEI_EMWeighting() returns an array with at least one or three dimensions -depending if the variable is zonal (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. -} -\description{ -This function implements the computation to obtain the weighted -ensemble means for SFSs using a normalized weights array, -} -\examples{ -# 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) -dim(res) -# time dataset -# 2 3 -# 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) -dim(res) -# time lat lon -# 2 2 3 - -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} -\references{ -Regionally improved seasonal forecast of precipitation through Best -estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -} - diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd index eb68a933..fa2f1525 100644 --- a/man/BEI_PDFBest.Rd +++ b/man/BEI_PDFBest.Rd @@ -85,8 +85,8 @@ method_BC <- 'ME' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) dim(res) -time statistic season - 1 2 2 +# time statistic season +# 1 2 2 # Example 2 for the BEI_PDFBest function index_obs<- rnorm(10, sd = 3) dim(index_obs) <- c(time = 5, season = 2) diff --git a/man/BEI_Weights.Rd b/man/BEI_Weights.Rd index 84301b58..bf5fbd42 100644 --- a/man/BEI_Weights.Rd +++ b/man/BEI_Weights.Rd @@ -17,6 +17,8 @@ must have the same lenght in both parameters, dimensions 'statistic'. The firt statistic is the parameter 'mean' of the PDF and the second statistic is the parameter 'standard deviation' of the PDF.} + +\item{time_dim_name}{A character string indicating the name of the temporal dimension, by default 'time'.} } \value{ BEI_Weights() returns a normalized weights array with the same diff --git a/tests/testthat/test-CST_BEI_Weighting.R b/tests/testthat/test-CST_BEI_Weighting.R index 2a6cc009..dd5da615 100644 --- a/tests/testthat/test-CST_BEI_Weighting.R +++ b/tests/testthat/test-CST_BEI_Weighting.R @@ -13,7 +13,6 @@ test_that("basic use case", { 4.6, 4.8, 12.6, 12.8, 20.6, 20.8, 28.6, 28.8, 36.6, 36.8, 44.6, 44.8), dim = c(time = 2, lat = 3, lon = 2, dataset =2)) - expect_equal(CST_BEI_Weighting(var_exp, aweights, type = 'ensembleMean', time_dim_name = 'time')$data, result, tolerance=1e-4) diff --git a/tests/testthat/test-CST_WeightIndex.R b/tests/testthat/test-CST_WeightIndex.R deleted file mode 100644 index e4a29c4f..00000000 --- a/tests/testthat/test-CST_WeightIndex.R +++ /dev/null @@ -1,148 +0,0 @@ -context("Generic tests") -test_that("basic use case", { - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 2 * 1) - dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - result <- array(c(0.4521103, 0.4017633, 0.3620216, 0.3545549, - 0.1858681, 0.2436818), - dim = c(time = 2, dataset = 1, member = 3, - season = 1)) - - expect_equal(CST_WeightIndex(index_exp, pdf_exp)$data, result, tolerance=1e-4) - -}) - -test_that("Sanity checks", { - expect_error( - CST_WeightIndex(index_exp = 1), - paste0("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - class(index_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = 1), - paste0("Parameter 'index_exp' and 'pdf_exp' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 2 * 1) - dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("Element 'data' from parameters 'index_exp' and 'pdf_exp'", - " should have dimmension names.")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 2 * 1) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("Element 'data' from parameters 'index_exp' and 'pdf_exp'", - " should have dimmension names.")) - - index_exp <- 1 : (2 * 1 * 3 * 1 * 2) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1, - statistic = 2) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 2 * 1) - dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("The number of dimensions in the component 'data' ", - "of the parameter 'index_exp' must be 4")) - - index_exp <- 1 : (3 * 2 * 1 * 1) - dim(index_exp) <- c(member = 3, time = 2, dataset = 1, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 2 * 1) - dim(pdf_exp) <- c(time = 2, statistic = 2, season = 1) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'index_exp'")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 2) - dim(pdf_exp) <- c(time = 2, statistic = 2) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("The number of dimensions in the component 'data' ", - "of the parameter 'pdf_exp' must be 3")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 1 * 2) - dim(pdf_exp) <- c(statistic = 2, season = 1, time = 2) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("Wrong dimensions of the component 'data' of parameter 'pdf_exp'")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 4 * 1) - dim(pdf_exp) <- c(time = 2, statistic = 4, season = 1) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("The length of the dimension 'statistic' in the component 'data' ", - "of the parameter 'pdf_exp' must be equal to 2.")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (5 * 2 * 1) - dim(pdf_exp) <- c(time = 5, statistic = 2, season = 1) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("The length of the dimensions 'time' in the component 'data' ", - "of the parameter 'index_exp' and 'pdf_exp' must be equals.")) - - index_exp <- 1 : (2 * 1 * 3 * 1) - dim(index_exp) <- c(time = 2, dataset = 1, member = 3, season = 1) - index_exp <- list(data = index_exp) - pdf_exp <- 1 : (2 * 2 * 3) - dim(pdf_exp) <- c(time = 2, statistic = 2, season = 3) - pdf_exp <- list(data = pdf_exp) - class(index_exp) <- 's2dv_cube' - class(pdf_exp) <- 's2dv_cube' - expect_error( - CST_WeightIndex(index_exp = index_exp, pdf_exp = pdf_exp), - paste0("The length of the dimensions 'season' in the component 'data' ", - "of the parameter 'index_exp' and 'pdf_exp' must be equals.")) -}) -- GitLab From 14c428f599490fdddf9048376917eaeff27a4d3a Mon Sep 17 00:00:00 2001 From: EROTEIDA SANCHEZ GARCIA Date: Thu, 24 Oct 2019 17:34:33 +0200 Subject: [PATCH 33/40] Fixing errors --- R/BEI_PDFBest.R | 6 ++---- R/CST_BEI_Weighting.R | 2 -- man/BEI_PDFBest.Rd | 4 ++-- tests/testthat/test-CST_BEI_Weighting.R | 16 +--------------- vignettes/BestEstimationIndex_vignette.Rmd | 5 +++-- 5 files changed, 8 insertions(+), 25 deletions(-) diff --git a/R/BEI_PDFBest.R b/R/BEI_PDFBest.R index b1c45fe4..3ded82c2 100644 --- a/R/BEI_PDFBest.R +++ b/R/BEI_PDFBest.R @@ -90,8 +90,8 @@ #' method_BC <- c('LMEV', 'ME') #' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) #' dim(res) -#' time statistic season -#' 1 2 2 +#' # time statistic season +#' # 1 2 2 #'@export BEI_PDFBest <- function(index_obs, index_hind1, index_hind2, @@ -552,8 +552,6 @@ PDFIndexHind <- function(index_hind, index_obs, method ='ME', #' index_hind <- 1 : (10 * 3) #' dim(index_hind) <- c(time = 10, member = 3) #' res <- .PDFIndexHind(index_hind, index_obs) -#' str(res) -#' # num [1:10, 1:4] 11 12 13 14 15 16 17 18 19 20 ... #' dim(res) #' # time statistic #' # 10 4 diff --git a/R/CST_BEI_Weighting.R b/R/CST_BEI_Weighting.R index 6f6033b1..c893ea2e 100644 --- a/R/CST_BEI_Weighting.R +++ b/R/CST_BEI_Weighting.R @@ -233,8 +233,6 @@ BEI_EMWeighting <- function(var_exp, aweights, time_dim_name = 'time') { #' aweights <- c(0.28, 0.15, 0.69, 0.64, 0.42, 0.17) #' dim(aweights) <- c(time = 2, member = 3) #' res <- .BEI_EMWeighting(var_exp, aweights) -#' str(res) -#' # num [1:2(1d)] 4.45 3.88 #' dim(res) #' # time #' # 2 diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd index fa2f1525..c41961d1 100644 --- a/man/BEI_PDFBest.Rd +++ b/man/BEI_PDFBest.Rd @@ -101,8 +101,8 @@ dim(index_fcst2) <- c(time = 1, member = 9, season = 2) method_BC <- c('LMEV', 'ME') res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) dim(res) -time statistic season - 1 2 2 +# time statistic season +# 1 2 2 } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/tests/testthat/test-CST_BEI_Weighting.R b/tests/testthat/test-CST_BEI_Weighting.R index dd5da615..4ea6b384 100644 --- a/tests/testthat/test-CST_BEI_Weighting.R +++ b/tests/testthat/test-CST_BEI_Weighting.R @@ -22,11 +22,8 @@ test_that("basic use case", { dim(aweights) <- c(time = 2, member = 3) var_exp <- list(data = var_exp) class(var_exp) <- 's2dv_cube' - - result <- array(c(0.5, 0.1, 0.0, 0.4, 0.5, 0.5, - 0.5, 0.1, 0.0, 0.4, 0.5, 0.5), + result <- array(c(0.5, 0.1, 0.0, 0.4, 0.5, 0.5, 0.5, 0.1, 0.0, 0.4, 0.5, 0.5), dim = c(time = 2, tercil = 3, lat = 1, lon = 2)) - expect_equal(CST_BEI_Weighting(var_exp, aweights, type = 'probs', time_dim_name = 'time')$data, result, tolerance=1e-4) @@ -64,22 +61,11 @@ test_that("Sanity checks", { "Parameter 'time_dim_name' must be a character string indicating", " the name of the temporal dimension.") - expect_warning( - CST_BEI_Weighting(var_exp, aweights, type = 'probs', time_dim_name = c('sdate', 'pepe')), - "Parameter 'time_dim_name' has length greater than 1 and ", - "only the first element will be used.") - expect_error( CST_BEI_Weighting(var_exp, aweights, type = 2), "Parameter 'type' must be a character string, 'probs' or 'ensembleMean', ", "indicating the type of output.") - expect_warning( - CST_BEI_Weighting(var_exp, aweights, type = c('probs', 'ensmeble'), - time_dim_name = c('sdate', 'pepe')), - "Parameter 'type' has length greater than 1 and ", - "only the first element will be used.") - expect_error( CST_BEI_Weighting(var_exp = 1, aweights), "Parameter 'var_exp' must be of the class 's2dv_cube', ", diff --git a/vignettes/BestEstimationIndex_vignette.Rmd b/vignettes/BestEstimationIndex_vignette.Rmd index 634434e5..00f8cc6a 100644 --- a/vignettes/BestEstimationIndex_vignette.Rmd +++ b/vignettes/BestEstimationIndex_vignette.Rmd @@ -113,8 +113,9 @@ prob_prec_raw <- CST_BEI_Weighting(lonlat_prec, aweights_raw, type = 'probs', ti A map with the probability that the total precipitation will be in the lower/normal/upper tercile based on the Best Estimated Index NAO could be obtained using the 'PlotEquiMap' function or 'PlotMostLikelyQuantileMap' function from 'CSToools' package. -The following figures show the results obtained for real data: winter precipitation (November to March) from 1997 to 2016 over Iberia Peninsula, combining the ECMWF-S5 dynamical model and the S-ClimWaRe empirical model from AEMET system to obtain the Best Estimation of Index NAO. - +The following figures show the probabilities for lower tercile for precipitation from November to March 2012/13 for ECMWF S5 system applying the methodology exposed or not, obtained using real data: +- NAO indices from the ECMWF-S5 dynamical model and the S-ClimWaRe empirical model from AEMET from 1997 to 2016 for computing the Best Estimation of Index NAO fro this hindcast period. +- The winter precipitation (from November to March) from 1997 to 2016 over Iberia Peninsula from he ECMWF-S5 dynamical model with resolution 0.5º x 0.5º, to weighting with the previous Best Estimation of Index NAO. ![](./Figures/BestEstimationIndex_fig1.png){width=70%} -- GitLab From 53e702b53a3e02e0be7a884397af81356d7042d0 Mon Sep 17 00:00:00 2001 From: EROTEIDA SANCHEZ GARCIA Date: Fri, 25 Oct 2019 10:03:16 +0200 Subject: [PATCH 34/40] Fixing tests errors --- tests/testthat/test-BEI_Weights.R | 2 ++ tests/testthat/test-CST_BEI_Weighting.R | 6 +++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/tests/testthat/test-BEI_Weights.R b/tests/testthat/test-BEI_Weights.R index 59b2fca1..fc234744 100644 --- a/tests/testthat/test-BEI_Weights.R +++ b/tests/testthat/test-BEI_Weights.R @@ -24,6 +24,8 @@ test_that("Sanity checks", { BEI_Weights(index_weight = 1), "Parameter 'index_weight' must be an array.") + index_weight <- 1 : (3 * 3) + dim(index_weight) <- c(sdate = 3, member = 3) expect_error( BEI_Weights(index_weight, pdf_weight = 1), "Parameter 'pdf_weight' must be an array.") diff --git a/tests/testthat/test-CST_BEI_Weighting.R b/tests/testthat/test-CST_BEI_Weighting.R index 4ea6b384..48d968e5 100644 --- a/tests/testthat/test-CST_BEI_Weighting.R +++ b/tests/testthat/test-CST_BEI_Weighting.R @@ -70,7 +70,11 @@ test_that("Sanity checks", { CST_BEI_Weighting(var_exp = 1, aweights), "Parameter 'var_exp' must be of the class 's2dv_cube', ", "as output by CSTools::CST_Load.") - + + var_exp <- 1 : (2 * 3) + dim(var_exp) <- c(sdate = 2, member = 3) + var_exp <- list(data = var_exp) + class(var_exp) <- 's2dv_cube' expect_error( CST_BEI_Weighting(var_exp, aweights = 2), "Parameter 'aweights' must be an array.") -- GitLab From c73461bf5e77133796cd0c51df53de1fb36fb1c8 Mon Sep 17 00:00:00 2001 From: EROTEIDA SANCHEZ GARCIA Date: Fri, 25 Oct 2019 10:39:58 +0200 Subject: [PATCH 35/40] Changing name of vignette --- ...gnette.Rmd => BestEstimateIndex_vignette.Rmd} | 8 ++++---- ...Index_fig1.png => BestEstimateIndex_fig1.png} | Bin ...Index_fig2.png => BestEstimateIndex_fig2.png} | Bin vignettes/Figures/BestEstimationIndex_fig3.png | Bin 6939 -> 0 bytes 4 files changed, 4 insertions(+), 4 deletions(-) rename vignettes/{BestEstimationIndex_vignette.Rmd => BestEstimateIndex_vignette.Rmd} (94%) rename vignettes/Figures/{BestEstimationIndex_fig1.png => BestEstimateIndex_fig1.png} (100%) rename vignettes/Figures/{BestEstimationIndex_fig2.png => BestEstimateIndex_fig2.png} (100%) delete mode 100644 vignettes/Figures/BestEstimationIndex_fig3.png diff --git a/vignettes/BestEstimationIndex_vignette.Rmd b/vignettes/BestEstimateIndex_vignette.Rmd similarity index 94% rename from vignettes/BestEstimationIndex_vignette.Rmd rename to vignettes/BestEstimateIndex_vignette.Rmd index 00f8cc6a..010c0223 100644 --- a/vignettes/BestEstimationIndex_vignette.Rmd +++ b/vignettes/BestEstimateIndex_vignette.Rmd @@ -55,7 +55,7 @@ lonlat_prec$data <- apply(lonlat_prec$data, c(1, 2, 3, 5, 6), sum) ``` -## 1- Best Estimation Index NAO +## 1- Best Estimate Index NAO The function `PDFBest` performes the next steps: - improves the PDF NAO index for each SFS (it could use bias correction method) and @@ -111,14 +111,14 @@ prob_prec_raw <- CST_BEI_Weighting(lonlat_prec, aweights_raw, type = 'probs', ti ``` -A map with the probability that the total precipitation will be in the lower/normal/upper tercile based on the Best Estimated Index NAO could be obtained using the 'PlotEquiMap' function or 'PlotMostLikelyQuantileMap' function from 'CSToools' package. +A map with the probability that the total precipitation will be in the lower/normal/upper tercile based on the Best Estimate Index NAO could be obtained using the 'PlotEquiMap' function or 'PlotMostLikelyQuantileMap' function from 'CSToools' package. The following figures show the probabilities for lower tercile for precipitation from November to March 2012/13 for ECMWF S5 system applying the methodology exposed or not, obtained using real data: - NAO indices from the ECMWF-S5 dynamical model and the S-ClimWaRe empirical model from AEMET from 1997 to 2016 for computing the Best Estimation of Index NAO fro this hindcast period. - The winter precipitation (from November to March) from 1997 to 2016 over Iberia Peninsula from he ECMWF-S5 dynamical model with resolution 0.5º x 0.5º, to weighting with the previous Best Estimation of Index NAO. -![](./Figures/BestEstimationIndex_fig1.png){width=70%} +![](./Figures/BestEstimateIndex_fig1.png){width=70%} @@ -127,4 +127,4 @@ March 2013, for example, will be in the lower tercile from ECMWF Seasonal Foreca -![](./Figures/BestEstimationIndex_fig2.png){width=70%} +![](./Figures/BestEstimateIndex_fig2.png){width=70%} diff --git a/vignettes/Figures/BestEstimationIndex_fig1.png b/vignettes/Figures/BestEstimateIndex_fig1.png similarity index 100% rename from vignettes/Figures/BestEstimationIndex_fig1.png rename to vignettes/Figures/BestEstimateIndex_fig1.png diff --git a/vignettes/Figures/BestEstimationIndex_fig2.png b/vignettes/Figures/BestEstimateIndex_fig2.png similarity index 100% rename from vignettes/Figures/BestEstimationIndex_fig2.png rename to vignettes/Figures/BestEstimateIndex_fig2.png diff --git a/vignettes/Figures/BestEstimationIndex_fig3.png b/vignettes/Figures/BestEstimationIndex_fig3.png deleted file mode 100644 index 9086e9367a926b069c0ae69e7964d8c71e229478..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6939 zcmeHMc~BEpw~tzjRgwB!P!Z7rQe;y^Qb9-&3kF%N$YuzOB9t{O4Pi@SZG{#TG{`Qb zmQ6qcvTsRL1Vol75JD1?C;}lsh^<-6&;H}huRd;hoZp0tB z?0@*}?kP2YyTgU1{U^6?|L#0}_kpa(Kc)T9nuV}QGTb+Lg{4C-amp`qip}Kkv^H{m ziA@E@lCw;+cycmqY=DV!USihAi{}GfT@Cl|fj}k#D#=?Q5ZimtH$%1^uZ2Kve`N@{ z<+phUcn97DLe4i{p$f&p#FEgR#^Uhn8$-SS7#Zzx)o2gCH2KXs~^Tx*zl zwa4Y@S~>)>!Y7Nl1*dLwySi94ic-Gh9DJ$+Kkk?txzNgYPXzgpEKg>-WN+MO_-%rG zvBwhvdGW=5$oD6I*$VmE@t>i8GVu>4{-MG@qVVsdg;mvSyC4vi=D`K7ldyFSNmfPY z{itoKJ!h(IA7D=ps>{m{5nZEiGlabVRWLIf9^7}P8Qt8#Z=QeMh+dn=)VGBNUO*1K zmdwxW&U>fIe6Y$K>Pl|Qf2Z1bPnEeFoo8zoV0y;3wiDRQTO#*-RAP`z3<7frf$K{j zFI$t6yivN)e)0Y#a_=CCYb1+s&lxy8bU!%kwfRVpMo6gLXpq{$2f>H)!k_e+kI?Tv zrKLaVGcymcrDSi?u7%uE6dU-C)oFWJST;6l^TxC11V&|I!4mmS)8P5mVL#o*y!>Q| zqhQ{n^GNxtL$e`=X6uSIOp7%_ROp!m;Izr@L$0{eM<>v3jpCnBR(B>q6op8Y?_%5+ z;JsGHfp!_McRi2!du9%$T-p@75+-;J4WC<>C(Y`xYGZBAyMn z5PKCH7=Vopz*clRsGlyj3jn*Oc9oR%GnL+KJCKgFDqzJJSuPc@V~m71@qHP%$%O5# zMdvq2atfL}AVhm9=lzI!Joc;|bIG2vLiuWd-LCfen(kkdeH~I?zfI3=jx$`tIC*T= z15|JyR9VKUH?rLFYoIhcW7}iMr94q6m&+L{;;|kWEDwczxdUwZ>tGQoi(5x-?{vkf zXn+B?CO)DRtOhsYCv2y4_2t?ik6&Knfl-u*%X(KIZy%9MMZJ6r>T?v0by*J=&M;Z0 z4)9JCdV-m^Wo*1=-Z5Jc5I#giP4BQ`eAK6Ya036Un15DSn9_A?A@3m$U>)-TdkDm; 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z2PCl-Ey1ZhFuQ6xp`CFqnyL~=&;f7A#fh$FPAtNjNL^!v<`{ZKgEVF}c~g?sd-aIt zV&}&wG+>N1g!SyEaf}a9GE#o%&>@GEPs(Kfj5aUeh_=+N?+R3rwt6ol zD;o${O8ma7Rr6nZKEB*Cm5oS0JDm$if@3;RkQ6Se@oRGIF+r=f1_JvKHL2e{6Q?lVfH zM6W$`b@l?zQ`CHBqVh>3Wn-|)(~yK{j(Bl<>&NbU1t Date: Fri, 25 Oct 2019 12:08:53 +0200 Subject: [PATCH 36/40] Adding something at vignette --- vignettes/BestEstimateIndex_vignette.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/BestEstimateIndex_vignette.Rmd b/vignettes/BestEstimateIndex_vignette.Rmd index 010c0223..a3ea8ece 100644 --- a/vignettes/BestEstimateIndex_vignette.Rmd +++ b/vignettes/BestEstimateIndex_vignette.Rmd @@ -93,7 +93,7 @@ The corrected precipitation field is obtained by running: ``` em_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'ensembleMean', time_dim_name = 'sdate') -prob_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'probs', ) +prob_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'probs', time_dim_name = 'sdate') ``` ## Comparison and visualization -- GitLab From 1e7f02fa0f5013b98b26d880955ef9daee02d1d7 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Wed, 30 Oct 2019 11:51:15 +0000 Subject: [PATCH 37/40] Adding maps of Most Likely Tercil in vignette --- DESCRIPTION | 2 +- man/BEI_PDFBest.Rd | 7 +++---- man/BEI_Weights.Rd | 7 +++---- man/CST_Anomaly.Rd | 7 +++---- man/CST_BEI_Weighting.Rd | 7 +++---- man/CST_BiasCorrection.Rd | 7 +++---- man/CST_Calibration.Rd | 7 +++---- man/CST_Load.Rd | 1 - man/CST_MultiMetric.Rd | 9 ++++----- man/CST_MultivarRMSE.Rd | 7 +++---- man/CST_RFSlope.Rd | 1 - man/CST_RFWeights.Rd | 7 +++---- man/CST_RainFARM.Rd | 7 +++---- man/PlotCombinedMap.Rd | 15 +++++++-------- man/PlotForecastPDF.Rd | 10 +++++----- man/PlotMostLikelyQuantileMap.Rd | 7 +++---- man/RFSlope.Rd | 1 - man/RainFARM.Rd | 8 ++++---- man/areave_data.Rd | 1 - man/lonlat_data.Rd | 1 - man/lonlat_prec.Rd | 1 - vignettes/BestEstimateIndex_vignette.Rmd | 6 +++++- vignettes/Figures/BestEstimateIndex_fig3.png | Bin 0 -> 66665 bytes vignettes/Figures/BestEstimateIndex_fig4.png | Bin 0 -> 67192 bytes 24 files changed, 56 insertions(+), 70 deletions(-) create mode 100644 vignettes/Figures/BestEstimateIndex_fig3.png create mode 100644 vignettes/Figures/BestEstimateIndex_fig4.png diff --git a/DESCRIPTION b/DESCRIPTION index 0ac6c809..c2f9fd6e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -55,4 +55,4 @@ VignetteBuilder: knitr License: Apache License 2.0 Encoding: UTF-8 LazyData: true -RoxygenNote: 5.0.0 +RoxygenNote: 6.1.1 diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd index c41961d1..ae0d749a 100644 --- a/man/BEI_PDFBest.Rd +++ b/man/BEI_PDFBest.Rd @@ -104,12 +104,11 @@ dim(res) # time statistic season # 1 2 2 } -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } - +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/BEI_Weights.Rd b/man/BEI_Weights.Rd index bf5fbd42..71c096be 100644 --- a/man/BEI_Weights.Rd +++ b/man/BEI_Weights.Rd @@ -41,13 +41,12 @@ dim(res) # sdate dataset member season # 10 3 5 1 -} -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} } \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } - +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/CST_Anomaly.Rd b/man/CST_Anomaly.Rd index e1c31f0c..b214a31a 100644 --- a/man/CST_Anomaly.Rd +++ b/man/CST_Anomaly.Rd @@ -53,13 +53,12 @@ str(anom3) anom4 <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = FALSE) str(anom4) +} +\seealso{ +\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} } \author{ Perez-Zanon Nuria, \email{nuria.perez@bsc.es} Pena Jesus, \email{jesus.pena@bsc.es} } -\seealso{ -\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} -} - diff --git a/man/CST_BEI_Weighting.Rd b/man/CST_BEI_Weighting.Rd index fe4144f3..42a67e89 100644 --- a/man/CST_BEI_Weighting.Rd +++ b/man/CST_BEI_Weighting.Rd @@ -60,12 +60,11 @@ dim(res_CST$data) # time lat lon dataset # 2 3 2 2 } -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } - +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} diff --git a/man/CST_BiasCorrection.Rd b/man/CST_BiasCorrection.Rd index e8a82af0..a1b415fb 100644 --- a/man/CST_BiasCorrection.Rd +++ b/man/CST_BiasCorrection.Rd @@ -35,10 +35,9 @@ attr(obs, 'class') <- 's2dv_cube' a <- CST_BiasCorrection(exp = exp, obs = obs) str(a) } -\author{ -Verónica Torralba, \email{veronica.torralba@bsc.es} -} \references{ Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) } - +\author{ +Verónica Torralba, \email{veronica.torralba@bsc.es} +} diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd index 396d87ca..58b12617 100644 --- a/man/CST_Calibration.Rd +++ b/man/CST_Calibration.Rd @@ -25,13 +25,12 @@ c(exp, obs) \%<-\% areave_data exp_calibrated <- CST_Calibration(exp = exp, obs = obs) str(exp_calibrated) } -\author{ -Verónica Torralba, \email{veronica.torralba@bsc.es} -} \references{ Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x } \seealso{ \code{\link{CST_Load}} } - +\author{ +Verónica Torralba, \email{veronica.torralba@bsc.es} +} diff --git a/man/CST_Load.Rd b/man/CST_Load.Rd index 1fee022c..bf03ba42 100644 --- a/man/CST_Load.Rd +++ b/man/CST_Load.Rd @@ -47,4 +47,3 @@ obs <- CSTools::lonlat_data$obs \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} } - diff --git a/man/CST_MultiMetric.Rd b/man/CST_MultiMetric.Rd index 079a5588..8e3ce593 100644 --- a/man/CST_MultiMetric.Rd +++ b/man/CST_MultiMetric.Rd @@ -37,15 +37,14 @@ c(ano_exp, ano_obs) \%<-\% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb a <- CST_MultiMetric(exp = ano_exp, obs = ano_obs) str(a) } -\author{ -Mishra Niti, \email{niti.mishra@bsc.es} - -Perez-Zanon Nuria, \email{nuria.perez@bsc.es} -} \references{ Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dverification]{Corr}}, \code{\link[s2dverification]{RMS}}, \code{\link[s2dverification]{RMSSS}} and \code{\link{CST_Load}} } +\author{ +Mishra Niti, \email{niti.mishra@bsc.es} +Perez-Zanon Nuria, \email{nuria.perez@bsc.es} +} diff --git a/man/CST_MultivarRMSE.Rd b/man/CST_MultivarRMSE.Rd index 685eaf77..24af608c 100644 --- a/man/CST_MultivarRMSE.Rd +++ b/man/CST_MultivarRMSE.Rd @@ -56,10 +56,9 @@ weight <- c(1, 2) a <- CST_MultivarRMSE(exp = ano_exp, obs = ano_obs, weight = weight) str(a) } -\author{ -Deborah Verfaillie, \email{deborah.verfaillie@bsc.es} -} \seealso{ \code{\link[s2dverification]{RMS}} and \code{\link{CST_Load}} } - +\author{ +Deborah Verfaillie, \email{deborah.verfaillie@bsc.es} +} diff --git a/man/CST_RFSlope.Rd b/man/CST_RFSlope.Rd index d2b5aec0..0c4e1671 100644 --- a/man/CST_RFSlope.Rd +++ b/man/CST_RFSlope.Rd @@ -50,4 +50,3 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } - diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd index 08a7b850..ef5ebe4d 100644 --- a/man/CST_RFWeights.Rd +++ b/man/CST_RFWeights.Rd @@ -47,9 +47,6 @@ nf <- 8 ww <- CST_RFWeights("./worldclim.nc", nf, lon, lat, fsmooth = TRUE) } } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} \references{ Terzago, S., Palazzi, E., & von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: @@ -57,4 +54,6 @@ A simple method to reproduce a realistic fine-scale climatology. Natural Hazards and Earth System Sciences, 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 . } - +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd index ca32e2d8..8df8f1f4 100644 --- a/man/CST_RainFARM.Rd +++ b/man/CST_RainFARM.Rd @@ -92,12 +92,11 @@ dim(res$data) # dataset member sdate ftime lat lon realization # 1 2 3 4 64 64 3 } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} \references{ Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 ; D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724. } - +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd index 6857c64d..62e26904 100644 --- a/man/PlotCombinedMap.Rd +++ b/man/PlotCombinedMap.Rd @@ -5,10 +5,10 @@ \title{Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function} \usage{ PlotCombinedMap(maps, lon, lat, map_select_fun, display_range, - map_dim = "map", brks = NULL, cols = NULL, col_unknown_map = "white", - mask = NULL, col_mask = "grey", bar_titles = NULL, legend_scale = 1, - fileout = NULL, width = 8, height = 5, size_units = "in", res = 100, - ...) + map_dim = "map", brks = NULL, cols = NULL, + col_unknown_map = "white", mask = NULL, col_mask = "grey", + bar_titles = NULL, legend_scale = 1, fileout = NULL, width = 8, + height = 5, size_units = "in", res = 100, ...) } \arguments{ \item{maps}{List of matrices to plot, each with (longitude, latitude) dimensions, or 3-dimensional array with the dimensions (longitude, latitude, map). Dimension names are required.} @@ -67,12 +67,11 @@ PlotCombinedMap(list(a, b, c), lons, lats, bar_titles = paste('\% of belonging to', c('a', 'b', 'c')), brks = 20, width = 10, height = 8) } +\seealso{ +\code{PlotCombinedMap} and \code{PlotEquiMap} +} \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} Veronica Torralba, \email{veronica.torralba@bsc.es} } -\seealso{ -\code{PlotCombinedMap} and \code{PlotEquiMap} -} - diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd index 3d1d65c3..c1959ee8 100644 --- a/man/PlotForecastPDF.Rd +++ b/man/PlotForecastPDF.Rd @@ -4,10 +4,11 @@ \alias{PlotForecastPDF} \title{Plot one or multiple ensemble forecast pdfs for the same event} \usage{ -PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, obs = NULL, - plotfile = NULL, title = "Set a title", var.name = "Varname (units)", - fcst.names = NULL, add.ensmemb = c("above", "below", "no"), - color.set = c("ggplot", "s2s4e", "hydro")) +PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, + obs = NULL, plotfile = NULL, title = "Set a title", + var.name = "Varname (units)", fcst.names = NULL, + add.ensmemb = c("above", "below", "no"), color.set = c("ggplot", + "s2s4e", "hydro")) } \arguments{ \item{fcst}{a dataframe or array containing all the ensember members for each frecast. If \code{'fcst'} is an array, it should have two labelled dimensions, and one of them should be \code{'members'}. If \code{'fcsts'} is a data.frame, each column shoul be a separate forecast, with the rows beeing the different ensemble members.} @@ -49,4 +50,3 @@ PlotForecastPDF(fcsts2, c(-0.66, 0.66), extreme.limits = c(-1.2, 1.2), \author{ Llorenç Lledó \email{llledo@bsc.es} } - diff --git a/man/PlotMostLikelyQuantileMap.Rd b/man/PlotMostLikelyQuantileMap.Rd index 6c92850e..0d984ede 100644 --- a/man/PlotMostLikelyQuantileMap.Rd +++ b/man/PlotMostLikelyQuantileMap.Rd @@ -109,11 +109,10 @@ PlotMostLikelyQuantileMap(bins, lons, lats, mask = 1 - (w1 + w2 / max(c(w1, w2))), brks = 20, width = 10, height = 8) -} -\author{ -Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es} } \seealso{ \code{PlotCombinedMap} and \code{PlotEquiMap} } - +\author{ +Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es} +} diff --git a/man/RFSlope.Rd b/man/RFSlope.Rd index 09a24ff5..5308ef8c 100644 --- a/man/RFSlope.Rd +++ b/man/RFSlope.Rd @@ -60,4 +60,3 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } - diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd index 3ef2a2f6..35e7c2a5 100644 --- a/man/RainFARM.Rd +++ b/man/RainFARM.Rd @@ -4,9 +4,10 @@ \alias{RainFARM} \title{RainFARM stochastic precipitation downscaling (reduced version)} \usage{ -RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, kmin = 1, - fglob = FALSE, fsmooth = TRUE, time_dim = NULL, lon_dim = "lon", - lat_dim = "lat", drop_realization_dim = FALSE, verbose = FALSE) +RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, + kmin = 1, fglob = FALSE, fsmooth = TRUE, time_dim = NULL, + lon_dim = "lon", lat_dim = "lat", drop_realization_dim = FALSE, + verbose = FALSE) } \arguments{ \item{data}{Precipitation array to downscale. @@ -113,4 +114,3 @@ dim(res$data) \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } - diff --git a/man/areave_data.Rd b/man/areave_data.Rd index cc79c85c..a772220a 100644 --- a/man/areave_data.Rd +++ b/man/areave_data.Rd @@ -41,4 +41,3 @@ areave_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} - diff --git a/man/lonlat_data.Rd b/man/lonlat_data.Rd index eca7abac..0c6ee30f 100644 --- a/man/lonlat_data.Rd +++ b/man/lonlat_data.Rd @@ -41,4 +41,3 @@ lonlat_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} - diff --git a/man/lonlat_prec.Rd b/man/lonlat_prec.Rd index 69cb94e8..345e3cab 100644 --- a/man/lonlat_prec.Rd +++ b/man/lonlat_prec.Rd @@ -29,4 +29,3 @@ lonlat_prec <- CST_Load('prlr', exp = list(infile), obs = NULL, Jost von Hardenberg \email{j.vonhardenberg@isac.cnr.it} } \keyword{data} - diff --git a/vignettes/BestEstimateIndex_vignette.Rmd b/vignettes/BestEstimateIndex_vignette.Rmd index a3ea8ece..e577eeb2 100644 --- a/vignettes/BestEstimateIndex_vignette.Rmd +++ b/vignettes/BestEstimateIndex_vignette.Rmd @@ -93,7 +93,7 @@ The corrected precipitation field is obtained by running: ``` em_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'ensembleMean', time_dim_name = 'sdate') -prob_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'probs', time_dim_name = 'sdate') +prob_prec <- CST_BEI_Weighting(lonlat_prec, weights_hind, type = 'probs', ) ``` ## Comparison and visualization @@ -120,6 +120,8 @@ The following figures show the probabilities for lower tercile for precipitation ![](./Figures/BestEstimateIndex_fig1.png){width=70%} +![](./Figures/BestEstimateIndex_fig3.png){width=70%} + In a similar way we can plot the map with the probability that the total precipitation from November 2012 to @@ -128,3 +130,5 @@ March 2013, for example, will be in the lower tercile from ECMWF Seasonal Foreca ![](./Figures/BestEstimateIndex_fig2.png){width=70%} + +![](./Figures/BestEstimateIndex_fig4.png){width=70%} \ No newline at end of file diff --git a/vignettes/Figures/BestEstimateIndex_fig3.png 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Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' -#'@param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with at least a temporal dimension (by default 'time'), -#' which must be greater than 2. -#'@param index_hind1 Index (e.g. NAO index) array from a SFS (named SFS1) -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' at the same time. -#'@param index_hind2 Index (e.g. NAO index) array from a SFS (named SFS2) -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_fcst1 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS1 -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_fcst2 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS2 -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param method_BC A character vector of maximun length 2 indicating the bias correction -#'methodology to be applied on each SFS. If it is 'none' or any of its elements is 'none', -#'the bias correction won't be applied. Available methods developped are "ME" and "LMEV". -#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#'@param na.rm Logical (default = FALSE). Should missing values be removed? -#' -#' @return BEI_PDFBest() returns an array -#' with at least a temporal dimension, by default 'time' and dimension 'statistic', -#' equal to 2. -#' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDFs. -#' The second statistic is the parameter 'standard deviation' of the PDF for -#' the best estimation combining the two SFSs PDFs. -#' If index_fcst1 and/or index_fcst2 are null, returns the values for hindcast period. -#' Otherwise, it returns the values for a forecast year. -#'@import multiApply -#' -#'@examples -#' # Example 1 for the BEI_PDFBest function -#' index_obs<- rnorm(10, sd = 3) -#' dim(index_obs) <- c(time = 5, season = 2) -#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) -#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) -#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) -#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) -#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) -#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) -#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) -#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) -#' method_BC <- 'ME' -#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, -#' index_fcst2, method_BC) -#' dim(res) -#' # time statistic season -#' # 1 2 2 -#' # Example 2 for the BEI_PDFBest function -#' index_obs<- rnorm(10, sd = 3) -#' dim(index_obs) <- c(time = 5, season = 2) -#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) -#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) -#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) -#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) -#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) -#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) -#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) -#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) -#' method_BC <- c('LMEV', 'ME') -#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) -#' dim(res) -#' # time statistic season -#' # 1 2 2 -#'@export - -BEI_PDFBest <- function(index_obs, index_hind1, index_hind2, - index_fcst1 = NULL, index_fcst2 = NULL, method_BC = 'none', - time_dim_name = 'time', na.rm = FALSE) { - - if (!is.logical(na.rm)) { - stop("Parameter 'na.rm' must be a logical value.") - } - if (!is.character(time_dim_name)) { - stop("Parameter 'time_dim_name' must be a character string ", - "indicating the name of the temporal dimension.") - } - if (length(time_dim_name) > 1) { - warning("Parameter 'time_dim_name' has length greater than 1 and ", - "only the first element will be used.") - time_dim_name <- time_dim_name[1] - } - if (!is.character(method_BC) || !is.vector(method_BC)){ - stop("Parameter 'method_BC' must be a character vector.") - } - if (!(length(method_BC) == 1 || length(method_BC) == 2)){ - stop("Length of parameter 'method_BC' must be 1 or 2.") - } - if(!all(method_BC %in% c('ME', 'LMEV', 'none'))){ - stop("Elements of parameter 'method_BC' must be equals to ", - "'none, 'ME' or 'LMEV'") - } - if (!is.array(index_obs)) { - stop("Parameter 'index_obs' must be an array.") - } - if (!is.array(index_hind1)) { - stop("Parameter 'index_hind1' must be an array.") - } - if (!is.array(index_hind2)) { - stop("Parameter 'index_hind2' must be an array.") - } - if (is.null(names(dim(index_hind1))) || - is.null(names(dim(index_obs))) || - is.null(names(dim(index_hind2)))) { - stop("Parameters 'index_obs', 'index_hind1' and 'index_hind2' ", - "should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_obs)))) { - stop("Parameter 'index_obs' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_hind1)))) { - stop("Parameter 'index_hind1' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_hind2)))) { - stop("Parameter 'index_hind2' must have temporal dimension.") - } - if (dim(index_obs)[time_dim_name] <= 2) { - stop("Length of temporal dimension ", - "of parameter 'index_obs', 'index_hind1' and 'index_hind2' ", - "must be greater than 2.") - } - if (dim(index_obs)[time_dim_name] != dim(index_hind1)[time_dim_name]) { - stop("Length of temporal dimensions ", - "of parameter 'index_obs' and 'index_hind1' must be equals.") - } - if (dim(index_hind1)[time_dim_name] != dim(index_hind2)[time_dim_name]) { - stop("Length of temporal dimensions ", - "of parameter 'index_hind1' and 'index_hind2' must be equals.") - } - if('member' %in% names(dim(index_hind1)) & - 'statistic' %in% names(dim(index_hind1))) { - stop("Parameter 'index_hind1' must have at least ", - "dimension 'member' or 'statistic', not 'member' and 'statistic' ", - "together.") - } - if('member' %in% names(dim(index_hind2)) & - 'statistic' %in% names(dim(index_hind2))) { - stop("Parameter 'index_hind2' must have at least ", - "dimension 'member' or 'statistic', not 'member' and 'statistic' ", - "together.") - } - if(!('member' %in% names(dim(index_hind1))) & - !('statistic' %in% names(dim(index_hind1)))) { - stop("Parameter 'index_hind1' must have dimension ", - "'member' or 'statistic'") - } - if(!('member' %in% names(dim(index_hind2))) & - !('statistic' %in% names(dim(index_hind2)))) { - stop("Parameter 'index_hind2' must have dimension ", - "'member' or 'statistic'") - } - if ('member' %in% names(dim(index_hind1))){ - if (dim(index_hind1)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind1' must be greater than 1.") - } - } - if ('member' %in% names(dim(index_hind2))){ - if (dim(index_hind2)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind2' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_hind1))){ - if (dim(index_hind1)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind1' must be equal to 2.") - } - } - if ('statistic' %in% names(dim(index_hind2))){ - if (dim(index_hind2)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind2' must be equal to 2.") - } - } - if (!is.null(index_fcst1)){ - if (!is.array(index_fcst1)) { - stop("Parameter 'index_fcst1' must be an array.") - } - if (is.null(names(dim(index_fcst1)))){ - stop("Parameters 'index_fcst1' should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_fcst1)))) { - stop("Parameter 'index_fcst1' must have temporal dimension.") - } - if (dim(index_fcst1)[time_dim_name] != 1) { - stop("Length of temporal dimension ", - "of parameter 'index_fcst1' must be equal to 1.") - } - if((length(names(dim(index_hind1))) != length(names(dim(index_fcst1)))) - || (!all(names(dim(index_hind1)) == names(dim(index_fcst1))))){ - stop("Dimension names of parameter 'index_hind1' and 'index_fcst1' ", - "must be equals.") - } - if ('member' %in% names(dim(index_fcst1))){ - if (dim(index_fcst1)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_fcst1' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_fcst1))){ - if (dim(index_fcst1)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_fcst1' must be equal to 2.") - } - } - } - if (!is.null(index_fcst2)){ - if (!is.array(index_fcst2)) { - stop("Parameter 'index_fcst2' must be an array.") - } - if (is.null(names(dim(index_fcst2)))){ - stop("Parameters 'index_fcst2' should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_fcst2)))) { - stop("Parameter 'index_fcst2' must have temporal dimension.") - } - if (dim(index_fcst2)[time_dim_name] != 1) { - stop("Length of temporal dimension ", - "of parameter 'index_fcst2' must be equal to 1.") - } - if((length(names(dim(index_hind2))) != length(names(dim(index_fcst2)))) - || (!all(names(dim(index_hind2)) == names(dim(index_fcst2))))){ - stop("Dimension names of parameter 'index_hind2' and 'index_fcst2' ", - "must be equals.") - } - if ('member' %in% names(dim(index_fcst2))){ - if (dim(index_fcst2)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_fcst2' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_fcst1))){ - if (dim(index_fcst1)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_fcst1' must be equal to 2.") - } - } - } - - if (all(method_BC == 'none')){ - method_BC1 <- 'ME' - method_BC2 <- 'ME' - bc_dataset1 <- FALSE - bc_dataset2 <- FALSE - } else if (length(method_BC) == 1){ - method_BC1 <- method_BC - method_BC2 <- method_BC - bc_dataset1 <- TRUE - bc_dataset2 <- TRUE - } else { - if(method_BC[1] == 'none'){ - method_BC1 <- 'ME' - bc_dataset1 <- FALSE - } else { - method_BC1 <- method_BC[1] - bc_dataset1 <- TRUE - } - if(method_BC[2] == 'none'){ - method_BC2 <- 'ME' - bc_dataset2 <- FALSE - } else { - method_BC2 <- method_BC[2] - bc_dataset2 <- TRUE - } - } - - pdf_hind1 <- PDFIndexHind(index_hind1, index_obs, method = method_BC1, - time_dim_name = time_dim_name, na.rm = na.rm) - pdf_hind2 <- PDFIndexHind(index_hind2, index_obs, method = method_BC2, - time_dim_name = time_dim_name, na.rm = na.rm) - if (is.null(index_fcst1) || is.null(index_fcst2)){ - pdf_best <- Apply(list(pdf_hind1, pdf_hind2), - target_dims = list('statistic', - 'statistic'), - fun = .BEI_PDFBest, - bc_dataset1, bc_dataset2) - } else { - pdf_fcst1 <- PDFIndexFcst(index_hind1, index_obs, index_fcst1, - method_BC1, time_dim_name = time_dim_name, - na.rm = na.rm) - pdf_fcst2 <- PDFIndexFcst(index_hind2, index_obs, index_fcst2, - method_BC2, time_dim_name = time_dim_name, - na.rm = na.rm) - pdf_best <- Apply(list(pdf_fcst1, pdf_fcst2), - target_dims = list('statistic', - 'statistic'), - fun = .BEI_PDFBest, - bc_dataset1, bc_dataset2) - } - Dim <- names(dim(index_hind1)) - if('member' %in% Dim){ - pos_aux <- match('member', Dim) - Dim[pos_aux] <- 'statistic' - } - pos <- match(Dim, names(dim(pdf_best$output1))) - res <- aperm(pdf_best$output1,pos) - names(dim(res)) <- Dim - return(res) -} - -#' Atomic BEI_PDFBest -#'@param pdf_1 Statistics array for the first SFS PDF with one dimension -#' 'statistic' equal to 4. -#' @param pdf_2 Statistics array for the second SFS PDF with one dimension -#' 'statistic' equal to 4. -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDFs for the first SFS has been computed -#' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDFs for the second SFS has been computed -#' with bias corrected. -#' -#' @return .BEI_PDFBest returns an array with dimensions (statistic = 2). -#' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDF. -#' The second statistic is the parameter 'standard deviation' of the PDF for -#' the best estimation combining the two SFSs PDF. -#' -#' @examples -#' # Example for the Atomic BEI_PDFBest function -#' pdf_1 <- c(1.1,0.6,1.6,0.9) -#' dim(pdf_1) <- c(statistic = 4) -#' pdf_2 <- c(1,0.5,1.5,0.8) -#' dim(pdf_2) <- c(statistic = 4) -#' res <- .BEI_PDFBest(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = FALSE) -#' str(res) -#' dim(res) -#' # statistic -#' # 2 -#' @noRd -.BEI_PDFBest <- function(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { - if(bc_dataset1){ - # apply bias correction to model 1 - mean_1 <- pdf_1[3] - sigma_1 <- pdf_1[4] - } else{ - # not bias correction to model 1 - mean_1 <- pdf_1[1] - sigma_1 <- pdf_1[2] - } - if(bc_dataset2){ - # apply bias correction to model 2 - mean_2 <- pdf_2[3] - sigma_2 <- pdf_2[4] - } else { - # not bias correction to model 2 - mean_2 <- pdf_2[1] - sigma_2 <- pdf_2[2] - } - a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) - a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) - pdf_mean <- a_1*mean_1 + a_2*mean_2 - pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) - data <- c(pdf_mean, pdf_sigma) - dim(data) <- c(statistic = 2) - return(data) -} - -#'Computing the Index PDFs for a dataset of SFSs for a hindcats period. -#' -#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' -#'@description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from SFSs for a hindcast period. -#' -#'@references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' -#'@param index_hind Index (e.g. NAO index) array from SFSs -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with at least a temporal dimension (by default 'time'), -#' which must be greater than 2. -#'@param method A character string indicating which methodology is applied -#' to compute the PDFs. One of "ME" (default) or "LMEV". -#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#'@param na.rm Logical (default = FALSE). Should missing values be removed? -#' -#'@return an array with at least two dimensions (time, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @import multiApply -#' @examples -#' # Example for the PDFIndexHind function -#' # Example 1 -#' index_obs <- 1 : (5 * 3 ) -#' dim(index_obs) <- c(time = 5, season = 3) -#' index_hind <- 1 : (5 * 4 * 3) -#' dim(index_hind) <- c(time = 5, member = 4, season = 3) -#' res <- PDFIndexHind(index_hind, index_obs) -#' dim(res) -#' # time statistic season -#' # 5 4 3 -#' # Example 2 -#' index_obs <- 1 : (5 * 3) -#' dim(index_obs) <- c(time = 5, season = 3) -#' index_hind <- 1 : (5 * 2 * 3) -#' dim(index_hind) <- c(time = 5, statistic = 2, season = 3) -#' res <- PDFIndexHind(index_hind, index_obs) -#' dim(res) -#' # time statistic season -#' # 5 4 3 -#'@noRd -PDFIndexHind <- function(index_hind, index_obs, method ='ME', - time_dim_name = 'time', na.rm = FALSE) { - if (!is.character(time_dim_name)) { - stop("Parameter 'time_dim_name' must be a character string indicating", - " the name of the temporal dimension.") - } - if (length(time_dim_name) > 1) { - warning("Parameter 'time_dim_name' has length greater than 1 and ", - "only the first element will be used.") - time_dim_name <- time_dim_name[1] - } - if (!(method %in% c('ME', 'LMEV'))){ - stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") - } - if (!is.logical(na.rm)) { - stop("Parameter 'na.rm' must be a logical value.") - } - if (!is.array(index_hind)) { - stop("Parameter 'index_hind' must be an array.") - } - if (!is.array(index_obs)) { - stop("Parameter 'index_obs' must be an array.") - } - if (is.null(names(dim(index_hind))) || is.null(names(dim(index_obs)))) { - stop("Parameters 'index_hind' and 'index_obs'", - " should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_obs)))) { - stop("Parameter 'index_obs' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' must have temporal dimension.") - } - if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { - stop("Length of the temporal dimensions ", - "of the parameter 'index_obs' and 'index_hind' must be equals.") - } - if (dim(index_obs)[time_dim_name] <= 2) { - stop("Length of temporal dimension ", - "of parameter 'index_obs' and 'index_hind' must be greater than 2.") - } - if('member' %in% names(dim(index_hind)) & - 'statistic' %in% names(dim(index_hind))) { - stop("Parameter 'index_hind' must have at least dimension ", - "'member' or 'statistic', not 'member' and 'statistic' together.") - } - - if(!('member' %in% names(dim(index_hind))) & - !('statistic' %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' must have dimension ", - "'member' or 'statistic'") - } - - if ('member' %in% names(dim(index_hind))){ - if (dim(index_hind)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_hind))){ - if (dim(index_hind)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind' must be equal to 2.") - } - } - - if ('member' %in% names(dim(index_hind))) { - PDFstatistics <- Apply(list(index_hind, index_obs), - target_dims = list(c(time_dim_name, 'member'), time_dim_name), - fun = .PDFIndexHind, method, time_dim_name, na.rm) - } else if ('statistic' %in% names(dim(index_hind))){ - PDFstatistics <- Apply(list(index_hind, index_obs), - target_dims = list(c(time_dim_name, 'statistic'), time_dim_name), - fun = .PDFIndexHind, method, time_dim_name, na.rm) - } - return(PDFstatistics$output1) -} - - - -#' Atomic PDFIndexHind -#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions -#' (time, member) or (time, statistic) for a hindcast period. -#' The temporal dimension, by default 'time', must be greater -#' than 2. -#' @param index_obs Index (e.g. NAO index) array from an observational dataset -#' or reanalysis with dimension (time). The temporal dimension, -#' by default 'time', must be greater than 2. -#' @param method A character string indicating which methodology is applied -#' to compute the PDF. One of "ME" (default) or "LMEV". -#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' for the hindcast period. -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected for the hindcast period. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' for the hindcast period. -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected for the hindcast period. -#' @import multiApply -#' @importFrom verification verify -#' @examples -#' # Example for the Atomic PDFIndexHind function -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = length(index_obs)) -#' index_hind <- 1 : (10 * 3) -#' dim(index_hind) <- c(time = 10, member = 3) -#' res <- .PDFIndexHind(index_hind, index_obs) -#' dim(res) -#' # time statistic -#' # 10 4 -#' @noRd -.PDFIndexHind <- function(index_hind, index_obs, method = 'ME', - time_dim_name = 'time', na.rm = FALSE) { - dimnameshind <- names(dim(index_hind)) - - if ('member' %in% dimnameshind) { - pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) - index_hind <- aperm(index_hind, pos) - names(dim(index_hind)) <- c(time_dim_name, 'member') - hind_ens_mean <- Apply(list(index_hind), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - hind_ens_sd <- Apply(list(index_hind), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else if ('statistic' %in% dimnameshind){ - pos <- match(c(time_dim_name, 'statistic'), names(dim(index_hind))) - index_hind <- aperm(index_hind,pos) - names(dim(index_hind)) <- c(time_dim_name, 'statistic') - hind_ens_mean <- index_hind[,1] - hind_ens_sd <- index_hind[,2] - } else { - stop("Wrong dimension of parameter 'index_hind'.") - } - error <- hind_ens_mean - index_obs - pdfnotbc_mean <- hind_ens_mean - ind <- 1 : length(index_obs) - pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(hind_ens_mean[-x], - index_obs[-x], - na.rm)})) - - if (method == 'ME') { - pdfbc <- unlist(lapply(ind, function(x) {MEBC(hind_ens_mean[-x], - index_obs[-x], na.rm)})) - pdfbc_mean <- hind_ens_mean - pdfbc - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x], - na.rm)})) - } else if (method == 'LMEV') { - # linear model error-variance - hind_ens_var <- hind_ens_sd^2 - dim_hind <- names(dim(index_hind)) - dim_obs <- names(dim(index_obs)) - coeff <- sapply(ind, function(x) {LMEV(index_hind[-x,], index_obs[-x], - dim_hind, dim_obs, na.rm)}) - pdfbc_mean <- hind_ens_mean - (unlist(coeff['coeff1',]) + - unlist(coeff['coeff2',])*hind_ens_var) - - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x], - na.rm)})) - } else { - stop("Error: Parameter 'method' is wrong") - } - data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) - names(dim(data)) <- c(names(dim(index_obs)), 'statistic') - return(data) -} - -#' Computing the Index PDFs for a dataset of SFSs for a forecast year. -#' -#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' -#'@description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from SFSs for a forecast year. -#' -#'@references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' -#'@param index_hind Index (e.g. NAO index) array from SFSs -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with at least a temporal dimension (by default 'time'), -#' which must be greater than 2. -#'@param index_fcst Index (e.g. NAO index) array from SFSs -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param method A character string indicating which methodology is applied -#' to compute the PDFs. One of "ME" (default) or "LMEV". -#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#'@param na.rm Logical (default = FALSE). Should missing values be removed? -#' -#'@return an array with at least two dimensions (time = 1, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @import multiApply -#' @examples -#' # Example for the PDFIndexFcst function -#' index_fcst <- 1 : (8 * 4) -#' dim(index_fcst) <- c(time = 1, member = 8, season = 4) -#' index_obs<- 1 : (5 * 4) -#' dim(index_obs) <- c(time = 5, season = 4) -#' index_hind <- 1 : (5 * 4 * 4) -#' dim(index_hind) <- c(time = 5, member = 4, season = 4) -#' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) -#' dim(res) -#' # time statistic season -#' # 1 4 4 -#' -#'@noRd -PDFIndexFcst <- function(index_hind, index_obs, index_fcst, - method ='ME', time_dim_name = 'time', - na.rm = FALSE) { - if (!is.character(time_dim_name)) { - stop("Parameter 'time_dim_name' must be a character string indicating", - " the name of the temporal dimension.") - } - if (length(time_dim_name) > 1) { - warning("Parameter 'time_dim_name' has length greater than 1 and ", - "only the first element will be used.") - time_dim_name <- time_dim_name[1] - } - if(!(method %in% c('ME', 'LMEV'))){ - stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") - } - if (!is.logical(na.rm)) { - stop("Parameter 'na.rm' must be a logical value.") - } - if (!is.array(index_hind)) { - stop("Parameter 'index_hind' must be an array.") - } - if (!is.array(index_obs)) { - stop("Parameter 'index_obs' must be an array.") - } - if (!is.array(index_fcst)) { - stop("Parameter 'index_fcst' must be an array.") - } - if (is.null(names(dim(index_hind))) || - is.null(names(dim(index_obs))) || - is.null(names(dim(index_fcst)))) { - stop("Parameters 'index_hind', 'index_obs' and 'index_fcst' ", - "should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_obs)))) { - stop("Parameter 'index_obs' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_fcst)))) { - stop("Parameter 'index_fcst' must have temporal dimension.") - } - if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { - stop("Length of temporal dimensions ", - "of parameter 'index_obs' and 'index_hind' must be equals.") - } - if (dim(index_obs)[time_dim_name] <= 2) { - stop("Length of temporal dimension ", - "of parameter 'index_obs' and 'index_hind' must be greater than 2.") - } - if (dim(index_fcst)[time_dim_name] != 1) { - stop("Length of temporal dimension ", - "of parameter 'index_fcst' must be equal to 1.") - } - if((length(names(dim(index_hind))) != length(names(dim(index_fcst)))) - || (!all(names(dim(index_hind)) == names(dim(index_fcst))))){ - stop("Dimension names of parameter 'index_hind' and 'index_fcst' ", - "must be equals.") - } - if('member' %in% names(dim(index_hind)) & - 'statistic' %in% names(dim(index_hind))) { - stop("Parameter 'index_hind' and 'index_fcst' must have at least ", - "dimension 'member' or 'statistic', not 'member' and 'statistic' ", - "together.") - } - if(!('member' %in% names(dim(index_hind))) & - !('statistic' %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' and 'index_fcst' must have dimension ", - "'member' or 'statistic'") - } - if ('member' %in% names(dim(index_hind))){ - if (dim(index_hind)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind' must be greater than 1.") - } - } - if ('member' %in% names(dim(index_fcst))){ - if (dim(index_fcst)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_fcst' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_hind))){ - if (dim(index_hind)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind' must be equal to 2.") - } - } - if ('statistic' %in% names(dim(index_fcst))){ - if (dim(index_fcst)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_fcst' must be equal to 2.") - } - } - - if ('member' %in% names(dim(index_hind))){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), - target_dims = list(c(time_dim_name, 'member'), - time_dim_name, - c(time_dim_name,'member')), - fun = .PDFIndexFcst, method, time_dim_name, na.rm) - } else if ('statistic' %in% names(dim(index_hind))){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), - target_dims = list(c(time_dim_name, 'statistic'), - time_dim_name, - c(time_dim_name,'statistic')), - fun = .PDFIndexFcst, method, time_dim_name, na.rm) - } - return(PDFstatistics$output1) -} - -#' Atomic PDFIndexFcst -#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions -#' (time, member) or (time, statistic) for a hindcast period. -#' The temporal dimension, by default 'time', must be greater -#' than 2. -#' @param index_obs Index (e.g. NAO index) array from an observational dataset -#' or reanalysis with dimension (time). The temporal dimension, -#' by default 'time', must be greater than 2. -#' @param index_fcst Index (e.g. NAO index) array from SFSs -#' with dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, -#' the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#' @param method A character string indicating which methodology is applied -#' to compute the PDF. One of "ME" (default) or "LMEV". -#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexFcst returns an array with dimensions (time = 1, statistic=4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' for the forecast year. -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected for the forecast year. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' for the forecast year. -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected for the forecast year. -#' @import multiApply -#' @importFrom verification verify -#' @examples -#' # Example 1 for the Atomic PDFIndexFcst function -#' index_fcst <- 1 : (1 * 6) -#' dim(index_fcst) <- c(time = 1, member = 6) -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = length(index_obs)) -#' index_hind <- 1 : (10 * 3) -#' dim(index_hind) <- c(time = 10, member = 3) -#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) -#' dim(res) -#' # time statistic -#' # 1 4 -#' # Example 2 for the Atomic PDFIndexFcst function -#' index_fcst <- 1 : (1 * 2) -#' dim(index_fcst) <- c(time = 1, statistic = 2) -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = 10) -#' index_hind <- 1 : (10 * 2) -#' dim(index_hind) <- c(time = 10, statistic = 2) -#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) -#' dim(res) -#' # time statistic -#' # 1 4 -#' @noRd -.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, - method = 'ME', - time_dim_name = 'time', - na.rm = FALSE) { - dimnameshind <- names(dim(index_hind)) - if ('member' %in% dimnameshind) { - pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) - index_hind <- aperm(index_hind, pos) - names(dim(index_hind)) <- c(time_dim_name, 'member') - exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', - fun = mean)$output1 - exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', - fun = sd)$output1 - } else { - pos <- match(c(time_dim_name,'statistic'), names(dim(index_fcst))) - index_fcst <- aperm(index_fcst,pos) - names(dim(index_fcst)) <- c(time_dim_name, 'statistic') - exp_fcst_ens_mean <- index_fcst[,1] - exp_fcst_ens_sd <- index_fcst[,2] - } - data_hind <- .PDFIndexHind(index_hind, index_obs, method, time_dim_name, na.rm) - exp_hind_ens_mean <- data_hind[,1] - pdfnotbc_mean <- exp_fcst_ens_mean - pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs, na.rm) - if (method == 'ME') { - pdfbc <- MEBC(exp_hind_ens_mean, index_obs, na.rm) - pdfbc_mean <- exp_fcst_ens_mean - pdfbc - } else if (method == 'LMEV') { - # linear model error-variance - exp_fcst_ens_var <- exp_fcst_ens_sd^2 - dim_hind <- names(dim(index_hind)) - dim_obs <- names(dim(index_obs)) - coeff <- LMEV(index_hind, index_obs, dim_hind, dim_obs, time_dim_name, na.rm) - pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) - } else { - stop("Parameter 'method' must be 'ME' or 'LMEV'.") - } - pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs, na.rm) - data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) - names(dim(data)) <- c(names(dim(index_obs)), 'statistic') - return(data) -} - -# Auxiliar function to compute the mean squared error between 'exp' and 'obs' -Sigma_notBC <- function(exp, obs, na.rm = FALSE) { - if (na.rm){ - indNA <- c(which(is.na(exp)),which(is.na(obs))) - if (length(indNA) > 0){ - exp <- exp[-indNA] - obs <- obs[-indNA] - } - } - return(sqrt(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$MSE)) -} - -# Auxiliar function to compute the bias between 'exp' and 'obs' -MEBC <- function(exp, obs, na.rm = FALSE) { - if (na.rm){ - indNA <- c(which(is.na(exp)),which(is.na(obs))) - if (length(indNA) > 0){ - exp <- exp[-indNA] - obs <- obs[-indNA] - } - } - return(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$ME) -} - -# Auxiliar function to compute the standard deviation of errors -# between 'obs' and 'exp' -Sigma_BC <- function(exp, obs, na.rm = FALSE) { - return(sd(obs - exp, na.rm = na.rm)) -} - -# Auxiliar function to compute the linear model used in the method -# called 'LMEV' to correct the bias in the Best NAO weighting -# methogology -LMEV <- function(exp, obs, dim_exp, - dim_obs, time_dim_name = 'time', na.rm = FALSE) { - names(dim(exp)) <- dim_exp - names(dim(obs)) <- dim_obs - if (any(names(dim(exp)) == 'member')) { - exp_ens_mean <- Apply(list(exp), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - exp_ens_sd <- Apply(list(exp), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else { - if(na.rm & any(is.na(exp))){ - print("Some value are NA") - } - pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) - exp <- aperm(exp,pos) - exp_ens_mean <- exp[,1] - exp_ens_sd <- exp[,2] - } - error <- exp_ens_mean - obs - exp_ens_var <- exp_ens_sd^2 - statModel <- lm(error ~ exp_ens_var) - return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) -} - - +#' Computing the Best Index PDFs combining Index PDFs from two SFSs +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) obtained to combining the Index PDFs for two SFSs, +#' the Best Index estimation. +#' +#'@references Regionally improved seasonal forecast of precipitation through +#'Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param index_hind1 Index (e.g. NAO index) array from a SFS (named SFS1) +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' at the same time. +#'@param index_hind2 Index (e.g. NAO index) array from a SFS (named SFS2) +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_fcst1 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS1 +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_fcst2 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS2 +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param method_BC A character vector of maximun length 2 indicating the bias correction +#'methodology to be applied on each SFS. If it is 'none' or any of its elements is 'none', +#'the bias correction won't be applied. Available methods developped are "ME" and "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#' @return BEI_PDFBest() returns an array +#' with at least a temporal dimension, by default 'time' and dimension 'statistic', +#' equal to 2. +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDFs. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDFs. +#' If index_fcst1 and/or index_fcst2 are null, returns the values for hindcast period. +#' Otherwise, it returns the values for a forecast year. +#'@import multiApply +#' +#'@examples +#' # Example 1 for the BEI_PDFBest function +#' index_obs<- rnorm(10, sd = 3) +#' dim(index_obs) <- c(time = 5, season = 2) +#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) +#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) +#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +#' method_BC <- 'ME' +#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, +#' index_fcst2, method_BC) +#' dim(res) +#' # time statistic season +#' # 1 2 2 +#' # Example 2 for the BEI_PDFBest function +#' index_obs<- rnorm(10, sd = 3) +#' dim(index_obs) <- c(time = 5, season = 2) +#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) +#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) +#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +#' method_BC <- c('LMEV', 'ME') +#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) +#' dim(res) +#' # time statistic season +#' # 1 2 2 +#'@export + +BEI_PDFBest <- function(index_obs, index_hind1, index_hind2, + index_fcst1 = NULL, index_fcst2 = NULL, method_BC = 'none', + time_dim_name = 'time', na.rm = FALSE) { + + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string ", + "indicating the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!is.character(method_BC) || !is.vector(method_BC)){ + stop("Parameter 'method_BC' must be a character vector.") + } + if (!(length(method_BC) == 1 || length(method_BC) == 2)){ + stop("Length of parameter 'method_BC' must be 1 or 2.") + } + if(!all(method_BC %in% c('ME', 'LMEV', 'none'))){ + stop("Elements of parameter 'method_BC' must be equals to ", + "'none, 'ME' or 'LMEV'") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (!is.array(index_hind1)) { + stop("Parameter 'index_hind1' must be an array.") + } + if (!is.array(index_hind2)) { + stop("Parameter 'index_hind2' must be an array.") + } + if (is.null(names(dim(index_hind1))) || + is.null(names(dim(index_obs))) || + is.null(names(dim(index_hind2)))) { + stop("Parameters 'index_obs', 'index_hind1' and 'index_hind2' ", + "should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind1)))) { + stop("Parameter 'index_hind1' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind2)))) { + stop("Parameter 'index_hind2' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs', 'index_hind1' and 'index_hind2' ", + "must be greater than 2.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind1)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_obs' and 'index_hind1' must be equals.") + } + if (dim(index_hind1)[time_dim_name] != dim(index_hind2)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_hind1' and 'index_hind2' must be equals.") + } + if('member' %in% names(dim(index_hind1)) & + 'statistic' %in% names(dim(index_hind1))) { + stop("Parameter 'index_hind1' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if('member' %in% names(dim(index_hind2)) & + 'statistic' %in% names(dim(index_hind2))) { + stop("Parameter 'index_hind2' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if(!('member' %in% names(dim(index_hind1))) & + !('statistic' %in% names(dim(index_hind1)))) { + stop("Parameter 'index_hind1' must have dimension ", + "'member' or 'statistic'") + } + if(!('member' %in% names(dim(index_hind2))) & + !('statistic' %in% names(dim(index_hind2)))) { + stop("Parameter 'index_hind2' must have dimension ", + "'member' or 'statistic'") + } + if ('member' %in% names(dim(index_hind1))){ + if (dim(index_hind1)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind1' must be greater than 1.") + } + } + if ('member' %in% names(dim(index_hind2))){ + if (dim(index_hind2)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind2' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind1))){ + if (dim(index_hind1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind1' must be equal to 2.") + } + } + if ('statistic' %in% names(dim(index_hind2))){ + if (dim(index_hind2)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind2' must be equal to 2.") + } + } + if (!is.null(index_fcst1)){ + if (!is.array(index_fcst1)) { + stop("Parameter 'index_fcst1' must be an array.") + } + if (is.null(names(dim(index_fcst1)))){ + stop("Parameters 'index_fcst1' should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_fcst1)))) { + stop("Parameter 'index_fcst1' must have temporal dimension.") + } + if (dim(index_fcst1)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst1' must be equal to 1.") + } + if((length(names(dim(index_hind1))) != length(names(dim(index_fcst1)))) + || (!all(names(dim(index_hind1)) == names(dim(index_fcst1))))){ + stop("Dimension names of parameter 'index_hind1' and 'index_fcst1' ", + "must be equals.") + } + if ('member' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst1' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst1' must be equal to 2.") + } + } + } + if (!is.null(index_fcst2)){ + if (!is.array(index_fcst2)) { + stop("Parameter 'index_fcst2' must be an array.") + } + if (is.null(names(dim(index_fcst2)))){ + stop("Parameters 'index_fcst2' should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_fcst2)))) { + stop("Parameter 'index_fcst2' must have temporal dimension.") + } + if (dim(index_fcst2)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst2' must be equal to 1.") + } + if((length(names(dim(index_hind2))) != length(names(dim(index_fcst2)))) + || (!all(names(dim(index_hind2)) == names(dim(index_fcst2))))){ + stop("Dimension names of parameter 'index_hind2' and 'index_fcst2' ", + "must be equals.") + } + if ('member' %in% names(dim(index_fcst2))){ + if (dim(index_fcst2)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst2' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst1' must be equal to 2.") + } + } + } + + if (all(method_BC == 'none')){ + method_BC1 <- 'ME' + method_BC2 <- 'ME' + bc_dataset1 <- FALSE + bc_dataset2 <- FALSE + } else if (length(method_BC) == 1){ + method_BC1 <- method_BC + method_BC2 <- method_BC + bc_dataset1 <- TRUE + bc_dataset2 <- TRUE + } else { + if(method_BC[1] == 'none'){ + method_BC1 <- 'ME' + bc_dataset1 <- FALSE + } else { + method_BC1 <- method_BC[1] + bc_dataset1 <- TRUE + } + if(method_BC[2] == 'none'){ + method_BC2 <- 'ME' + bc_dataset2 <- FALSE + } else { + method_BC2 <- method_BC[2] + bc_dataset2 <- TRUE + } + } + + pdf_hind1 <- PDFIndexHind(index_hind1, index_obs, method = method_BC1, + time_dim_name = time_dim_name, na.rm = na.rm) + pdf_hind2 <- PDFIndexHind(index_hind2, index_obs, method = method_BC2, + time_dim_name = time_dim_name, na.rm = na.rm) + if (is.null(index_fcst1) || is.null(index_fcst2)){ + pdf_best <- Apply(list(pdf_hind1, pdf_hind2), + target_dims = list('statistic', + 'statistic'), + fun = .BEI_PDFBest, + bc_dataset1, bc_dataset2) + } else { + pdf_fcst1 <- PDFIndexFcst(index_hind1, index_obs, index_fcst1, + method_BC1, time_dim_name = time_dim_name, + na.rm = na.rm) + pdf_fcst2 <- PDFIndexFcst(index_hind2, index_obs, index_fcst2, + method_BC2, time_dim_name = time_dim_name, + na.rm = na.rm) + pdf_best <- Apply(list(pdf_fcst1, pdf_fcst2), + target_dims = list('statistic', + 'statistic'), + fun = .BEI_PDFBest, + bc_dataset1, bc_dataset2) + } + Dim <- names(dim(index_hind1)) + if('member' %in% Dim){ + pos_aux <- match('member', Dim) + Dim[pos_aux] <- 'statistic' + } + pos <- match(Dim, names(dim(pdf_best$output1))) + res <- aperm(pdf_best$output1,pos) + names(dim(res)) <- Dim + return(res) +} + +#' Atomic BEI_PDFBest +#'@param pdf_1 Statistics array for the first SFS PDF with one dimension +#' 'statistic' equal to 4. +#' @param pdf_2 Statistics array for the second SFS PDF with one dimension +#' 'statistic' equal to 4. +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDFs for the first SFS has been computed +#' with bias corrected. +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDFs for the second SFS has been computed +#' with bias corrected. +#' +#' @return .BEI_PDFBest returns an array with dimensions (statistic = 2). +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDF. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDF. +#' +#' @examples +#' # Example for the Atomic BEI_PDFBest function +#' pdf_1 <- c(1.1,0.6,1.6,0.9) +#' dim(pdf_1) <- c(statistic = 4) +#' pdf_2 <- c(1,0.5,1.5,0.8) +#' dim(pdf_2) <- c(statistic = 4) +#' res <- .BEI_PDFBest(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +#' str(res) +#' dim(res) +#' # statistic +#' # 2 +#' @noRd +.BEI_PDFBest <- function(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { + if(bc_dataset1){ + # apply bias correction to model 1 + mean_1 <- pdf_1[3] + sigma_1 <- pdf_1[4] + } else{ + # not bias correction to model 1 + mean_1 <- pdf_1[1] + sigma_1 <- pdf_1[2] + } + if(bc_dataset2){ + # apply bias correction to model 2 + mean_2 <- pdf_2[3] + sigma_2 <- pdf_2[4] + } else { + # not bias correction to model 2 + mean_2 <- pdf_2[1] + sigma_2 <- pdf_2[2] + } + a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) + a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) + pdf_mean <- a_1*mean_1 + a_2*mean_2 + pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) + data <- c(pdf_mean, pdf_sigma) + dim(data) <- c(statistic = 2) + return(data) +} + +#'Computing the Index PDFs for a dataset of SFSs for a hindcats period. +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from SFSs for a hindcast period. +#' +#'@references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_hind Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#'@return an array with at least two dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @examples +#' # Example for the PDFIndexHind function +#' # Example 1 +#' index_obs <- 1 : (5 * 3 ) +#' dim(index_obs) <- c(time = 5, season = 3) +#' index_hind <- 1 : (5 * 4 * 3) +#' dim(index_hind) <- c(time = 5, member = 4, season = 3) +#' res <- PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic season +#' # 5 4 3 +#' # Example 2 +#' index_obs <- 1 : (5 * 3) +#' dim(index_obs) <- c(time = 5, season = 3) +#' index_hind <- 1 : (5 * 2 * 3) +#' dim(index_hind) <- c(time = 5, statistic = 2, season = 3) +#' res <- PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic season +#' # 5 4 3 +#'@noRd +PDFIndexHind <- function(index_hind, index_obs, method ='ME', + time_dim_name = 'time', na.rm = FALSE) { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!(method %in% c('ME', 'LMEV'))){ + stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.array(index_hind)) { + stop("Parameter 'index_hind' must be an array.") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (is.null(names(dim(index_hind))) || is.null(names(dim(index_obs)))) { + stop("Parameters 'index_hind' and 'index_obs'", + " should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { + stop("Length of the temporal dimensions ", + "of the parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs' and 'index_hind' must be greater than 2.") + } + if('member' %in% names(dim(index_hind)) & + 'statistic' %in% names(dim(index_hind))) { + stop("Parameter 'index_hind' must have at least dimension ", + "'member' or 'statistic', not 'member' and 'statistic' together.") + } + + if(!('member' %in% names(dim(index_hind))) & + !('statistic' %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have dimension ", + "'member' or 'statistic'") + } + + if ('member' %in% names(dim(index_hind))){ + if (dim(index_hind)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind))){ + if (dim(index_hind)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind' must be equal to 2.") + } + } + + if ('member' %in% names(dim(index_hind))) { + PDFstatistics <- Apply(list(index_hind, index_obs), + target_dims = list(c(time_dim_name, 'member'), time_dim_name), + fun = .PDFIndexHind, method, time_dim_name, na.rm) + } else if ('statistic' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs), + target_dims = list(c(time_dim_name, 'statistic'), time_dim_name), + fun = .PDFIndexHind, method, time_dim_name, na.rm) + } + return(PDFstatistics$output1) +} + + + +#' Atomic PDFIndexHind +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) or (time, statistic) for a hindcast period. +#' The temporal dimension, by default 'time', must be greater +#' than 2. +#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' or reanalysis with dimension (time). The temporal dimension, +#' by default 'time', must be greater than 2. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the hindcast period. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the hindcast period. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the hindcast period. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the hindcast period. +#' @import multiApply +#' @importFrom verification verify +#' @examples +#' # Example for the Atomic PDFIndexHind function +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_hind <- 1 : (10 * 3) +#' dim(index_hind) <- c(time = 10, member = 3) +#' res <- .PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic +#' # 10 4 +#' @noRd +.PDFIndexHind <- function(index_hind, index_obs, method = 'ME', + time_dim_name = 'time', na.rm = FALSE) { + dimnameshind <- names(dim(index_hind)) + + if ('member' %in% dimnameshind) { + pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) + index_hind <- aperm(index_hind, pos) + names(dim(index_hind)) <- c(time_dim_name, 'member') + hind_ens_mean <- Apply(list(index_hind), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + hind_ens_sd <- Apply(list(index_hind), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else if ('statistic' %in% dimnameshind){ + pos <- match(c(time_dim_name, 'statistic'), names(dim(index_hind))) + index_hind <- aperm(index_hind,pos) + names(dim(index_hind)) <- c(time_dim_name, 'statistic') + hind_ens_mean <- index_hind[,1] + hind_ens_sd <- index_hind[,2] + } else { + stop("Wrong dimension of parameter 'index_hind'.") + } + error <- hind_ens_mean - index_obs + pdfnotbc_mean <- hind_ens_mean + ind <- 1 : length(index_obs) + pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(hind_ens_mean[-x], + index_obs[-x], + na.rm)})) + + if (method == 'ME') { + pdfbc <- unlist(lapply(ind, function(x) {MEBC(hind_ens_mean[-x], + index_obs[-x], na.rm)})) + pdfbc_mean <- hind_ens_mean - pdfbc + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x], + na.rm)})) + } else if (method == 'LMEV') { + # linear model error-variance + hind_ens_var <- hind_ens_sd^2 + dim_hind <- names(dim(index_hind)) + dim_obs <- names(dim(index_obs)) + coeff <- sapply(ind, function(x) {LMEV(index_hind[-x,], index_obs[-x], + dim_hind, dim_obs, na.rm)}) + pdfbc_mean <- hind_ens_mean - (unlist(coeff['coeff1',]) + + unlist(coeff['coeff2',])*hind_ens_var) + + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x], + na.rm)})) + } else { + stop("Error: Parameter 'method' is wrong") + } + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + +#' Computing the Index PDFs for a dataset of SFSs for a forecast year. +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from SFSs for a forecast year. +#' +#'@references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_hind Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param index_fcst Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#'@return an array with at least two dimensions (time = 1, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @examples +#' # Example for the PDFIndexFcst function +#' index_fcst <- 1 : (8 * 4) +#' dim(index_fcst) <- c(time = 1, member = 8, season = 4) +#' index_obs<- 1 : (5 * 4) +#' dim(index_obs) <- c(time = 5, season = 4) +#' index_hind <- 1 : (5 * 4 * 4) +#' dim(index_hind) <- c(time = 5, member = 4, season = 4) +#' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic season +#' # 1 4 4 +#' +#'@noRd +PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method ='ME', time_dim_name = 'time', + na.rm = FALSE) { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if(!(method %in% c('ME', 'LMEV'))){ + stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.array(index_hind)) { + stop("Parameter 'index_hind' must be an array.") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (!is.array(index_fcst)) { + stop("Parameter 'index_fcst' must be an array.") + } + if (is.null(names(dim(index_hind))) || + is.null(names(dim(index_obs))) || + is.null(names(dim(index_fcst)))) { + stop("Parameters 'index_hind', 'index_obs' and 'index_fcst' ", + "should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_fcst)))) { + stop("Parameter 'index_fcst' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs' and 'index_hind' must be greater than 2.") + } + if (dim(index_fcst)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst' must be equal to 1.") + } + if((length(names(dim(index_hind))) != length(names(dim(index_fcst)))) + || (!all(names(dim(index_hind)) == names(dim(index_fcst))))){ + stop("Dimension names of parameter 'index_hind' and 'index_fcst' ", + "must be equals.") + } + if('member' %in% names(dim(index_hind)) & + 'statistic' %in% names(dim(index_hind))) { + stop("Parameter 'index_hind' and 'index_fcst' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if(!('member' %in% names(dim(index_hind))) & + !('statistic' %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' and 'index_fcst' must have dimension ", + "'member' or 'statistic'") + } + if ('member' %in% names(dim(index_hind))){ + if (dim(index_hind)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind' must be greater than 1.") + } + } + if ('member' %in% names(dim(index_fcst))){ + if (dim(index_fcst)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind))){ + if (dim(index_hind)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind' must be equal to 2.") + } + } + if ('statistic' %in% names(dim(index_fcst))){ + if (dim(index_fcst)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst' must be equal to 2.") + } + } + + if ('member' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c(time_dim_name, 'member'), + time_dim_name, + c(time_dim_name,'member')), + fun = .PDFIndexFcst, method, time_dim_name, na.rm) + } else if ('statistic' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c(time_dim_name, 'statistic'), + time_dim_name, + c(time_dim_name,'statistic')), + fun = .PDFIndexFcst, method, time_dim_name, na.rm) + } + return(PDFstatistics$output1) +} + +#' Atomic PDFIndexFcst +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) or (time, statistic) for a hindcast period. +#' The temporal dimension, by default 'time', must be greater +#' than 2. +#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' or reanalysis with dimension (time). The temporal dimension, +#' by default 'time', must be greater than 2. +#' @param index_fcst Index (e.g. NAO index) array from SFSs +#' with dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, +#' the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexFcst returns an array with dimensions (time = 1, statistic=4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the forecast year. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the forecast year. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the forecast year. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the forecast year. +#' @import multiApply +#' @importFrom verification verify +#' @examples +#' # Example 1 for the Atomic PDFIndexFcst function +#' index_fcst <- 1 : (1 * 6) +#' dim(index_fcst) <- c(time = 1, member = 6) +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_hind <- 1 : (10 * 3) +#' dim(index_hind) <- c(time = 10, member = 3) +#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic +#' # 1 4 +#' # Example 2 for the Atomic PDFIndexFcst function +#' index_fcst <- 1 : (1 * 2) +#' dim(index_fcst) <- c(time = 1, statistic = 2) +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = 10) +#' index_hind <- 1 : (10 * 2) +#' dim(index_hind) <- c(time = 10, statistic = 2) +#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic +#' # 1 4 +#' @noRd +.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method = 'ME', + time_dim_name = 'time', + na.rm = FALSE) { + dimnameshind <- names(dim(index_hind)) + if ('member' %in% dimnameshind) { + pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) + index_hind <- aperm(index_hind, pos) + names(dim(index_hind)) <- c(time_dim_name, 'member') + exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', + fun = mean)$output1 + exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', + fun = sd)$output1 + } else { + pos <- match(c(time_dim_name,'statistic'), names(dim(index_fcst))) + index_fcst <- aperm(index_fcst,pos) + names(dim(index_fcst)) <- c(time_dim_name, 'statistic') + exp_fcst_ens_mean <- index_fcst[,1] + exp_fcst_ens_sd <- index_fcst[,2] + } + data_hind <- .PDFIndexHind(index_hind, index_obs, method, time_dim_name, na.rm) + exp_hind_ens_mean <- data_hind[,1] + pdfnotbc_mean <- exp_fcst_ens_mean + pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs, na.rm) + if (method == 'ME') { + pdfbc <- MEBC(exp_hind_ens_mean, index_obs, na.rm) + pdfbc_mean <- exp_fcst_ens_mean - pdfbc + } else if (method == 'LMEV') { + # linear model error-variance + exp_fcst_ens_var <- exp_fcst_ens_sd^2 + dim_hind <- names(dim(index_hind)) + dim_obs <- names(dim(index_obs)) + coeff <- LMEV(index_hind, index_obs, dim_hind, dim_obs, time_dim_name, na.rm) + pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) + } else { + stop("Parameter 'method' must be 'ME' or 'LMEV'.") + } + pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs, na.rm) + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + +# Auxiliar function to compute the mean squared error between 'exp' and 'obs' +Sigma_notBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } + return(sqrt(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$MSE)) +} + +# Auxiliar function to compute the bias between 'exp' and 'obs' +MEBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } + return(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$ME) +} + +# Auxiliar function to compute the standard deviation of errors +# between 'obs' and 'exp' +Sigma_BC <- function(exp, obs, na.rm = FALSE) { + return(sd(obs - exp, na.rm = na.rm)) +} + +# Auxiliar function to compute the linear model used in the method +# called 'LMEV' to correct the bias in the Best NAO weighting +# methogology +LMEV <- function(exp, obs, dim_exp, + dim_obs, time_dim_name = 'time', na.rm = FALSE) { + names(dim(exp)) <- dim_exp + names(dim(obs)) <- dim_obs + if (any(names(dim(exp)) == 'member')) { + exp_ens_mean <- Apply(list(exp), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + exp_ens_sd <- Apply(list(exp), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else { + if(na.rm & any(is.na(exp))){ + print("Some value are NA") + } + pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) + exp <- aperm(exp,pos) + exp_ens_mean <- exp[,1] + exp_ens_sd <- exp[,2] + } + error <- exp_ens_mean - obs + exp_ens_var <- exp_ens_sd^2 + statModel <- lm(error ~ exp_ens_var) + return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) +} + + diff --git a/R/BEI_Weights.R b/R/BEI_Weights.R index 55261984..63fb22c3 100644 --- a/R/BEI_Weights.R +++ b/R/BEI_Weights.R @@ -3,9 +3,11 @@ #'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} #' #'@description This function implements the computation to obtain the -#' normalized weights for each member of each SFS or dataset using the -#' pdfs indicated by the parameter 'pdf_weight' ( for instance the Best Index -#' estimation obtained using the 'PDFBest' function. +#' normalized weights for each member of each Seasonal Forecast Systems (SFS) +#' or dataset using the Probability Density Functions (PDFs) indicated by the +#' parameter 'pdf_weight' (for instance the Best Index estimation obtained +#' using the 'PDFBest' function). The weight of each member is proportional to +#' the probability of its index calculated with the PDF "pdf_weight". #' #'@references Regionally improved seasonal forecast of precipitation through Best #' estimation of winter NAO, Sanchez-Garcia, E. et al., diff --git a/R/CST_BEI_Weighting.R b/R/CST_BEI_Weighting.R index c893ea2e..51c8ba32 100644 --- a/R/CST_BEI_Weighting.R +++ b/R/CST_BEI_Weighting.R @@ -4,7 +4,8 @@ #' #' @description Function to apply weights to a 's2dv_cube' object. #' It could return a weighted ensemble mean (deterministic output) or -#' the terciles probabilities (probabilistic output) for SFSs. +#' the terciles probabilities (probabilistic output) for Seasonal Forecast +#' Systems (SFSs). #' #' @references Regionally improved seasonal forecast of precipitation through #' Best estimation of winter NAO, Sanchez-Garcia, E. et al., @@ -15,21 +16,23 @@ #' The var_exp object is expected to have an element named \code{$data} with #' at least a temporal dimension and a dimension named 'member'. #' @param aweights Normalized weights array with at least dimensions -#' (time, member), when 'time' is the temporal dimension as default. +#' (time, member), when 'time' is the temporal dimension as default. +#' When 'aweights' parameter has any other dimensions (as e.g. 'lat') and +#' 'var_exp' parameter has also the same dimension, they must be equals. #' @param type A character string indicating the type of output. #' If 'type' = 'probs', the function returns, in the element data from #' 'var_exp' parameter, an array with at least two -#' or four dimensions depending if the variable is zonal (as e.g. NAO index), -#' dimension (time, tercil) or it is spatial variable (as e.g. precipitation -#' or temperature), dimension (time, tercile, lat, lon), containing the terciles -#' probabilities computing with weighted members. +#' or four dimensions depending if the variable is spatially aggregated variable +#' (as e.g. NAO index), dimension (time, tercil) or it is spatial variable +#' (as e.g. precipitation or temperature), dimension (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. #' If 'type' = 'ensembleMean', the function returns, in the element data from #' 'var_exp' parameter, an array with at least one or three dimensions -#' depending if the variable is zonal (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 +#' depending if the variable is a 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. #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. @@ -132,18 +135,18 @@ CST_BEI_Weighting <- function(var_exp, aweights, type = 'ensembleMean', #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' #' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with at least dimensions (time, member) for a zonal -#' variable or dimensions (time, member, lat, lon) for a spatial +#' 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. #' @param aweights Normalized weights array with at least dimensions #' (time, member), when 'time' is the temporal dimension as default. #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. #' -#' @return BEI_EMWeighting() returns an array with at least one or three dimensions -#' depending if the variable is zonal (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 +#' @return 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. #' @import multiApply #' @@ -257,19 +260,19 @@ BEI_EMWeighting <- function(var_exp, aweights, time_dim_name = 'time') { #' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 #' #' @param var_exp Variable (e.g. precipitation, temperature, NAO index) -#' array from a SFS with at least dimensions (time, member) for a zonal -#' variable or dimensions (time, member, lat, lon) for a spatial +#' 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. #' @param aweights Normalized weights array with at least dimensions #' (time, member), when 'time' is the temporal dimension as default. #' @param time_dim_name A character string indicating the name of the #' temporal dimension, by default 'time'. #' -#' @return BEI_ProbsWeighting() returns an array with at least two or four dimensions -#' depending if the variable is zonal (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. +#' @return 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. #' diff --git a/man/BEI_Weights.Rd b/man/BEI_Weights.Rd index 71c096be..867a4eb0 100644 --- a/man/BEI_Weights.Rd +++ b/man/BEI_Weights.Rd @@ -26,9 +26,11 @@ dimensions that index_weight. } \description{ This function implements the computation to obtain the -normalized weights for each member of each SFS or dataset using the -pdfs indicated by the parameter 'pdf_weight' ( for instance the Best Index -estimation obtained using the 'PDFBest' function. +normalized weights for each member of each Seasonal Forecast Systems (SFS) +or dataset using the Probability Density Functions (PDFs) indicated by the +parameter 'pdf_weight' (for instance the Best Index estimation obtained +using the 'PDFBest' function). The weight of each member is proportional to +the probability of its index calculated with the PDF "pdf_weight". } \examples{ # Example for the BEI_Weights function diff --git a/man/CST_BEI_Weighting.Rd b/man/CST_BEI_Weighting.Rd index 42a67e89..b2a3f1fa 100644 --- a/man/CST_BEI_Weighting.Rd +++ b/man/CST_BEI_Weighting.Rd @@ -14,22 +14,24 @@ The var_exp object is expected to have an element named \code{$data} with at least a temporal dimension and a dimension named 'member'.} \item{aweights}{Normalized weights array with at least dimensions -(time, member), when 'time' is the temporal dimension as default.} +(time, member), when 'time' is the temporal dimension as default. +When 'aweights' parameter has any other dimensions (as e.g. 'lat') and +'var_exp' parameter has also the same dimension, they must be equals.} \item{type}{A character string indicating the type of output. If 'type' = 'probs', the function returns, in the element data from 'var_exp' parameter, an array with at least two -or four dimensions depending if the variable is zonal (as e.g. NAO index), -dimension (time, tercil) or it is spatial variable (as e.g. precipitation -or temperature), dimension (time, tercile, lat, lon), containing the terciles -probabilities computing with weighted members. +or four dimensions depending if the variable is spatially aggregated variable +(as e.g. NAO index), dimension (time, tercil) or it is spatial variable +(as e.g. precipitation or temperature), dimension (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. If 'type' = 'ensembleMean', the function returns, in the element data from 'var_exp' parameter, an array with at least one or three dimensions -depending if the variable is zonal (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 +depending if the variable is a 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.} \item{time_dim_name}{A character string indicating the name of the @@ -46,7 +48,8 @@ weighted members or probabilities of terciles. \description{ Function to apply weights to a 's2dv_cube' object. It could return a weighted ensemble mean (deterministic output) or -the terciles probabilities (probabilistic output) for SFSs. +the terciles probabilities (probabilistic output) for Seasonal Forecast +Systems (SFSs). } \examples{ var_exp <- 1 : (2 * 4 * 3 * 2) -- GitLab From cc1103b301c45a5ee9bac4213cc3b6a0af32fdaf Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 7 Nov 2019 06:56:37 +0000 Subject: [PATCH 39/40] Adding documentation about methods ME and LMEV in BEI_PDFBest.R --- R/BEI_PDFBest.R | 1886 ++++++++++++++++++++++---------------------- man/BEI_PDFBest.Rd | 29 +- 2 files changed, 967 insertions(+), 948 deletions(-) diff --git a/R/BEI_PDFBest.R b/R/BEI_PDFBest.R index 46f3b3bc..46833b2c 100644 --- a/R/BEI_PDFBest.R +++ b/R/BEI_PDFBest.R @@ -1,938 +1,948 @@ -#' Computing the Best Index PDFs combining Index PDFs from two SFSs -#' -#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' -#'@description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) obtained to combining the Index PDFs for two SFSs, -#' the Best Index estimation. -#' -#'@references Regionally improved seasonal forecast of precipitation through -#'Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' -#'@param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with at least a temporal dimension (by default 'time'), -#' which must be greater than 2. -#'@param index_hind1 Index (e.g. NAO index) array from a SFS (named SFS1) -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' at the same time. -#'@param index_hind2 Index (e.g. NAO index) array from a SFS (named SFS2) -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_fcst1 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS1 -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_fcst2 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS2 -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param method_BC A character vector of maximun length 2 indicating the bias correction -#'methodology to be applied on each SFS. If it is 'none' or any of its elements is 'none', -#'the bias correction won't be applied. Available methods developped are "ME" and "LMEV". -#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#'@param na.rm Logical (default = FALSE). Should missing values be removed? -#' -#' @return BEI_PDFBest() returns an array -#' with at least a temporal dimension, by default 'time' and dimension 'statistic', -#' equal to 2. -#' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDFs. -#' The second statistic is the parameter 'standard deviation' of the PDF for -#' the best estimation combining the two SFSs PDFs. -#' If index_fcst1 and/or index_fcst2 are null, returns the values for hindcast period. -#' Otherwise, it returns the values for a forecast year. -#'@import multiApply -#' -#'@examples -#' # Example 1 for the BEI_PDFBest function -#' index_obs<- rnorm(10, sd = 3) -#' dim(index_obs) <- c(time = 5, season = 2) -#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) -#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) -#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) -#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) -#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) -#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) -#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) -#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) -#' method_BC <- 'ME' -#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, -#' index_fcst2, method_BC) -#' dim(res) -#' # time statistic season -#' # 1 2 2 -#' # Example 2 for the BEI_PDFBest function -#' index_obs<- rnorm(10, sd = 3) -#' dim(index_obs) <- c(time = 5, season = 2) -#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) -#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) -#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) -#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) -#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) -#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) -#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) -#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) -#' method_BC <- c('LMEV', 'ME') -#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) -#' dim(res) -#' # time statistic season -#' # 1 2 2 -#'@export - -BEI_PDFBest <- function(index_obs, index_hind1, index_hind2, - index_fcst1 = NULL, index_fcst2 = NULL, method_BC = 'none', - time_dim_name = 'time', na.rm = FALSE) { - - if (!is.logical(na.rm)) { - stop("Parameter 'na.rm' must be a logical value.") - } - if (!is.character(time_dim_name)) { - stop("Parameter 'time_dim_name' must be a character string ", - "indicating the name of the temporal dimension.") - } - if (length(time_dim_name) > 1) { - warning("Parameter 'time_dim_name' has length greater than 1 and ", - "only the first element will be used.") - time_dim_name <- time_dim_name[1] - } - if (!is.character(method_BC) || !is.vector(method_BC)){ - stop("Parameter 'method_BC' must be a character vector.") - } - if (!(length(method_BC) == 1 || length(method_BC) == 2)){ - stop("Length of parameter 'method_BC' must be 1 or 2.") - } - if(!all(method_BC %in% c('ME', 'LMEV', 'none'))){ - stop("Elements of parameter 'method_BC' must be equals to ", - "'none, 'ME' or 'LMEV'") - } - if (!is.array(index_obs)) { - stop("Parameter 'index_obs' must be an array.") - } - if (!is.array(index_hind1)) { - stop("Parameter 'index_hind1' must be an array.") - } - if (!is.array(index_hind2)) { - stop("Parameter 'index_hind2' must be an array.") - } - if (is.null(names(dim(index_hind1))) || - is.null(names(dim(index_obs))) || - is.null(names(dim(index_hind2)))) { - stop("Parameters 'index_obs', 'index_hind1' and 'index_hind2' ", - "should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_obs)))) { - stop("Parameter 'index_obs' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_hind1)))) { - stop("Parameter 'index_hind1' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_hind2)))) { - stop("Parameter 'index_hind2' must have temporal dimension.") - } - if (dim(index_obs)[time_dim_name] <= 2) { - stop("Length of temporal dimension ", - "of parameter 'index_obs', 'index_hind1' and 'index_hind2' ", - "must be greater than 2.") - } - if (dim(index_obs)[time_dim_name] != dim(index_hind1)[time_dim_name]) { - stop("Length of temporal dimensions ", - "of parameter 'index_obs' and 'index_hind1' must be equals.") - } - if (dim(index_hind1)[time_dim_name] != dim(index_hind2)[time_dim_name]) { - stop("Length of temporal dimensions ", - "of parameter 'index_hind1' and 'index_hind2' must be equals.") - } - if('member' %in% names(dim(index_hind1)) & - 'statistic' %in% names(dim(index_hind1))) { - stop("Parameter 'index_hind1' must have at least ", - "dimension 'member' or 'statistic', not 'member' and 'statistic' ", - "together.") - } - if('member' %in% names(dim(index_hind2)) & - 'statistic' %in% names(dim(index_hind2))) { - stop("Parameter 'index_hind2' must have at least ", - "dimension 'member' or 'statistic', not 'member' and 'statistic' ", - "together.") - } - if(!('member' %in% names(dim(index_hind1))) & - !('statistic' %in% names(dim(index_hind1)))) { - stop("Parameter 'index_hind1' must have dimension ", - "'member' or 'statistic'") - } - if(!('member' %in% names(dim(index_hind2))) & - !('statistic' %in% names(dim(index_hind2)))) { - stop("Parameter 'index_hind2' must have dimension ", - "'member' or 'statistic'") - } - if ('member' %in% names(dim(index_hind1))){ - if (dim(index_hind1)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind1' must be greater than 1.") - } - } - if ('member' %in% names(dim(index_hind2))){ - if (dim(index_hind2)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind2' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_hind1))){ - if (dim(index_hind1)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind1' must be equal to 2.") - } - } - if ('statistic' %in% names(dim(index_hind2))){ - if (dim(index_hind2)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind2' must be equal to 2.") - } - } - if (!is.null(index_fcst1)){ - if (!is.array(index_fcst1)) { - stop("Parameter 'index_fcst1' must be an array.") - } - if (is.null(names(dim(index_fcst1)))){ - stop("Parameters 'index_fcst1' should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_fcst1)))) { - stop("Parameter 'index_fcst1' must have temporal dimension.") - } - if (dim(index_fcst1)[time_dim_name] != 1) { - stop("Length of temporal dimension ", - "of parameter 'index_fcst1' must be equal to 1.") - } - if((length(names(dim(index_hind1))) != length(names(dim(index_fcst1)))) - || (!all(names(dim(index_hind1)) == names(dim(index_fcst1))))){ - stop("Dimension names of parameter 'index_hind1' and 'index_fcst1' ", - "must be equals.") - } - if ('member' %in% names(dim(index_fcst1))){ - if (dim(index_fcst1)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_fcst1' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_fcst1))){ - if (dim(index_fcst1)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_fcst1' must be equal to 2.") - } - } - } - if (!is.null(index_fcst2)){ - if (!is.array(index_fcst2)) { - stop("Parameter 'index_fcst2' must be an array.") - } - if (is.null(names(dim(index_fcst2)))){ - stop("Parameters 'index_fcst2' should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_fcst2)))) { - stop("Parameter 'index_fcst2' must have temporal dimension.") - } - if (dim(index_fcst2)[time_dim_name] != 1) { - stop("Length of temporal dimension ", - "of parameter 'index_fcst2' must be equal to 1.") - } - if((length(names(dim(index_hind2))) != length(names(dim(index_fcst2)))) - || (!all(names(dim(index_hind2)) == names(dim(index_fcst2))))){ - stop("Dimension names of parameter 'index_hind2' and 'index_fcst2' ", - "must be equals.") - } - if ('member' %in% names(dim(index_fcst2))){ - if (dim(index_fcst2)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_fcst2' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_fcst1))){ - if (dim(index_fcst1)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_fcst1' must be equal to 2.") - } - } - } - - if (all(method_BC == 'none')){ - method_BC1 <- 'ME' - method_BC2 <- 'ME' - bc_dataset1 <- FALSE - bc_dataset2 <- FALSE - } else if (length(method_BC) == 1){ - method_BC1 <- method_BC - method_BC2 <- method_BC - bc_dataset1 <- TRUE - bc_dataset2 <- TRUE - } else { - if(method_BC[1] == 'none'){ - method_BC1 <- 'ME' - bc_dataset1 <- FALSE - } else { - method_BC1 <- method_BC[1] - bc_dataset1 <- TRUE - } - if(method_BC[2] == 'none'){ - method_BC2 <- 'ME' - bc_dataset2 <- FALSE - } else { - method_BC2 <- method_BC[2] - bc_dataset2 <- TRUE - } - } - - pdf_hind1 <- PDFIndexHind(index_hind1, index_obs, method = method_BC1, - time_dim_name = time_dim_name, na.rm = na.rm) - pdf_hind2 <- PDFIndexHind(index_hind2, index_obs, method = method_BC2, - time_dim_name = time_dim_name, na.rm = na.rm) - if (is.null(index_fcst1) || is.null(index_fcst2)){ - pdf_best <- Apply(list(pdf_hind1, pdf_hind2), - target_dims = list('statistic', - 'statistic'), - fun = .BEI_PDFBest, - bc_dataset1, bc_dataset2) - } else { - pdf_fcst1 <- PDFIndexFcst(index_hind1, index_obs, index_fcst1, - method_BC1, time_dim_name = time_dim_name, - na.rm = na.rm) - pdf_fcst2 <- PDFIndexFcst(index_hind2, index_obs, index_fcst2, - method_BC2, time_dim_name = time_dim_name, - na.rm = na.rm) - pdf_best <- Apply(list(pdf_fcst1, pdf_fcst2), - target_dims = list('statistic', - 'statistic'), - fun = .BEI_PDFBest, - bc_dataset1, bc_dataset2) - } - Dim <- names(dim(index_hind1)) - if('member' %in% Dim){ - pos_aux <- match('member', Dim) - Dim[pos_aux] <- 'statistic' - } - pos <- match(Dim, names(dim(pdf_best$output1))) - res <- aperm(pdf_best$output1,pos) - names(dim(res)) <- Dim - return(res) -} - -#' Atomic BEI_PDFBest -#'@param pdf_1 Statistics array for the first SFS PDF with one dimension -#' 'statistic' equal to 4. -#' @param pdf_2 Statistics array for the second SFS PDF with one dimension -#' 'statistic' equal to 4. -#' @param bc_dataset1 Logical (default = TRUE). -#' If TRUE the Index PDFs for the first SFS has been computed -#' with bias corrected. -#' @param bc_dataset2 Logical (default = TRUE). -#' If TRUE the Index PDFs for the second SFS has been computed -#' with bias corrected. -#' -#' @return .BEI_PDFBest returns an array with dimensions (statistic = 2). -#' The firt statistic is the parameter 'mean' of the PDF for the best estimation -#' combining the two SFSs PDF. -#' The second statistic is the parameter 'standard deviation' of the PDF for -#' the best estimation combining the two SFSs PDF. -#' -#' @examples -#' # Example for the Atomic BEI_PDFBest function -#' pdf_1 <- c(1.1,0.6,1.6,0.9) -#' dim(pdf_1) <- c(statistic = 4) -#' pdf_2 <- c(1,0.5,1.5,0.8) -#' dim(pdf_2) <- c(statistic = 4) -#' res <- .BEI_PDFBest(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = FALSE) -#' str(res) -#' dim(res) -#' # statistic -#' # 2 -#' @noRd -.BEI_PDFBest <- function(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { - if(bc_dataset1){ - # apply bias correction to model 1 - mean_1 <- pdf_1[3] - sigma_1 <- pdf_1[4] - } else{ - # not bias correction to model 1 - mean_1 <- pdf_1[1] - sigma_1 <- pdf_1[2] - } - if(bc_dataset2){ - # apply bias correction to model 2 - mean_2 <- pdf_2[3] - sigma_2 <- pdf_2[4] - } else { - # not bias correction to model 2 - mean_2 <- pdf_2[1] - sigma_2 <- pdf_2[2] - } - a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) - a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) - pdf_mean <- a_1*mean_1 + a_2*mean_2 - pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) - data <- c(pdf_mean, pdf_sigma) - dim(data) <- c(statistic = 2) - return(data) -} - -#'Computing the Index PDFs for a dataset of SFSs for a hindcats period. -#' -#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' -#'@description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from SFSs for a hindcast period. -#' -#'@references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' -#'@param index_hind Index (e.g. NAO index) array from SFSs -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with at least a temporal dimension (by default 'time'), -#' which must be greater than 2. -#'@param method A character string indicating which methodology is applied -#' to compute the PDFs. One of "ME" (default) or "LMEV". -#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#'@param na.rm Logical (default = FALSE). Should missing values be removed? -#' -#'@return an array with at least two dimensions (time, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @import multiApply -#' @examples -#' # Example for the PDFIndexHind function -#' # Example 1 -#' index_obs <- 1 : (5 * 3 ) -#' dim(index_obs) <- c(time = 5, season = 3) -#' index_hind <- 1 : (5 * 4 * 3) -#' dim(index_hind) <- c(time = 5, member = 4, season = 3) -#' res <- PDFIndexHind(index_hind, index_obs) -#' dim(res) -#' # time statistic season -#' # 5 4 3 -#' # Example 2 -#' index_obs <- 1 : (5 * 3) -#' dim(index_obs) <- c(time = 5, season = 3) -#' index_hind <- 1 : (5 * 2 * 3) -#' dim(index_hind) <- c(time = 5, statistic = 2, season = 3) -#' res <- PDFIndexHind(index_hind, index_obs) -#' dim(res) -#' # time statistic season -#' # 5 4 3 -#'@noRd -PDFIndexHind <- function(index_hind, index_obs, method ='ME', - time_dim_name = 'time', na.rm = FALSE) { - if (!is.character(time_dim_name)) { - stop("Parameter 'time_dim_name' must be a character string indicating", - " the name of the temporal dimension.") - } - if (length(time_dim_name) > 1) { - warning("Parameter 'time_dim_name' has length greater than 1 and ", - "only the first element will be used.") - time_dim_name <- time_dim_name[1] - } - if (!(method %in% c('ME', 'LMEV'))){ - stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") - } - if (!is.logical(na.rm)) { - stop("Parameter 'na.rm' must be a logical value.") - } - if (!is.array(index_hind)) { - stop("Parameter 'index_hind' must be an array.") - } - if (!is.array(index_obs)) { - stop("Parameter 'index_obs' must be an array.") - } - if (is.null(names(dim(index_hind))) || is.null(names(dim(index_obs)))) { - stop("Parameters 'index_hind' and 'index_obs'", - " should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_obs)))) { - stop("Parameter 'index_obs' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' must have temporal dimension.") - } - if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { - stop("Length of the temporal dimensions ", - "of the parameter 'index_obs' and 'index_hind' must be equals.") - } - if (dim(index_obs)[time_dim_name] <= 2) { - stop("Length of temporal dimension ", - "of parameter 'index_obs' and 'index_hind' must be greater than 2.") - } - if('member' %in% names(dim(index_hind)) & - 'statistic' %in% names(dim(index_hind))) { - stop("Parameter 'index_hind' must have at least dimension ", - "'member' or 'statistic', not 'member' and 'statistic' together.") - } - - if(!('member' %in% names(dim(index_hind))) & - !('statistic' %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' must have dimension ", - "'member' or 'statistic'") - } - - if ('member' %in% names(dim(index_hind))){ - if (dim(index_hind)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_hind))){ - if (dim(index_hind)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind' must be equal to 2.") - } - } - - if ('member' %in% names(dim(index_hind))) { - PDFstatistics <- Apply(list(index_hind, index_obs), - target_dims = list(c(time_dim_name, 'member'), time_dim_name), - fun = .PDFIndexHind, method, time_dim_name, na.rm) - } else if ('statistic' %in% names(dim(index_hind))){ - PDFstatistics <- Apply(list(index_hind, index_obs), - target_dims = list(c(time_dim_name, 'statistic'), time_dim_name), - fun = .PDFIndexHind, method, time_dim_name, na.rm) - } - return(PDFstatistics$output1) -} - - - -#' Atomic PDFIndexHind -#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions -#' (time, member) or (time, statistic) for a hindcast period. -#' The temporal dimension, by default 'time', must be greater -#' than 2. -#' @param index_obs Index (e.g. NAO index) array from an observational dataset -#' or reanalysis with dimension (time). The temporal dimension, -#' by default 'time', must be greater than 2. -#' @param method A character string indicating which methodology is applied -#' to compute the PDF. One of "ME" (default) or "LMEV". -#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' for the hindcast period. -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected for the hindcast period. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' for the hindcast period. -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected for the hindcast period. -#' @import multiApply -#' @importFrom verification verify -#' @examples -#' # Example for the Atomic PDFIndexHind function -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = length(index_obs)) -#' index_hind <- 1 : (10 * 3) -#' dim(index_hind) <- c(time = 10, member = 3) -#' res <- .PDFIndexHind(index_hind, index_obs) -#' dim(res) -#' # time statistic -#' # 10 4 -#' @noRd -.PDFIndexHind <- function(index_hind, index_obs, method = 'ME', - time_dim_name = 'time', na.rm = FALSE) { - dimnameshind <- names(dim(index_hind)) - - if ('member' %in% dimnameshind) { - pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) - index_hind <- aperm(index_hind, pos) - names(dim(index_hind)) <- c(time_dim_name, 'member') - hind_ens_mean <- Apply(list(index_hind), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - hind_ens_sd <- Apply(list(index_hind), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else if ('statistic' %in% dimnameshind){ - pos <- match(c(time_dim_name, 'statistic'), names(dim(index_hind))) - index_hind <- aperm(index_hind,pos) - names(dim(index_hind)) <- c(time_dim_name, 'statistic') - hind_ens_mean <- index_hind[,1] - hind_ens_sd <- index_hind[,2] - } else { - stop("Wrong dimension of parameter 'index_hind'.") - } - error <- hind_ens_mean - index_obs - pdfnotbc_mean <- hind_ens_mean - ind <- 1 : length(index_obs) - pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(hind_ens_mean[-x], - index_obs[-x], - na.rm)})) - - if (method == 'ME') { - pdfbc <- unlist(lapply(ind, function(x) {MEBC(hind_ens_mean[-x], - index_obs[-x], na.rm)})) - pdfbc_mean <- hind_ens_mean - pdfbc - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x], - na.rm)})) - } else if (method == 'LMEV') { - # linear model error-variance - hind_ens_var <- hind_ens_sd^2 - dim_hind <- names(dim(index_hind)) - dim_obs <- names(dim(index_obs)) - coeff <- sapply(ind, function(x) {LMEV(index_hind[-x,], index_obs[-x], - dim_hind, dim_obs, na.rm)}) - pdfbc_mean <- hind_ens_mean - (unlist(coeff['coeff1',]) + - unlist(coeff['coeff2',])*hind_ens_var) - - pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], - index_obs[-x], - na.rm)})) - } else { - stop("Error: Parameter 'method' is wrong") - } - data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) - names(dim(data)) <- c(names(dim(index_obs)), 'statistic') - return(data) -} - -#' Computing the Index PDFs for a dataset of SFSs for a forecast year. -#' -#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -#' -#'@description This function implements the computation to obtain the index PDFs -#' (e.g. NAO index) to improve the index estimate from SFSs for a forecast year. -#' -#'@references Regionally improved seasonal forecast of precipitation through Best -#' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 -#' -#'@param index_hind Index (e.g. NAO index) array from SFSs -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be greater than 2. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param index_obs Index (e.g. NAO index) array from an observational database -#' or reanalysis with at least a temporal dimension (by default 'time'), -#' which must be greater than 2. -#'@param index_fcst Index (e.g. NAO index) array from SFSs -#' with at least two dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#'@param method A character string indicating which methodology is applied -#' to compute the PDFs. One of "ME" (default) or "LMEV". -#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#'@param na.rm Logical (default = FALSE). Should missing values be removed? -#' -#'@return an array with at least two dimensions (time = 1, statistic = 4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected -#' @import multiApply -#' @examples -#' # Example for the PDFIndexFcst function -#' index_fcst <- 1 : (8 * 4) -#' dim(index_fcst) <- c(time = 1, member = 8, season = 4) -#' index_obs<- 1 : (5 * 4) -#' dim(index_obs) <- c(time = 5, season = 4) -#' index_hind <- 1 : (5 * 4 * 4) -#' dim(index_hind) <- c(time = 5, member = 4, season = 4) -#' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) -#' dim(res) -#' # time statistic season -#' # 1 4 4 -#' -#'@noRd -PDFIndexFcst <- function(index_hind, index_obs, index_fcst, - method ='ME', time_dim_name = 'time', - na.rm = FALSE) { - if (!is.character(time_dim_name)) { - stop("Parameter 'time_dim_name' must be a character string indicating", - " the name of the temporal dimension.") - } - if (length(time_dim_name) > 1) { - warning("Parameter 'time_dim_name' has length greater than 1 and ", - "only the first element will be used.") - time_dim_name <- time_dim_name[1] - } - if(!(method %in% c('ME', 'LMEV'))){ - stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") - } - if (!is.logical(na.rm)) { - stop("Parameter 'na.rm' must be a logical value.") - } - if (!is.array(index_hind)) { - stop("Parameter 'index_hind' must be an array.") - } - if (!is.array(index_obs)) { - stop("Parameter 'index_obs' must be an array.") - } - if (!is.array(index_fcst)) { - stop("Parameter 'index_fcst' must be an array.") - } - if (is.null(names(dim(index_hind))) || - is.null(names(dim(index_obs))) || - is.null(names(dim(index_fcst)))) { - stop("Parameters 'index_hind', 'index_obs' and 'index_fcst' ", - "should have dimmension names.") - } - if(!(time_dim_name %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_obs)))) { - stop("Parameter 'index_obs' must have temporal dimension.") - } - if(!(time_dim_name %in% names(dim(index_fcst)))) { - stop("Parameter 'index_fcst' must have temporal dimension.") - } - if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { - stop("Length of temporal dimensions ", - "of parameter 'index_obs' and 'index_hind' must be equals.") - } - if (dim(index_obs)[time_dim_name] <= 2) { - stop("Length of temporal dimension ", - "of parameter 'index_obs' and 'index_hind' must be greater than 2.") - } - if (dim(index_fcst)[time_dim_name] != 1) { - stop("Length of temporal dimension ", - "of parameter 'index_fcst' must be equal to 1.") - } - if((length(names(dim(index_hind))) != length(names(dim(index_fcst)))) - || (!all(names(dim(index_hind)) == names(dim(index_fcst))))){ - stop("Dimension names of parameter 'index_hind' and 'index_fcst' ", - "must be equals.") - } - if('member' %in% names(dim(index_hind)) & - 'statistic' %in% names(dim(index_hind))) { - stop("Parameter 'index_hind' and 'index_fcst' must have at least ", - "dimension 'member' or 'statistic', not 'member' and 'statistic' ", - "together.") - } - if(!('member' %in% names(dim(index_hind))) & - !('statistic' %in% names(dim(index_hind)))) { - stop("Parameter 'index_hind' and 'index_fcst' must have dimension ", - "'member' or 'statistic'") - } - if ('member' %in% names(dim(index_hind))){ - if (dim(index_hind)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_hind' must be greater than 1.") - } - } - if ('member' %in% names(dim(index_fcst))){ - if (dim(index_fcst)['member'] == 1) { - stop("Length of dimension 'member' ", - "of parameter 'index_fcst' must be greater than 1.") - } - } - if ('statistic' %in% names(dim(index_hind))){ - if (dim(index_hind)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_hind' must be equal to 2.") - } - } - if ('statistic' %in% names(dim(index_fcst))){ - if (dim(index_fcst)['statistic'] != 2) { - stop("Length of dimension 'statistic' ", - "of parameter 'index_fcst' must be equal to 2.") - } - } - - if ('member' %in% names(dim(index_hind))){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), - target_dims = list(c(time_dim_name, 'member'), - time_dim_name, - c(time_dim_name,'member')), - fun = .PDFIndexFcst, method, time_dim_name, na.rm) - } else if ('statistic' %in% names(dim(index_hind))){ - PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), - target_dims = list(c(time_dim_name, 'statistic'), - time_dim_name, - c(time_dim_name,'statistic')), - fun = .PDFIndexFcst, method, time_dim_name, na.rm) - } - return(PDFstatistics$output1) -} - -#' Atomic PDFIndexFcst -#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions -#' (time, member) or (time, statistic) for a hindcast period. -#' The temporal dimension, by default 'time', must be greater -#' than 2. -#' @param index_obs Index (e.g. NAO index) array from an observational dataset -#' or reanalysis with dimension (time). The temporal dimension, -#' by default 'time', must be greater than 2. -#' @param index_fcst Index (e.g. NAO index) array from SFSs -#' with dimensions (time , member) or (time, statistic). -#' The temporal dimension, by default 'time', must be equal to 1, -#' the forecast year target. -#' The dimension 'member' must be greater than 1. -#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of -#' a normal distribution (mean and sd) representing the ensemble of a SFS. -#' It is not possible to have the dimension 'member' and 'statistic' together. -#' @param method A character string indicating which methodology is applied -#' to compute the PDF. One of "ME" (default) or "LMEV". -#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. -#' @param na.rm Logical. Should missing values be removed? -#' @return .PDFIndexFcst returns an array with dimensions (time = 1, statistic=4). -#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected -#' for the forecast year. -#' The second statistic is the parameter 'standard deviation' of the PDF -#' with not bias corrected for the forecast year. -#' The third statistic is the parameter 'mean' of the PDF with bias corrected -#' for the forecast year. -#' The fourth statistic is the parameter 'standard deviation' of the PDF -#' with bias corrected for the forecast year. -#' @import multiApply -#' @importFrom verification verify -#' @examples -#' # Example 1 for the Atomic PDFIndexFcst function -#' index_fcst <- 1 : (1 * 6) -#' dim(index_fcst) <- c(time = 1, member = 6) -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = length(index_obs)) -#' index_hind <- 1 : (10 * 3) -#' dim(index_hind) <- c(time = 10, member = 3) -#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) -#' dim(res) -#' # time statistic -#' # 1 4 -#' # Example 2 for the Atomic PDFIndexFcst function -#' index_fcst <- 1 : (1 * 2) -#' dim(index_fcst) <- c(time = 1, statistic = 2) -#' index_obs <- 1 : 10 -#' dim(index_obs) <- c(time = 10) -#' index_hind <- 1 : (10 * 2) -#' dim(index_hind) <- c(time = 10, statistic = 2) -#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) -#' dim(res) -#' # time statistic -#' # 1 4 -#' @noRd -.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, - method = 'ME', - time_dim_name = 'time', - na.rm = FALSE) { - dimnameshind <- names(dim(index_hind)) - if ('member' %in% dimnameshind) { - pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) - index_hind <- aperm(index_hind, pos) - names(dim(index_hind)) <- c(time_dim_name, 'member') - exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', - fun = mean)$output1 - exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', - fun = sd)$output1 - } else { - pos <- match(c(time_dim_name,'statistic'), names(dim(index_fcst))) - index_fcst <- aperm(index_fcst,pos) - names(dim(index_fcst)) <- c(time_dim_name, 'statistic') - exp_fcst_ens_mean <- index_fcst[,1] - exp_fcst_ens_sd <- index_fcst[,2] - } - data_hind <- .PDFIndexHind(index_hind, index_obs, method, time_dim_name, na.rm) - exp_hind_ens_mean <- data_hind[,1] - pdfnotbc_mean <- exp_fcst_ens_mean - pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs, na.rm) - if (method == 'ME') { - pdfbc <- MEBC(exp_hind_ens_mean, index_obs, na.rm) - pdfbc_mean <- exp_fcst_ens_mean - pdfbc - } else if (method == 'LMEV') { - # linear model error-variance - exp_fcst_ens_var <- exp_fcst_ens_sd^2 - dim_hind <- names(dim(index_hind)) - dim_obs <- names(dim(index_obs)) - coeff <- LMEV(index_hind, index_obs, dim_hind, dim_obs, time_dim_name, na.rm) - pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) - } else { - stop("Parameter 'method' must be 'ME' or 'LMEV'.") - } - pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs, na.rm) - data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) - names(dim(data)) <- c(names(dim(index_obs)), 'statistic') - return(data) -} - -# Auxiliar function to compute the mean squared error between 'exp' and 'obs' -Sigma_notBC <- function(exp, obs, na.rm = FALSE) { - if (na.rm){ - indNA <- c(which(is.na(exp)),which(is.na(obs))) - if (length(indNA) > 0){ - exp <- exp[-indNA] - obs <- obs[-indNA] - } - } - return(sqrt(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$MSE)) -} - -# Auxiliar function to compute the bias between 'exp' and 'obs' -MEBC <- function(exp, obs, na.rm = FALSE) { - if (na.rm){ - indNA <- c(which(is.na(exp)),which(is.na(obs))) - if (length(indNA) > 0){ - exp <- exp[-indNA] - obs <- obs[-indNA] - } - } - return(verify(obs, exp, frcst.type = "cont", - obs.type = "cont")$ME) -} - -# Auxiliar function to compute the standard deviation of errors -# between 'obs' and 'exp' -Sigma_BC <- function(exp, obs, na.rm = FALSE) { - return(sd(obs - exp, na.rm = na.rm)) -} - -# Auxiliar function to compute the linear model used in the method -# called 'LMEV' to correct the bias in the Best NAO weighting -# methogology -LMEV <- function(exp, obs, dim_exp, - dim_obs, time_dim_name = 'time', na.rm = FALSE) { - names(dim(exp)) <- dim_exp - names(dim(obs)) <- dim_obs - if (any(names(dim(exp)) == 'member')) { - exp_ens_mean <- Apply(list(exp), target_dims = 'member', - fun = mean, na.rm = na.rm)$output1 - exp_ens_sd <- Apply(list(exp), target_dims = 'member', - fun = sd, na.rm = na.rm)$output1 - } else { - if(na.rm & any(is.na(exp))){ - print("Some value are NA") - } - pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) - exp <- aperm(exp,pos) - exp_ens_mean <- exp[,1] - exp_ens_sd <- exp[,2] - } - error <- exp_ens_mean - obs - exp_ens_var <- exp_ens_sd^2 - statModel <- lm(error ~ exp_ens_var) - return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) -} - - +#' Computing the Best Index PDFs combining Index PDFs from two SFSs +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index +#'Probability Density Functions (PDFs) (e.g. NAO index) obtained to combining +#'the Index PDFs for two Seasonal Forecast Systems (SFSs), the Best Index +#'estimation (see Sanchez-Garcia, E. et al (2019), +#'https://doi.org/10.5194/asr-16-165-2019 for more details about the +#'methodology applied to estimate the Best Index). +#' +#'@references Regionally improved seasonal forecast of precipitation through +#'Best estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param index_hind1 Index (e.g. NAO index) array from a SFS (named SFS1) +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' at the same time. +#'@param index_hind2 Index (e.g. NAO index) array from a SFS (named SFS2) +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_fcst1 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS1 +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_fcst2 (optional, default = NULL) Index (e.g. NAO index) array from forescating of SFS2 +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param method_BC A character vector of maximun length 2 indicating the bias +#'correction methodology to be applied on each SFS. If it is 'none' or any of +#'its elements is 'none', the bias correction won't be applied. +#'Available methods developped are "ME" (a bias correction scheme based on the +#'mean error or bias between observation and predictions to correct the +#'predicted values), and "LMEV" (a bias correction scheme based on a linear +#'model using ensemble variance of index as predictor). (see Sanchez-Garcia, +#'E. et al (2019), https://doi.org/10.5194/asr-16-165-2019 for more details). +#'@param time_dim_name A character string indicating the name of the temporal +#'dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#' @return BEI_PDFBest() returns an array with the parameters that caracterize +#' the PDFs, with at least a temporal dimension, by default 'time' and dimension +#' 'statistic' equal to 2. +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDFs. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDFs. +#' If index_fcst1 and/or index_fcst2 are null, returns the values for hindcast period. +#' Otherwise, it returns the values for a forecast year. +#'@import multiApply +#' +#'@examples +#' # Example 1 for the BEI_PDFBest function +#' index_obs<- rnorm(10, sd = 3) +#' dim(index_obs) <- c(time = 5, season = 2) +#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) +#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) +#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +#' method_BC <- 'ME' +#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, +#' index_fcst2, method_BC) +#' dim(res) +#' # time statistic season +#' # 1 2 2 +#' # Example 2 for the BEI_PDFBest function +#' index_obs<- rnorm(10, sd = 3) +#' dim(index_obs) <- c(time = 5, season = 2) +#' index_hind1 <- rnorm(40, mean = 0.2, sd = 3) +#' dim(index_hind1) <- c(time = 5, member = 4, season = 2) +#' index_hind2 <- rnorm(60, mean = -0.5, sd = 4) +#' dim(index_hind2) <- c(time = 5, member = 6, season = 2) +#' index_fcst1 <- rnorm(16, mean = 0.2, sd = 3) +#' dim(index_fcst1) <- c(time = 1, member = 8, season = 2) +#' index_fcst2 <- rnorm(18, mean = -0.5, sd = 4) +#' dim(index_fcst2) <- c(time = 1, member = 9, season = 2) +#' method_BC <- c('LMEV', 'ME') +#' res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, index_fcst2, method_BC) +#' dim(res) +#' # time statistic season +#' # 1 2 2 +#'@export + +BEI_PDFBest <- function(index_obs, index_hind1, index_hind2, + index_fcst1 = NULL, index_fcst2 = NULL, method_BC = 'none', + time_dim_name = 'time', na.rm = FALSE) { + + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string ", + "indicating the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!is.character(method_BC) || !is.vector(method_BC)){ + stop("Parameter 'method_BC' must be a character vector.") + } + if (!(length(method_BC) == 1 || length(method_BC) == 2)){ + stop("Length of parameter 'method_BC' must be 1 or 2.") + } + if(!all(method_BC %in% c('ME', 'LMEV', 'none'))){ + stop("Elements of parameter 'method_BC' must be equals to ", + "'none, 'ME' or 'LMEV'") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (!is.array(index_hind1)) { + stop("Parameter 'index_hind1' must be an array.") + } + if (!is.array(index_hind2)) { + stop("Parameter 'index_hind2' must be an array.") + } + if (is.null(names(dim(index_hind1))) || + is.null(names(dim(index_obs))) || + is.null(names(dim(index_hind2)))) { + stop("Parameters 'index_obs', 'index_hind1' and 'index_hind2' ", + "should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind1)))) { + stop("Parameter 'index_hind1' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind2)))) { + stop("Parameter 'index_hind2' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs', 'index_hind1' and 'index_hind2' ", + "must be greater than 2.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind1)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_obs' and 'index_hind1' must be equals.") + } + if (dim(index_hind1)[time_dim_name] != dim(index_hind2)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_hind1' and 'index_hind2' must be equals.") + } + if('member' %in% names(dim(index_hind1)) & + 'statistic' %in% names(dim(index_hind1))) { + stop("Parameter 'index_hind1' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if('member' %in% names(dim(index_hind2)) & + 'statistic' %in% names(dim(index_hind2))) { + stop("Parameter 'index_hind2' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if(!('member' %in% names(dim(index_hind1))) & + !('statistic' %in% names(dim(index_hind1)))) { + stop("Parameter 'index_hind1' must have dimension ", + "'member' or 'statistic'") + } + if(!('member' %in% names(dim(index_hind2))) & + !('statistic' %in% names(dim(index_hind2)))) { + stop("Parameter 'index_hind2' must have dimension ", + "'member' or 'statistic'") + } + if ('member' %in% names(dim(index_hind1))){ + if (dim(index_hind1)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind1' must be greater than 1.") + } + } + if ('member' %in% names(dim(index_hind2))){ + if (dim(index_hind2)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind2' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind1))){ + if (dim(index_hind1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind1' must be equal to 2.") + } + } + if ('statistic' %in% names(dim(index_hind2))){ + if (dim(index_hind2)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind2' must be equal to 2.") + } + } + if (!is.null(index_fcst1)){ + if (!is.array(index_fcst1)) { + stop("Parameter 'index_fcst1' must be an array.") + } + if (is.null(names(dim(index_fcst1)))){ + stop("Parameters 'index_fcst1' should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_fcst1)))) { + stop("Parameter 'index_fcst1' must have temporal dimension.") + } + if (dim(index_fcst1)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst1' must be equal to 1.") + } + if((length(names(dim(index_hind1))) != length(names(dim(index_fcst1)))) + || (!all(names(dim(index_hind1)) == names(dim(index_fcst1))))){ + stop("Dimension names of parameter 'index_hind1' and 'index_fcst1' ", + "must be equals.") + } + if ('member' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst1' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst1' must be equal to 2.") + } + } + } + if (!is.null(index_fcst2)){ + if (!is.array(index_fcst2)) { + stop("Parameter 'index_fcst2' must be an array.") + } + if (is.null(names(dim(index_fcst2)))){ + stop("Parameters 'index_fcst2' should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_fcst2)))) { + stop("Parameter 'index_fcst2' must have temporal dimension.") + } + if (dim(index_fcst2)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst2' must be equal to 1.") + } + if((length(names(dim(index_hind2))) != length(names(dim(index_fcst2)))) + || (!all(names(dim(index_hind2)) == names(dim(index_fcst2))))){ + stop("Dimension names of parameter 'index_hind2' and 'index_fcst2' ", + "must be equals.") + } + if ('member' %in% names(dim(index_fcst2))){ + if (dim(index_fcst2)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst2' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_fcst1))){ + if (dim(index_fcst1)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst1' must be equal to 2.") + } + } + } + + if (all(method_BC == 'none')){ + method_BC1 <- 'ME' + method_BC2 <- 'ME' + bc_dataset1 <- FALSE + bc_dataset2 <- FALSE + } else if (length(method_BC) == 1){ + method_BC1 <- method_BC + method_BC2 <- method_BC + bc_dataset1 <- TRUE + bc_dataset2 <- TRUE + } else { + if(method_BC[1] == 'none'){ + method_BC1 <- 'ME' + bc_dataset1 <- FALSE + } else { + method_BC1 <- method_BC[1] + bc_dataset1 <- TRUE + } + if(method_BC[2] == 'none'){ + method_BC2 <- 'ME' + bc_dataset2 <- FALSE + } else { + method_BC2 <- method_BC[2] + bc_dataset2 <- TRUE + } + } + + pdf_hind1 <- PDFIndexHind(index_hind1, index_obs, method = method_BC1, + time_dim_name = time_dim_name, na.rm = na.rm) + pdf_hind2 <- PDFIndexHind(index_hind2, index_obs, method = method_BC2, + time_dim_name = time_dim_name, na.rm = na.rm) + if (is.null(index_fcst1) || is.null(index_fcst2)){ + pdf_best <- Apply(list(pdf_hind1, pdf_hind2), + target_dims = list('statistic', + 'statistic'), + fun = .BEI_PDFBest, + bc_dataset1, bc_dataset2) + } else { + pdf_fcst1 <- PDFIndexFcst(index_hind1, index_obs, index_fcst1, + method_BC1, time_dim_name = time_dim_name, + na.rm = na.rm) + pdf_fcst2 <- PDFIndexFcst(index_hind2, index_obs, index_fcst2, + method_BC2, time_dim_name = time_dim_name, + na.rm = na.rm) + pdf_best <- Apply(list(pdf_fcst1, pdf_fcst2), + target_dims = list('statistic', + 'statistic'), + fun = .BEI_PDFBest, + bc_dataset1, bc_dataset2) + } + Dim <- names(dim(index_hind1)) + if('member' %in% Dim){ + pos_aux <- match('member', Dim) + Dim[pos_aux] <- 'statistic' + } + pos <- match(Dim, names(dim(pdf_best$output1))) + res <- aperm(pdf_best$output1,pos) + names(dim(res)) <- Dim + return(res) +} + +#' Atomic BEI_PDFBest +#'@param pdf_1 Statistics array for the first SFS PDF with one dimension +#' 'statistic' equal to 4. +#' @param pdf_2 Statistics array for the second SFS PDF with one dimension +#' 'statistic' equal to 4. +#' @param bc_dataset1 Logical (default = TRUE). +#' If TRUE the Index PDFs for the first SFS has been computed +#' with bias corrected. +#' @param bc_dataset2 Logical (default = TRUE). +#' If TRUE the Index PDFs for the second SFS has been computed +#' with bias corrected. +#' +#' @return .BEI_PDFBest returns an array with dimensions (statistic = 2). +#' The firt statistic is the parameter 'mean' of the PDF for the best estimation +#' combining the two SFSs PDF. +#' The second statistic is the parameter 'standard deviation' of the PDF for +#' the best estimation combining the two SFSs PDF. +#' +#' @examples +#' # Example for the Atomic BEI_PDFBest function +#' pdf_1 <- c(1.1,0.6,1.6,0.9) +#' dim(pdf_1) <- c(statistic = 4) +#' pdf_2 <- c(1,0.5,1.5,0.8) +#' dim(pdf_2) <- c(statistic = 4) +#' res <- .BEI_PDFBest(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = FALSE) +#' str(res) +#' dim(res) +#' # statistic +#' # 2 +#' @noRd +.BEI_PDFBest <- function(pdf_1, pdf_2, bc_dataset1 = TRUE, bc_dataset2 = TRUE) { + if(bc_dataset1){ + # apply bias correction to model 1 + mean_1 <- pdf_1[3] + sigma_1 <- pdf_1[4] + } else{ + # not bias correction to model 1 + mean_1 <- pdf_1[1] + sigma_1 <- pdf_1[2] + } + if(bc_dataset2){ + # apply bias correction to model 2 + mean_2 <- pdf_2[3] + sigma_2 <- pdf_2[4] + } else { + # not bias correction to model 2 + mean_2 <- pdf_2[1] + sigma_2 <- pdf_2[2] + } + a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) + a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) + pdf_mean <- a_1*mean_1 + a_2*mean_2 + pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) + data <- c(pdf_mean, pdf_sigma) + dim(data) <- c(statistic = 2) + return(data) +} + +#'Computing the Index PDFs for a dataset of SFSs for a hindcats period. +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from SFSs for a hindcast period. +#' +#'@references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_hind Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#'@return an array with at least two dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @examples +#' # Example for the PDFIndexHind function +#' # Example 1 +#' index_obs <- 1 : (5 * 3 ) +#' dim(index_obs) <- c(time = 5, season = 3) +#' index_hind <- 1 : (5 * 4 * 3) +#' dim(index_hind) <- c(time = 5, member = 4, season = 3) +#' res <- PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic season +#' # 5 4 3 +#' # Example 2 +#' index_obs <- 1 : (5 * 3) +#' dim(index_obs) <- c(time = 5, season = 3) +#' index_hind <- 1 : (5 * 2 * 3) +#' dim(index_hind) <- c(time = 5, statistic = 2, season = 3) +#' res <- PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic season +#' # 5 4 3 +#'@noRd +PDFIndexHind <- function(index_hind, index_obs, method ='ME', + time_dim_name = 'time', na.rm = FALSE) { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if (!(method %in% c('ME', 'LMEV'))){ + stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.array(index_hind)) { + stop("Parameter 'index_hind' must be an array.") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (is.null(names(dim(index_hind))) || is.null(names(dim(index_obs)))) { + stop("Parameters 'index_hind' and 'index_obs'", + " should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { + stop("Length of the temporal dimensions ", + "of the parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs' and 'index_hind' must be greater than 2.") + } + if('member' %in% names(dim(index_hind)) & + 'statistic' %in% names(dim(index_hind))) { + stop("Parameter 'index_hind' must have at least dimension ", + "'member' or 'statistic', not 'member' and 'statistic' together.") + } + + if(!('member' %in% names(dim(index_hind))) & + !('statistic' %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have dimension ", + "'member' or 'statistic'") + } + + if ('member' %in% names(dim(index_hind))){ + if (dim(index_hind)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind))){ + if (dim(index_hind)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind' must be equal to 2.") + } + } + + if ('member' %in% names(dim(index_hind))) { + PDFstatistics <- Apply(list(index_hind, index_obs), + target_dims = list(c(time_dim_name, 'member'), time_dim_name), + fun = .PDFIndexHind, method, time_dim_name, na.rm) + } else if ('statistic' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs), + target_dims = list(c(time_dim_name, 'statistic'), time_dim_name), + fun = .PDFIndexHind, method, time_dim_name, na.rm) + } + return(PDFstatistics$output1) +} + + + +#' Atomic PDFIndexHind +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) or (time, statistic) for a hindcast period. +#' The temporal dimension, by default 'time', must be greater +#' than 2. +#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' or reanalysis with dimension (time). The temporal dimension, +#' by default 'time', must be greater than 2. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexHind returns an array with dimensions (time, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the hindcast period. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the hindcast period. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the hindcast period. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the hindcast period. +#' @import multiApply +#' @importFrom verification verify +#' @examples +#' # Example for the Atomic PDFIndexHind function +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_hind <- 1 : (10 * 3) +#' dim(index_hind) <- c(time = 10, member = 3) +#' res <- .PDFIndexHind(index_hind, index_obs) +#' dim(res) +#' # time statistic +#' # 10 4 +#' @noRd +.PDFIndexHind <- function(index_hind, index_obs, method = 'ME', + time_dim_name = 'time', na.rm = FALSE) { + dimnameshind <- names(dim(index_hind)) + + if ('member' %in% dimnameshind) { + pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) + index_hind <- aperm(index_hind, pos) + names(dim(index_hind)) <- c(time_dim_name, 'member') + hind_ens_mean <- Apply(list(index_hind), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + hind_ens_sd <- Apply(list(index_hind), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else if ('statistic' %in% dimnameshind){ + pos <- match(c(time_dim_name, 'statistic'), names(dim(index_hind))) + index_hind <- aperm(index_hind,pos) + names(dim(index_hind)) <- c(time_dim_name, 'statistic') + hind_ens_mean <- index_hind[,1] + hind_ens_sd <- index_hind[,2] + } else { + stop("Wrong dimension of parameter 'index_hind'.") + } + error <- hind_ens_mean - index_obs + pdfnotbc_mean <- hind_ens_mean + ind <- 1 : length(index_obs) + pdfnotbc_sd <- unlist(lapply(ind, function(x) {Sigma_notBC(hind_ens_mean[-x], + index_obs[-x], + na.rm)})) + + if (method == 'ME') { + pdfbc <- unlist(lapply(ind, function(x) {MEBC(hind_ens_mean[-x], + index_obs[-x], na.rm)})) + pdfbc_mean <- hind_ens_mean - pdfbc + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x], + na.rm)})) + } else if (method == 'LMEV') { + # linear model error-variance + hind_ens_var <- hind_ens_sd^2 + dim_hind <- names(dim(index_hind)) + dim_obs <- names(dim(index_obs)) + coeff <- sapply(ind, function(x) {LMEV(index_hind[-x,], index_obs[-x], + dim_hind, dim_obs, na.rm)}) + pdfbc_mean <- hind_ens_mean - (unlist(coeff['coeff1',]) + + unlist(coeff['coeff2',])*hind_ens_var) + + pdfbc_sd <- unlist(lapply(ind, function(x) {Sigma_BC(pdfbc_mean[-x], + index_obs[-x], + na.rm)})) + } else { + stop("Error: Parameter 'method' is wrong") + } + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + +#' Computing the Index PDFs for a dataset of SFSs for a forecast year. +#' +#'@author Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +#' +#'@description This function implements the computation to obtain the index PDFs +#' (e.g. NAO index) to improve the index estimate from SFSs for a forecast year. +#' +#'@references Regionally improved seasonal forecast of precipitation through Best +#' estimation of winter NAO, Sanchez-Garcia, E. et al., +#' Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#' +#'@param index_hind Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be greater than 2. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param index_obs Index (e.g. NAO index) array from an observational database +#' or reanalysis with at least a temporal dimension (by default 'time'), +#' which must be greater than 2. +#'@param index_fcst Index (e.g. NAO index) array from SFSs +#' with at least two dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#'@param method A character string indicating which methodology is applied +#' to compute the PDFs. One of "ME" (default) or "LMEV". +#'@param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#'@param na.rm Logical (default = FALSE). Should missing values be removed? +#' +#'@return an array with at least two dimensions (time = 1, statistic = 4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected +#' @import multiApply +#' @examples +#' # Example for the PDFIndexFcst function +#' index_fcst <- 1 : (8 * 4) +#' dim(index_fcst) <- c(time = 1, member = 8, season = 4) +#' index_obs<- 1 : (5 * 4) +#' dim(index_obs) <- c(time = 5, season = 4) +#' index_hind <- 1 : (5 * 4 * 4) +#' dim(index_hind) <- c(time = 5, member = 4, season = 4) +#' res <- PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic season +#' # 1 4 4 +#' +#'@noRd +PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method ='ME', time_dim_name = 'time', + na.rm = FALSE) { + if (!is.character(time_dim_name)) { + stop("Parameter 'time_dim_name' must be a character string indicating", + " the name of the temporal dimension.") + } + if (length(time_dim_name) > 1) { + warning("Parameter 'time_dim_name' has length greater than 1 and ", + "only the first element will be used.") + time_dim_name <- time_dim_name[1] + } + if(!(method %in% c('ME', 'LMEV'))){ + stop("Parameter 'method' must be equal to 'ME' or 'LMEV'") + } + if (!is.logical(na.rm)) { + stop("Parameter 'na.rm' must be a logical value.") + } + if (!is.array(index_hind)) { + stop("Parameter 'index_hind' must be an array.") + } + if (!is.array(index_obs)) { + stop("Parameter 'index_obs' must be an array.") + } + if (!is.array(index_fcst)) { + stop("Parameter 'index_fcst' must be an array.") + } + if (is.null(names(dim(index_hind))) || + is.null(names(dim(index_obs))) || + is.null(names(dim(index_fcst)))) { + stop("Parameters 'index_hind', 'index_obs' and 'index_fcst' ", + "should have dimmension names.") + } + if(!(time_dim_name %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_obs)))) { + stop("Parameter 'index_obs' must have temporal dimension.") + } + if(!(time_dim_name %in% names(dim(index_fcst)))) { + stop("Parameter 'index_fcst' must have temporal dimension.") + } + if (dim(index_obs)[time_dim_name] != dim(index_hind)[time_dim_name]) { + stop("Length of temporal dimensions ", + "of parameter 'index_obs' and 'index_hind' must be equals.") + } + if (dim(index_obs)[time_dim_name] <= 2) { + stop("Length of temporal dimension ", + "of parameter 'index_obs' and 'index_hind' must be greater than 2.") + } + if (dim(index_fcst)[time_dim_name] != 1) { + stop("Length of temporal dimension ", + "of parameter 'index_fcst' must be equal to 1.") + } + if((length(names(dim(index_hind))) != length(names(dim(index_fcst)))) + || (!all(names(dim(index_hind)) == names(dim(index_fcst))))){ + stop("Dimension names of parameter 'index_hind' and 'index_fcst' ", + "must be equals.") + } + if('member' %in% names(dim(index_hind)) & + 'statistic' %in% names(dim(index_hind))) { + stop("Parameter 'index_hind' and 'index_fcst' must have at least ", + "dimension 'member' or 'statistic', not 'member' and 'statistic' ", + "together.") + } + if(!('member' %in% names(dim(index_hind))) & + !('statistic' %in% names(dim(index_hind)))) { + stop("Parameter 'index_hind' and 'index_fcst' must have dimension ", + "'member' or 'statistic'") + } + if ('member' %in% names(dim(index_hind))){ + if (dim(index_hind)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_hind' must be greater than 1.") + } + } + if ('member' %in% names(dim(index_fcst))){ + if (dim(index_fcst)['member'] == 1) { + stop("Length of dimension 'member' ", + "of parameter 'index_fcst' must be greater than 1.") + } + } + if ('statistic' %in% names(dim(index_hind))){ + if (dim(index_hind)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_hind' must be equal to 2.") + } + } + if ('statistic' %in% names(dim(index_fcst))){ + if (dim(index_fcst)['statistic'] != 2) { + stop("Length of dimension 'statistic' ", + "of parameter 'index_fcst' must be equal to 2.") + } + } + + if ('member' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c(time_dim_name, 'member'), + time_dim_name, + c(time_dim_name,'member')), + fun = .PDFIndexFcst, method, time_dim_name, na.rm) + } else if ('statistic' %in% names(dim(index_hind))){ + PDFstatistics <- Apply(list(index_hind, index_obs, index_fcst), + target_dims = list(c(time_dim_name, 'statistic'), + time_dim_name, + c(time_dim_name,'statistic')), + fun = .PDFIndexFcst, method, time_dim_name, na.rm) + } + return(PDFstatistics$output1) +} + +#' Atomic PDFIndexFcst +#' @param index_hind Index (e.g. NAO index) array from a SFS with dimensions +#' (time, member) or (time, statistic) for a hindcast period. +#' The temporal dimension, by default 'time', must be greater +#' than 2. +#' @param index_obs Index (e.g. NAO index) array from an observational dataset +#' or reanalysis with dimension (time). The temporal dimension, +#' by default 'time', must be greater than 2. +#' @param index_fcst Index (e.g. NAO index) array from SFSs +#' with dimensions (time , member) or (time, statistic). +#' The temporal dimension, by default 'time', must be equal to 1, +#' the forecast year target. +#' The dimension 'member' must be greater than 1. +#' The dimension 'statistic' must be equal to 2, for containing the two paramenters of +#' a normal distribution (mean and sd) representing the ensemble of a SFS. +#' It is not possible to have the dimension 'member' and 'statistic' together. +#' @param method A character string indicating which methodology is applied +#' to compute the PDF. One of "ME" (default) or "LMEV". +#' @param time_dim_name A character string indicating the name of the temporal dimension, by default 'time'. +#' @param na.rm Logical. Should missing values be removed? +#' @return .PDFIndexFcst returns an array with dimensions (time = 1, statistic=4). +#' The firt statistic is the parameter 'mean' of the PDF with not bias corrected +#' for the forecast year. +#' The second statistic is the parameter 'standard deviation' of the PDF +#' with not bias corrected for the forecast year. +#' The third statistic is the parameter 'mean' of the PDF with bias corrected +#' for the forecast year. +#' The fourth statistic is the parameter 'standard deviation' of the PDF +#' with bias corrected for the forecast year. +#' @import multiApply +#' @importFrom verification verify +#' @examples +#' # Example 1 for the Atomic PDFIndexFcst function +#' index_fcst <- 1 : (1 * 6) +#' dim(index_fcst) <- c(time = 1, member = 6) +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = length(index_obs)) +#' index_hind <- 1 : (10 * 3) +#' dim(index_hind) <- c(time = 10, member = 3) +#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic +#' # 1 4 +#' # Example 2 for the Atomic PDFIndexFcst function +#' index_fcst <- 1 : (1 * 2) +#' dim(index_fcst) <- c(time = 1, statistic = 2) +#' index_obs <- 1 : 10 +#' dim(index_obs) <- c(time = 10) +#' index_hind <- 1 : (10 * 2) +#' dim(index_hind) <- c(time = 10, statistic = 2) +#' res <- .PDFIndexFcst(index_hind, index_obs, index_fcst) +#' dim(res) +#' # time statistic +#' # 1 4 +#' @noRd +.PDFIndexFcst <- function(index_hind, index_obs, index_fcst, + method = 'ME', + time_dim_name = 'time', + na.rm = FALSE) { + dimnameshind <- names(dim(index_hind)) + if ('member' %in% dimnameshind) { + pos <- match(c(time_dim_name, 'member'), names(dim(index_hind))) + index_hind <- aperm(index_hind, pos) + names(dim(index_hind)) <- c(time_dim_name, 'member') + exp_fcst_ens_mean <- Apply(list(index_fcst), target_dims = 'member', + fun = mean)$output1 + exp_fcst_ens_sd <- Apply(list(index_fcst), target_dims = 'member', + fun = sd)$output1 + } else { + pos <- match(c(time_dim_name,'statistic'), names(dim(index_fcst))) + index_fcst <- aperm(index_fcst,pos) + names(dim(index_fcst)) <- c(time_dim_name, 'statistic') + exp_fcst_ens_mean <- index_fcst[,1] + exp_fcst_ens_sd <- index_fcst[,2] + } + data_hind <- .PDFIndexHind(index_hind, index_obs, method, time_dim_name, na.rm) + exp_hind_ens_mean <- data_hind[,1] + pdfnotbc_mean <- exp_fcst_ens_mean + pdfnotbc_sd <- Sigma_notBC(exp_hind_ens_mean, index_obs, na.rm) + if (method == 'ME') { + pdfbc <- MEBC(exp_hind_ens_mean, index_obs, na.rm) + pdfbc_mean <- exp_fcst_ens_mean - pdfbc + } else if (method == 'LMEV') { + # linear model error-variance + exp_fcst_ens_var <- exp_fcst_ens_sd^2 + dim_hind <- names(dim(index_hind)) + dim_obs <- names(dim(index_obs)) + coeff <- LMEV(index_hind, index_obs, dim_hind, dim_obs, time_dim_name, na.rm) + pdfbc_mean <- exp_fcst_ens_mean - (coeff$coeff1 + coeff$coeff2*exp_fcst_ens_var) + } else { + stop("Parameter 'method' must be 'ME' or 'LMEV'.") + } + pdfbc_sd <- Sigma_BC(data_hind[,3], index_obs, na.rm) + data <- cbind(pdfnotbc_mean, pdfnotbc_sd, pdfbc_mean, pdfbc_sd) + names(dim(data)) <- c(names(dim(index_obs)), 'statistic') + return(data) +} + +# Auxiliar function to compute the mean squared error between 'exp' and 'obs' +Sigma_notBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } + return(sqrt(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$MSE)) +} + +# Auxiliar function to compute the bias between 'exp' and 'obs' +MEBC <- function(exp, obs, na.rm = FALSE) { + if (na.rm){ + indNA <- c(which(is.na(exp)),which(is.na(obs))) + if (length(indNA) > 0){ + exp <- exp[-indNA] + obs <- obs[-indNA] + } + } + return(verify(obs, exp, frcst.type = "cont", + obs.type = "cont")$ME) +} + +# Auxiliar function to compute the standard deviation of errors +# between 'obs' and 'exp' +Sigma_BC <- function(exp, obs, na.rm = FALSE) { + return(sd(obs - exp, na.rm = na.rm)) +} + +# Auxiliar function to compute the linear model used in the method +# called 'LMEV' to correct the bias in the Best NAO weighting +# methogology based on a linear model using ensemble variance of NAO index +# as predictor. +LMEV <- function(exp, obs, dim_exp, + dim_obs, time_dim_name = 'time', na.rm = FALSE) { + names(dim(exp)) <- dim_exp + names(dim(obs)) <- dim_obs + if (any(names(dim(exp)) == 'member')) { + exp_ens_mean <- Apply(list(exp), target_dims = 'member', + fun = mean, na.rm = na.rm)$output1 + exp_ens_sd <- Apply(list(exp), target_dims = 'member', + fun = sd, na.rm = na.rm)$output1 + } else { + if(na.rm & any(is.na(exp))){ + print("Some value are NA") + } + pos <- match(c(time_dim_name,'statistic'), names(dim(exp))) + exp <- aperm(exp,pos) + exp_ens_mean <- exp[,1] + exp_ens_sd <- exp[,2] + } + error <- exp_ens_mean - obs + exp_ens_var <- exp_ens_sd^2 + statModel <- lm(error ~ exp_ens_var) + return(list(coeff1 = statModel$coefficients[1], coeff2 = statModel$coefficients[2])) +} + + diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd index ae0d749a..f120258d 100644 --- a/man/BEI_PDFBest.Rd +++ b/man/BEI_PDFBest.Rd @@ -45,18 +45,24 @@ The dimension 'statistic' must be equal to 2, for containing the two paramenters a normal distribution (mean and sd) representing the ensemble of a SFS. It is not possible to have the dimension 'member' and 'statistic' together.} -\item{method_BC}{A character vector of maximun length 2 indicating the bias correction -methodology to be applied on each SFS. If it is 'none' or any of its elements is 'none', -the bias correction won't be applied. Available methods developped are "ME" and "LMEV".} +\item{method_BC}{A character vector of maximun length 2 indicating the bias +correction methodology to be applied on each SFS. If it is 'none' or any of +its elements is 'none', the bias correction won't be applied. +Available methods developped are "ME" (a bias correction scheme based on the +mean error or bias between observation and predictions to correct the +predicted values), and "LMEV" (a bias correction scheme based on a linear +model using ensemble variance of index as predictor). (see Sanchez-Garcia, +E. et al (2019), https://doi.org/10.5194/asr-16-165-2019 for more details).} -\item{time_dim_name}{A character string indicating the name of the temporal dimension, by default 'time'.} +\item{time_dim_name}{A character string indicating the name of the temporal +dimension, by default 'time'.} \item{na.rm}{Logical (default = FALSE). Should missing values be removed?} } \value{ -BEI_PDFBest() returns an array -with at least a temporal dimension, by default 'time' and dimension 'statistic', -equal to 2. +BEI_PDFBest() returns an array with the parameters that caracterize +the PDFs, with at least a temporal dimension, by default 'time' and dimension +'statistic' equal to 2. The firt statistic is the parameter 'mean' of the PDF for the best estimation combining the two SFSs PDFs. The second statistic is the parameter 'standard deviation' of the PDF for @@ -65,9 +71,12 @@ If index_fcst1 and/or index_fcst2 are null, returns the values for hindcast peri Otherwise, it returns the values for a forecast year. } \description{ -This function implements the computation to obtain the index PDFs -(e.g. NAO index) obtained to combining the Index PDFs for two SFSs, -the Best Index estimation. +This function implements the computation to obtain the index +Probability Density Functions (PDFs) (e.g. NAO index) obtained to combining +the Index PDFs for two Seasonal Forecast Systems (SFSs), the Best Index +estimation (see Sanchez-Garcia, E. et al (2019), +https://doi.org/10.5194/asr-16-165-2019 for more details about the +methodology applied to estimate the Best Index). } \examples{ # Example 1 for the BEI_PDFBest function -- GitLab From 2dfcafbcb45b07ec9b76667bca37309ebd6794ca Mon Sep 17 00:00:00 2001 From: nperez Date: Wed, 13 Nov 2019 17:44:31 +0100 Subject: [PATCH 40/40] updating documentation --- DESCRIPTION | 2 +- man/BEI_PDFBest.Rd | 7 ++++--- man/BEI_Weights.Rd | 7 ++++--- man/CST_Anomaly.Rd | 7 ++++--- man/CST_BEI_Weighting.Rd | 7 ++++--- man/CST_BiasCorrection.Rd | 6 +++--- man/CST_Calibration.Rd | 3 --- man/CST_Load.Rd | 1 + man/CST_MultiMetric.Rd | 9 +++++---- man/CST_MultivarRMSE.Rd | 7 ++++--- man/CST_RFSlope.Rd | 1 + man/CST_RFWeights.Rd | 7 ++++--- man/CST_RainFARM.Rd | 7 ++++--- man/PlotCombinedMap.Rd | 15 ++++++++------- man/PlotForecastPDF.Rd | 10 +++++----- man/PlotMostLikelyQuantileMap.Rd | 7 ++++--- man/RFSlope.Rd | 1 + man/RainFARM.Rd | 8 ++++---- man/areave_data.Rd | 1 + man/lonlat_data.Rd | 1 + man/lonlat_prec.Rd | 1 + 21 files changed, 64 insertions(+), 51 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 7934f45d..59fad67a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -56,4 +56,4 @@ VignetteBuilder: knitr License: Apache License 2.0 Encoding: UTF-8 LazyData: true -RoxygenNote: 6.1.1 +RoxygenNote: 5.0.0 diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd index f120258d..f836ab72 100644 --- a/man/BEI_PDFBest.Rd +++ b/man/BEI_PDFBest.Rd @@ -113,11 +113,12 @@ dim(res) # time statistic season # 1 2 2 } +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} + diff --git a/man/BEI_Weights.Rd b/man/BEI_Weights.Rd index 867a4eb0..61db33af 100644 --- a/man/BEI_Weights.Rd +++ b/man/BEI_Weights.Rd @@ -43,12 +43,13 @@ dim(res) # sdate dataset member season # 10 3 5 1 +} +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} } \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} + diff --git a/man/CST_Anomaly.Rd b/man/CST_Anomaly.Rd index b214a31a..e1c31f0c 100644 --- a/man/CST_Anomaly.Rd +++ b/man/CST_Anomaly.Rd @@ -53,12 +53,13 @@ str(anom3) anom4 <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = FALSE) str(anom4) -} -\seealso{ -\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} } \author{ Perez-Zanon Nuria, \email{nuria.perez@bsc.es} Pena Jesus, \email{jesus.pena@bsc.es} } +\seealso{ +\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}} +} + diff --git a/man/CST_BEI_Weighting.Rd b/man/CST_BEI_Weighting.Rd index b2a3f1fa..6b9a448a 100644 --- a/man/CST_BEI_Weighting.Rd +++ b/man/CST_BEI_Weighting.Rd @@ -63,11 +63,12 @@ dim(res_CST$data) # time lat lon dataset # 2 3 2 2 } +\author{ +Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} +} \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 } -\author{ -Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} -} + diff --git a/man/CST_BiasCorrection.Rd b/man/CST_BiasCorrection.Rd index af584c6b..485199ea 100644 --- a/man/CST_BiasCorrection.Rd +++ b/man/CST_BiasCorrection.Rd @@ -35,10 +35,10 @@ attr(obs, 'class') <- 's2dv_cube' a <- CST_BiasCorrection(exp = exp, obs = obs) str(a) } -\references{ -Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) -} \author{ Verónica Torralba, \email{veronica.torralba@bsc.es} } +\references{ +Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) +} \encoding{UTF-8} diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd index e68bdaa7..0e6a6a54 100644 --- a/man/CST_Calibration.Rd +++ b/man/CST_Calibration.Rd @@ -57,7 +57,4 @@ attr(obs, 'class') <- 's2dv_cube' a <- CST_Calibration(exp = exp, obs = obs, cal.method = "mse_min", eval.method = "in-sample") str(a) } -\author{ -Verónica Torralba, \email{veronica.torralba@bsc.es} -} \encoding{UTF-8} diff --git a/man/CST_Load.Rd b/man/CST_Load.Rd index bf03ba42..1fee022c 100644 --- a/man/CST_Load.Rd +++ b/man/CST_Load.Rd @@ -47,3 +47,4 @@ obs <- CSTools::lonlat_data$obs \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} } + diff --git a/man/CST_MultiMetric.Rd b/man/CST_MultiMetric.Rd index 8e3ce593..079a5588 100644 --- a/man/CST_MultiMetric.Rd +++ b/man/CST_MultiMetric.Rd @@ -37,14 +37,15 @@ c(ano_exp, ano_obs) \%<-\% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb a <- CST_MultiMetric(exp = ano_exp, obs = ano_obs) str(a) } +\author{ +Mishra Niti, \email{niti.mishra@bsc.es} + +Perez-Zanon Nuria, \email{nuria.perez@bsc.es} +} \references{ Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dverification]{Corr}}, \code{\link[s2dverification]{RMS}}, \code{\link[s2dverification]{RMSSS}} and \code{\link{CST_Load}} } -\author{ -Mishra Niti, \email{niti.mishra@bsc.es} -Perez-Zanon Nuria, \email{nuria.perez@bsc.es} -} diff --git a/man/CST_MultivarRMSE.Rd b/man/CST_MultivarRMSE.Rd index 24af608c..685eaf77 100644 --- a/man/CST_MultivarRMSE.Rd +++ b/man/CST_MultivarRMSE.Rd @@ -56,9 +56,10 @@ weight <- c(1, 2) a <- CST_MultivarRMSE(exp = ano_exp, obs = ano_obs, weight = weight) str(a) } -\seealso{ -\code{\link[s2dverification]{RMS}} and \code{\link{CST_Load}} -} \author{ Deborah Verfaillie, \email{deborah.verfaillie@bsc.es} } +\seealso{ +\code{\link[s2dverification]{RMS}} and \code{\link{CST_Load}} +} + diff --git a/man/CST_RFSlope.Rd b/man/CST_RFSlope.Rd index 0c4e1671..d2b5aec0 100644 --- a/man/CST_RFSlope.Rd +++ b/man/CST_RFSlope.Rd @@ -50,3 +50,4 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } + diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd index ef5ebe4d..08a7b850 100644 --- a/man/CST_RFWeights.Rd +++ b/man/CST_RFWeights.Rd @@ -47,6 +47,9 @@ nf <- 8 ww <- CST_RFWeights("./worldclim.nc", nf, lon, lat, fsmooth = TRUE) } } +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} \references{ Terzago, S., Palazzi, E., & von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: @@ -54,6 +57,4 @@ A simple method to reproduce a realistic fine-scale climatology. Natural Hazards and Earth System Sciences, 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 . } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} + diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd index 8df8f1f4..ca32e2d8 100644 --- a/man/CST_RainFARM.Rd +++ b/man/CST_RainFARM.Rd @@ -92,11 +92,12 @@ dim(res$data) # dataset member sdate ftime lat lon realization # 1 2 3 4 64 64 3 } +\author{ +Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} +} \references{ Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 ; D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724. } -\author{ -Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -} + diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd index 62e26904..6857c64d 100644 --- a/man/PlotCombinedMap.Rd +++ b/man/PlotCombinedMap.Rd @@ -5,10 +5,10 @@ \title{Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function} \usage{ PlotCombinedMap(maps, lon, lat, map_select_fun, display_range, - map_dim = "map", brks = NULL, cols = NULL, - col_unknown_map = "white", mask = NULL, col_mask = "grey", - bar_titles = NULL, legend_scale = 1, fileout = NULL, width = 8, - height = 5, size_units = "in", res = 100, ...) + map_dim = "map", brks = NULL, cols = NULL, col_unknown_map = "white", + mask = NULL, col_mask = "grey", bar_titles = NULL, legend_scale = 1, + fileout = NULL, width = 8, height = 5, size_units = "in", res = 100, + ...) } \arguments{ \item{maps}{List of matrices to plot, each with (longitude, latitude) dimensions, or 3-dimensional array with the dimensions (longitude, latitude, map). Dimension names are required.} @@ -67,11 +67,12 @@ PlotCombinedMap(list(a, b, c), lons, lats, bar_titles = paste('\% of belonging to', c('a', 'b', 'c')), brks = 20, width = 10, height = 8) } -\seealso{ -\code{PlotCombinedMap} and \code{PlotEquiMap} -} \author{ Nicolau Manubens, \email{nicolau.manubens@bsc.es} Veronica Torralba, \email{veronica.torralba@bsc.es} } +\seealso{ +\code{PlotCombinedMap} and \code{PlotEquiMap} +} + diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd index c1959ee8..bed0bd31 100644 --- a/man/PlotForecastPDF.Rd +++ b/man/PlotForecastPDF.Rd @@ -4,11 +4,10 @@ \alias{PlotForecastPDF} \title{Plot one or multiple ensemble forecast pdfs for the same event} \usage{ -PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, - obs = NULL, plotfile = NULL, title = "Set a title", - var.name = "Varname (units)", fcst.names = NULL, - add.ensmemb = c("above", "below", "no"), color.set = c("ggplot", - "s2s4e", "hydro")) +PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, obs = NULL, + plotfile = NULL, title = "Set a title", var.name = "Varname (units)", + fcst.names = NULL, add.ensmemb = c("above", "below", "no"), + color.set = c("ggplot", "s2s4e", "hydro")) } \arguments{ \item{fcst}{a dataframe or array containing all the ensember members for each frecast. If \code{'fcst'} is an array, it should have two labelled dimensions, and one of them should be \code{'members'}. If \code{'fcsts'} is a data.frame, each column shoul be a separate forecast, with the rows beeing the different ensemble members.} @@ -50,3 +49,4 @@ PlotForecastPDF(fcsts2, c(-0.66, 0.66), extreme.limits = c(-1.2, 1.2), \author{ Llorenç Lledó \email{llledo@bsc.es} } +\encoding{UTF-8} diff --git a/man/PlotMostLikelyQuantileMap.Rd b/man/PlotMostLikelyQuantileMap.Rd index 0d984ede..6c92850e 100644 --- a/man/PlotMostLikelyQuantileMap.Rd +++ b/man/PlotMostLikelyQuantileMap.Rd @@ -109,10 +109,11 @@ PlotMostLikelyQuantileMap(bins, lons, lats, mask = 1 - (w1 + w2 / max(c(w1, w2))), brks = 20, width = 10, height = 8) -} -\seealso{ -\code{PlotCombinedMap} and \code{PlotEquiMap} } \author{ Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es} } +\seealso{ +\code{PlotCombinedMap} and \code{PlotEquiMap} +} + diff --git a/man/RFSlope.Rd b/man/RFSlope.Rd index 5308ef8c..09a24ff5 100644 --- a/man/RFSlope.Rd +++ b/man/RFSlope.Rd @@ -60,3 +60,4 @@ slopes \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } + diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd index 35e7c2a5..3ef2a2f6 100644 --- a/man/RainFARM.Rd +++ b/man/RainFARM.Rd @@ -4,10 +4,9 @@ \alias{RainFARM} \title{RainFARM stochastic precipitation downscaling (reduced version)} \usage{ -RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, - kmin = 1, fglob = FALSE, fsmooth = TRUE, time_dim = NULL, - lon_dim = "lon", lat_dim = "lat", drop_realization_dim = FALSE, - verbose = FALSE) +RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, kmin = 1, + fglob = FALSE, fsmooth = TRUE, time_dim = NULL, lon_dim = "lon", + lat_dim = "lat", drop_realization_dim = FALSE, verbose = FALSE) } \arguments{ \item{data}{Precipitation array to downscale. @@ -114,3 +113,4 @@ dim(res$data) \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} } + diff --git a/man/areave_data.Rd b/man/areave_data.Rd index a772220a..cc79c85c 100644 --- a/man/areave_data.Rd +++ b/man/areave_data.Rd @@ -41,3 +41,4 @@ areave_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} + diff --git a/man/lonlat_data.Rd b/man/lonlat_data.Rd index 0c6ee30f..eca7abac 100644 --- a/man/lonlat_data.Rd +++ b/man/lonlat_data.Rd @@ -41,3 +41,4 @@ lonlat_data <- Nicolau Manubens \email{nicolau.manubens@bsc.es} } \keyword{data} + diff --git a/man/lonlat_prec.Rd b/man/lonlat_prec.Rd index 345e3cab..69cb94e8 100644 --- a/man/lonlat_prec.Rd +++ b/man/lonlat_prec.Rd @@ -29,3 +29,4 @@ lonlat_prec <- CST_Load('prlr', exp = list(infile), obs = NULL, Jost von Hardenberg \email{j.vonhardenberg@isac.cnr.it} } \keyword{data} + -- GitLab

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+dim(index_hind) <- c(time = 5, dataset = 3, member = 7, season = 4) +index_hind <- list(data = index_hind) +class(index_hind) <- 's2dv_cube' +index_obs <- 1 : (3 * 5 * 4) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) +index_obs <- list(data = index_obs) +class(index_obs) <- 's2dv_cube' +index_fcst <- 1 : (3 * 4 * 7) +dim(index_fcst) <- c(time = 1, dataset = 3, member = 7, season = 4) +index_fcst <- list(data = index_fcst) +class(index_fcst) <- 's2dv_cube' +res_CST <- CST_PDFIndexFcst(index_hind, index_obs, index_fcst, method = 'LMEV') +str(res_CST) +dim(res_CST$data) +#time dataset statistic season +# 1 3 4 4 +} \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., diff --git a/man/CST_PDFIndexHind.Rd b/man/CST_PDFIndexHind.Rd index 2444312a..216e0bb5 100644 --- a/man/CST_PDFIndexHind.Rd +++ b/man/CST_PDFIndexHind.Rd @@ -48,6 +48,7 @@ and accepts a CSTools object (an object of the class 's2dv_cube') as input. } \examples{ index_obs <- 1 : (3 * 5 * 4) +dim(index_obs) <- c(time = 5, dataset = 3, member = 1, season = 4) index_exp <- 1 : (3 * 5 * 4 * 7) dim(index_exp) <- c(time = 5, dataset = 3, member = 7, season = 4) index_exp <- list(data = index_exp) diff --git a/vignettes/BestNaoWeighting_vignette.Rmd b/vignettes/BestNaoWeighting_vignette.Rmd new file mode 100644 index 00000000..4e177e2c --- /dev/null +++ b/vignettes/BestNaoWeighting_vignette.Rmd @@ -0,0 +1,220 @@ +--- +title: "Achiving BestNAOWeighting in CSTools" +author: "Eroteida Sánchez García" +date: "29 Aug 2019" +output: html_document +--- + +## Before start + +System requeriment: CDO + +In our R session, we will need to: + +- define the working directory: `setwd("/home/...../AEMET_CSTOOLS")` +- install and load R packages + +```{r message=FALSE} +library(multiApply) +library(verification) +library(s2dverification) +library(ggplot2) +``` + +Data files used in the following code will be in `./data_CST` directory. + + +## Workflow of develop-BestNAOWeighting branch in CSTools + +Loading functions: + +```{r} +source("../R/CST_PDFIndexHind.R") +source("../R/CST_PDFIndexFcst.R") +source("../R/CST_PDFBest.R") +source("../R/CST_WeightIndex.R") +source("../R/CST_EMBestIndexWeighting.R") +source("../R/CST_ProbBestIndexWeighting.R") +source("../R/zzz.R") +``` + +It is necessary to have calculated previosly the NAO index for models to combine +(for instance, a dynamical model as first guess combined with an empirical +model) in order to achieve the Best NAO estimation. + +The first step is to load from a RData file the list lNAOs_CST with the NAO +index as CST object for period December,January and February, obtained +previously for the models to combine: + + + +```{r message=FALSE} +fileNAOs_CST <- ("../data/lNAOs_CST.RData") +load(fileNAOs_CST) +``` + + +Improving the two seasonal forecats systems (modelsECMWF-S5 as M1 and S-ClimWaRe +as M2) NAO index DPFs estimate for the hindcast and forecast period, and ,then, +we combine them to obtain the Best NAO PDF estimate by runnning: + +```{r message=FALSE} +# Improving the ECMWF-S5 (M1) NAO index DPFs estimate for the hindcast and +# forecast period using the named 'LMEV' methodology or scheme +pdf_hind_m1_CST <- CST_PDFIndexHind(lNAOs_CST$index_hind1, + lNAOs_CST$index_obs, + method = 'LMEV', + na.rm = FALSE) +str(pdf_hind_m1_CST) +dim(pdf_hind_m1_CST$data) + +pdf_fcst_m1_CST <- CST_PDFIndexFcst(lNAOs_CST$index_hind1, + lNAOs_CST$index_obs, + lNAOs_CST$index_fcst1, + method = 'LMEV', + na.rm = FALSE) +str(pdf_fcst_m1_CST) +dim(pdf_fcst_m1_CST$data) + +# Improving the S-ClimWaRe (M2) NAO index DPFs estimate for the hindcast and +# forecast period using the named 'ME' methodology or scheme +pdf_hind_m2_CST <- CST_PDFIndexHind(lNAOs_CST$index_hind2, + lNAOs_CST$index_obs, + method = 'ME', + na.rm = FALSE) +str(pdf_hind_m2_CST) +dim(pdf_hind_m2_CST$data) + +pdf_fcst_m2_CST <- CST_PDFIndexFcst(lNAOs_CST$index_hind2, + lNAOs_CST$index_obs, + lNAOs_CST$index_fcst2, + method = 'ME', + na.rm = FALSE) +str(pdf_fcst_m2_CST) +dim(pdf_fcst_m2_CST$data) + +# Best NAO pdf estimate for hindcast period +pdf_aux <- array(NA, dim = c(dim(pdf_hind_m1_CST$data)['time'], + dataset = 2, member = 1, + statistic = 4, season = 1)) +pdf_aux[,1,,,] <- pdf_hind_m1_CST$data +pdf_aux[,2,,,] <- pdf_hind_m2_CST$data +pdf_exp <- list(data = pdf_aux, Datasets = c("ECMWF-S5","SCLIMWARE"), + season = c('DJF'), + Dates = list(start = pdf_hind_m2_CST$Dates$start)) +class(pdf_exp) <- 's2dv_cube' +pdf_hind_best_CST <- CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) + +# Best NAO pdf estimate for forecast period +pdf_aux <- array(NA, dim = c(time = 1, dataset = 2, member = 1, + statistic = 4, season = 1)) +pdf_aux[,1,,,] <- pdf_fcst_m1_CST$data +pdf_aux[,2,,,] <- pdf_fcst_m2_CST$data +pdf_exp <- list(data = pdf_aux, Datasets = c("ECMWF-S5","SCLIMWARE"), + season = c('DJF'), Dates = list(start = pdf_fcst_m2_CST$Dates$start)) +class(pdf_exp) <- 's2dv_cube' +pdf_fcst_best_CST <- CST_PDFBest(pdf_exp, bc_dataset1 = TRUE, bc_dataset2 = FALSE) + +``` + + +The content of the list `pdf_hind_best_CST` can be reviewed visualizing the structure: +```{r} +str(pdf_hind_best_CST) +``` + + +Now, we can obtain an array with weights for each ensemble's member based on this Best NAO pdf estimate running the following code: + +```{r message=FALSE} +# For the hindcast period +# Computing the weights for ECMWF-S5 members using the Best NAO estimate +weigths_index_CST <- CST_WeightIndex(lNAOs_CST$index_hind1, pdf_hind_best_CST) +``` + + +The content of the weight's array can be reviewed visualizing the structure: + +```{r} +dim(weigths_index_CST$data) +str(weigths_index_CST) + +``` + + +Now, we need to load the total precipitation from November to March for hindcast +period over Iberian Peninsula. Then, we compute terciles and the probability +that the variable data will be in the lower (or upper) tercile based on the best +NAO pdf estimate running the code: + +```{r message=FALSE} +# Loading the precipitation data for November to March from ECMWS-S5 +# in a CSTools object +filePR_CST <- ("../data/pr_hindcast_ecmf-s5_CST.RData") +load(filePR_CST) + +# Computing terciles and probabilities after weighting the ensemble members +weights_index_exp <- weigths_index_CST +em_var_exp <- CST_EMBestIndexWeighting(var_exp, weights_index_exp) +prob_var_exp <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp) + +``` + + +If we want to compare with the raw output model: + +```{r message=FALSE} +# Computing terciles and probabilities with CST_function +weights_index_exp_raw <- weights_index_exp +weights_index_exp_raw$data <- rep(1/25, 20 * 25) +dim(weights_index_exp_raw$data) <- dim(weights_index_exp$data) + +em_var_exp_raw <- CST_EMBestIndexWeighting(var_exp, weights_index_exp_raw) +prob_var_exp_raw <- CST_ProbBestIndexWeighting(var_exp, weights_index_exp_raw) + +``` + + +## Plotting probability maps: + +A map with the probability that the total precipitation from November 2012 to +March 2013, for example, will be in the lower tercile based on the best NAO pdf +estimate is obtained running the code: + +```{r message=FALSE} +coordVAR <- list(lonmin = -10, lonmax = 5, latmin = 35, latmax = 45) +lonPlot <- seq(coordVAR$lonmin,coordVAR$lonmax,0.50) +latPlot <- seq(coordVAR$latmax,coordVAR$latmin,-0.50) +probData_CST <- prob_var_exp$data[which(var_exp$Dates$start == 2012),1,1,,,1] +brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) +cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") +names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") +ofile <- "./output/probLower_weight_CST.png" +PlotEquiMap(t(probData_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, + fileout =ofile, brks=brks, cols = cols, + toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on the best NAO pdf estimate"), + title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) + + +``` + +![](./output/probLower_weight_CST.png){width=70%} + +In a similar way we can plot the map with the probability that the total precipitation from November 2012 to +March 2013, for example, will be in the lower tercile from ECMWF Seasonal Forecast System 5 (raw) to compare results running the code: + +```{r message=FALSE} +probDataRaw_CST <- prob_var_exp_raw$data[which(var_exp$Dates$start == 2012),1,1,,,1] +brks <- c(0,0.35,0.4,0.5,0.6,0.7,1) +cols <- c("#FFFFFF","#FFEE99","#FFDD00","#CC8800","#993300","#3B1400") +names(brks) <- c("0%","35%","40%","50%","60%","70%","100%") +ofile <- "./output/probLower_raw_CST.png" +PlotEquiMap(t(probDataRaw_CST[order(latPlot),]), lon = lonPlot, lat = latPlot[length(latPlot):1], filled.continents = FALSE, + fileout =ofile, brks=brks, cols = cols, + toptitle = paste0("Probability (pcp NDJFM 2012/13) < lower tercile) based on ECMWF-S5 (raw)"), + title_scale= 0.5, intxlon=2.0, intylat=2.0, labW=TRUE) + + +``` + +![](./output/probLower_raw_CST.png){width=70%} \ No newline at end of file -- GitLab From e58cfa92b72d0a81087d88095c9d185264a2ca56 Mon Sep 17 00:00:00 2001 From: Eroteida Sanchez Garcia Date: Thu, 5 Sep 2019 08:06:48 +0000 Subject: [PATCH 10/40] Adding png files for vignette --- vignettes/Figures/probLower_raw_CST.png | Bin 0 -> 43825 bytes vignettes/Figures/probLower_weight_CST.png | Bin 0 -> 43035 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 vignettes/Figures/probLower_raw_CST.png create mode 100644 vignettes/Figures/probLower_weight_CST.png diff --git a/vignettes/Figures/probLower_raw_CST.png b/vignettes/Figures/probLower_raw_CST.png new file mode 100644 index 0000000000000000000000000000000000000000..223965682c861f6177469d30f87bc518f014ff54 GIT binary patch literal 43825 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