## scaleGrid.R Grid scaling
##
## Copyright (C) 2017 Santander Meteorology Group (http://www.meteo.unican.es)
##
## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program. If not, see <http://www.gnu.org/licenses/>.
#' @title Grid scaling
#' @description Scale a grid (or compute anomalies)
#' @param grid Input grid to be rescaled
#' @param base Reference baseline whose climatology will be subtracted to the input grid. If \code{NULL} (the default),
#' the climatology is directly computed from the input \code{grid} via \code{\link{climatology}}, either member-by-member
#' or using the ensemble mean climatology, as specified by the \code{by.member} flag. Note that \code{base}
#' must NOT be a climatology (the base climatology is internally calculated via the argument \code{clim.fun}).
#' @param ref Reference grid. After subtracting to grid its climatology (as defined by \code{grid.clim}), the mean
#' climatology of this additional grid is added. Default to NULL, so no reference grid is used.
#' @param clim.fun Function to compute the climatology. Default to mean.
#' See \code{\link{climatology}} for details on function definition.
#' @param by.member Logical. In case of multimember grids, should the climatology be computed sepparately
#' for each member (\code{by.member=TRUE}), or a single climatology calculated from the ensemble mean
#' (\code{by.member=FALSE})?. Default to \code{TRUE}. Argument passed to \code{\link{climatology}}.
#' @param spatial.frame Character string indicating whether to perform the local scaling considering the climatolopgical value
#' at the field scale (\code{"field"}) or per gridbox (\code{"gridbox"}, the default).
#' @param time.frame Character indicating the time frame to perform the scaling. Possible values are
#' \code{"none"}, which considers the climatological mean of the whole period given in
#' \code{base} and/or \code{ref}, \code{"monthly"}, that performs the calculation on a monthly basis
#' and \code{"daily"}, for a julian day-based approach. See details.
#' @param window.width Default to NULL. Only applied if time.frame = "daily". In that case the daily mean is removed with a
#' moving average whose size is defined by the window.width parameter.
#' @param type Character string. Either \code{"center"}, \code{"standardize"} or \code{"ratio"}, depending on wheter the correction factor are anomalies (e.g. temperature...),
#' standardized anomalies or applied as a ratio (e.g. precipitation, wind speed...). See details
#' @param skip.season.check Logical flag. Should the internal checker \code{\link{checkSeason}} be skipped?. By
#' default, the function undertakes an automatic seasonal check. This can be skipped if needed, mainly for internal use
#' (e.g. in cross-validation setups)
#'
#' @template templateParallelParams
#' @details In the \code{type = "center"} set up the reference grid (\code{ref}) is used to correct the input grid, as follows:
#'
#' \deqn{grid'_{t} = grid - base_{clim} + ref_{clim}}
#'
#' In the case of \code{type = "standardize"}:
#'
#' \deqn{grid'_{t} = (grid - base_{clim})/ base_{sd} * ref_{sd} + ref_{clim}}
#'
#' In the case of \code{type = "ratio"}:
#'
#' \deqn{grid'_{t} = (grid / base_{clim}) * ref_{clim}}
#'
#' , where \emph{grid'_{t}} is each time slice \emph{t} of the input grid, and \emph{base_clim} corresponds to the baseline climatology (by default the grid climatology), \emph{base_sd} is the standard deviation of the baseline climate signal,thus yielding an anomaly
#' w.r.t. the base, \emph{ref_clim} is a reference climatological value added to the previously calculated anomaly and \emph{ref_sd} is the standard deviation of the reference climate signal. The \code{"center"} or \code{"standardize"} version
#' is preferably applied to unbounded variables (e.g. temperature) and the \code{"ratio"} to variables with a lower bound
#' (e.g. precipitation, because it also preserves the frequency).
#'
#' Depending on the value of the argument \code{time.frame}, the baseline and reference climatologies are calculated for
#' the whole season (\code{time.frame = "none"}), month by month \code{time.frame = "monthly"}
#' or by day-of-the-year (julian days) (\code{time.frame = "daily"}). Thus,
#' if both \code{base} and \code{ref} are set to \code{NULL}, the output grid corresponds to the anomaly field of the
#' input \code{grid} w.r.t. its own mean. The way \emph{base_clim} and \emph{ref_clim_grid} are computed in case of multimember grids is controlled
#' by the argument \code{by.member}. By default the climatological mean is used, but this can be changed
#' by the user through the argument \code{clim.fun}, that is passed to \code{\link{climatology}}.
#'
#' The \code{ref} usually corresponds to the control (historical, 20C3M...) run of the GCM in the training period in climate change applications,
#' or the hindcast data for the training period in s2d applications. Note that by default \code{ref = NULL}.
#'
#' @importFrom abind abind asub
#' @importFrom stats na.omit
#' @importFrom magrittr %>% %<>%
#' @importFrom parallel stopCluster
#' @return A locally scaled grid
#' @author J. Bedia
#' @export
#' @examples \donttest{
#' require(climate4R.datasets)
#' ## ANOMALIES
#' data("NCEP_Iberia_psl")
#' # Define average aggregation function
#' f = list(FUN = "mean", na.rm = TRUE)
#' psl <- aggregateGrid(NCEP_Iberia_psl, aggr.y = f) # get interannual DJF series
#'
#' ## When 'base' and 'ref' are not supplied,
#' ## the input grid climatology (by default the mean) is subtracted, thus yielding anomalies:
#' psl.anom <- scaleGrid(psl)
#' # spatial aggregation of the output (for plotting the time series)
#' psl.anom.iberia <- aggregateGrid(psl.anom, aggr.lat = f, aggr.lon = f)
#' plot(getYearsAsINDEX(psl.anom.iberia), psl.anom.iberia$Data, ty = 'b',
#' ylab = "MSLP anomalies (Pa)", xlab = "year",
#' main = "NCEP Reanalysis - Mean SLP anomalies (DJF)")
#' grid()
#' abline(h = 0, lty = 2, col = "blue")
#' # In the particular case of multimember grids, the anomalies are computed for each member
#' # by subtracting either their own mean (by.member = TRUE) or the
#' # multimember mean climatology (by.member = FALSE)
#' data("CFS_Iberia_tas")
#' a <- scaleGrid(CFS_Iberia_tas, by.member = FALSE)
#' aa <- aggregateGrid(a, aggr.lat = f, aggr.lon = f, aggr.m = f, aggr.y = f)
#' # Example, member 4 time series
#' plot(as.Date(getRefDates(aa)), aa$Data[4,], ty = "o", xlab = "Date",
#' ylab = "Tmean anomaly (degC)", main = "Tmean DJF anomalies, Member 4")
#' abline(h = 0, lty = 2, col = "blue")
#' grid()
#' b <- scaleGrid(CFS_Iberia_tas, by.member = TRUE) # almost identical in this case
#' bb <- aggregateGrid(b, aggr.lat = f, aggr.lon = f, aggr.m = f, aggr.y = f)
#' lines(as.Date(getRefDates(aa)), bb$Data[4,], col = "red", ty = "l")
#' legend("bottomright", c("by.member = FALSE", "by.member = TRUE"), lty = 1, col = c(1,2))
#'
#' # In this example, the anomalies are calculated using a different period specifying a "base".
#' # Note that "base" could also be a grid of a different dataset, for instance a
#' # reanalysisdata(EOBS_Iberia_tas)
#' data("EOBS_Iberia_tas")
#' grid <- subsetGrid(EOBS_Iberia_tas, years = 1999:2000)
#' base <- subsetGrid(EOBS_Iberia_tas, years = 1998)
#' lc <- scaleGrid(grid = grid, base = base)
#' plot(lc$Data[,,15,15], ty = 'l', ylim = c(-10,10),
#' xlab = "time", ylab = "Anomaly (degC)",
#' main = "Anomalies w.r.t. 1998")
#' abline(h = 0, lty = 2, col = "grey30")
#' str(lc)
#' # The anomalies are calculated on a monthly basis using the 'time.frame' argument:
#' lc.m <- scaleGrid(grid = grid, base = base, time.frame = "monthly")
#' lines(lc.m$Data[,15,15], col = "red")
#' lc.d <- scaleGrid(grid = grid, base = base, time.frame = "daily")
#' lines(lc.d$Data[,15,15], col = "blue")
#' legend("topleft", c("none", "monthly","daily"), title = "'time.frame'",
#' lty = 1, col = c(1,2,4), bty = "n")
#'
#' # An example in which the reference climatology is added to the anomalies:
#' # (not quite meaningful, though)
#' grid <- subsetGrid(EOBS_Iberia_tas, years = 1999)
#' base <- subsetGrid(EOBS_Iberia_tas, years = 1998)
#' ref <- subsetGrid(EOBS_Iberia_tas, years = 2000)
#' lc.ref <- scaleGrid(grid = grid, base = base, ref = ref)
#' lc.ref.m <- scaleGrid(grid = grid, base = base, ref = ref, time.frame = "monthly")
#' plot(grid$Data[,15,15], ty = "l", ylim = c(-7.5,10))
#' lines(lc.ref$Data[,,15,15], col = "blue")
#' lines(lc.ref.m$Data[,15,15], col = "red")
#'
#' # An example using the "ratio" type:
#' data("EOBS_Iberia_pr")
#' grid <- subsetGrid(EOBS_Iberia_pr, years = 1999)
#' base <- subsetGrid(EOBS_Iberia_pr, years = 1998)
#' ref <- subsetGrid(EOBS_Iberia_pr, years = 2000)
#' fun <- list(FUN = "sum")
#' # We plot the climatologies (total accumulated precip) to have a visual comparison of
#' # the input grid, the grid used as base and the reference
#' mg <- makeMultiGrid(climatology(grid, clim.fun = fun),
#' climatology(base, clim.fun = fun),
#' climatology(ref, clim.fun = fun), skip.temporal.check = TRUE)
#' require(visualizeR)
#' spatialPlot(mg, names.attr = c("input_grid", "base", "ref"))
#' # Note that for precipitation we use a scaling factor rather than an addition:
#' grid.corr <- scaleGrid(grid = grid, base = base, ref = ref,
#' time.frame = "monthly", type = "ratio")
#' mg.corr <- makeMultiGrid(climatology(grid, clim.fun = fun), climatology(grid.corr, clim.fun = fun))
#' spatialPlot(mg.corr, at = seq(0,350,10), names.attr = c("Raw","Scaled"))
#' # An example using the "standardize" type:
#' data("EOBS_Iberia_pr")
#' grid <- subsetGrid(EOBS_Iberia_pr, years = 1999)
#' base <- subsetGrid(EOBS_Iberia_pr, years = 1998)
#' ref <- subsetGrid(EOBS_Iberia_pr, years = 2000)
#' grid.corr <- scaleGrid(grid = grid, base = NULL, ref = NULL,
#' type = "standardize")
#' head(apply(grid.corr$Data,MARGIN = c(3,4),mean))
#' head(apply(grid.corr$Data,MARGIN = c(3,4),sd))
#' }
scaleGrid <- function(grid,
base = NULL,
ref = NULL,
clim.fun = list(FUN = "mean", na.rm = TRUE),
by.member = TRUE,
time.frame = c("none", "monthly", "daily"),
spatial.frame = c("gridbox","field"),
type = "center",
window.width = NULL,
parallel = FALSE,
max.ncores = 16,
ncores = NULL,
skip.season.check = FALSE) {
time.frame <- match.arg(time.frame, choices = c("none", "monthly", "daily"))
type <- match.arg(type, choices = c("center", "standardize", "ratio"))
spatial.frame <- match.arg(spatial.frame, choices = c("gridbox", "field"))
stopifnot(is.logical(skip.season.check))
if (time.frame == "none") {
message("[", Sys.time(), "] - Scaling ...")
out <- gridScale.(grid, base, ref, clim.fun, by.member, type, parallel, max.ncores, ncores, spatial.frame, skip.season.check)
message("[", Sys.time(), "] - Done")
} else if (time.frame == "monthly") {
message("[", Sys.time(), "] - Scaling by months ...")
months <- getSeason(grid)
aux.list <- lapply(1:length(months), function(x) {
grid1 <- subsetGrid(grid, season = months[x])
base1 <- if (!is.null(base)) {
subsetGrid(base, season = months[x])
} else {
NULL
}
ref1 <- if (!is.null(ref)) {
subsetGrid(ref, season = months[x])
} else {
NULL
}
gridScale.(grid1, base1, ref1, clim.fun, by.member, type, parallel, max.ncores, ncores, spatial.frame, skip.season.check)
})
out <- do.call("bindGrid", c(aux.list, dimension = "time"))
message("[", Sys.time(), "] - Done")
} else if (time.frame == "daily") {
doys.grid <- grid %>% getRefDates() %>% toJulian()
if (!is.null(base)) {
doys.base <- base %>% getRefDates() %>% toJulian()
}
if (!is.null(ref)) {
doys.ref <- ref %>% getRefDates() %>% toJulian()
}
juliancalendary <- c(1:366,1:366,1:366)
if (is.null(window.width)) {
window.width <- 0
}
message("[", Sys.time(), "] - Scaling by julian days ...")
aux.list <- lapply(unique(doys.grid), function(x) {
window <- juliancalendary[(x+366-floor(window.width/2)):(x+366+floor(window.width/2))]
index_grid <- lapply(1:length(window), FUN = function(z) which(doys.grid == window[z])) %>% unlist() %>% sort()
targets <- which(doys.grid == juliancalendary[x+366])
targets <- lapply(1:length(targets),FUN = function(z) which(index_grid == targets[z])) %>% unlist()
grid1 <- subsetDimension(grid, dimension = "time", indices = index_grid)
if (!is.null(base)) {
window <- juliancalendary[(x+366-floor(window.width/2)):(x+366+floor(window.width/2))]
index_base <- lapply(1:length(window), FUN = function(z) which(doys.base == window[z])) %>% unlist() %>% sort()
base1 <- subsetDimension(base, dimension = "time", indices = index_base)
} else {
base1 <- base
}
if (!is.null(ref)) {
window <- juliancalendary[(x+366-floor(window.width/2)):(x+366+floor(window.width/2))]
index_ref <- lapply(1:length(window), FUN = function(z) which(doys.ref == window[z])) %>% unlist() %>% sort()
ref1 <- subsetDimension(ref, dimension = "time", indices = index_ref)
} else {
ref1 <- ref
}
gridScale.(grid1, base1, ref1, clim.fun, by.member, type, parallel, max.ncores, ncores, spatial.frame, skip.season.check, targets = targets)
})
out <- do.call("bindGrid", c(aux.list, dimension = "time"))
message("[", Sys.time(), "] - Done")
}
invisible(out)
}
#' @title Local scaling internal
#' @description Internal workhorse for local scaling
#' @keywords internal
#' @importFrom magrittr %>%
#' @importFrom abind abind
#' @importFrom parallel stopCluster
#' @author J Bedia
gridScale. <- function(grid, base, ref, clim.fun, by.member, type, parallel, max.ncores, ncores,
spatial.frame, skip.season.check,targets = NULL) {
grid <- redim(grid)
if (is.null(base)) {
base.m <- suppressMessages({
climatology(grid, clim.fun, by.member, parallel, max.ncores, ncores)
}) %>% redim()
base.std <- 1
ref.std <- 1
if (type == "standardize") {
base.std <- suppressMessages({
climatology(grid, clim.fun = list(FUN = "sd", na.rm = TRUE), by.member, parallel, max.ncores, ncores)
}) %>% redim()
base.std <- base.std$Data}
if (spatial.frame == "field") {
getDim.base <- attr(base.m$Data, "dimensions")
ind <- c(which(getDim(base.m) == "time"), which(getDim(base.m) == "lat"), which(getDim(base.m) == "lon"))
base.m$Data <- array(data = apply(base.m$Data, MARGIN = -ind, FUN = function(Z) {mean(Z,na.rm = TRUE)}),dim = dim(base.m$Data))
attr(base.m$Data,"dimensions") <- getDim.base
if ((type == "standardize")) {
base.std <- array(data = apply(grid$Data, MARGIN = -ind, FUN = function(Z) {sd(Z,na.rm = TRUE)}),dim = dim(base.m$Data))
}
}
} else {
if (!skip.season.check) checkSeason(grid, base)
checkDim(grid, base, dimensions = c("lat", "lon"))
base.m <- suppressMessages({
climatology(base, clim.fun, by.member, parallel, max.ncores, ncores)
}) %>% redim()
base.std <- 1
ref.std <- 1
if ((type == "standardize")) {
base.std <- suppressMessages({
climatology(base, clim.fun = list(FUN = "sd", na.rm = TRUE), by.member, parallel, max.ncores, ncores)
}) %>% redim()
base.std <- base.std$Data}
if (spatial.frame == "field") {
getDim.base <- attr(base.m$Data, "dimensions")
ind <- c(which(getDim(base.m) == "time"), which(getDim(base.m) == "lat"), which(getDim(base.m) == "lon"))
base.m$Data <- array(data = apply(base.m$Data, MARGIN = -ind, FUN = function(Z) {mean(Z,na.rm = TRUE)}),dim = dim(base.m$Data))
attr(base.m$Data,"dimensions") <- getDim.base
if (type == "standardize") {
base.std <- array(data = apply(redim(base)$Data, MARGIN = -ind, FUN = function(Z) {sd(Z,na.rm = TRUE)}),dim = dim(base.m$Data))
}
}
}
if (!is.null(ref)) {
checkDim(grid, ref, dimensions = c("lat", "lon"))
if (!skip.season.check) checkSeason(grid, ref)
ref.m <- suppressMessages({
climatology(ref, clim.fun, by.member, parallel, max.ncores,ncores)
}) %>% redim()
if (type == "standardize") {
ref.std <- suppressMessages({
climatology(ref, clim.fun = list(FUN = "sd", na.rm = TRUE), by.member, parallel, max.ncores, ncores)
}) %>% redim()
ref.std <- ref.std$Data}
if (spatial.frame == "field") {
getDim.ref <- attr(ref.m$Data, "dimensions")
ind <- c(which(getDim(ref.m) == "time"), which(getDim(ref.m) == "lat"), which(getDim(ref.m) == "lon"))
ref.m$Data <- array(data = apply(ref.m$Data, MARGIN = -ind, FUN = function(Z) {mean(Z,na.rm = TRUE)}),dim = dim(ref.m$Data))
attr(ref.m$Data,"dimensions") <- getDim.ref
if (type == "standardize") {
ref.std <- array(data = apply(redim(ref)$Data, MARGIN = -ind, FUN = function(Z) {sd(Z,na.rm = TRUE)}),dim = dim(ref.m$Data))
}
}
ref <- ref.m
} else {
ref <- list()
ref[["Data"]] <- array(0, getShape(base.m))
attr(ref[["Data"]], "dimensions") <- getDim(base.m)
}
parallel.pars <- parallelCheck(parallel, max.ncores, ncores)
lapply_fun <- selectPar.pplyFun(parallel.pars, .pplyFUN = "lapply")
if (parallel.pars$hasparallel) on.exit(parallel::stopCluster(parallel.pars$cl))
clim <- grid[["Data"]]
dimNames <- getDim(grid)
ind.time <- grep("^time", dimNames)
n.times <- getShape(grid, "time")
Xc <- base.m[["Data"]]
Xref <- ref[["Data"]]
if (!is.null(targets)) {
clim <- asub(clim, idx = targets, dims = ind.time, drop = FALSE)
attr(clim,"dimensions") <- dimNames
n.times <- dim(clim)[ind.time]
} else {
targets <- 1:n.times
}
aux.list <- gridScale.type(clim, n.times, ind.time, Xc, Xref, type, lapply_fun, base.std, ref.std)
Xc <- Xref <- base <- base.m <- base.std <- ref <- ref.std <- NULL
grid[["Data"]] <- do.call("abind", c(aux.list, along = ind.time)) %>% unname()
aux.list <- NULL
attr(grid[["Data"]], "dimensions") <- dimNames
grid[["Dates"]]$start <- grid[["Dates"]]$start[targets]
grid[["Dates"]]$end <- grid[["Dates"]]$end[targets]
return(grid)
}
#' @title Local scaling type internal
#' @description Internal for center/standardize/ratio local scaling
#' @keywords internal
#' @importFrom magrittr %>%
#' @importFrom abind abind
#' @importFrom parallel stopCluster
#' @author J Bedia
gridScale.type <- function(clim, n.times, ind.time, Xc, Xref, type, lapply_fun, base.std, ref.std) {
if (type != "ratio") {
lapply_fun(1:n.times, function(x) {
X <- asub(clim, idx = x, dims = ind.time, drop = FALSE)
if (dim(X)[1] != dim(Xc)[1]) {
aux <- lapply(1:dim(X)[1], function(i) {((X[i, , , , drop = FALSE] - Xc) / base.std) * ref.std + Xref})
do.call("abind", c(aux, along = 1)) %>% unname()
} else {
((X - Xc) / base.std ) * ref.std + Xref
}
})
} else {
lapply_fun(1:n.times, function(x) {
X <- asub(clim, idx = x, dims = ind.time, drop = FALSE)
if (dim(X)[1] != dim(Xc)[1]) {
aux <- lapply(1:dim(X)[1], function(i) (X[i, , , , drop = FALSE] / Xc) * Xref)
do.call("abind", c(aux, along = 1)) %>% unname()
} else {
(X / Xc) * Xref
}
})
}
}
#' @title Convert to julian
#' @description Internal for converting date to julian day
#' @keywords internal
#' @author J BaƱo-Medina
toJulian <- function(dates) {
dates <- as.Date(dates)
# f<-as.Date("1976-02-29")
p<-c(31,29,31,30,31,30,31,31,30,31,30,31,30,31)
sapply(1:length(dates),FUN = function(z) {sum(p[1:as.numeric(format(dates[z],"%m"))-1])+as.numeric(format(dates[z],"%d"))})
}
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