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#'Compute climatological weights for RainFARM stochastic precipitation downscaling
#'
#'@author Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
#'
#'@description Compute climatological ("orographic") weights from a fine-scale
#'precipitation climatology file.
#'@references Terzago, S., Palazzi, E., & von Hardenberg, J. (2018).
#'Stochastic downscaling of precipitation in complex orography:
#'A simple method to reproduce a realistic fine-scale climatology.
#'Natural Hazards and Earth System Sciences, 18(11),
#'2825-2840. \doi{10.5194/nhess-18-2825-2018}.
#'@param climfile Filename of a fine-scale precipitation climatology. The file
#' is expected to be in NetCDF format and should contain at least one
#' precipitation field. If several fields at different times are provided,
#' a climatology is derived by time averaging. Suitable climatology files could
#' be for example a fine-scale precipitation climatology from a high-resolution
#' regional climate model (see e.g. Terzago et al. 2018), a local
#' high-resolution gridded climatology from observations, or a reconstruction
#' such as those which can be downloaded from the WORLDCLIM
#' (\url{https://www.worldclim.org}) or CHELSA (\url{https://chelsa-climate.org/})
#' websites. The latter data will need to be converted to NetCDF format before
#' being used (see for example the GDAL tools (\url{https://gdal.org/}). It
#' could also be an 's2dv_cube' object.
#'@param nf Refinement factor for downscaling (the output resolution is
#' increased by this factor).
#'@param lon Vector of longitudes.
#'@param lat Vector of latitudes. The number of longitudes and latitudes is
#' expected to be even and the same. If not the function will perform a
#' subsetting to ensure this condition.
#'@param varname Name of the variable to be read from \code{climfile}.
#'@param fsmooth Logical to use smooth conservation (default) or large-scale
#' box-average conservation.
#'@param lonname A character string indicating the name of the longitudinal
#' dimension set as 'lon' by default.
#'@param latname A character string indicating the name of the latitudinal
#' dimension set as 'lat' by default.
#'@param ncores An integer that indicates the number of cores for parallel
#' computations using multiApply function. The default value is one.
#'
#'@return An object of class 's2dv_cube' containing in matrix \code{data} the
#'weights with dimensions (lon, lat).
#'@examples
#'# Create weights to be used with the CST_RainFARM() or RainFARM() functions
#'# using an external random data in the form of 's2dv_cube'.
#'obs <- rnorm(2 * 3 * 4 * 8 * 8)
#'dim(obs) <- c(dataset = 1, member = 2, sdate = 3, ftime = 4, lat = 8, lon = 8)
#'lon <- seq(10, 13.5, 0.5)
#'lat <- seq(40, 43.5, 0.5)
#'coords <- list(lon = lon, lat = lat)
#'data <- list(data = obs, coords = coords)
#'class(data) <- "s2dv_cube"
#'res <- CST_RFWeights(climfile = data, nf = 3, lon, lat, lonname = 'lon',
#' latname = 'lat', fsmooth = TRUE)
#'@import ncdf4
#'@import rainfarmr
#'@import multiApply
#'@importFrom utils tail
#'@importFrom utils head
#'@export
CST_RFWeights <- function(climfile, nf, lon, lat, varname = NULL, fsmooth = TRUE,
lonname = 'lon', latname = 'lat', ncores = NULL) {
if (!inherits(climfile, "s2dv_cube")) {
if (!is.null(varname) & !is.character(varname)) {
stop("Parameter 'varname' must be a character string indicating the name",
" of the variable to be read from the file.")
}
}
# Ensure input grid is square and with even dimensions
if ((length(lat) != length(lon)) | (length(lon) %% 2 == 1)) {
warning("Input data are expected to be on a square grid",
" with an even number of pixels per side.")
nmin <- min(length(lon), length(lat))
nmin <- floor(nmin / 2) * 2
lon <- lon[1:nmin]
lat <- lat[1:nmin]
warning("The input data have been cut to the range.")
warning(paste0("lon: [", lon[1], ", ", lon[length(lon)], "] ",
" lat: [", lat[1], ", ", lat[length(lat)], "]"))
}
if (is.character(climfile)) {
ncin <- nc_open(climfile)
latin <- ncvar_get(ncin, grep(latname, attributes(ncin$dim)$names,
value = TRUE))
lonin <- ncvar_get(ncin, grep(lonname, attributes(ncin$dim)$names,
value = TRUE))
if (varname == "") {
varname <- grep("bnds", attributes(ncin$var)$names,
invert = TRUE, value = TRUE)[1]
}
zclim <- ncvar_get(ncin, varname)
nc_close(ncin)
} else if (inherits(climfile, "s2dv_cube")) {
# Check object structure
if (!all(c('data', 'coords') %in% names(climfile))) {
stop("Parameter 'climfile' must have 'data' and 'coords' elements ",
"within the 's2dv_cube' structure.")
}
# Check coordinates
if (!any(names(climfile$coords) %in% .KnownLonNames()) |
!any(names(climfile$coords) %in% .KnownLatNames())) {
stop("Spatial coordinate names do not match any of the names accepted by ",
"the package.")
}
loncoordname <- names(climfile$coords)[[which(names(climfile$coords) %in% .KnownLonNames())]]
latcoordname <- names(climfile$coords)[[which(names(climfile$coords) %in% .KnownLatNames())]]
zclim <- climfile$data
latin <- as.vector(climfile$coords[[latcoordname]])
lonin <- as.vector(climfile$coords[[loncoordname]])
} else {
stop("Parameter 'climfile' is expected to be a character string indicating",
" the path to the files or an object of class 's2dv_cube'.")
}
# Check dim names and order
if (length(names(dim(zclim))) < 1) {
stop("The dataset provided in 'climfile' requires dimension names.")
}
result <- RF_Weights(zclim, latin, lonin, nf, lat, lon, fsmooth = fsmooth,
lonname = lonname, latname = latname, ncores = ncores)
if (inherits(climfile, "s2dv_cube")) {
climfile$data <- result$data
climfile$coords[[loncoordname]] <- result[[lonname]]
climfile$coords[[latcoordname]] <- result[[latname]]
} else {
climfile <- NULL
climfile$data <- result
climfile$coords[[lonname]] <- result[[lonname]]
climfile$coords[[latname]] <- result[[latname]]
}
return(climfile)
}
#'Compute climatological weights for RainFARM stochastic precipitation downscaling
#'
#'@author Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
#'
#'@description Compute climatological ("orographic") weights from a fine-scale
#'precipitation climatology file.
#'@references Terzago, S., Palazzi, E., & von Hardenberg, J. (2018).
#'Stochastic downscaling of precipitation in complex orography:
#'A simple method to reproduce a realistic fine-scale climatology.
#'Natural Hazards and Earth System Sciences, 18(11),
#'2825-2840. \doi{10.5194/nhess-18-2825-2018}.
#'@param zclim A multi-dimensional array with named dimension containing at
#' least one precipiation field with spatial dimensions.
#'@param lonin A vector indicating the longitudinal coordinates corresponding to
#' the \code{zclim} parameter.
#'@param latin A vector indicating the latitudinal coordinates corresponding to
#' the \code{zclim} parameter.
#'@param nf Refinement factor for downscaling (the output resolution is
#' increased by this factor).
#'@param lon Vector of longitudes.
#'@param lat Vector of latitudes. The number of longitudes and latitudes is
#' expected to be even and the same. If not the function will perform a
#' subsetting to ensure this condition.
#'@param fsmooth Logical to use smooth conservation (default) or large-scale
#' box-average conservation.
#'@param lonname A character string indicating the name of the longitudinal
#' dimension set as 'lon' by default.
#'@param latname A character string indicating the name of the latitudinal
#' dimension set as 'lat' by default.
#'@param ncores An integer that indicates the number of cores for parallel
#' computations using multiApply function. The default value is one.
#'
#'@return An object of class 's2dv_cube' containing in matrix \code{data} the
#'weights with dimensions (lon, lat).
#'@examples
#'a <- array(1:2500, c(lat = 50, lon = 50))
#'res <- RF_Weights(a, seq(0.1 ,5, 0.1), seq(0.1 ,5, 0.1),
#' nf = 5, lat = 1:5, lon = 1:5)
#'@import ncdf4
#'@import rainfarmr
#'@import multiApply
#'@importFrom utils tail
#'@importFrom utils head
#'@export
RF_Weights <- function(zclim, latin, lonin, nf, lat, lon, fsmooth = TRUE,
lonname = 'lon', latname = 'lat', ncores = NULL) {
x <- Apply(list(zclim), target_dims = c(lonname, latname), fun = rf_weights,
latin = latin, lonin = lonin, nf = nf, lat = lat, lon = lon,
lonname = lonname, latname = latname,
fsmooth = fsmooth, ncores = ncores)$output1
grid <- lon_lat_fine(lon, lat, nf)
res <- NULL
res$data <- x
res[[lonname]] <- grid$lon
res[[latname]] <- grid$lon
return(res)
}
rf_weights <- function(zclim, latin, lonin, nf, lat, lon, lonname = 'lon',
latname = 'lat', fsmooth = TRUE) {
# Check if lon and lat need to be reversed
if (lat[1] > lat[2]) {
lat <- rev(lat)
frev <- TRUE
} else {
frev <- FALSE
}
if (latin[1] > latin[2]) {
latin <- rev(latin)
zclim <- zclim[, seq(dim(zclim)[2], 1)]
}
# If lon is not monotonic make it so
if (lon[1] > tail(lon, 1)) {
lon <- (lon - 360) * (lon > tail(lon, 1)) + lon * (lon <= tail(lon, 1))
}
# Is the reference climatology global in the zonal direction ?
# Test if the the first and the last longitude are close
dx <- abs((tail(lonin, 1) %% 360) - (lonin[1] %% 360))
# Shortest distance on a torus
if (dx > 180) {
dx <- 360 - dx
}
# If this distance is smaller than twice the grid spacing we are global
if (dx <= (lonin[2] - lonin[1]) * 2) {
# Is the target area not fully inside the reference climatology area ?
if (lon[1] < lonin[1]) {
# Shift lonin to the west by 180 degrees
nn <- length(lonin)
zclim0 <- zclim
zclim[1:(nn / 2), ] <- zclim0[(nn / 2 + 1):nn, ]
zclim[(nn / 2 + 1):nn, ] <- zclim0[1:(nn / 2), ]
lonin <- lonin - 180
} else if (tail(lon, 1) > tail(lonin, 1)) {
# Shift lonin to the east by 180 degrees
nn <- length(lonin)
zclim0 <- zclim
zclim[1:(nn / 2), ] <- zclim0[(nn / 2 + 1):nn, ]
zclim[(nn / 2 + 1):nn, ] <- zclim0[1:(nn / 2), ]
lonin <- lonin + 180
}
}
ww <- rfweights(zclim, lonin, latin, lon, lat, nf, fsmooth = fsmooth)
if (frev) {
ww <- ww[, seq(dim(ww)[2], 1)]
}
attributes(dim(ww))$names <- c(lonname, latname)
return(ww)
}
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