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#!/usr/bin/env Rscript
rm(list=ls()); gc();
### Creation date: 9th June 2021
# Author: N. Pérez-Zanón
# Adapted from the original version Terzago et al. 2020
# https://drive.google.com/file/d/1qp2gbtKdBl4XmsyOeaEhFENwpeUuJwkf/view
# Refer CSTools package and manuscript when using it.
# ----------------------------------------
# This code is using divided in 4 steps plus visualization of the results
# Taking advantage of
# STEP 1: Compute Slope values for RainFARM downscaling method
# STEP 2: Apply Quantile Mapping Correction to Seasonal Precipitation Forecast
# STEP 3: Compute Weights for RainFARM downscaling method
# STEP 4: Apply RainFARM downscaling method
# ----------------------------------------
# Note: STEP 3 requires a machine with internet connexion.
# In this file, the lines are commented since they have been run and the
# result saved on disk, then the result is loaded.
# ----------------------------------------
#
# Load required libraries and setup output directory:
library(CSTools)
library(ClimProjDiags)
library(zeallot)
library(ragg)
dir_output <- '/esarchive/scratch/nperez/CSTools_manuscript/v20210603/' #slash end
# --------------------------------------------
# STEP 1:
# --------------------------------------------
era5 <- list(name = 'era5',
path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc')
years <- unlist(lapply(1993:2018, function(x){
paste0(x, sprintf("%02d",1:12), '01')}))
era5 <- CST_Load(var = 'prlr',
exp = list(era5),
sdates = years, nmember = 1,
storefreq = "daily", sampleperiod = 1,
latmin = 37.5, latmax = 53.25, lonmin = 2.5, lonmax = 18.25,
output = 'lonlat', nprocs = 1)
era5$data <- era5$data * 24 * 3600 * 1000 # with or without this line -> same result
era5 <- CST_SplitDim(era5, split_dim = 'sdate', indices = rep(1:12, 26))
slope <- CST_RFSlope(era5, time_dim = c('sdate', 'ftime'), kmin = 5)
save(slope, file = paste0(dir_output, 'Slope.RDS'), version = 2)
slope_plot <- slope
# --------------------------------------------
# STEP 2:
# --------------------------------------------
StartDates <- paste0(1993:2018, '1101')
exp <- list(name = 'ecmwfS5',
path = "/esarchive/exp/ecmwf/system5c3s/$STORE_FREQ$_mean/$VAR_NAME$_s0-24h/$VAR_NAME$_$START_DATE$.nc")
obs <- list(name = 'era5',
path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc')
#obs <- list(name = 'era5', path = '/esarchive/scratch/nperez/ERA5/daily_total_prlr_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc')
c(exp, obs) %<-% CST_Load(var = 'prlr', exp = list(exp), obs = list(obs),
sdates = StartDates, nmember = 25,
storefreq = "daily", sampleperiod = 1,
latmin = 42, latmax = 49, lonmin = 4, lonmax = 11,
output = 'lonlat', nprocs = 1)
exp <- CST_SplitDim(exp, split_dim = c('ftime'))
obs <- CST_SplitDim(obs, split_dim = c('ftime'))
exp$data <- exp$data * 24 * 3600 * 1000
obs$data <- obs$data * 24 * 3600 * 1000
exp$data[which(exp$data < 0)] <- 0
exp.qm <- CST_QuantileMapping(exp, obs, method = "QUANT",
wet.day = FALSE,
sample_dims = c('member', 'sdate', 'ftime'),
ncores = 4)
save(exp.qm, file = paste0(dir_output, 'ExpQM.RDS'), version = 2)
# --------------------------------------------
# STEP 3:
# --------------------------------------------
#library(raster); library(s2dv); library(CSTools)
#worldclim <- getData("worldclim", var = "prec", res = 0.5, lon = 5, lat = 45)
#wc_month <- lapply(1:12, FUN = function(x) {
# res <- crop(worldclim[[x]],
# extent(3.5, 11.5, 41.5, 49.5))
# res <- as.array(res)
# names(dim(res)) <- c('lat', 'lon', 'month')
# return(res)
# })
#xy <- xyFromCell(crop(worldclim[[1]], extent(3.5, 11.5, 41.5, 49.5)),
# 1:length(crop(worldclim[[1]], extent(3.5, 11.5, 41.5, 49.5))))
#lons <- unique(xy[,1])
#lats <- unique(xy[,2])
#wc_month <- unlist(wc_month)
#dim(wc_month) <- c(lat = length(lats), lon = length(lons), monthly = 12)
#wc_month <- Reorder(wc_month, c('lon', 'lat', 'monthly'))
#wc_month <- s2dv_cube(data = wc_month, lon = lons, lat = lats,
# Datasets = 'WorldClim')
#weight <- CST_RFWeights(wc_month, lon = 4:11, lat = 49:42, nf = 100)
#save(weight, file = paste0(dir_output, 'weightsRF100.RDS'))
load(paste0(dir_output, 'weightsRF100.RDS'))
# --------------------------------------------
# Visualization
# --------------------------------------------
agg_png(paste0(dir_output, "RF100_WeightsDec.png"),
width = 1000, height = 1100, units = 'px',res = 144)
PlotEquiMap(weight$data[,,12], lon = weight$lon, lat = weight$lat,
filled.continents = FALSE, title_scale = 1,
intylat = 2, intxlon = 2,
toptitle = 'December Weights RF 100')
dev.off()
# --------------------------------------------
# STEP 4:
# --------------------------------------------
weights <- Subset(weight$data, along = 'monthly', indices = c(11, 12, 1:6))
slope <- Subset(slope, along = 'monthly', indices = c(11, 12, 1:6),
drop = 'non-selected')
k = 1 # To create the member ID
#-----
# To be removed when CSTools 4.0.1 is published:
source("/gpfs/scratch/bsc32/bsc32339/CSTools_manuscript/CST_SaveExp.R")
library(multiApply)
library(ncdf4)
library(s2dv)
#-----
for (realizations in 1:10) {
for (member in 1:25) {
result <- data # to store the data
result$data <- NULL
for (month in 1:8) {
data <- exp.qm # to take the correct piece of data
data$data <- data$data[1, member, , , , , month]
fs <- CST_RainFARM(data, nf = 100,
weights = weights, slope = slope[month],
kmin = 1, nens = 1, verbose = TRUE,
nprocs = 8,
drop_realization = TRUE)
print(dim(fs$data))
result$data <- abind::abind(result$data, fs$data, along = 5)
if (month == 2 & member == 1 & realization == 1) {
# ----------------------------
# Visualization:
# ----------------------------
agg_png(paste0(dir_output, "RF100_Down_11dec.png"),
width = 1000, height = 1100, units = 'px',res = 144)
PlotEquiMap(fs$data[1,11,,],lon = fs$lon, lat = fs$lat,
filled.continents = FALSE, bar_limits = c(0,40),
intylat = 2, intxlon = 2, title_scale = 1,
triangle_ends = c(TRUE, FALSE),
toptitle = 'Downsacaled RF 100', units = 'precipitation (mm)')
dev.off()
# PlotForecastPDF
library(abind)
obsts <- MeanDims(obs$data, c('lat', 'lon'), na.rm = T)
print(quantile(obsts, c(0.1, 0.3, 0.6, 0.9), na.rm = T))
expts <- MeanDims(exp$data, c('lat', 'lon'), na.rm = T)
exp.qmts <- MeanDims(exp.qm$data, c('lat', 'lon'), na.rm = T)
empty <- array(NA, c(dataset = 2, member = 225, sdate = 26,
ftime = 31, monthly = 8))
data <- abind(expts, exp.qmts, along = 1)
names(dim(data)) <- names(dim(expts))
data <- abind(data, empty, along = 2)
names(dim(data)) <- names(dim(expts))
fsts <- MeanDims(fs$data, c('lat', 'lon'), na.rm = T)
print(dim(fsts))
print(dim(data))
data <- abind(data, fsts, along = 1)
names(dim(data)) <- c('dataset', 'members', 'sdate', 'ftime', 'monthly')
agg_png(paste0(dir_output, "/FiguresPDF_RF100_11DecemberAll2.png"),
width = 750, height = 650, units = 'px', res = 144)
PlotForecastPDF(data[,,1,11,2], tercile.limits = c(0.58, 2.4),
obs = obsts[,,1,11,2],
extreme.limits = c(0.06, 7.26),
color.set = 'hydro', add.ensmemb = 'no',
var.name = "Precipitation (mm)",
title = "Forecasts issued on Nov 1993 for 11th December 1993",
fcst.names = c("ECMWFS5C3S", "Bias Adjusted", "Downscaled"))
dev.off()
}
result$lon <- fs$lon
result$lat <- fs$lat
result <- CST_MergeDims(result, merge_dims = c("ftime", "monthly"),
na.rm = TRUE)
result$Dataset <- paste0('RF100_ECMWFC3S_QM_member_', member, '_real_',
realizations)
result$Dates[[1]] <- exp$Dates[[1]]
CST_SaveExp(result, destination = dir_output,
extra_string = paste0('member', k))
gc()
k = k + 1
rm(list= list('fs', 'result', 'data'))
}
}