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# Author: Bert Van Schaeybroeck
# Use Case 3: Seasonal forecasts for a river flow
# -----------------------------------------------
rm(list = ls())
library(CSTools)
library(s2dverification)
library(CSTools)
library(ClimProjDiags)
#SETUP PARAMETERS (TO FIX BEFORE RUNNING SCRIPT):
#------------------------------------------------
var.to.use <- "prlr" #which variable to correct (prlr=precipitation, tasmin, tasmax, tas)
init.yr <- 1993 #initial year (for ECMWF Sys5 = 1993)
end.yr <- 2019 #end year (for ECMWF Sys5 = 2019)
amt.ftime <- 214
n.cores.to.use <- 20
use.chirps <- T
eval.method.to.use <- "leave-one-out"
domain.high.res <- "greece_high_res"
domain.low.res <- "greece_low_res"
sdate.mon.to.use <- 5 #Month of startdate for ECMWF Sys5 possibilities are 5 (May) or 11 (November)
sdate.day.to.use <- 1 #Day of startdate for ECMWF Sys5 only possibility is 1
make.plot.msk <- F #mask to indicate if figures need to be made based on the output of the Analog function.
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#LOCAL PARAMETERS (to be adjusted for each working system)
#---------------------------------------------------------
dir.rdata <- "/mnt/HDS_URCLIM/URCLIM/bertvs/medscope/data/greece_rdata/"
#dir.scripts <- "/mnt/netapp/home/bertvs/ARCHIVE_bertvs/R/medscope/D3.1/"
dir.scratch <- "/scratch/bertvs/"
#Base path for C3S forecast (experiment) dataset:
dir.c3s <- "/mnt/HDS_URCLIM/URCLIM/bertvs/medscope/data/"
#Base path for ERA5 reference or obs dataset:
dir.era5 <- "/mnt/HDS_BREGILABEPOC/BREGILABEPOC/era5/europe/daily/per_mon/"
#Base path for CHIRPS (reference or obs) rainfall dataset:
dir.chirps <- "/mnt/HDS_MEDYCLIM/MEDYCLIM/PREDANTAR/climate_data/obs/chirps/"
dir.chirps.low.res <- paste0(dir.chirps, "/", domain.low.res, "/per_mon/")
dir.chirps.high.res <- paste0(dir.chirps, "/", domain.high.res, "/per_mon/")
#AUXILIARY FUNCTIONS
#-------------------
set.msk <- function(x, msk, const){
x[msk] = const
return(x)
}
#FIXED PARAMETERS:
#-----------------
greece.coor.vec <- c(
lonmin = 18.975,
lonmax = 24.025,
latmin = 37.975,
latmax = 43.025)
greece.coor.lst <- list(
lon.min = 18.975,
lon.max = 24.025,
lat.min = 37.975,
lat.max = 43.025)
coor.to.use <- greece.coor.lst
europe.coor <- list(
lon.min = -27,
lon.max = 45,
lat.min = 33,
lat.max = 73.5)
#Large-scale pressure field metadata (necessary for analogs)
var.msl <- "mean_sea_level_pressure"
nc.var.name.msl <- "msl"
#Depending on the variable loaded, different datasets and metadata are used
if(var.to.use == "prlr"){ #Precipitation
var.era5 <- "total_precipitation"
time.era5 <- "daily"
nc.var.name.era5 <- "tp"
var.chirps <- "precip"
time.chirps <- "daily"
nc.var.name.chirps <- "precip"
cal.meth.to.use <- "bias" #method for bias calibration
chirps.low.res.daily <- list(
name = "chirps_low_res",
path = paste0(dir.chirps.low.res, "chirps-v2.0.$YEAR$.days_greece_low_res-$MONTH$.nc"),
nc_var_name = nc.var.name.chirps)
chirps.high.res.daily <- list(
name = "chirps_high_res",
path = paste0(dir.chirps.high.res,
"chirps-v2.0.$YEAR$.days_greece_high_res-$MONTH$.nc"),
nc_var_name = nc.var.name.chirps)
#unit conversions
mul.cor.era5 <- 1000 * 24
add.cor.era5 <- 0
mul.cor.chirps <- 1
add.cor.chirps <- 0
mul.cor.exp <- 1000 * 3600 * 24
add.cor.exp <- 0
if(use.chirps){
add.cor.obs <- add.cor.chirps
mul.cor.obs <- mul.cor.chirps
} else {
add.cor.obs <- add.cor.era5
mul.cor.obs <- mul.cor.er5
}
} else if(var.to.use == "tas"){
var.era5 <- "2m_temperature"
time.era5 <- "daily"
nc.var.name.era5 <- "t2m"
cal.meth.to.use <- "mse_min"
#unit conversions
mul.cor.era5 <- 0
add.cor.era5 <- 0
mul.cor.exp <- 0
add.cor.exp <- 0
} else if(var.to.use == "tasmin"){
var.era5 <- "2m_temperature"
time.era5 <- "daily_min"
nc_var_name.era5 <- "t2m"
cal.meth.to.use <- "mse_min" #method for bias calibration
#unit conversions
mul.cor.era5 <- 0
add.cor.era5 <- 0
mul.cor.exp <- 0
add.cor.exp <- 0
} else if(var.to.use == "tasmax"){
var.era5 <- "2m_temperature"
time.era5 <- "daily_max"
nc_var_name.era5 <- "t2m"
cal.meth.to.use <- "mse_min" #method for bias calibration
#unit conversions
mul.cor.era5 <- 0
add.cor.era5 <- 0
mul.cor.exp <- 0
add.cor.exp <- 0
}
#Experiment path specification:
ecmwf.s5.daily <- list(
name = "ecmwfS5",
path = paste0(dir.c3s,
"C3S/$EXP_NAME$/$STORE_FREQ$/$VAR_NAME$/",
"$VAR_NAME$_$START_DATE$.nc"))
#Reference or obs path specifications (ERA5 data available over Europe):
era5.daily <- list(name = "era5",
path = paste0(dir.era5, "era5-", time.era5,
"-europe-", var.era5, "-$YEAR$-$MONTH$.nc"),
nc_var_name = nc.var.name.era5)
#Reference or obs path specifications for pressure field (ERA5 data available over Europe):
msl.era5.daily <- list(name = "msl",
path = paste0(dir.era5, "era5-", time.era5,
"-europe-", var.msl, "-$YEAR$-$MONTH$.nc"),
nc_var_name = nc.var.name.msl)
#UNIVERSAL PARAMETERS:
#---------------------
amt.mon.per.yr <- 12
amt.day.per.mon <- 31
sdate.day.str <- formatC(sdate.day.to.use, width = 2, flag = "0")
sdate.mon.str <- formatC(sdate.mon.to.use, width = 2, flag = "0")
day.lst <- formatC(seq(1, amt.day.per.mon), width = 2, flag = "0")
yr.lst <- seq(init.yr, end.yr)
amt.yr <- length(yr.lst)
sdate.lst <- paste0(yr.lst, sdate.mon.str, sdate.day.str )
#START
#-----
#1. LOAD THE DATA
#----------------
if(use.chirps){
obs.set.to.use <- chirps.low.res.daily
} else {
obs.set.to.use <- era5.daily
}
#Load mean sea level pressure field from ERA5 (no need to set the units)
file.to.load <- paste0(dir.rdata, "msl_all.RData")
if(file.exists(file.to.load)){
load(file.to.load, verbose = T)
} else {
msl.all <- CST_Load(
var = "psl", #nc.var.name.msl,
obs = list(msl.era5.daily),
exp = list(ecmwf.s5.daily),
nmember = NULL,
sdates = sdate.lst,
lonmin = europe.coor$lon.min,
lonmax = europe.coor$lon.max,
latmin = europe.coor$lat.min,
latmax = europe.coor$lat.max,
output = "lonlat",
storefreq = "daily",
nprocs = n.cores.to.use)
save(file = file.to.load, msl.all)
}
#Data manipulation: first split lead times per month. Then merge all data per month and all sdates.
#This merged dataset will be used to calibrate (per month) and find analogs (per month).
obs.msl.eur.split <- CST_SplitDim(msl.all$obs, split_dim = c("ftime"))
exp.msl.eur.split <- CST_SplitDim(msl.all$exp, split_dim = c("ftime"))
obs.msl.eur.merge <- CST_MergeDims(
obs.msl.eur.split,
merge_dims = c("sdate", "ftime"),
rename_dim = "sdate")
exp.msl.eur.merge <- CST_MergeDims(
exp.msl.eur.split,
merge_dims = c("sdate", "ftime"),
rename_dim = "sdate")
obs.msl.eur.merge.an <- CST_Anomaly(exp = obs.msl.eur.merge, dim_anom = 3)
exp.msl.eur.merge.an <- CST_Anomaly(exp = exp.msl.eur.merge, dim_anom = 3)
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#Load observational and forecast set of variable that needs to be calibrated and downscaled:
file.to.load <- paste0(dir.rdata, "data_all.RData")
if(file.exists(file.to.load)){
load(file.to.load, verbose = T)
} else {
data.all <- CST_Load(
var = var.to.use,
obs = list(obs.set.to.use),
exp = list(ecmwf.s5.daily),
nmember = NULL,
sdates = sdate.lst,
lonmin = coor.to.use$lon.min,
lonmax = coor.to.use$lon.max,
latmin = coor.to.use$lat.min,
latmax = coor.to.use$lat.max,
output = "lonlat",
storefreq = "daily",
nprocs = n.cores.to.use)
save(file = file.to.load, data.all)
}
#Set the units:
data.all$obs$data <- data.all$obs$data * mul.cor.obs + add.cor.obs
data.all$exp$data <- data.all$exp$data * mul.cor.exp + add.cor.exp
#Data manipulation: first split lead times per month. Then merge all data per month and all sdates.
#This merged dataset will be used to calibrate (per month) and find analogs (per month).
obs.split <- CST_SplitDim(data.all$obs, split_dim = c("ftime"))
exp.split <- CST_SplitDim(data.all$exp, split_dim = c("ftime"))
obs.merge <- CST_MergeDims(
obs.split,
merge_dims = c("sdate", "ftime"),
rename_dim = "sdate")
exp.merge <- CST_MergeDims(
exp.split,
merge_dims = c("sdate", "ftime"),
rename_dim = "sdate")
#Calibrate the exp data (per month)
cal.merge <- CST_Calibration(
exp = exp.merge,
obs = obs.merge,
cal.method = cal.meth.to.use,
eval.method = eval.method.to.use)
cal.merge$data[cal.merge$data < 0] <- 0
#LOAD HIGH RES CHIRPS DATA
file.to.load <- paste0(dir.rdata, "obs_high_res.RData")
if(file.exists(file.to.load)){
load(file.to.load, verbose = T)
} else {
obs.high.res <- CST_Load(var = var.to.use,
obs = list(chirps.high.res.daily),
exp = NULL,
sdates = sdate.lst,
nmember = 1,
leadtimemax = amt.ftime,
sampleperiod = 1,
lonmin = coor.to.use$lon.min,
lonmax = coor.to.use$lon.max,
latmin = coor.to.use$lat.min,
latmax = coor.to.use$lat.max,
output = "lonlat",
storefreq = "daily",
nprocs = n.cores.to.use)
save(file = file.to.load, obs.high.res)
}
#set the units
obs.high.res$data <- obs.high.res$data * mul.cor.chirps +
add.cor.chirps
#split per month
obs.high.res.split <- CST_SplitDim(
obs.high.res,
split_dim = c("ftime"))
#merge lead times and sdates
obs.high.res.merge <- CST_MergeDims(
obs.high.res.split,
merge_dims = c("sdate", "ftime"),
rename_dim = "sdate")
#LOAD LOW RES CHIRPS DATA
file.to.load <- paste0(dir.rdata, "obs_low_res.RData")
if(file.exists(file.to.load)){
load(file.to.load, verbose = T)
} else {
obs.low.res <- CST_Load(var = var.to.use,
obs = list(chirps.low.res.daily),
exp = NULL,
sdates = sdate.lst,
nmember = 1,
leadtimemax = amt.ftime,
sampleperiod = 1,
lonmin = coor.to.use$lon.min,
lonmax = coor.to.use$lon.max,
latmin = coor.to.use$lat.min,
latmax = coor.to.use$lat.max,
output = "lonlat",
storefreq = "daily",
nprocs = n.cores.to.use)
save(file = file.to.load, obs.low.res)
}
#set units
obs.low.res$data <- obs.low.res$data * mul.cor.chirps +
add.cor.chirps
#split per month
obs.low.res.split <- CST_SplitDim(
obs.low.res,
split_dim = c("ftime"))
#merge lead times and sdates
obs.low.res.merge <- CST_MergeDims(
obs.low.res.split,
merge_dims = c("sdate", "ftime"),
rename_dim = "sdate")
#2. PROCESS THE DATA
#-------------------
#amount of ensemble members from experiment. For ECMWF Sys5 it is 25:
amt.mbr <- as.numeric(dim(cal.merge$data)["member"])
lon.low.res <- as.vector(cal.merge$coords$lon)
lat.low.res <- as.vector(cal.merge$coords$lat)
lon.high.res <- as.vector(obs.high.res$coords$lon)
lat.high.res <- as.vector(obs.high.res$coords$lat)
lon.eur <- as.vector(obs.msl.eur.merge.an$coords$lon)
lat.eur <- as.vector(obs.msl.eur.merge.an$coords$lat)
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#amount of lead times in months. For ECMWF Sys5 it is 7:
amt.lead.mon <- as.numeric(dim(cal.merge$data)["monthly"])
mon.seq.tmp <- seq(sdate.mon.to.use, sdate.mon.to.use + amt.lead.mon - 1)
mon.seq.tmp <- ((mon.seq.tmp - 1) %% amt.mon.per.yr) + 1
lead.mon.lst <- formatC(mon.seq.tmp, width = 2, flag = "0")
#amount of starting days from experiment. For ECMWF Sys5 it is 837:
amt.sdate <- as.numeric(dim(cal.merge$data)["sdate"])
sub.time <- outer(
as.vector(t(outer(yr.lst, day.lst, paste, sep="-"))),
lead.mon.lst,
paste, sep = "-")
#This step is necessary to set the non-existent dates to NA
sub.time <- format(as.Date(sub.time, format("%Y-%d-%m")), "%Y-%m-%d")
dim(sub.time) <- c(sdate = amt.yr * amt.day.per.mon, time = amt.lead.mon)
cal.high.res.merge <- obs.high.res.merge
cal.high.res.merge$data[] <- NA
#Determine spatial points with all obs.high.res.merge (CHIRPS) data equal to NA. These are the points over sea.
is.na.high.res.obs <- apply(
obs.high.res.merge$data,
MARGIN = c(4, 5),
FUN = function(x){all(is.na(x))})
#Determine spatial points with all obs.low.res.merge (CHIRPS) data equal to NA. These are the points over sea.
is.na.low.res.obs <- apply(
obs.low.res.merge$data,
MARGIN = c(4, 5),
FUN = function(x){all(is.na(x))})
#Set all calibrated exp data (cal.merge) equal to NA at the sea point.
cal.merge.tmp = Apply(
data = list(x = cal.merge$data),
target_dims = list(x = c("lat", "lon")),
fun = set.msk,
msk = is.na.low.res.obs,
const = 0,
output_dims = list(c("lat", "lon"))
)$output1
dex.match <- match(names(dim(cal.merge$data)), names(dim(cal.merge.tmp)))
cal.merge$data <- aperm(cal.merge.tmp, dex.match)
rm(cal.merge.tmp)
#2. PROCESS THE DATA
#-------------------
i.dataset <- 1
i.mbr.obs <- 1
for(i.mbr in seq(1, amt.mbr)){
for(i.mon in seq(1, amt.lead.mon)){
for(i.sdate in seq(1, amt.sdate)){
date.to.use <- sub.time[ i.sdate, i.mon]
date.an.lst <- sub.time[ , i.mon]
cat("i.mbr = ", i.mbr, ", i.mon =", i.mon, ", i.sdate = ",
i.sdate, "date: ", date.to.use,"\n")
#Extract the (calibrated) forecast that you want to downscale:
exp.low.res.tmp <- exp.merge$data[i.dataset, i.mbr, i.sdate, , , i.mon]
cal.low.res.tmp <- cal.merge$data[i.dataset, i.mbr, i.sdate, , , i.mon]
#Extract the large-scale pressure field of that day
exp.msl.eur.tmp <- exp.msl.eur.merge.an$data[i.dataset, i.mbr, i.sdate, , , i.mon]
#Extract all observations that will be used to find analogs
obs.msl.eur.tmp <- obs.msl.eur.merge.an$data[i.dataset, i.mbr.obs, , , , i.mon]#-i.sdate
obs.low.res.tmp <- obs.low.res.merge$data[i.dataset, i.mbr.obs, , , , i.mon] #-i.sdate
obs.high.res.tmp <- obs.high.res.merge$data[i.dataset, i.mbr.obs, , , , i.mon] #-i.sdate
names(dim(obs.high.res.tmp)) <- c("time", "lat", "lon")
names(dim(obs.low.res.tmp)) <- c("time", "lat", "lon")
names(dim(obs.msl.eur.tmp)) <- c("time", "lat", "lon")
if(!is.na(date.to.use) & !all(is.na(cal.low.res.tmp))){
obs.low.res.tmp[is.na(obs.low.res.tmp)] <- 0
res <- Analogs(
expL = exp.msl.eur.tmp,
obsL = obs.msl.eur.tmp,
time_obsL = date.an.lst,
obsVar = obs.low.res.tmp,
expVar = exp.low.res.tmp,
lonVar = lon.low.res,
latVar = lat.low.res,
lonL = lon.eur,
latL = lat.eur,
region = greece.coor.vec,
criteria = "Local_dist",
time_expL = date.to.use,
excludeTime = date.to.use,
AnalogsInfo = T,
nAnalogs = 1000)
if(make.plot.msk){
corr.date <- as.character(res$dates[1]) #select the date of the most
corr.dex <- which(date.an.lst == corr.date)
#The following figure shows the uncalibrated raw model field (analogous to Fig. 9a)
file.fig <- paste0("mbr_", i.mbr, "_mon_", i.mon,
"_sdate_", date.to.use, "_exp.low.res.pdf")
pdf(file = file.fig)
PlotEquiMap(
exp.low.res.tmp[ , ],
lon = obs.low.res.merge$coords$lon,
lat = obs.low.res.merge$coords$lat,
filled.continents = F,
intylat = 2,
intxlon = 2,
title_scale = 0.7, #bar_limits = c(0, 60),
units = "precipitation (mm)")
dev.off()
#The following figure includes the calibrated model field (analogous to Fig. 9b)
file.fig <- paste0("mbr_", i.mbr, "_mon_", i.mon,
"_sdate_", date.to.use, "_cal.low.res.pdf")
pdf(file = file.fig)
PlotEquiMap(
cal.low.res.tmp,
lon = obs.low.res.merge$coords$lon,
lat = obs.low.res.merge$coords$lat,
filled.continents = F,
intylat = 2,
intxlon = 2,
title_scale = 0.7, #bar_limits = c(0, 60),
units = "precipitation (mm)")
#The following figure includes the analog upscaled field (analogous to Fig. 9c)
file.fig <- paste0("mbr_", i.mbr, "_mon_", i.mon,
"_sdate_", date.to.use, "_obs.low.res.pdf")
pdf(file = file.fig)
PlotEquiMap(
obs.low.res.tmp[corr.dex, , ],
lon = obs.low.res.merge$coords$lon,
lat = obs.low.res.merge$coords$lat,
filled.continents = F,
intylat = 2,
intxlon = 2,
title_scale = 0.7, #bar_limits = c(0, 60),
units = "precipitation (mm)")
dev.off()
#The following figure includes the analog field (analogous to Fig. 9d)
file.fig <- paste0("mbr_", i.mbr, "_mon_", i.mon,
"_sdate_", date.to.use, "_obs.high.res.pdf")
pdf(file = file.fig)
PlotEquiMap(
obs.high.res.tmp[corr.dex, , ],
lon = obs.high.res.merge$coords$lon,
lat = obs.high.res.merge$coords$lat,
filled.continents = F,
intylat = 2,
intxlon = 2,
title_scale = 0.7, #bar_limits = c(0, 60),
units = "precipitation (mm)")
dev.off()
}