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downscaling_area_estudio.R
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downscaling_area_estudio.R
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library(ncdf4)
library(loadeR)
library(visualizeR)
library(downscaleR)
library(filesstrings) # para mover archivos entre carpetas
library(climate4R.climdex)
library(climate4R.value)
rm(list=ls())
dev.off()
setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/')
source('funcion_identificador_de_variables_era5_CEDA.R')
# filtro de datos ----
meses <- 1
anhos <- 2010:2014
latitud <- c(-49,-36) # area de estudio CCR
longitud <- c(-75, -72) # area de estudio CCR
# latitud <- c(-48, -46) # area de estudio WRF
# longitud <- c(-74, -71) # area de estudio WRF
# fin ---
# ps ----
# Lectura de datos
setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/descargas_ERA5/copernicus/')
ERA5 <- 'ERA5_2000_al_2014.nc'
# ej <- nc_open(ERA5, verbose = TRUE)
di <- dataInventory(ERA5)
di$sp
C4R.vocabulary()
ps.mean <- loadGridData(dataset = ERA5,
var = "sp",
aggr.d = "mean",
aggr.m = "mean",
lonLim = longitud,
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(ps.mean)
# plot
spatialPlot(climatology(ps.mean), backdrop.theme = "countries", color.theme = "YlGnBu",
main = '')
# fin ---
# tas ----
# Lectura de datos
# ta <- 'ta_day_ACCESS-ESM1-5_historical_r1i1p1f1_gn_20100101-20141231.nc'
#C4R.vocabulary()
tas.mean <- loadGridData(dataset = ERA5,
var = "t2m",
aggr.d = "mean",
aggr.m = "mean",
lonLim = longitud,#+c(-1, +1),
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(tas.mean)
# plot
spatialPlot(climatology(tas.mean), backdrop.theme = "countries", color.theme = "RdYlBu",
rev.colors = TRUE, main = '')
# fin ---
# pr ----
# Lectura de datos
# pr <- 'pr_day_ACCESS-ESM1-5_historical_r1i1p1f1_gn_20000101-20141231.nc'
# C4R.vocabulary()
pr.sum <- loadGridData(dataset = ERA5,
var = "tp",
aggr.d = "sum",
aggr.m = "sum",
lonLim = longitud,
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(pr.sum)
# plot
spatialPlot(climatology(pr.sum), backdrop.theme = "countries", color.theme = "YlGnBu",
main = '')
# fin ---
# u10 ----
# Lectura de datos
u10.mean <- loadGridData(dataset = ERA5,
var = "u10",
aggr.d = "mean",
aggr.m = "mean",
lonLim = longitud,
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(u10.mean)
# plot
spatialPlot(climatology(u10.mean), backdrop.theme = "countries", color.theme = "jet.colors",
main = '')
# fin ---
# v10 ----
# Lectura de datos
v10.mean <- loadGridData(dataset = ERA5,
var = "v10",
aggr.d = "mean",
aggr.m = "mean",
lonLim = longitud,
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(v10.mean)
# plot
spatialPlot(climatology(v10.mean), backdrop.theme = "countries", color.theme = "jet.colors",
main = '')
# fin ---
# # Z ----
#
# # Lectura de datos
# z.mean <- loadGridData(dataset = ERA5,
# var = "z",
# aggr.d = "mean",
# aggr.m = "mean",
# lonLim = longitud,
# latLim= latitud,
# season= meses,
# years = anhos,
# time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
# str(z.mean)
#
# # plot
# spatialPlot(climatology(z.mean), backdrop.theme = "countries", color.theme = "BrBG",
# main = '')
# # fin ---
# stack ----
x <- makeMultiGrid(ps.mean, tas.mean)#, u10.mean, v10.mean)#, z.mean)
# fin ---
# Estaciones metereologicas ----
# Lectura de datos
nombre.carpeta <- 'datos_transformados_a_ASCII'
setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/')
stationInfo(nombre.carpeta)
y <- loadStationData(dataset = nombre.carpeta,
var="precip",
years = anhos)
# Datos presencia/ausencia de precipitacion
y <- binaryGrid(y, condition = "GE", # GE: greater or equal
threshold = 1, # umbral para discriminar entre presencia y ausencia
partial = TRUE)
y_bin <- binaryGrid(y, condition = "GE",
threshold = 1)
# fin ---
# Calibracion ----
# Configuration of method M1 en Bedia et al. (2020)
vars <- c("sp", "t2m")#, "u10", "v10")#, "z")
folds <- list(2010, 2011)
# m1
spatial.pars.M1 <- list(which.combine = vars,
v.exp = .95, # PCs explaining the 95 % of total variance are retained (Bedia et al., 2020)
rot = FALSE) # non-rotated
M1cv.bin <- downscaleCV(x = x, y = y_bin,
method = "GLM",
family = binomial(link = "logit"),
folds = folds,
prepareData.args = list(global.vars = NULL,
local.predictors = NULL,
spatial.predictors = spatial.pars.M1,
combined.only = TRUE))
# In the logistic regression model, downscaleCV returns
# a multigrid with two output prediction grids, storing
# the variables prob and bin. The first contains the grid
# probability of rain for every day and the second is a
# binary prediction indicating whether it rained or not (Bedia et al., 2020).
M1cv.bin <- subsetGrid(M1cv.bin, var = "bin") # bin: grilla binaria (si llovio o no)
M1cv.cont <- downscaleCV(x = x, y = y,
method = "GLM",
family = Gamma(link = "log"),
condition = "GE",
threshold = 1,
folds = folds,
prepareData.args = list(global.vars = NULL,
local.predictors = NULL,
spatial.predictors = spatial.pars.M1,
combined.only = TRUE))
M1cv <- gridArithmetics(M1cv.bin, M1cv.cont, operator = "*")
# plot pp predicha vs observada
aggr.pars <- list(FUN = "sum", na.rm = TRUE)
pred.M1 <- aggregateGrid(M1cv, aggr.m = aggr.pars)
obs <- aggregateGrid(y, aggr.m = aggr.pars)
temporalPlot(pred.M1, obs)
# m2
spatial.pars.M2 <- list(which.combine
= NULL,
v.exp = .95)
M2cv.bin <- downscaleCV(x = x, y = y_bin,
method = "GLM",
family = binomial(link = "logit"),
folds = folds,
prepareData.args = list(global.vars = NULL,
local.predictors = NULL,
spatial.predictors = spatial.pars.M2,
combined.only = TRUE))
M2cv.bin <- subsetGrid(M2cv.bin, var = "bin") # bin: grilla binaria (si llovio o no)
M2cv.cont <- downscaleCV(x = x, y = y,
method = "GLM",
family = Gamma(link = "log"),
condition = "GE",
threshold = 1,
folds = folds,
prepareData.args = list(global.vars = NULL,
local.predictors = NULL,
spatial.predictors = spatial.pars.M2,
combined.only = TRUE))
M2cv <- gridArithmetics(M2cv.bin, M2cv.cont, operator = "*")
# plot pp predicha vs observada
aggr.pars <- list(FUN = "sum", na.rm = TRUE)
pred.M2 <- aggregateGrid(M2cv, aggr.m = aggr.pars)
obs <- aggregateGrid(y, aggr.m = aggr.pars)
temporalPlot(pred.M2, obs)
# m3
local.pars.M3 <- list(n = 1, vars = getVarNames(x))
scaling.pars <- list(type = "standardize",
spatial.frame = "gridbox")
M3cv.bin <- downscaleCV(x = x, y = y_bin,
method = "GLM",
family = binomial(link = "logit"),
folds = folds,
prepareData.args = list(global.vars = NULL,
local.predictors = local.pars.M3,
spatial.predictors = NULL,
combined.only = TRUE))
M3cv.bin <- subsetGrid(M3cv.bin, var = "bin") # bin: grilla binaria (si llovio o no)
M3cv.cont <- downscaleCV(x = x, y = y,
method = "GLM",
family = Gamma(link = "log"),
condition = "GE", threshold
= 1,
folds = folds,
scaleGrid.args = scaling.pars,
prepareData.args =
list(global.vars = NULL,
local.predictors
= local.pars.M3,
spatial.predictors
= NULL))
M3cv <- gridArithmetics(M3cv.bin, M3cv.cont, operator = "*")
# plot pp predicha vs observada
aggr.pars <- list(FUN = "sum", na.rm = TRUE)
pred.M3 <- aggregateGrid(M3cv, aggr.m = aggr.pars)
obs <- aggregateGrid(y, aggr.m = aggr.pars)
temporalPlot(pred.M3, obs)
# m4
local.pars.M4 <- list(n = 4, vars = vars)
scaling.pars <- list(type = "standardize",
spatial.frame = "gridbox")
M4cv.bin <- downscaleCV(x = x, y = y_bin,
method = "GLM",
family = binomial(link = "logit"),
folds = folds,
prepareData.args = list(global.vars = NULL,
local.predictors = local.pars.M4,
spatial.predictors = NULL,
combined.only = TRUE))
M4cv.bin <- subsetGrid(M4cv.bin, var = "bin") # bin: grilla binaria (si llovio o no)
M4cv.cont <- downscaleCV(x = x, y = y,
method = "GLM",
family = Gamma(link = "log"),
condition = "GE", threshold
= 1,
folds = folds,
scaleGrid.args = scaling.pars,
prepareData.args =
list(global.vars = NULL,
local.predictors
= local.pars.M4,
spatial.predictors
= NULL))
M4cv <- gridArithmetics(M4cv.bin, M4cv.cont, operator = "*")
# plot pp predicha vs observada
aggr.pars <- list(FUN = "sum", na.rm = TRUE)
pred.M4 <- aggregateGrid(M4cv, aggr.m = aggr.pars)
obs <- aggregateGrid(y, aggr.m = aggr.pars)
temporalPlot(pred.M4, obs)
# m5
scaling.pars.M5 <- list(type = "standardize",
spatial.frame = "field")
M5cv <- downscaleCV(x = x, y = y,
method = "analogs", n.analogs
= 1,
folds = folds,
scaleGrid.args = scaling.pars.M5,
prepareData.args =
list(global.vars = vars,
local.predictors = NULL,
spatial.predictors = NULL))
# plot pp predicha vs observada
aggr.pars <- list(FUN = "sum", na.rm = TRUE)
pred.M5 <- aggregateGrid(M5cv, aggr.m = aggr.pars)
obs <- aggregateGrid(y, aggr.m = aggr.pars)
temporalPlot(pred.M5, obs)
# m6
spatial.pars.M6 <- list(which.combine = vars,
v.exp = .95, # PCs explaining the 95 % of total variance are retained (Bedia et al., 2020)
rot = FALSE) # non-rotated
M6cv.bin <- downscaleCV(x = x, y = y_bin,
method = "GLM",
family = binomial(link = "logit"),
folds = folds,
prepareData.args = list(global.vars = NULL,
local.predictors = NULL,
spatial.predictors = spatial.pars.M6,
combined.only = TRUE))
M6cv.bin <- subsetGrid(M6cv.bin, var = "bin") # bin: grilla binaria (si llovio o no)
M6.cont <- downscaleCV(x = x, y = y,
method = "analogs", n.analogs
= 1,
folds = folds,
prepareData.args =
list(global.vars = NULL,
local.predictors = NULL,
spatial.predictors =
spatial.pars.M6,
combined.only = TRUE))
M6cv <- gridArithmetics(M6cv.bin, M6.cont, operator = "*")
# plot pp predicha vs observada
aggr.pars <- list(FUN = "sum", na.rm = TRUE)
pred.M6 <- aggregateGrid(M6cv, aggr.m = aggr.pars)
obs <- aggregateGrid(y, aggr.m = aggr.pars)
temporalPlot(pred.M6, obs)
# fin ---
# Validation ----
R01.ratio <- valueMeasure(y, x = M6cv,
measure.code = "ratio",
index.code = "R01")$Measure
spatialPlot(R01.ratio, backdrop.theme = "countries")
# fin ---
# Futuro ---
# Method intercomparison experiment
# M1.L <- list(local.predictors =
# list(n = 1, vars = vars),
# spatial.predictors =
# list(v.exp = .95,
# which.combine = vars))
M6.L <- list(local.predictors = list(n = 1,
vars = vars))
# Standardization
x_scale <- scaleGrid(x, type = "standardize")
# Predictor config (M6-L method)
M6.L <- prepareData(x_scale, y)#, local.predictors = M6.L)
# SDS model training
model.M6L <- downscaleTrain(M6.L,
method = "analogs",
n.analogs = 1)
# fin ---
# psl CMIP6 ----
setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/descargas_CMIP6/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/day/psl/')
psl.mean.cmip6 <- loadGridData(dataset = 'psl_day_ACCESS-ESM1-5_historical_r1i1p1f1_gn_20000101-20141231.nc',
var = "psl",
aggr.d = "mean",
aggr.m = "mean",
lonLim = longitud,
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(psl.mean.cmip6)
# plot
spatialPlot(climatology(psl.mean.cmip6), backdrop.theme = "countries", color.theme = "YlGnBu",
main = '')
# fin ---
# pr CMIP6 ----
setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/descargas_CMIP6/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/day/pr/')
pr.sum.cmip6 <- loadGridData(dataset = 'pr_day_ACCESS-ESM1-5_historical_r1i1p1f1_gn_20000101-20141231.nc',
var = "pr",
aggr.d = "sum",
aggr.m = "sum",
lonLim = longitud,
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(pr.sum.cmip6)
# plot
spatialPlot(climatology(pr.sum.cmip6), backdrop.theme = "countries", color.theme = "YlGnBu",
main = '')
# fin ---
# tas CMIP6 ----
setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/descargas_CMIP6/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/day/tas/')
# C4R.vocabulary()
tas.mean.cmip6 <- loadGridData(dataset = 'tas_day_ACCESS-ESM1-5_historical_r1i1p1f1_gn_20000101-20141231.nc',
var = "tas",
aggr.d = "mean",
aggr.m = "mean",
lonLim = longitud,
latLim= latitud,
season= meses,
years = anhos,
time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
str(tas.mean.cmip6)
# plot
spatialPlot(climatology(tas.mean.cmip6), backdrop.theme = "countries", color.theme = "YlGnBu",
main = '')
# fin ---
# # uas CMIP6 ----
# setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/descargas_CMIP6/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/day/uas/')
#
# # C4R.vocabulary()
# uas.mean.cmip6 <- loadGridData(dataset = 'uas_day_ACCESS-ESM1-5_historical_r1i1p1f1_gn_20000101-20141231.nc',
# var = "uas",
# aggr.d = "mean",
# aggr.m = "mean",
# lonLim = longitud,
# latLim= latitud,
# season= meses,
# years = anhos,
# time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
# str(uas.mean.cmip6)
#
# # plot
# spatialPlot(climatology(uas.mean.cmip6), backdrop.theme = "countries", color.theme = "YlGnBu",
# main = '')
#
# # fin ---
#
#
#
#
#
# # vas CMIP6 ----
# setwd('C:/Users/Usuario/Documents/Francisco/proyecto_DownscaleR/descargas_CMIP6/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/day/vas/')
#
# # C4R.vocabulary()
# vas.mean.cmip6 <- loadGridData(dataset = 'vas_day_ACCESS-ESM1-5_historical_r1i1p1f1_gn_20000101-20141231.nc',
# var = "vas",
# aggr.d = "mean",
# aggr.m = "mean",
# lonLim = longitud,
# latLim= latitud,
# season= meses,
# years = anhos,
# time = 'DD') # obtain daily (aggr.d) or monthly (aggr.m) data )
# str(vas.mean.cmip6)
#
# # plot
# spatialPlot(climatology(vas.mean.cmip6), backdrop.theme = "countries", color.theme = "YlGnBu",
# main = '')
#
# # fin ---
# ERA 5 Y CMIP6 CSIRO, TIENEN DIFERENTES DIMENSIONES, POR ESO NO CORRE LO DE ABAJO!
# PROBAR CON EJEMPLO DE OTRO PAPER
xh <- makeMultiGrid(psl.mean.cmip6, tas.mean.cmip6)#, uas.mean.cmip6, vas.mean.cmip6)
xh <- interpGrid(xh, new.coordinates = getGrid(x))
xh <- scaleGrid(xh, base = xh, ref = x,
type = "center",
spatial.frame = "gridbox",
time.frame = "monthly")
xh <- scaleGrid(xh, base = x, type =
"standardize")
h_analog <- prepareNewData(newdata = xh,
data.struc = M6L)
f_analog <- prepareNewData(newdata = xf,
data.struc = M6L)
hist_ocu_glm <- downscalePredict(newdata
= h_analog,
model = model.M6L)
# fin ---
# Apuntes ----
# Apuntes ERA 5 (para descargar datos!!!!): ERA 5 comes in two streams: the ‘oper’ stream is a single member
# analysis at highest resolution, and the ‘enda’ stream is a 10-member ensemble at
# lower resolution - both are supported (Fuente: https://brohan.org/IRData/subdata/data_era5.html).
# Finally (panel e), future projections from the same
# model are loaded from the RCP8.5 scenario (2071-2100)
# and bias adjusted using the biasCorrection function.
# fin ---