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bid_clima_corrections.txt
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modelos_all <- c("bcc_csm1_1","csiro_mk3_6_0","inm_cm4","miroc_miroc5","mri_cgcm3",
"ncc_noresm1_m","mohc_hadgem2_cc","ipsl_cm5b_lr",
"mpi_esm_mr","bnu_esm","ipsl_cm5a_lr","miroc_esm_chem",
"cccma_canesm2","gfld_esm2g","ipsl_cm5a_mr","gfld_esm2g",
"mohc_hadgem2_es","cnrm_cm5","miroc_esm",
"mpi_esm_lr")
set.seed(1000)
################################################
##Proyecto BID corrección data precipitation
################################################
modelos <- c("bcc_csm1_1", "bnu_esm", "cccma_canesm2", "gfld_esm2g", "inm_cm4", "ipsl_cm5a_lr", "miroc_miroc5",
"mpi_esm_mr", "mri_cgcm3", "ncc_noresm1_m")
for(i in 1:10){
load(paste("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[i],"/Futuro/Precipitation.Rdat",sep=""))
Prec=Srad
save(Prec,file=paste0("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[i],"/Futuro/Precipitation.Rdat"))
print(i)
}
################################################
##Proyecto BID corrección data radiación solar
################################################
modelos <- c("bcc_csm1_1", "bnu_esm", "cccma_canesm2", "gfld_esm2g", "inm_cm4", "ipsl_cm5a_lr", "miroc_miroc5",
"mpi_esm_mr", "mri_cgcm3", "ncc_noresm1_m")
for(i in 1:10){
load(paste("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[i],"/Futuro/Srad_old.RDat",sep=""))
num_pixel <- 8000
num_años <- 28
for(año in 1:num_años) {
for(pixel in 1:num_pixel) {
srad <- unlist(Prec[[pixel]][año])/11.5740741
pos=which(srad<1.5)
srad[pos]=1.5
Prec[[pixel]][año]=list(srad)
}
}
Srad=Prec
save(Srad,file=paste0("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[i],"/Futuro/Srad.Rdat"))
print(i)
}
####Poner a correr el cambio de nombre para todos los modelos (Prec y Srad)
################################################
##Proyecto BID corrección data temperaturas
################################################
#install.packages("readr")
#install.packages("tidyr")
#install.packages("dplyr")
library(readr)
library(tidyr)
library(dplyr)
set.seed(1000)
Tmax=list()
Tmin=list()
for(j in 1:28){
Tmax[[j]]= matrix(NA, nrow = 8199 ,ncol=365)
Tmin[[j]]=matrix(NA, nrow = 8199 ,ncol=365)
}
modelos <- c("bcc_csm1_1", "bnu_esm", "cccma_canesm2", "gfld_esm2g", "inm_cm4", "ipsl_cm5a_lr", "miroc_miroc5",
"mpi_esm_mr", "mri_cgcm3", "ncc_noresm1_m")
for(k in 1:10){
load(paste("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/",modelos[k],".RDat",sep=""))
num_pixel <- 8199
num_años <- 28
for( pixel in 1:num_pixel){
Tmax_all=list()
Tmin_all=list()
for(año in 1:num_años) {
Tmax_all[[año]]<- TempMax[[año]][pixel,]
Tmin_all[[año]]<- TempMin[[año]][pixel,]
}
test_tmax=do.call("rbind",Tmax_all)
rownames(test_tmax)=2021:2048
x=melt(test_tmax)
test_tmin=do.call("rbind",Tmin_all)
rownames(test_tmin)=2021:2048
y=melt(test_tmin)
int_tmax= x%>%
separate(X2,c("x","day","month"))%>%
arrange(X1)
int_tmin= y%>%
separate(X2,c("x","day","month"))%>%
arrange(X1)
####Identificacion de casos con tmin>tmax
pos=which(int_tmax[,5] < int_tmin[,5])
if(length(pos)>0){
dif=0
for(i in 1:length(pos)){
pos_f=c(pos[i]-c(1:5),pos[i]+c(1:5))
dif=mean(abs(int_tmax[pos_f[which(pos_f>0 & pos_f<=nrow(int_tmax))],5]-int_tmin[pos_f[which(pos_f>0 & pos_f<=nrow(int_tmin))],5]),na.rm=T)
int_tmax[pos[i],5]=int_tmin[pos[i],5]+runif(1,0,dif/2)
int_tmin[pos[i],5]=int_tmax[pos[i],5]-runif(1,0,dif/2)
}
}
###Identificación de datos atípicos
month=1:12
siz=c(31,28,31,30,31,30,31,31,30,31,30,31)
int_tmax$month=rep(month,siz)
int_tmin$month=rep(month,siz)
int2_tmax=int_tmax%>%
group_by(month)%>%
summarise(li=mean(value,na.rm=T)-(3.5*sd(value,na.rm=T)),ls=mean(value,na.rm=T)+(3.5*sd(value,na.rm=T)))
int2_tmax=as.data.frame(int2_tmax)
int2_tmax=int2_tmax[order(as.numeric(int2_tmax[,1])),]
int2_tmin=int_tmin%>%
group_by(month)%>%
summarise(li=mean(value,na.rm=T)-(3.5*sd(value,na.rm=T)),ls=mean(value,na.rm=T)+(3.5*sd(value,na.rm=T)))
int2_tmin=as.data.frame(int2_tmin)
int2_tmin=int2_tmin[order(as.numeric(int2_tmin[,1])),]
lim_inf_tmax=rep(rep(int2_tmax[,2],siz),28)
lim_sup_tmax=rep(rep(int2_tmax[,3],siz),28)
pos_inf_tmax=which(int_tmax[,5]<lim_inf_tmax )
pos_sup_tmax=which(int_tmax[,5]>lim_sup_tmax)
lim_inf_tmin=rep(rep(int2_tmin[,2],siz),28)
lim_sup_tmin=rep(rep(int2_tmin[,3],siz),28)
pos_inf_tmin=which(int_tmin[,5]<lim_inf_tmin)
pos_sup_tmin=which(int_tmin[,5]>lim_sup_tmin)
int_tmax[pos_inf_tmax,5]=lim_inf_tmax[pos_inf_tmax]
int_tmax[pos_sup_tmax,5]=lim_sup_tmax[pos_sup_tmax]
int_tmin[pos_inf_tmin,5]=lim_inf_tmin[pos_inf_tmin]
int_tmin[pos_sup_tmin,5]=lim_sup_tmin[pos_sup_tmin]
years=2021:2048
for(j in 1:28){
Tmax[[j]][pixel,]=filter(int_tmax,X1==years[j])[,5]
Tmin[[j]][pixel,]=filter(int_tmin,X1==years[j])[,5]
}
}
save(Tmax,Tmin,file=paste0("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/Temperatura.Rdat"))
print(k)
}
##############################################
#########Corrección años bisiestos############
##############################################
modelos <- c("bcc_csm1_1", "bnu_esm", "cccma_canesm2", "gfld_esm2g", "inm_cm4", "ipsl_cm5a_lr", "miroc_miroc5",
"mpi_esm_mr", "mri_cgcm3", "ncc_noresm1_m")
####Precipitation
años_bisiestos=seq(4,28,4)
num_pixel <- 8000
for(k in 1:10){
load(paste("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/Precipitation.RDat",sep=""))
for(año in años_bisiestos){
for(pixel in 1:num_pixel){
Prec[[pixel]][año][[1]][60]=unlist(Prec[[pixel]][año])[59]
}
}
save(Prec,file=paste0("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/Precipitation.Rdat"))
print(k)
}
####Temperaturas
años_bisiestos=seq(4,28,4)
num_pixel <- 8199
modelos <- c("bcc_csm1_1", "bnu_esm", "cccma_canesm2", "gfld_esm2g", "inm_cm4", "ipsl_cm5a_lr", "miroc_miroc5",
"mpi_esm_mr", "mri_cgcm3", "ncc_noresm1_m")
for(k in 1:10){
load(paste("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/Temperatura.RDat",sep=""))
Tmax1=list()
Tmin1=list()
for(pixel in 1:num_pixel){
tx=list()
tm=list()
for(año in 1:28){
if(año%in%años_bisiestos){
tm[[año]]=c(Tmin[[año]][pixel,][1:59],Tmin[[año]][pixel,][59],Tmin[[año]][pixel,][60:365])
tx[[año]]=c(Tmax[[año]][pixel,][1:59],Tmax[[año]][pixel,][59],Tmax[[año]][pixel,][60:365])
}else{
tm[[año]]=Tmin[[año]][pixel,]
tx[[año]]=Tmax[[año]][pixel,]
}
Tmax1[[pixel]]=tx
Tmin1[[pixel]]=tm
}
}
Tmax=Tmax1
Tmin=Tmin1
save(Tmax,Tmin,file=paste0("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/Temperatura.Rdat"))
print(k)
}
#####Radiacion solar
años_bisiestos=seq(4,28,4)
num_pixel <- 8000
for(k in 1:10){
load(paste("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/Srad.RDat",sep=""))
for(año in años_bisiestos){
for(pixel in 1:num_pixel){
Srad[[pixel]][año][[1]][60]=unlist(Srad[[pixel]][año])[59]
}
}
save(Srad,file=paste0("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/14-ObjectsR/",modelos[k],"/Futuro/Srad.Rdat"))
print(k)
}
#######################################################################################
#############Corrección datos WDF######################################################
#######################################################################################
load("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/wfd/ValorWFD.RDat")
num_pixel <- 8199
num_años <- 30
error <- array(NA, dim = c(8199, 30))
for(año in 1:num_años) {
for(pixel in 1:num_pixel) {
srad <- Srad[[año]][pixel,]/11.5740741
pos=which(srad<1.5)
#srad[pos]=1.5
#Srad[[año]][pixel,]=srad
error[pixel, año] <- length(pos)
}
}
num_pixel <- 8199
num_años <- 30
for(año in 1:num_años) {
for(pixel in 1:num_pixel) {
prec <- Prec[[año]][pixel,]*86400
pos=which(prec<0)
#prec[pos]=0
#Prec[[año]][pixel,]=prec
error[pixel, año] <- length(pos)
}
}
num_pixel <- 8199
num_años <- 30
for(año in 1:num_años) {
for(pixel in 1:num_pixel) {
Tmax <- TempMax[[año]][pixel, ]
Tmin <- TempMin[[año]][pixel, ]
error[pixel, año] <- length(which ( Tmax < Tmin ) )
}
}
################################################
##Proyecto BID corrección data temperaturas
################################################
#install.packages("readr")
#install.packages("tidyr")
#install.packages("dplyr")
install.packages("plyr")
library(readr)
library(tidyr)
library(dplyr)
library(plyr)
library(reshape)
set.seed(1000)
siz_y=c(365, 366, 365, 365, 365 ,366 ,365, 365 ,365 ,366, 365 ,365 ,365 ,366, 365, 365 ,365 ,366 ,365,365 ,365 ,366 ,365 ,365 ,365 ,366, 365 ,365, 365, 366)
Tmax=list()
Tmin=list()
for(j in 1:30){
Tmax[[j]]= matrix(NA, nrow = 8199 ,ncol=siz_y[j])
Tmin[[j]]=matrix(NA, nrow = 8199 ,ncol=siz_y[j])
}
num_pixel <- 8199
num_años <- 30
for( pixel in 1:num_pixel){
Tmax_all=list()
Tmin_all=list()
for(año in 1:num_años) {
Tmax_all[[año]]<- TempMax[[año]][pixel,]-273.15
Tmin_all[[año]]<- TempMin[[año]][pixel,]-273.15
}
int_tmax= as.data.frame(unlist(Tmax_all))
names(int_tmax)="value"
int_tmin= as.data.frame(unlist(Tmin_all))
names(int_tmin)="value"
siz_y=c(365, 366, 365, 365, 365 ,366 ,365, 365 ,365 ,366, 365 ,365 ,365 ,366, 365, 365 ,365 ,366 ,365,365 ,365 ,366 ,365 ,365 ,365 ,366, 365 ,365, 365, 366)
int_tmax$year=rep(1971:2000,siz_y)
int_tmin$year=rep(1971:2000,siz_y)
####Identificacion de casos con tmin>tmax
pos=which(int_tmax[,1] < int_tmin[,1])
if(length(pos)>0){
dif=0
for(i in 1:length(pos)){
pos_f=c(pos[i]-c(1:5),pos[i]+c(1:5))
dif=mean(abs(int_tmax[pos_f[which(pos_f>0 & pos_f<=nrow(int_tmax))],1]-int_tmin[pos_f[which(pos_f>0 & pos_f<=nrow(int_tmin))],1]),na.rm=T)
int_tmax[pos[i],1]=int_tmin[pos[i],1]+runif(1,0,dif/2)
int_tmin[pos[i],1]=int_tmax[pos[i],1]-runif(1,0,dif/2)
}
}
###Identificación de datos atípicos
month=1:12
siz=c(31,28,31,30,31,30,31,31,30,31,30,31)
siz2=c(31,29,31,30,31,30,31,31,30,31,30,31)
siz_f=c(siz,siz2,rep(siz,3),siz2,rep(siz,3),siz2,rep(siz,3),siz2,rep(siz,3),siz2,rep(siz,3),siz2,rep(siz,3),siz2,rep(siz,3),siz2)
int_tmax$month=rep(rep(month,30),siz_f)
int_tmin$month=rep(rep(month,30),siz_f)
interv=function(x){
res=cbind(li=mean(x,na.rm=T)-(3.5*sd(x,na.rm=T)),ls=mean(x,na.rm=T)+(3.5*sd(x,na.rm=T)))
return(res)
}
int2_tmax= aggregate(int_tmax[,1],list(int_tmax$month),interv)
int2_tmin= aggregate(int_tmin[,1],list(int_tmin$month),interv)
lim_inf_tmax=rep(rep(int2_tmax[,2][,1],30),siz_f)
lim_sup_tmax=rep(rep(int2_tmax[,2][,2],30),siz_f)
pos_inf_tmax=which(int_tmax[,1]<lim_inf_tmax )
pos_sup_tmax=which(int_tmax[,1]>lim_sup_tmax)
lim_inf_tmin=rep(rep(int2_tmin[,2][,1],30),siz_f)
lim_sup_tmin=rep(rep(int2_tmin[,2][,2],30),siz_f)
pos_inf_tmin=which(int_tmin[,1]<lim_inf_tmin)
pos_sup_tmin=which(int_tmin[,1]>lim_sup_tmin)
int_tmax[pos_inf_tmax,1]=lim_inf_tmax[pos_inf_tmax]
int_tmax[pos_sup_tmax,1]=lim_sup_tmax[pos_sup_tmax]
int_tmin[pos_inf_tmin,1]=lim_inf_tmin[pos_inf_tmin]
int_tmin[pos_sup_tmin,1]=lim_sup_tmin[pos_sup_tmin]
years=1971:2000
for(j in 1:30){
Tmax[[j]][pixel,]=filter(int_tmax,year==years[j])[,1]
Tmin[[j]][pixel,]=filter(int_tmin,year==years[j])[,1]
}
}
save(Srad,Prec,Tmax,Tmin,file=paste0("/mnt/workspace_cluster_3/bid-cc-agricultural-sector/14-ObjectsR/wfd/WDF_all_new.Rdat"))