This is a function in R helping retrieve a series of points in a NETCDF files and generate a well-structured CSV format on the output side.
rm(list=ls())
#==Load packages======
library("dplyr")
library("tibble")
library('ncdf4')
library("ncdf4.helpers")
getwd()## [1] "C:/Users/De-RoOt/Documents/Blog/N1/CRU_data"
For this exercise, I am going to use the High-resolution gridded datasets - Climatic Research Unit and retrieve some stations values in different climatic zones.
The latest version of CRU (Ts 4.03) provide gridded data from Jan. 1901 to Dec. 2018 which can be downloaded from this
link.
#=====Get data path
(data<-list.files(pattern="*.nc"))## [1] "CRU_WA_pre.nc" "CRU_WA_tmax.nc" "CRU_WA_tmean.nc"The stations data are arranged as follows
#===stations informations =======
zone_points<- as_tibble(read.table("Climatic_Zone_Points.csv", header=TRUE,sep=","))
zone_points$Stations<-as.character(zone_points$Stations)#Format colunm to character
zone_points$Climatic.Zones<-as.character(zone_points$Climatic.Zones)#Format colunm to character
#print
zone_points## # A tibble: 3 x 4
## Stations Climatic.Zones Longitude Latitude
## <chr> <chr> <dbl> <dbl>
## 1 Cotonou Guinean 2.31 6.40
## 2 SAVE Soudano-Guinean 2.66 8.18
## 3 Kandi Soudanien 3.22 11.0Extraction Function
#Function of extraction
Extraction <-function(data_path,grid_info){
nfiles<-length(data_path) #number of files in the path
# Repeat operation for each files in the data path
for (nfil in 1:nfiles){
modExt1<-NULL #final ouptut variable
print(data_path[nfil])
nc_brick<-NULL
fileName<-basename(data_path[nfil])# File name without the path
#Variable name
spl<-unlist(strsplit(fileName, "[.]"))#split the filename in different compoenents
varlabel<-paste(spl[1])
#Netcdf multi-layer raster object
nc_brick<-brick(data_path[nfil])
dataget<-nc_open(data_path[nfil],write=FALSE)
#time axis of dataget
ts<-nc.get.time.series(dataget)
#Get a list of names of data variables
var<-nc.get.variable.list(dataget)
# Retrieve, day, month and year from ts
day<-format(ts, "%d")
year<-format(ts, "%Y")
month<-format(ts,"%m")
date<-format(ts,"%Y-%m-%d")
#New dataset with date,year, month, day colunms
times<-as.data.frame(cbind(date,year,month,day))
#length of the time series
tslen<-length(ts)
#=Extract variable values for each station and given lon and lat===========
points<-length(unique(grid_info$Stations)) # Number of grid point to extract
for (p in 1:points){
print(grid_info$Stations[p])
consol_tmp<-NULL
grid_val<-NULL
#Extract values
grid_val<-extract(nc_brick,matrix(c(grid_info$Longitude[p],grid_info$Latitude[p]), ncol = 2))
grid_val<-as.data.frame(t(grid_val), row.names = F)
#descriptive colunms (Stations, Climate.Zones,Longitude,Longitude)
info_val<-cbind(rep(grid_info$Stations[p],tslen),rep(grid_info$Climatic.Zones[p],tslen),
rep(grid_info$Longitude[p],tslen),rep(grid_info$Latitude[p],tslen))
#Consolidated file output
consol_tmp<-cbind(times,info_val,grid_val)
#Model output file
modExt1<-rbind(modExt1,consol_tmp)
#rm(grid_val,consol_tmp)
}
#Rename colnames
colnames(modExt1)<-c('Date','Year','Month', 'Day', 'Stations','Climatic_Zones','Longitude','Latitude',varlabel)
modExt1<-as_tibble(modExt1)
#Output file name
filelabel<-paste(spl[1],'_daily','.csv',sep='')
#Output file in csv file
write.csv(modExt1,filelabel)
print (paste('Output file ',nfil,filelabel))
print ("======Content printing===========================")
print (modExt1[,-1])
print ("======End printing===============================")
}
}Operate the function Extraction on the netcdf files in the directory CRU Data
Extraction(data,zone_points)## [1] "CRU_WA_pre.nc"
## [1] "Cotonou"
## [1] "SAVE"
## [1] "Kandi"
## [1] "Output file 1 CRU_WA_pre_daily.csv"
## [1] "======Content printing==========================="
## # A tibble: 4,176 x 8
## Year Month Day Stations Climatic_Zones Longitude Latitude CRU_WA_pre
## <fct> <fct> <fct> <fct> <fct> <fct> <fct> <dbl>
## 1 1901 01 16 Cotonou Guinean 2.30763 6.39958 14.2
## 2 1901 02 15 Cotonou Guinean 2.30763 6.39958 18.4
## 3 1901 03 16 Cotonou Guinean 2.30763 6.39958 155.
## 4 1901 04 16 Cotonou Guinean 2.30763 6.39958 73.6
## 5 1901 05 16 Cotonou Guinean 2.30763 6.39958 185.
## 6 1901 06 16 Cotonou Guinean 2.30763 6.39958 180.
## 7 1901 07 16 Cotonou Guinean 2.30763 6.39958 243.
## 8 1901 08 16 Cotonou Guinean 2.30763 6.39958 132.
## 9 1901 09 16 Cotonou Guinean 2.30763 6.39958 368
## 10 1901 10 16 Cotonou Guinean 2.30763 6.39958 251.
## # ... with 4,166 more rows
## [1] "======End printing==============================="
## [1] "CRU_WA_tmax.nc"
## [1] "Cotonou"
## [1] "SAVE"
## [1] "Kandi"
## [1] "Output file 2 CRU_WA_tmax_daily.csv"
## [1] "======Content printing==========================="
## # A tibble: 4,176 x 8
## Year Month Day Stations Climatic_Zones Longitude Latitude CRU_WA_tmax
## <fct> <fct> <fct> <fct> <fct> <fct> <fct> <dbl>
## 1 1901 01 16 Cotonou Guinean 2.30763 6.39958 32.2
## 2 1901 02 15 Cotonou Guinean 2.30763 6.39958 33.5
## 3 1901 03 16 Cotonou Guinean 2.30763 6.39958 32.4
## 4 1901 04 16 Cotonou Guinean 2.30763 6.39958 32.3
## 5 1901 05 16 Cotonou Guinean 2.30763 6.39958 30.7
## 6 1901 06 16 Cotonou Guinean 2.30763 6.39958 29.4
## 7 1901 07 16 Cotonou Guinean 2.30763 6.39958 28.5
## 8 1901 08 16 Cotonou Guinean 2.30763 6.39958 27.9
## 9 1901 09 16 Cotonou Guinean 2.30763 6.39958 28.8
## 10 1901 10 16 Cotonou Guinean 2.30763 6.39958 30.5
## # ... with 4,166 more rows
## [1] "======End printing==============================="
## [1] "CRU_WA_tmean.nc"
## [1] "Cotonou"
## [1] "SAVE"
## [1] "Kandi"
## [1] "Output file 3 CRU_WA_tmean_daily.csv"
## [1] "======Content printing==========================="
## # A tibble: 4,176 x 8
## Year Month Day Stations Climatic_Zones Longitude Latitude
## <fct> <fct> <fct> <fct> <fct> <fct> <fct>
## 1 1901 01 16 Cotonou Guinean 2.30763 6.39958
## 2 1901 02 15 Cotonou Guinean 2.30763 6.39958
## 3 1901 03 16 Cotonou Guinean 2.30763 6.39958
## 4 1901 04 16 Cotonou Guinean 2.30763 6.39958
## 5 1901 05 16 Cotonou Guinean 2.30763 6.39958
## 6 1901 06 16 Cotonou Guinean 2.30763 6.39958
## 7 1901 07 16 Cotonou Guinean 2.30763 6.39958
## 8 1901 08 16 Cotonou Guinean 2.30763 6.39958
## 9 1901 09 16 Cotonou Guinean 2.30763 6.39958
## 10 1901 10 16 Cotonou Guinean 2.30763 6.39958
## # ... with 4,166 more rows, and 1 more variable: CRU_WA_tmean <dbl>
## [1] "======End printing==============================="