Introduction

.libPaths("/nobackup/users/dirksen/R/x86_64-redhat-linux-gnu-library/3.3/")
library(R.utils)
library(raster)
library(ncdf4)
library(rts)
library(rgdal)
#source("../functions/generate_raster_based_on_netcdf.R")
sourceDirectory("R")
pro <- CRS("+init=epsg:28992")
WGS84<-CRS("+init=epsg:4326")

mymap.unpro=readOGR(dsn='../data/NaturalEarthData/ne_10m_admin_0_countries',layer="ne_10m_admin_0_countries") # Read in (unprojected) map data
mymap.pro=spTransform(mymap.unpro, WGS84) # Reproject the map

mymap.unpro_lakes=readOGR(dsn='../data/NaturalEarthData/ne_10m_lakes',layer="ne_10m_lakes") # Read in (unprojected) map data
mymap.pro_lakes=spTransform(mymap.unpro_lakes, WGS84) # Reproject the map

fun <- function() {
  plot(mymap.pro,add=TRUE)
  plot(mymap.pro_lakes,add=TRUE)
}

Untar and unzip the SARAH files from ftp server

#untar all the files
#system("ls *.tar | xargs -i tar xf {}")

#gz.files<-list.files("/nobackup/users/dirksen/SARAH/",pattern=".nc.gz",full.names = TRUE)

#for (i in 1:length(gz.files)){
#  gz<-gz.files[i]
#gunzip(gz)
#}

Create rasters from NetCDF files

function: generate_raster_based_on_netcdf

nc.files<-list.files("/nobackup/users/dirksen/SARAH/",pattern=".nc",full.names = TRUE)
save_dir<-"../data/SARAH_raster/raster_datum.grd"
start_time<-as.POSIXct("1983-01-01 00:00:00") 

results<-lapply(nc.files[1:5],
                generate_raster_based_on_netcdf,
                save_dir = save_dir,
                start_time = start_time,
                varname = "SIS")

Climatologies based on SARAH

function: calculate_time_average_raster

raster.path<-"../data/SARAH_raster/"
time.format<-"raster_%Y-%m-%d.grd"
time.period<-"month"
ext<-extent(-11.5,15.1,45.8,61.1) #Northwestern europe

new_raster<-calculate_time_average_raster(raster.path=raster.path,
                              ext=ext,
                              time.format=time.format,
                              time.period=time.period)
saveRDS(new_raster,"../results/time_averaged_raster.rds")
grdfiles<- list.files("/nobackup/users/dirksen/data/SARAH_raster/",pattern=".grd")
sarah.datums<-as.Date(grdfiles,format = time.format)
sarah.months<-months(sarah.datums)


sarah.months.unique<-unique(sarah.months)
I<-which(sarah.months==sarah.months.unique[1])

Jan.files<-list.files("/nobackup/users/dirksen/data/SARAH_raster/",pattern=".grd",full.names = TRUE)[I]
Jan.st<-stack(Jan.files)

Jan.pca<-rasterPCA

Visualization

month.mean<-readRDS("../results/time_averaged_raster.rds")

kleur.breaks<-seq(18,245,by=0.5)
kleur.cols<-colorRampPalette(c("green","yellow","orange"))(length(kleur.breaks-1))
plot.new()
par(mar=c(0,0,0,0), oma=c(0,0,0,0))

#png(filename="../fig/month_mean_SARAH.png", width = 3200, height = 2200, units = "px",res=300)

plot(month.mean,addfun=fun,col=kleur.cols,
     #ext=extent(month.mean),
     legend.args=list(text='W/m2', side=3, cex=0.5
     ))

#dev.off()

SNOW cover

\url(link)[http://nsidc.org/data/g02156]

format: zipped asci files (without meta data) or GeoTIFF (with projection and so, only 1km and 4km) name: ims%Y%yd_4km_v1.2.asc.gz

library(RCurl)
library(lubridate)
library(dplyr)
url<-"ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02156/GIS/1km/"

name.format<-"imsYYYYDDD_1km_GIS_v1.3.tif.gz"

start<-as.Date("2014-01-01")
yday(start)<-336
start<-as.POSIXct(start)

stop<-as.Date("2014-01-01")
yday(stop)<-365
end<-as.POSIXct(stop)

time.vector<-seq(start,end,by="day")

time.vector<-format(time.vector,format="%Y%j")

first.folder<-format(start,format="%Y")
first.file<-gsub("YYYYDDD",time.vector[1],name.format)

#now transfered using filezilla, somehow rstudio doesnt download this data?
library(httr)
url.file<-paste0(url,first.folder,"/",first.file)
output.file<-"test.tif.gz"

out<-GET(url.file)

#files<-getURL(paste0(url,first.folder,"/",first.file))

#url<-"ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02156/" #link to the asci files


MariekeDirk/Solar-Radiation-Europe documentation built on May 23, 2019, 10:33 p.m.