Disentangle Things by Ivan Hanigan

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long-term-climatology-contextual-data-for-ecological-research

Studies of extreme weather events such as drought require long term climate data
these are available at continental scale derived from
observations from a network of weather stations that are interpolated to a surface
I have been working on techniques with R and online resources (the Australian Water Availabilty Project AWAP) to make working with these long term climatology datasets easier.
The package is in development at https://github.com/swish-climate-impact-assessment/awaptools

Case Study

aim is need to look at seasonal rainfall means.
first thing is to download the data (I’m also working on a Rstudio server to host these data, as a Virtual Lab).
data = multiple years of monthly rainfall data in a raster grid format.
aim = combine rainfall in a seasonal basis in one grid
(i.e. M-J-J-A-S-O 1900, 1901 etc.) calculate mean of each cell.
assumption1 = filenames have year, month embedded so they will be sorted in order when listed
assumption2 = all months are available, from 1:12 for all years in
study period
notes:
this requires the files are listed in the right order by name, and all months are present. might be better to use grep on the file name and strsplit/substr to extract the month identifier more precisely?

Results

alttext

I’m looking for collaboration on this!

I’ve been in contact with:
https://github.com/RationShop/rain_r/wiki/The-Rain-Project
I apologise to windoze users but feel a bit like this guy when recomending how to install:

quote:

probably the easiest way to do this is to use
Hadley's devtools package.  Assuming you have devtools and my package's
dependencies.  If you're using Linux or the BSD's, this should just
work.  Welcome to the good life, player.  I think this will work out
of the box on a Mac.  I have no idea if this will work on Windows; how
you strange people get anything done amazes me.  At the least,
Im guessing you have to install Rtools first.  
You could also just source all the R scripts like 
some kind of barbarian

R Code:

# depends
require(swishdbtools)
if(!require(raster)) install.packages("raster", dependencies = T); require(raster)
if(!require(rgdal)) install.packages("rgdal", dependencies = T); require(rgdal)

# on linux can install direct, on windoze you configure Rtools
require(devtools)
install_github("awaptools", "swish-climate-impact-assessment")
require(awaptools)

homedir <- "~/data/AWAP_GRIDS/data"
outdir <- "~/data/AWAP_GRIDS/data-seasonal-vignette"
 
# first make sure there are no left over files from previous runs
#oldfiles <- list.files(pattern = '.tif', full.names=T) 
#for(oldfile in oldfiles)
#{
#  print(oldfile)
#  file.remove(oldfile)
#}
################################################
setwd(homedir)
 
# local customisations
workdir  <- homedir
setwd(workdir)
# don't change this
years <- c(1900:2014)
lengthYears <- length(years)
# change this
startdate <- "2013-01-01"
enddate <- "2014-01-31"
# do
load_monthly(start_date = startdate, end_date = enddate)
 
# do
filelist <- dir(pattern = "grid.Z$")
for(fname in filelist)
{
  #fname <- filelist[1]
  unzip_monthly(fname, aggregation_factor = 1)
  fin <- gsub(".grid.Z", ".grid", fname)
  fout <- gsub(".grid.Z", ".tif", fname)
  r <- raster(fin)
  writeRaster(r, fout, format="GTiff",  overwrite = TRUE)
  file.remove(fin)
}
 
cfiles <- list.files(pattern = '.tif', full.names=T) 
# loop thru
# NEED TO SET THE FILESOFSEASEON_I counter EACH TIME YOU start
 
 
for(season in c("hot", "cool"))
{
  # season <- "hot" # for labelling
  if(season == "cool")
  {
    filesOfSeason_i <- c(5,6,7,8,9,10)  
    endat <- lengthYears
  } else {
    filesOfSeason_i <- c(11,12,13,14,15,16) 
    endat <- lengthYears - 1
  }
  
  for (year in 1:endat){ 
    ## setup for checking month 
    # year  <- 1 #endat
    
    
    ## checking
    print(cat("####################\n\n"))
    print(cfiles[filesOfSeason_i])
    
    b <- brick(stack(cfiles[filesOfSeason_i])) 
    ## calculate mean 
    m <- mean(b) 
    ## checking 
    # image(m) 
    writeRaster(m, file.path(outdir,sprintf("season_%s_%s.tif", season, year)), drivername="GTiff")
    filesOfSeason_i <- filesOfSeason_i + 12
  } 
}
 
##### now we will overall average
setwd(outdir)
for(season in c("cool", "hot"))
{
  cfiles <- list.files(pattern = season, full.names=T)   
  print(cfiles)
  b <- brick(stack(cfiles)) 
  ## calculate mean 
  m <- mean(b) 
  ## checking 
  # image(m) 
  writeRaster(m, file.path(outdir,sprintf("season_%s.tif", season)), drivername="GTiff")
}
 
# qc
cool <- raster("season_cool.tif")
hot <- raster("season_hot.tif")
par(mfrow = c(2,1))
image(cool)
image(hot)
 
# just summer rainfall
png("season_hot.png")
image(hot)
dev.off()

Posted in extreme weather events Drought