Nothing
R/get_climate_data.R
In climstats: R functions for working with station and gridded climate data.
#' Get/Preprocess Climate Data from Remote Sources
#'
#' This function downloads and preprocesses climate data from a variety of
#' sources.
#'
#'
#' @param climate_source A datasource to fetch climate data from. The
#' following datasources are currently supported: \itemize{ \item
#' "PRISM-4km-ppt": PRISM 4km monthly precipitation grids \item
#' "PRISM-4km-tmin": PRISM 4km monthly minimum temperature grids \item
#' "PRISM-4km-tmax": PRISM 4km monthly maximum temperature grids \item
#' "PRISM-4km-spi": PRISM 4km monthly standardized precipitation index grids
#' \item "PRISM-4km-tdmean": PRISM 4km monthly dewpoint grids \item
#' "PRISM-4km-elev": PRISM 4km elevation data \item "NARR-monthlymean-wnd":
#' North American Regional Reanalysis \cr monthly mean wind velocity \item
#' "NARR-longtermmonthlymean-wnd": North American Regional Reanalysis long-term
#' monthly mean wind velocity \item "generic": arbitrary local files.
#' local_files must be set if this is used. }
#' @param date_range A vector of length 2 containing the starting date and
#' ending date of a range of climate data to download.
#' @param dates Unused at present.
#' @param download_folder The target download directory of the raw data.
#' @param final_folder The final directory to store the preprocessed grd files.
#' @param standardize standardize=TRUE converts the downloaded dataset to
#' standard units used by eto and water balance \itemize{ \item precipitation:
#' average mm H20 per day \item temperature: deg C }
#' @param snow_nthreads The number of threads (CPUs) to use when processing.
#' If snow_nthreads>1, the algorithm will attempt to use the multiple
#' processing package snow via MPI. CURRENTLY UNSUPPORTED.
#' @param overwrite overwrite=TRUE will allow overwriting of downloaded and
#' preprocessed files. Setting overwrite=FALSE (default) lets the process pick
#' up where it left off if interrupted.
#' @param wnd_speed_height_correction wnd_speed_height_correction=TRUE
#' (default) corrects wind speed data to wind speed at 2m above the terrain
#' using the wind profile relationship described in
#' http://www.fao.org/docrep/x0490e/x0490e07.htm equation 47.
#' @param verbose verbose=TRUE will print process information.
#' @param enable_download If enable_download=TRUE (default), data will be
#' downloaded, otherwise download will be skipped.
#' @param local_files A search string for local files which will be used to
#' create the climate stack when climate_source="generic"
#' @param preconvert If preconvert=TRUE (default), raw data will be
#' preconverted to format=raster before any further processing occurs.
#' @param output_basename A string representing the output files. Note that
#' most formats have their own defaults which will override this.
#' @param proj A PROJ string which will be used for climate_source='generic'.
#' @param zvalue The zvalue applied to the output raster (only used with
#' climate_source= 'generic'). This should be a character string of dates
#' for each of the output layers. If zvalue='months' and the output is exactly
#' 12 bands, the zvalue will be set to dates corresponding to the first of each
#' month.
#' @return Returns a RasterLayer, RasterBrick or RasterStack object, as well as
#' saving the objects to disk.
#' @author Jonathan A. Greenberg
# @seealso \code{\link[climstats]{eto}},
# \code{\link[climstats]{climate_summaries}}
#' @references \itemize{ \item PRISM data:
#' \url{http://www.prism.oregonstate.edu/} \item PRISM terms of use:
#' \url{http://www.prism.oregonstate.edu/terms.phtml} \item PRISM metadata:
#' \url{http://www.prism.oregonstate.edu/docs/meta} } \itemize{ \item NCEP
#' North American Regional Reanalysis: \url{http://www.esrl.noaa.gov/psd/ \cr
#' data/gridded/data.narr.html} \item NCEP NARR Data Description:
#' \url{ftp://ftp.cdc.noaa.gov/Datasets/ \cr NARR/README} }
#' @keywords climate
#' @examples \dontrun{
#' # Download and decompress PRISM monthly precipitation data
#' # but do not post-process it.
#' ppt=get_climate_data("PRISM-4km-ppt",
#' date_range=c("1999/1/1","1999/12/31"),
#' standardize=FALSE, enable_download=TRUE)
#' # Note the raw, decompressed downloaded data.
#' dir()
#' # Download and post-process PRISM monthly precipitation data
#' # (note that as long as overwrite=FALSE, it will not redownload the files,
#' # just post-process the files already acquired during the previous step.
#' ppt=get_climate_data("PRISM-4km-ppt",
#' date_range=c("1999/1/1","1999/12/31"),
#' standardize=TRUE, enable_download=TRUE, overwrite=TRUE)
#' # Note the raw raster format files (*_raw.grd), and the corrected raster
#' # format data (*_climstats.grd).
#' dir()
#' summary(ppt)
#' getZ(ppt)
#' }
get_climate_data <- function(
climate_source,
date_range,
dates,
download_folder,
final_folder,
standardize=TRUE,
snow_nthreads=1,
overwrite=FALSE,
wnd_speed_height_correction=TRUE,
verbose=FALSE,
enable_download=FALSE,
local_files,
preconvert=TRUE,
output_basename="climstats_data",
proj,
zvalue)
{
# climate_source:
# PRISM-800m-ppt: PRISM 800m monthly precipitation grids
# PRISM-800m-tmin: PRISM 800m monthly min temperature grids
# PRISM-800m-tmax: PRISM 800m monthly max temperature grids
# PRISM-800m-spi: PRISM 800m monthly standardized precipitation index grids
# PRISM-800m-tdmean: PRISM 800m monthly dewpoint grids
# dates: a vector of individual dates. get_climate_data will download the product with the nearest matching date.
# startdate/enddate: used in lieu of dates.
# standardize=TRUE downloads the data, converts the units to climstats standards, and saves the
# data out as a raster or brick in the native raster format (grd).
# standardize=FALSE only downloads the data.
if(missing(download_folder))
{
download_folder=getwd()
}
setwd(download_folder)
if(missing(final_folder))
{
final_folder=getwd()
}
if( climate_source=="PRISM-4km-elev")
{
require("R.utils")
basepath="ftp://prism.oregonstate.edu/pub/prism/us/grids"
prism_filenames="us_25m_dem.asc.gz"
prism_filenames_gunzipped="us_25m_dem.asc"
prism_path=paste(basepath,prism_filenames,sep="/")
if(enable_download)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the gunzipped or decompressed file is not present...
(!overwrite & !file.exists(prism_filenames_gunzipped) & !file.exists(prism_filenames))
)
{
download.file(prism_path,destfile=basename(prism_path))
}
}
if(overwrite | (!overwrite & !file.exists(prism_filenames_gunzipped)))
{
gunzip(prism_filenames,remove=TRUE,overwrite=TRUE)
}
if(standardize)
{
require("raster")
setOptions(setfileext=FALSE)
if(overwrite |
(!overwrite & !file.exists(paste(prism_filenames_gunzipped,"_climstats.grd",sep=""))))
{
temp_raster=raster(prism_filenames_gunzipped)
writeRaster(temp_raster,paste(prism_filenames_gunzipped,"_climstats.grd",sep=""),format="raster",overwrite=TRUE)
}
setOptions(setfileext=TRUE)
elev=raster(paste(prism_filenames_gunzipped,"_climstats.grd",sep=""))
projection(elev)="+proj=longlat +ellps=WGS84 +datum=WGS84 +lon_wrap"
return(elev)
}
} # END PRISM-4km-elev
if( climate_source=="PRISM-4km-ppt" |
climate_source=="PRISM-4km-tmin" |
climate_source=="PRISM-4km-tmax" |
climate_source=="PRISM-4km-spi" |
climate_source=="PRISM-4km-tdmean")
{
require("R.utils")
basepath="ftp://prism.oregonstate.edu/pub/prism/us/grids"
yearfolderstart=seq(1890,2010,by=10)
yearfolderend=seq(1899,2019,by=10)
yearfolders=paste(yearfolderstart,yearfolderend,sep="-")
# PRISM is monthly data, so we figure the range based on months.
if(missing(dates))
{
startdate=as.Date(date_range[1])
enddate=as.Date(date_range[2])
dates=seq.Date(startdate,enddate,by="month")
if(verbose)
{
print("Downloading/preparing the following dates...")
print(dates)
}
}
years_text=format(dates,"%Y")
months_text=format(dates,"%m")
dates_N=length(dates)
prism_type=strsplit(climate_source,"-")[[1]][3]
prism_filenames=paste(paste("us",prism_type,years_text,sep="_"),months_text,"gz",sep=".")
# Figure out the full path
prism_path=vector(mode="character",length=dates_N)
for (i in 1:dates_N)
{
temp_year=as.numeric(years_text[i])
temp_yearfolderstart_check=yearfolderstart <= temp_year
temp_yearfolderend_check=yearfolderend >= temp_year
temp_yearfolder_check=temp_yearfolderstart_check & temp_yearfolderend_check
temp_yearfolder=yearfolders[temp_yearfolder_check]
prism_path[i]=paste(basepath,prism_type,temp_yearfolder,prism_filenames[i],sep="/")
}
prism_filenames_gunzipped=paste(paste("us",prism_type,years_text,sep="_"),months_text,sep=".")
raster_names_list=as.list(prism_filenames_gunzipped)
# Download and extract the files
if(enable_download)
{
for(i in 1:dates_N)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the gunzipped or decompressed file is not present...
(!overwrite & !file.exists(prism_filenames_gunzipped[[i]]) & !file.exists(prism_filenames[[i]]))
)
{
download.file(prism_path[i],destfile=basename(prism_path[i]))
}
}
}
for(i in 1:dates_N)
{
if(overwrite | (!overwrite & !file.exists(prism_filenames_gunzipped[[i]])))
{
gunzip(prism_filenames[i],remove=TRUE)
}
}
if(standardize)
{
require("raster")
# Set up each file as a raster.
# prism_filenames_gunzipped=paste(paste("us",prism_type,years_text,sep="_"),months_text,sep=".")
# raster_names_list=as.list(prism_filenames_gunzipped)
# raster_list=sapply(raster_names_list,raster,simplify=FALSE)
# For PRISM ASCIIs, its faster if we pre-convert them before we tweak them any further.
setOptions(setfileext=FALSE)
for (i in (1:dates_N))
{
if(overwrite |
(!overwrite & !file.exists(paste(prism_filenames_gunzipped[i],"_raw.grd",sep="")))
)
{
temp_raster=raster(raster_names_list[[i]])
writeRaster(temp_raster,paste(prism_filenames_gunzipped[i],"_raw.grd",sep=""),format="raster",overwrite=TRUE)
}
}
setOptions(setfileext=TRUE)
# raster_list=sapply(paste(raster_names_list,"_raw.grd",sep=""),raster,simplify=FALSE)
raster_raw_list=as.list(paste(raster_names_list,"_raw.gri",sep=""))
# stack_raw=stack(raster_raw_list)
# Now assign the proper dates.
days_in_months=c(31,28,31,30,31,30,31,31,30,31,30,31)
middays_in_months=ceiling(days_in_months/2)
dates_raster=vector("character",length=dates_N)
for (i in (1:dates_N))
{
temp_date_vector=as.numeric(strsplit(strsplit(prism_filenames_gunzipped[i],"_")[[1]][3],"[.]")[[1]])
if (temp_date_vector[2] >= 1 & temp_date_vector[2] <=12)
{
dates_raster[i]=paste(temp_date_vector[1],temp_date_vector[2],middays_in_months[temp_date_vector[2]],sep="-")
} else
{
dates_raster[i]="NA"
}
}
dates_raster=as.Date(dates_raster)
# for (i in (1:dates_N))
# {
# raster_list[[i]]@zvalue=as.character(dates_raster[i])
# }
climstats_filenames=vector(mode="character",length=dates_N)
for(i in (1:dates_N))
{
climstats_filenames[i]=paste(prism_filenames_gunzipped[i],"_climstats.grd",sep="")
}
if(prism_type=="ppt")
{
# if((snow_nthreads) > 1)
# {
# require("snow")
# cl <- makeCluster(snow_nthreads, type = "MPI")
# final_raster_list=clusterMap(cl,apply_gains_offsets,raster_list,MoreArgs=list(gains=(1/100),divide_by_days_in_month=TRUE))
# stopCluster(cl)
# } else
# final_raster_list=mapply(apply_gains_offsets,raster_list,MoreArgs=list(gains=(1/100),divide_by_days_in_month=TRUE))
# }
setOptions(setfileext=FALSE)
for (i in (1:dates_N))
{
if(overwrite |
(!overwrite & !file.exists(climstats_filenames[i]))
)
{
setwd(download_folder)
temp_raster=raster(raster_raw_list[[i]])
temp_raster@zvalue=as.character(dates_raster[i])
temp_raster@zname="Date/time"
temp_raster_standardized=apply_gains_offsets(temp_raster,gains=(1/100),divide_by_days_in_month=TRUE)
setwd(final_folder)
writeRaster(temp_raster_standardized,climstats_filenames[i],format="raster",overwrite=TRUE)
}
}
setOptions(setfileext=TRUE)
}
if(prism_type=="tmin" | prism_type=="tmax")
{
setOptions(setfileext=FALSE)
for (i in (1:dates_N))
{
if(overwrite |
(!overwrite & !file.exists(paste(prism_filenames_gunzipped[i],"_climstats.grd",sep="")))
)
{
setwd(download_folder)
temp_raster=raster(raster_raw_list[[i]])
temp_raster@zvalue=as.character(dates_raster[i])
temp_raster@zname="Date/time"
temp_raster_standardized=apply_gains_offsets(temp_raster,gains=(1/100),divide_by_days_in_month=FALSE)
projection(temp_raster_standardized)="+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
setwd(final_folder)
writeRaster(temp_raster_standardized,climstats_filenames[i],format="raster",overwrite=TRUE)
}
}
setOptions(setfileext=TRUE)
}
setwd(final_folder)
climstats_stack=stack(as.list(climstats_filenames))
climstats_stack@zvalue=as.character(dates_raster)
climstats_stack@zname="Date/time"
projection(climstats_stack)="+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
return(climstats_stack)
}
} # End PRISM monthly climate data
# North American Regional Reanalysis data (monthly mean)
# http://www.esrl.noaa.gov/psd/data/gridded/data.narr.html
# This isn't working right now, don't use.
if(climate_source=="NARR-monthlymean-rad_sw" | climate_source=="NARR-monthlymean-rad_lw")
{
require("ncdf")
basepath="ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.derived/surface_gauss"
if(climate_source=="NARR-monthlymean-rad_sw")
{
download_filenames="nswrs.mon.mean.nc"
download_path=paste(basepath,download_filenames,sep="/")
}
if(climate_source=="NARR-monthlymean-rad_sw")
{
download_filenames="nlwrs.mon.mean.nc"
download_path=paste(basepath,download_filenames,sep="/")
}
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the file is not present...
(!overwrite & !file.exists(download_filenames[i]))
)
{
download.file(download_path[i],destfile=download_filenames[i])
}
if(standardize)
{
narr_brick_list=vector(mode="list",length=length(download_filenames))
for(i in 1:length(download_filenames))
{
narr_brick_list[[i]]=brick(download_filenames)
}
}
}
# BEGIN NARR-longtermmonthlymean-wnd
if(climate_source=="NARR-longtermmonthlymean-wnd")
{
require("ncdf")
basepath="ftp://ftp.cdc.noaa.gov/Datasets/NARR/Derived/monolevel"
# if(missing(dates))
# {
# dates=seq.Date(startdate,enddate,by="month")
# }
# dates_N=length(dates)
if(!missing(download_folder))
{
setwd(download_folder)
}
# Set up filenames to download.
if(climate_source=="NARR-longtermmonthlymean-wnd")
{
download_filenames=c("uwnd.10m.mon.ltm.nc","vwnd.10m.mon.ltm.nc")
download_path=paste(basepath,download_filenames,sep="/")
}
for(i in 1:length(download_filenames))
{
if(enable_download)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the file is not present...
(!overwrite & !file.exists(download_filenames[i]))
)
{
download.file(download_path[i],destfile=download_filenames[i])
}
}
}
# Preprocess the data if requested.
if(standardize)
{
narr_brick_list=vector(mode="list",length=length(download_filenames))
for(i in 1:length(download_filenames))
{
narr_brick_list[[i]]=brick(download_filenames)
}
if(climate_source=="NARR-longtermmonthlymean-wnd")
{
if(!wnd_speed_height_correction)
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]])
} else
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]],10)
}
if(!missing(final_folder))
{
setwd(final_folder)
}
setOptions(setfileext=FALSE)
projection(wnd)="+proj=lcc +lat_1=50 +lat_2=50 +lat_0=50 +lon_0=-107 +x_0=5632642 +y_0=4612546"
writeRaster(wnd,"wnd.10m.mon.ltm.grd",format="raster",overwrite=TRUE)
setOptions(setfileext=TRUE)
return(wnd)
}
}
} # END NARR-longtermmonthlymean-wnd
# BEGIN NARR-monthlymean-wnd
if(climate_source=="NARR-monthlymean-wnd")
{
require("ncdf")
basepath="ftp://ftp.cdc.noaa.gov/Datasets/NARR/Monthlies/monolevel"
# if(missing(dates))
# {
# dates=seq.Date(startdate,enddate,by="month")
# }
# dates_N=length(dates)
if(!missing(download_folder))
{
setwd(download_folder)
}
# Set up filenames to download.
if(climate_source=="NARR-monthlymean-wnd")
{
download_filenames=c("uwnd.10m.mon.mean.nc","vwnd.10m.mon.mean.nc")
download_path=paste(basepath,download_filenames,sep="/")
}
for(i in 1:length(download_filenames))
{
if(enable_download)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the file is not present...
(!overwrite & !file.exists(download_filenames[i]))
)
{
download.file(download_path[i],destfile=download_filenames[i])
}
}
}
# Preprocess the data if requested.
if(standardize)
{
narr_brick_list=vector(mode="list",length=length(download_filenames))
for(i in 1:length(download_filenames))
{
narr_brick_list[[i]]=brick(download_filenames)
}
if(climate_source=="NARR-monthlymean-wnd")
{
if(!wnd_speed_height_correction)
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]])
} else
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]],10)
}
if(!missing(final_folder))
{
setwd(final_folder)
}
setOptions(setfileext=FALSE)
projection(wnd)="+proj=lcc +lat_1=50 +lat_2=50 +lat_0=50 +lon_0=-107 +x_0=5632642 +y_0=4612546"
writeRaster(wnd,"wnd.10m.mon.ltm.grd",format="raster",overwrite=TRUE)
setOptions(setfileext=TRUE)
return(wnd)
}
}
} # END NCEP-monthlymean-wnd
# Needs passwords, boo. Not pursuing this...
if(climate_source=="PGF-daily-rad_sw" | climate_source=="PGF-daily-rad_lw")
{
if(climate_source=="PGF-daily-rad_sw")
{
basepath="http://dss.ucar.edu/datazone/dsszone/ds314.0/daily/"
pgf_prefix="dlwrf_daily_"
}
#dlwrf_daily_1948-1948.nc
if(missing(dates))
{
startdate=as.Date(date_range[1])
enddate=as.Date(date_range[2])
dates=seq.Date(startdate,enddate,by="year")
if(verbose)
{
print("Downloading/preparing the following dates...")
print(dates)
}
}
years=format(dates,"%Y")
pgf_filenames=paste(pgf_prefix,years,"-",years,".nc",sep="")
dates_N=length(pgf_filenames)
pgf_path=paste(basepath,pgf_filenames,sep="")
for(i in 1:dates_N)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the gunzipped or decompressed file is not present...
(!overwrite & !file.exists(pgf_filenames[[i]]))
)
{
download.file(pgf_path[i],destfile=basename(pgf_path[i]))
}
}
}
if(climate_source=="generic")
{
# Search for the local files
input_files=dir(dirname(local_files),
pattern=basename(local_files),
full.names=TRUE)
input_files_N=length(input_files)
if (input_files_N==0)
{
print("No files found, exiting")
return()
}
# TODO: add preconversion here.
raster_object_from_files=stack(sort(input_files))
if(!missing(proj))
{
projection(raster_object_from_files)=proj
}
if(!missing(zvalue))
{
# raster_object_from_files@zname=zname
if(zvalue=="months")
{
if (nlayers(raster_object_from_files)!=12)
# || length(zvalue) != 12
{
print("zvalue=months requires exactly 12 layers in the files and zvalue to be of length 12...")
return()
}
raster_object_from_files@zvalue=as.character(seq.Date(as.Date("9999-01-01"),as.Date("9999-12-31"),by="month"))
}else if(zvalue=="monthsyears"){
if(missing(dates))
{
startdate=as.Date(date_range[1])
enddate=as.Date(date_range[2])
dates=seq.Date(startdate,enddate,by="month")
}
raster_object_from_files@zvalue=as.character(dates)
}else
{
raster_object_from_files@zvalue=zvalue
}
}
return(raster_object_from_files)
} # END generic
# return(final_raster_list)
}
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#' Get/Preprocess Climate Data from Remote Sources
#'
#' This function downloads and preprocesses climate data from a variety of
#' sources.
#'
#'
#' @param climate_source A datasource to fetch climate data from. The
#' following datasources are currently supported: \itemize{ \item
#' "PRISM-4km-ppt": PRISM 4km monthly precipitation grids \item
#' "PRISM-4km-tmin": PRISM 4km monthly minimum temperature grids \item
#' "PRISM-4km-tmax": PRISM 4km monthly maximum temperature grids \item
#' "PRISM-4km-spi": PRISM 4km monthly standardized precipitation index grids
#' \item "PRISM-4km-tdmean": PRISM 4km monthly dewpoint grids \item
#' "PRISM-4km-elev": PRISM 4km elevation data \item "NARR-monthlymean-wnd":
#' North American Regional Reanalysis \cr monthly mean wind velocity \item
#' "NARR-longtermmonthlymean-wnd": North American Regional Reanalysis long-term
#' monthly mean wind velocity \item "generic": arbitrary local files.
#' local_files must be set if this is used. }
#' @param date_range A vector of length 2 containing the starting date and
#' ending date of a range of climate data to download.
#' @param dates Unused at present.
#' @param download_folder The target download directory of the raw data.
#' @param final_folder The final directory to store the preprocessed grd files.
#' @param standardize standardize=TRUE converts the downloaded dataset to
#' standard units used by eto and water balance \itemize{ \item precipitation:
#' average mm H20 per day \item temperature: deg C }
#' @param snow_nthreads The number of threads (CPUs) to use when processing.
#' If snow_nthreads>1, the algorithm will attempt to use the multiple
#' processing package snow via MPI. CURRENTLY UNSUPPORTED.
#' @param overwrite overwrite=TRUE will allow overwriting of downloaded and
#' preprocessed files. Setting overwrite=FALSE (default) lets the process pick
#' up where it left off if interrupted.
#' @param wnd_speed_height_correction wnd_speed_height_correction=TRUE
#' (default) corrects wind speed data to wind speed at 2m above the terrain
#' using the wind profile relationship described in
#' http://www.fao.org/docrep/x0490e/x0490e07.htm equation 47.
#' @param verbose verbose=TRUE will print process information.
#' @param enable_download If enable_download=TRUE (default), data will be
#' downloaded, otherwise download will be skipped.
#' @param local_files A search string for local files which will be used to
#' create the climate stack when climate_source="generic"
#' @param preconvert If preconvert=TRUE (default), raw data will be
#' preconverted to format=raster before any further processing occurs.
#' @param output_basename A string representing the output files. Note that
#' most formats have their own defaults which will override this.
#' @param proj A PROJ string which will be used for climate_source='generic'.
#' @param zvalue The zvalue applied to the output raster (only used with
#' climate_source= 'generic'). This should be a character string of dates
#' for each of the output layers. If zvalue='months' and the output is exactly
#' 12 bands, the zvalue will be set to dates corresponding to the first of each
#' month.
#' @return Returns a RasterLayer, RasterBrick or RasterStack object, as well as
#' saving the objects to disk.
#' @author Jonathan A. Greenberg
# @seealso \code{\link[climstats]{eto}},
# \code{\link[climstats]{climate_summaries}}
#' @references \itemize{ \item PRISM data:
#' \url{http://www.prism.oregonstate.edu/} \item PRISM terms of use:
#' \url{http://www.prism.oregonstate.edu/terms.phtml} \item PRISM metadata:
#' \url{http://www.prism.oregonstate.edu/docs/meta} } \itemize{ \item NCEP
#' North American Regional Reanalysis: \url{http://www.esrl.noaa.gov/psd/ \cr
#' data/gridded/data.narr.html} \item NCEP NARR Data Description:
#' \url{ftp://ftp.cdc.noaa.gov/Datasets/ \cr NARR/README} }
#' @keywords climate
#' @examples \dontrun{
#' # Download and decompress PRISM monthly precipitation data
#' # but do not post-process it.
#' ppt=get_climate_data("PRISM-4km-ppt",
#' date_range=c("1999/1/1","1999/12/31"),
#' standardize=FALSE, enable_download=TRUE)
#' # Note the raw, decompressed downloaded data.
#' dir()
#' # Download and post-process PRISM monthly precipitation data
#' # (note that as long as overwrite=FALSE, it will not redownload the files,
#' # just post-process the files already acquired during the previous step.
#' ppt=get_climate_data("PRISM-4km-ppt",
#' date_range=c("1999/1/1","1999/12/31"),
#' standardize=TRUE, enable_download=TRUE, overwrite=TRUE)
#' # Note the raw raster format files (*_raw.grd), and the corrected raster
#' # format data (*_climstats.grd).
#' dir()
#' summary(ppt)
#' getZ(ppt)
#' }
get_climate_data <- function(
climate_source,
date_range,
dates,
download_folder,
final_folder,
standardize=TRUE,
snow_nthreads=1,
overwrite=FALSE,
wnd_speed_height_correction=TRUE,
verbose=FALSE,
enable_download=FALSE,
local_files,
preconvert=TRUE,
output_basename="climstats_data",
proj,
zvalue)
{
# climate_source:
# PRISM-800m-ppt: PRISM 800m monthly precipitation grids
# PRISM-800m-tmin: PRISM 800m monthly min temperature grids
# PRISM-800m-tmax: PRISM 800m monthly max temperature grids
# PRISM-800m-spi: PRISM 800m monthly standardized precipitation index grids
# PRISM-800m-tdmean: PRISM 800m monthly dewpoint grids
# dates: a vector of individual dates. get_climate_data will download the product with the nearest matching date.
# startdate/enddate: used in lieu of dates.
# standardize=TRUE downloads the data, converts the units to climstats standards, and saves the
# data out as a raster or brick in the native raster format (grd).
# standardize=FALSE only downloads the data.
if(missing(download_folder))
{
download_folder=getwd()
}
setwd(download_folder)
if(missing(final_folder))
{
final_folder=getwd()
}
if( climate_source=="PRISM-4km-elev")
{
require("R.utils")
basepath="ftp://prism.oregonstate.edu/pub/prism/us/grids"
prism_filenames="us_25m_dem.asc.gz"
prism_filenames_gunzipped="us_25m_dem.asc"
prism_path=paste(basepath,prism_filenames,sep="/")
if(enable_download)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the gunzipped or decompressed file is not present...
(!overwrite & !file.exists(prism_filenames_gunzipped) & !file.exists(prism_filenames))
)
{
download.file(prism_path,destfile=basename(prism_path))
}
}
if(overwrite | (!overwrite & !file.exists(prism_filenames_gunzipped)))
{
gunzip(prism_filenames,remove=TRUE,overwrite=TRUE)
}
if(standardize)
{
require("raster")
setOptions(setfileext=FALSE)
if(overwrite |
(!overwrite & !file.exists(paste(prism_filenames_gunzipped,"_climstats.grd",sep=""))))
{
temp_raster=raster(prism_filenames_gunzipped)
writeRaster(temp_raster,paste(prism_filenames_gunzipped,"_climstats.grd",sep=""),format="raster",overwrite=TRUE)
}
setOptions(setfileext=TRUE)
elev=raster(paste(prism_filenames_gunzipped,"_climstats.grd",sep=""))
projection(elev)="+proj=longlat +ellps=WGS84 +datum=WGS84 +lon_wrap"
return(elev)
}
} # END PRISM-4km-elev
if( climate_source=="PRISM-4km-ppt" |
climate_source=="PRISM-4km-tmin" |
climate_source=="PRISM-4km-tmax" |
climate_source=="PRISM-4km-spi" |
climate_source=="PRISM-4km-tdmean")
{
require("R.utils")
basepath="ftp://prism.oregonstate.edu/pub/prism/us/grids"
yearfolderstart=seq(1890,2010,by=10)
yearfolderend=seq(1899,2019,by=10)
yearfolders=paste(yearfolderstart,yearfolderend,sep="-")
# PRISM is monthly data, so we figure the range based on months.
if(missing(dates))
{
startdate=as.Date(date_range[1])
enddate=as.Date(date_range[2])
dates=seq.Date(startdate,enddate,by="month")
if(verbose)
{
print("Downloading/preparing the following dates...")
print(dates)
}
}
years_text=format(dates,"%Y")
months_text=format(dates,"%m")
dates_N=length(dates)
prism_type=strsplit(climate_source,"-")[[1]][3]
prism_filenames=paste(paste("us",prism_type,years_text,sep="_"),months_text,"gz",sep=".")
# Figure out the full path
prism_path=vector(mode="character",length=dates_N)
for (i in 1:dates_N)
{
temp_year=as.numeric(years_text[i])
temp_yearfolderstart_check=yearfolderstart <= temp_year
temp_yearfolderend_check=yearfolderend >= temp_year
temp_yearfolder_check=temp_yearfolderstart_check & temp_yearfolderend_check
temp_yearfolder=yearfolders[temp_yearfolder_check]
prism_path[i]=paste(basepath,prism_type,temp_yearfolder,prism_filenames[i],sep="/")
}
prism_filenames_gunzipped=paste(paste("us",prism_type,years_text,sep="_"),months_text,sep=".")
raster_names_list=as.list(prism_filenames_gunzipped)
# Download and extract the files
if(enable_download)
{
for(i in 1:dates_N)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the gunzipped or decompressed file is not present...
(!overwrite & !file.exists(prism_filenames_gunzipped[[i]]) & !file.exists(prism_filenames[[i]]))
)
{
download.file(prism_path[i],destfile=basename(prism_path[i]))
}
}
}
for(i in 1:dates_N)
{
if(overwrite | (!overwrite & !file.exists(prism_filenames_gunzipped[[i]])))
{
gunzip(prism_filenames[i],remove=TRUE)
}
}
if(standardize)
{
require("raster")
# Set up each file as a raster.
# prism_filenames_gunzipped=paste(paste("us",prism_type,years_text,sep="_"),months_text,sep=".")
# raster_names_list=as.list(prism_filenames_gunzipped)
# raster_list=sapply(raster_names_list,raster,simplify=FALSE)
# For PRISM ASCIIs, its faster if we pre-convert them before we tweak them any further.
setOptions(setfileext=FALSE)
for (i in (1:dates_N))
{
if(overwrite |
(!overwrite & !file.exists(paste(prism_filenames_gunzipped[i],"_raw.grd",sep="")))
)
{
temp_raster=raster(raster_names_list[[i]])
writeRaster(temp_raster,paste(prism_filenames_gunzipped[i],"_raw.grd",sep=""),format="raster",overwrite=TRUE)
}
}
setOptions(setfileext=TRUE)
# raster_list=sapply(paste(raster_names_list,"_raw.grd",sep=""),raster,simplify=FALSE)
raster_raw_list=as.list(paste(raster_names_list,"_raw.gri",sep=""))
# stack_raw=stack(raster_raw_list)
# Now assign the proper dates.
days_in_months=c(31,28,31,30,31,30,31,31,30,31,30,31)
middays_in_months=ceiling(days_in_months/2)
dates_raster=vector("character",length=dates_N)
for (i in (1:dates_N))
{
temp_date_vector=as.numeric(strsplit(strsplit(prism_filenames_gunzipped[i],"_")[[1]][3],"[.]")[[1]])
if (temp_date_vector[2] >= 1 & temp_date_vector[2] <=12)
{
dates_raster[i]=paste(temp_date_vector[1],temp_date_vector[2],middays_in_months[temp_date_vector[2]],sep="-")
} else
{
dates_raster[i]="NA"
}
}
dates_raster=as.Date(dates_raster)
# for (i in (1:dates_N))
# {
# raster_list[[i]]@zvalue=as.character(dates_raster[i])
# }
climstats_filenames=vector(mode="character",length=dates_N)
for(i in (1:dates_N))
{
climstats_filenames[i]=paste(prism_filenames_gunzipped[i],"_climstats.grd",sep="")
}
if(prism_type=="ppt")
{
# if((snow_nthreads) > 1)
# {
# require("snow")
# cl <- makeCluster(snow_nthreads, type = "MPI")
# final_raster_list=clusterMap(cl,apply_gains_offsets,raster_list,MoreArgs=list(gains=(1/100),divide_by_days_in_month=TRUE))
# stopCluster(cl)
# } else
# final_raster_list=mapply(apply_gains_offsets,raster_list,MoreArgs=list(gains=(1/100),divide_by_days_in_month=TRUE))
# }
setOptions(setfileext=FALSE)
for (i in (1:dates_N))
{
if(overwrite |
(!overwrite & !file.exists(climstats_filenames[i]))
)
{
setwd(download_folder)
temp_raster=raster(raster_raw_list[[i]])
temp_raster@zvalue=as.character(dates_raster[i])
temp_raster@zname="Date/time"
temp_raster_standardized=apply_gains_offsets(temp_raster,gains=(1/100),divide_by_days_in_month=TRUE)
setwd(final_folder)
writeRaster(temp_raster_standardized,climstats_filenames[i],format="raster",overwrite=TRUE)
}
}
setOptions(setfileext=TRUE)
}
if(prism_type=="tmin" | prism_type=="tmax")
{
setOptions(setfileext=FALSE)
for (i in (1:dates_N))
{
if(overwrite |
(!overwrite & !file.exists(paste(prism_filenames_gunzipped[i],"_climstats.grd",sep="")))
)
{
setwd(download_folder)
temp_raster=raster(raster_raw_list[[i]])
temp_raster@zvalue=as.character(dates_raster[i])
temp_raster@zname="Date/time"
temp_raster_standardized=apply_gains_offsets(temp_raster,gains=(1/100),divide_by_days_in_month=FALSE)
projection(temp_raster_standardized)="+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
setwd(final_folder)
writeRaster(temp_raster_standardized,climstats_filenames[i],format="raster",overwrite=TRUE)
}
}
setOptions(setfileext=TRUE)
}
setwd(final_folder)
climstats_stack=stack(as.list(climstats_filenames))
climstats_stack@zvalue=as.character(dates_raster)
climstats_stack@zname="Date/time"
projection(climstats_stack)="+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
return(climstats_stack)
}
} # End PRISM monthly climate data
# North American Regional Reanalysis data (monthly mean)
# http://www.esrl.noaa.gov/psd/data/gridded/data.narr.html
# This isn't working right now, don't use.
if(climate_source=="NARR-monthlymean-rad_sw" | climate_source=="NARR-monthlymean-rad_lw")
{
require("ncdf")
basepath="ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.derived/surface_gauss"
if(climate_source=="NARR-monthlymean-rad_sw")
{
download_filenames="nswrs.mon.mean.nc"
download_path=paste(basepath,download_filenames,sep="/")
}
if(climate_source=="NARR-monthlymean-rad_sw")
{
download_filenames="nlwrs.mon.mean.nc"
download_path=paste(basepath,download_filenames,sep="/")
}
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the file is not present...
(!overwrite & !file.exists(download_filenames[i]))
)
{
download.file(download_path[i],destfile=download_filenames[i])
}
if(standardize)
{
narr_brick_list=vector(mode="list",length=length(download_filenames))
for(i in 1:length(download_filenames))
{
narr_brick_list[[i]]=brick(download_filenames)
}
}
}
# BEGIN NARR-longtermmonthlymean-wnd
if(climate_source=="NARR-longtermmonthlymean-wnd")
{
require("ncdf")
basepath="ftp://ftp.cdc.noaa.gov/Datasets/NARR/Derived/monolevel"
# if(missing(dates))
# {
# dates=seq.Date(startdate,enddate,by="month")
# }
# dates_N=length(dates)
if(!missing(download_folder))
{
setwd(download_folder)
}
# Set up filenames to download.
if(climate_source=="NARR-longtermmonthlymean-wnd")
{
download_filenames=c("uwnd.10m.mon.ltm.nc","vwnd.10m.mon.ltm.nc")
download_path=paste(basepath,download_filenames,sep="/")
}
for(i in 1:length(download_filenames))
{
if(enable_download)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the file is not present...
(!overwrite & !file.exists(download_filenames[i]))
)
{
download.file(download_path[i],destfile=download_filenames[i])
}
}
}
# Preprocess the data if requested.
if(standardize)
{
narr_brick_list=vector(mode="list",length=length(download_filenames))
for(i in 1:length(download_filenames))
{
narr_brick_list[[i]]=brick(download_filenames)
}
if(climate_source=="NARR-longtermmonthlymean-wnd")
{
if(!wnd_speed_height_correction)
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]])
} else
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]],10)
}
if(!missing(final_folder))
{
setwd(final_folder)
}
setOptions(setfileext=FALSE)
projection(wnd)="+proj=lcc +lat_1=50 +lat_2=50 +lat_0=50 +lon_0=-107 +x_0=5632642 +y_0=4612546"
writeRaster(wnd,"wnd.10m.mon.ltm.grd",format="raster",overwrite=TRUE)
setOptions(setfileext=TRUE)
return(wnd)
}
}
} # END NARR-longtermmonthlymean-wnd
# BEGIN NARR-monthlymean-wnd
if(climate_source=="NARR-monthlymean-wnd")
{
require("ncdf")
basepath="ftp://ftp.cdc.noaa.gov/Datasets/NARR/Monthlies/monolevel"
# if(missing(dates))
# {
# dates=seq.Date(startdate,enddate,by="month")
# }
# dates_N=length(dates)
if(!missing(download_folder))
{
setwd(download_folder)
}
# Set up filenames to download.
if(climate_source=="NARR-monthlymean-wnd")
{
download_filenames=c("uwnd.10m.mon.mean.nc","vwnd.10m.mon.mean.nc")
download_path=paste(basepath,download_filenames,sep="/")
}
for(i in 1:length(download_filenames))
{
if(enable_download)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the file is not present...
(!overwrite & !file.exists(download_filenames[i]))
)
{
download.file(download_path[i],destfile=download_filenames[i])
}
}
}
# Preprocess the data if requested.
if(standardize)
{
narr_brick_list=vector(mode="list",length=length(download_filenames))
for(i in 1:length(download_filenames))
{
narr_brick_list[[i]]=brick(download_filenames)
}
if(climate_source=="NARR-monthlymean-wnd")
{
if(!wnd_speed_height_correction)
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]])
} else
{
wnd=windvectors_to_wind(narr_brick_list[[1]],narr_brick_list[[2]],10)
}
if(!missing(final_folder))
{
setwd(final_folder)
}
setOptions(setfileext=FALSE)
projection(wnd)="+proj=lcc +lat_1=50 +lat_2=50 +lat_0=50 +lon_0=-107 +x_0=5632642 +y_0=4612546"
writeRaster(wnd,"wnd.10m.mon.ltm.grd",format="raster",overwrite=TRUE)
setOptions(setfileext=TRUE)
return(wnd)
}
}
} # END NCEP-monthlymean-wnd
# Needs passwords, boo. Not pursuing this...
if(climate_source=="PGF-daily-rad_sw" | climate_source=="PGF-daily-rad_lw")
{
if(climate_source=="PGF-daily-rad_sw")
{
basepath="http://dss.ucar.edu/datazone/dsszone/ds314.0/daily/"
pgf_prefix="dlwrf_daily_"
}
#dlwrf_daily_1948-1948.nc
if(missing(dates))
{
startdate=as.Date(date_range[1])
enddate=as.Date(date_range[2])
dates=seq.Date(startdate,enddate,by="year")
if(verbose)
{
print("Downloading/preparing the following dates...")
print(dates)
}
}
years=format(dates,"%Y")
pgf_filenames=paste(pgf_prefix,years,"-",years,".nc",sep="")
dates_N=length(pgf_filenames)
pgf_path=paste(basepath,pgf_filenames,sep="")
for(i in 1:dates_N)
{
if(
# If overwrites are allowed...
overwrite |
# If overwrites are disabled but the gunzipped or decompressed file is not present...
(!overwrite & !file.exists(pgf_filenames[[i]]))
)
{
download.file(pgf_path[i],destfile=basename(pgf_path[i]))
}
}
}
if(climate_source=="generic")
{
# Search for the local files
input_files=dir(dirname(local_files),
pattern=basename(local_files),
full.names=TRUE)
input_files_N=length(input_files)
if (input_files_N==0)
{
print("No files found, exiting")
return()
}
# TODO: add preconversion here.
raster_object_from_files=stack(sort(input_files))
if(!missing(proj))
{
projection(raster_object_from_files)=proj
}
if(!missing(zvalue))
{
# raster_object_from_files@zname=zname
if(zvalue=="months")
{
if (nlayers(raster_object_from_files)!=12)
# || length(zvalue) != 12
{
print("zvalue=months requires exactly 12 layers in the files and zvalue to be of length 12...")
return()
}
raster_object_from_files@zvalue=as.character(seq.Date(as.Date("9999-01-01"),as.Date("9999-12-31"),by="month"))
}else if(zvalue=="monthsyears"){
if(missing(dates))
{
startdate=as.Date(date_range[1])
enddate=as.Date(date_range[2])
dates=seq.Date(startdate,enddate,by="month")
}
raster_object_from_files@zvalue=as.character(dates)
}else
{
raster_object_from_files@zvalue=zvalue
}
}
return(raster_object_from_files)
} # END generic
# return(final_raster_list)
}
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