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#! /bin/R
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### Script to download and process the NDP-026D station cloud dataset
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setwd("~/acrobates/adamw/projects/interp/data/NDP026D")
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library(multicore)
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library(latticeExtra)
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library(doMC)
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library(rasterVis)
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library(rgdal)
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library(reshape)
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library(hexbin)
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## register parallel processing
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#registerDoMC(10)
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#beginCluster(10)
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## available here http://cdiac.ornl.gov/epubs/ndp/ndp026d/ndp026d.html
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## Get station locations
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system("wget -N -nd http://cdiac.ornl.gov/ftp/ndp026d/cat01/01_STID -P data/")
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st=read.table("data/01_STID",skip=1)
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colnames(st)=c("StaID","LAT","LON","ELEV","ny1","fy1","ly1","ny7","fy7","ly7","SDC","b5c")
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st$lat=st$LAT/100
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st$lon=st$LON/100
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st$lon[st$lon>180]=st$lon[st$lon>180]-360
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st=st[,c("StaID","ELEV","lat","lon")]
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colnames(st)=c("id","elev","lat","lon")
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write.csv(st,"stations.csv",row.names=F)
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coordinates(st)=c("lon","lat")
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## download data
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system("wget -N -nd ftp://cdiac.ornl.gov/pub/ndp026d/cat67_78/* -A '.tc.Z' -P data/")
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system("gunzip data/*.Z")
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## define FWF widths
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f162=c(5,5,4,7,7,7,4) #format 162
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c162=c("StaID","YR","Nobs","Amt","Fq","AWP","NC")
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## use monthly timeseries
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cld=do.call(rbind.data.frame,mclapply(sprintf("%02d",1:12),function(m) {
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d=read.fwf(list.files("data",pattern=paste("MNYDC.",m,".tc",sep=""),full=T),skip=1,widths=f162)
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colnames(d)=c162
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d$month=as.numeric(m)
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print(m)
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return(d)}
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))
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## add lat/lon
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cld[,c("lat","lon")]=st[match(cld$StaID,st$id),c("lat","lon")]
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## drop missing values
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cld=cld[,!grepl("Fq|AWP|NC",colnames(cld))]
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cld$Amt[cld$Amt<0]=NA
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#cld$Fq[cld$Fq<0]=NA
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#cld$AWP[cld$AWP<0]=NA
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#cld$NC[cld$NC<0]=NA
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#cld=cld[cld$Nobs>0,]
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## add the MOD09 data to cld
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#### Evaluate MOD35 Cloud data
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mod09=brick("~/acrobates/adamw/projects/cloud/data/mod09.nc")
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## overlay the data with 32km diameter (16km radius) buffer
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## buffer size from Dybbroe, et al. (2005) doi:10.1175/JAM-2189.1.
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buf=16000
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#mod09sta=lapply(1:nlayers(mod09),function(l) {print(l); extract(mod09[[l]],st,buffer=buf,fun=mean,na.rm=T,df=T)[,2]})
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bins=cut(1:nrow(st),100)
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mod09sta=lapply(levels(bins),function(lb) {
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l=which(bins==lb)
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td=extract(mod09,st[l,],buffer=buf,fun=mean,na.rm=T,df=T)
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td$id=st$id[l]
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print(lb)#as.vector(c(l,td[,1:4])))
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write.table(td,"valid.csv",append=T,col.names=F,quote=F,sep=",",row.names=F)
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td
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})#,mc.cores=3)
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#mod09sta=extract(mod09,st,buffer=buf,fun=mean,na.rm=T,df=T)
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mod09st=read.csv("valid.csv",header=F)[,-c(1,2)]
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#mod09st=do.call(rbind.data.frame,mod09sta)
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#mod09st=mod09st[,!is.na(colnames(mod09st))]
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colnames(mod09st)=c(names(mod09),"id")
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#mod09st$id=st$id
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mod09stl=melt(mod09st,id.vars="id")
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mod09stl[,c("year","month")]=do.call(rbind,strsplit(sub("X","",mod09stl$variable),"[.]"))[,1:2]
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## add it to cld
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cld$mod09=mod09stl$value[match(paste(cld$StaID,cld$YR,cld$month),paste(mod09stl$id,mod09stl$year,as.numeric(mod09stl$month)))]
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## LULC
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#system(paste("gdalwarp -r near -co \"COMPRESS=LZW\" -tr ",paste(res(mod09),collapse=" ",sep=""),
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# "-tap -multi -t_srs \"", projection(mod09),"\" /mnt/data/jetzlab/Data/environ/global/landcover/MODIS/MCD12Q1_IGBP_2005_v51.tif ../modis/mod12/MCD12Q1_IGBP_2005_v51.tif"))
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lulc=raster("../modis/mod12/MCD12Q1_IGBP_2005_v51.tif")
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#lulc=ratify(lulc)
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require(plotKML); data(worldgrids_pal) #load IGBP palette
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IGBP=data.frame(ID=0:16,col=worldgrids_pal$IGBP[-c(18,19)],stringsAsFactors=F)
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IGBP$class=rownames(IGBP);rownames(IGBP)=1:nrow(IGBP)
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levels(lulc)=list(IGBP)
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#lulc=crop(lulc,mod09)
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Mode <- function(x) {
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ux <- na.omit(unique(x))
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ux[which.max(tabulate(match(x, ux)))]
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}
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lulcst=extract(lulc,st,fun=Mode,buffer=buf,df=T)
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colnames(lulcst)=c("id","lulc")
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## add it to cld
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cld$lulc=lulcst$lulc[match(cld$StaID,lulcst$id)]
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#cld$lulc=factor(as.integer(cld$lulc),labels=IGBP$class[sort(unique(cld$lulc))])
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## update cld column names
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colnames(cld)[grep("Amt",colnames(cld))]="cld"
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cld$cld=cld$cld/100
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## calculate means and sds
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cldm=do.call(rbind.data.frame,by(cld,list(month=as.factor(cld$month),StaID=as.factor(cld$StaID)),function(x){
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data.frame(
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month=x$month[1],
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lulc=x$lulc[1],
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StaID=x$StaID[1],
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mod09=mean(x$mod09,na.rm=T),
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mod09sd=sd(x$mod09,na.rm=T),
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cld=mean(x$cld[x$Nobs>10],na.rm=T),
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cldsd=sd(x$cld[x$Nobs>10],na.rm=T))}))
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cldm[,c("lat","lon")]=coordinates(st)[match(cldm$StaID,st$id),c("lat","lon")]
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## means by year
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cldy=do.call(rbind.data.frame,by(cld,list(year=as.factor(cld$YR),StaID=as.factor(cld$StaID)),function(x){
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data.frame(
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year=x$YR[1],
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StaID=x$StaID[1],
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lulc=x$lulc[1],
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mod09=mean(x$mod09,na.rm=T),
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mod09sd=sd(x$mod09,na.rm=T),
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cld=mean(x$Amt[x$Nobs>10]/100,na.rm=T),
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cldsd=sd(x$Amt[x$Nobs>10]/100,na.rm=T))}))
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cldy[,c("lat","lon")]=coordinates(st)[match(cldy$StaID,st$id),c("lat","lon")]
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## overall mean
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clda=do.call(rbind.data.frame,by(cld,list(StaID=as.factor(cld$StaID)),function(x){
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data.frame(
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StaID=x$StaID[1],
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lulc=x$lulc[1],
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mod09=mean(x$mod09,na.rm=T),
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mod09sd=sd(x$mod09,na.rm=T),
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cld=mean(x$cld[x$Nobs>10],na.rm=T),
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cldsd=sd(x$cld[x$Nobs>10],na.rm=T))}))
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clda[,c("lat","lon")]=coordinates(st)[match(clda$StaID,st$id),c("lat","lon")]
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## write out the tables
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write.csv(cld,file="cld.csv",row.names=F)
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write.csv(cldy,file="cldy.csv",row.names=F)
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write.csv(cldm,file="cldm.csv",row.names=F)
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write.csv(clda,file="clda.csv",row.names=F
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)
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#########################################################################
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##################
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###
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cld=read.csv("cld.csv")
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cldm=read.csv("cldm.csv")
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cldy=read.csv("cldy.csv")
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clda=read.csv("clda.csv")
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st=read.csv("stations.csv")
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### remove mod09==0 due to mosaic problem (remove when fixed)
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cld=cld[!is.na(cld$lat)&cld$mod09!=0,]
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cldm=cldm[!is.na(cldm$lat)&cldm$mod09!=0,]
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cldy=cldy[!is.na(cldy$lat)&cldy$mod09!=0,]
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## month factors
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cld$month2=factor(cld$month,labels=month.name)
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cldm$month2=factor(cldm$month,labels=month.name)
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coordinates(st)=c("lon","lat")
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projection(st)=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
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##make spatial object
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cldms=cldm
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coordinates(cldms)=c("lon","lat")
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projection(cldms)=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
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##make spatial object
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cldys=cldy
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coordinates(cldys)=c("lon","lat")
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projection(cldys)=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
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#### Evaluate MOD35 Cloud data
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mod09=brick("~/acrobates/adamw/projects/cloud/data/mod09.nc")
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mod09c=brick("~/acrobates/adamw/projects/cloud/data/mod09_clim.nc",varname="CF");names(mod09c)=month.name
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mod09c2=raster("~/acrobates/adamw/projects/cloud/data/mod09_clim.nc",varname="CF",nl=1)
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### get monthly climatologies for each station
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#cldc=do.call(rbind.data.frame,by(cld,list(id=cld$StaID,month=cld$month),function(x){
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# x$mod09[x$mod09==0]=NA
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# data.frame(id=x$StaID[1],month=x$month[1],Nobs=sum(x$Nobs,na.rm=T),Amt=mean(x$Amt,na.rm=T),mod09=mean(x$mod09,na.rm=T))
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# }))
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## read in global coasts for nice plotting
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library(maptools)
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data(wrld_simpl)
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coast <- unionSpatialPolygons(wrld_simpl, rep("land",nrow(wrld_simpl)), threshold=5)
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coast=as(coast,"SpatialLines")
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#coast=spTransform(coast,CRS(projection(mod35)))
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n=100
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at=seq(0,100,length=n)
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colr=colorRampPalette(c("black","green","red"))
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cols=colr(n)
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pdf("/home/adamw/acrobates/adamw/projects/cloud/output/validation.pdf",width=11,height=8.5)
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### maps of mod09 and NDP
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## map of stations
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xyplot(lat~lon,data=data.frame(coordinates(st)),pch=16,cex=.5, main="NDP-026D Cloud Climatology Stations",ylab="Latitude",xlab="Longitude")+
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layer(sp.lines(coast,col="grey"),under=T)
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levelplot(mod09c,col.regions=colr(100),at=seq(0,100,len=100),margin=F,maxpixels=1e5,main="MOD09 Cloud Frequency",ylab="Latitude",xlab="Longitude")
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#p2=xyplot(lat~lon|month2,data=cldm,col=as.character(cut(cldm$cld,seq(0,100,len=100),labels=colr(99))),pch=16,cex=.1,auto.key=T,asp=1,
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# main="NDP-026D Cloud Climatology Stations",ylab="Latitude",xlab="Longitude",layout=c(12,1))+
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# layer(sp.lines(coast,col="black",lwd=.1),under=F)
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#v_month=c(p1,p2,layout=c(12,2),x.same=T,y.same=T,merge.legends=T)
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#print(v_month)
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#xyplot(lat~lon|month2,groups=cut(cldm$cld,seq(0,100,len=5)),data=cldm,pch=".",cex=.2,auto.key=T,
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# main="Mean Monthly Cloud Coverage",ylab="Latitude",xlab="Longitude",
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# par.settings = list(superpose.symbol= list(pch=16,col=c("blue","green","yellow","red"))))+
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# layer(sp.lines(coast,col="grey"),under=T)
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### heatmap of mod09 vs. NDP for all months
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hmcols=colorRampPalette(c("grey","blue","red"))
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tr=c(0,27)
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colkey <- draw.colorkey(list(col = hmcols(tr[2]), at = tr[1]:tr[2],height=.25))
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xyplot(cld~mod09,data=cld[cld$Nobs>10,],panel=function(x,y,subscripts){
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n=150
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bins=seq(0,100,len=n)
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tb=melt(as.matrix(table(
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x=cut(x,bins,labels=bins[-1]),
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y=cut(y,bins,labels=bins[-1]))))
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qat=tr[1]:tr[2]#unique(tb$value)
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print(qat)
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panel.levelplot(tb$x,tb$y,tb$value,at=qat,col.regions=c("transparent",hmcols(length(qat))),subscripts=subscripts)
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},asp=1,scales=list(at=seq(0,100,len=6)),ylab="NDP Mean Cloud Amount (%)",xlab="MOD09 Cloud Frequency (%)",
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legend= list(right = list(fun = colkey,title="Station Count")))+
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layer(panel.abline(0,1,col="black",lwd=2))+
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layer(panel.ablineq(lm(y ~ x), r.sq = TRUE,at = 0.6,pos=1, offset=22,digits=2,col="blue"), style = 1)
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xyplot(cld~mod09|month2,data=cld[cld$Nobs>10,],panel=function(x,y,subscripts){
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n=50
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bins=seq(0,100,len=n)
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tb=melt(as.matrix(table(
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x=cut(x,bins,labels=bins[-1]),
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y=cut(y,bins,labels=bins[-1]))))
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qat=unique(tb$value)
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print(qat)
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qat=0:26
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qat=tr[1]:tr[2]#unique(tb$value)
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panel.levelplot(tb$x,tb$y,tb$value,at=qat,col.regions=c("transparent",hmcols(length(qat))),subscripts=1:nrow(tb))
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layer(panel.abline(0,1,col="black",lwd=2))+
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layer(panel.ablineq(lm(y ~ x), r.sq = TRUE,at = 0.6,pos=1, offset=0,digits=2,col="blue"), style = 1)
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},asp=1,scales=list(at=seq(0,100,len=6),useRaster=T,colorkey=list(width=.5,title="Number of Stations")),
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ylab="NDP Mean Cloud Amount (%)",xlab="MOD09 Cloud Frequency (%)",
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legend= list(right = list(fun = colkey)))+ layer(panel.abline(0,1,col="black",lwd=2))
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xyplot(cld~mod09,data=clda,cex=0.5,pch=16)+
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layer(panel.abline(lm(y~x),col="blue"))+
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# layer(panel.lines(x,predict(lm(y~x),type="prediction")))+
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layer(panel.loess(x,y,col="blue",span=.2))+
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layer(panel.abline(0,1,col="red"))+
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277
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layer(panel.segments(mod09,cld-cldsd,mod09,cld+cldsd,col="grey"),data=clda,under=T,magicdots=T)
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278
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279
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## all monthly values
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280
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#xyplot(cld~mod09|as.factor(month),data=cld[cld$Nobs>75,],cex=.2,pch=16,subscripts=T)+
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281
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# layer(panel.abline(lm(y~x),col="blue"))+
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282
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# layer(panel.abline(0,1,col="red"))
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283
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284
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## Monthly Climatologies
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285
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for(i in 1:2){
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286
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p1=xyplot(cld~mod09|month2,data=cldm,cex=.2,pch=16,subscripts=T,ylab="NDP Mean Cloud Amount",xlab="MOD09 Cloud Frequency (%)")+
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287
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layer(panel.lines(1:100,predict(lm(y~x),newdata=data.frame(x=1:100)),col="green"))+
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288
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layer(panel.lines(1:100,predict(lm(y~x+I(x^3)),newdata=data.frame(x=1:100)),col="blue"))+
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289
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layer(panel.abline(0,1,col="red"))
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290
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if(i==2){
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291
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p1=p1+layer(panel.segments(mod09[subscripts],cld[subscripts]-cldsd[subscripts],mod09[subscripts],cld[subscripts]+cldsd[subscripts],subscripts=subscripts,col="grey"),data=cldm,under=T,magicdots=T)
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292
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p1=p1+layer(panel.segments(mod09[subscripts]-mod09sd[subscripts],cld[subscripts],mod09[subscripts]+mod09sd[subscripts],cld[subscripts],subscripts=subscripts,col="grey"),data=cldm,under=T,magicdots=T)
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293
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}
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294
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print(p1)
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295
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}
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|
296
|
|
|
297
|
dev.off()
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298
|
|
|
299
|
|
|
300
|
summary(lm(Amt~mod09,data=cld))
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301
|
summary(lm(cld~mod09_10+as.factor(lulc),data=d))
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302
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summary(lm(cld~mod09_10+as.factor(lulc),data=d))
|
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303
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|
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304
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### exploratory plots
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305
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xyplot(cld~mod09_10,groups=lulc,data=d@data,pch=16,cex=.5)+layer(panel.abline(0,1,col="red"))
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306
|
xyplot(cld~mod09_10+mod35c5_10|as.factor(lulc),data=d@data,type=c("p","r"),pch=16,cex=.25,auto.key=T)+layer(panel.abline(0,1,col="green"))
|
|
307
|
xyplot(cld~mod35_10|as.factor(lulc),data=d@data,pch=16,cex=.5)+layer(panel.abline(0,1,col="red"))
|
|
308
|
xyplot(mod35_10~mod09_10|as.factor(lulc),data=d@data,pch=16,cex=.5)+layer(panel.abline(0,1,col="red"))
|
|
309
|
|
|
310
|
densityplot(stack(mod35,mod09))
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|
311
|
boxplot(mod35,lulc)
|
|
312
|
|
|
313
|
bwplot(mod09~mod35|cut(y,5),data=stack(mod09,mod35))
|
|
314
|
|
|
315
|
## add a color key
|
|
316
|
breaks=seq(0,100,by=25)
|
|
317
|
cldm$cut=cut(cldm$cld,breaks)
|
|
318
|
cp=colorRampPalette(c("blue","orange","red"))
|
|
319
|
cols=cp(length(at))
|
|
320
|
|
|
321
|
|
|
322
|
|
|
323
|
## write a pdf
|
|
324
|
#dir.create("output")
|
|
325
|
pdf("output/NDP026d.pdf",width=11,height=8.5)
|
|
326
|
|
|
327
|
|
|
328
|
|
|
329
|
## Validation
|
|
330
|
m=10
|
|
331
|
zlim=c(40,100)
|
|
332
|
dr=subset(mod35,subset=m);projection(dr)=projection(mod35)
|
|
333
|
ds=cldms[cldms$month==m,]
|
|
334
|
plot(dr,col=cp(100),zlim=zlim,main="Comparison of MOD35 Cloud Frequency and NDP-026D Station Cloud Climatologies",
|
|
335
|
ylab="Northing (m)",xlab="Easting (m)",sub="MOD35 is proportion of cloudy days, while NDP-026D is Mean Cloud Coverage")
|
|
336
|
plot(ds,add=T,pch=21,cex=3,lwd=2,fg="black",bg=as.character(cut(ds$cld,breaks=seq(zlim[1],zlim[2],len=5),labels=cp(4))))
|
|
337
|
#legend("topright",legend=seq(zlim[1],zlim[2],len=5),pch=16,col=cp(length(breaks)))
|
|
338
|
|
|
339
|
|
|
340
|
xyplot(mod35~cld,data=mod35v,subscripts=T,auto.key=T,panel=function(x,y,subscripts){
|
|
341
|
td=mod35v[subscripts,]
|
|
342
|
# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
|
|
343
|
panel.xyplot(x,y,subscripts=subscripts,type=c("p","smooth"),pch=16,col="black")
|
|
344
|
# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
|
|
345
|
},ylab="MOD35 Proportion Cloudy Days",xlab="NDP-026D Mean Monthly Cloud Amount",
|
|
346
|
main="Comparison of MOD35 Cloud Mask and Station Cloud Climatologies")
|
|
347
|
|
|
348
|
#xyplot(mod35~cld|month,data=mod35v,subscripts=T,auto.key=T,panel=function(x,y,subscripts){
|
|
349
|
# td=mod35v[subscripts,]
|
|
350
|
# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
|
|
351
|
# panel.xyplot(x,y,subscripts=subscripts,type=c("p","smooth"),pch=16,col="black")
|
|
352
|
# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
|
|
353
|
# },ylab="MOD35 Proportion Cloudy Days",xlab="NDP-026D Mean Monthly Cloud Amount",
|
|
354
|
# main="Comparison of MOD35 Cloud Mask and Station Cloud Climatologies")
|
|
355
|
|
|
356
|
|
|
357
|
dev.off()
|
|
358
|
|
|
359
|
graphics.off()
|
