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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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## register parallel processing
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registerDoMC(20)
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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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## 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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## get monthly mean cloud amount MMCF
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#system("wget -N -nd ftp://cdiac.ornl.gov/pub/ndp026d/cat08_09/* -A '.tc.Z' -P data/")
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#system("gunzip data/*.Z")
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#f121=c(6,6,6,7,6,7,6,2) #format 121
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#c121=c("StaID","NobD","AvgDy","NobN","AvgNt","NobDN","AvgDN","Acode")
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#cld=do.call(rbind.data.frame,lapply(sprintf("%02d",1:12),function(m) {
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# d=read.fwf(list.files("data",pattern=paste("MMCA.",m,".tc",sep=""),full=T),skip=1,widths=f162)
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# colnames(d)=c121
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# d$month=as.numeric(m)
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# return(d)}
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# ))
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## define FWF widths
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f162=c(5,5,4,7,7,7,4) #format 162
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c162=c("StaID","YR","Nobs","Amt","Fq","AWP","NC")
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## use monthly timeseries
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cld=do.call(rbind.data.frame,mclapply(sprintf("%02d",1:12),function(m) {
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d=read.fwf(list.files("data",pattern=paste("MNYDC.",m,".tc",sep=""),full=T),skip=1,widths=f162)
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colnames(d)=c162
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d$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$StaID,st$StaID),c("lat","lon")]
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## drop missing values
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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$Nobs>0,]
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## calculate means and sds
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cldm=do.call(rbind.data.frame,by(cld,list(month=as.factor(cld$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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StaID=x$StaID[1],
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cld=mean(x$Amt[x$Nobs>10]/100,na.rm=T),
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cldsd=sd(x$Amt[x$Nobs>10]/100,na.rm=T))}))
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cldm[,c("lat","lon")]=st[match(cldm$StaID,st$StaID),c("lat","lon")]
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## means by year
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cldy=do.call(rbind.data.frame,by(cld,list(year=as.factor(cld$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$StaID[1],
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cld=mean(x$Amt[x$Nobs>10]/100,na.rm=T),
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cldsd=sd(x$Amt[x$Nobs>10]/100,na.rm=T))}))
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cldy[,c("lat","lon")]=st[match(cldy$StaID,st$StaID),c("lat","lon")]
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#cldm=foreach(m=unique(cld$month),.combine='rbind')%:%
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# foreach(s=unique(cld$StaID),.combine="rbind") %dopar% {
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# x=cld[cld$month==m&cld$StaID==s,]
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# data.frame(
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# month=x$month[1],
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# StaID=x$StaID[1],
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# Amt=mean(x$Amt[x$Nobs>10],na.rm=T)/100)}
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## write out the tables
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write.csv(cldy,file="cldy.csv")
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write.csv(cldm,file="cldm.csv")
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#########################################################################
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##################
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###
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cldm=read.csv("cldm.csv")
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cldy=read.csv("cldy.csv")
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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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mod35=brick("../modis/mod35/MOD35_h11v08.nc",varname="CLD01")
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mod35sd=brick("../modis/mod35/MOD35_h11v08.nc",varname="CLD_sd")
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projection(mod35)="+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs"
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### use data from google earth engine
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mod35=raster("../modis/mod09/global_2009/MOD35_2009.tif")
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mod09=raster("../modis/mod09/global_2009/MOD09_2009.tif")
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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$class=rownames(IGBP);rownames(IGBP)=1:nrow(IGBP)
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levels(lulc)=list(IGBP)
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lulc=crop(lulc,mod09)
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n=100
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at=seq(0,100,length=n)
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colr=colorRampPalette(c("black","green","red"))
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cols=colr(n)
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dif=mod35-mod09
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bwplot(dif~as.factor(lulc))
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levelplot(mod35,col.regions=cols,at=at,margins=F,maxpixels=1e6)#,xlim=c(-100,-50),ylim=c(0,10))
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levelplot(lulc,att="class",col.regions=levels(lulc)[[1]]$col,margin=F,maxpixels=1e6)
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#cldys=spTransform(cldys,CRS(projection(mod35)))
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mod35v=foreach(m=unique(cldm$month),.combine="rbind") %do% {
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dr=subset(mod35,subset=m);projection(dr)=projection(mod35)
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dr2=subset(mod35sd,subset=m);projection(dr2)=projection(mod35)
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ds=cldms[cldms$month==m,]
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ds$mod35=unlist(extract(dr,ds,buffer=10,fun=mean,na.rm=T))
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# ds$mod35sd=extract(dr2,ds,buffer=10)
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print(m)
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return(ds@data[!is.na(ds$mod35),])}
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y=2009
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d=cldys[cldys$year==y,]
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d$mod35_10=unlist(extract(mod35,d,buffer=10000,fun=mean,na.rm=T))
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d$mod09_10=unlist(extract(mod09,d,buffer=10000,fun=mean,na.rm=T))
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d$dif=d$mod35_10-d$mod09_10
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d$dif2=d$mod35_10-d$cld
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d$lulc=unlist(extract(lulc,d))
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d$lulc_10=unlist(extract(lulc,d,buffer=10000,fun=mode,na.rm=T))
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d$lulc=factor(d$lulc,labels=IGBP$class)
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save(d,file="annualsummary.Rdata")
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## quick model to explore fit
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plot(cld~mod35,groups=lulc,data=d)
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summary(lm(cld~mod35+as.factor(lulc),data=d))
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summary(lm(cld~mod09_10,data=d))
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summary(lm(cld~mod09_10+as.factor(lulc),data=d))
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summary(lm(cld~mod09_10+as.factor(lulc),data=d))
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### exploratory plots
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xyplot(cld~mod09_10,groups=lulc,data=d@data,pch=16,cex=.5)+layer(panel.abline(0,1,col="red"))
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xyplot(cld~mod09_10+mod35_10|as.factor(lulc),data=d@data,type=c("p","r"),pch=16,cex=.25,auto.key=T)+layer(panel.abline(0,1,col="green"))
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xyplot(cld~mod35_10|as.factor(lulc),data=d@data,pch=16,cex=.5)+layer(panel.abline(0,1,col="red"))
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xyplot(mod35_10~mod09_10|as.factor(lulc),data=d@data,pch=16,cex=.5)+layer(panel.abline(0,1,col="red"))
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densityplot(stack(mod35,mod09))
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boxplot(mod35,lulc)
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bwplot(mod09~mod35|cut(y,5),data=stack(mod09,mod35))
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## month factors
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cldm$month2=factor(cldm$month,labels=month.name)
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## add a color key
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breaks=seq(0,100,by=25)
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cldm$cut=cut(cldm$cld,breaks)
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cp=colorRampPalette(c("blue","orange","red"))
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cols=cp(length(at))
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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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## write a pdf
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#dir.create("output")
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pdf("output/NDP026d.pdf",width=11,height=8.5)
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## map of stations
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xyplot(lat~lon,data=st,pch=16,cex=.5,col="black",auto.key=T,
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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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xyplot(lat~lon|month2,groups=cut,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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## Validation
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m=10
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zlim=c(40,100)
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dr=subset(mod35,subset=m);projection(dr)=projection(mod35)
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ds=cldms[cldms$month==m,]
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plot(dr,col=cp(100),zlim=zlim,main="Comparison of MOD35 Cloud Frequency and NDP-026D Station Cloud Climatologies",
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ylab="Northing (m)",xlab="Easting (m)",sub="MOD35 is proportion of cloudy days, while NDP-026D is Mean Cloud Coverage")
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plot(ds,add=T,pch=21,cex=3,lwd=2,fg="black",bg=as.character(cut(ds$cld,breaks=seq(zlim[1],zlim[2],len=5),labels=cp(4))))
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#legend("topright",legend=seq(zlim[1],zlim[2],len=5),pch=16,col=cp(length(breaks)))
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xyplot(mod35~cld,data=mod35v,subscripts=T,auto.key=T,panel=function(x,y,subscripts){
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td=mod35v[subscripts,]
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# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
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panel.xyplot(x,y,subscripts=subscripts,type=c("p","smooth"),pch=16,col="black")
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# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
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},ylab="MOD35 Proportion Cloudy Days",xlab="NDP-026D Mean Monthly Cloud Amount",
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main="Comparison of MOD35 Cloud Mask and Station Cloud Climatologies")
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#xyplot(mod35~cld|month,data=mod35v,subscripts=T,auto.key=T,panel=function(x,y,subscripts){
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# td=mod35v[subscripts,]
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# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
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# panel.xyplot(x,y,subscripts=subscripts,type=c("p","smooth"),pch=16,col="black")
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# panel.segments(x-td$cldsd[subscripts],y,x+td$cldsd[subscripts],y,subscripts=subscripts)
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# },ylab="MOD35 Proportion Cloudy Days",xlab="NDP-026D Mean Monthly Cloud Amount",
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# main="Comparison of MOD35 Cloud Mask and Station Cloud Climatologies")
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dev.off()
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graphics.off()
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