Revision ac284468
Added by Adam Wilson almost 11 years ago
| climate/research/cloud/MOD09/MOD09_Visualize.R | ||
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r="Venezuela" |
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## print global map with box for region |
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png(paste("output/CF_mean_regbox.png",sep=""),width=1920,height=round(1920*d1),res=300,pointsize=46,bg="white")
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print(levelplot(mc[[1]],col.regions=cols(100),at=seq(1,100,len=100),margin=F,maxpixels=2.1e6,ylim=c(extent(coast2)@ymin,extent(coast2)@ymax), |
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main=paste(month.name[i]),cex.main=3,scales=list(draw=F),cuts=99,ylab="",xlab="")+ |
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layer(panel.polygon(c(extent(world)@xmin,extent(world)@xmin,extent(world)@xmax,extent(world)@xmax),y=c(extent(world)@ymin,extent(world)@ymax,extent(world)@ymax,extent(world)@ymin),col="black"),under=T)+ |
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layer(sp.lines(coast2,col="black"),under=F)+ |
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layer(sp.lines(world,col="black"),under=F)+ |
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layer(sp.lines(regs[[r]],col="blue",lwd=2),under=F))
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layer(sp.lines(as(regs2[[r]],"SpatialLines"),col="blue",lwd=2),under=F))
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dev.off() |
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# ylab.right = "Cloud Frequency (%)",par.settings = list(layout.widths = list(axis.key.padding = 0.1,axis.left=0.6,ylab.right = 3,right.padding=2)), |
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| climate/research/cloud/MOD09/ee/ee_compile.R | ||
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## mask no data regions (with less than 1 observation per day within that month) |
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biasf=function(cf,cfsd,nobs,pobs) {
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## drop data in areass with nobs<1 or pobs<=50 or where sd=0 and cf=0 or 50 (some polar regions)
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## drop data in areas with nobs<1 or pobs<=50 or where sd=0 and cf=0 or 50 (some polar regions) |
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drop=nobs<=0|pobs<=50|(cf==0&cfsd==0)|(cf==50&cfsd==0) |
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cf[drop]=NA |
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cfsd[drop]=NA |
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return(c(cf=cf,cfsd=cfsd,nobs=nobs,pobs=pobs))} |
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mod2=overlay(mod,fun=biasf,unstack=TRUE,filename=ttif1,format="GTiff", |
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dataType="INT1U",overwrite=T,NAflag=255, options=c("COMPRESS=LZW","BIGTIFF=YES"))
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| ... | ... | |
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ttif2=paste(tmpfs,"/",s,"_",m,"_wgs84.tif",sep="") |
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ncfile=paste("data/mcd09nc/",s,"_",m,".nc",sep="")
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## set up processing chunks |
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nrw=nrow(mod) |
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nby=20 |
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nrwg=seq(1,nrw,by=nby) |
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writeLines(paste("Processing ",length(nrwg)," groups and",nrw,"lines"))
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modpts=sampleRandom(cmod, size=10000, na.rm=TRUE, xy=T, sp=T) |
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rm(cmod) #remove temporary raster to save space |
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modpts=modpts[modpts$nobs>0,] #drop data from missing tiles |
| ... | ... | |
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modbeta1=coef(modlm1)["pobs"] |
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writeLines(paste(date," slope:",round(modbeta1,4))) |
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## Smooth data to remove large-grain variability |
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# system(paste("pkfilter -f mean -dx 99 -dy 99 -ot Byte -i ",df$path[df$month==m&df$sensor==s][1]," -o ",ttif1))
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## mask no data regions (with less than 1 observation per day within that month) |
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## use model above to correct for orbital artifacts |
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