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###################################################################################
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###  R code to interpolate monthly climatologies 
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## connect to server of choice
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#system("ssh litoria")
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#R
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library(sp)
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#library(spgrass6)
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library(rgdal)
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library(reshape)
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library(ncdf4)
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#library(geosphere)
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#library(rgeos)
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library(multicore)
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library(raster)
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library(rasterVis)
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library(lattice)
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library(latticeExtra)
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#library(rgl)
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#library(hdf5)
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#library(heR.Misc)
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#library(car)
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library(mgcv)
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library(sampling)
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X11.options(type="Xlib")
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ncores=20  #number of threads to use
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setwd("/home/adamw/acrobates/projects/interp")
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## get MODLAND tile information
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tb=read.table("http://landweb.nascom.nasa.gov/developers/sn_tiles/sn_bound_10deg.txt",skip=6,nrows=648,header=T)
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tb$tile=paste("h",sprintf("%02d",tb$ih),"v",sprintf("%02d",tb$iv),sep="")
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save(tb,file="modlandTiles.Rdata")
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#tile="h11v08"   #can move this to submit script if needed
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tile="h21v09"  #Kenya
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tile="h09v04"   #oregon
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tiles=c("h11v08","h09v04")
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psin=CRS("+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs")
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tile_bb=tb[tb$tile==tile,] ## identify tile of interest
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roi_ll=extent(tile_bb$lon_min,tile_bb$lon_max,tile_bb$lat_min,tile_bb$lat_max) 
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dmod06="data/modis/mod06/summary"
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outdir=paste("data/tiles/",tile,"/",sep="")
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##########################
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#### Organize the data
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months=seq(as.Date("2000-01-15"),as.Date("2000-12-15"),by="month")
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getmod06<-function(variable,month=NA){
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  d=brick(list.files(dmod06,pattern=paste("MOD06_",tile,".nc$",sep=""),full=T),varname=toupper(variable))
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  if(!is.na(month)) {
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    d=subset(d,subset=month)
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    names(d)=variable
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  }
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  if(is.na(month)){
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    setZ(d,format(as.Date(d@z$Date),"%m"),name="time")
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    layerNames(d) <- as.character(format(as.Date(d@z$Date),"%b")) #paste(variable,format(as.Date(d@z$Date),"%m"))
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  }
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  projection(d)=psin
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  return(d)
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}
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cer=getmod06("cer")
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cld=getmod06("cld")
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cot=getmod06("cot")
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cer20=getmod06("cer20")
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pcol=colorRampPalette(c("brown","red","yellow","darkgreen"))
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### create data dir for tiled data
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ddir=paste("data/tiles/",tile,sep="")
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if(!file.exists(ddir)) dir.create(ddir)
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## load WorldClim data for comparison (download then uncompress)
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#system("wget -P data/worldclim/ http://biogeo.ucdavis.edu/data/climate/worldclim/1_4/grid/cur/prec_30s_bil.zip",wait=F)
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#system("wget -P data/worldclim/ http://biogeo.ucdavis.edu/data/climate/worldclim/1_4/grid/cur/alt_30s_bil.zip",wait=F)
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### load WORLDCLIM elevation 
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if(!file.exists(paste(ddir,"/dem_",tile,".tif",sep=""))){
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  dem=raster(list.files("data/worldclim/alt_30s_bil/",pattern="bil$",full=T))
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  projection(dem)=CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
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  dem=crop(dem,roi_ll)
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  dem[dem>30000]=NA
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  dem=projectRaster(dem,cer)
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  writeRaster(dem,file=paste("data/tiles/",tile,"/dem_",tile,".tif",sep=""),format="GTiff",overwrite=T)
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}
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dem=raster(paste("data/tiles/",tile,"/dem_",tile,".tif",sep=""))
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names(dem)="dem"
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### get station data, subset to stations in region, and transform to sinusoidal
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dm=readOGR(ddir,paste("station_monthly_",tile,"_PRCP",sep=""))
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colnames(dm@data)[grep("elevation",colnames(dm@data))]="elev"
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colnames(dm@data)[grep("value",colnames(dm@data))]="ppt"
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#xyplot(latitude~longitude|month,data=dm@data)
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## transform to sinusoidal to overlay with raster data
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dm2=spTransform(dm,CRS(projection(cer)))
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dm2@data[,c("x","y")]=coordinates(dm2)
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## limit data to station-months with at least 300 daily observations (~10 years)
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dm2=dm2[dm2$count>300,]
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## create coordinate rasters
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### create monthly raster bricks for prediction
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getd<-function(vars=c("cer","cot","cer20","cld"),month){
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  pdata=stack(lapply(vars,function(v){getmod06(v,month=month)}))
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  x=rasterFromXYZ(coordinates(dem)[,c(1,2,1)])
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  y=rasterFromXYZ(coordinates(dem)[,c(1,2,2)])
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  pdata=stack(pdata,dem,x,y)
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  return(pdata)
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}
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## Set up models to compare
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####################################
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#### build table comparing various metrics
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models=data.frame(model=c(
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#  "ppt~s(y)+ s(x)",
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#  "ppt~s(y,x)",
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  "lppt~s(y,x)",
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  "lppt~s(y,x)+s(dem)",
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  "lppt~s(y,x,dem)",
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  "lppt~s(y,x,dem)+s(cld)",
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  "lppt~s(y,x)+s(dem)+s(cld)",
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  "lppt~s(y,x)+s(cld)",
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  "lppt~s(y,x)+s(cot)",
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  "lppt~s(y,x)+s(dem)+s(cot)",
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  "lppt~s(y,x,dem)+s(cot)",
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  "lppt~s(y,x)+s(cer20)",
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  "lppt~s(y,x)+s(dem)+cld+cot+cer20",
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  "lppt~s(y,x,dem)+cld+cot+cer20",
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  "lppt~s(y,x)+s(dem)+s(cld,cot,cer20)",
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  "lppt~s(y,x)+s(dem)+s(cld)+s(cot)+s(cer20)"))
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  months=1:12
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## add category for each model
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models$type="Spatial"
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models$type[grepl("cer|cot|cer20",models$model)]="MOD06"
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models$type[grepl("cld",models$model)&!grepl("cer|cot|cer20",models$model)]="MOD35"
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## build the list of models/months to process
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mm=expand.grid(model=models$model,months=months,stringsAsFactors=F)
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mm$mid=match(mm$model,models$model)
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### Sample validation stations
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prop=0.1
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### run it
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fitmod<-function(i,prop=0.1,predict=F, nv,...){
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  model=as.character(mm$model[i])
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  month=mm$month[i]
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  mid=mm$mid[i]
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### extract data
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  dr=getd(month=month)
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  ##  subset data for this month
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  dt=dm2[dm2$month==month,]
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  ## add log+1 ppt
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  dt$lppt=log(dt$ppt+1)
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  ## extract for points from dm2
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  dr2=subset(dr,subset=grep("^x$|^y$",names(dr),invert=T))
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  ds=cbind.data.frame(dt@data,extract(dr2,dt))
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### flag stations to hold out for validation
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  ts2=do.call(rbind.data.frame,lapply(1:nv,function(r) {
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    ds$validation=F
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    ds$validation[sample(1:nrow(ds),size=as.integer(prop*nrow(ds)))]=T
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### fit model
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    mod<<-try(gam(as.formula(model),data=ds[!ds$validation,]),silent=T)
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      ## if fitting fails, return a NULL
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      if(class(mod)[[1]]=="try-error")  return(NULL)
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### Model Validation
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      vd=ds[ds$validation,]  # extract validation points
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      y=as.data.frame(predict(mod,ds,se.fit=T))      # make predictions
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      if(attr(terms(mod),"variables")[[2]]=="lppt"){  #if modeling log(ppt+1), transform back to real units
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        y$fit=exp(y$fit)-1
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        y$se.fit=exp(y$se.fit)-1
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      }
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      ds[,c("pred","pred.se")]=cbind(y$fit,y$se.fit)
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      ds$model=model
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      ds$modelid=mid
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      vlm=lm(pred~ppt,data=ds[ds$validation,])       # lm() to summarize predictive fit
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    if(r==nv) ds<<-ds  #if on last iteration, save predictions
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### summarize validation
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      s1=summary(mod)
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      s2=data.frame(
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        model.r2=s1$r.sq,
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        model.dev=s1$dev.expl,
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        model.aic=AIC(mod),
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        valid.rmse=sqrt(mean((vd$ppt-y$fit[ds$validation])^2,na.rm=T)),
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        valid.nrmse=sqrt(mean((vd$ppt-y$fit[ds$validation])^2,na.rm=T))/mean(vd$ppt+.1,na.rm=T),
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        valid.mer=mean(vd$ppt-y$fit[ds$validation],na.rm=T),
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        valid.mae=mean(abs(vd$ppt-y$fit[ds$validation]),na.rm=T),
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        valid.r2=summary(vlm)$r.squared)
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      return(s2)}))
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  ## summarize the gcv datasets
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  if(nrow(ts2)==0) return(NULL)
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  s2a=data.frame(mean=apply(ts2,2,mean,na.rm=T))
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  s2b=t(apply(ts2,2,quantile,c(0.025,0.975),na.rm=T))
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  colnames(s2b)=paste("Q",c(2.5,97.5),sep="")
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  s2=cbind.data.frame(tile=tile, model=model, modelid=mid, month=month,
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    metric=rownames(s2a),s2a,s2b)
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  rownames(s2)=1:nrow(s2)
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### add some attributes to the object to help ID it later
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      attr(mod,"month")=ds$month[1]
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      attr(mod,"model")=model
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      attr(mod,"modelid")=mid
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### generate predictions?
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  if(predict){
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    ## write prediction
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    ncfile=paste(outdir,tile,"_pred_",ds$month[1],"_",mid[1],".nc",sep="")
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    predict(dr,mod,filename=ncfile,format="CDF",overwrite=T)
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    ## add some attributes to nc file
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    ncopath=""
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    ## add other attributes
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    system(paste(ncopath,"ncrename -v variable,ppt ",ncfile,sep=""))
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    system(paste(ncopath,"ncatted -a units,ppt,o,c,\"mm\" ",
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                 "-a model,ppt,o,c,\"",model,"\" ",
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                 "-a long_name,ppt,o,c,\"Mean Monthly Precipitation\" ",
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                 ncfile,sep=""))
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    ## create NC file to hold summary data and then append it to output file
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    ## add table for validation metrics
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    s2_dim1=ncdim_def("metrics1",units="",vals=1:ncol(s2),unlim=FALSE,create_dimvar=T,longname="Model Fitting and Validation Metrics")
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    s2_dim2=ncdim_def("metrics2",units="",vals=1:nrow(s2),unlim=FALSE,create_dimvar=T,longname="Model Fitting and Validation Metrics")
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    s2_var=ncvar_def("modelmetrics",units="varies",list(s2_dim1,s2_dim2),missval=-999,longname="Model Fitting and Validation Metrics",
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      prec="float")
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    ## simplify data table for incorporation into netcdf file
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    ds2=ds[,-1];ds2$validation=ifelse(ds2$validation,1,0)
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    ds2=as.matrix(ds2)
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    data_dim1=ncdim_def("data",units="",vals=1:ncol(ds2),unlim=FALSE,create_dimvar=T,longname="Data used in model fitting")
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    data_dim2=ncdim_def("stations",units="",vals=1:nrow(ds2),unlim=FALSE,create_dimvar=T,longname="Stations used in model fitting")
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    data_var=ncvar_def("modeldata",units="varies",list(data_dim1,data_dim2),missval=-999,longname="Data used in model fitting",
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      prec="float")
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    tncf=paste(tempdir(),"/",tile,month,mid,"metrics.nc",sep="") #temp file to hold metric data
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    nc_create(filename=tncf,vars=list(data_var,s2_var))
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    tnc=nc_open(tncf,write=T)
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#    ncvar_put(tnc,s2_var,vals=s2)  ## put in validatation data
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    ncvar_put(tnc,data_var,vals=ds2)  ## put in validatation data
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    nc_close(tnc)  ## close the file
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  system(paste(ncopath,"ncks -A ",tncf," ",ncfile,sep=""))
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  system(paste(ncopath,"ncatted -a value,modelmetrics,o,c,\"",paste(1:ncol(s2),":",colnames(s2),collapse="
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",sep=""),"\" ",ncfile,sep=""))
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  ## also add metrics as variable attributes for easier viewing
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  for(c in 1:ncol(s2))  system(paste(ncopath,"ncatted -a ",colnames(s2)[c],",ppt,o,",
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                                    ifelse(class(s2[1,c])!="numeric","c,\"","d,"),s2[1,c],
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                                    ifelse(class(s2[1,c])!="numeric","\" "," "),ncfile,sep=""))
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  ## Add time dimension to ease merging files later
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  system(paste(ncopath,"ncecat -O -u time ",ncfile," ",ncfile,sep=""))
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  cat(paste("
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    netcdf time {
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      dimensions:
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        time = 1 ;
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      variables:
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        int time(time) ;
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      time:units = \"months since 2000-01-01 00:00:00\" ;
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      time:calendar = \"gregorian\";
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      time:long_name = \"time of observation\"; 
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    data:
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      time=",as.integer(ds$month[1]-1),";
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    }"),file=paste(tempdir(),"/time.cdl",sep=""))
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  system(paste("ncgen -o ",tempdir(),"/time.nc ",tempdir(),"/time.cdl",sep=""))
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  system(paste(ncopath,"ncks -A ",tempdir(),"/time.nc ",ncfile,sep=""))
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  ## global attributes
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  system(paste(ncopath,"ncatted -a title,global,o,c,\"Interpolated Monthly Precipitation\" ",
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               "-a institution,global,o,c,\"Yale University\" ",
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               "-a source,global,o,c,\"Interpolated from station data\" ",
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               "-a contact,global,o,c,\"adam.wilson@yale.edu\" ",
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               ncfile,sep=""))
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  }
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  print(paste("Finished model ",model," for month",ds$month[1]))
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  return(list(model=mod,summary=s2,data=ds))
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}
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mods=lapply(1:nrow(mm),fitmod,predict=F,nv=100)
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## extract summary tables and write them
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sums=do.call(rbind.data.frame,lapply(mods,function(m) return(m$summary)))
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write.csv(sums,paste(outdir,tile,"_validation.csv",sep=""))
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pred=lapply(mods,function(m) return(m$data))
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### messy error handling to remove tables with incorrect number of columns...
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nullpred=which(!do.call(c,lapply(pred,function(x) ncol(x)==20&!is.null(x))))
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pred=pred[nullpred]
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pred=do.call(rbind.data.frame,pred)
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pred$tile=tile
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write.csv(pred,paste(outdir,tile,"_predictions.csv",sep=""))
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### read them back
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#sums=do.call(rbind.data.frame,lapply(tiles,function(tile) read.csv(paste("data/tiles/",tile,"/",tile,"_validation.csv",sep=""))))
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sums=read.csv(paste(outdir,tile,"_validation.csv",sep=""))
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pred=read.csv(paste(outdir,tile,"_predictions.csv",sep=""))
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#sums=rbind.data.frame(sums,read.csv(paste("data/tiles/",tiles[1],"/",tiles[1],"_validation.csv",sep="")))
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#pred=read.csv(paste(outdir,tiles[1],"_predictions.csv",sep=""))
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## reshape summary validation data
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#sumsl=melt(sums,id.vars=c("tile","model","modelid","month"))
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sum2=cast(sums[,-1],model+modelid~metric,value="mean",fun=function(x) c(median=round(median(x,na.rm=T),2),sd=round(sd(x,na.rm=T),2)));sum2
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#by(sum2,tile,function(x) rank(apply(cbind(rank(x$valid.rmse_median),rank(-x$valid.r2_median)),1,mean,na.rm=T)))
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#sum2$rank=rank(apply(cbind(rank(sum2$valid.rmse_median),rank(-sum2$valid.r2_median)),1,mean,na.rm=T))
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## add sorting variables across months
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sum2$rank=rank(apply(cbind(rank(sum2$valid.rmse_median),rank(-sum2$valid.r2_median)),1,mean,na.rm=T))
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sum2[order(sum2$rank),c("model","modelid","valid.r2_median","valid.rmse_median","valid.nrmse_median","rank"),] #"valid.mer_mean",
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sums$fmodel=factor(sums$model,ordered=T,levels=rev(sum2$model[order(sum2$rank)]))
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## add sorting variables for each month
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sumsl2=cast(model~month~metric,value="mean",data=sums)
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sumsl2[,,"valid.r2"]
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#combineLimits(useOuterStrips(xyplot(value~month|model+variable,data=sumsl,scale=list(relation="free"))))
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sums2=sums[sums$metric%in%c("valid.r2","valid.rmse"),]
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###############################################################
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###############################################################
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### Draw some plots
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library(maptools)
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coast=getRgshhsMap(fn="/home/adamw/acrobates/Global/GSHHS_shp/gshhs/gshhs_l.b",
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  xlim=c(360+roi_ll@xmin,360+roi_ll@xmax),ylim=c(roi_ll@ymin,roi_ll@ymax),level=1)
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coast=as(coast,"SpatialLines")
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coast=spTransform(coast,psin)
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roi=spTransform(roi,psin)
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roil=as(roi,"SpatialLines")
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### quantile function
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gq=function(x,n=10,cut=F) {
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  if(!cut) return(unique(quantile(x,seq(0,1,len=n+1),na.rm=T)))
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  if(cut)  return(cut(x,unique(quantile(x,seq(0,1,len=n+1),na.rm=T))))
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}
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bgyr=colorRampPalette(c("brown","yellow","green","darkgreen","blue"))
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X11.options(type="cairo")
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png(paste("output/ModelComparisonRasters_",tile,"_%02d.png",sep=""),width=3000,height=2000,res=300,bg="transparent",type="cairo-png")
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## COT climatologies
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at=unique(seq(0,30,len=100))
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p=levelplot(cot,xlab.top=title,at=at,col.regions=bgyr(length(at)))+layer(sp.lines(coast, lwd=1.2, col='black'))
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print(p)
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## all monthly predictions
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p1=brick(stack(list.files(outdir,pattern="pred_.*_10.nc$",full=T),varname="ppt"))
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projection(p1)=psin
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names(p1)=month.name
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title=""#"Interpolated Precipitation"
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at=unique(quantile(as.matrix(p1),seq(0,1,len=100),na.rm=T))
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at=unique(seq(min(p1@data@min),max(p1@data@max),len=100))
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p=levelplot(p1,xlab.top=title,at=at,col.regions=bgyr(length(at)))#+layer(sp.lines(roil, lwd=1.2, col='black'))
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print(p)
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## compare two models
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p2=brick(stack(list.files(outdir,pattern="pred_3_3.nc|pred_3_12.nc",full=T)[2:1],varname="ppt"))
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projection(p2)=psin
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names(p2)=c("X+Y+Elevation","X+Y+Elevation+MOD06")
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at=unique(seq(min(p2@data@min),max(p2@data@max),len=100))
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p=levelplot(p2,xlab.top=title,at=at,col.regions=bgyr(length(at)))+layer(sp.lines(coast, lwd=1.2, col='black'))
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print(p)
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dev.off()
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png(paste("output/dem_",tile,".png",sep=""),width=3000,height=3000,res=300,bg="transparent")
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at=unique(seq(min(dem@data@min),max(dem@data@max),len=100))
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st=unique(coordinates(dm2))
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levelplot(dem,col.regions=terrain.colors(100),at=at,margin=F)+
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  layer(panel.xyplot(st[,1],st[,2],cex=.5,pch=3,col="black"))
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dev.off()
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pdf(paste("output/ModelComparison_",tile,".pdf",sep=""),width=11,height=5,useDingbats=F)
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bwplot(fmodel~mean|metric,data=sums2,scale=list(x=list(relation="free")),subscripts=T,panel=function(x,y,subscripts){
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  panel.abline(v=mean(x))
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  types=models$type[match(sort(unique(y)),models$model)]
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  panel.bwplot(x,y,fill=ifelse(types=="Spatial","grey",ifelse(types=="MOD35","dodgerblue","orangered")))
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  #  panel.text(x,jitter(as.numeric(y),factor=.5),labels=as.character(sums2$month[subscripts]),col="grey40",cex=.3)
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  panel.xyplot(x,y,pch=16,col="grey48",cex=.5)
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},
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                                        #strip=strip.custom(factor.levels=c("Validation R^2","Validation RMSE")),
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       main="Comparison of Validation Metrics",sub="Vertical line indicates overall mean",
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       key=list(space="right",rect=list(col=c("orangered","dodgerblue","grey")),text=list(c("MOD06","MOD35","Spatial"))))
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dev.off()
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### illustration of GAM for IBS poster
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#mm
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i=56
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mm[i,]
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tm=fitmod(i,predict=F,nv=1)
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    ## partial residual plots
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var=4
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    fv <- predict(tm$mod,type="terms") ## get term estimates
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    v=sub("[)]","",sub("s[(]","",colnames(fv)))[var]
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    ## compute partial residuals for first smooth...
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    prsd1 <- residuals(tm$mod,type="working") + fv[,var]
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pdf(paste("output/GAMexample.pdf"),width=11,height=6,useDingbats=F)
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plot(tm$mod,select=var,las=1,rug=F,cex.lab=2,cex.axis=2) ## plot first smooth
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    points(tm$mod$model[,v],prsd1,pch=16,cex=.5,col="red")
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dev.off()
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xyplot(mean~month|metric,groups=model,type="l",data=sums,scale=list(y=list(relation="free")),auto.key=list(space="right"))#+layer(panel.xyplot(x,y,pch=16,col="grey"),under=T)+layer(panel.abline(v=mean(x)))
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sums$type=as.factor(models$type[match(sums$model,models$model)])
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xyplot(mean~month|metric,groups=model,type="l",data=sums2,panel=function(x,y,subscripts,groups){
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  tsums=sums[subscripts,]
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  panel.xyplot(x,y,groups=groups,type="l",subscripts=subscripts,col=ifelse(tsums$type=="Spatial","grey20",ifelse(tsums$type=="MOD35","blue","red")))
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  panel.segments(tsums$month,tsums$Q2.5,tsums$month,tsums$Q97.5,groups=groups,subscripts=subscripts)
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},subscripts=T,scale=list(y=list(relation="free")),
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       key=list(space="right",text=list(c("Spatial","MOD35","MOD06")),lines=list(col=c("grey20","blue","red"))))
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                                        #+layer(panel.xyplot(x,y,pch=16,col="grey"),under=T)+layer(panel.abline(v=mean(x)))
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xyplot(pred~ppt|as.factor(month),groups=validation,data=pred,panel=function(x,y,subscripts,groups){
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  panel.xyplot(x,y,groups=groups,subscripts=subscripts)
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  panel.abline(0,1,col="red",lwd=2)
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},scales=list(relation="free"),
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       main="Predicted vs. Observed mean monthly precipitation at validation stations",
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       sub="Line is y=x",auto.key=T)
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## plot prediction rasters
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p=raster(ncfile,varname="ppt")
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plot3D(dem, maxpixels=100000,zfac=.5, drape=p, rev=FALSE, adjust=TRUE)
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###################################################################
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###################################################################
447 86629ea0 Adam M. Wilson
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### add some additional variables
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mod06s$month=factor(mod06s$month,labels=format(as.Date(paste("2000",1:12,"15",sep="-")),"%b"))
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mod06s$lppt=log(mod06s$ppt)
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mod06s$glon=cut(mod06s$lon,gq(mod06s$lon,n=5),include.lowest=T,ordered=T)#gq(mod06s$lon,n=3))
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mod06s$glon2=cut(mod06s$lon,breaks=c(-125,-122,-115),labels=c("Coastal","Inland"),include.lowest=T,ordered=T)#gq(mod06s$lon,n=3))
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mod06s$gelev=cut(mod06s$elev,breaks=gq(mod06s$elev,n=3),labels=c("Low","Mid","High"),include.lowest=T,ordered=T)
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mod06s$gbin=factor(paste(mod06s$gelev,mod06s$glon2,sep="_"),levels=c("Low_Coastal","Mid_Coastal","High_Coastal","Low_Inland","Mid_Inland","High_Inland"),ordered=T)
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mod06s$LWP_mean=(2/3)*mod06s$CER_mean*mod06s$COT_mean
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## melt it
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mod06sl=melt(mod06s[,!grepl("lppt",colnames(mod06s))],id.vars=c("id","lon","lat","elev","month","ppt","glon","glon2","gelev","gbin"))
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levels(mod06sl$variable)=c("Effective Radius (um)","Very Cloudy Days (%)","Cloudy Days (%)","Optical Thickness (%)","Liquid Water Path")
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pdf("output/MOD06_summary.pdf",width=11,height=8.5)
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# % cloudy maps
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title="Cloudiness (% cloudy days) "
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at=unique(quantile(as.matrix(cld),seq(0,1,len=100),na.rm=T))
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p=levelplot(cld,xlab.top=title,at=at,col.regions=bgyr(length(at)))#+layer(sp.lines(roil, lwd=1.2, col='black'))
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print(p)
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#bwplot(cer,main=title,ylab="Cloud Effective Radius (microns)")
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# CER maps
471
title="Cloud Effective Radius (microns)"
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at=quantile(as.matrix(cer),seq(0,1,len=100),na.rm=T)
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p=levelplot(cer,xlab.top=title,at=at,col.regions=bgyr(length(at)))#+layer(sp.lines(roil, lwd=1.2, col='black'))
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print(p)
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#bwplot(cer,main=title,ylab="Cloud Effective Radius (microns)")
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# CER20 maps
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title="% Days with Cloud Effective Radius > 20 microns"
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at=unique(quantile(as.matrix(cer20),seq(0,1,len=100),na.rm=T))
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p=levelplot(cer20,xlab.top=title,at=at,col.regions=bgyr(length(at)))#+layer(sp.lines(roil, lwd=1.2, col='black'))
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print(p)
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#bwplot(cer,main=title,ylab="Cloud Effective Radius (microns)")
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# COT maps
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title="Cloud Optical Thickness (%)"
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at=quantile(as.matrix(cot),seq(0,1,len=100),na.rm=T)
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p=levelplot(cot,xlab.top=title,at=at,col.regions=bgyr(length(at)))#+layer(sp.lines(roil, lwd=0.8, col='black'))
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print(p)
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#bwplot(cot,xlab.top=title,ylab="Cloud Optical Thickness (%)")
490
dev.off()
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### Calculate the slope of each line
498
mod06s.sl=dapply(mod06s,list(id=mod06s$id),function(x){
499
  lm1=lm(log(x$ppt)~x$CER_mean,)
500
  data.frame(lat=x$lat[1],lon=x$lon[1],elev=x$elev[1],intcpt=coefficients(lm1)[1],cer=coefficients(lm1)[2],r2=summary(lm1)$r.squared)
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})
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mod06s.sl$cex=gq(mod06s.sl$r2,n=5,cut=T)
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mod06s.sl$cer.s=gq(mod06s.sl$cer,n=5,cut=T)
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###  and plot it on a map
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xyplot(lat~lon,group=cer.s,data=mod06s.sl,par.settings = list(superpose.symbol = list(pch =16, col=bgyr(5),cex=1)),auto.key=list(space="right",title="Slope Coefficient"),asp=1,
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       main="Slopes of linear regressions {log(ppt)~CloudEffectiveRadius}")+
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  layer(sp.lines(roi_geo, lwd=1.2, col='black'))
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### look for relationships with longitude
511
xyplot(cer~lon,group=cut(mod06s.sl$elev,gq(mod06s.sl$elev,n=5)),data=mod06s.sl,
512
       par.settings = list(superpose.symbol = list(col=bgyr(5),pch=16,cex=1)),auto.key=list(space="right",title="Station Elevation"),
513
       ylab="Slope of lm(ppt~EffectiveRadius)",xlab="Longitude",main="Precipitation~Effective Radius relationship by latitude")
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############################################################
517
### simple regression to get spatial residuals
518
m="01"
519
mod06s2=mod06s#[mod06s$month==m,]
520
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lm1=lm(log(ppt)~CER_mean*month*lon,data=mod06s2); summary(lm1)
522
mod06s2$pred=exp(predict(lm1,mod06s2))
523
mod06s2$resid=mod06s2$pred-mod06s2$ppt
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mod06s2$residg=gq(mod06s2$resid,n=5,cut=T)
525
mod06s2$presid=mod06s2$resid/mod06s2$ppt
526
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for(l in c(F,T)){
528
## all months
529
  xyplot(pred~ppt,groups=gelev,data=mod06s2,
530
       par.settings = list(superpose.symbol = list(col=bgyr(3),pch=16,cex=.75)),auto.key=list(space="right",title="Station Elevation"),
531
       scales=list(log=l),
532
       ylab="Predicted Mean Monthly Precipitation (mm)",xlab="Observed Mean Monthly Precipitation (mm)",main="Predicted vs. Observed for Simple Model",
533
       sub="Red line is y=x")+
534
  layer(panel.abline(0,1,col="red"))
535
536
## month by month
537
  print(xyplot(pred~ppt|month,groups=gelev,data=mod06s2,
538
       par.settings = list(superpose.symbol = list(col=bgyr(3),pch=16,cex=.75)),auto.key=list(space="right",title="Station Elevation"),
539
       scales=list(log=l),
540
       ylab="Predicted Mean Monthly Precipitation (mm)",xlab="Observed Mean Monthly Precipitation (mm)",main="Predicted vs. Observed for Simple Model",
541
       sub="Red line is y=x")+
542
  layer(panel.abline(0,1,col="red"))
543
)}
544
545
## residuals by month
546
xyplot(lat~lon|month,group=residg,data=mod06s2,
547
       par.settings = list(superpose.symbol = list(pch =16, col=bgyr(5),cex=.5)),
548
       auto.key=list(space="right",title="Residuals"),
549
       main="Spatial plot of monthly residuals")+
550
    layer(sp.lines(roi_geo, lwd=1.2, col='black'))
551
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dev.off()
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load("data/modis/pointsummary.Rdata")
565
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dsl=melt(ds,id.vars=c("id","date","ppt","lon","lat"),measure.vars=  c("Cloud_Water_Path","Cloud_Effective_Radius","Cloud_Optical_Thickness"))
568
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dsl=dsl[!is.nan(dsl$value),]
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574
####
575
## mean annual precip
576
dp=d[d$variable=="ppt",]
577
dp$year=format(dp$date,"%Y")
578
dm=tapply(dp$value,list(id=dp$id,year=dp$year),sum,na.rm=T)
579
dms=apply(dm,1,mean,na.rm=T)
580
dms=data.frame(id=names(dms),ppt=dms/10)
581
582
dslm=tapply(dsl$value,list(id=dsl$id,variable=dsl$variable),mean,na.rm=T)
583
dslm=data.frame(id=rownames(dslm),dslm)
584
585
dms=merge(dms,dslm)
586
dmsl=melt(dms,id.vars=c("id","ppt"))
587
588
summary(lm(ppt~Cloud_Effective_Radius,data=dms))
589
summary(lm(ppt~Cloud_Water_Path,data=dms))
590
summary(lm(ppt~Cloud_Optical_Thickness,data=dms))
591
summary(lm(ppt~Cloud_Effective_Radius+Cloud_Water_Path+Cloud_Optical_Thickness,data=dms))
592
593
594
#### draw some plots
595
#pdf("output/MOD06_summary.pdf",width=11,height=8.5)
596
png("output/MOD06_summary_%d.png",width=1024,height=780)
597
598
 ## daily data
599
xyplot(value~ppt/10|variable,data=dsl,
600
       scales=list(relation="free"),type=c("p","r"),
601
       pch=16,cex=.5,layout=c(3,1))
602
603
604
densityplot(~value|variable,groups=cut(dsl$ppt,c(0,50,100,500)),data=dsl,auto.key=T,
605
            scales=list(relation="free"),plot.points=F)
606
607
## annual means
608
609
xyplot(value~ppt|variable,data=dmsl,
610
       scales=list(relation="free"),type=c("p","r"),pch=16,cex=0.5,layout=c(3,1),
611
       xlab="Mean Annual Precipitation (mm)",ylab="Mean value")
612
613
densityplot(~value|variable,groups=cut(dsl$ppt,c(0,50,100,500)),data=dmsl,auto.key=T,
614
            scales=list(relation="free"),plot.points=F)
615
616
617
## plot some swaths
618
619
nc1=raster(fs$path[3],varname="Cloud_Effective_Radius")
620
nc2=raster(fs$path[4],varname="Cloud_Effective_Radius")
621
nc3=raster(fs$path[5],varname="Cloud_Effective_Radius")
622
623
nc1[nc1<=0]=NA
624
nc2[nc2<=0]=NA
625
nc3[nc3<=0]=NA
626
627
plot(roi)
628
plot(nc3)
629
630
plot(nc1,add=T)
631
plot(nc2,add=T)
632
633
634
dev.off()
635
636
637 165c04fb Adam M. Wilson
638
639
640
641
642
643
####################
644
####################  OLD JUNK BELOW!
645
####################
646
647
648
#levelplot(cer)#+points(x,y,data=dm2@data)
649
650
### extract MOD06 data for each station
651
stcer=extract(cer,dm2,fun=mean);colnames(stcer)=paste("cer_mean_",as.numeric(format(as.Date(cer@z$Date),"%m")),sep="")
652
stcerp=extract(cerp,dm2,fun=mean);colnames(stcerp)=paste("cerp_mean_",as.numeric(format(as.Date(cerp@z$Date),"%m")),sep="")
653
stcer20=extract(cer20,dm2,fun=mean);colnames(stcer20)=paste("cer20_mean_",as.numeric(format(as.Date(cer20@z$Date),"%m")),sep="")
654
stcot=extract(cot,dm2);colnames(stcot)=paste("cot_mean_",as.numeric(format(as.Date(cot@z$Date),"%m")),sep="")
655
stcld=extract(cld,dm2);colnames(stcld)=paste("cld_mean_",as.numeric(format(as.Date(cld@z$Date),"%m")),sep="")
656
stdem=extract(dem,dm2)
657
### generate new design matrix
658
mod06=cbind.data.frame(station=dm$station,stcer,stcerp,stcot,stcld,stcer20)
659
mod06l=melt(mod06,id.vars=c("station"));colnames(mod06l)[grep("value",colnames(mod06l))]="mod06"
660
mod06l[,c("variable","moment","month")]=do.call(rbind,strsplit(as.character(mod06l$variable),"_"))
661
mod06l=unique(mod06l)
662
mod06l=cast(mod06l,station+moment+month~variable,value="mod06")
663
mod06l=merge(dm2@data,mod06l,by=c("station","month"))
664
mod06l=mod06l[!is.na(mod06l$cer),]
665
666
mod06l=mod06l[order(mod06l$month),]
667
mod06l$lppt=log(mod06l$ppt+1)
668
669
#xyplot(latitude~longitude|as.factor(month),groups=cut(mod06l$ppt,breaks=quantile(mod06l$ppt,seq(0,1,len=10))),data=mod06l,auto.key=T,col=grey(seq(0,1,len=10)),pch=16,cex=.5)
670
#plot(cer)