#Debias the data
debias_drivers = function(nldas_path, dscale_path, dbiased_path, shortwave=FALSE){
dscale = read.csv(dscale_path, header=TRUE)
nldas = read.csv(nldas_path, header=TRUE)
dbiased = dscale
names(nldas) = paste0('nldas_', names(nldas))
names(nldas)[1] = 'time'
overlap = merge(nldas, dscale, by='time')
#Debias wind with a multiplier
wnd_multip = 1/(mean(overlap$WindSpeed)/mean(overlap$nldas_WindSpeed))
dbiased$WindSpeed = dbiased$WindSpeed*wnd_multip
#debias airT with linear model
air_lm = lm(nldas_AirTemp~AirTemp, overlap)
dbiased$AirTemp = predict(air_lm, dbiased)
#lets try shortwave if requested
if(shortwave){
dbiased$ShortWave = dbiased$ShortWave + (mean(overlap$nldas_ShortWave) - mean(overlap$ShortWave))
}
if(missing(dbiased_path)){
return(dbiased)
}else{
write.csv(dbiased, dbiased_path, row.names=FALSE, quote=FALSE)
}
}
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