var<-"TMAX" # variable being interpolated

#out_prefix<-"_365d_gam_cai_lst_comb3_10102013"                #User defined output prefix

out_suffix_modis <-"_05302013"                       #pattern to find tiles produced previously     

#interpolation_method<-c("gam_fusion","gam_CAI","gam_daily") #other otpions to be added later

#interpolation_method<-c("gwr_CAI") #other otpions to be added later

#interpolation_method<-c("kriging_CAI") 

#interpolation_method<-c("kriging_daily") #other otpions to be added later

#out_path<-"/home/parmentier/Data/IPLANT_project/Oregon_interpolation/Oregon_03142013/output_data"

out_path <- "/data/project/layers/commons/Oregon_interpolation/output_data"

#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd

#dates_selected<-c("20100101","20100102","20100301","20100302","20100501","20100502","20100701","20100702","20100901","20100902","20101101","20101102")

dates_selected<-"" # if empty string then predict for the full year specified earlier

screen_data_training<- FALSE #screen training data for NA and use same input training for all models fitted

use_clim_image <- TRUE # use predicted image as a base...rather than average Tmin at the station for delta

interp_method2 <- NULL #other options are "gwr" and "kriging"

#interp_method2 <- "gam" #other options are "gwr" and "kriging"

#list_models<-c("y_var ~ lat*lon + elev_s") #,

#               "y_var ~ lat*lon + elev_s + N_w",

#debug(debug_fun_test)

#debug_fun_test(list_param_raster_prediction)

############## STAGE 5: OUTPUT ANALYSES ##################