#debug(runClim_KGFusion)

    clim_method_mod_obj<-mclapply(1:length(sampling_month_obj$ghcn_data), list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 11) #This is the end bracket from mclapply(...) statement

    #Use function to extract list

#DATE: 09/04/2013                                                                                 

  #inserted #

  sampling_month_obj<-list_param$sampling_month_obj

  ghcn.month.subsets<-sampling_month_obj$ghcn_data

  sampling_month_dat <- sampling_month_obj$sampling_dat

  sampling_month_index <- sampling_month_obj$sampling_index

  #prop_month<-0 #proportion retained for validation...

  #run_samp<-1 #sample number, can be introduced later...

  prop_month <- sampling_month_dat$prop[j] #proportion retained for validation...

  run_samp <- sampling_month_dat$run_samp[j] #sample number if multisampling...

  #will need create mulitple prediction at daily!!! could be complicated

  #possibility is to average per proportion !!!

  date_month <-strptime(sampling_month_dat$date[j], "%Y%m%d")   # interpolation date being processed

  month_no <-strftime(date_month, "%m")          # current month of the date being processed

  LST_month<-paste("mm_",month_no,sep="") # name of LST month to be matched

  LST_name <-LST_month  

  #### STEP 2: PREPARE DATA

  #change here...use training data...

  ###Regression part 1: Creating a validation dataset by creating training and testing datasets

  #LST_name <-lst_avg[j] # name of LST month to be matched

  #data_month$LST<-data_month[[LST_name]]

  dataset_month <-ghcn.month.subsets[[j]]

  mod_LST <- ghcn.month.subsets[[j]][,match(LST_month, names(ghcn.month.subsets[[j]]))]  #Match interpolation date and monthly LST average

  dataset_month$LST <- as.data.frame(mod_LST)[,1] #Add the variable LST to the dataset

  #change here...

  dst$LST<-dst[[LST_month]] #Add the variable LST to the monthly dataset

  proj_str<-proj4string(dst) #get the local projection information from monthly data

  ind.training <- sampling_month_index[[j]]

  ind.testing  <- setdiff(1:nrow(dataset_month), ind.training)

  data_month_s <- dataset_month[ind.training, ]   #Training dataset currently used in the modeling

  data_month_v <- dataset_month[ind.testing, ]    #Testing/validation dataset using input sampling

  data_month <- data_month_s #training data for  monthhly predictions...

  #date_proc<-strptime(sampling_dat$date[i], "%Y%m%d")   # interpolation date being processed

  #mo<-as.integer(strftime(date_proc, "%m"))          # current month of the date being processed

  #day<-as.integer(strftime(date_proc, "%d"))

  #year<-as.integer(strftime(date_proc, "%Y"))

  ## end of pasted

  #end of insert...09/04

  #data_month<-dst[dst$month==j,] #Subsetting dataset for the relevant month of the date being processed

  #LST_name<-lst_avg[j] # name of LST month to be matched

  #data_month$LST<-data_month[[LST_name]]

  #If CAI model then...

  #TMax to model..., add precip later

  #if (var=="TMAX"){   

  #  dataset_month$y_var<-dataset_month$TMax #Adding TMax as the variable modeled

  #}

  #if (var=="TMIN"){   

  #  dataset_month$y_var<-dataset_month$TMin #Adding TMin as the variable modeled

  #}

  ##Change name...

    data_name<-paste(var,"_bias_LST_month_",as.integer(month_no),"_",cname[k],"_",prop_month,

  #for (k in 1:length(list_out_filename)){

  #  #j indicate which month is predicted

  #  data_name<-paste(var,"_clim_month_",as.integer(month_no),"_",cname[k],"_",prop_month,

  #                   "_",run_samp,sep="")

  #  raster_name<-file.path(out_path,paste("CAI_",data_name,out_prefix,".tif", sep=""))

  #  list_out_filename[[k]]<-raster_name

  #}

    data_name<-paste(var,"_clim_LST_month_",as.integer(month_no),"_",names(rast_clim_list)[k],"_",prop_month,

    data_name<-paste(var,"_bias_LST_month_",as.integer(month_no),"_",model_name,"_",prop_month,

    raster_name_bias<-file.path(out_path,paste("fusion_",interpolation_method,"_",data_name,out_prefix,".tif", sep=""))

    data_name<-paste(var,"_clim_LST_month_",as.integer(month_no),"_",model_name,"_",prop_month,

    raster_name_clim<-file.path(out_path,paste("fusion_",interpolation_method,"_",data_name,out_prefix,".tif", sep=""))

  #Prepare object to return

  clim_obj<-list(rast_bias_list,rast_clim_list,data_month,data_month_v,sampling_month_dat[j,],mod_list,list_formulas)

  names(clim_obj)<-c("bias","clim","data_month","data_month_v","sampling_month_dat","mod","formulas")

  save(clim_obj,file= file.path(out_path,paste("clim_obj_fusion_month_",as.integer(month_no),"_",var,"_",prop_month,

                                               "_",run_samp,"_",out_prefix,".RData",sep="")))