date_f<-strptime(tb_diagnostic$date, "%Y%m%d")   # interpolation date being processed

  tb_diagnostic$month<-strftime(date_f, "%m")          # current month of the date being processed

  mod_names<-sort(unique(tb_diagnostic$pred_mod)) #models that have accuracy metrics

  tb_mod_list<-lapply(mod_names, function(k) subset(tb_diagnostic, pred_mod==k)) #this creates a list of 5 based on models names

  names(tb_mod_list)<-mod_names

  agg_by_month <-function(tb_mod_list,j,metric_names){

    for (k in 1:length(metric_names)){

      metric_ac<-metric_names[k]

      mod_pat<-glob2rx(paste(metric_ac,"_*",sep=""))   

      mod_var<-grep(mod_pat,names(mod_metrics),value=TRUE) # using grep with "value" extracts the matching names     

      d_month<-aggregate(metric_n~month, data=tb_mod_list[[j]], mean)  #Calculate monthly mean for every station in OR

    }

    return(d_month)

  }

  test<-lapply(1:length(tb_mod_list),FUN=agg_by_month,tb_mod_list=tb_mod_list)

  t<-melt(tb_diagnostic,

          #measure=mod_var, 

          id=c("date","pred_mod","prop","month"),

          na.rm=F)

  test<-cast(t,pred_mod+month~variable,mean)

  tb_mod_list<-lapply(mod_names, function(k) subset(tb_diagnostic, pred_mod==k)) #this creates a list of 5 based on models names

  for (k in 1:tb_mod_m_ist){

    for (j in 1:length(metric_names)){

      metric_ac<-metric_names[j]

      mod_pat<-glob2rx(paste(metric_ac,"_*",sep=""))   

      mod_var<-grep(mod_pat,names(mod_metrics),value=TRUE) # using grep with "value" extracts the matching names     

      #browser()

      test<-mod_metrics[mod_var]

      png(paste("boxplot_metric_",metric_ac, out_prefix,".png", sep=""))

      boxplot(test,outline=FALSE,horizontal=FALSE,cex=0.5,

              ylab=paste(metric_ac,"in degree C",sep=" "))

      #legend("bottomleft",legend=paste(names(rows_total),":",rows_total,sep=""),cex=0.7,bty="n")

      title(as.character(t(paste(t(names(rows_total)),":",rows_total,sep=""))),cex=0.8)

      dev.off()

    }

  }