Revision 6c21df27
Added by Benoit Parmentier almost 12 years ago
| climate/research/oregon/interpolation/function_methods_comparison_assessment_part7.R | ||
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#for(i in 1:length(dates)){
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accuracy_comp_CAI_fus_function <- function(i){
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date_selected<-dates[i] |
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## Get the relevant raster layers with prediction for fusion and CAI |
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oldpath<-getwd() |
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setwd(path_data_cai) |
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file_pat<-glob2rx(paste("*tmax_predicted*",date_selected,"*_365d_GAM_CAI2_const_all_10312012.rst",sep="")) #Search for files in relation to fusion
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lf_cai2c<-list.files(pattern=file_pat) #Search for files in relation to fusion |
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rast_cai2c<-stack(lf_cai2c) #lf_cai2c CAI results with constant sampling over 365 dates |
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rast_cai2c<-mask(rast_cai2c,mask_ELEV_SRTM) |
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oldpath<-getwd() |
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setwd(path_data_fus) |
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file_pat<-glob2rx(paste("*tmax_predicted*",date_selected,"*_365d_GAM_fusion_const_all_lstd_11022012.rst",sep="")) #Search for files in relation to fusion
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lf_fus1c<-list.files(pattern=file_pat) #Search for files in relation to fusion |
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rast_fus1c<-stack(lf_fus1c) |
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rast_fus1c<-mask(rast_fus1c,mask_ELEV_SRTM) |
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#PLOT ALL MODELS |
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#Prepare for plotting |
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setwd(path) #set path to the output path |
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rast_fus_pred<-raster(rast_fus1c,1) # Select the first model from the stack i.e fusion with kriging for both steps |
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rast_cai_pred<-raster(rast_cai2c,1) |
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layerNames(rast_cai_pred)<-paste("cai",date_selected,sep="_")
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layerNames(rast_fus_pred)<-paste("fus",date_selected,sep="_")
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rast_pred2<-stack(rast_fus_pred,rast_cai_pred) |
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#function to extract training and test from object from object models created earlier during interpolation... |
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#load training and testing date for the specified date for fusion and CAI |
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data_vf<-station_data_interp(date_selected,file.path(path_data_fus,obj_mod_fus_name),training=FALSE,testing=TRUE) |
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#data_sf<-station_data_interp(date_selected,file.path(path_data_fus,obj_mod_fus_name),training=TRUE,testing=FALSE) |
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data_vc<-station_data_interp(date_selected,file.path(path_data_cai,obj_mod_cai_name),training=FALSE,testing=TRUE) |
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#data_sc<-station_data_interp(date_selected,file.path(path_data_cai,obj_mod_cai_name),training=TRUE,testing=FALSE) |
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date_selected_snot<-strptime(date_selected,"%Y%m%d") |
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snot_selected <-snot_OR_2010_sp[snot_OR_2010_sp$date_formatted==date_selected_snot,] |
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#snot_selected<-na.omit(as.data.frame(snot_OR_2010_sp[snot_OR_2010_sp$date==90110,])) |
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rast_diff_fc<-rast_fus_pred-rast_cai_pred |
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LC_stack<-stack(LC1,LC2,LC3,LC4,LC6,LC7) |
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rast_pred3<-stack(rast_diff_fc,rast_pred2,ELEV_SRTM,LC_stack) |
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layerNames(rast_pred3)<-c("diff_fc","fus","CAI","ELEV_SRTM","LC1","LC2","LC3","LC4","LC6","LC7") #extract amount of veg...
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#extract predicted tmax corresponding to |
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extract_snot<-extract(rast_pred3,snot_selected) #return value from extract is a matrix (with input SPDF) |
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snot_data_selected<-cbind(as.data.frame(snot_selected),extract_snot) #bind data together |
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snot_data_selected$res_f<-snot_data_selected$fus-snot_data_selected$tmax #calculate the residuals for Fusion |
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snot_data_selected$res_c<-snot_data_selected$CAI-snot_data_selected$tmax #calculate the residuals for CAI |
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#snot_data_selected<-(na.omit(as.data.frame(snot_data_selected))) #remove rows containing NA, this may need to be modified later. |
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###fig3: Plot predicted vs observed tmax |
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#fig3a: FUS |
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png(paste("fig3_testing_scatterplot_pred_fus_CAI_observed_SNOT_GHCN_",date_selected,out_prefix,".png", sep=""))
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par(mfrow=c(1,2)) |
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x_range<-range(c(data_vf$pred_mod7,snot_data_selected$fus,data_vc$pred_mod9,snot_data_selected$CAI),na.rm=T) |
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y_range<-range(c(data_vf$dailyTmax,snot_data_selected$tmax,data_vc$dailyTmax,snot_data_selected$tmax),na.rm=T) |
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plot(data_vf$pred_mod7,data_vf$dailyTmax, ylab="Observed daily tmax (C)", xlab="Fusion predicted daily tmax (C)", |
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ylim=y_range,xlim=x_range) |
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#text(data_vf$pred_mod7,data_vf$dailyTmax,labels=data_vf$idx,pos=3) |
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abline(0,1) #takes intercept at 0 and slope as 1 so display 1:1 ine |
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grid(lwd=0.5,col="black") |
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points(snot_data_selected$fus,snot_data_selected$tmax,pch=2,co="red") |
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title(paste("Testing stations tmax fusion vs daily tmax",date_selected,sep=" "))
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legend("topleft",legend=c("GHCN", "SNOT"),
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cex=1.2, col=c("black","red"),
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pch=c(1,2)) |
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#fig 3b: CAI |
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#x_range<-range(c(data_vc$pred_mod9,snot_data_selected$CAI)) |
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#y_range<-range(c(data_vc$dailyTmax,snot_data_selected$tmax)) |
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plot(data_vc$pred_mod9,data_vc$dailyTmax, ylab="Observed daily tmax (C)", xlab="CAI predicted daily tmax (C)", |
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ylim=y_range,xlim=x_range) |
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#text(data_vc$pred_mod9,data_vc$dailyTmax,labels=data_vf$idx,pos=3) |
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abline(0,1) #takes intercept at 0 and slope as 1 so display 1:1 ine |
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grid(lwd=0.5,col="black") |
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points(snot_data_selected$CAI,snot_data_selected$tmax,pch=2,co="red") |
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#text(snot_data_selected$CAI,snot_data_selected$tmax,labels=1:nrow(snot_data_selected),pos=3) |
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#title(paste("Testing stations tmax CAI vs daily tmax",date_selected,sep=" "))
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legend("topleft",legend=c("GHCN", "SNOT"),
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cex=1.2, col=c("black","red"),
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pch=c(1,2)) |
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#savePlot(paste("fig3_testing_scatterplot_pred_fus_CAI_observed_SNOT_GHCN_",date_selected,out_prefix,".png", sep=""), type="png")
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dev.off() |
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##### Fig4a: ELEV-CAI |
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png(paste("fig4_testing_scatterplot_pred_fus_CIA_elev_SNOT_GHCN_",date_selected,out_prefix,".png", sep=""))
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par(mfrow=c(1,2)) |
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y_range<-range(c(data_vc$pred_mod9,snot_data_selected$CAI),na.rm=T) |
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#y_range<-range(c(data_vc$pred_mod9,snot_data_selected$CAI),na.rm=T) |
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x_range<-range(c(data_vc$ELEV_SRTM,snot_data_selected$ELEV_SRTM),na.rm=T) |
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lm_mod1<-lm(data_vc$pred_mod9~data_vc$ELEV_SRTM) |
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lm_mod2<-lm(snot_data_selected$CAI~snot_data_selected$ELEV_SRTM) |
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plot(data_vc$ELEV_SRTM,data_vc$pred_mod9,ylab="Observed daily tmax (C)", xlab="Elevation (m)", |
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ylim=y_range,xlim=x_range) |
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#text(data_vc$ELEV_SRTM,data_vc$pred_mod9,labels=data_vc$idx,pos=3) |
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abline(lm_mod1) #takes intercept at 0 and slope as 1 so display 1:1 ine |
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abline(lm_mod2,col="red") #takes intercept at 0 and slope as 1 so display 1:1 ine |
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grid(lwd=0.5, col="black") |
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points(snot_data_selected$ELEV_SRTM,snot_data_selected$CAI,pch=2,co="red") |
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title(paste("Testing stations tmax CAI vs elevation",date_selected,sep=" "))
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legend("topleft",legend=c("GHCN", "SNOT"),
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cex=1.2, col=c("black","red"),
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pch=c(1,2)) |
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#Fig4bELEV-FUS |
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y_range<-range(c(data_vf$pred_mod7,snot_data_selected$fus),na.rm=T) |
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x_range<-range(c(data_vf$ELEV_SRTM,snot_data_selected$ELEV_SRTM),na.rm=T) |
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lm_mod1<-lm(data_vf$pred_mod7~data_vf$ELEV_SRTM) |
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lm_mod2<-lm(snot_data_selected$fus~snot_data_selected$ELEV_SRTM) |
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plot(data_vf$ELEV_SRTM,data_vf$pred_mod7,ylab="Observed daily tmax (C)", xlab="Elevation (m)", |
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ylim=y_range,xlim=x_range) |
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#text(data_vc$ELEV_SRTM,data_vc$pred_mod9,labels=data_vc$idx,pos=3) |
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abline(lm_mod1) #takes intercept at 0 and slope as 1 so display 1:1 ine |
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abline(lm_mod2,col="red") #takes intercept at 0 and slope as 1 so display 1:1 ine |
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grid(lwd=0.5, col="black") |
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points(snot_data_selected$ELEV_SRTM,snot_data_selected$fus,pch=2,co="red") |
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title(paste("Testing stations tmax vs elevation",date_selected,sep=" "))
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legend("topleft",legend=c("GHCN", "SNOT"),
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cex=1.2, col=c("black","red"),
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pch=c(1,2)) |
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#savePlot(paste("fig4_testing_scatterplot_pred_fus_CIA_elev_SNOT_GHCN_",date_selected,out_prefix,".png", sep=""), type="png")
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dev.off() |
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############ ACCURACY METRICS AND RESIDUALS ############# |
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#START FIG 5 |
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#####Fig5a: CAI vs FUSION: difference by plotting on in terms of the other |
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png(paste("fig5_testing_scatterplot_pred_fus_CAI_observed_SNOT_GHCN_",date_selected,out_prefix,".png", sep=""))
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par(mfrow=c(1,2)) |
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lm_mod<-lm(snot_data_selected$CAI~snot_data_selected$fus) |
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y_range<-range(c(data_vc$pred_mod9,snot_data_selected$CAI),na.rm=T) |
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x_range<-range(c(data_vf$pred_mod7,snot_data_selected$fus),na.rm=T) |
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plot(data_vf$pred_mod7,data_vc$pred_mod9,ylab="Predicted CAI daily tmax (C)", xlab="Predicted fusion daily tmax (C)", |
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ylim=y_range,xlim=x_range) |
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#text(data_vc$ELEV_SRTM,data_vc$dailyTmax,labels=data_vc$idx,pos=3) |
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abline(0,1) #takes intercept at 0 and slope as 1 so display 1:1 ine |
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abline(lm_mod,col="red") |
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grid(lwd=0.5, col="black") |
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points(snot_data_selected$fus,snot_data_selected$CAI,pch=2,co="red") |
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title(paste("Testing stations predicted tmax fusion vs CAI tmax",date_selected,sep=" "))
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legend("topleft",legend=c("GHCN", "SNOT"),
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cex=1.2, col=c("black","red"),
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pch=c(1,2)) |
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####Fig5b: diff vs elev: difference by plotting on in terms of elev |
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diff_fc<-data_vf$pred_mod7-data_vc$pred_mod9 |
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plot(snot_data_selected$ELEV_SRTM,snot_data_selected$diff_fc,pch=2,col="red") |
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lm_mod<-lm(snot_data_selected$diff_fc~snot_data_selected$ELEV_SRTM) |
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abline(lm_mod,col="red") |
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points(data_vf$ELEV_SRTM,diff_fc) |
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lm_mod<-lm(diff_fc~data_vf$ELEV_SRTM) |
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abline(lm_mod) |
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legend("topleft",legend=c("GHCN", "SNOT"),
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cex=1.2, col=c("black","red"),
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pch=c(1,2)) |
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title(paste("Prediction tmax difference and elevation ",sep=""))
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dev.off() |
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#savePlot(paste("fig5_testing_scatterplot_pred_fus_CAI_observed_SNOT_GHCN_",date_selected,out_prefix,".png", sep=""), type="png")
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#DO diff IN TERM OF ELEVATION CLASSES as well as diff.. |
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#### START FIG 6: difference fc vs elev |
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#fig6a |
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png(paste("fig6_elevation_classes_diff_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""))
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par(mfrow=c(1,2)) |
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brks<-c(0,500,1000,1500,2000,2500,4000) |
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lab_brks<-1:6 |
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elev_rcstat<-cut(snot_data_selected$ELEV_SRTM,breaks=brks,labels=lab_brks,right=F) |
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snot_data_selected$elev_rec<-elev_rcstat |
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y_range<-range(c(snot_data_selected$diff_fc),na.rm=T) |
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x_range<-range(c(elev_rcstat),na.rm=T) |
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plot(elev_rcstat,snot_data_selected$diff_fc, ylab="diff_fc", xlab="ELEV_SRTM (m) ", |
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ylim=y_range, xlim=x_range) |
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#text(elev_rcstat,diff_cf,labels=data_vf$idx,pos=3) |
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grid(lwd=0.5,col="black") |
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title(paste("SNOT stations diff f vs Elevation",date_selected,sep=" "))
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###With fewer classes...fig6b |
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brks<-c(0,1000,2000,3000,4000) |
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lab_brks<-1:4 |
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elev_rcstat<-cut(snot_data_selected$ELEV_SRTM,breaks=brks,labels=lab_brks,right=F) |
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snot_data_selected$elev_rec<-elev_rcstat |
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y_range<-range(c(snot_data_selected$diff_fc),na.rm=T) |
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x_range<-range(c(elev_rcstat),na.rm=T) |
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plot(elev_rcstat,snot_data_selected$diff_fc, ylab="diff_fc", xlab="ELEV_SRTM (m) ", |
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ylim=y_range, xlim=x_range) |
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#text(elev_rcstat,diff_cf,labels=data_vf$idx,pos=3) |
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grid(lwd=0.5,col="black") |
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title(paste("SNOT stations diff f vs Elevation",date_selected,sep=" "))
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#savePlot(paste("fig6_elevation_classes_diff_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""), type="png")
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dev.off() |
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#START FIG 7 with residuals |
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#fig 7a |
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png(paste("fig7_elevation_classes_residuals_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""))
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par(mfrow=c(1,2)) |
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brks<-c(0,1000,2000,3000,4000) |
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lab_brks<-1:4 |
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elev_rcstat<-cut(snot_data_selected$ELEV_SRTM,breaks=brks,labels=lab_brks,right=F) |
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snot_data_selected$elev_rec<-elev_rcstat |
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y_range<-range(c(snot_data_selected$res_f,snot_data_selected$res_c),na.rm=T) |
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x_range<-range(c(elev_rcstat),na.rm=T) |
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plot(elev_rcstat,snot_data_selected$res_f, ylab="res_f", xlab="ELEV_SRTM (m) ", |
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ylim=y_range, xlim=x_range) |
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#text(elev_rcstat,diff_cf,labels=data_vf$idx,pos=3) |
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grid(lwd=0.5,col="black") |
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title(paste("SNOT stations residuals fusion vs Elevation",date_selected,sep=" "))
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#fig 7b |
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elev_rcstat<-cut(snot_data_selected$ELEV_SRTM,breaks=brks,labels=lab_brks,right=F) |
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y_range<-range(c(snot_data_selected$res_c,snot_data_selected$res_f),na.rm=T) |
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x_range<-range(c(elev_rcstat)) |
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plot(elev_rcstat,snot_data_selected$res_c, ylab="res_c", xlab="ELEV_SRTM (m) ", |
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ylim=y_range, xlim=x_range) |
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#text(elev_rcstat,diff_cf,labels=data_vf$idx,pos=3) |
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grid(lwd=0.5,col="black") |
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title(paste("SNOT stations residuals CAI vs Elevation",date_selected,sep=" "))
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#savePlot(paste("fig7_elevation_classes_residuals_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""), type="png")
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dev.off() |
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####### COMPARE CAI FUSION USING SNOTEL DATA WITH ACCURACY METRICS############### |
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################ RESIDUALS and MAE etc. ##################### |
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browser() |
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### Run for full list of date? --365 |
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ac_tab_snot_fus<-calc_accuracy_metrics(snot_data_selected$tmax,snot_data_selected$fus) |
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ac_tab_snot_cai<-calc_accuracy_metrics(snot_data_selected$tmax,snot_data_selected$CAI) |
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ac_tab_ghcn_fus<-calc_accuracy_metrics(data_vf$dailyTmax,data_vf$pred_mod7) |
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ac_tab_ghcn_cai<-calc_accuracy_metrics(data_vc$dailyTmax,data_vc$pred_mod9) |
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ac_tab<-do.call(rbind,list(ac_tab_snot_fus,ac_tab_snot_cai,ac_tab_ghcn_fus,ac_tab_ghcn_cai)) |
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ac_tab$mod_id<-c("snot_fus","snot_cai","ghcn_fus","ghcn_cai")
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ac_tab$date<-date_selected |
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list_ac_tab[[i]]<-ac_tab #storing the accuracy metric data.frame in a list... |
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#save(list_ac_tab,) |
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save(list_ac_tab,file= paste("list_ac_tab_", date_selected,out_prefix,".RData",sep=""))
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#FIG8: boxplot of residuals for methods (fus, cai) using SNOT and GHCN data |
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#fig8a |
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png(paste("fig8_residuals_boxplot_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""))
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par(mfrow=c(1,2)) |
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y_range<-range(c(snot_data_selected$res_f,snot_data_selected$res_c,data_vf$res_mod7,data_vc$res_mod9),na.rm=T) |
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boxplot(snot_data_selected$res_f,snot_data_selected$res_c,names=c("FUS","CAI"),ylim=y_range,ylab="Residuals tmax degree C")
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title(paste("Residuals for fusion and CAI methods for SNOT data ",date_selected,sep=" "))
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#fig8b |
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boxplot(data_vf$res_mod7,data_vc$res_mod9,names=c("FUS","CAI"),ylim=y_range,ylab="Residuals tmax degree C")
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title(paste("Residuals for fusion and CAI methods for GHCN data ",date_selected,sep=" "))
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#savePlot(paste("fig8_residuals_boxplot_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""), type="png")
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|
| 246 |
dev.off() |
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| 247 |
mae_fun<-function(residuals){
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|
| 248 |
mean(abs(residuals),na.rm=T) |
|
| 249 |
} |
|
| 250 |
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|
| 251 |
mean_diff_fc<-aggregate(diff_fc~elev_rec,data=snot_data_selected,mean) |
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| 252 |
mean_mae_c<-aggregate(res_c~elev_rec,data=snot_data_selected,mae_fun) |
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| 253 |
mean_mae_f<-aggregate(res_f~elev_rec,data=snot_data_selected,mae_fun) |
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| 254 |
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| 255 |
####FIG 9: plot MAE for fusion and CAI as well as boxplots of both thechnique |
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| 256 |
#fig 9a: boxplot of residuals for MAE and CAI |
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| 257 |
png(paste("fig9_residuals_boxplot_MAE_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""))
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|
| 258 |
par(mfrow=c(1,2)) |
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| 259 |
height<-cbind(snot_data_selected$res_f,snot_data_selected$res_c) |
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| 260 |
boxplot(height,names=c("FUS","CAI"),ylab="Residuals tmax degree C")
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| 261 |
title(paste("Residuals for fusion and CAI methods for SNOT data ",date_selected,sep=" "))
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|
| 262 |
#par(new=TRUE) |
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| 263 |
#abline(h=ac_tab[1,1],col="red") |
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| 264 |
points(1,ac_tab[1,1],pch=5,col="red") |
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| 265 |
points(2,ac_tab[2,1],pch=5,col="black") |
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| 266 |
legend("bottom",legend=c("FUS_MAE", "CAI_MAE"),
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|
| 267 |
cex=0.8, col=c("red","black"),
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| 268 |
pch=c(2,1)) |
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| 269 |
#fig 9b: MAE per 3 elevation classes:0-1000,1000-2000,2000-3000,3000-4000 |
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| 270 |
y_range<-c(0,max(c(mean_mae_c[,2],mean_mae_f[,2]),na.rm=T)) |
|
| 271 |
plot(1:3,mean_mae_c[,2],ylim=y_range,type="n",ylab="MAE in degree C",xlab="elevation classes") |
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| 272 |
points(mean_mae_c,ylim=y_range) |
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| 273 |
lines(1:3,mean_mae_c[,2],col="black") |
|
| 274 |
par(new=TRUE) # key: ask for new plot without erasing old |
|
| 275 |
points(mean_mae_f,ylim=y_range) |
|
| 276 |
lines(1:3,mean_mae_f[,2],col="red") |
|
| 277 |
legend("bottom",legend=c("FUS_MAE", "CAI_MAE"),
|
|
| 278 |
cex=0.8, col=c("red","black"),
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|
| 279 |
pch=c(2,1)) |
|
| 280 |
title(paste("MAE per elevation classes for SNOT data ",date_selected,sep=" "))
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|
| 281 |
#savePlot(paste("fig9_residuals_boxplot_MAE_SNOT_GHCN_network",date_selected,out_prefix,".png", sep=""), type="png")
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|
| 282 |
dev.off() |
|
| 283 |
### LM MODELS for difference and elevation categories |
|
| 284 |
## Are the differences plotted on fig 9 significant?? |
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| 285 |
diffelev_mod<-lm(diff_fc~elev_rec,data=snot_data_selected) |
|
| 286 |
summary(diffelev_mod) |
|
| 287 |
##LM MODEL MAE PER ELEVATION CLASS: residuals for CAI |
|
| 288 |
diffelev_mod<-lm(res_c~elev_rec,data=snot_data_selected) |
|
| 289 |
summary(diffelev_mod) |
|
| 290 |
##LM MODEL MAE PER ELEVATION CLASS: residuals for Fusions |
|
| 291 |
diffelev_mod<-lm(res_f~elev_rec,data=snot_data_selected) |
|
| 292 |
summary(diffelev_mod) |
|
| 293 |
|
|
| 294 |
### LM MODELS for RESIDUALS BETWEEN CAI AND FUSION |
|
| 295 |
## Are the differences plotted on fig 9 significant?? |
|
| 296 |
## STORE THE p values...?? overall and per cat? |
|
| 297 |
|
|
| 298 |
#diffelev_mod<-lm(res_f~elev_rec,data=snot_data_selected) |
|
| 299 |
#table(snot_data_selected$elev_rec) #Number of observation per class |
|
| 300 |
#max(snot_data_selected$E_STRM) |
|
| 301 |
|
|
| 302 |
#res |
|
| 303 |
|
|
| 304 |
############################################# |
|
| 305 |
#USING BOTH validation and training |
|
| 306 |
#This part is exploratory.... |
|
| 307 |
################## EXAMINING RESIDUALS AND DIFFERENCES IN LAND COVER......############ |
|
| 308 |
###### |
|
| 309 |
|
|
| 310 |
#LC_names<-c("LC1_rec","LC2_rec","LC3_rec","LC4_rec","LC6_rec")
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|
| 311 |
suf_name<-c("rec1")
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|
| 312 |
sum_var<-c("diff_fc")
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|
| 313 |
LC_names<-c("LC1","LC2","LC3","LC4","LC6")
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|
| 314 |
brks<-c(-1,20,40,60,80,101) |
|
| 315 |
lab_brks<-seq(1,5,1) |
|
| 316 |
#var_name<-LC_names; suffix<-"rec1"; s_function<-"mean";df<-snot_data_selected;summary_var<-"diff_fc" |
|
| 317 |
#reclassify_df(snot_data_selected,LC_names,var_name,brks,lab_brks,suffix,summary_var) |
|
| 318 |
|
|
| 319 |
#Calculate mean per land cover percentage |
|
| 320 |
data_agg<-reclassify_df(snot_data_selected,LC_names,brks,lab_brks,suf_name,sum_var) |
|
| 321 |
data_lc<-data_agg[[1]] |
|
| 322 |
snot_data_selected<-data_agg[[2]] |
|
| 323 |
|
|
| 324 |
by_name<-"rec1" |
|
| 325 |
df_lc_diff_fc<-merge_multiple_df(data_lc,by_name) |
|
| 326 |
|
|
| 327 |
###### FIG10: PLOT LAND COVER |
|
| 328 |
png(paste("fig10_diff_prediction_tmax_diff_res_f_land cover",date_selected,out_prefix,".png", sep=""))
|
|
| 329 |
par(mfrow=c(1,2)) |
|
| 330 |
zones_stat<-df_lc_diff_fc #first land cover |
|
| 331 |
#names(zones_stat)<-c("lab_brks","LC")
|
|
| 332 |
y_range<-range(as.vector(t(zones_stat[,-1])),na.rm=T) |
|
| 333 |
lab_brks_mid<-c(10,30,50,70,90) |
|
| 334 |
plot(lab_brks_mid,zones_stat[,2],type="b",ylim=y_range,col="black", lwd=2, |
|
| 335 |
ylab="difference between fusion and CAI",xlab="land cover percent classes") |
|
| 336 |
lines(lab_brks_mid,zones_stat[,3],col="red",type="b") |
|
| 337 |
lines(lab_brks_mid,zones_stat[,4],col="blue",type="b") |
|
| 338 |
lines(lab_brks_mid,zones_stat[,5],col="darkgreen",type="b") |
|
| 339 |
lines(lab_brks_mid,zones_stat[,6],col="purple",type="b") |
|
| 340 |
legend("topleft",legend=c("LC1_forest", "LC2_shrub", "LC3_grass", "LC4_crop", "LC6_urban"),
|
|
| 341 |
cex=1.2, col=c("black","red","blue","darkgreen","purple"),
|
|
| 342 |
lty=1,lwd=1.8) |
|
| 343 |
title(paste("Prediction tmax difference and land cover ",date_selected,sep=""))
|
|
| 344 |
|
|
| 345 |
###NOW USE RESIDUALS FOR FUSION |
|
| 346 |
sum_var<-"res_f" |
|
| 347 |
suf_name<-"rec2" |
|
| 348 |
data_agg2<-reclassify_df(snot_data_selected,LC_names,brks,lab_brks,suf_name,sum_var) |
|
| 349 |
data_resf_lc<-data_agg2[[1]] |
|
| 350 |
#snot_data_selected<-data_agg[[2]] |
|
| 351 |
|
|
| 352 |
by_name<-"rec2" |
|
| 353 |
df_lc_resf<-merge_multiple_df(data_resf_lc,by_name) |
|
| 354 |
|
|
| 355 |
zones_stat<-df_lc_resf #first land cover |
|
| 356 |
#names(zones_stat)<-c("lab_brks","LC")
|
|
| 357 |
lab_brks_mid<-c(10,30,50,70,90) |
|
| 358 |
plot(lab_brks_mid,zones_stat[,2],type="b",ylim=y_range,col="black",lwd=2, |
|
| 359 |
ylab="tmax residuals fusion ",xlab="land cover percent classes") |
|
| 360 |
lines(lab_brks_mid,zones_stat[,3],col="red",type="b") |
|
| 361 |
lines(lab_brks_mid,zones_stat[,4],col="blue",type="b") |
|
| 362 |
lines(lab_brks_mid,zones_stat[,5],col="darkgreen",type="b") |
|
| 363 |
lines(lab_brks_mid,zones_stat[,6],col="purple",type="b") |
|
| 364 |
legend("topleft",legend=c("LC1_forest", "LC2_shrub", "LC3_grass", "LC4_crop", "LC6_urban"),
|
|
| 365 |
cex=1.2, col=c("black","red","blue","darkgreen","purple"),
|
|
| 366 |
lty=1,lwd=1.2) |
|
| 367 |
title(paste("Prediction tmax residuals and land cover ",date_selected,sep=""))
|
|
| 368 |
#savePlot(paste("fig10_diff_prediction_tmax_diff_res_f_land cover",date_selected,out_prefix,".png", sep=""), type="png")
|
|
| 369 |
dev.off() |
|
| 370 |
#### FIGURE11: res_f and res_c per land cover |
|
| 371 |
|
|
| 372 |
sum_var<-"res_c" |
|
| 373 |
suf_name<-"rec3" |
|
| 374 |
data_agg3<-reclassify_df(snot_data_selected,LC_names,brks,lab_brks,suf_name,sum_var) |
|
| 375 |
data_resc_lc<-data_agg3[[1]] |
|
| 376 |
snot_data_selected<-data_agg3[[2]] |
|
| 377 |
|
|
| 378 |
by_name<-"rec3" |
|
| 379 |
df_lc_resc<-merge_multiple_df(data_resc_lc,by_name) |
|
| 380 |
|
|
| 381 |
zones_stat<-df_lc_resc #first land cover |
|
| 382 |
#names(zones_stat)<-c("lab_brks","LC")
|
|
| 383 |
png(paste("fig11_prediction_tmax_res_f_res_c_land cover",date_selected,out_prefix,".png", sep=""))
|
|
| 384 |
par(mfrow=c(1,2)) |
|
| 385 |
y_range<-range(as.vector(t(zones_stat[,-1])),na.rm=T) |
|
| 386 |
lab_brks_mid<-c(10,30,50,70,90) |
|
| 387 |
plot(lab_brks_mid,zones_stat[,2],type="b",ylim=y_range,col="black",lwd=2, |
|
| 388 |
ylab="tmax residuals CAI",xlab="land cover percent classes") |
|
| 389 |
lines(lab_brks_mid,zones_stat[,3],col="red",type="b") |
|
| 390 |
lines(lab_brks_mid,zones_stat[,4],col="blue",type="b") |
|
| 391 |
lines(lab_brks_mid,zones_stat[,5],col="darkgreen",type="b") |
|
| 392 |
lines(lab_brks_mid,zones_stat[,6],col="purple",type="b") |
|
| 393 |
legend("topleft",legend=c("LC1_forest", "LC2_shrub", "LC3_grass", "LC4_crop", "LC6_urban"),
|
|
| 394 |
cex=1.2, col=c("black","red","blue","darkgreen","purple"),
|
|
| 395 |
lty=1,lwd=1.2) |
|
| 396 |
title(paste("Prediction tmax residuals CAI and land cover ",date_selected,sep=""))
|
|
| 397 |
|
|
| 398 |
#fig11b |
|
| 399 |
zones_stat<-df_lc_resf #first land cover |
|
| 400 |
#names(zones_stat)<-c("lab_brks","LC")
|
|
| 401 |
y_range<-range(as.vector(t(zones_stat[,-1])),na.rm=T) |
|
| 402 |
lab_brks_mid<-c(10,30,50,70,90) |
|
| 403 |
plot(lab_brks_mid,zones_stat[,2],type="b",ylim=y_range,col="black",lwd=2, |
|
| 404 |
ylab="tmax residuals fusion ",xlab="land cover percent classes") |
|
| 405 |
lines(lab_brks_mid,zones_stat[,3],col="red",type="b") |
|
| 406 |
lines(lab_brks_mid,zones_stat[,4],col="blue",type="b") |
|
| 407 |
lines(lab_brks_mid,zones_stat[,5],col="darkgreen",type="b") |
|
| 408 |
lines(lab_brks_mid,zones_stat[,6],col="purple",type="b") |
|
| 409 |
legend("topleft",legend=c("LC1_forest", "LC2_shrub", "LC3_grass", "LC4_crop", "LC6_urban"),
|
|
| 410 |
cex=1.2, col=c("black","red","blue","darkgreen","purple"),
|
|
| 411 |
lty=1,lwd=1.2) |
|
| 412 |
title(paste("Prediction tmax residuals and land cover ",date_selected,sep=""))
|
|
| 413 |
#savePlot(paste("fig10_diff_prediction_tmax_diff_res_f_land cover",date_selected,out_prefix,".png", sep=""), type="png")
|
|
| 414 |
#savePlot(paste("fig11_prediction_tmax_res_f_res_c_land cover",date_selected,out_prefix,".png", sep=""), type="png")
|
|
| 415 |
dev.off() |
|
| 416 |
ac_data_obj<-list(snot_data_selected,data_vf,data_vc,ac_tab) |
|
| 417 |
names(ac_data_obj)<-c("snot_data_selected","data_vf", "data_vc","ac_tab")
|
|
| 418 |
return(ac_data_obj) |
|
| 419 |
} |
|
Also available in: Unified diff
Methods comp part7-task#491- function for residuals analyses comparison SNOTEL and GHCN through dates