Revision 17673d6a
Added by Benoit Parmentier over 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part2.R | ||
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############################## INTERPOLATION OF TEMPERATURES ####################################### |
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############################## INTERPOLATION OF TEMPERATURES #######################################
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####################### Script for assessment of scaling up on NEX : part2 ############################## |
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#This script uses the worklfow code applied to the globe. Results currently reside on NEX/PLEIADES NASA. |
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#Accuracy methods are added in the the function scripts to evaluate results. |
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#Analyses, figures, tables and data are also produced in the script. |
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#AUTHOR: Benoit Parmentier |
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#CREATED ON: 03/23/2014 |
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#MODIFIED ON: 05/13/2015
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#MODIFIED ON: 05/26/2015
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#Version: 4 |
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#PROJECT: Environmental Layers project |
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#COMMENTS: analyses for run 10 global analyses,all regions 1500x4500km with additional tiles to increase overlap |
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interpolation_method <- c("gam_CAI") #PARAM2
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#out_suffix<-"run10_global_analyses_01282015" |
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#out_suffix <- "output_run10_1000x3000_global_analyses_02102015" |
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out_suffix <- "run10_1500x4500_global_analyses_pred_2003_05122015" #PARAM3
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out_dir <- "/data/project/layers/commons/NEX_data/output_run10_1500x4500_global_analyses_pred_2003_05122015" #PARAM4
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out_suffix <- "run10_1500x4500_global_analyses_pred_2010_05262015" #PARAM3
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out_dir <- "/data/project/layers/commons/NEX_data/output_run10_1500x4500_global_analyses_pred_2010_05262015" #PARAM4
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create_out_dir_param <- FALSE #PARAM 5 |
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mosaic_plot <- FALSE #PARAM6 |
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#if daily mosaics NULL then mosaicas all days of the year |
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day_to_mosaic <- c("20030101","20030102","20030103","20030104","20030105",
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"20030301","20030302","20030303","20030304","20030305", |
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"20030501","20030502","20030503","20030504","20030505", |
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"20030701","20030702","20030703","20030704","20030705", |
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"20030901","20030902","20030903","20030904","20030905", |
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"20031101","20031102","20031103","20031104","20031105") #PARAM7 |
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day_to_mosaic <- c("20100829","20100830","20100831",
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"20100901","20100902","20100903") |
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#CRS_locs_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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CRS_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84 #CONSTANT1
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CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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summary_metrics_v_all <- summary_metrics_v |
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#deal with additional tiles... |
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if(reg_modified==T){
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summary_metrics_v_tmp <- summary_metrics_v |
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#summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg_1b"] <- "reg1" |
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#summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg_1c"] <- "reg1" |
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#summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg_3b"] <- "reg3" |
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summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg5b"] <- "reg5" |
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summary_metrics_v_tmp$reg_all <- summary_metrics_v$reg |
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### |
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summary_metrics_v<- summary_metrics_v_tmp |
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### |
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tb_tmp <- tb |
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#tb_tmp$reg[tb_tmp$reg=="reg_1b"] <- "reg1" |
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#tb_tmp$reg[tb_tmp$reg=="reg_1c"] <- "reg1" |
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#tb_tmp$reg[tb_tmp$reg=="reg_3b"] <- "reg3" |
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tb_tmp$reg[tb_tmp$reg=="reg5b"] <- "reg5" |
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### |
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tb <- tb_tmp |
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} |
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# if(reg_modified==T){
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# |
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# summary_metrics_v_tmp <- summary_metrics_v
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# #summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg_1b"] <- "reg1"
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# #summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg_1c"] <- "reg1"
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# #summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg_3b"] <- "reg3"
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# summary_metrics_v_tmp$reg[summary_metrics_v_tmp$reg=="reg5b"] <- "reg5"
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# |
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# summary_metrics_v_tmp$reg_all <- summary_metrics_v$reg
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# ###
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# summary_metrics_v<- summary_metrics_v_tmp
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# |
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# ###
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# tb_tmp <- tb
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# #tb_tmp$reg[tb_tmp$reg=="reg_1b"] <- "reg1"
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# #tb_tmp$reg[tb_tmp$reg=="reg_1c"] <- "reg1"
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# #tb_tmp$reg[tb_tmp$reg=="reg_3b"] <- "reg3"
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# tb_tmp$reg[tb_tmp$reg=="reg5b"] <- "reg5"
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# |
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# ###
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# tb <- tb_tmp
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# }
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table(summary_metrics_v_all$reg) |
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table(summary_metrics_v$reg) |
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} |
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## Number of tiles with information: |
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sum(df_tile_processed$metrics_v) #20,number of tiles with raster object
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length(df_tile_processed$metrics_v) #25,number of tiles in the region
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sum(df_tile_processed$metrics_v) #26,number of tiles with raster object
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length(df_tile_processed$metrics_v) #26,number of tiles in the region
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sum(df_tile_processed$metrics_v)/length(df_tile_processed$metrics_v) #80 of tiles with info |
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#coordinates |
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# pattern=paste("^world_mosaics.*.tif$",sep=""),full.names=T)
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lf_world_pred <-list.files(path=file.path(out_dir,"mosaics"), |
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pattern=paste("^reg5.*.",out_suffix,".tif$",sep=""),full.names=T)
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pattern=paste("^reg4.*.",".tif$",sep=""),full.names=T)
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#mosaic_list_mean <- test_list |
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#out_rastnames <- "world_test_mosaic_20100101" |
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#world_m_list <- mclapply(11:30, list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = num_cores) #This is the end bracket from mclapply(...) statement |
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world_m_list <- mclapply(1:length(l_dates), list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = num_cores) #This is the end bracket from mclapply(...) statement |
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s_pred <- stack(lf_raster_fname) |
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res_pix <- 1500 |
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col_mfrow <- 3 |
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row_mfrow <- 2 |
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png(filename=paste("Figure10_levelplot_combined_",region_name,"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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levelplot(s_pred,layers=1:6,col.regions=rev(terrain.colors(255)),cex=4) |
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dev.off() |
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# blues<- designer.colors(6, c( "blue", "white") ) |
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# reds <- designer.colors(6, c( "white","red") ) |
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# colorTable<- c( blues[-6], reds) |
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# breaks with a gap of 10 to 17 assigned the white color |
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# brks<- c(seq( 1, 10,,6), seq( 17, 25,,6)) |
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# image.plot( x,y,z,breaks=brks, col=colorTable) |
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# |
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#lf_world_mask_reg <- vector("list",length=length(lf_world_pred))
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#for(i in 1:length(lf_world_pred)){
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# |
Also available in: Unified diff
scaling up assessment part2, South America with additional tiles, figures and analyses