Ensemble mean plots of AMBER results (bias, bias scores, etc)
This function plots ensemble mean, minimum, and maximum values of a statistical metric computed by scores.grid.time and scores.grid.notime.
plotEnsembleMean(long.name, metric, mod.path.list, modelIDs, myVariables, shp.filename = system.file("extdata/ne_110m_land/ne_110m_land.shp", package = "amber"), my.xlim = c(-180, 180), my.ylim = c(-60, 85), plot.width = 5, plot.height = 7, outputDir = FALSE, subcaption = "")
long.name |
A string that gives the full name of the variable, e.g. 'Gross primary productivity' |
metric |
A string that specifies what statistical metric should be plotted. This includes for instance 'bias', 'crmse', 'phase', 'iav', 'bias-score', 'rmse-score', 'phase-score', and 'iav-score'. |
mod.path.list |
A List of directories where AMBER output is stored for different model runs, e.g. list(mod01.path, mod02.path, mod03.path) |
modelIDs |
An R object with the different model run IDs, e.g. c('CLASSIC.CRUJRAv2', 'CLASSIC.GSWP3W5E5', 'CLASSIC.CRUNCEP') |
myVariables |
An R object with the variable names of interest, e.g. c('GPP.FluxCom', 'RECO.FluxCom'). |
shp.filename |
A string that gives the coastline shapefile |
my.xlim |
An R object that gives the longitude range that you wish to plot, e.g. c(-180, 180) |
my.ylim |
An R object that gives the longitude range that you wish to plot, e.g. c(-90, 90) |
plot.width |
Number that gives the plot width, e.g. 8 |
plot.height |
Number that gives the plot height, e.g. 4 |
outputDir |
A string that gives the output directory, e.g. '/home/project/study'. The output will only be written if the user specifies an output directory. |
subcaption |
A string that defines the subcaption of the figure, e.g. '(a)'. |
Figures in PDF format.
library(amber) library(classInt) library(doParallel) library(foreach) library(Hmisc) library(latex2exp) library(ncdf4) library(parallel) library(raster) library(rgdal) library(rgeos) library(scico) library(sp) library(stats) library(utils) library(viridis) library(xtable) long.name <- 'Gross Primary Productivity' metric <- 'mod-mean' mod01.path <- paste(system.file('extdata', package = 'amber'), 'model01', sep = '/') mod02.path <- paste(system.file('extdata', package = 'amber'), 'model02', sep = '/') mod.path.list <- list(mod01.path, mod02.path) modelIDs <- c('CLASSIC.CRUJRAv2', 'CLASSIC.GSWP3W5E5') myVariables <- c('GPP-GOSIF', 'GPP-MODIS') plotEnsembleMean(long.name, metric, mod.path.list, modelIDs, myVariables, plot.width = 5, plot.height = 5.5)
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