1.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
1.2 +++ b/fire/24x.table+tseries.ncl Mon Jan 26 22:08:20 2009 -0500
1.3 @@ -0,0 +1,784 @@
1.4 +;********************************************************
1.5 +; using observed biome class
1.6 +; landfrac applied to area only.
1.7 +;
1.8 +; required command line input parameters:
1.9 +; ncl 'model_name="10cn" model_grid="T42" dirm="/.../ film="..."' 01.npp.ncl
1.10 +;
1.11 +; histogram normalized by rain and compute correleration
1.12 +;**************************************************************
1.13 +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"
1.14 +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl"
1.15 +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"
1.16 +;**************************************************************
1.17 +procedure set_line(lines:string,nline:integer,newlines:string)
1.18 +begin
1.19 +; add line to ascci/html file
1.20 +
1.21 + nnewlines = dimsizes(newlines)
1.22 + if(nline+nnewlines-1.ge.dimsizes(lines))
1.23 + print("set_line: bad index, not setting anything.")
1.24 + return
1.25 + end if
1.26 + lines(nline:nline+nnewlines-1) = newlines
1.27 +; print ("lines = " + lines(nline:nline+nnewlines-1))
1.28 + nline = nline + nnewlines
1.29 + return
1.30 +end
1.31 +;**************************************************************
1.32 +; Main code.
1.33 +begin
1.34 +
1.35 + plot_type = "ps"
1.36 + plot_type_new = "png"
1.37 +
1.38 +;---------------------------------------------------
1.39 +; model name and grid
1.40 +
1.41 + model_grid = "T42"
1.42 +
1.43 + model_name = "cn"
1.44 + model_name1 = "i01.06cn"
1.45 + model_name2 = "i01.10cn"
1.46 +
1.47 +;------------------------------------------------
1.48 +; read biome data: observed
1.49 +
1.50 + biome_name_ob = "MODIS LandCover"
1.51 +
1.52 + diro = "/fis/cgd/cseg/people/jeff/clamp_data/lai/ob/"
1.53 + filo = "land_class_"+model_grid+".nc"
1.54 +
1.55 + fo = addfile(diro+filo,"r")
1.56 +
1.57 + classob = tofloat(fo->LAND_CLASS)
1.58 +
1.59 + delete (fo)
1.60 +
1.61 +; observed data has 20 land-type classes
1.62 +
1.63 + nclass_ob = 20
1.64 +
1.65 +;---------------------------------------------------
1.66 +; get biome data: model
1.67 +
1.68 + biome_name_mod = "Model PFT Class"
1.69 +
1.70 + dirm = "/fis/cgd/cseg/people/jeff/clamp_data/model/"
1.71 + film = "class_pft_"+model_grid+".nc"
1.72 + fm = addfile(dirm+film,"r")
1.73 +
1.74 + classmod = fm->CLASS_PFT
1.75 +
1.76 + delete (fm)
1.77 +
1.78 +; model data has 17 land-type classes
1.79 +
1.80 + nclass_mod = 17
1.81 +
1.82 +;--------------------------------------------------
1.83 +; get model data: landmask, landfrac and area
1.84 +
1.85 + dirm = "/fis/cgd/cseg/people/jeff/surface_data/"
1.86 + film = "lnd_T42.nc"
1.87 + fm = addfile (dirm+film,"r")
1.88 +
1.89 + landmask = fm->landmask
1.90 + landfrac = fm->landfrac
1.91 + area = fm->area
1.92 +
1.93 + delete (fm)
1.94 +
1.95 +; change area from km**2 to m**2
1.96 + area = area * 1.e6
1.97 +
1.98 +;---------------------------------------------------
1.99 +; take into account landfrac
1.100 +
1.101 + area = area * landfrac
1.102 +
1.103 +;----------------------------------------------------
1.104 +; read data: time series, model
1.105 +
1.106 + dirm = "/fis/cgd/cseg/people/jeff/clamp_data/model/"
1.107 + film = model_name2 + "_Fire_C_1979-2004_monthly.nc"
1.108 + fm = addfile (dirm+film,"r")
1.109 +
1.110 + data_mod = fm->COL_FIRE_CLOSS(18:25,:,:,:)
1.111 +
1.112 + delete (fm)
1.113 +
1.114 +; Units for these variables are:
1.115 +; g C/m^2/s
1.116 +
1.117 +; change unit to g C/m^2/month
1.118 +
1.119 + nsec_per_month = 60*60*24*30
1.120 +
1.121 + data_mod = data_mod * nsec_per_month
1.122 +
1.123 + data_mod@unit = "gC/m2/month"
1.124 +;----------------------------------------------------
1.125 +; read data: time series, observed
1.126 +
1.127 + dirm = "/fis/cgd/cseg/people/jeff/fire_data/ob/GFEDv2_C/"
1.128 + film = "Fire_C_1997-2006_monthly_"+ model_grid+".nc"
1.129 + fm = addfile (dirm+film,"r")
1.130 +
1.131 + data_ob = fm->FIRE_C(0:7,:,:,:)
1.132 +
1.133 + delete (fm)
1.134 +
1.135 + ob_name = "GFEDv2"
1.136 +
1.137 +; Units for these variables are:
1.138 +; g C/m^2/month
1.139 +
1.140 +;-------------------------------------------------------------
1.141 +; html table1 data
1.142 +
1.143 +; column (not including header column)
1.144 +
1.145 + col_head = (/"Observed Fire_Flux (PgC/yr)" \
1.146 + ,"Model Fire_Flux (PgC/yr)" \
1.147 + ,"Correlation Coefficient" \
1.148 + ,"Ratio model/observed" \
1.149 + ,"M_score" \
1.150 + ,"Timeseries plot" \
1.151 + /)
1.152 +
1.153 + ncol = dimsizes(col_head)
1.154 +
1.155 +; row (not including header row)
1.156 +
1.157 +;----------------------------------------------------
1.158 +; using observed biome class:
1.159 + row_head = (/"Evergreen Needleleaf Forests" \
1.160 + ,"Evergreen Broadleaf Forests" \
1.161 + ,"Deciduous Needleleaf Forest" \
1.162 + ,"Deciduous Broadleaf Forests" \
1.163 + ,"Mixed Forests" \
1.164 + ,"Closed Bushlands" \
1.165 + ,"Open Bushlands" \
1.166 + ,"Woody Savannas (S. Hem.)" \
1.167 + ,"Savannas (S. Hem.)" \
1.168 + ,"Grasslands" \
1.169 + ,"Permanent Wetlands" \
1.170 + ,"Croplands" \
1.171 + ,"Cropland/Natural Vegetation Mosaic" \
1.172 + ,"Barren or Sparsely Vegetated" \
1.173 + ,"Woody Savannas (N. Hem.)" \
1.174 + ,"Savannas (N. Hem.)" \
1.175 + ,"All Biome" \
1.176 + /)
1.177 +
1.178 +
1.179 +; using model biome class:
1.180 +; row_head = (/"Not Vegetated" \
1.181 +; ,"Needleleaf Evergreen Temperate Tree" \
1.182 +; ,"Needleleaf Evergreen Boreal Tree" \
1.183 +;; ,"Needleleaf Deciduous Boreal Tree" \
1.184 +; ,"Broadleaf Evergreen Tropical Tree" \
1.185 +; ,"Broadleaf Evergreen Temperate Tree" \
1.186 +; ,"Broadleaf Deciduous Tropical Tree" \
1.187 +; ,"Broadleaf Deciduous Temperate Tree" \
1.188 +;; ,"Broadleaf Deciduous Boreal Tree" \
1.189 +;; ,"Broadleaf Evergreen Shrub" \
1.190 +; ,"Broadleaf Deciduous Temperate Shrub" \
1.191 +; ,"Broadleaf Deciduous Boreal Shrub" \
1.192 +; ,"C3 Arctic Grass" \
1.193 +; ,"C3 Non-Arctic Grass" \
1.194 +; ,"C4 Grass" \
1.195 +; ,"Corn" \
1.196 +;; ,"Wheat" \
1.197 +; ,"All Biome" \
1.198 +; /)
1.199 + nrow = dimsizes(row_head)
1.200 +
1.201 +; arrays to be passed to table.
1.202 + text = new ((/nrow, ncol/),string )
1.203 +
1.204 +;*****************************************************************
1.205 +; (A) get time-mean
1.206 +;*****************************************************************
1.207 +
1.208 + x = dim_avg_Wrap(data_mod(lat|:,lon|:,month|:,year|:))
1.209 + data_mod_m = dim_avg_Wrap( x(lat|:,lon|:,month|:))
1.210 + delete (x)
1.211 +
1.212 + x = dim_avg_Wrap( data_ob(lat|:,lon|:,month|:,year|:))
1.213 + data_ob_m = dim_avg_Wrap( x(lat|:,lon|:,month|:))
1.214 + delete (x)
1.215 +
1.216 +;----------------------------------------------------
1.217 +; compute correlation coef: space
1.218 +
1.219 + landmask_1d = ndtooned(landmask)
1.220 + data_mod_1d = ndtooned(data_mod_m)
1.221 + data_ob_1d = ndtooned(data_ob_m )
1.222 + area_1d = ndtooned(area)
1.223 + landfrac_1d = ndtooned(landfrac)
1.224 +
1.225 + good = ind(landmask_1d .gt. 0.)
1.226 +
1.227 + global_mod = sum(data_mod_1d(good)*area_1d(good)) * 1.e-15 * 12.
1.228 + global_ob = sum(data_ob_1d(good) *area_1d(good)) * 1.e-15 * 12.
1.229 +
1.230 + print (global_mod)
1.231 + print (global_ob)
1.232 +
1.233 + global_area= sum(area_1d)
1.234 + global_land= sum(area_1d(good))
1.235 + print (global_area)
1.236 + print (global_land)
1.237 +
1.238 + cc_space = esccr(data_mod_1d(good)*landfrac_1d(good),data_ob_1d(good)*landfrac_1d(good),0)
1.239 +
1.240 + delete (landmask_1d)
1.241 + delete (landfrac_1d)
1.242 +; delete (area_1d)
1.243 + delete (data_mod_1d)
1.244 + delete (data_ob_1d)
1.245 + delete (good)
1.246 +
1.247 +;----------------------------------------------------
1.248 +; compute M_global
1.249 +
1.250 + score_max = 1.
1.251 +
1.252 + Mscore1 = cc_space * cc_space * score_max
1.253 +
1.254 + M_global = sprintf("%.2f", Mscore1)
1.255 +
1.256 +;----------------------------------------------------
1.257 +; global res
1.258 +
1.259 + resg = True ; Use plot options
1.260 + resg@cnFillOn = True ; Turn on color fill
1.261 + resg@gsnSpreadColors = True ; use full colormap
1.262 + resg@cnLinesOn = False ; Turn off contourn lines
1.263 + resg@mpFillOn = False ; Turn off map fill
1.264 + resg@cnLevelSelectionMode = "ManualLevels" ; Manual contour invtervals
1.265 +
1.266 +;----------------------------------------------------
1.267 +; global contour: model vs ob
1.268 +
1.269 + plot_name = "global_model_vs_ob"
1.270 +
1.271 + wks = gsn_open_wks (plot_type,plot_name)
1.272 + gsn_define_colormap(wks,"gui_default")
1.273 +
1.274 + plot=new(3,graphic) ; create graphic array
1.275 +
1.276 + resg@gsnFrame = False ; Do not draw plot
1.277 + resg@gsnDraw = False ; Do not advance frame
1.278 +
1.279 +;----------------------
1.280 +; plot correlation coef
1.281 +
1.282 + gRes = True
1.283 + gRes@txFontHeightF = 0.02
1.284 + gRes@txAngleF = 90
1.285 +
1.286 + correlation_text = "(correlation coef = "+sprintf("%.2f", cc_space)+")"
1.287 +
1.288 + gsn_text_ndc(wks,correlation_text,0.20,0.50,gRes)
1.289 +
1.290 +;-----------------------
1.291 +; plot ob
1.292 +
1.293 + data_ob_m = where(landmask .gt. 0., data_ob_m, data_ob_m@_FillValue)
1.294 +
1.295 + title = ob_name
1.296 + resg@tiMainString = title
1.297 +
1.298 + resg@cnMinLevelValF = 1.
1.299 + resg@cnMaxLevelValF = 10.
1.300 + resg@cnLevelSpacingF = 1.
1.301 +
1.302 + plot(0) = gsn_csm_contour_map_ce(wks,data_ob_m,resg)
1.303 +
1.304 +;-----------------------
1.305 +; plot model
1.306 +
1.307 + data_mod_m = where(landmask .gt. 0., data_mod_m, data_mod_m@_FillValue)
1.308 +
1.309 + title = "Model "+ model_name
1.310 + resg@tiMainString = title
1.311 +
1.312 + resg@cnMinLevelValF = 1.
1.313 + resg@cnMaxLevelValF = 10.
1.314 + resg@cnLevelSpacingF = 1.
1.315 +
1.316 + plot(1) = gsn_csm_contour_map_ce(wks,data_mod_m,resg)
1.317 +
1.318 +;-----------------------
1.319 +; plot model-ob
1.320 +
1.321 + resg@cnMinLevelValF = -8.
1.322 + resg@cnMaxLevelValF = 2.
1.323 + resg@cnLevelSpacingF = 1.
1.324 +
1.325 + zz = data_ob_m
1.326 + zz = data_mod_m - data_ob_m
1.327 + title = "Model_"+model_name+" - Observed"
1.328 + resg@tiMainString = title
1.329 +
1.330 + plot(2) = gsn_csm_contour_map_ce(wks,zz,resg)
1.331 +
1.332 +; plot panel
1.333 +
1.334 + pres = True ; panel plot mods desired
1.335 + pres@gsnMaximize = True ; fill the page
1.336 +
1.337 + gsn_panel(wks,plot,(/3,1/),pres) ; create panel plot
1.338 +
1.339 + system("convert "+plot_name+"."+plot_type+" "+plot_name+"."+plot_type_new+";"+ \
1.340 + "rm "+plot_name+"."+plot_type)
1.341 +
1.342 + clear (wks)
1.343 + delete (plot)
1.344 +
1.345 + delete (data_ob_m)
1.346 + delete (data_mod_m)
1.347 + delete (zz)
1.348 +
1.349 + resg@gsnFrame = True ; Do advance frame
1.350 + resg@gsnDraw = True ; Do draw plot
1.351 +
1.352 +;*******************************************************************
1.353 +; (B) Time series : per biome
1.354 +;*******************************************************************
1.355 +
1.356 + data_n = 2
1.357 +
1.358 + dsizes = dimsizes(data_mod)
1.359 + nyear = dsizes(0)
1.360 + nmonth = dsizes(1)
1.361 + ntime = nyear * nmonth
1.362 +
1.363 + year_start = 1997
1.364 + year_end = 2004
1.365 +
1.366 +;-------------------------------------------
1.367 +; Calculate "nice" bins for binning the data
1.368 +
1.369 +; using ob biome class
1.370 + nclass = nclass_ob
1.371 +; using model biome class
1.372 +; nclass = nclass_mod
1.373 +
1.374 + range = fspan(0,nclass,nclass+1)
1.375 +
1.376 +; Use this range information to grab all the values in a
1.377 +; particular range, and then take an average.
1.378 +
1.379 + nx = dimsizes(range) - 1
1.380 +
1.381 +;-------------------------------------------
1.382 +; put data into bins
1.383 +
1.384 +; using observed biome class
1.385 + base = ndtooned(classob)
1.386 +; using model biome class
1.387 +; base = ndtooned(classmod)
1.388 +
1.389 +; output
1.390 +
1.391 + area_bin = new((/nx/),float)
1.392 + yvalues = new((/ntime,data_n,nx/),float)
1.393 +
1.394 +; Loop through each range, using base.
1.395 +
1.396 + do i=0,nx-1
1.397 +
1.398 + if (i.ne.(nx-1)) then
1.399 + idx = ind((base.ge.range(i)).and.(base.lt.range(i+1)))
1.400 + else
1.401 + idx = ind(base.ge.range(i))
1.402 + end if
1.403 +;---------------------
1.404 +; for area
1.405 +
1.406 + if (.not.any(ismissing(idx))) then
1.407 + area_bin(i) = sum(area_1d(idx))
1.408 + else
1.409 + area_bin(i) = area_bin@_FillValue
1.410 + end if
1.411 +
1.412 +;#############################################################
1.413 +;using observed biome class:
1.414 +; set the following 4 classes to _FillValue:
1.415 +; Water Bodies(0), Urban and Build-Up(13),
1.416 +; Permenant Snow and Ice(15), Unclassified(17)
1.417 +
1.418 + if (i.eq.0 .or. i.eq.13 .or. i.eq.15 .or. i.eq.17) then
1.419 + area_bin(i) = yvalues@_FillValue
1.420 + end if
1.421 +;#############################################################
1.422 +
1.423 +
1.424 +;#############################################################
1.425 +; using model biome class:
1.426 +; set the following 4 classes to _FillValue:
1.427 +; (3)Needleleaf Deciduous Boreal Tree,
1.428 +; (8)Broadleaf Deciduous Boreal Tree,
1.429 +; (9)Broadleaf Evergreen Shrub,
1.430 +; (16)Wheat
1.431 +
1.432 +; if (i.eq.3 .or. i.eq.8 .or. i.eq.9 .or. i.eq.16) then
1.433 +; area_bin(i) = area_bin@_FillValue
1.434 +; end if
1.435 +;#############################################################
1.436 +
1.437 +;---------------------
1.438 +; for data_mod and data_ob
1.439 +
1.440 + do n = 0,data_n-1
1.441 +
1.442 + t = -1
1.443 + do m = 0,nyear-1
1.444 + do k = 0,nmonth-1
1.445 +
1.446 + t = t + 1
1.447 +
1.448 + if (n.eq.0) then
1.449 + data = ndtooned(data_ob(m,k,:,:))
1.450 + end if
1.451 +
1.452 + if (n.eq.1) then
1.453 + data = ndtooned(data_mod(m,k,:,:))
1.454 + end if
1.455 +
1.456 +; Calculate average
1.457 +
1.458 + if (.not.any(ismissing(idx))) then
1.459 + yvalues(t,n,i) = sum(data(idx)*area_1d(idx))
1.460 + else
1.461 + yvalues(t,n,i) = yvalues@_FillValue
1.462 + end if
1.463 +
1.464 +;#############################################################
1.465 +;using observed biome class:
1.466 +; set the following 4 classes to _FillValue:
1.467 +; Water Bodies(0), Urban and Build-Up(13),
1.468 +; Permenant Snow and Ice(15), Unclassified(17)
1.469 +
1.470 + if (i.eq.0 .or. i.eq.13 .or. i.eq.15 .or. i.eq.17) then
1.471 + yvalues(t,n,i) = yvalues@_FillValue
1.472 + end if
1.473 +;#############################################################
1.474 +
1.475 +
1.476 +;#############################################################
1.477 +; using model biome class:
1.478 +; set the following 4 classes to _FillValue:
1.479 +; (3)Needleleaf Deciduous Boreal Tree,
1.480 +; (8)Broadleaf Deciduous Boreal Tree,
1.481 +; (9)Broadleaf Evergreen Shrub,
1.482 +; (16)Wheat
1.483 +
1.484 +; if (i.eq.3 .or. i.eq.8 .or. i.eq.9 .or. i.eq.16) then
1.485 +; yvalues(t,n,i) = yvalues@_FillValue
1.486 +; end if
1.487 +;#############################################################
1.488 +
1.489 + end do
1.490 + end do
1.491 +
1.492 + delete(data)
1.493 + end do
1.494 +
1.495 + delete(idx)
1.496 + end do
1.497 +
1.498 + delete (base)
1.499 + delete (data_mod)
1.500 + delete (data_ob)
1.501 +
1.502 + global_bin = sum(area_bin)
1.503 + print (global_bin)
1.504 +
1.505 +;----------------------------------------------------------------
1.506 +; get area_good
1.507 +
1.508 + good = ind(.not.ismissing(area_bin))
1.509 +
1.510 + area_g = area_bin(good)
1.511 +
1.512 + n_biome = dimsizes(good)
1.513 +
1.514 + global_good = sum(area_g)
1.515 + print (global_good)
1.516 +
1.517 +;----------------------------------------------------------------
1.518 +; data for tseries plot
1.519 +
1.520 + yvalues_g = new((/ntime,data_n,n_biome/),float)
1.521 +
1.522 + yvalues_g@units = "TgC/month"
1.523 +
1.524 +; change unit to Tg C/month
1.525 +; change unit from g to Tg (Tera gram)
1.526 + factor_unit = 1.e-12
1.527 +
1.528 + yvalues_g = yvalues(:,:,good) * factor_unit
1.529 +
1.530 + delete (good)
1.531 +
1.532 +;-------------------------------------------------------------------
1.533 +; general settings for line plot
1.534 +
1.535 + res = True
1.536 + res@xyDashPatterns = (/0,0/) ; make lines solid
1.537 + res@xyLineThicknesses = (/2.0,2.0/) ; make lines thicker
1.538 + res@xyLineColors = (/"blue","red"/) ; line color
1.539 +
1.540 + res@trXMinF = year_start
1.541 + res@trXMaxF = year_end + 1
1.542 +
1.543 + res@vpHeightF = 0.4 ; change aspect ratio of plot
1.544 +; res@vpWidthF = 0.8
1.545 + res@vpWidthF = 0.75
1.546 +
1.547 + res@tiMainFontHeightF = 0.025 ; size of title
1.548 +
1.549 + res@tmXBFormat = "f" ; not to add trailing zeros
1.550 +
1.551 +; res@gsnMaximize = True
1.552 +
1.553 +;----------------------------------------------
1.554 +; Add a boxed legend using the simple method
1.555 +
1.556 + res@pmLegendDisplayMode = "Always"
1.557 +; res@pmLegendWidthF = 0.1
1.558 + res@pmLegendWidthF = 0.08
1.559 + res@pmLegendHeightF = 0.06
1.560 + res@pmLegendOrthogonalPosF = -1.17
1.561 +; res@pmLegendOrthogonalPosF = -1.00 ;(downward)
1.562 +; res@pmLegendOrthogonalPosF = -0.30 ;(downward)
1.563 +
1.564 +; res@pmLegendParallelPosF = 0.18
1.565 + res@pmLegendParallelPosF = 0.23 ;(rightward)
1.566 + res@pmLegendParallelPosF = 0.73 ;(rightward)
1.567 + res@pmLegendParallelPosF = 0.83 ;(rightward)
1.568 +
1.569 +; res@lgPerimOn = False
1.570 + res@lgLabelFontHeightF = 0.015
1.571 + res@xyExplicitLegendLabels = (/"observed",model_name/)
1.572 +
1.573 +;*******************************************************************
1.574 +; (A) time series plot: monthly ( 2 lines per plot)
1.575 +;*******************************************************************
1.576 +
1.577 +; x-axis in time series plot
1.578 +
1.579 + timeI = new((/ntime/),integer)
1.580 + timeF = new((/ntime/),float)
1.581 + timeI = ispan(1,ntime,1)
1.582 + timeF = year_start + (timeI-1)/12.
1.583 + timeF@long_name = "year"
1.584 +
1.585 + plot_data = new((/2,ntime/),float)
1.586 + plot_data@long_name = "TgC/month"
1.587 +
1.588 +;----------------------------------------------
1.589 +; time series plot : per biome
1.590 +
1.591 + do m = 0, n_biome-1
1.592 +
1.593 + plot_name = "monthly_biome_"+ m
1.594 +
1.595 + wks = gsn_open_wks (plot_type,plot_name)
1.596 +
1.597 + title = "Fire : "+ row_head(m)
1.598 + res@tiMainString = title
1.599 +
1.600 + plot_data(0,:) = yvalues_g(:,0,m)
1.601 + plot_data(1,:) = yvalues_g(:,1,m)
1.602 +
1.603 + plot = gsn_csm_xy(wks,timeF,plot_data,res)
1.604 +
1.605 + system("convert "+plot_name+"."+plot_type+" "+plot_name+"."+plot_type_new+";"+ \
1.606 + "rm "+plot_name+"."+plot_type)
1.607 +
1.608 + clear (wks)
1.609 + delete (plot)
1.610 +
1.611 + end do
1.612 +
1.613 +;------------------------------------------
1.614 +; data for table : per biome
1.615 +
1.616 +; unit change from TgC/month to PgC/month
1.617 + unit_factor = 1.e-3
1.618 +
1.619 + score_max = 1.
1.620 +
1.621 + tmp_ob = new((/ntime/),float)
1.622 + tmp_mod = new((/ntime/),float)
1.623 +
1.624 + total_ob = new((/n_biome/),float)
1.625 + total_mod = new((/n_biome/),float)
1.626 + Mscore2 = new((/n_biome/),float)
1.627 +
1.628 + do m = 0, n_biome-1
1.629 +
1.630 + tmp_ob = yvalues_g(:,0,m)
1.631 + tmp_mod = yvalues_g(:,1,m)
1.632 +
1.633 + total_ob(m) = avg(month_to_annual(tmp_ob, 0)) * unit_factor
1.634 + total_mod(m) = avg(month_to_annual(tmp_mod,0)) * unit_factor
1.635 +
1.636 + cc_time = esccr(tmp_mod,tmp_ob,0)
1.637 +
1.638 + ratio = total_mod(m)/total_ob(m)
1.639 +
1.640 + good = ind(tmp_ob .ne. 0. .and. tmp_mod .ne. 0.)
1.641 +
1.642 + bias = sum( abs( tmp_mod(good)-tmp_ob(good) )/( abs(tmp_mod(good))+abs(tmp_ob(good)) ) )
1.643 + Mscore2(m) = (1.- (bias/dimsizes(good)))*score_max
1.644 +
1.645 + delete (good)
1.646 +
1.647 + text(m,0) = sprintf("%.2f",total_ob(m))
1.648 + text(m,1) = sprintf("%.2f",total_mod(m))
1.649 + text(m,2) = sprintf("%.2f",cc_time)
1.650 + text(m,3) = sprintf("%.2f",ratio)
1.651 + text(m,4) = sprintf("%.2f",Mscore2(m))
1.652 + text(m,5) = "<a href=./monthly_biome_"+m+".png>model_vs_ob</a>"
1.653 + end do
1.654 +
1.655 + delete (tmp_ob)
1.656 + delete (tmp_mod)
1.657 +
1.658 +;--------------------------------------------
1.659 +; time series plot: all biome
1.660 +
1.661 + plot_name = "monthly_global"
1.662 +
1.663 + wks = gsn_open_wks (plot_type,plot_name)
1.664 +
1.665 + title = "Fire : "+ row_head(n_biome)
1.666 + res@tiMainString = title
1.667 +
1.668 + do k = 0,ntime-1
1.669 + plot_data(0,k) = sum(yvalues_g(k,0,:))
1.670 + plot_data(1,k) = sum(yvalues_g(k,1,:))
1.671 + end do
1.672 +
1.673 + plot = gsn_csm_xy(wks,timeF,plot_data,res)
1.674 +
1.675 + system("convert "+plot_name+"."+plot_type+" "+plot_name+"."+plot_type_new+";"+ \
1.676 + "rm "+plot_name+"."+plot_type)
1.677 +
1.678 + clear (wks)
1.679 + delete (plot)
1.680 +
1.681 +;------------------------------------------
1.682 +; data for table : global
1.683 +
1.684 + score_max = 1.
1.685 +
1.686 + tmp_ob = ndtooned(yvalues_g(:,0,:))
1.687 + tmp_mod = ndtooned(yvalues_g(:,1,:))
1.688 +
1.689 + cc_time = esccr(tmp_mod,tmp_ob,0)
1.690 +
1.691 + ratio = sum(total_mod)/sum(total_ob)
1.692 +
1.693 + good = ind(tmp_ob .ne. 0. .and. tmp_mod .ne. 0.)
1.694 +
1.695 + bias = sum( abs( tmp_mod(good)-tmp_ob(good) )/( abs(tmp_mod(good))+abs(tmp_ob(good)) ) )
1.696 + Mscore3 = (1.- (bias/dimsizes(good)))*score_max
1.697 +
1.698 + print (Mscore3)
1.699 +
1.700 + delete (good)
1.701 +
1.702 + text(nrow-1,0) = sprintf("%.2f",sum(total_ob))
1.703 + text(nrow-1,1) = sprintf("%.2f",sum(total_mod))
1.704 + text(nrow-1,2) = sprintf("%.2f",cc_time)
1.705 + text(nrow-1,3) = sprintf("%.2f",ratio)
1.706 +; text(nrow-1,4) = sprintf("%.2f",avg(Mscore2))
1.707 + text(nrow-1,4) = sprintf("%.2f", Mscore3)
1.708 + text(nrow-1,5) = "<a href=./monthly_global.png>model_vs_ob</a>"
1.709 +
1.710 +;**************************************************
1.711 +; create html table
1.712 +;**************************************************
1.713 +
1.714 + header_text = "<H1>Fire Emission (1997-2004): Model "+model_name+"</H1>"
1.715 +
1.716 + header = (/"<HTML>" \
1.717 + ,"<HEAD>" \
1.718 + ,"<TITLE>CLAMP metrics</TITLE>" \
1.719 + ,"</HEAD>" \
1.720 + ,header_text \
1.721 + /)
1.722 + footer = "</HTML>"
1.723 +
1.724 + table_header = (/ \
1.725 + "<table border=1 cellspacing=0 cellpadding=3 width=60%>" \
1.726 + ,"<tr>" \
1.727 + ," <th bgcolor=DDDDDD >Biome Type</th>" \
1.728 + ," <th bgcolor=DDDDDD >"+col_head(0)+"</th>" \
1.729 + ," <th bgcolor=DDDDDD >"+col_head(1)+"</th>" \
1.730 + ," <th bgcolor=DDDDDD >"+col_head(2)+"</th>" \
1.731 + ," <th bgcolor=DDDDDD >"+col_head(3)+"</th>" \
1.732 + ," <th bgcolor=DDDDDD >"+col_head(4)+"</th>" \
1.733 + ," <th bgcolor=DDDDDD >"+col_head(5)+"</th>" \
1.734 + ,"</tr>" \
1.735 + /)
1.736 + table_footer = "</table>"
1.737 + row_header = "<tr>"
1.738 + row_footer = "</tr>"
1.739 +
1.740 + lines = new(50000,string)
1.741 + nline = 0
1.742 +
1.743 + set_line(lines,nline,header)
1.744 + set_line(lines,nline,table_header)
1.745 +;-----------------------------------------------
1.746 +;row of table
1.747 +
1.748 + do n = 0,nrow-1
1.749 + set_line(lines,nline,row_header)
1.750 +
1.751 + txt0 = row_head(n)
1.752 + txt1 = text(n,0)
1.753 + txt2 = text(n,1)
1.754 + txt3 = text(n,2)
1.755 + txt4 = text(n,3)
1.756 + txt5 = text(n,4)
1.757 + txt6 = text(n,5)
1.758 +
1.759 + set_line(lines,nline,"<th>"+txt0+"</th>")
1.760 + set_line(lines,nline,"<th>"+txt1+"</th>")
1.761 + set_line(lines,nline,"<th>"+txt2+"</th>")
1.762 + set_line(lines,nline,"<th>"+txt3+"</th>")
1.763 + set_line(lines,nline,"<th>"+txt4+"</th>")
1.764 + set_line(lines,nline,"<th>"+txt5+"</th>")
1.765 + set_line(lines,nline,"<th>"+txt6+"</th>")
1.766 +
1.767 + set_line(lines,nline,row_footer)
1.768 + end do
1.769 +;-----------------------------------------------
1.770 + set_line(lines,nline,table_footer)
1.771 + set_line(lines,nline,footer)
1.772 +
1.773 +; Now write to an HTML file.
1.774 +
1.775 + output_html = "table_fire.html"
1.776 +
1.777 + idx = ind(.not.ismissing(lines))
1.778 + if(.not.any(ismissing(idx))) then
1.779 + asciiwrite(output_html,lines(idx))
1.780 + else
1.781 + print ("error?")
1.782 + end if
1.783 +
1.784 + delete (idx)
1.785 +
1.786 +end
1.787 +