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1 ;******************************************************** |
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2 ; required command line input parameters: |
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3 ; ncl 'model_name="10cn" model_grid="T42" dirm="/.../ film="..."' 01.npp.ncl |
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4 ; |
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5 ; histogram normalized by rain and compute correleration |
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6 ;************************************************************** |
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7 load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl.test" |
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8 load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl.test" |
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9 load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" |
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10 ;************************************************************** |
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11 procedure set_line(lines:string,nline:integer,newlines:string) |
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12 begin |
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13 ; add line to ascci/html file |
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14 |
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15 nnewlines = dimsizes(newlines) |
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16 if(nline+nnewlines-1.ge.dimsizes(lines)) |
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17 print("set_line: bad index, not setting anything.") |
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18 return |
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19 end if |
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20 lines(nline:nline+nnewlines-1) = newlines |
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21 ; print ("lines = " + lines(nline:nline+nnewlines-1)) |
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22 nline = nline + nnewlines |
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23 return |
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24 end |
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25 ;************************************************************** |
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26 ; Main code. |
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27 begin |
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28 |
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29 plot_type = "ps" |
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30 plot_type_new = "png" |
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31 |
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32 ;************************************************ |
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33 ; read data: model |
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34 ;************************************************ |
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35 co2_i = 283.1878 |
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36 co2_f = 364.1252 |
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37 |
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38 model_grid = "T42" |
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39 |
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40 model_name_i = "i01.07cn" |
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41 model_name_f = "i01.10cn" |
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42 |
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43 ;model_name_i = "i01.07casa" |
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44 ;model_name_f = "i01.10casa" |
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45 |
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46 model_name = model_name_f |
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47 |
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48 dirm = "/fis/cgd/cseg/people/jeff/clamp_data/model/" |
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49 film_i = model_name_i + "_1990-2004_ANN_climo.nc" |
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50 film_f = model_name_f + "_1990-2004_ANN_climo.nc" |
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51 |
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52 fm_i = addfile (dirm+film_i,"r") |
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53 fm_f = addfile (dirm+film_f,"r") |
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54 |
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55 xm = fm_f->lon |
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56 ym = fm_f->lat |
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57 |
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58 npp_i = fm_i->NPP |
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59 npp_f = fm_f->NPP |
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60 |
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61 ;Units for these variables are: |
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62 ;npp_i: g C/m^2/s |
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63 |
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64 nsec_per_year = 60*60*24*365 |
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65 |
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66 npp_i = npp_i * nsec_per_year |
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67 npp_f = npp_f * nsec_per_year |
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68 |
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69 ;=================================================== |
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70 ; read data: observed -station |
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71 ;=================================================== |
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72 |
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73 station = (/"DukeFACE" \ |
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74 ,"AspenFACE" \ |
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75 ,"ORNL-FACE" \ |
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76 ,"POP-EUROFACE" \ |
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77 /) |
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78 |
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79 lat_ob = (/ 35.58, 45.40, 35.54, 42.22/) |
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80 lon_ob = (/-79.05, -89.37, -84.20, 11.48/) |
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81 lon_ob = where(lon_ob.lt.0.,lon_ob+360.,lon_ob) |
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82 ;print (lon_ob) |
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83 |
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84 n_sta = dimsizes(station) |
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85 beta_4_ob = new((/n_sta/),float) |
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86 beta_4_ob = 0.60 |
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87 |
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88 ;=================================================== |
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89 ; get model data at station |
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90 ;=================================================== |
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91 |
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92 npp_i_4 =linint2_points(xm,ym,npp_i,True,lon_ob,lat_ob,0) |
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93 |
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94 npp_f_4 =linint2_points(xm,ym,npp_f,True,lon_ob,lat_ob,0) |
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95 |
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96 ;print (npp_i_4) |
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97 ;print (npp_f_4) |
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98 |
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99 ;============================ |
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100 ;compute beta_4 |
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101 ;============================ |
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102 |
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103 beta_4 = new((/n_sta/),float) |
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104 |
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105 beta_4 = ((npp_f_4/npp_i_4) - 1.)/log(co2_f/co2_i) |
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106 |
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107 beta_4_avg = avg(beta_4) |
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108 |
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109 ;print (beta_4) |
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110 ;print (beta_4_avg) |
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111 |
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112 ;M_beta = abs((beta_4_avg/beta_4_ob) - 1.)* 3. |
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113 |
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114 bias = sum(abs(beta_4-beta_4_ob)/(abs(beta_4)+abs(beta_4_ob))) |
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115 M_beta = (1. - (bias/n_sta))*3. |
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116 |
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117 print (M_beta) |
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118 |
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119 ;========================= |
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120 ; for html table - station |
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121 ;========================= |
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122 |
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123 output_html = "table_station.html" |
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124 |
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125 ; column (not including header column) |
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126 |
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127 col_head = (/"Latitude","Longitude","CO2_i","CO2_f","NPP_i","NPP_f","Beta_model","Beta_ob"/) |
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128 |
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129 ncol = dimsizes(col_head) |
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130 |
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131 ; row (not including header row) |
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132 row_head = (/"DukeFACE" \ |
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133 ,"AspenFACE" \ |
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134 ,"ORNL-FACE" \ |
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135 ,"POP-EUROFACE" \ |
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136 ,"All Station" \ |
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137 /) |
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138 nrow = dimsizes(row_head) |
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139 |
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140 ; arrays to be passed to table. |
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141 text4 = new ((/nrow, ncol/),string ) |
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142 |
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143 do i=0,nrow-2 |
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144 text4(i,0) = sprintf("%.1f",lat_ob(i)) |
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145 text4(i,1) = sprintf("%.1f",lon_ob(i)) |
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146 text4(i,2) = sprintf("%.1f",co2_i) |
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147 text4(i,3) = sprintf("%.1f",co2_f) |
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148 text4(i,4) = sprintf("%.1f",npp_i_4(0,i)) |
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149 text4(i,5) = sprintf("%.1f",npp_f_4(0,i)) |
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150 text4(i,6) = sprintf("%.2f",beta_4(i)) |
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151 text4(i,7) = "-" |
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152 end do |
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153 text4(nrow-1,0) = "-" |
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154 text4(nrow-1,1) = "-" |
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155 text4(nrow-1,2) = "-" |
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156 text4(nrow-1,3) = "-" |
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157 text4(nrow-1,4) = "-" |
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158 text4(nrow-1,5) = "-" |
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159 text4(nrow-1,6) = sprintf("%.2f",beta_4_avg) |
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160 text4(nrow-1,7) = sprintf("%.2f",avg(beta_4_ob)) |
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161 |
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162 ;----------- |
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163 ; html table |
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164 ;----------- |
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165 |
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166 header_text = "<H1>Beta Factor: Model "+model_name+"</H1>" |
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167 |
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168 header = (/"<HTML>" \ |
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169 ,"<HEAD>" \ |
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170 ,"<TITLE>CLAMP metrics</TITLE>" \ |
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171 ,"</HEAD>" \ |
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172 ,header_text \ |
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173 /) |
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174 footer = "</HTML>" |
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175 |
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176 table_header = (/ \ |
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177 "<table border=1 cellspacing=0 cellpadding=3 width=80%>" \ |
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178 ,"<tr>" \ |
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179 ," <th bgcolor=DDDDDD >Station</th>" \ |
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180 ," <th bgcolor=DDDDDD >"+col_head(0)+"</th>" \ |
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181 ," <th bgcolor=DDDDDD >"+col_head(1)+"</th>" \ |
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182 ," <th bgcolor=DDDDDD >"+col_head(2)+"</th>" \ |
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183 ," <th bgcolor=DDDDDD >"+col_head(3)+"</th>" \ |
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184 ," <th bgcolor=DDDDDD >"+col_head(4)+"</th>" \ |
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185 ," <th bgcolor=DDDDDD >"+col_head(5)+"</th>" \ |
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186 ," <th bgcolor=DDDDDD >"+col_head(6)+"</th>" \ |
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187 ," <th bgcolor=DDDDDD >"+col_head(7)+"</th>" \ |
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188 ,"</tr>" \ |
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189 /) |
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190 table_footer = "</table>" |
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191 row_header = "<tr>" |
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192 row_footer = "</tr>" |
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193 |
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194 lines = new(50000,string) |
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195 nline = 0 |
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196 |
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197 set_line(lines,nline,header) |
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198 set_line(lines,nline,table_header) |
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199 ;----------------------------------------------- |
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200 ;row of table |
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201 |
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202 do n = 0,nrow-1 |
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203 set_line(lines,nline,row_header) |
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204 |
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205 txt1 = row_head(n) |
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206 txt2 = text4(n,0) |
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207 txt3 = text4(n,1) |
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208 txt4 = text4(n,2) |
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209 txt5 = text4(n,3) |
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210 txt6 = text4(n,4) |
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211 txt7 = text4(n,5) |
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212 txt8 = text4(n,6) |
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213 txt9 = text4(n,7) |
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214 |
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215 set_line(lines,nline,"<th>"+txt1+"</th>") |
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216 set_line(lines,nline,"<th>"+txt2+"</th>") |
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217 set_line(lines,nline,"<th>"+txt3+"</th>") |
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218 set_line(lines,nline,"<th>"+txt4+"</th>") |
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219 set_line(lines,nline,"<th>"+txt5+"</th>") |
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220 set_line(lines,nline,"<th>"+txt6+"</th>") |
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221 set_line(lines,nline,"<th>"+txt7+"</th>") |
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222 set_line(lines,nline,"<th>"+txt8+"</th>") |
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223 set_line(lines,nline,"<th>"+txt9+"</th>") |
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224 |
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225 set_line(lines,nline,row_footer) |
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226 end do |
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227 ;----------------------------------------------- |
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228 set_line(lines,nline,table_footer) |
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229 set_line(lines,nline,footer) |
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230 |
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231 ; Now write to an HTML file. |
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232 idx = ind(.not.ismissing(lines)) |
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233 if(.not.any(ismissing(idx))) then |
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234 asciiwrite(output_html,lines(idx)) |
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235 else |
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236 print ("error?") |
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237 end if |
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238 |
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239 delete (col_head) |
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240 delete (row_head) |
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241 delete (text4) |
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242 delete (table_header) |
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243 delete (idx) |
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244 |
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245 ;************************************************ |
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246 ; read data: observed-2 |
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247 ;************************************************ |
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248 |
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249 ob_name = "MODIS MOD 15A2 2000-2005" |
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250 |
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251 diro = "/fis/cgd/cseg/people/jeff/clamp_data/lai/ob/" |
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252 filo = "land_class_"+model_grid+".nc" |
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253 |
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254 fo = addfile(diro+filo,"r") |
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255 |
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256 classob = tofloat(fo->LAND_CLASS) |
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257 |
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258 ; observed data has 20 land-type classes |
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259 nclass = 20 |
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260 |
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261 ;******************************************************************* |
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262 ; Calculate "nice" bins for binning the data in equally spaced ranges |
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263 ;******************************************************************** |
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264 |
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265 nclassn = nclass + 1 |
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266 range = fspan(0,nclassn-1,nclassn) |
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267 ; print (range) |
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268 |
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269 ; Use this range information to grab all the values in a |
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270 ; particular range, and then take an average. |
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271 |
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272 nr = dimsizes(range) |
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273 nx = nr-1 |
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274 xvalues = new((/2,nx/),float) |
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275 xvalues(0,:) = range(0:nr-2) + (range(1:)-range(0:nr-2))/2. |
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276 dx = xvalues(0,1) - xvalues(0,0) ; range width |
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277 dx4 = dx/4 ; 1/4 of the range |
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278 xvalues(1,:) = xvalues(0,:) - dx/5. |
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279 ; get data |
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280 |
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281 base_1D = ndtooned(classob) |
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282 data1_1D = ndtooned(npp_i) |
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283 data2_1D = ndtooned(npp_f) |
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284 |
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285 ; output |
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286 |
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287 yvalues = new((/2,nx/),float) |
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288 count = new((/2,nx/),float) |
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289 |
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290 do nd=0,1 |
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291 |
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292 ; See if we are doing data1 (nd=0) or data2 (nd=1). |
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293 |
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294 base = base_1D |
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295 |
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296 if(nd.eq.0) then |
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297 data = data1_1D |
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298 else |
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299 data = data2_1D |
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300 end if |
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301 |
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302 ; Loop through each range, using base. |
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303 |
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304 do i=0,nr-2 |
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305 if (i.ne.(nr-2)) then |
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306 ; print("") |
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307 ; print("In range ["+range(i)+","+range(i+1)+")") |
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308 idx = ind((base.ge.range(i)).and.(base.lt.range(i+1))) |
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309 else |
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310 ; print("") |
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311 ; print("In range ["+range(i)+",)") |
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312 idx = ind(base.ge.range(i)) |
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313 end if |
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314 |
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315 ; Calculate average |
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316 |
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317 if(.not.any(ismissing(idx))) then |
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318 yvalues(nd,i) = avg(data(idx)) |
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319 count(nd,i) = dimsizes(idx) |
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320 else |
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321 yvalues(nd,i) = yvalues@_FillValue |
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322 count(nd,i) = 0 |
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323 end if |
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324 |
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325 ;############################################################# |
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326 ; set the following 4 classes to _FillValue: |
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327 ; Water Bodies(0), Urban and Build-Up(13), |
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328 ; Permenant Snow and Ice(15), Unclassified(17) |
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329 |
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330 if (i.eq.0 .or. i.eq.13 .or. i.eq.15 .or. i.eq.17) then |
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331 yvalues(nd,i) = yvalues@_FillValue |
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332 count(nd,i) = 0 |
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333 end if |
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334 ;############################################################# |
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335 |
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336 ; print(nd + ": " + count(nd,i) + " points, avg = " + yvalues(nd,i)) |
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337 |
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338 ; Clean up for next time in loop. |
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339 |
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340 delete(idx) |
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341 end do |
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342 |
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343 delete(data) |
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344 end do |
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345 |
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346 ;============================ |
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347 ;compute beta |
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348 ;============================ |
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349 |
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350 u = yvalues(0,:) |
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351 v = yvalues(1,:) |
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352 u_count = count(0,:) |
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353 v_count = count(1,:) |
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354 |
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355 good = ind(.not.ismissing(u) .and. .not.ismissing(v)) |
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356 |
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357 uu = u(good) |
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358 vv = v(good) |
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359 uu_count = u_count(good) |
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360 vv_count = v_count(good) |
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361 |
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362 n_biome = dimsizes(uu) |
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363 beta_biome = new((/n_biome/),float) |
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364 |
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365 beta_biome = ((vv/uu) - 1.)/log(co2_f/co2_i) |
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366 |
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367 ;beta_biome_avg = avg(beta_biome) |
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368 beta_biome_avg = (sum(vv*vv_count)/sum(uu*uu_count) - 1.)/log(co2_f/co2_i) |
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369 |
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370 print (beta_biome_avg) |
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371 |
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372 ;=========================== |
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373 ; for html table - biome |
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374 ;=========================== |
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375 |
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376 output_html = "table_biome.html" |
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377 |
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378 ; column (not including header column) |
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379 |
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380 col_head = (/"CO2_i","CO2_f","NPP_i","NPP_f","Beta_model"/) |
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381 |
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382 ncol = dimsizes(col_head) |
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383 |
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384 ; row (not including header row) |
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385 ; 3 classes removed: Water Bodies, Urban and Build-Up, Unclassified |
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386 ; function "good" removed the last 2 classes |
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387 ; text4(i,2) = sprintf("%.2f",uu(i+1)) remove the first class |
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388 |
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389 row_head = (/"Evergreen Needleleaf Forests" \ |
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390 ,"Evergreen Broadleaf Forests" \ |
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391 ,"Deciduous Needleleaf Forest" \ |
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392 ,"Deciduous Broadleaf Forests" \ |
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393 ,"Mixed Forests" \ |
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394 ,"Closed Bushlands" \ |
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395 ,"Open Bushlands" \ |
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396 ,"Woody Savannas (S. Hem.)" \ |
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397 ,"Savannas (S. Hem.)" \ |
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398 ,"Grasslands" \ |
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399 ,"Permanent Wetlands" \ |
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400 ,"Croplands" \ |
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401 ,"Cropland/Natural Vegetation Mosaic" \ |
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402 ,"Barren or Sparsely Vegetated" \ |
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403 ,"Woody Savannas (N. Hem.)" \ |
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404 ,"Savannas (N. Hem.)" \ |
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405 ,"All Biome" \ |
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406 /) |
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407 nrow = dimsizes(row_head) |
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408 |
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409 ; arrays to be passed to table. |
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410 text4 = new ((/nrow, ncol/),string ) |
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411 |
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412 do i=0,nrow-2 |
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413 text4(i,0) = sprintf("%.1f",co2_i) |
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414 text4(i,1) = sprintf("%.1f",co2_f) |
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415 text4(i,2) = sprintf("%.1f",uu(i)) |
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416 text4(i,3) = sprintf("%.1f",vv(i)) |
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417 text4(i,4) = sprintf("%.2f",beta_biome(i)) |
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418 end do |
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419 text4(nrow-1,0) = "-" |
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420 text4(nrow-1,1) = "-" |
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421 text4(nrow-1,2) = "-" |
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422 text4(nrow-1,3) = "-" |
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423 text4(nrow-1,4) = sprintf("%.2f",beta_biome_avg) |
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424 |
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425 ;************************************************** |
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426 ; html table |
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427 ;************************************************** |
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428 |
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429 header_text = "<H1>Beta Factor: Model "+model_name+"</H1>" |
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430 |
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431 header = (/"<HTML>" \ |
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432 ,"<HEAD>" \ |
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433 ,"<TITLE>CLAMP metrics</TITLE>" \ |
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434 ,"</HEAD>" \ |
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435 ,header_text \ |
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436 /) |
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437 footer = "</HTML>" |
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438 |
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439 table_header = (/ \ |
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440 "<table border=1 cellspacing=0 cellpadding=3 width=80%>" \ |
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441 ,"<tr>" \ |
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442 ," <th bgcolor=DDDDDD >Biome Class</th>" \ |
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443 ," <th bgcolor=DDDDDD >"+col_head(0)+"</th>" \ |
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444 ," <th bgcolor=DDDDDD >"+col_head(1)+"</th>" \ |
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445 ," <th bgcolor=DDDDDD >"+col_head(2)+"</th>" \ |
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446 ," <th bgcolor=DDDDDD >"+col_head(3)+"</th>" \ |
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447 ," <th bgcolor=DDDDDD >"+col_head(4)+"</th>" \ |
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448 ,"</tr>" \ |
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449 /) |
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450 table_footer = "</table>" |
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451 row_header = "<tr>" |
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452 row_footer = "</tr>" |
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453 |
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454 lines = new(50000,string) |
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455 nline = 0 |
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456 |
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457 set_line(lines,nline,header) |
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458 set_line(lines,nline,table_header) |
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459 ;----------------------------------------------- |
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460 ;row of table |
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461 |
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462 do n = 0,nrow-1 |
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463 set_line(lines,nline,row_header) |
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464 |
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465 txt1 = row_head(n) |
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466 txt2 = text4(n,0) |
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467 txt3 = text4(n,1) |
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468 txt4 = text4(n,2) |
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469 txt5 = text4(n,3) |
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470 txt6 = text4(n,4) |
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471 |
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472 set_line(lines,nline,"<th>"+txt1+"</th>") |
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473 set_line(lines,nline,"<th>"+txt2+"</th>") |
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474 set_line(lines,nline,"<th>"+txt3+"</th>") |
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475 set_line(lines,nline,"<th>"+txt4+"</th>") |
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476 set_line(lines,nline,"<th>"+txt5+"</th>") |
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477 set_line(lines,nline,"<th>"+txt6+"</th>") |
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478 |
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479 set_line(lines,nline,row_footer) |
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480 end do |
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481 ;----------------------------------------------- |
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482 set_line(lines,nline,table_footer) |
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483 set_line(lines,nline,footer) |
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484 |
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485 ; Now write to an HTML file. |
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486 idx = ind(.not.ismissing(lines)) |
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487 if(.not.any(ismissing(idx))) then |
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488 asciiwrite(output_html,lines(idx)) |
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489 else |
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490 print ("error?") |
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491 end if |
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492 |
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493 end |
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494 |