1 | import cdms2 |
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2 | import numpy as N |
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3 | from constantes import * |
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4 | from math import * |
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5 | import copy |
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6 | |
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7 | #------------------------------------------------------------------------------------------- |
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8 | # function write_nc |
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9 | def write_nc(listvar,listvar2,listvar3,val_lon,val_lat,year_start,year_end,name,name_path): |
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10 | timear = N.ma.arange(len(listvar[0]), dtype=N.float) |
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11 | timear = timear*1800 |
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12 | time = cdms2.createAxis(timear, id='tstep') |
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13 | time.units = "seconds since "+str(year_start)+"-01-01 00:00:00" |
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14 | time.origin = str(year_start)+"-01-01 00:00:00" |
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15 | time.calendar = "gregorian" |
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16 | |
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17 | zlevelar = N.ma.arange(1, dtype=N.float) |
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18 | zlevelar = zlevelar+2 |
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19 | zlevel = cdms2.createAxis(zlevelar, id='lev') |
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20 | zlevel.units = "m" |
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21 | zlevel.long_name = "Vertical levels" |
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22 | |
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23 | latar = N.ma.arange(1, dtype=N.float) |
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24 | latar = latar+val_lat |
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25 | lat = cdms2.createAxis(latar, id='lat') |
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26 | lat.units = "degrees_north" |
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27 | lat.long_name = "Latitude" |
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28 | |
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29 | |
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30 | lonar = N.ma.arange(1, dtype=N.float) |
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31 | lonar = latar+val_lon |
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32 | lon = cdms2.createAxis(lonar, id='lon') |
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33 | lon.units = "degrees_east" |
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34 | lon.long_name = "Longitude" |
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35 | |
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36 | g = cdms2.createUniformGrid(val_lat, 1, 0, val_lon, 1, 0) |
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37 | |
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38 | tair_ar = N.array(listvar[id_tair]) |
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39 | tair_ar.shape = (len(listvar[id_tair]),1,1,1) |
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40 | tair = cdms2.createVariable(tair_ar, id='Tair', axes = (time,zlevel,lat,lon)) |
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41 | tair.long_name = "Near surface air temperature" |
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42 | tair.units = "K" |
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43 | tair.missing_value = "1.e+20f" |
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44 | |
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45 | tair_qc_ar = N.array(listvar2[id_tair]) |
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46 | tair_qc_ar.shape = (len(listvar2[id_tair]),1,1,1) |
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47 | tair_qc = cdms2.createVariable(tair_qc_ar, id='tair_qc', axes = (time,zlevel,lat,lon)) |
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48 | tair_qc.long_name = "Quality check for Tair" |
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49 | tair_qc.units = "-" |
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50 | tair_qc.missing_value = "1.e+20f" |
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51 | |
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52 | psurf_ar = N.array(listvar[id_psurf]) |
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53 | psurf_ar.shape = (len(listvar[id_psurf]),1,1) |
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54 | psurf = cdms2.createVariable(psurf_ar, id='PSurf', axes = (time,lat,lon)) |
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55 | psurf.long_name = "Surface pressure" |
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56 | psurf.units = "Pa" |
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57 | psurf.missing_value = "1.e+20f" |
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58 | |
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59 | psurf_qc_ar = N.array(listvar2[id_psurf]) |
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60 | psurf_qc_ar.shape = (len(listvar2[id_psurf]),1,1) |
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61 | psurf_qc = cdms2.createVariable(psurf_qc_ar, id='psurf_qc', axes = (time,lat,lon)) |
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62 | psurf_qc.long_name = "quality check for Psurf" |
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63 | psurf_qc.units = "-" |
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64 | psurf_qc.missing_value = "1.e+20f" |
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65 | |
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66 | qair_ar = N.array(listvar[id_qair]) |
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67 | qair_ar.shape = (len(listvar[id_qair]),1,1,1) |
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68 | qair = cdms2.createVariable(qair_ar, id='Qair', axes = (time,zlevel,lat,lon)) |
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69 | qair.long_name = "Near Surface specific humidity" |
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70 | qair.units = "kg/kg" |
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71 | qair.missing_value = "1.e+20f" |
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72 | |
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73 | temp=listvar2[id_qair]*listvar2[id_tair]*listvar2[id_psurf] |
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74 | qair_qc_ar = N.array(temp) |
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75 | qair_qc_ar.shape = (len(temp),1,1,1) |
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76 | qair_qc = cdms2.createVariable(qair_qc_ar, id='qair_qc', axes = (time,zlevel,lat,lon)) |
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77 | qair_qc.long_name = "Quality check for qair" |
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78 | qair_qc.units = "-" |
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79 | qair_qc.missing_value = "1.e+20f" |
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80 | |
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81 | wind_n_ar = N.array(listvar[id_ws]) |
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82 | wind_n_ar.shape = (len(listvar[id_ws]),1,1,1) |
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83 | wind_n = cdms2.createVariable(wind_n_ar, id='Wind_N', axes = (time,zlevel,lat,lon)) |
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84 | wind_n.long_name = "Near surface northward wind component" |
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85 | wind_n.units = "m/s" |
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86 | wind_n.missing_value = "1.e+20f" |
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87 | |
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88 | wind_n_qc_ar= N.array(listvar2[id_ws]) |
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89 | wind_n_qc_ar.shape = (len(listvar2[id_ws]),1,1,1) |
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90 | wind_n_qc = cdms2.createVariable(wind_n_qc_ar, id='wind_n_qc', axes = (time,zlevel,lat,lon)) |
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91 | wind_n_qc.long_name = "Quality check for wind_n" |
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92 | wind_n_qc.units = "-" |
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93 | wind_n_qc.missing_value = "1.e+20f" |
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94 | |
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95 | wind_e_ar = N.zeros(len(listvar[id_ws])) |
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96 | wind_e_ar.shape = (len(listvar[id_ws]),1,1,1) |
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97 | wind_e = cdms2.createVariable(wind_e_ar, id='Wind_E', axes = (time,zlevel,lat,lon)) |
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98 | wind_e.long_name = "Near surface eastward wind component" |
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99 | wind_e.units = "m/s" |
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100 | wind_e.missing_value = "1.e+20f" |
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101 | |
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102 | wind_e_qc_ar = N.zeros(len(listvar2[id_ws])) |
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103 | wind_e_qc_ar.shape = (len(listvar2[id_ws]),1,1,1) |
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104 | wind_e_qc = cdms2.createVariable(wind_e_qc_ar, id='Wind_e_qc', axes = (time,zlevel,lat,lon)) |
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105 | wind_e_qc.long_name = "Quality check for wind_e" |
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106 | wind_e_qc.units = "-" |
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107 | wind_e_qc.missing_value = "1.e+20f" |
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108 | |
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109 | rainf_ar = N.array(listvar[id_rain]) |
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110 | rainf_ar.shape = (len(listvar[id_rain]),1,1) |
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111 | rainf = cdms2.createVariable(rainf_ar, id='Rainf', axes = (time,lat,lon)) |
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112 | rainf.long_name = "Rainfall rate" |
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113 | rainf.units = "kg/m^2s" |
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114 | rainf.missing_value = "1.e+20f" |
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115 | |
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116 | rainf_qc_ar = N.array(listvar2[id_precip]) |
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117 | rainf_qc_ar.shape = (len(listvar2[id_precip]),1,1) |
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118 | rainf_qc = cdms2.createVariable(rainf_qc_ar, id='Rainf_qc', axes = (time,lat,lon)) |
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119 | rainf_qc.long_name = "Quality check for Rainf" |
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120 | rainf_qc.units = "-" |
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121 | rainf_qc.missing_value = "1.e+20f" |
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122 | |
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123 | snowf_ar = N.array(listvar[id_snow]) |
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124 | snowf_ar.shape = (len(listvar[id_snow]),1,1) |
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125 | snowf = cdms2.createVariable(snowf_ar, id='Snowf', axes = (time,lat,lon)) |
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126 | snowf.long_name = "Snowfall rate" |
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127 | snowf.units = "kg/m^2s" |
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128 | snowf.missing_value = "1.e+20f" |
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129 | |
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130 | snowf_qc_ar = N.array(listvar2[id_precip]) |
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131 | snowf_qc_ar.shape = (len(listvar2[id_precip]),1,1) |
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132 | snowf_qc = cdms2.createVariable(snowf_qc_ar, id='Snowf_qc', axes = (time,lat,lon)) |
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133 | snowf_qc.long_name = "Quality check for Snowf" |
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134 | snowf_qc.units = "-" |
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135 | snowf_qc.missing_value = "1.e+20f" |
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136 | |
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137 | swdown_ar = N.array(listvar[id_swdown]) |
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138 | swdown_ar.shape = (len(listvar[id_swdown]),1,1) |
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139 | swdown = cdms2.createVariable(swdown_ar, id='SWdown', axes = (time,lat,lon)) |
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140 | swdown.long_name = "Surface incident shortwave radiation" |
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141 | swdown.units = "W/m^2" |
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142 | swdown.missing_value = "1.e+20f" |
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143 | |
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144 | swdown_qc_ar = N.array(listvar2[id_swdown]) |
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145 | swdown_qc_ar.shape = (len(listvar2[id_swdown]),1,1) |
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146 | swdown_qc = cdms2.createVariable(swdown_qc_ar, id='SWdown_qc', axes = (time,lat,lon)) |
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147 | swdown_qc.long_name = "Quality check for Swdown" |
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148 | swdown_qc.units = "-" |
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149 | swdown_qc.missing_value = "1.e+20f" |
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150 | |
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151 | lwdown_ar = N.array(listvar[id_lwdown]) |
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152 | lwdown_ar.shape = (len(listvar[id_lwdown]),1,1) |
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153 | lwdown = cdms2.createVariable(lwdown_ar, id='LWdown', axes = (time,lat,lon)) |
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154 | lwdown.long_name = "Surface incident longwave radiation" |
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155 | lwdown.units = "W/m^2" |
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156 | lwdown.missing_value = "1.e+20f" |
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157 | |
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158 | lwdown_qc_ar = N.array(listvar2[id_lwdown]) |
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159 | lwdown_qc_ar.shape = (len(listvar2[id_lwdown]),1,1) |
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160 | lwdown_qc = cdms2.createVariable(lwdown_qc_ar, id='LWdown_qc', axes = (time,lat,lon)) |
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161 | lwdown_qc.long_name = "Quality check for Lwdown" |
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162 | lwdown_qc.units = "-" |
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163 | lwdown_qc.missing_value = "1.e+20f" |
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164 | |
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165 | Ts1_f_ar = N.array(listvar3[id_Ts1_f]) |
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166 | Ts1_f_ar.shape = (len(listvar3[id_Ts1_f]),1,1) |
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167 | Ts1_f = cdms2.createVariable(Ts1_f_ar, id='TSOIL', axes = (time,lat,lon)) |
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168 | Ts1_f.long_name = "Soil Temperature" |
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169 | Ts1_f.units = "degC" |
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170 | Ts1_f.missing_value = -9999. |
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171 | |
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172 | Rh_ar = N.array(listvar3[id_Rh]) |
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173 | Rh_ar.shape = (len(listvar3[id_Rh]),1,1) |
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174 | Rh = cdms2.createVariable(Rh_ar, id='RH', axes = (time,lat,lon)) |
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175 | Rh.long_name = "Relative Humidity" |
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176 | Rh.units = "%" |
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177 | Rh.missing_value = -9999. |
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178 | |
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179 | NEE_f_ar = N.array(listvar3[id_NEE_f]) |
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180 | NEE_f_ar.shape = (len(listvar3[id_NEE_f]),1,1) |
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181 | NEE_f = cdms2.createVariable(NEE_f_ar, id='NEE', axes = (time,lat,lon)) |
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182 | NEE_f.long_name = "Net Ecosystem Exchange" |
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183 | NEE_f.units = "gC/m2/tstep" |
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184 | NEE_f.missing_value = -9999. |
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185 | |
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186 | H_f_ar = N.array(listvar3[id_H_f]) |
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187 | H_f_ar.shape = (len(listvar3[id_H_f]),1,1) |
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188 | H_f = cdms2.createVariable(H_f_ar, id='Fh', axes = (time,lat,lon)) |
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189 | H_f.long_name = "Sensible Heat Flux" |
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190 | H_f.units = "W/m2" |
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191 | H_f.missing_value = -9999. |
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192 | |
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193 | LE_f_ar = N.array(listvar3[id_LE_f]) |
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194 | LE_f_ar.shape = (len(listvar3[id_LE_f]),1,1) |
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195 | LE_f = cdms2.createVariable(LE_f_ar, id='Fle', axes = (time,lat,lon)) |
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196 | LE_f.long_name = "Latent Heat Flux" |
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197 | LE_f.units = "W/m2" |
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198 | LE_f.missing_value = -9999. |
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199 | |
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200 | Epot_f_ar = N.array(listvar3[id_Epot_f]) |
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201 | Epot_f_ar.shape = (len(listvar3[id_Epot_f]),1,1) |
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202 | Epot_f = cdms2.createVariable(Epot_f_ar, id='ET', axes = (time,lat,lon)) |
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203 | Epot_f.long_name = "Evapotranspiration" |
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204 | Epot_f.units = "mm/tstep" |
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205 | Epot_f.missing_value = -9999. |
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206 | |
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207 | SWC1_f_ar = N.array(listvar3[id_SWC1_f]) |
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208 | SWC1_f_ar.shape = (len(listvar3[id_SWC1_f]),1,1) |
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209 | SWC1_f = cdms2.createVariable(SWC1_f_ar, id='SWC', axes = (time,lat,lon)) |
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210 | SWC1_f.long_name = "Soil Water Content" |
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211 | SWC1_f.units = "VOL%" |
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212 | SWC1_f.missing_value = -9999. |
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213 | |
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214 | gsurf_f_ar = N.array(listvar3[id_gsurf_f]) |
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215 | gsurf_f_ar.shape = (len(listvar3[id_gsurf_f]),1,1) |
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216 | gsurf_f = cdms2.createVariable(gsurf_f_ar, id='GS', axes = (time,lat,lon)) |
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217 | gsurf_f.long_name = "Canopy conductance" |
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218 | gsurf_f.units = "mm/s" |
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219 | gsurf_f.missing_value = -9999. |
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220 | |
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221 | GPP_f_ar = N.array(listvar3[id_GPP_f]) |
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222 | GPP_f_ar.shape = (len(listvar3[id_GPP_f]),1,1) |
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223 | GPP_f = cdms2.createVariable(GPP_f_ar, id='GPP', axes = (time,lat,lon)) |
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224 | GPP_f.long_name = "Gross Primary Production" |
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225 | GPP_f.units = "gC/m2/tstep" |
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226 | GPP_f.missing_value = -9999. |
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227 | |
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228 | Reco_ar = N.array(listvar3[id_Reco]) |
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229 | Reco_ar.shape = (len(listvar3[id_Reco]),1,1) |
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230 | Reco = cdms2.createVariable(Reco_ar, id='Reco', axes = (time,lat,lon)) |
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231 | Reco.long_name = "Ecosystem Respiration" |
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232 | Reco.units = "gC/m2/tstep" |
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233 | Reco.missing_value = -9999. |
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234 | |
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235 | f = cdms2.open(name_path+name+'_'+str(year_start)+'-'+str(year_end)+'.nc', 'w') |
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236 | f.write(tair,axes=None) |
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237 | f.write(qair,axes=None) |
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238 | f.write(psurf,axes=None) |
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239 | f.write(wind_n,axes=None) |
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240 | f.write(wind_e,axes=None) |
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241 | f.write(rainf,axes=None) |
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242 | f.write(snowf,axes=None) |
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243 | f.write(swdown,axes=None) |
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244 | f.write(lwdown,axes=None) |
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245 | |
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246 | f.write(tair_qc,axes=None) |
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247 | f.write(qair_qc,axes=None) |
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248 | f.write(psurf_qc,axes=None) |
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249 | f.write(wind_n_qc,axes=None) |
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250 | f.write(wind_e_qc,axes=None) |
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251 | f.write(rainf_qc,axes=None) |
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252 | f.write(snowf_qc,axes=None) |
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253 | f.write(swdown_qc,axes=None) |
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254 | f.write(lwdown_qc,axes=None) |
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255 | |
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256 | f.write(Ts1_f,axes=None) |
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257 | f.write(Rh,axes=None) |
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258 | f.write(NEE_f,axes=None) |
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259 | f.write(H_f,axes=None) |
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260 | f.write(LE_f,axes=None) |
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261 | f.write(Epot_f,axes=None) |
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262 | f.write(SWC1_f,axes=None) |
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263 | f.write(gsurf_f,axes=None) |
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264 | f.write(GPP_f,axes=None) |
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265 | f.write(Reco,axes=None) |
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266 | |
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267 | f.close() |
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268 | # end function write_nc |
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269 | #------------------------------------------------------------------------------------------- |
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270 | |
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271 | |
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272 | |
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273 | def prepare_for_orchidee(weather_gapfill): |
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274 | weather_out=copy.deepcopy(weather_gapfill) |
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275 | # Conversion from VPD (Vapour Pressure Deficit, Pa) to Qair (Near Surface Specific Humidity, kg/kg) |
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276 | for i in range(len(weather_out[id_vpd])): |
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277 | # Magnus Tetens (Murray, 1967) http://cires.colorado.edu/~voemel/vp.html |
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278 | if(weather_out[id_tair][i]<273.15): |
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279 | eg=c74*exp(c70*(weather_out[id_tair][i]-273.15)/(weather_out[id_tair][i]-c71)) |
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280 | else: |
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281 | eg=c74*exp(c72*(weather_out[id_tair][i]-273.15)/(weather_out[id_tair][i]-c73)) |
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282 | |
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283 | weather_out[id_vpd][i]=(eg-weather_out[id_vpd][i])*c75/(weather_out[id_psurf][i]-(eg-weather_out[id_vpd][i])*c76) |
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284 | |
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285 | weather_out.append(N.zeros(len(weather_out[id_precip]))) |
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286 | |
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287 | for i in range(len(weather_out[id_precip])): |
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288 | if(weather_out[id_tair][i]<273.15): |
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289 | weather_out[id_snow][i]=weather_out[id_precip][i] |
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290 | weather_out[id_rain][i]=0. |
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291 | |
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292 | return weather_out |
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293 | |
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294 | |
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295 | |
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296 | #------------------------------------------------------------------------------------------- |
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