| 1 | #!/usr/bin/env python |
|---|
| 2 | # -*- coding: utf-8 -*- |
|---|
| 3 | import string |
|---|
| 4 | import numpy as np |
|---|
| 5 | import matplotlib.pyplot as plt |
|---|
| 6 | from pylab import * |
|---|
| 7 | from mpl_toolkits.basemap import Basemap |
|---|
| 8 | from mpl_toolkits.basemap import shiftgrid, cm |
|---|
| 9 | from netCDF4 import Dataset |
|---|
| 10 | import arctic_map # function to regrid coast limits |
|---|
| 11 | import cartesian_grid_test # function to convert grid from polar to cartesian |
|---|
| 12 | import scipy.special |
|---|
| 13 | import ffgrid2 |
|---|
| 14 | import map_ffgrid |
|---|
| 15 | from matplotlib import colors |
|---|
| 16 | from matplotlib.font_manager import FontProperties |
|---|
| 17 | import map_cartesian_grid |
|---|
| 18 | |
|---|
| 19 | |
|---|
| 20 | |
|---|
| 21 | |
|---|
| 22 | MONTH = np.array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']) |
|---|
| 23 | month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER']) |
|---|
| 24 | month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) |
|---|
| 25 | M = len(month) |
|---|
| 26 | |
|---|
| 27 | |
|---|
| 28 | ######################## |
|---|
| 29 | # grid characteristics # |
|---|
| 30 | ######################## |
|---|
| 31 | x0 = -3000. # min limit of grid |
|---|
| 32 | x1 = 2500. # max limit of grid |
|---|
| 33 | dx = 40. |
|---|
| 34 | xvec = np.arange(x0, x1+dx, dx) |
|---|
| 35 | nx = len(xvec) |
|---|
| 36 | y0 = -3000. # min limit of grid |
|---|
| 37 | y1 = 3000. # max limit of grid |
|---|
| 38 | dy = 40. |
|---|
| 39 | yvec = np.arange(y0, y1+dy, dy) |
|---|
| 40 | ny = len(yvec) |
|---|
| 41 | |
|---|
| 42 | |
|---|
| 43 | frequ = 89 |
|---|
| 44 | |
|---|
| 45 | ##################### |
|---|
| 46 | # read NETCDF files # |
|---|
| 47 | ##################### |
|---|
| 48 | emis_spec_month = np.zeros([M, ny, nx], float) |
|---|
| 49 | emis_lamb_month = np.zeros([M, ny, nx], float) |
|---|
| 50 | ratio_emis = np.zeros([M, ny, nx], float) |
|---|
| 51 | ratio_emis_vec = np.zeros([M, ny * nx], float) |
|---|
| 52 | spec_emis_vec = np.zeros([M, ny * nx], float) |
|---|
| 53 | lamb_emis_vec = np.zeros([M, ny * nx], float) |
|---|
| 54 | print 'read data from files' |
|---|
| 55 | for imo in range (0, M): |
|---|
| 56 | print 'month: ' + month[imo] |
|---|
| 57 | ### emissivity ### |
|---|
| 58 | print 'open emissivity file' |
|---|
| 59 | fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSUB' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC') |
|---|
| 60 | xdist = fichier_emis.variables['longitude'][:] |
|---|
| 61 | ydist = fichier_emis.variables['latitude'][:] |
|---|
| 62 | day = fichier_emis.variables['days'][:] |
|---|
| 63 | emis_spec = fichier_emis.variables['e_spec'][:] |
|---|
| 64 | emis_lamb = fichier_emis.variables['e_lamb'][:] |
|---|
| 65 | fichier_emis.close() |
|---|
| 66 | ##### calculate monthly mean of emis ##### |
|---|
| 67 | for ilat in range(0, len(ydist)): |
|---|
| 68 | for ilon in range (0, len(xdist)): |
|---|
| 69 | emis_spec_month[imo, ilat, ilon] = mean(emis_spec[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(emis_spec[ilat, ilon, 0 : month_day[imo]]) == False)]) |
|---|
| 70 | emis_lamb_month[imo, ilat, ilon] = mean(emis_lamb[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(emis_lamb[ilat, ilon, 0 : month_day[imo]]) == False)]) |
|---|
| 71 | ### monthly emissivity ratio ### |
|---|
| 72 | print 'open emissivity ratio file' |
|---|
| 73 | fichier_ratio = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_lamb-spec_ratio_near_nadir_AMSUB' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC') |
|---|
| 74 | xdist = fichier_ratio.variables['longitude'][:] |
|---|
| 75 | ydist = fichier_ratio.variables['latitude'][:] |
|---|
| 76 | ratio_emis[imo, :, :] = fichier_ratio.variables['emis_ratio'][:] |
|---|
| 77 | fichier_ratio.close() |
|---|
| 78 | ### reshape in monthly vectors all data in 2D arrays ### |
|---|
| 79 | print 'reshape data' |
|---|
| 80 | ratio_emis_vec[imo, :] = reshape(ratio_emis[imo, :, :], size(ratio_emis[imo, :, :])) |
|---|
| 81 | spec_emis_vec[imo, :] = reshape(emis_spec_month[imo, :, :], size(emis_spec_month[imo, :, :])) |
|---|
| 82 | lamb_emis_vec[imo, :] = reshape(emis_lamb_month[imo, :, :], size(emis_lamb_month[imo, :, :])) |
|---|
| 83 | |
|---|
| 84 | |
|---|
| 85 | ''' |
|---|
| 86 | # test: |
|---|
| 87 | ion() |
|---|
| 88 | x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() |
|---|
| 89 | x_coast = x_ind |
|---|
| 90 | y_coast = y_ind |
|---|
| 91 | z_coast = z_ind |
|---|
| 92 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xdist, ydist, emis_spec_month[0, :, :], 0.45, 1.02, 0.01, cm.s3pcpn_l_r, 'test') |
|---|
| 93 | ''' |
|---|
| 94 | |
|---|
| 95 | |
|---|
| 96 | c = np.array(['r', 'b', 'c', 'm', 'y', 'g']) |
|---|
| 97 | #limit_coef_spec = np.array([0.6, 0.6, 0.7, 0.8, 0.75]) # i1 = AMSUA23GHz / i2 = AMSUA30GHz / i3 = AMSUA50GHz / i4 = AMSUA89GHz / i5 = AMSUB89GHz |
|---|
| 98 | #limit_coef_lamb = np.array([0.6, 0.6, 0.8, 0.8, 0.77]) # i1 = AMSUA23GHz / i2 = AMSUA30GHz / i3 = AMSUA50GHz / i4 = AMSUA89GHz / i5 = AMSUB89GHz |
|---|
| 99 | #idata = 0 |
|---|
| 100 | fontP = FontProperties() |
|---|
| 101 | fontP.set_size('small') |
|---|
| 102 | print 'distribution of emissivity values' |
|---|
| 103 | ################################### |
|---|
| 104 | # distribution of SPEC emissivity # |
|---|
| 105 | ################################### |
|---|
| 106 | print 'spec' |
|---|
| 107 | hist_vals_spec = np.zeros([M, 50], float) |
|---|
| 108 | corresp_emis_spec = np.zeros([M, 50], float) |
|---|
| 109 | for imo in range (0, M): |
|---|
| 110 | hist_vals_spec[imo, :] = hist(spec_emis_vec[imo, :][nonzero(isnan(spec_emis_vec[imo,:]) == False)], bins = 50, normed = True, histtype='step')[0] |
|---|
| 111 | for ibin in range (0, 50): |
|---|
| 112 | corresp_emis_spec[imo, ibin] = mean(hist(spec_emis_vec[imo, :][nonzero(isnan(spec_emis_vec[imo,:]) == False)], bins = 50, normed = True, histtype='step')[1][ibin : ibin + 2]) |
|---|
| 113 | |
|---|
| 114 | ## plot first six months of spec emissivity histograms ## |
|---|
| 115 | ion() |
|---|
| 116 | figure() |
|---|
| 117 | for imo in range (0, 6): |
|---|
| 118 | plot(corresp_emis_spec[imo], hist_vals_spec[imo], c = str(c[imo]), label = str(month[imo])) |
|---|
| 119 | |
|---|
| 120 | grid() |
|---|
| 121 | xlim(corresp_emis_spec.min() - 0.02, 1.02) |
|---|
| 122 | xlabel('emissivity SPEC') |
|---|
| 123 | ylabel('frequency of occurence') |
|---|
| 124 | legend(loc = 2, prop = fontP) |
|---|
| 125 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_spec_AMSUA' + str(frequ) + '_JANUARY-JUNE_2009.png') |
|---|
| 126 | ## plot six following months of spec emissivity histograms ## |
|---|
| 127 | figure() |
|---|
| 128 | for imo in range (6, M): |
|---|
| 129 | plot(corresp_emis_spec[imo], hist_vals_spec[imo], c = str(c[imo - 6]), label = str(month[imo])) |
|---|
| 130 | |
|---|
| 131 | grid() |
|---|
| 132 | xlim(corresp_emis_spec.min() - 0.02, 1.02) |
|---|
| 133 | xlabel('emissivity SPEC') |
|---|
| 134 | ylabel('frequency of occurence') |
|---|
| 135 | legend(loc = 9, prop = fontP) |
|---|
| 136 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_spec_AMSUA' + str(frequ) + '_JULY-DECEMBER_2009.png') |
|---|
| 137 | |
|---|
| 138 | # find the emissivity value corresponding to the threshold of ice/no_ice limit |
|---|
| 139 | emis_lim_spec = np.zeros([M], float) |
|---|
| 140 | for imo in range (0, M): |
|---|
| 141 | print 'month ' + str(month[imo]) |
|---|
| 142 | bb = np.where((corresp_emis_spec[imo, :] > 0.7) & (corresp_emis_spec[imo, :] < 0.77))[0] # only consider low emissivity values (open sea) // CHANGE EMISSIVITY VALUE ACCORDING TO FREQUENCY |
|---|
| 143 | aa = np.where(hist_vals_spec[imo, bb] == min(hist_vals_spec[imo, bb]))[0] # of these latter values, only consider emissivity values lower than 1/4*peak emissivity |
|---|
| 144 | #cc = np.where(hist_vals_spec[imo, bb] == max(hist_vals_spec[imo, bb]))[0] # which emissivity index corresponds to peak emissivity |
|---|
| 145 | #dd = np.where(aa > cc[0])[0] |
|---|
| 146 | emis_lim_spec[imo] = corresp_emis_spec[imo, bb][aa][0] |
|---|
| 147 | |
|---|
| 148 | # plot monthly evolution of emissivity spec threshold used for delimitation |
|---|
| 149 | figure() |
|---|
| 150 | plot(np.arange(0, M, 1), emis_lim_spec, 'b-+', label = 'emis SPEC') |
|---|
| 151 | ylabel('emissivity SPEC threshold') |
|---|
| 152 | grid() |
|---|
| 153 | xticks(np.arange(0, M, 1), month, rotation = 25) |
|---|
| 154 | legend(loc = 2) |
|---|
| 155 | xlim(-1, M) |
|---|
| 156 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUB/emis_lim_frequ_SPEC_AMSUB' + str(frequ) + '_2009.png') |
|---|
| 157 | |
|---|
| 158 | |
|---|
| 159 | ################################### |
|---|
| 160 | # distribution of LAMB emissivity # |
|---|
| 161 | ################################### |
|---|
| 162 | print 'lamb' |
|---|
| 163 | hist_vals_lamb = np.zeros([M, 50], float) |
|---|
| 164 | corresp_emis_lamb = np.zeros([M, 50], float) |
|---|
| 165 | for imo in range (0, M): |
|---|
| 166 | hist_vals_lamb[imo, :] = hist(lamb_emis_vec[imo, :][nonzero(isnan(lamb_emis_vec[imo,:]) == False)], bins = 50, normed = True, histtype='step')[0] |
|---|
| 167 | for ibin in range (0, 50): |
|---|
| 168 | corresp_emis_lamb[imo, ibin] = mean(hist(lamb_emis_vec[imo, :][nonzero(isnan(lamb_emis_vec[imo,:]) == False)], bins = 50, normed = True, histtype='step')[1][ibin : ibin + 2]) |
|---|
| 169 | |
|---|
| 170 | ## plot first six months of spec emissivity histograms ## |
|---|
| 171 | figure() |
|---|
| 172 | for imo in range (0, 6): |
|---|
| 173 | plot(corresp_emis_lamb[imo], hist_vals_lamb[imo], c = str(c[imo]), label = str(month[imo])) |
|---|
| 174 | |
|---|
| 175 | grid() |
|---|
| 176 | xlim(corresp_emis_lamb.min() - 0.02, 1.02) |
|---|
| 177 | xlabel('emissivity LAMB') |
|---|
| 178 | ylabel('frequency of occurence') |
|---|
| 179 | legend(loc = 2, prop = fontP) |
|---|
| 180 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_lamb_AMSUA' + str(frequ) + '_JANUARY-JUNE_2009.png') |
|---|
| 181 | ## plot six following months of spec emissivity histograms ## |
|---|
| 182 | figure() |
|---|
| 183 | for imo in range (6, M): |
|---|
| 184 | plot(corresp_emis_lamb[imo], hist_vals_lamb[imo], c = str(c[imo - 6]), label = str(month[imo])) |
|---|
| 185 | |
|---|
| 186 | grid() |
|---|
| 187 | xlim(corresp_emis_lamb.min() - 0.02, 1.02) |
|---|
| 188 | xlabel('emissivity LAMB') |
|---|
| 189 | ylabel('frequency of occurence') |
|---|
| 190 | legend(loc = 9, prop = fontP) |
|---|
| 191 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_lamb_AMSUA' + str(frequ) + '_JULY-DECEMBER_2009.png') |
|---|
| 192 | |
|---|
| 193 | # find the emissivity value corresponding to the threshold of ice/no_ice limit |
|---|
| 194 | emis_lim_lamb = np.zeros([M], float) |
|---|
| 195 | for imo in range (0, M): |
|---|
| 196 | print 'month ' + str(month[imo]) |
|---|
| 197 | bb = np.where((corresp_emis_lamb[imo, :] > 0.72) & (corresp_emis_lamb[imo, :] < 0.8))[0] # only consider low emissivity values (open sea) // CHANGE EMISSIVITY VALUE ACCORDING TO FREQUENCY |
|---|
| 198 | aa = np.where(hist_vals_lamb[imo, bb] == min(hist_vals_lamb[imo, bb]))[0] # of these latter values, only consider emissivity values lower than 1/4*peak emissivity |
|---|
| 199 | #cc = np.where(hist_vals_lamb[imo, bb] == max(hist_vals_lamb[imo, bb]))[0] # which emissivity index corresponds to peak emissivity |
|---|
| 200 | #dd = np.where(aa > cc[0])[0] |
|---|
| 201 | emis_lim_lamb[imo] = corresp_emis_lamb[imo, bb][aa][0] |
|---|
| 202 | |
|---|
| 203 | # plot monthly evolution of emissivity spec threshold used for delimitation |
|---|
| 204 | figure() |
|---|
| 205 | plot(np.arange(0, M, 1), emis_lim_lamb, 'b-+', label = 'emis LAMB') |
|---|
| 206 | ylabel('emissivity LAMB threshold') |
|---|
| 207 | grid() |
|---|
| 208 | xticks(np.arange(0, M, 1), month, rotation = 25) |
|---|
| 209 | legend(loc = 2) |
|---|
| 210 | xlim(-1, M) |
|---|
| 211 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUB/emis_lim_frequ_LAMB_AMSUB' + str(frequ) + '_2009.png') |
|---|
| 212 | |
|---|
| 213 | |
|---|
| 214 | |
|---|
| 215 | ################################### |
|---|
| 216 | # distribution of emissivity rate # |
|---|
| 217 | ################################### |
|---|
| 218 | print 'rate' |
|---|
| 219 | hist_vals_rate = np.zeros([M, 50], float) |
|---|
| 220 | corresp_emis_rate = np.zeros([M, 50], float) |
|---|
| 221 | for imo in range (0, M): |
|---|
| 222 | hist_vals_rate[imo, :] = hist(ratio_emis_vec[imo, :][nonzero(isnan(ratio_emis_vec[imo,:]) == False)], bins = 50, normed = True, histtype='step')[0] |
|---|
| 223 | for ibin in range (0, 50): |
|---|
| 224 | corresp_emis_rate[imo, ibin] = mean(hist(ratio_emis_vec[imo, :][nonzero(isnan(ratio_emis_vec[imo,:]) == False)], bins = 50, normed = True, histtype='step')[1][ibin : ibin + 2]) |
|---|
| 225 | |
|---|
| 226 | ## plot first six months of spec emissivity histograms ## |
|---|
| 227 | figure() |
|---|
| 228 | for imo in range (0, 6): |
|---|
| 229 | plot(corresp_emis_rate[imo], hist_vals_rate[imo], c = str(c[imo]), label = str(month[imo])) |
|---|
| 230 | |
|---|
| 231 | grid() |
|---|
| 232 | xlim(corresp_emis_rate.min() - 0.02, corresp_emis_rate.max() + 0.02) |
|---|
| 233 | xlabel('emissivity rate (%)') |
|---|
| 234 | ylabel('frequency of occurence') |
|---|
| 235 | legend(prop = fontP) |
|---|
| 236 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_rate_AMSUA'+str(frequ)+'_JANUARY-JUNE_2009.png') |
|---|
| 237 | ## plot six following months of spec emissivity histograms ## |
|---|
| 238 | figure() |
|---|
| 239 | for imo in range (6, M): |
|---|
| 240 | plot(corresp_emis_rate[imo], hist_vals_rate[imo], c = str(c[imo - 6]), label = str(month[imo])) |
|---|
| 241 | |
|---|
| 242 | grid() |
|---|
| 243 | xlim(corresp_emis_rate.min() - 0.02, corresp_emis_rate.max() + 0.02) |
|---|
| 244 | xlabel('emissivity rate (%)') |
|---|
| 245 | ylabel('frequency of occurence') |
|---|
| 246 | legend(loc = 9, prop = fontP) |
|---|
| 247 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_rate_AMSUA'+str(frequ)+'_JULY-DECEMBER_2009.png') |
|---|
| 248 | |
|---|
| 249 | # no definition of threshold for ICE / NO_ICE delimitation because no apparent distinction signal // use of this signal for internal ice classification |
|---|
| 250 | ''' |
|---|
| 251 | # find the emissivity value corresponding to the threshold of ice/no_ice limit |
|---|
| 252 | emis_lim_rate = np.zeros([M], float) |
|---|
| 253 | for imo in range (0, M): |
|---|
| 254 | bb = np.where((corresp_emis_rate[imo, :] < 0.6))[0] # only consider low emissivity values (open sea) // CHANGE EMISSIVITY VALUE ACCORDING TO FREQUENCY |
|---|
| 255 | aa = np.where(hist_vals_rate[imo, bb] < max(hist_vals_rate[imo, bb]) / 4.)[0] # of these latter values, only consider emissivity values lower than 1/4*peak emissivity |
|---|
| 256 | cc = np.where(hist_vals_rate[imo, bb] == max(hist_vals_rate[imo, bb]))[0] # which emissivity index corresponds to peak emissivity |
|---|
| 257 | dd = np.where(aa > cc[0])[0] |
|---|
| 258 | emis_lim_rate[imo] = corresp_emis_rate[imo, bb][aa][dd[0]] |
|---|
| 259 | |
|---|
| 260 | # plot monthly evolution of emissivity spec threshold used for delimitation |
|---|
| 261 | figure() |
|---|
| 262 | plot(np.arange(0, M, 1), emis_lim_rate, 'b-+', label = 'emis LAMB') |
|---|
| 263 | ylabel('emissivity rate threshold - AMSUB 89GHz') |
|---|
| 264 | grid() |
|---|
| 265 | xticks(np.arange(0, M, 1), month, rotation = 25) |
|---|
| 266 | legend(loc = 2) |
|---|
| 267 | xlim(-1, M) |
|---|
| 268 | plt.savefig('/usr/home/lahlod/twice_d/figure_output_ARCTIC/figure_output_CEN/ice_class_AMSUA/emis_lim_frequ_rate_AMSUA89_2009.png') |
|---|
| 269 | ''' |
|---|
| 270 | |
|---|
| 271 | |
|---|
| 272 | ############################################################################## |
|---|
| 273 | # plot comparison of emissivity thresholds between spec and lamb assumptions # |
|---|
| 274 | ############################################################################## |
|---|
| 275 | figure() |
|---|
| 276 | plot(np.arange(0, M, 1), emis_lim_spec, 'b-+', label = 'emis SPEC') |
|---|
| 277 | plot(np.arange(0, M, 1), emis_lim_lamb, 'g-+', label = 'emis LAMB') |
|---|
| 278 | ylabel('emissivity threshold') |
|---|
| 279 | grid() |
|---|
| 280 | xticks(np.arange(0, M, 1), month, rotation = 25) |
|---|
| 281 | legend(loc = 2, prop = fontP) |
|---|
| 282 | xlim(-1, M) |
|---|
| 283 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUB/emis_lim_frequ_spec_and_lamb_AMSUB' + str(frequ) + '_2009.png') |
|---|
| 284 | |
|---|
| 285 | |
|---|
| 286 | |
|---|
| 287 | |
|---|
| 288 | ################################################################ |
|---|
| 289 | # delimitation ice - no_ice with emissivity or ratio threshold # |
|---|
| 290 | ################################################################ |
|---|
| 291 | print 'definition of ice extent' |
|---|
| 292 | x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() |
|---|
| 293 | x_coast = x_ind |
|---|
| 294 | y_coast = y_ind |
|---|
| 295 | z_coast = z_ind |
|---|
| 296 | #### SPEC emiss #### |
|---|
| 297 | print 'spec' |
|---|
| 298 | ice_zone_spec = np.zeros([M, 151, 139], float) |
|---|
| 299 | for imo in range (0, M): |
|---|
| 300 | print 'month: ' + str(month[imo]) |
|---|
| 301 | for ilat in range (0, 151): |
|---|
| 302 | for ilon in range (0, 139): |
|---|
| 303 | if (isnan(emis_spec_month[imo, ilat, ilon]) == True): |
|---|
| 304 | ice_zone_spec[imo, ilat, ilon] = NaN |
|---|
| 305 | else: |
|---|
| 306 | if (emis_spec_month[imo, ilat, ilon] <= emis_lim_spec[imo]): # use the monthly SPEC emissivity threshold |
|---|
| 307 | ice_zone_spec[imo, ilat, ilon] = NaN |
|---|
| 308 | else: |
|---|
| 309 | ice_zone_spec[imo, ilat, ilon] = emis_spec_month[imo, ilat, ilon] |
|---|
| 310 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xdist, ydist, ice_zone_spec[8, :, :], 0.5, 1.02, 0.01, cm.s3pcpn_l_r, 'Sea ice emissivity spec (threshold : emis_SPEC > ' + str(emis_lim_spec[imo])[0:6] + ')') |
|---|
| 311 | title(str(month[imo]) + ' 2009 - AMSUA ' + str(frequ) + 'GHz') |
|---|
| 312 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/AMSUA' + str(frequ) + '_ice_emis_spec_thresh' + '_' + month[imo] + '2009.png') |
|---|
| 313 | |
|---|
| 314 | #### LAMB emiss #### |
|---|
| 315 | print 'lamb' |
|---|
| 316 | ice_zone_lamb = np.zeros([M, 151, 139], float) |
|---|
| 317 | for imo in range (0, M): |
|---|
| 318 | print 'month: ' + str(month[imo]) |
|---|
| 319 | for ilat in range (0, 151): |
|---|
| 320 | for ilon in range (0, 139): |
|---|
| 321 | if (isnan(emis_lamb_month[imo, ilat, ilon]) == True): |
|---|
| 322 | ice_zone_lamb[imo, ilat, ilon] = NaN |
|---|
| 323 | else: |
|---|
| 324 | if (emis_lamb_month[imo, ilat, ilon] <= emis_lim_lamb[imo]): # use the monthly SPEC emissivity threshold |
|---|
| 325 | ice_zone_lamb[imo, ilat, ilon] = NaN |
|---|
| 326 | else: |
|---|
| 327 | ice_zone_lamb[imo, ilat, ilon] = emis_lamb_month[imo, ilat, ilon] |
|---|
| 328 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xdist, ydist, ice_zone_lamb[imo, :, :], 0.5, 1.02, 0.01, cm.s3pcpn_l_r, 'Sea ice emissivity lamb (threshold : emis_LAMB > ' + str(emis_lim_lamb[imo])[0:6] + ')') |
|---|
| 329 | title(str(month[imo]) + ' 2009 - AMSUA ' + str(frequ) + 'GHz') |
|---|
| 330 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/AMSUA' + str(frequ) + '_ice_emis_lamb_thresh' + '_' + month[imo] + '2009.png') |
|---|
| 331 | |
|---|
| 332 | |
|---|
| 333 | |
|---|
| 334 | ########################### |
|---|
| 335 | # calculation of ice area # |
|---|
| 336 | ########################### |
|---|
| 337 | # nb of pixels near pole = 926 |
|---|
| 338 | print 'calculation of ice area' |
|---|
| 339 | pix_area = 40. * 40. |
|---|
| 340 | #### SPEC #### |
|---|
| 341 | print 'spec' |
|---|
| 342 | nb_pix_spec = np.zeros([M], float) |
|---|
| 343 | total_ice_area_spec = np.zeros([M], float) |
|---|
| 344 | for imo in range (0, M): |
|---|
| 345 | ice_spec = reshape(ice_zone_spec[imo, :, :], size(ice_zone_spec[imo, :, :])) |
|---|
| 346 | nb_pix_spec[imo] = len(np.where(isnan(ice_spec) == False)[0]) |
|---|
| 347 | total_ice_area_spec[imo] = (pix_area * nb_pix_spec[imo]) + (926. * pix_area) |
|---|
| 348 | |
|---|
| 349 | |
|---|
| 350 | #### LAMB #### |
|---|
| 351 | print 'lamb' |
|---|
| 352 | nb_pix_lamb = np.zeros([M], float) |
|---|
| 353 | total_ice_area_lamb = np.zeros([M], float) |
|---|
| 354 | for imo in range (0, M): |
|---|
| 355 | ice_lamb = reshape(ice_zone_lamb[imo, :, :], size(ice_zone_lamb[imo, :, :])) |
|---|
| 356 | nb_pix_lamb[imo] = len(np.where(isnan(ice_lamb) == False)[0]) |
|---|
| 357 | total_ice_area_lamb[imo] = (pix_area * nb_pix_lamb[imo]) + (926. * pix_area) |
|---|
| 358 | |
|---|
| 359 | |
|---|
| 360 | |
|---|
| 361 | ########################################## STACK DATA ################################################### |
|---|
| 362 | |
|---|
| 363 | ###################### |
|---|
| 364 | # stack in .dat file # |
|---|
| 365 | ###################### |
|---|
| 366 | print 'start stacking in .dat file' |
|---|
| 367 | data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/AMSUB'+str(frequ)+'_data_classification_parameters_ice_no-ice_2009.dat', 'a') |
|---|
| 368 | for imo in range (0, M): |
|---|
| 369 | for ii in range (0, 50): |
|---|
| 370 | data_classif.write(('%(months)10s %(hist_vals_spec)10.5f %(corresp_emis_spec)10.5f %(hist_vals_lamb)10.5f %(corresp_emis_lamb)10.5f %(hist_vals_rate)10.5f %(corresp_emis_rate)10.5f %(emis_lim_spec)10.5f %(emis_lim_lamb)10.5f %(spec_ice_area)10.5f %(lamb_ice_area)10.5f \n' % { |
|---|
| 371 | 'months':month[imo], |
|---|
| 372 | 'hist_vals_spec':hist_vals_spec[imo, ii], |
|---|
| 373 | 'corresp_emis_spec':corresp_emis_spec[imo, ii], |
|---|
| 374 | 'hist_vals_lamb':hist_vals_lamb[imo, ii], |
|---|
| 375 | 'corresp_emis_lamb':corresp_emis_lamb[imo, ii], |
|---|
| 376 | 'hist_vals_rate':hist_vals_rate[imo, ii], |
|---|
| 377 | 'corresp_emis_rate':corresp_emis_rate[imo, ii], |
|---|
| 378 | 'emis_lim_spec':emis_lim_spec[imo], |
|---|
| 379 | 'emis_lim_lamb':emis_lim_lamb[imo], |
|---|
| 380 | 'spec_ice_area':total_ice_area_spec[imo], |
|---|
| 381 | 'lamb_ice_area':total_ice_area_lamb[imo], |
|---|
| 382 | })) |
|---|
| 383 | |
|---|
| 384 | data_classif.close() |
|---|
| 385 | |
|---|
| 386 | |
|---|
| 387 | |
|---|
| 388 | ######################## |
|---|
| 389 | # stack in netcdf file # |
|---|
| 390 | ######################## |
|---|
| 391 | print 'start stacking' |
|---|
| 392 | for imo in range (0, M): |
|---|
| 393 | print 'stack in file month ' + str(month[imo]) |
|---|
| 394 | rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/cartesian_grid_map_ice_no-ice_' + month[imo] + '2009_AMSUB' + str(frequ) + '_spec_lamb_thresholds.nc', 'w', format='NETCDF3_CLASSIC') |
|---|
| 395 | rootgrp.createDimension('longitude', len(xdist)) |
|---|
| 396 | rootgrp.createDimension('latitude', len(ydist)) |
|---|
| 397 | nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',)) |
|---|
| 398 | nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) |
|---|
| 399 | nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude')) |
|---|
| 400 | nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude')) |
|---|
| 401 | nc_lon[:] = xdist |
|---|
| 402 | nc_lat[:] = ydist |
|---|
| 403 | nc_ice_spec[:] = ice_zone_spec[imo, :, :] |
|---|
| 404 | nc_ice_lamb[:] = ice_zone_lamb[imo, :, :] |
|---|
| 405 | rootgrp.close() |
|---|
| 406 | |
|---|
| 407 | |
|---|
| 408 | |
|---|
| 409 | |
|---|
| 410 | |
|---|
| 411 | ''' |
|---|
| 412 | ################################### |
|---|
| 413 | # plot time evolution of ice area # |
|---|
| 414 | ################################### |
|---|
| 415 | figure() |
|---|
| 416 | plot(total_ice_area, '-+b', label = 'AMSUB SPEC') |
|---|
| 417 | plot(total_ice_area_osi, '-^r', label = 'OSISAF') |
|---|
| 418 | plot(total_ice_area_l, '-og', label = 'AMSUB LAMB') |
|---|
| 419 | legend(loc = 3) |
|---|
| 420 | xlim(-1, M) |
|---|
| 421 | ylim(0.2*1e7, 1.3*1e7) |
|---|
| 422 | xticks(np.arange(0, M, 1), month, rotation = 25) |
|---|
| 423 | ylabel('totale ice area (square km)') |
|---|
| 424 | grid() |
|---|
| 425 | plt.savefig('/usr/home/lahlod/twice_d/figure_output_ARCTIC/figure_output_CEN/ice_pix_area/total_ice_area_AMSUB_SPEC_LAMB_OSISAF_2009.png') |
|---|
| 426 | ''' |
|---|
| 427 | |
|---|
| 428 | |
|---|
| 429 | |
|---|
| 430 | |
|---|
| 431 | |
|---|
| 432 | |
|---|
| 433 | |
|---|
![(please configure the [header_logo] section in trac.ini)](/twice/chrome/site/your_project_logo.png){kind=logo}
