| 1 | #!/usr/bin/env python |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | import string |
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| 4 | import numpy as np |
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| 5 | import matplotlib.pyplot as plt |
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| 6 | from pylab import * |
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| 7 | from mpl_toolkits.basemap import Basemap |
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| 8 | from mpl_toolkits.basemap import shiftgrid, cm |
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| 9 | from netCDF4 import Dataset |
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| 10 | import ffgrid2 |
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| 11 | |
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| 12 | |
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| 13 | |
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| 14 | |
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| 15 | ## ATTENTION: lecture des fichiers NETCDF AMSUA - definition des variables dans "read_netcdf_AMSUA_test.py" ## |
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| 16 | ## lon |
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| 17 | ## lat |
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| 18 | ## jrs_MONTH |
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| 19 | ## chan |
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| 20 | len_month = np.array([31, 28, 31, 30, 31, 30, 31]) |
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| 21 | ######################### |
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| 22 | ## moyennes mensuelles ## |
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| 23 | ######################### |
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| 24 | monthly_emis_JAN = np.zeros([len(chan), len(lat), len(lon)], float) |
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| 25 | monthly_emis_FEB = np.zeros([len(chan), len(lat), len(lon)], float) |
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| 26 | monthly_emis_MAR = np.zeros([len(chan), len(lat), len(lon)], float) |
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| 27 | monthly_emis_APR = np.zeros([len(chan), len(lat), len(lon)], float) |
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| 28 | monthly_emis_MAY = np.zeros([len(chan), len(lat), len(lon)], float) |
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| 29 | monthly_emis_JUN = np.zeros([len(chan), len(lat), len(lon)], float) |
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| 30 | monthly_emis_JUL = np.zeros([len(chan), len(lat), len(lon)], float) |
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| 31 | for ich in range (0, len(chan)): |
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| 32 | for ilon in range (0, len(lon)): |
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| 33 | for ilat in range (0, len(lat)): |
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| 34 | monthly_emis_JAN[ich, ilat, ilon] = mean(emis_JAN[ich, ilat, ilon, :][nonzero(isnan(emis_JAN[ich, ilat, ilon, :]) == False)]) |
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| 35 | monthly_emis_FEB[ich, ilat, ilon] = mean(emis_FEB[ich, ilat, ilon, :][nonzero(isnan(emis_FEB[ich, ilat, ilon, :]) == False)]) |
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| 36 | monthly_emis_MAR[ich, ilat, ilon] = mean(emis_MAR[ich, ilat, ilon, :][nonzero(isnan(emis_MAR[ich, ilat, ilon, :]) == False)]) |
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| 37 | monthly_emis_APR[ich, ilat, ilon] = mean(emis_APR[ich, ilat, ilon, :][nonzero(isnan(emis_APR[ich, ilat, ilon, :]) == False)]) |
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| 38 | monthly_emis_MAY[ich, ilat, ilon] = mean(emis_MAY[ich, ilat, ilon, :][nonzero(isnan(emis_MAY[ich, ilat, ilon, :]) == False)]) |
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| 39 | monthly_emis_JUN[ich, ilat, ilon] = mean(emis_JUN[ich, ilat, ilon, :][nonzero(isnan(emis_JUN[ich, ilat, ilon, :]) == False)]) |
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| 40 | monthly_emis_JUL[ich, ilat, ilon] = mean(emis_JUL[ich, ilat, ilon, :][nonzero(isnan(emis_JUL[ich, ilat, ilon, :]) == False)]) |
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| 41 | |
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| 42 | monthly_emis = [monthly_emis_JAN, monthly_emis_FEB, monthly_emis_MAR, monthly_emis_APR, monthly_emis_MAY, monthly_emis_JUN, monthly_emis_JUL] |
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| 43 | |
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| 44 | ############################## |
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| 45 | ## plot moyennes mensuelles ## |
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| 46 | ############################## |
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| 47 | ## evolution de l'emissivite moyenne mensuelle dans une tranche de latitude ## |
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| 48 | plt.ion() |
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| 49 | for ilat in range (2, 7): |
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| 50 | plt.figure() |
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| 51 | plt.plot(lon, monthly_emis_JAN[0, ilat, :], 'r--', label = 'JAN') |
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| 52 | plt.plot(lon, monthly_emis_FEB[0, ilat, :], 'g--', label = 'FEB') |
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| 53 | plt.plot(lon, monthly_emis_MAR[0, ilat, :], 'b--', label = 'MAR') |
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| 54 | plt.plot(lon, monthly_emis_APR[0, ilat, :], 'm', label = 'APR') |
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| 55 | plt.plot(lon, monthly_emis_MAY[0, ilat, :], 'y', label = 'MAY') |
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| 56 | plt.plot(lon, monthly_emis_JUN[0, ilat, :], 'k', label = 'JUN') |
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| 57 | plt.plot(lon, monthly_emis_JUL[0, ilat, :], 'c', label = 'JUL') |
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| 58 | plt.xticks(np.arange(-180, 200, 20), rotation = 45) |
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| 59 | legend(bbox_to_anchor = (0., 1.02, 1., 0.01), loc = 3, ncol = 7) |
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| 60 | plt.ylabel('emissivity') |
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| 61 | plt.xlabel('longitude') |
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| 62 | plt.title('latitude: ' + str(lat[ilat]) + ' - CH1 - AMSUA') |
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| 63 | grid(True) |
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| 64 | plt.savefig('/usr/home/lahlod/twice_d/figures_output_ANTARC/AMSUA/emis_lon_lat' + str(lat[ilat]) + '_plt_FEB-APR-JUL_ch1_AMSUA.png') |
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| 65 | |
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| 66 | |
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| 67 | ## evolution biais dans une tranche de latitude ## |
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| 68 | plt.figure() |
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| 69 | lat_color = np.array(['r', 'm', 'b', 'g', 'c']) |
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| 70 | for ilat in range (2, 7): |
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| 71 | plt.plot(lon, (monthly_emis_JAN[0, ilat, :] / monthly_emis_JUL[0, ilat, :]), lat_color[ilat-2], label = str(lat[ilat])) |
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| 72 | |
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| 73 | legend(bbox_to_anchor = (0., 1.04), loc = 3, ncol = 5) |
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| 74 | plt.xticks(np.arange(-180, 200, 20), rotation = 45) |
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| 75 | plt.ylabel('emissivity bias') |
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| 76 | plt.xlabel('longitude') |
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| 77 | grid(True) |
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| 78 | plt.title('emiss(JAN)/emiss(JUL)') |
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| 79 | plt.plot(lon, np.ones([len(lon)]), 'k--') |
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| 80 | |
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| 81 | |
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| 82 | ################ |
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| 83 | ## emissivite ## |
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| 84 | ################ |
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| 85 | Njr = np.sum(len_month) |
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| 86 | emis_glob = np.zeros([len(chan), len(lat), len(lon), Njr], float) |
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| 87 | for ich in range (0, len(chan)): |
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| 88 | for ilon in range (0, len(lon)): |
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| 89 | for ilat in range (0, len(lat)): |
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| 90 | e1 = np.append(emis_JAN[ich, ilat, ilon, :], emis_FEB[ich, ilat, ilon, :]) |
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| 91 | e2 = np.append(e1, emis_MAR[ich, ilat, ilon, :]) |
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| 92 | e3 = np.append(e2, emis_APR[ich, ilat, ilon, :]) |
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| 93 | e4 = np.append(e3, emis_MAY[ich, ilat, ilon, :]) |
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| 94 | e5 = np.append(e4, emis_JUN[ich, ilat, ilon, :]) |
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| 95 | e6 = np.append(e5, emis_JUL[ich, ilat, ilon, :]) |
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| 96 | emis_glob[ich, ilat, ilon, :] = e6 |
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| 97 | |
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| 98 | y_time, x_space = np.meshgrid(arange(0, Njr, 1), lon) |
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| 99 | for ilat in range (2, 7): |
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| 100 | plt.figure() |
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| 101 | plt.title('emissivity - AMSUA - latitude ' + str(lat[ilat])) |
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| 102 | for ich in range (0, len(chan)): |
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| 103 | plt.subplot(2, 1, ich+1) |
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| 104 | plt.pcolor(x_space, y_time, emis_glob[ich, ilat, :, :], cmap=cm.s3pcpn_l_r, vmin = 0.5, vmax = 0.95) |
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| 105 | plt.axis([-180., 180., 0, Njr]) |
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| 106 | cb = plt.colorbar() |
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| 107 | cb.set_label('CH ' + str(chan[ich]) + 'GHz') |
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| 108 | plt.yticks(np.array([0, 31, 59, 90, 120, 151, 181]), np.array(['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL'])) |
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| 109 | plt.xticks(np.arange(-180, 200, 20), rotation = 30) |
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| 110 | plt.grid(True) |
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| 111 | plt.savefig('/usr/home/lahlod/twice_d/figures_output_ANTARC/AMSUA/hov_emis_lat' + str(lat[ilat]) + '_ch1-ch15_AMSUA.png') |
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| 112 | |
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| 113 | |
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| 114 | |
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| 115 | ######################### |
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| 116 | ## anomalie emissivite ## |
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| 117 | ######################### |
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| 118 | emis_anom_glob = np.zeros([len(chan), len(lat), len(lon), Njr], float) |
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| 119 | for ich in range (0, len(chan)): |
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| 120 | for ilon in range (0, len(lon)): |
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| 121 | for ilat in range (0, len(lat)): |
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| 122 | for indjr in range (15, Njr - 15): # moyenne glissante centree |
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| 123 | emis_anom_glob[ich, ilat, ilon, indjr] = emis_glob[ich, ilat, ilon, indjr][nonzero(emis_glob[ich, ilat, ilon, indjr] != 0.)] - mean(emis_glob[ich, ilat, ilon, indjr - 15 : indjr + 15][nonzero(emis_glob[ich, ilat, ilon, indjr - 15 : indjr + 15] != 0.)]) |
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| 124 | |
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| 125 | |
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| 126 | |
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| 127 | ################################################################### |
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| 128 | ## variation saisonniÚre emissivite en fonction de type de glace ## |
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| 129 | ################################################################### |
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| 130 | |
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| 131 | #### definition des zones d etaude #### |
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| 132 | |
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| 133 | # zone1 et 2 = lon ([30E - 70E] et [90E - 160E]) tranche de lat [70S] |
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| 134 | lat1 = np.where(lat == -70.)[0][0] |
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| 135 | lon1 = np.where(lon == 30.)[0][0] |
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| 136 | lon2 = np.where(lon == 75.)[0][0] |
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| 137 | lon3 = np.where(lon == 90.)[0][0] |
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| 138 | lon4 = np.where(lon == 165.)[0][0] |
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| 139 | # zone3 = lon ([100W - 80W] tranche de lat [80S] |
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| 140 | lat2 = np.where(lat == -80.)[0][0] |
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| 141 | lon5 = np.where(lon == -100.)[0][0] |
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| 142 | lon6 = np.where(lon == -75.)[0][0] |
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| 143 | # zone4 = lon ([20W - 20E] tranche de lat [75S] |
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| 144 | lat3 = np.where(lat == -75.)[0][0] |
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| 145 | lon7 = np.where(lon == -20.)[0][0] |
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| 146 | lon8 = np.where(lon == 25.)[0][0] |
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| 147 | # zone5 = lon ([90E - 160E] tranche de lat [80S] |
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| 148 | lon3 = np.where(lon == 90.)[0][0] |
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| 149 | lon4 = np.where(lon == 165.)[0][0] |
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| 150 | # zone6 = lon ([180W - 160W] tranche de lat [75S] |
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| 151 | lon9 = np.where(lon == -180.)[0][0] |
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| 152 | lon10 = np.where(lon == -155.)[0][0] |
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| 153 | # zone7 = lon ([180E - 160E] tranche de lat [80S] |
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| 154 | lon11 = np.where(lon == 160.)[0][0] |
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| 155 | lon12 = np.where(lon == 180.)[0][0] |
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| 156 | # zone8 = lon ([180E - 160E] tranche de lat [75S] |
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| 157 | lon11 = np.where(lon == 160.)[0][0] |
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| 158 | lon12 = np.where(lon == 180.)[0][0] |
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| 159 | # zone9 = lon ([40E - 90E] tranche de lat [80S] |
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| 160 | lon13 = np.where(lon == 40.)[0][0] |
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| 161 | lon14 = np.where(lon == 95.)[0][0] |
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| 162 | # zone10 = lon ([120W - 140W] tranche de lat [80S] |
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| 163 | lon15 = np.where(lon == -140.)[0][0] |
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| 164 | lon16 = np.where(lon == -115.)[0][0] |
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| 165 | |
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| 166 | #### emissivite #### |
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| 167 | |
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| 168 | emis_glob_jr = np.zeros([len(chan), Njr], float) |
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| 169 | for ich in range (0, len(chan)): |
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| 170 | for indjr in range (0, Njr): |
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| 171 | emis_glob_jr[ich, indjr] = mean(emis_glob[ich, lat2, lon3 : lon4, indjr]) |
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| 172 | |
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| 173 | |
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| 174 | plt.ion() |
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| 175 | plt.figure() |
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| 176 | color_chan = np.array(['b', 'r']) |
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| 177 | for ich in range (0, len(chan)): |
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| 178 | plt.plot(emis_glob_jr[ich, :][nonzero(emis_glob_jr[ich, :] != 0.)], color_chan[ich], label = str(chan[ich]) + 'GHz') |
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| 179 | |
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| 180 | legend(loc = 4) |
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| 181 | plt.xlim(0, 212) |
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| 182 | plt.xticks(np.array([0, 31, 59, 90, 120, 151, 181]), np.array(['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL']), rotation = 30) |
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| 183 | plt.ylabel('emissivitty') |
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| 184 | grid(True) |
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| 185 | plt.title('Seasonal variation of emissivity - zone[120W-140W;80S] - AMSUA (nadir)') |
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| 186 | |
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| 187 | #### anomalie d emissivite (sur une moyenne glissante) #### |
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| 188 | |
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| 189 | emis_anom_glob_jr = np.zeros([len(chan), Njr], float) |
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| 190 | for ich in range (0, len(chan)): |
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| 191 | for indjr in range (0, Njr): |
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| 192 | emis_anom_glob_jr[ich, indjr] = mean(emis_anom_glob[ich, lat3, lon11 : lon12, indjr][nonzero(isnan(emis_anom_glob[ich, lat3, lon11 : lon12, indjr]) == False)]) |
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| 193 | |
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| 194 | |
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| 195 | plt.ion() |
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| 196 | plt.figure() |
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| 197 | color_chan = np.array(['b', 'r']) |
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| 198 | for ich in range (0, len(chan)): |
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| 199 | plt.plot(emis_anom_glob_jr[ich, :], color_chan[ich], label = str(chan[ich]) + 'GHz') |
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| 200 | |
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| 201 | legend(loc = 4) |
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| 202 | plt.xlim(0, 212) |
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| 203 | plt.xticks(np.array([0, 31, 59, 90, 120, 151, 181]), np.array(['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL']), rotation = 30) |
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| 204 | plt.ylabel('emissivity anomaly') |
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| 205 | grid(True) |
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| 206 | plt.title('Seasonal variation of emissivity anomaly - zone[30E-70E;70S] - AMSUA (nadir)') |
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| 207 | |
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| 208 | #### gradient de frequence #### |
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| 209 | |
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| 210 | monthly_emis = np.array([monthly_emis_JAN, monthly_emis_FEB, monthly_emis_MAR, monthly_emis_APR, monthly_emis_MAY, monthly_emis_JUN, monthly_emis_JUL]) |
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| 211 | monthly_emis_mo = np.zeros([len(month), len(chan)], float) |
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| 212 | for imo in range (0, len(month)): |
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| 213 | for ich in range(0, len(chan)): |
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| 214 | monthly_emis_mo[imo, ich] = mean(monthly_emis[imo, ich, lat2, lon15 : lon16]) |
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| 215 | |
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| 216 | |
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| 217 | monthly_emis_diff = monthly_emis_mo[:, 0] / monthly_emis_mo[:, 1] |
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| 218 | |
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| 219 | |
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| 220 | plt.ion() |
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| 221 | plt.figure() |
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| 222 | color_chan = np.array(['b', 'r']) |
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| 223 | subplot(2,1,1) |
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| 224 | for ich in range (0, len(chan)): |
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| 225 | plt.plot(monthly_emis_mo[:, ich], color_chan[ich], label = str(chan[ich]) + 'GHz') |
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| 226 | |
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| 227 | plt.ylabel('emissivity') |
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| 228 | legend(loc = 4) |
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| 229 | grid(True) |
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| 230 | plt.title('Seasonal emissivity bias - zone[140W-120W;80S] - AMSUA (nadir)') |
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| 231 | plt.xticks(np.arange(0, 7, 1), np.array(['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL']), rotation = 30) |
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| 232 | subplot(2,1,2) |
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| 233 | plot(monthly_emis_diff, 'k', label = 'bias') |
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| 234 | plot(np.ones([len(month)]), 'g--') |
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| 235 | plt.ylabel('bias (CH1/CH15)') |
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| 236 | plt.xticks(np.arange(0, 7, 1), np.array(['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL']), rotation = 30) |
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| 237 | grid(True) |
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| 238 | |
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| 239 | |
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| 240 | |
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| 241 | |
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| 242 | ############################### |
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| 243 | ## biais emissivite par mois ## |
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| 244 | ############################### |
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| 245 | biais_month = np.zeros([len(month), len(lat), len(lon)], float) |
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| 246 | for imo in range (0, len(month)): |
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| 247 | biais_month[imo, :, :] = monthly_emis[imo][0, :, :] - monthly_emis[imo][1, :, :] |
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| 248 | |
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| 249 | for imo in range (0, len(month)): |
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| 250 | plt.figure() |
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| 251 | plt.title('AMSUA [CH1-CH2] - '+str(month[imo])+' - (lat -80)') |
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| 252 | vlines(lon, [0], biais_month[imo, 2, :]) |
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| 253 | xlabel('longitude') |
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| 254 | ylabel('bias(emissivity)') |
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| 255 | xlim(-180, 180) |
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| 256 | xticks(np.arange(-180., 200., 20), rotation = 30) |
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| 257 | grid(True) |
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| 258 | plt.savefig('/usr/home/lahlod/twice_d/figures_output_ANTARC/AMSUA/vlines_bias_emis_ch1-ch15_lat-80_'+str(month[imo])+'_AMSUA.png') |
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| 259 | |
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| 260 | |
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| 261 | ############################# |
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| 262 | ## var emissivite par mois ## |
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| 263 | ############################# |
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| 264 | var = np.zeros([len(lat), len(lon)], float) |
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| 265 | for ilon in range (0, len(lon)): |
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| 266 | for ilat in range (0, len(lat)): |
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| 267 | var[ilat, ilon] = np.sum((biais_month[:, ilat, ilon] - mean(biais_month[:, ilat, ilon]))**2) |
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| 268 | |
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| 269 | var = var/7 |
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| 270 | for ilat in range (2, 7): |
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| 271 | plt.figure() |
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| 272 | vlines(lon, [0], var[ilat, :]) |
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| 273 | xlabel('longitude') |
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| 274 | ylabel('var(emiss_bias)') |
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| 275 | plt.title('AMSUA [CH1-CH2] - lat '+str(lat[ilat]) ) |
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| 276 | xlim(-180, 180) |
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| 277 | xticks(np.arange(-180., 200., 20), rotation = 30) |
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| 278 | grid(True) |
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| 279 | plt.savefig('/usr/home/lahlod/twice_d/figures_output_ANTARC/AMSUA/vlines_var_bias-emis_ch1-ch15_lat-'+str(lat[ilat])+'_AMSUA.png') |
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| 297 | |
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| 298 | plt.figure() |
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| 299 | plt.ion() |
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| 300 | #vlines(biais_month[0, 2]) |
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| 301 | #plt.subplot(2, 1, 1) |
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| 302 | m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) |
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| 303 | m.drawcoastlines(linewidth = 1) |
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| 304 | m.drawparallels(np.arange(-90., -30., 20)) |
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| 305 | m.drawmeridians(np.arange(-180., 180., 20)) |
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| 306 | xii,yii = m(*np.meshgrid(lon, lat)) |
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| 307 | clevs = (arange(0., 0.05, 0.0001)) |
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| 308 | cs = m.contourf(xii, yii, var, clevs, cmap=cm.s3pcpn_l_r) |
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| 309 | cbar = colorbar(cs) |
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| 310 | cbar.set_label('CH1 - 23.8GHz') |
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| 311 | plt.title('var(emis) JUL - AMSUA') |
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| 312 | xticks(arange(-180, 200, 20), rotation = 45) |
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| 313 | yticks(arange(-90, -10, 20)) |
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| 314 | plt.subplot(2, 1, 2) |
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| 315 | m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) |
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| 316 | m.drawcoastlines(linewidth = 1) |
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| 317 | m.drawparallels(np.arange(-90., -30., 20)) |
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| 318 | m.drawmeridians(np.arange(-180., 180., 20)) |
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| 319 | xii,yii = m(*np.meshgrid(lon, lat)) |
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| 320 | clevs = (arange(0., 0.0035, 0.00001)) |
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| 321 | cs = m.contourf(xii, yii, var_JUL[1, :, :], clevs, cmap=cm.s3pcpn_l_r) |
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| 322 | cbar = colorbar(cs) |
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| 323 | cbar.set_label('CH15 - 89GHz') |
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| 324 | xticks(arange(-180, 200, 20), rotation = 45) |
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| 325 | yticks(arange(-90, -10, 20)) |
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| 326 | |
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| 327 | |
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| 328 | |
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| 329 | |
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| 330 | |
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![(please configure the [header_logo] section in trac.ini)](/twice/chrome/site/your_project_logo.png){kind=logo}
