[45] | 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 arctic_map # function to regrid coast limits |
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| 11 | import cartesian_grid_test # function to convert grid from polar to cartesian |
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| 12 | import scipy.special |
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| 13 | import ffgrid2 |
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| 14 | import map_ffgrid |
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| 15 | from matplotlib import colors |
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| 16 | from matplotlib.font_manager import FontProperties |
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| 17 | import map_cartesian_grid |
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| 18 | |
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| 19 | |
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| 20 | |
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| 21 | |
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| 22 | MONTH = np.array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']) |
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| 23 | month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER']) |
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| 24 | month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) |
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| 25 | M = len(month) |
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| 26 | |
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[46] | 27 | frequ = np.array([23, 30, 50, 89]) |
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[45] | 28 | |
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| 29 | |
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| 30 | ################################# |
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| 31 | # read .dat files of AMSUA data # |
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| 32 | ################################# |
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| 33 | AS = np.zeros([len(frequ), M], float) |
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| 34 | AL = np.zeros([len(frequ), M], float) |
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| 35 | for ifr in range (0, len(frequ)): |
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| 36 | fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/AMSUA' + str(frequ[ifr]) + '_data_classification_parameters_ice_no-ice_2009.dat','r') |
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| 37 | numlines = 0 |
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| 38 | for line in fichier: numlines += 1 |
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| 39 | fichier.close() |
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| 40 | fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/AMSUA' + str(frequ[ifr]) + '_data_classification_parameters_ice_no-ice_2009.dat','r') |
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| 41 | nbtotal = numlines - 1 |
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| 42 | iligne = 0 |
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| 43 | tot_area_spec = np.zeros([nbtotal],float) |
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| 44 | tot_area_lamb = np.zeros([nbtotal],float) |
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| 45 | while (iligne < nbtotal) : |
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| 46 | line=fichier.readline() |
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| 47 | # exemple : line = "0.22 2.3 5.0 6" |
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| 48 | liste = line.split() |
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| 49 | # exemple : listeCoord ['0.22', '2.3', '5.0', '6'] (liste de chaine de caract?es) |
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| 50 | tot_area_spec[iligne] = float(liste[9]) |
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| 51 | tot_area_lamb[iligne] = float(liste[10]) |
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| 52 | iligne=iligne+1 |
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| 53 | fichier.close() |
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| 54 | vec = np.arange(0, nbtotal + 51, 50) |
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| 55 | area_s = np.zeros([M], float) |
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| 56 | area_l = np.zeros([M], float) |
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| 57 | for imo in range (0, M): |
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| 58 | area_s[imo] = tot_area_spec[imo + vec[imo]] |
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| 59 | area_l[imo] = tot_area_lamb[imo + vec[imo]] |
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| 60 | AS[ifr, :] = area_s[:] |
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| 61 | AL[ifr, :] = area_l[:] |
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| 62 | |
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| 63 | |
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| 64 | |
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| 65 | ################################# |
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[46] | 66 | # read .dat files of AMSUB data # |
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| 67 | ################################# |
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| 68 | fichier_B = open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/AMSUB89_data_classification_parameters_ice_no-ice_2009.dat','r') |
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| 69 | numlines = 0 |
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| 70 | for line in fichier_B: numlines += 1 |
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| 71 | |
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| 72 | fichier_B.close() |
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| 73 | fichier_B = open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/AMSUB89_data_classification_parameters_ice_no-ice_2009.dat','r') |
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| 74 | nbtotal = numlines - 1 |
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| 75 | iligne = 0 |
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| 76 | tot_area_spec_B = np.zeros([nbtotal],float) |
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| 77 | tot_area_lamb_B = np.zeros([nbtotal],float) |
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| 78 | while (iligne < nbtotal) : |
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| 79 | line=fichier_B.readline() |
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| 80 | # exemple : line = "0.22 2.3 5.0 6" |
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| 81 | liste = line.split() |
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| 82 | # exemple : listeCoord ['0.22', '2.3', '5.0', '6'] (liste de chaine de caract?es) |
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| 83 | tot_area_spec_B[iligne] = float(liste[9]) |
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| 84 | tot_area_lamb_B[iligne] = float(liste[10]) |
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| 85 | iligne=iligne+1 |
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| 86 | |
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| 87 | fichier_B.close() |
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| 88 | area_s_B = np.zeros([M], float) |
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| 89 | area_l_B = np.zeros([M], float) |
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| 90 | for imo in range (0, M): |
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| 91 | area_s_B[imo] = tot_area_spec_B[imo + vec[imo]] |
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| 92 | area_l_B[imo] = tot_area_lamb_B[imo + vec[imo]] |
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| 93 | |
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| 94 | |
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| 95 | |
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| 96 | ################################# |
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[45] | 97 | # read .dat file of OSISAF data # |
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| 98 | ################################# |
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| 99 | fichier_osi = open('/net/argos/data/parvati/lahlod/ARCTIC/OSISAF_Ice_Types/OSISAF_daily_ice_no-ice_2009.dat','r') |
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| 100 | numlines = 0 |
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| 101 | for line in fichier_osi: numlines += 1 |
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| 102 | |
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| 103 | fichier_osi.close() |
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| 104 | fichier_osi = open('/net/argos/data/parvati/lahlod/ARCTIC/OSISAF_Ice_Types/OSISAF_daily_ice_no-ice_2009.dat','r') |
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| 105 | nbtotal = numlines - 1 |
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| 106 | iligne = 0 |
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| 107 | mo = np.zeros([nbtotal],object) |
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| 108 | tot_area_osi = np.zeros([nbtotal],float) |
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| 109 | while (iligne < nbtotal) : |
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| 110 | line=fichier_osi.readline() |
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| 111 | # exemple : line = "0.22 2.3 5.0 6" |
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| 112 | liste = line.split() |
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| 113 | # exemple : listeCoord ['0.22', '2.3', '5.0', '6'] (liste de chaine de caract?es) |
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| 114 | mo[iligne] = str(liste[0]) |
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| 115 | tot_area_osi[iligne] = float(liste[2]) |
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| 116 | iligne=iligne+1 |
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| 117 | |
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| 118 | fichier_osi.close() |
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| 119 | vec_osi = np.array([0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334]) |
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| 120 | area_osi = np.zeros([M], float) |
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| 121 | for imo in range (0, M): |
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| 122 | area_osi[imo] = tot_area_osi[imo + vec_osi[imo]] |
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| 123 | |
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| 124 | |
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| 125 | |
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| 126 | ################################### |
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| 127 | # plot time evolution of ice area # |
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| 128 | ################################### |
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[54] | 129 | ion() |
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[45] | 130 | figure() |
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[46] | 131 | plot(AS[0, :], 'c', label = 'AMSUA spec_23GHz') |
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| 132 | plot(AL[0, :], 'c--', label = 'AMSUA lamb_23GHz') |
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| 133 | plot(AS[1, :], 'r', label = 'AMSUA spec_30GHz') |
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| 134 | plot(AL[1, :], 'r--', label = 'AMSUA lamb_30GHz') |
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| 135 | plot(AS[2, :], 'm', label = 'AMSUA spec_50GHz') |
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| 136 | plot(AL[2, :], 'm--', label = 'AMSUA lamb_50GHz') |
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| 137 | plot(AS[3, :], 'g', label = 'AMSUA spec_89GHz') |
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| 138 | plot(AL[3, :], 'g--', label = 'AMSUA lamb_89GHz') |
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| 139 | plot(area_s_B[:], 'b', label = 'AMSUB spec_89GHz') |
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| 140 | plot(area_l_B[:], 'b--', label = 'AMSUB lamb_89GHz') |
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[54] | 141 | plot(area_osi + 1500000, 'k', label = 'OSISAF + 1.5e7 (correction)') |
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[45] | 142 | fontP = FontProperties() |
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| 143 | fontP.set_size('small') |
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| 144 | legend(loc = 3, prop = fontP) |
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| 145 | ylabel('total ice area (in square km)') |
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| 146 | xticks(np.arange(0, M, 1), month, rotation = 25) |
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| 147 | xlim(-1, M) |
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| 148 | grid() |
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[54] | 149 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/total_ice_area/compar_total_ice_area_AMSUA_AMSUB_SPEC_LAMB_corrected_OSISAF_2009.png') |
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[45] | 150 | |
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| 151 | |
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| 152 | |
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[46] | 153 | ############################### |
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| 154 | # calculation of bias and std # |
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| 155 | ############################### |
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[47] | 156 | a = np.zeros([M], float) |
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| 157 | b = np.zeros([M], float) |
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| 158 | c = np.zeros([M], float) |
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| 159 | d = np.zeros([M], float) |
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| 160 | e = np.zeros([M], float) |
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| 161 | f = np.zeros([M], float) |
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| 162 | g = np.zeros([M], float) |
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| 163 | h = np.zeros([M], float) |
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| 164 | i = np.zeros([M], float) |
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| 165 | j = np.zeros([M], float) |
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| 166 | for imo in range (0, M): |
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[48] | 167 | a[imo] = (AS[0, imo] - area_osi[imo]) / area_osi[imo] |
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| 168 | b[imo] = (AL[0, imo] - area_osi[imo]) / area_osi[imo] |
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| 169 | c[imo] = (AS[1, imo] - area_osi[imo]) / area_osi[imo] |
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| 170 | d[imo] = (AL[1, imo] - area_osi[imo]) / area_osi[imo] |
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| 171 | e[imo] = (AS[2, imo] - area_osi[imo]) / area_osi[imo] |
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| 172 | f[imo] = (AL[2, imo] - area_osi[imo]) / area_osi[imo] |
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| 173 | g[imo] = (AS[3, imo] - area_osi[imo]) / area_osi[imo] |
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| 174 | h[imo] = (AL[3, imo] - area_osi[imo]) / area_osi[imo] |
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| 175 | i[imo] = (area_s_B[imo] - area_osi[imo]) / area_osi[imo] |
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| 176 | j[imo] = (area_l_B[imo] - area_osi[imo]) / area_osi[imo] |
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[47] | 177 | |
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| 178 | |
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[46] | 179 | figure() |
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[47] | 180 | plot(a, 'c', label = 'AMSUA spec 23GHz') |
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| 181 | plot(b, 'c--', label = 'AMSUA lamb 23GHz') |
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| 182 | plot(c, 'r', label = 'AMSUA spec 30GHz') |
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| 183 | plot(d, 'r--', label = 'AMSUA lamb 30GHz') |
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| 184 | plot(e, 'm', label = 'AMSUA spec 50GHz') |
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| 185 | plot(f, 'm--', label = 'AMSUA lamb 50GHz') |
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| 186 | plot(g, 'g', label = 'AMSUA spec 89GHz') |
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| 187 | plot(h, 'g--', label = 'AMSUA lamb 89GHz') |
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| 188 | plot(i, 'b', label = 'AMSUB spec 89GHz') |
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| 189 | plot(j, 'b--', label = 'AMSUB lamb 89GHz') |
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| 190 | plot(np.arange(0, M, 1), np.zeros([M], float), 'k') |
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[46] | 191 | fontP = FontProperties() |
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| 192 | fontP.set_size('small') |
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| 193 | legend(loc = 3, prop = fontP) |
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[47] | 194 | ylabel('bias of total ice area') |
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[46] | 195 | xticks(np.arange(0, M, 1), month, rotation = 25) |
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| 196 | xlim(-1, M) |
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| 197 | grid() |
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[47] | 198 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/total_ice_area/bias_total_ice_area_AMSU_OSI_2009.png') |
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[45] | 199 | |
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