| 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 | # time period characteristics # |
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| 22 | ############################### |
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| 23 | MONTH = np.array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']) |
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| 24 | month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER']) |
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| 25 | month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) |
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| 26 | M = len(month) |
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| 27 | |
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| 28 | |
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| 29 | ######################## |
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| 30 | # grid characteristics # |
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| 31 | ######################## |
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| 32 | x0 = -3000. # min limit of grid |
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| 33 | x1 = 2500. # max limit of grid |
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| 34 | dx = 40. |
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| 35 | xvec = np.arange(x0, x1+dx, dx) |
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| 36 | nx = len(xvec) |
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| 37 | y0 = -3000. # min limit of grid |
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| 38 | y1 = 3000. # max limit of grid |
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| 39 | dy = 40. |
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| 40 | yvec = np.arange(y0, y1+dy, dy) |
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| 41 | ny = len(yvec) |
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| 42 | |
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| 43 | |
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| 44 | |
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| 45 | |
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| 46 | ################################################################## |
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| 47 | # compute monthly means of emissivity parameters on sea ice area # |
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| 48 | ################################################################## |
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| 49 | spec_emis89 = np.zeros([M, ny, nx], float) |
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| 50 | spec_emis23 = np.zeros([M, ny, nx], float) |
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| 51 | |
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| 52 | #std_spec_emis89 = np.zeros([M, ny, nx], float) |
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| 53 | #std_spec_emis23 = np.zeros([M, ny, nx], float) |
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| 54 | |
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| 55 | grad_ratio = np.zeros([M, ny, nx], float) |
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| 56 | |
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| 57 | spec_anom89 = np.zeros([M, ny, nx], float) |
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| 58 | ratio_anom89 = np.zeros([M, ny, nx], float) |
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| 59 | |
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| 60 | spec_anom23 = np.zeros([M, ny, nx], float) |
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| 61 | |
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| 62 | for imo in range (0, M): |
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| 63 | print 'compute emissivity parameters for month ' + month[imo] |
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| 64 | # open daily spec emissivity at 89GHz |
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| 65 | fichier_daily89 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUA89_spec_thresholds.nc', 'r', format='NETCDF3_CLASSIC') |
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| 66 | a = fichier_daily89.variables['spec_ice_area'][:] |
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| 67 | fichier_daily89.close() |
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| 68 | # open daily spec emissivity at 23GHz |
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| 69 | fichier_daily23 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUA23_spec_thresholds.nc', 'r', format='NETCDF3_CLASSIC') |
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| 70 | b = fichier_daily23.variables['spec_ice_area'][:] |
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| 71 | fichier_daily23.close() |
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| 72 | ## compute monthly mean of spec emissivity at 89GHz |
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| 73 | print 'monthly spec emis' |
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| 74 | for ilon in range (0, nx): |
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| 75 | for ilat in range (0, ny): |
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| 76 | spec_emis89[imo, ilat, ilon] = mean(a[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(a[ilat, ilon, 0 : month_day[imo]]) == False)]) |
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| 77 | #std_spec_emis89[imo, ilat, ilon] = sqrt((1. / (month_day[imo] - 1.)) * sum((a[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(a[ilat, ilon, 0 : month_day[imo]]) == False)] - spec_emis89[imo, ilat, ilon])**2.)) |
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| 78 | spec_emis23[imo, ilat, ilon] = mean(b[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(b[ilat, ilon, 0 : month_day[imo]]) == False)]) |
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| 79 | #std_spec_emis23[imo, ilat, ilon] = sqrt((1. / (month_day[imo] - 1.)) * sum((b[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(b[ilat, ilon, 0 : month_day[imo]]) == False)] - spec_emis23[imo, ilat, ilon])**2.)) |
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| 80 | # compute monthly gradient ratio 23-89GHz |
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| 81 | print 'monthly gradient ratio 23-89GHz' |
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| 82 | grad_ratio[imo, :, :] = spec_emis23[imo, :, :] - spec_emis89[imo, :, :] |
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| 83 | # open anomaly of spec emissivity and emissivity ratio anomaly at 89GHz |
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| 84 | fichier_anom89 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_param_anomaly_' + month[imo] + '2009_AMSUA89.nc', 'r', format = 'NETCDF3_CLASSIC') |
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| 85 | spec_anom89[imo, :, :] = fichier_anom89.variables['spec_anomaly'][:] |
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| 86 | ratio_anom89[imo, :, :] = fichier_anom89.variables['ratio_anomaly'][:] |
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| 87 | fichier_anom89.close() |
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| 88 | # open anomaly of spec emissivity anomaly at 23GHz |
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| 89 | fichier_anom23 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_param_anomaly_' + month[imo] + '2009_AMSUA23.nc', 'r', format = 'NETCDF3_CLASSIC') |
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| 90 | spec_anom23[imo, :, :] = fichier_anom23.variables['spec_anomaly'][:] |
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| 91 | fichier_anom23.close() |
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| 92 | |
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| 93 | |
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| 94 | |
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| 95 | cumul = abs(spec_anom89) + abs(spec_anom23) + abs(ratio_anom89) + abs(grad_ratio) |
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| 96 | cumul_index = np.zeros([M, ny, nx], float) |
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| 97 | for imo in range (0, M): |
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| 98 | print 'month ' + month[imo] |
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| 99 | max_cumul = cumul[imo, :, :][nonzero(isnan(cumul[imo, :, :]) == False)].max() |
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| 100 | for ilon in range (0, nx): |
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| 101 | for ilat in range (0, ny): |
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| 102 | cumul_index[imo, ilat, ilon] = cumul[imo, ilat, ilon] / max_cumul |
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| 103 | |
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| 104 | |
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| 105 | |
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| 106 | |
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| 107 | |
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| 108 | ############################################## |
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| 109 | # maps monthly mean of emissivity parameters # |
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| 110 | ############################################## |
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| 111 | #ion() |
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| 112 | x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() |
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| 113 | x_coast = x_ind |
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| 114 | y_coast = y_ind |
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| 115 | z_coast = z_ind |
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| 116 | for imo in range (0, M): |
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| 117 | print 'map month ' + month[imo] |
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| 118 | # cumulation index |
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| 119 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, cumul_index[imo, :, :], 0., 1., 0.01, cm.s3pcpn_l_r, 'monthly cumulation index') |
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| 120 | title('AMSUA - ' + month[imo] + ' 2009') |
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| 121 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/cumul_params/cumul_all_parameters/map_cumulation_index_' + MONTH[imo] + month[imo] + '2009_sea_ice_extent.png') |
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| 122 | ''' |
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| 123 | # emis spec std 23GHz |
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| 124 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, std_spec_emis23[imo, :, :], 0., 0.12, 0.001, cm.s3pcpn_l_r, 'monthly emis spec std') |
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| 125 | title('AMSUA 23GHz - ' + month[imo] + ' 2009') |
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| 126 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/emiss_spec/std/std_emis_spec_map_AMSUA23_' + MONTH[imo] + month[imo] + '_2009_arctic_sea_ice.png') |
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| 127 | # emis spec std 89GHz |
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| 128 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, std_spec_emis89[imo, :, :], 0., 0.2, 0.001, cm.s3pcpn_l_r, 'monthly emis spec std') |
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| 129 | title('AMSUA 89GHz - ' + month[imo] + ' 2009') |
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| 130 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/emiss_spec/std/std_emis_spec_map_AMSUA89_' + MONTH[imo] + month[imo] + '_2009_arctic_sea_ice.png') |
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| 131 | # emis spec anomaly AMSUA23 |
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| 132 | print 'map emis spec anomaly AMSUA23' |
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| 133 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, spec_anom23[imo, :, :], -0.1, 0.2, 0.01, cm.s3pcpn_l_r, 'monthly emis spec anomaly') |
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| 134 | title('AMSUA 23GHz - ' + month[imo] + ' 2009') |
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| 135 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/emiss_anomaly/spec/anomaly_spec_map_AMSUA23_' + MONTH[imo] + month[imo] + '_2009_arctic_sea_ice.png') |
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| 136 | # emis spec anomaly AMSUA89 |
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| 137 | print 'map emis spec anomaly AMSUA89' |
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| 138 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, spec_anom89[imo, :, :], -0.2, 0.2, 0.01, cm.s3pcpn_l_r, 'monthly emis spec anomaly') |
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| 139 | title('AMSUA 89GHz - ' + month[imo] + ' 2009') |
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| 140 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/emiss_anomaly/spec/anomaly_spec_map_AMSUA89_' + MONTH[imo] + month[imo] + '_2009_arctic_sea_ice.png') |
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| 141 | # emis gradient ratio |
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| 142 | print 'map gradient ratio' |
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| 143 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, grad_ratio[imo, :, :], -0.2, 0.3, 0.01, cm.s3pcpn_l_r, 'monthly gradient ratio') |
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| 144 | title('AMSUA [23-89]GHz - ' + month[imo] + ' 2009') |
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| 145 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/gradient_ratio_23-89/gradient_ratio_map_AMSUA23-89_spec_' + MONTH[imo] + month[imo] + '_2009_arctic_sea_ice.png') |
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| 146 | # emis ratio anomaly AMSUA89 |
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| 147 | print 'map emis ratio anomaly AMSUA89' |
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| 148 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, ratio_anom89[imo, :, :], -2, 2, 0.1, cm.s3pcpn_l_r, 'monthly emis ratio anomaly') |
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| 149 | title('AMSUA 89GHz - ' + month[imo] + ' 2009') |
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| 150 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/emiss_anomaly/ratio/anomaly_ratio_map_AMSUA89_' + MONTH[imo] + month[imo] + '_2009_arctic_sea_ice.png') |
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| 151 | ''' |
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