[47] | 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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[48] | 7 | from mpl_toolkits.basemap import Basemap |
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| 8 | from mpl_toolkits.basemap import shiftgrid, cm |
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[47] | 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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[48] | 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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[47] | 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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[48] | 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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[47] | 27 | |
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| 28 | |
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[48] | 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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[47] | 42 | |
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| 43 | |
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[48] | 44 | ################################################################################################################## |
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| 45 | # We devide the loop in two steps : |
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[54] | 46 | # - first loop concerns JANUARY, FEBRUARY, MARCH, APRIL, SEPTEMBER, OCTOBER, NOVEMBER, DECEMBER - use of AMSUA23GHz SPEC emissivity to seperate ice from no-ice zones |
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| 47 | # - second loop concerns MAY, JUNE, JULY, AUGUST - use of AMSUA89GHz SPEC emissivity to seperate ice from no_ice zones |
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[48] | 48 | ################################################################################################################## |
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[56] | 49 | |
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| 50 | |
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[54] | 51 | frequ = 89 # apply threshold on this frequency |
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[47] | 52 | |
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| 53 | |
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[54] | 54 | # daily parameter (2D-array) on ARCTIC area |
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| 55 | emis_spec = np.zeros([M, ny, nx, 31], float) |
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| 56 | emis_lamb = np.zeros([M, ny, nx, 31], float) |
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| 57 | emis_diff = np.zeros([M, ny, nx, 31], float) |
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| 58 | emis_ratio = np.zeros([M, ny, nx, 31], float) |
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| 59 | |
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| 60 | # daily parameter (2D-array) on ARCTIC SEA ICE area |
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| 61 | daily_spec_ice = np.zeros([M, ny, nx, 31], float) |
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| 62 | daily_lamb_ice = np.zeros([M, ny, nx, 31], float) |
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| 63 | daily_diff_ice = np.zeros([M, ny, nx, 31], float) |
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| 64 | daily_ratio_ice = np.zeros([M, ny, nx, 31], float) |
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| 65 | |
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| 66 | |
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| 67 | months1 = np.array([0, 1, 2, 3, 8, 9, 10, 11]) # use AMSUA 23GHz to delimit ice/no_ice for JANUARY, FEBRUARY, MARCH, APRIL, SEPTEMBER, OCTOBER, NOVEMBER, DECEMBER |
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[48] | 68 | for imo in months1: |
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| 69 | print 'month ' + month[imo] |
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| 70 | ################################################################################## |
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| 71 | # choice of AMSUA 23GHz delimitation ice/no_ice for the sub_classification study # |
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| 72 | ################################################################################## |
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| 73 | print 'open threshold file' |
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| 74 | fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/cartesian_grid_map_ice_no-ice_' + str(month[imo]) + '2009_AMSUA23_spec_lamb_thresholds.nc', 'r', format='NETCDF3_CLASSIC') |
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[56] | 75 | spec_lim = fichier_emis.variables['spec_ice_area'][:] # sea ice pixels defined with spec emis at 23GHz |
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[48] | 76 | #lamb_lim = fichier_emis.variables['lamb_ice_area'][:] |
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| 77 | fichier_emis.close() |
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| 78 | ######################################################### |
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| 79 | # application of AMSUA 23GHz delimitation to other data # |
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| 80 | ######################################################### |
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| 81 | print 'open emissivity to sub_classify file' |
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| 82 | ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB) |
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[54] | 83 | fichier_e = 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') |
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[48] | 84 | day = fichier_e.variables['days'][:] |
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[54] | 85 | emis_spec[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec'][:] |
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| 86 | emis_lamb[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_lamb'][:] |
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| 87 | emis_diff[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec_lamb'][:] |
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[48] | 88 | fichier_e.close() |
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[54] | 89 | # calculate emis ratio daily |
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| 90 | for ijr in range (0, month_day[imo]): |
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| 91 | for ilon in range (0, nx): |
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| 92 | for ilat in range (0, ny): |
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| 93 | emis_ratio[imo, ilat, ilon, ijr] = ((emis_lamb[imo, ilat, ilon, ijr] - emis_spec[imo, ilat, ilon, ijr]) / emis_spec[imo, ilat, ilon, ijr]) * 100. |
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[56] | 94 | # create 2D-array of emissivity spec, lamb, diff and ratio on sea ice extent only, defined by AMSUA 23GHz spec emiss threshold |
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| 95 | if (isnan(spec_lim[ilat, ilon]) == True): # if pixel of sea ice extent defined with spec_emiss_23_threshold corresponds to 'no_ice', then compute NaN in pixel |
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[54] | 96 | daily_spec_ice[imo, ilat, ilon, ijr] = NaN |
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| 97 | daily_lamb_ice[imo, ilat, ilon, ijr] = NaN |
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| 98 | daily_diff_ice[imo, ilat, ilon, ijr] = NaN |
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| 99 | daily_ratio_ice[imo, ilat, ilon, ijr] = NaN |
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[56] | 100 | else: # if pixel of sea ice extent defined with spec_emiss_23_threshold corresponds to 'ice', then compute value of emis spec, emis lamb or emis diff in pixel |
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[54] | 101 | daily_spec_ice[imo, ilat, ilon, ijr] = emis_spec[imo, ilat, ilon, ijr] |
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| 102 | daily_lamb_ice[imo, ilat, ilon, ijr] = emis_lamb[imo, ilat, ilon, ijr] |
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| 103 | daily_diff_ice[imo, ilat, ilon, ijr] = emis_diff[imo, ilat, ilon, ijr] |
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| 104 | daily_ratio_ice[imo, ilat, ilon, ijr] = emis_ratio[imo, ilat, ilon, ijr] |
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[48] | 105 | ######################## |
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| 106 | # stack in netcdf file # |
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| 107 | ######################## |
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| 108 | print 'stack in file month ' + str(month[imo]) |
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[54] | 109 | rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUB' + str(frequ) + '_spec_thresholds.nc', 'w', format='NETCDF3_CLASSIC') |
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| 110 | rootgrp.createDimension('longitude', nx) |
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| 111 | rootgrp.createDimension('latitude', ny) |
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| 112 | rootgrp.createDimension('days', month_day[imo]) |
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[48] | 113 | nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',)) |
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| 114 | nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) |
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[54] | 115 | nc_days = rootgrp.createVariable('days', 'f', ('days',)) |
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| 116 | nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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| 117 | nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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| 118 | nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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| 119 | nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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[48] | 120 | nc_lon[:] = xvec |
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| 121 | nc_lat[:] = yvec |
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[54] | 122 | nc_days[:] = np.arange(0, month_day[imo]) |
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| 123 | nc_ice_spec[:] = daily_spec_ice[imo, :, :, 0 : month_day[imo]] |
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| 124 | nc_ice_lamb[:] = daily_lamb_ice[imo, :, :, 0 : month_day[imo]] |
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| 125 | nc_ice_diff[:] = daily_diff_ice[imo, :, :, 0 : month_day[imo]] |
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| 126 | nc_ice_ratio[:] = daily_ratio_ice[imo, :, :, 0 : month_day[imo]] |
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[48] | 127 | rootgrp.close() |
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[47] | 128 | |
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| 129 | |
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[54] | 130 | |
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| 131 | |
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| 132 | months2 = np.array([4, 5, 6, 7])# use AMSUA 89GHz to delimit ice/no_ice for MAY, JUNE, JULY, AUGUST |
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[48] | 133 | for imo in months2: |
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| 134 | print 'month ' + month[imo] |
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| 135 | ################################################################################## |
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| 136 | # choice of AMSUA 23GHz delimitation ice/no_ice for the sub_classification study # |
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| 137 | ################################################################################## |
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| 138 | print 'open threshold file' |
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[54] | 139 | fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/cartesian_grid_map_ice_no-ice_' + str(month[imo]) + '2009_AMSUA89_spec_lamb_thresholds.nc', 'r', format='NETCDF3_CLASSIC') |
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[48] | 140 | spec_lim = fichier_emis.variables['spec_ice_area'][:] |
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| 141 | #lamb_lim = fichier_emis.variables['lamb_ice_area'][:] |
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| 142 | fichier_emis.close() |
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| 143 | ######################################################### |
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| 144 | # application of AMSUA 23GHz delimitation to other data # |
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| 145 | ######################################################### |
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| 146 | print 'open emissivity to sub_classify file' |
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| 147 | ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB) |
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[54] | 148 | fichier_e = 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') |
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[48] | 149 | day = fichier_e.variables['days'][:] |
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[54] | 150 | emis_spec[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec'][:] |
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| 151 | emis_lamb[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_lamb'][:] |
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| 152 | emis_diff[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec_lamb'][:] |
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[48] | 153 | fichier_e.close() |
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[54] | 154 | # calculate emis ratio daily |
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| 155 | for ijr in range (0, month_day[imo]): |
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| 156 | for ilon in range (0, nx): |
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| 157 | for ilat in range (0, ny): |
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| 158 | emis_ratio[imo, ilat, ilon, ijr] = ((emis_lamb[imo, ilat, ilon, ijr] - emis_spec[imo, ilat, ilon, ijr]) / emis_spec[imo, ilat, ilon, ijr]) * 100. |
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| 159 | if (isnan(spec_lim[ilat, ilon]) == True): |
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| 160 | daily_spec_ice[imo, ilat, ilon, ijr] = NaN |
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| 161 | daily_lamb_ice[imo, ilat, ilon, ijr] = NaN |
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| 162 | daily_diff_ice[imo, ilat, ilon, ijr] = NaN |
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| 163 | daily_ratio_ice[imo, ilat, ilon, ijr] = NaN |
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| 164 | else: |
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| 165 | daily_spec_ice[imo, ilat, ilon, ijr] = emis_spec[imo, ilat, ilon, ijr] |
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| 166 | daily_lamb_ice[imo, ilat, ilon, ijr] = emis_lamb[imo, ilat, ilon, ijr] |
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| 167 | daily_diff_ice[imo, ilat, ilon, ijr] = emis_diff[imo, ilat, ilon, ijr] |
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| 168 | daily_ratio_ice[imo, ilat, ilon, ijr] = emis_ratio[imo, ilat, ilon, ijr] |
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[48] | 169 | ######################## |
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| 170 | # stack in netcdf file # |
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| 171 | ######################## |
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| 172 | print 'stack in file month ' + str(month[imo]) |
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[54] | 173 | rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUB' + str(frequ) + '_spec_thresholds.nc', 'w', format='NETCDF3_CLASSIC') |
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| 174 | rootgrp.createDimension('longitude', nx) |
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| 175 | rootgrp.createDimension('latitude', ny) |
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| 176 | rootgrp.createDimension('days', month_day[imo]) |
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[48] | 177 | nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',)) |
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| 178 | nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) |
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[54] | 179 | nc_days = rootgrp.createVariable('days', 'f', ('days',)) |
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| 180 | nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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| 181 | nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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| 182 | nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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| 183 | nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude', 'days')) |
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[48] | 184 | nc_lon[:] = xvec |
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| 185 | nc_lat[:] = yvec |
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[54] | 186 | nc_days[:] = np.arange(0, month_day[imo]) |
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| 187 | nc_ice_spec[:] = daily_spec_ice[imo, :, :, 0 : month_day[imo]] |
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| 188 | nc_ice_lamb[:] = daily_lamb_ice[imo, :, :, 0 : month_day[imo]] |
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| 189 | nc_ice_diff[:] = daily_diff_ice[imo, :, :, 0 : month_day[imo]] |
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| 190 | nc_ice_ratio[:] = daily_ratio_ice[imo, :, :, 0 : month_day[imo]] |
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[48] | 191 | rootgrp.close() |
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[47] | 192 | |
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| 193 | |
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| 194 | |
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| 195 | |
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[54] | 196 | |
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[56] | 197 | ''' |
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[47] | 198 | # test: |
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| 199 | ion() |
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| 200 | x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() |
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| 201 | x_coast = x_ind |
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| 202 | y_coast = y_ind |
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| 203 | z_coast = z_ind |
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[48] | 204 | for imo in range (0, M): |
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| 205 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, ratio_ice[imo, :, :], 0., 4., 0.01, cm.s3pcpn_l_r, 'Sea ice extent - emissivity RATIO') |
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| 206 | title('AMSUA ' + str(frequ) + ' - ' + str(month[imo]) + ' 2009') |
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| 207 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps_sea_ice_extent/emiss_ratio_map_AMSUA'+str(frequ)+'_with_AMSUA23-and-30-spec_'+str(month[imo])+'_2009.png') |
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| 208 | ''' |
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| 209 | |
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| 210 | |
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