| 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 scipy.special |
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| 11 | import ffgrid2 |
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| 12 | import map_ffgrid |
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| 13 | import arctic_map # function to regrid coast limits |
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| 14 | import cartesian_grid_test # function to convert grid from polar to cartesian |
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| 15 | |
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| 16 | |
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| 17 | |
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| 18 | |
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| 19 | ############### |
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| 20 | # time period # |
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| 21 | ############### |
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| 22 | month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER']) |
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| 23 | month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) |
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| 24 | M = len(month) |
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| 25 | |
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| 26 | |
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| 27 | |
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| 28 | ######################## |
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| 29 | # grid characteristics # |
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| 30 | ######################## |
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| 31 | x0 = -3000. # min limit of grid |
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| 32 | x1 = 2500. # max limit of grid |
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| 33 | dx = 100. |
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| 34 | xvec = np.arange(x0, x1+dx, dx) |
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| 35 | nx = len(xvec) |
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| 36 | y0 = -3000. # min limit of grid |
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| 37 | y1 = 3000. # max limit of grid |
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| 38 | dy = 100. |
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| 39 | yvec = np.arange(y0, y1+dy, dy) |
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| 40 | ny = len(yvec) |
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| 41 | |
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| 42 | |
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| 43 | |
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| 44 | ############################# |
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| 45 | # grid data from .dat files # |
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| 46 | ############################# |
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| 47 | tsu = np.zeros([ny, nx, 31, M], float) |
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| 48 | '''tu = np.zeros([ny, nx, 31, M], float) |
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| 49 | td = np.zeros([ny, nx, 31, M], float) |
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| 50 | tbs = np.zeros([ny, nx, 31, M], float) |
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| 51 | tbl = np.zeros([ny, nx, 31, M], float) |
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| 52 | es = np.zeros([ny, nx, 31, M], float) |
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| 53 | el = np.zeros([ny, nx, 31, M], float) |
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| 54 | esl = np.zeros([ny, nx, 31, M], float) |
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| 55 | esl00 = np.zeros([ny, nx, 31, M], float) |
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| 56 | esl25 = np.zeros([ny, nx, 31, M], float) |
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| 57 | esl50 = np.zeros([ny, nx, 31, M], float) |
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| 58 | esl75 = np.zeros([ny, nx, 31, M], float) |
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| 59 | esl100 = np.zeros([ny, nx, 31, M], float)''' |
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| 60 | for imo in range (0, M): |
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| 61 | print 'month: ' + month[imo] |
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| 62 | fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/GLACE/AMSUA/GLACE_AMSUA_EMIS_' + month[imo] + '2009.DAT','r') |
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| 63 | numlines = 0 |
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| 64 | for line in fichier: numlines += 1 |
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| 65 | fichier.close() |
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| 66 | fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/GLACE/AMSUA/GLACE_AMSUA_EMIS_' + month[imo] + '2009.DAT','r') |
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| 67 | nbtotal = numlines-1 |
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| 68 | iligne = 0 |
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| 69 | jjr = np.zeros([nbtotal],float) |
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| 70 | lat = np.zeros([nbtotal],float) |
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| 71 | lon = np.zeros([nbtotal],float) |
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| 72 | '''e = np.zeros([nbtotal],float)''' |
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| 73 | ts = np.zeros([nbtotal],float) |
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| 74 | '''tup = np.zeros([nbtotal],float) |
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| 75 | tdn = np.zeros([nbtotal],float) |
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| 76 | tb = np.zeros([nbtotal],float) |
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| 77 | theta_eff = np.zeros([nbtotal],float) |
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| 78 | opac = np.zeros([nbtotal],float) |
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| 79 | tdn_lamb = np.zeros([nbtotal],float) |
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| 80 | tb_spec = np.zeros([nbtotal],float) |
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| 81 | tb_lamb = np.zeros([nbtotal],float) |
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| 82 | e_spec = np.zeros([nbtotal],float) |
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| 83 | e_lamb = np.zeros([nbtotal],float) |
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| 84 | e_spec_lamb = np.zeros([nbtotal],float) |
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| 85 | e_sl_00 = np.zeros([nbtotal],float) |
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| 86 | e_sl_25 = np.zeros([nbtotal],float) |
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| 87 | e_sl_50 = np.zeros([nbtotal],float) |
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| 88 | e_sl_75 = np.zeros([nbtotal],float) |
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| 89 | e_sl_100 = np.zeros([nbtotal],float)''' |
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| 90 | while (iligne < nbtotal) : |
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| 91 | line=fichier.readline() |
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| 92 | liste = line.split() |
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| 93 | jjr[iligne] = float(liste[4]) |
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| 94 | lat[iligne] = float(liste[1]) |
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| 95 | lon[iligne] = float(liste[0]) |
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| 96 | '''e[iligne] = float(liste[5])''' |
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| 97 | ts[iligne] = float(liste[8]) |
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| 98 | '''tup[iligne] = float(liste[7]) |
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| 99 | tdn[iligne] = float(liste[8]) |
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| 100 | tb[iligne] = float(liste[10]) |
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| 101 | theta_eff[iligne] = float(liste[12]) |
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| 102 | opac[iligne] = float(liste[11]) |
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| 103 | tdn_lamb[iligne] = float(liste[13]) |
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| 104 | tb_spec[iligne] = float(liste[14]) |
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| 105 | tb_lamb[iligne] = float(liste[15]) |
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| 106 | e_spec[iligne] = float(liste[16]) |
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| 107 | e_lamb[iligne] = float(liste[17]) |
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| 108 | e_spec_lamb[iligne] = float(liste[18]) |
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| 109 | e_sl_00[iligne] = float(liste[19]) |
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| 110 | e_sl_25[iligne] = float(liste[20]) |
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| 111 | e_sl_50[iligne] = float(liste[21]) |
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| 112 | e_sl_75[iligne] = float(liste[22]) |
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| 113 | e_sl_100[iligne] = float(liste[23])''' |
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| 114 | iligne=iligne+1 |
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| 115 | fichier.close() |
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| 116 | print 'ts' |
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| 117 | z0 = ts.min() |
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| 118 | z1 = ts.max() |
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| 119 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, ts, z0, z1, dx, dy) |
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| 120 | ts_day = zgrid_output |
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| 121 | tsu[:, :, 0 : month_day[imo], imo] = ts_day[:, :, :] |
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| 122 | '''print 'tup' |
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| 123 | z0 = tup.min() |
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| 124 | z1 = tup.max() |
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| 125 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, tup, z0, z1, dx, dy) |
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| 126 | tup_day = zgrid_output |
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| 127 | tu[:, :, 0 : month_day[imo], imo] = tup_day[:, :, :] |
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| 128 | print 'tdn' |
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| 129 | z0 = tdn.min() |
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| 130 | z1 = tdn.max() |
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| 131 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, tdn, z0, z1, dx, dy) |
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| 132 | tdn_day = zgrid_output |
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| 133 | td[:, :, 0 : month_day[imo], imo] = tdn_day[:, :, :] |
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| 134 | print 'tb spec' |
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| 135 | z0 = tb_spec.min() |
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| 136 | z1 = tb_spec.max() |
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| 137 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, tb_spec, z0, z1, dx, dy) |
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| 138 | tb_spec_day = zgrid_output |
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| 139 | tbs[:, :, 0 : month_day[imo], imo] = tb_spec_day[:, :, :] |
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| 140 | print 'tb lamb' |
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| 141 | z0 = tb_lamb.min() |
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| 142 | z1 = tb_lamb.max() |
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| 143 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, tb_lamb, z0, z1, dx, dy) |
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| 144 | tb_lamb_day = zgrid_output |
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| 145 | tbl[:, :, 0 : month_day[imo], imo] = tb_lamb_day[:, :, :] |
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| 146 | print 'emis spec' |
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| 147 | z0 = e_spec.min() |
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| 148 | z1 = e_spec.max() |
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| 149 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_spec, z0, z1, dx, dy) |
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| 150 | e_spec_day = zgrid_output |
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| 151 | es[:, :, 0 : month_day[imo], imo] = e_spec_day[:, :, :] |
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| 152 | print 'emis lamb' |
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| 153 | z0 = e_lamb.min() |
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| 154 | z1 = e_lamb.max() |
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| 155 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_lamb, z0, z1, dx, dy) |
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| 156 | e_lamb_day = zgrid_output |
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| 157 | el[:, :, 0 : month_day[imo], imo] = e_lamb_day[:, :, :] |
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| 158 | print 'emis spec lamb' |
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| 159 | z0 = e_spec_lamb.min() |
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| 160 | z1 = e_spec_lamb.max() |
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| 161 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_spec_lamb, z0, z1, dx, dy) |
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| 162 | e_spec_lamb_day = zgrid_output |
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| 163 | esl[:, :, 0 : month_day[imo], imo] = e_spec_lamb_day[:, :, :] |
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| 164 | print 'emis spec lamb 00' |
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| 165 | z0 = e_sl_00.min() |
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| 166 | z1 = e_sl_00.max() |
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| 167 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_sl_00, z0, z1, dx, dy) |
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| 168 | e_sl_00_day = zgrid_output |
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| 169 | esl00[:, :, 0 : month_day[imo], imo] = e_sl_00_day[:, :, :] |
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| 170 | print 'emis spec lamb 25' |
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| 171 | z0 = e_sl_25.min() |
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| 172 | z1 = e_sl_25.max() |
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| 173 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_sl_25, z0, z1, dx, dy) |
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| 174 | e_sl_25_day = zgrid_output |
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| 175 | esl25[:, :, 0 : month_day[imo], imo] = e_sl_25_day[:, :, :] |
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| 176 | print 'emis spec lamb 50' |
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| 177 | z0 = e_sl_50.min() |
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| 178 | z1 = e_sl_50.max() |
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| 179 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_sl_50, z0, z1, dx, dy) |
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| 180 | e_sl_50_day = zgrid_output |
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| 181 | esl50[:, :, 0 : month_day[imo], imo] = e_sl_50_day[:, :, :] |
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| 182 | print 'emis spec lamb 75' |
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| 183 | z0 = e_sl_75.min() |
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| 184 | z1 = e_sl_75.max() |
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| 185 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_sl_75, z0, z1, dx, dy) |
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| 186 | e_sl_75_day = zgrid_output |
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| 187 | esl75[:, :, 0 : month_day[imo], imo] = e_sl_75_day[:, :, :] |
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| 188 | print 'emis spec lamb 100' |
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| 189 | z0 = e_sl_100.min() |
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| 190 | z1 = e_sl_100.max() |
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| 191 | zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, e_sl_100, z0, z1, dx, dy) |
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| 192 | e_sl_100_day = zgrid_output |
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| 193 | esl100[:, :, 0 : month_day[imo], imo] = e_sl_100_day[:, :, :]''' |
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| 194 | ############################################### |
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| 195 | # stack gridded data spec lamb in NETCDF file # |
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| 196 | ############################################### |
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| 197 | print 'stacking of gridded data' |
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| 198 | rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_100/cartesian_grid_monthly_surf-temp_' + month[imo] + '2009.nc', 'w', format='NETCDF3_CLASSIC') |
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| 199 | rootgrp.createDimension('longitude', nx) |
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| 200 | rootgrp.createDimension('latitude', ny) |
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| 201 | rootgrp.createDimension('days', month_day[imo]) |
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| 202 | nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',)) |
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| 203 | nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) |
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| 204 | nc_day = rootgrp.createVariable('days', 'f', ('days',)) |
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| 205 | nc_ts = rootgrp.createVariable('ts', 'f', ('latitude', 'longitude', 'days')) |
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| 206 | '''nc_tup = rootgrp.createVariable('tup', 'f', ('latitude', 'longitude', 'days')) |
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| 207 | nc_tdn = rootgrp.createVariable('tdn', 'f', ('latitude', 'longitude', 'days')) |
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| 208 | nc_tb_spec = rootgrp.createVariable('tb_spec', 'f', ('latitude', 'longitude', 'days')) |
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| 209 | nc_tb_lamb = rootgrp.createVariable('tb_lamb', 'f', ('latitude', 'longitude', 'days')) |
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| 210 | nc_e_spec = rootgrp.createVariable('e_spec', 'f', ('latitude', 'longitude', 'days')) |
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| 211 | nc_e_lamb = rootgrp.createVariable('e_lamb', 'f', ('latitude', 'longitude', 'days')) |
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| 212 | nc_e_spec_lamb = rootgrp.createVariable('e_spec_lamb', 'f', ('latitude', 'longitude', 'days')) |
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| 213 | nc_e_sl_00 = rootgrp.createVariable('e_mixed_s00', 'f', ('latitude', 'longitude', 'days')) |
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| 214 | nc_e_sl_25 = rootgrp.createVariable('e_mixed_s25', 'f', ('latitude', 'longitude', 'days')) |
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| 215 | nc_e_sl_50 = rootgrp.createVariable('e_mixed_s50', 'f', ('latitude', 'longitude', 'days')) |
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| 216 | nc_e_sl_75 = rootgrp.createVariable('e_mixed_s75', 'f', ('latitude', 'longitude', 'days')) |
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| 217 | nc_e_sl_100 = rootgrp.createVariable('e_mixed_s100', 'f', ('latitude', 'longitude', 'days'))''' |
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| 218 | nc_lon[:] = xvec |
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| 219 | nc_lat[:] = yvec |
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| 220 | nc_ts[:] = tsu[:, :, 0 : month_day[imo], imo] |
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| 221 | '''nc_tup[:] = tu[:, :, 0 : month_day[imo], imo] |
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| 222 | nc_tdn[:] = td[:, :, 0 : month_day[imo], imo] |
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| 223 | nc_tb_spec[:] = tbs[:, :, 0 : month_day[imo], imo] |
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| 224 | nc_tb_lamb[:] = tbl[:, :, 0 : month_day[imo], imo] |
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| 225 | nc_e_spec[:] = es[:, :, 0 : month_day[imo], imo] |
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| 226 | nc_e_lamb[:] = el[:, :, 0 : month_day[imo], imo] |
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| 227 | nc_e_spec_lamb[:] = esl[:, :, 0 : month_day[imo], imo] |
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| 228 | nc_e_sl_00[:] = esl00[:, :, 0 : month_day[imo], imo] |
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| 229 | nc_e_sl_25[:] = esl25[:, :, 0 : month_day[imo], imo] |
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| 230 | nc_e_sl_50[:] = esl50[:, :, 0 : month_day[imo], imo] |
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| 231 | nc_e_sl_75[:] = esl75[:, :, 0 : month_day[imo], imo] |
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| 232 | nc_e_sl_100[:] = esl100[:, :, 0 : month_day[imo], imo]''' |
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| 233 | rootgrp.close() |
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| 234 | |
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| 235 | |
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| 236 | |
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| 237 | ############################################ |
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| 238 | # scatter plot theta_eff vs zenith_opacity # |
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| 239 | ############################################ |
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| 240 | '''plt.ion() |
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| 241 | plt.figure() |
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| 242 | scatter(opac, theta_eff, alpha = 0.4) |
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| 243 | xlim(0.135, 0.24) |
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| 244 | ylim(54.5, 56.3) |
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| 245 | xlabel('Zenith opacity') |
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| 246 | ylabel('Effective incidence angle (deg)') |
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| 247 | grid(True) |
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| 248 | plt.savefig('/mma/hermozol/Documents/figure_output/scatter_effective_incident_angle_vs_opacity_AMSUB_JANUARY2009.png')''' |
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| 249 | |
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