Access AIRS data from GES DISC via OPeNDAP

Copyright 2016 The HDF Group

Copyright 2014 John Evans

This example code illustrates how to access and visualize Atmospheric Infrared Sounder (AIRS) grid data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) in Python.

If you have any questions, suggestions, or comments on this example, please use the HDF-EOS Forum. If you would like to see an example of any other NASA HDF/HDF-EOS data product that is not listed in the HDF-EOS Comprehensive Examples page, feel free to contact us at eoshelp@hdfgroup.org or post it at the HDF-EOS Forum.

Tested under: Python 2.7.12 :: Anaconda 4.2.0 (x86_64)

Access AIRS data from GES DISC via OPeNDAP

This example assumes that you have configured .netrc, .urs_cookies and .dodsrc file properly according to the sources listed in the References section (below) because we will use the netCDF4 Python interface that is based on the netCDF-C library.

import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline

from mpl_toolkits.basemap import Basemap
from netCDF4 import Dataset

opendap_url = 'https://acdisc.gesdisc.eosdis.nasa.gov:443/opendap/ncml/Aqua_AIRS_\
Level3/AIRS3STD.006/2003/AIRS.2003.02.05.L3.RetStd_IR001.v6.0.9.0.G13222004955.hd\
f.ncml'
DATAFIELD_NAME = 'Topography'
nc = Dataset(opendap_url)
data = nc.variables[DATAFIELD_NAME][:,:]
latitude = nc.variables['Latitude'][:]
longitude = nc.variables['Longitude'][:]

# Verify that data is accessible with 
# username & password in ~/.netrc, 
# cookies in ~/.urs_cookies, 
# and remote OPeNDAP server access information in ~/.dodsrc.
print latitude

[ 89.5  88.5  87.5  86.5  85.5  84.5  83.5  82.5  81.5  80.5  79.5  78.5
  77.5  76.5  75.5  74.5  73.5  72.5  71.5  70.5  69.5  68.5  67.5  66.5
  65.5  64.5  63.5  62.5  61.5  60.5  59.5  58.5  57.5  56.5  55.5  54.5
  53.5  52.5  51.5  50.5  49.5  48.5  47.5  46.5  45.5  44.5  43.5  42.5
  41.5  40.5  39.5  38.5  37.5  36.5  35.5  34.5  33.5  32.5  31.5  30.5
  29.5  28.5  27.5  26.5  25.5  24.5  23.5  22.5  21.5  20.5  19.5  18.5
  17.5  16.5  15.5  14.5  13.5  12.5  11.5  10.5   9.5   8.5   7.5   6.5
   5.5   4.5   3.5   2.5   1.5   0.5  -0.5  -1.5  -2.5  -3.5  -4.5  -5.5
  -6.5  -7.5  -8.5  -9.5 -10.5 -11.5 -12.5 -13.5 -14.5 -15.5 -16.5 -17.5
 -18.5 -19.5 -20.5 -21.5 -22.5 -23.5 -24.5 -25.5 -26.5 -27.5 -28.5 -29.5
 -30.5 -31.5 -32.5 -33.5 -34.5 -35.5 -36.5 -37.5 -38.5 -39.5 -40.5 -41.5
 -42.5 -43.5 -44.5 -45.5 -46.5 -47.5 -48.5 -49.5 -50.5 -51.5 -52.5 -53.5
 -54.5 -55.5 -56.5 -57.5 -58.5 -59.5 -60.5 -61.5 -62.5 -63.5 -64.5 -65.5
 -66.5 -67.5 -68.5 -69.5 -70.5 -71.5 -72.5 -73.5 -74.5 -75.5 -76.5 -77.5
 -78.5 -79.5 -80.5 -81.5 -82.5 -83.5 -84.5 -85.5 -86.5 -87.5 -88.5 -89.5]

If Latitude values are printed correctly, let's plot the grid data on a map.

# Draw an equidistant cylindrical projection using the low resolution             
# coastline database.                                                             
m = Basemap(projection='cyl', resolution='l',
           llcrnrlat=-90, urcrnrlat = 90,
           llcrnrlon=-180, urcrnrlon = 180)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90., 120., 30.), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180., 181., 45.), labels=[0, 0, 0, 1])
m.pcolormesh(longitude, latitude, data, latlon=True, alpha=0.90)
cb = m.colorbar()
cb.set_label('Unit:m')
basename = os.path.basename(opendap_url)
plt.title('{0}\n {1}'.format(basename, DATAFIELD_NAME))
fig = plt.gcf()

References

• GES DISC Data Access webpage
• HDF-EOS Tools and Information Center: Usage of NCL to Access Remote HDF-EOS5 MLS Files