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Proceedings Paper

MODTRAN5 analysis of clear-sky, co-located space- and ground-based infrared atmospheric measurements: AERI, AIRS, CERES, MODIS
Author(s): Paul E. Lewis; Gail P. Anderson; Sylvia S. Shen; James Chetwynd; Miguel Roman; Crystal Schaaf; David D. Turner; David A. Rutan; Alexander Berk; David P. Miller; Robert Kroutil
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Paper Abstract

A set of 26 clear-sky, co-located, infrared data from NASA's space-based, downward looking Atmospheric Infrared Sounder (AIRS)1 and its Clouds and the Earth's Radiant Energy System (CERES)2 have been paired with the DOE's Southern Great Plains (SGP)3 ground-based, upward looking Atmospheric Emitted Radiance Interferometer (AERI)4. These data sets have then been simulated using the MODTRAN®55 (MOD5) radiative transfer code with standard auxiliary 'truth' data as input. Of particular interest is the impact, if any, of the large Ground Sampling Distance (GSD) of AIRS and CERES (minimum radii of approximately 13 and 26 km, respectively) vs. the soda-straw up-looking mode of AERI. The smaller Moderate Resolution Imaging Spectroradiometer (MODIS)6 GSD (<1 km) provides estimated distributions of land-type and albedo within the larger footprints. The SGP's coincident vertical profile sondes and Aeronet7 retrievals, along with other satellite data [Ozone Monitoring Instrument (OMI)8] constrain the surface type, column ozone and aerosol optical depth that existed during the measurement events. Initial MOD5 calculations, using these ancillary data as input, have replicated the AERI and AIRS measurements to within an average difference of ~1% over their entire spectral range. Using MODIS visible albedo9 retrievals to extend the albedo into the long wave (LW) only slightly improved the statistical comparison between the CERES and MOD5 broadband LW radiances agreement, from 3% to ~1.5%, while increasing the variance. While these results are not sufficient for specific instrument inversion algorithms, they suggest some confidence in the generic use of MODTRAN®5 to help integrate and spectrally extend assorted data sets for sensitivity studies of Climate Change, where the estimated required sensitivity is <1%.

Paper Details

Date Published: 27 April 2009
PDF: 7 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733410 (27 April 2009); doi: 10.1117/12.819920
Show Author Affiliations
Paul E. Lewis, National Geospatial-Intelligence Agency (United States)
Gail P. Anderson, Air Force Research Lab. (United States)
NOAA/ESRL (United States)
Sylvia S. Shen, The Aerospace Corp. (United States)
James Chetwynd, Air Force Research Lab. (United States)
Miguel Roman, Boston Univ. (United States)
Crystal Schaaf, Boston Univ. (United States)
David D. Turner, Univ. of Wisconsin, Madison (United States)
David A. Rutan, Science Systems and Applications, Inc. (United States)
Alexander Berk, Spectral Sciences, Inc. (United States)
David P. Miller, Northrop Grumman TASC (United States)
Robert Kroutil, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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