ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-9-9121-2009 Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels Liu X. 1 Zhou D. K. 1 Larar A. M. 1 Smith W. L. 2 Schluessel P. 3 Newman S. M. 4 Taylor J. P. 4 Wu W. 5 NASA Langley Research Center, Hampton, VA 23681, USA Hampton University, VA 23668, USA and University of Wisconsin, Madison, WI 53706, USA EUMETSAT, Am Kavalleriesand 31, 64 295 Darmstadt, Germany Met Office, Exeter, Devon, UK Science Systems and Applications, Inc., Hampton, VA 23666, USA 03 12 2009 9 23 9121 9142 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/9/9121/2009/acp-9-9121-2009.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/9/9121/2009/acp-9-9121-2009.pdf

The Infrared Atmospheric Sounding Interferometer (IASI) is an ultra-spectral satellite sensor with 8461 spectral channels. IASI spectra contain high information content on atmospheric, cloud, and surface properties. The instrument presents a challenge for using thousands of spectral channels in a physical retrieval system or in a Numerical Weather Prediction (NWP) data assimilation system. In this paper we describe a method of simultaneously retrieving atmospheric temperature, moisture, and cloud properties using all available IASI channels without sacrificing computational speed. The essence of the method is to convert the IASI channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Studies show that about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in the super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements. While no large-scale validation has been performed on any retrieval methodology presented in this paper, comparisons of the retrieved atmospheric profiles, sea surface temperatures, and surface emissivities with co-located ground- and aircraft-based measurements over four days in Spring 2007 over the South-Central United States indicate excellent agreement.

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