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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 23
Atmos. Chem. Phys., 9, 9121–9142, 2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: The IASI instrument onboard the METOP satellite: first...

Atmos. Chem. Phys., 9, 9121–9142, 2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Dec 2009

03 Dec 2009

Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels

X. Liu1, D. K. Zhou1, A. M. Larar1, W. L. Smith2, P. Schluessel3, S. M. Newman4, J. P. Taylor4, and W. Wu5 X. Liu et al.
  • 1NASA Langley Research Center, Hampton, VA 23681, USA
  • 2Hampton University, VA 23668, USA and University of Wisconsin, Madison, WI 53706, USA
  • 3EUMETSAT, Am Kavalleriesand 31, 64 295 Darmstadt, Germany
  • 4Met Office, Exeter, Devon, UK
  • 5Science Systems and Applications, Inc., Hampton, VA 23666, USA

Abstract. 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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