ACPD Atmospheric Chemistry and Physics Discussions ACPD 1680-7375 Copernicus GmbH Göttingen, Germany 10.5194/acpd-9-7589-2009 Technical note: Functional sliced inverse regression to infer temperature, water vapour and ozone from IASI data Amato U. 1 Antoniadis A. 2 De Feis I. 1 Masiello G. 3 Matricardi M. 4 Serio C. 3 Istituto per le Applicazioni del Calcolo ''Mauro Picone'' CNR, Napoli, Italy Laboratoire Jean Kuntzmann, Université Joseph Fourier, Grenoble, France Dipartimento di Ingegneria e Fisica dell'Ambiente, Università della Basilicata, Potenza, Italy European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK 23 03 2009 9 2 7589 7613 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-discuss.net/9/7589/2009/acpd-9-7589-2009.html The full text article is available as a PDF file from http://www.atmos-chem-phys-discuss.net/9/7589/2009/acpd-9-7589-2009.pdf

A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The methodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmospheric profiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by co-locating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysis.

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