ACPD Atmospheric Chemistry and Physics Discussions ACPD 1680-7375 Copernicus GmbH Göttingen, Germany 10.5194/acpd-8-18007-2008 A self-adapting and altitude-dependent regularization method for atmospheric profile retrievals Ridolfi M. 1 Sgheri L. 2 Dipartimento di Chimica Fisica e Inorganica, Università di Bologna, Bologna, Italy Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, Firenze, Italy 10 10 2008 8 5 18007 18037 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/8/18007/2008/acpd-8-18007-2008.html The full text article is available as a PDF file from http://www.atmos-chem-phys-discuss.net/8/18007/2008/acpd-8-18007-2008.pdf

MIPAS is a Fourier transform spectrometer, operating onboard of the ENVISAT satellite since July 2002.The online retrieval algorithm produces geolocated profiles of temperature and of volume mixing ratios of six key atmospheric constituents: H<sub>2</sub>O, O<sub>3</sub>, HNO<sub>3</sub>, CH<sub>4</sub>, N<sub>2</sub>O and NO<sub>2</sub>. In the validation phase, oscillations beyond the error bars were observed in several profiles, particularly in CH<sub>4</sub> and N<sub>2</sub>O. <br><br> To tackle this problem, a Tikhonov regularization scheme has been implemented in the retrieval algorithm. The applied regularization is however rather weak in order to preserve the vertical resolution of the profiles. <br><br> In this paper we present a self-adapting and altitude-dependent regularization approach that detects whether the analysed observations contain information about small-scale profile features, and determines the strength of the regularization accordingly. The objective of the method is to smooth out artificial oscillations as much as possible, while preserving the fine detail features of the profile when related information is detected in the observations. <br><br> The proposed method is checked for self consistency, its performance is tested on MIPAS observations and compared with that of a few scalar and altitude-dependent regularization schemes available in the literature. In all the considered cases the proposed scheme achieves a good performance, thanks to its altitude dependence and to the constrains employed, which are specific of the inversion problem under consideration. The proposed method is generally applicable to iterative Gauss-Newton algorithms for the retrieval of vertical distribution profiles from atmospheric remote sounding measurements.

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