Ozone profile smoothness as a priori information in the inversion of limb measurements

• Published in: Annales Geophysicae
• Main Authors: Sofieva, V. F., Tamminen, J., Haario, H., Kyrölä, E., Lehtinen, M.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2004
• Subjects: article; Verlagsveröffentlichung; Sodankylä
• Online Access: https://doi.org/10.5194/angeo-22-3411-2004; https://noa.gwlb.de/receive/cop_mods_00034570; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00034524/angeo-22-3411-2004.pdf; https://angeo.copernicus.org/articles/22/3411/2004/angeo-22-3411-2004.pdf

In this work we discuss inclusion of a priori information about the smoothness of atmospheric profiles in inversion algorithms. The smoothness requirement can be formulated in the form of Tikhonov-type regularization, where the smoothness of atmospheric profiles is considered as a constraint or in the form of Bayesian optimal estimation (maximum a posteriori method, MAP), where the smoothness of profiles can be included as a priori information. We develop further two recently proposed retrieval methods. One of them - Tikhonov-type regularization according to the target resolution - develops the classical Tikhonov regularization. The second method - maximum a posteriori method with smoothness a priori - effectively combines the ideas of the classical MAP method and Tikhonov-type regularization. We discuss a grid-independent formulation for the proposed inversion methods, thus isolating the choice of calculation grid from the question of how strong the smoothing should be. The discussed approaches are applied to the problem of ozone profile retrieval from stellar occultation measurements by the GOMOS instrument on board the Envisat satellite. Realistic simulations for the typical measurement conditions with smoothness a priori information created from 10-years analysis of ozone sounding at Sodankylä and analysis of the total retrieval error illustrate the advantages of the proposed methods. The proposed methods are equally applicable to other profile retrieval problems from remote sensing measurements.