Dissemin is shutting down on January 1st, 2025

Published in

European Geosciences Union, Atmospheric Chemistry and Physics, 24(8), p. 7697-7707, 2008

DOI: 10.5194/acp-8-7697-2008

European Geosciences Union, Atmospheric Chemistry and Physics Discussions, 3(8), p. 10791-10816

DOI: 10.5194/acpd-8-10791-2008

Links

Tools

Export citation

Search in Google Scholar

Aerosol model selection and uncertainty modelling by adaptive MCMC technique

Journal article published in 2008 by Marko Laine ORCID, Johanna Tamminen
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

Abstract. We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way. The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ). It uses Markov chain Monte Carlo (MCMC) technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use. We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.