Atmos. Chem. Phys. Discuss., 8, 10791-10816, 2008
www.atmos-chem-phys-discuss.net/8/10791/2008/
doi:10.5194/acpd-8-10791-2008
© Author(s) 2008. This work is distributed
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This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Aerosol model selection and uncertainty modelling by adaptive MCMC technique
M. Laine and J. Tamminen
Finnish Meteorological Institute, Helsinki, Finland

Abstract. We apply Bayesian model selection techniques on the statistical inversion problem of the GOMOS instrument. The motif is to study which type of aerosol model best fits the data and to show how the uncertainty of the aerosol model can be included in the error estimates. The competing models consist of various formulations, each having different unknown parameter vectors. We have developed an Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ) for sampling values from the posterior distributions of the unknowns of the models. The algorithm is easy to implement and can readily be employed for model selection as well as for model averaging, to properly take into account the uncertainty of the modelling.

Citation: Laine, M. and Tamminen, J.: Aerosol model selection and uncertainty modelling by adaptive MCMC technique, Atmos. Chem. Phys. Discuss., 8, 10791-10816, doi:10.5194/acpd-8-10791-2008, 2008.
 
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