Aerosol model selection and uncertainty modelling by RJMCMC technique

Abstract

We apply Bayesian model selection techniques on GOMOS inverse problem to obtain information on which type of aerosol model fits best the data and to show how the uncertainty of the aerosol model can be included in the error estimates. We use reversible jump Markov chain Monte Carlo (RJMCMC), which is a general extension to the Metropolis-Hastings MCMC algorithm that can explore parameter space of varying/unknown dimensionality. This makes it suitable for the model determination problem. This work is related to Envisat AO NORM.