Aerosol model selection and uncertainty modelling by adaptive MCMC technique

M. Laine (1) , J. Tamminen (1)

1 FMI - Finnish Meteorological Institute

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.

Domaines: Océan, Atmosphère

M. Laine, J. Tamminen. Aerosol model selection and uncertainty modelling by adaptive MCMC technique. Atmospheric Chemistry and Physics Discussions, 2008, 8 (3), pp.10791-10816. ⟨hal-00304233⟩