Aerosol model selection and uncertainty modelling by adaptive MCMC technique M. Laine and J. Tamminen Finnish Meteorological Institute, Helsinki, Finland
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.
Citation: Laine, M. and Tamminen, J.: Aerosol model selection and uncertainty modelling by adaptive MCMC technique, Atmos. Chem. Phys., 8, 7697-7707, doi:10.5194/acp-8-7697-2008, 2008.