HR: 09:45h
AN: A11F-08    [Abstracts]
TI: Atmospheric Tracer Inverse Modeling Using Markov Chain Monte Carlo (MCMC)
AU: * Kasibhatla, P
EM: psk9@duke.edu
AF: Nicholas School of the Environment and Earth Sciences, Duke University, Room A355 LSRC, Durham, NC 27708 United States
AB: In recent years, there has been an increasing emphasis on the use of Bayesian statistical estimation techniques to characterize the temporal and spatial variability of atmospheric trace gas sources and sinks. The applications have been varied in terms of the particular species of interest, as well as in terms of the spatial and temporal resolution of the estimated fluxes. However, one common characteristic has been the use of relatively simple statistical models for describing the measurement and chemical transport model error statistics and prior source statistics. For example, multivariate normal probability distribution functions (pdfs) are commonly used to model these quantities and inverse source estimates are derived for fixed values of pdf paramaters. While the advantage of this approach is that closed form analytical solutions for the a posteriori pdfs of interest are available, it is worth exploring Bayesian analysis approaches which allow for a more general treatment of error and prior source statistics. Here, we present an application of the Markov Chain Monte Carlo (MCMC) methodology to an atmospheric tracer inversion problem to demonstrate how more gereral statistical models for errors can be incorporated into the analysis in a relatively straightforward manner. The MCMC approach to Bayesian analysis, which has found wide application in a variety of fields, is a statistical simulation approach that involves computing moments of interest of the a posteriori pdf by efficiently sampling this pdf. The specific inverse problem that we focus on is the annual mean $CO_2$ source/sink estimation problem considered by the TransCom3 project. TransCom3 was a collaborative effort involving various modeling groups and followed a common modeling and analysis protocoal. As such, this problem provides a convenient case study to demonstrate the applicability of the MCMC methodology to atmospheric tracer source/sink estimation problems.
DE: 3260 Inverse theory
DE: 0300 ATMOSPHERIC COMPOSITION AND STRUCTURE
DE: 0322 Constituent sources and sinks
DE: 0365 Troposphere--composition and chemistry
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting