Abstract
For atmospheric releases, event reconstruction answers the critical questions - How much material was released? When? Where? and What are the potential consequences? Inaccurate estimation of the source term can lead to gross errors, time delays during a crisis, and even fatalities. We are developing a capability that seamlessly integrates observational data streams with predictive models in order to provide the best possible estimates of unknown source term parameters, as well as optimal and timely situation analyses consistent with both models and data. Our approach utilizes Bayesian inference and stochastic sampling methods (Markov Chain and Sequential Monte Carlo) to reformulate the inverse problem into a solution based on efficient sampling of an ensemble of predictive simulations, guided by statistical comparisons with data.
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