Sequential Monte-Carlo Based Framework for Dynamic Data-Driven Event Reconstruction for Atmospheric Release

Atmospheric releases of hazardous materials are highly effective means to impact large populations. We propose an atmospheric event reconstruction framework that couples observed data and predictive computer-intensive dispersion models via Bayesian methodology. Due to the complexity of the model framework, a sampling-based approach is taken for posterior inference that combines Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) strategies.