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

Conference · Mon Jul 17 00:00:00 EDT 2006

DOI:https://doi.org/10.1109/NSSPW.2006.4378840· OSTI ID:893974

Johannesson, G; Dyer, K; Hanley, W; Kosovic, B; Larsen, S; Loosmore, G; Lundquist, J; Mirin, A

The release of hazardous materials into the atmosphere can have a tremendous impact on dense 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.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 893974
• Report Number(s): UCRL-PROC-222915; TRN: US200701%%156
• Resource Relation: Conference: Presented at: Nonlinear Statistical Signal Processing Workshop, Cambridge, United Kingdom, Sep 13 - Sep 15, 2006
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
HAZARDOUS MATERIALS
PROCESSING
LAWRENCE LIVERMORE NATIONAL LABORATORY