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

Conference · Wed Nov 16 00:00:00 EST 2005

OSTI ID:883520

Johannesson, G; Chow, F K; Glascoe, L; Glaser, R E; Hanley, W G; Kosovic, B; Krnjajic, M; Larsen, S C; Lundquist, J K; Mirin, A A; Nitao, J J; Sugiyama, G A

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.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 883520
• Report Number(s): UCRL-PROC-217190; TRN: US200615%%18
• Resource Relation: Conference: Presented at: 2005 Joint Statistical Meetings, Minneapolis, MN, United States, Aug 07 - Aug 11, 2005
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
HAZARDOUS MATERIALS SPILLS
ENVIRONMENTAL TRANSPORT
FLOW MODELS
MONTE CARLO METHOD