Event reconstruction for atmospheric releases employing urban puff model UDM with stochastic inversion methodology

86th AMS Annual Meeting

Volumn , Issue , 2006, Pages

  Glascoe, Lee ^a   Neuman, Stephanie ^b   Kosović, Branko ^b   Dyer, Kathy ^b   Hanley, William ^b   Nitao, John ^b  

^a LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC RELEASE; BAYESIAN INFERENCE; CFD CODES; CFD MODELS; COMPUTATIONAL EFFORT; COMPUTATIONAL FLUID DYNAMICS SIMULATIONS; CONCENTRATION MEASUREMENT; CONTAMINANT PLUME; EMERGENCY RESPONSE; EVENT RECONSTRUCTION; FIELD CAMPAIGN; FINITE-ELEMENT; FLOW CONDITION; FLOW DIVERSION; FORWARD MODELS; GAUSSIAN PUFF; HIGH RESOLUTION; INVERSION METHODOLOGY; ISOLATED BUILDINGS; JOINT URBAN 2003; MARKOV CHAIN MONTE CARLO; NONLINEAR DISPERSION; OBSERVED DATA; OKLAHOMA CITY; POINT MEASUREMENT; PUFF MODEL; RAPID IDENTIFICATION; RELEASE RATE; SCIENCE TECHNOLOGIES; SHAPE AND SIZE; SOURCE IDENTIFICATION; SOURCE INVERSION; SOURCE LOCATION; SOURCE TERMS; SPLITTING TECHNIQUES; STOCHASTIC METHODOLOGY; STOCHASTIC SAMPLING; STOCHASTIC SAMPLING METHOD; TIME VARYING; URBAN DISPERSION; URBAN DISPERSION MODELS; URBAN ENVIRONMENTS; URBAN GEOMETRY;

BAYESIAN NETWORKS; COMPUTATIONAL FLUID DYNAMICS; DISPERSIONS; FLOW FIELDS; IDENTIFICATION (CONTROL SYSTEMS); INFERENCE ENGINES; MARKOV PROCESSES; MEASUREMENT ERRORS; PROBABILITY DISTRIBUTIONS; SECURITY OF DATA; STOCHASTIC SYSTEMS;

STOCHASTIC MODELS;

EID: 77949282633     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Aines, R., J. Nitao, R. Newmark, S. Carle, A. Ramirez, and W. Hanley, 2002: The Stochastic Engine Initiative: Improving Prediction of Behavior in Geologic Environments We Cannot Directly Observe Publication. Technical Report UCRL-ID-148221, Lawrence Livermore National Laboratory.
2. Allwine, K., 2004: Joint Urban 2003 field study and urban mesonets. Fourth Symposium on Planning, Nowcasting, and Forecasting in the Urban Zone, American Meteorological Society.
3. Chan, S., D. Stevens, and W. Smith, 2001: Validation of CFD urban dispersion models using high resolution wind tunnel data. 107.
4. Chow, F., B. Kosovic, and S. Chan, 2006: Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations. Annual American Meteorological Society Meeting, Atlanta, S. Hanna, ed.
5. Gelman, A., J. Carlin, H. Stern, and D. Rubin, 2003: Bayesian Data Analysis. Chapman and Hall, CRC.
6. Hall, D., A. Spanton, I. Griffiths, M. Hargrave, and S. Walker, 2003: The Urban Dispersion Model (UDM): Version 2.2 technical documentation release 1.1. Technical Report DSTL/TR04774, Porton Down, UK.
7. Johannesson, G., W. Hanley, and J. Nitao, 2004: Dynamic Bayesian models via Monte Carlo - an introduction with examples. Technical Report UCRL-PRES-207173, Lawrence Livermore National Laboratory, Livermore, CA.
8. Kosovic, B., G. Sugiyama, S. Chan, T. Chow, K. Dyer, L. Glascoe, R. Glaser, W. Hanley, G. Johannesson, S. Larsen, G. Loosmore, J. Lundquist, A. Mirin, S. Neu-man, J. Nitao, R. Serban, and C. Tong, 2005: Dynamic data-driven event reconstruction for atmospheric releases. Aerosols and Safety, 2005 Conference, Obninsk, Russia, number UCRL-PRES-215766.