A new urban boundary layer and dispersion parameterization for an emergency response modeling system: Tests with the Joint Urban 2003 data set |
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| Authors: | Luca Delle Monache Jeffrey Weil Matthew Simpson Marty Leach |
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| Institution: | 1. Lawrence Livermore National Laboratory, Livermore, CA, USA;2. National Center for Atmospheric Research, Boulder, CO, USA;3. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA;1. Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium;2. Umicore Hoboken, Dept. of Environmental Affairs, A. Greinerstraat 14, 2660 Hoboken, Belgium;1. University of Cincinnati, Department of Biomedical, Chemical, and Environmental Engineering, College of Engineering and Applied Science, P.O. Box 210012, Cincinnati, OH 45221-0012, USA;2. U.S. Center for Disease Control, National Institute of Occupational Safety and Health, 5555 Ridge Avenue, Cincinnati, OH 45213, USA;3. US EPA Office of Research and Development, National Risk Management Research Laboratory, Research Triangle Park, NC 27711, USA;4. US EPA Office of Research and Development, National Risk Management Research Laboratory, 26W Martin Luther King Dr., Cincinnati, OH 45268, USA;1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China;2. School of Engineering, RMIT University, Melbourne, Australia;1. Department of Internal Medicine, Division of Immunology/Allergy, University of Cincinnati, Cincinnati, Ohio;2. Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio;3. Division of Biostatistics and Epidemiology, Cincinnati Children''s Hospital Medical Center, Cincinnati, Ohio;4. Division of Asthma Research, Cincinnati Children''s Hospital Medical Center, Cincinnati, Ohio;5. Division of Immunology, Microbiology, and Allergy, Rush University Medical Center, Chicago, Illinois;1. Department of Mechanical Engineering, COPPE, Federal University of Rio de Janeiro, Centro de Tecnologia, Bloco G, Rio de Janeiro, RJ 21945-970, Brazil;2. Computational Nucleus for Air Quality Studies (NCQAr), Department of Meteorology, IGEO, Federal University of Rio de Janeiro, CCMN, Bloco H, Rio de Janeiro, RJ 21941-916, Brazil |
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| Abstract: | A new urban parameterization for a fast-running dispersion prediction modeling system suitable for emergency response situations is introduced. The parameterization represents the urban convective boundary layer in the dispersion prediction system developed by the National Atmospheric Release Advisory Center (NARAC) at Lawrence Livermore National Laboratory. The performance of the modeling system is tested with data collected during the field campaign Joint Urban 2003 (JU03), held in July 2003 in Oklahoma City, Oklahoma. Tests were performed using data from three intense operating periods held during daytime slightly unstable to unstable conditions. The system was run in operational mode using the meteorological data that would be available operationally at NARAC to test its effectiveness in emergency response conditions. The new parameterization considerably improves the performance of the original modeling system, by producing a better degree of pattern of correspondence between predictions and observations (as measured by Taylor diagrams), considerably reducing bias, and better capturing directional effects resulting in plume predictions whose shape and size better resemble the observations (via the measure of effectiveness). Furthermore, the new parameterization shows similar skills to urban modeling systems of similar or greater complexity. The parameterization performs the best at the three JU03 sensor arcs (1, 2, and 4 km downwind the release points), with fractional bias values ranging from 0.13 to 0.4, correlation values from 0.45 to 0.71, and centered root-mean-square error being reduced more than 50% in most cases. The urban parameterization has been tested with grid increments of 125, 250, 500 and 1000 m, performing best at 250 and 500 m. Finally, it has been found that representing the point source by a Gaussian distribution with an initial spread of particles leads to a better representation of the initial spread induced by near-source buildings, resulting in lower bias and improved correlation in downtown Oklahoma City. |
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