Assimilating concentration observations for transport and dispersion modeling in a meandering wind field |
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| Authors: | Sue Ellen Haupt Anke Beyer-Lout Kerrie J Long George S Young |
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| Institution: | 1. Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16804, USA;2. Meteorology Department, The Pennsylvania State University, State College, PA 16804, USA;1. Department of Earth & Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada;2. Agriculture and Agri-Food Canada, Lacombe, Alberta, Canada;3. Alberta Agriculture and Rural Development, Lacombe, Alberta, Canada;1. School of Materials Science and Engineering, Shandong Jianzhu University, 250101 Jinan, PR China;2. Science and Technology on Power Beam Processes Laboratory, Beijing Key Laboratory of High Power Beam Additive Manufacturing Technology and Equipment, Aeronautical Key Laboratory for Additive Manufacturing Technologies, AVIC Manufacturing Technology Institute, 100024 Beijing, PR China;1. LAGA, Université Paris 13 SPC, 99 Av J.B. Clement, 93430 Villetaneuse, France;2. LaboMAC & PM, Department Mathematics FSTM, Hassan II University Casablanca, Morocco;3. Department of Genie Civil, LASH EMI, Mohammed V University Rabat, Morocco;4. School of Engineering and Computing Sciences, University of Durham, South Road, Durham DH1 3LE, UK |
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| Abstract: | Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach. |
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