Data assimilation in meteorological pre-processors: Effects on atmospheric dispersion simulations |
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| Institution: | 1. Department of Chemical Engineering, Aristotle University of Thessaloniki, University Box 453, 54124 Thessaloniki, Greece;2. Environmental Research Laboratory, Institute of Nuclear Technology and Radiation Protection, NCSR ‘Demokritos’, 15310 Aghia Paraskevi, Greece;3. Department of Environmental Modelling, Institute of Mathematical Machine and System Problems, National Academy of Sciences of Ukraine, pr. Glushkova-42, Kiev 03187, Ukraine;4. Department of Engineering and Management of Energy Resources, University of Western Macedonia, Bakola and Sialvera Str., 50100 Kozani, Greece;1. Department of Civil Engineering, Monash University, Melbourne, Victoria 3800, Australia;2. School of Geography and Earth Sciences, and Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S4L8, Canada;1. Reseau National de Surveillance Aerobiologique (RNSA), Brussieu, France;2. Laboratory for Palynology, Department of Biology and Ecology, Faculty of Sciences University of Novi Sad, Novi Sad, Serbia;3. Research Group Aerobiology and Pollen information, Department of Oto-Rhino-Laryngology, Medical University of Vienna, Austria;4. National Pollen and Aerobiology Research Unit, University of Worcester, Henwick Grove, Worcester WR2 6AJ, UK;1. LISA, Laboratoire Interuniversitaire des Systèmes Atmosphériques, UMR CNRS 7583, Université Paris Est Créteil, Université Paris Diderot, 94010 Créteil Cedex, France;2. UMR 1391 ISPA, INRA-Bordeaux Sciences Agro, F-33140 Villenave d’Ornon, France;3. UMR1402 ECOSYS, INRA-AgroParisTech, Université Paris-Saclay, 78850 Thiverval-Grignon, France;4. UMR SADAPT, AgroParisTech, INRA, Université Paris-Saclay, 16 rue Claude Bernard, 75231 Paris, France;1. Department of Building Science, Tsinghua University, Beijing 100084, PR China;2. Department of Engineering Mechanics, Tsinghua University, Beijing 100084, PR China |
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| Abstract: | In previous work Kovalets, I., Andronopoulos, S., Bartzis, J.G., Gounaris, N., Kushchan, A., 2004. Introduction of data assimilation procedures in the meteorological pre-processor of atmospheric dispersion models used in emergency response systems. Atmospheric Environment 38, 457–467.] the authors have developed data assimilation (DA) procedures and implemented them in the frames of a diagnostic meteorological pre-processor (MPP) to enable simultaneous use of meteorological measurements with numerical weather prediction (NWP) data. The DA techniques were directly validated showing a clear improvement of the MPP output quality in comparison with meteorological measurement data. In the current paper it is demonstrated that the application of DA procedures in the MPP, to combine meteorological measurements with NWP data, has a noticeable positive effect on the performance of an atmospheric dispersion model (ADM) driven by the MPP output. This result is particularly important for emergency response systems used for accidental releases of pollutants, because it provides the possibility to combine meteorological measurements with NWP data in order to achieve more reliable dispersion predictions. This is also an indirect way to validate the DA procedures applied in the MPP. The above goal is achieved by applying the Lagrangian ADM DIPCOT driven by meteorological data calculated by the MPP code both with and without the use of DA procedures to simulate the first European tracer experiment (ETEX I). The performance of the ADM in each case was evaluated by comparing the predicted and the experimental concentrations with the use of statistical indices and concentration plots. The comparison of resulting concentrations using the different sets of meteorological data showed that the activation of DA in the MPP code clearly improves the performance of dispersion calculations in terms of plume shape and dimensions, location of maximum concentrations, statistical indices and time variation of concentration at the detectors locations. |
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