Particulate air quality model predictions using prognostic vs. diagnostic meteorology in central California |
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| Authors: | Jianlin Hu Qi Ying Jianjun Chen Abdullah Mahmud Zhan Zhao Shu-Hua Chen Michael J. Kleeman |
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| Affiliation: | 1. Department of Land, Air and Water Resources, University of California, Davis, 1 Shields Ave, Davis CA 95616, USA;2. Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA;3. Department of Civil and Environmental Engineering, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA;1. Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, NC 27599, USA;2. Institute for the Environment, University of North Carolina at Chapel Hill, 137 E. Franklin St., #648A, Chapel Hill, NC 27599-6116, USA;1. Atmospheric Chemistry Services, Okehampton, Devon EX20 1FB, UK;2. School of Chemistry, University of Bristol, Cantock''s Close, Bristol BS8 1TS, UK;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA;2. School of Energy and Environment, Southeast University, Nanjing, China;1. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China;2. Chengdu Academy of Environmental Sciences, Chengdu, 610072, China;3. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China;4. State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China |
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| Abstract: | Comparisons were made between three sets of meteorological fields used to support air quality predictions for the California Regional Particulate Air Quality Study (CRPAQS) winter episode from December 15, 2000 to January 6, 2001. The first set of fields was interpolated from observations using an objective analysis method. The second set of fields was generated using the WRF prognostic model without data assimilation. The third set of fields was generated using the WRF prognostic model with the four-dimensional data assimilation (FDDA) technique. The UCD/CIT air quality model was applied with each set of meteorological fields to predict the concentrations of airborne particulate matter and gaseous species in central California. The results show that the WRF model without data assimilation over-predicts surface wind speed by ～30% on average and consequently yields under-predictions for all PM and gaseous species except sulfate (S(VI)) and ozone(O3). The WRF model with FDDA improves the agreement between predicted and observed wind and temperature values and consequently yields improved predictions for all PM and gaseous species. Overall, diagnostic meteorological fields produced more accurate air quality predictions than either version of the WRF prognostic fields during this episode. Population-weighted average PM2.5 exposure is 40% higher using diagnostic meteorological fields compared to prognostic meteorological fields created without data assimilation. These results suggest diagnostic meteorological fields based on a dense measurement network are the preferred choice for air quality model studies during stagnant periods in locations with complex topography. |
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