Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006 |
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| Authors: | I Djalalova J Wilczak S McKeen G Grell S Peckham M Pagowski L DelleMonache J McQueen Y Tang P Lee J McHenry W Gong V Bouchet R Mathur |
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| Institution: | 1. Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA;2. Earth System Research Laboratory/Physical Sciences Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA;3. Earth System Research Laboratory/Chemical Sciences Division, National Oceanic and Atmospheric, Administration, Boulder, CO, USA;4. Earth System Research Laboratory/Global Systems Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA;5. National Center for Atmospheric Research, Boulder, CO, USA;6. National Weather Service/National Center for Environmental Prediction/Environmental Modeling Center, National Oceanic and Atmospheric Administration, Camp Springs, MD, USA;7. Air Resource Laboratory, National Oceanic and Atmospheric Administration, Silver Spring, MD, USA;8. Baron Advanced Meteorological Systems, Raleigh, NC, USA;9. Environment Canada, Science and Technology Branch, Downsview, Ontario, Canada;10. Environment Canada, Meteorological Service of Canada, Dorval, Quebec, Canada;11. Environmental Protection Agency/National Exposure Research Laboratory, Research Triangle, Park, NC, USA;1. Air Quality Forecasting Lab, North Carolina State University, Raleigh, NC 27695, USA;2. Barons Advanced Meteorological Systems, Raleigh, NC, USA;1. Department of Civil and Environmental Engineering, University of Macau, Macau, China;2. Department of Environment and Planning & CESAM, University of Aveiro, Portugal;3. Now at Institute of Energy and Climate Research, Troposphere (IEK-8), Jülich Research Center, Germany;1. Yangtze River Delta Center for Environmental Meteorology Prediction and Warning, Shanghai 200030, PR China;2. Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, PR China;1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;2. Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, Chinese Meteorological Administration, Beijing 100089, China;3. Korea Polar Research Institute, Incheon 406-840, Republic of Korea |
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| Abstract: | Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories. Evaluation of individual model and ensemble forecasts of surface ozone and particulate matter (PM) was performed using data from 119 EPA AIRNow ozone sites and 38 PM sites during a 50-day period in August and September of 2006. From the original set of models, two new bias-corrected model data sets were built, either by applying a simple running mean average to the past 7 days of data or by a Kalman-Filter approach. From the original and two bias-corrected data sets, three ensembles were created by a simple averaging of the seven models. For further improvements three additional weighted model ensembles were created, where individual model weights were calculated using the singular value decomposition method. All six of the ensembles are compared to the individual models and to each other in terms of root mean square error, correlation, and contingency and probabilistic statistics. In most cases, each of the ensembles show improved skill compared to the best of the individual models. The over all best ensemble technique was found to be the combination of Kalman-Filtering and weighted averaging. PM2.5 aerosol ensembles demonstrated significant improvement gains, mostly because the original model's skill was very low. |
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