Real-time bias-adjusted O3 and PM2.5 air quality index forecasts and their performance evaluations over the continental United States |
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| Authors: | Daiwen Kang Rohit Mathur S Trivikrama Rao |
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| Institution: | 1. Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China;2. Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China;1. Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA;2. Department of Civil & Environmental Engineering, University of California, Davis, CA 95616, USA;3. Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA;4. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA;1. College of Meteorology and Oceanology, National University of Defense Technology, Nanjing 211101, China;2. Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Edifício C8, Piso 3, 1749-016 Lisboa, Portugal;3. Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China |
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| Abstract: | The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA’s North American Mesoscale (NAM) meteorological model with EPA’s Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O3) and experimental fine particular matter (PM2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O3 and PM2.5 forecasts at discrete monitoring locations. The operational surface-level O3 and PM2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using near real-time hourly O3 and PM2.5 observations obtained from EPA’s AIRNow measurement network. The KF bias-adjusted forecasts were created daily, providing 24-h hourly bias-adjusted forecasts for O3 and PM2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 min of CPU on a single processor Linux machine to generate 24-h hourly bias-adjusted forecasts over the entire CONUS domain.The results show that the real-time KF bias-adjusted forecasts for both O3 and PM2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O3 and PM2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O3 and daily mean PM2.5 forecasts in terms of both discrete (i.e., reduced errors, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008. |
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