Improved space–time forecasting of next day ozone concentrations in the eastern US |
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| Authors: | Sujit K Sahu Stan Yip David M Holland |
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| Institution: | 1. School of Mathematics, University of Southampton, Southampton, SO17 1BJ, UK;2. US Environmental Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC 27711, USA;3. Exeter Climate Systems, University of Exeter, Exeter, EX4 4QJ, UK;1. ACRI-HE, 260 Route du Pin Montard, BP 234, 06904 Sophia Antipolis cedex, France;2. GeographR, 1 Rue de Taulignan, 84000 Avignon, France;1. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA;2. National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Chapel Hill, NC, USA;3. Institute of Epidemiology II, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Neuherberg, Germany;4. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA;5. Tel-Aviv University, Department of Geography and Human Environment, School of Geosciences, Israel;6. Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA;7. National Environmental Exposure Laboratory, US Environmental Protection Agency, Research Triangle Park, NC, USA;8. Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA;1. School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China;2. Commonwealth Scientific and Industrial Research Organization (CSIRO) Energy, North Ryde, Australia;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States;2. Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States;3. Department of Physics, University of Nevada, Reno, NV 89557, United States;4. Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States |
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| Abstract: | There is an urgent need to provide accurate air quality information and forecasts to the general public and environmental health decision-makers. This paper develops a hierarchical space–time model for daily 8-h maximum ozone concentration (O3) data covering much of the eastern United States. The model combines observed data and forecast output from a computer simulation model known as the Eta Community Multi-scale Air Quality (CMAQ) forecast model in a very flexible, yet computationally fast way, so that the next day forecasts can be computed in real-time operational mode. The model adjusts for spatio-temporal biases in the Eta CMAQ forecasts and avoids a change of support problem often encountered in data fusion settings where real data have been observed at point level monitoring sites, but the forecasts from the computer model are provided at grid cell levels. The model is validated with a large amount of set-aside data and is shown to provide much improved forecasts of daily O3 concentrations in the eastern United States. |
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