title: Spatio-temporal modeling for real-time ozone forecasting.
abstract: The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Suchreal-timeforecasting is now provided as spatial forecast maps ofcurrent8-hour average ozone defined as the average of the previous four hours, current hour, andpredictionsfor the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.
authors.affiliation: Department of Statistical Science at Duke University, Box 90251, Durham NC 27708-0251, USA.
authors.forename: Lucia
authors.initials: L
authors.lastname: Paci
authors.forename: Alan E
authors.initials: AE
authors.lastname: Gelfand
authors.forename: David M
authors.initials: DM
authors.lastname: Holland
keywords: Markov chain Monte Carlo; data fusion; hierarchical model; kriging; space-time covariance; time differencing