Improved space–time forecasting of next day ozone concentrations in the eastern US

• Published in: Atmospheric environment (1994) 2009, Vol.43 (3), p.494-501
• Main Authors: Sahu, Sujit K., Yip, Stan, Holland, David M.
• Format: Article
• Language: English
• Subjects: Air. Soil. Water. Waste. Feeding; Analysis methods; Applied sciences; Atmospheric pollution; Bayesian modeling; Biological and medical sciences; Data fusion; Environment. Living conditions; Exact sciences and technology; Hierarchical model; Markov chain Monte Carlo; Medical sciences; Pollutants physicochemistry study: properties, effects, reactions, transport and distribution; Pollution; Public health. Hygiene; Public health. Hygiene-occupational medicine; Spatial interpolation
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2008.10.028

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 (O 3) 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 O 3 concentrations in the eastern United States.