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Spatio-temporal modeling for real-time ozone forecasting.

Publication ,  Journal Article
Paci, L; Gelfand, AE; Holland, DM
Published in: Spatial statistics
May 2013

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. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for 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.

Duke Scholars

Published In

Spatial statistics

DOI

EISSN

2211-6753

ISSN

2211-6753

Publication Date

May 2013

Volume

4

Start / End Page

79 / 93

Related Subject Headings

  • 4905 Statistics
  • 0801 Artificial Intelligence and Image Processing
  • 0104 Statistics
 

Citation

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ICMJE
MLA
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Paci, L., Gelfand, A. E., & Holland, D. M. (2013). Spatio-temporal modeling for real-time ozone forecasting. Spatial Statistics, 4, 79–93. https://doi.org/10.1016/j.spasta.2013.04.003
Paci, Lucia, Alan E. Gelfand, and David M. Holland. “Spatio-temporal modeling for real-time ozone forecasting.Spatial Statistics 4 (May 2013): 79–93. https://doi.org/10.1016/j.spasta.2013.04.003.
Paci L, Gelfand AE, Holland DM. Spatio-temporal modeling for real-time ozone forecasting. Spatial statistics. 2013 May;4:79–93.
Paci, Lucia, et al. “Spatio-temporal modeling for real-time ozone forecasting.Spatial Statistics, vol. 4, May 2013, pp. 79–93. Epmc, doi:10.1016/j.spasta.2013.04.003.
Paci L, Gelfand AE, Holland DM. Spatio-temporal modeling for real-time ozone forecasting. Spatial statistics. 2013 May;4:79–93.
Journal cover image

Published In

Spatial statistics

DOI

EISSN

2211-6753

ISSN

2211-6753

Publication Date

May 2013

Volume

4

Start / End Page

79 / 93

Related Subject Headings

  • 4905 Statistics
  • 0801 Artificial Intelligence and Image Processing
  • 0104 Statistics