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

Sujit K. Sahu, Stan Yip, David M. Holland

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)494-501
Number of pages8
JournalAtmospheric Environment
Volume43
Issue number3
DOIs
Publication statusPublished - Jan 2009
Externally publishedYes

Keywords

  • Bayesian modeling
  • Data fusion
  • Hierarchical model
  • Markov chain Monte Carlo
  • Spatial interpolation

ASJC Scopus subject areas

  • Environmental Science(all)
  • Atmospheric Science

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