Improved space-time forecasting of next day ozone concentrations in the eastern U.S.
Improved space-time forecasting of next day ozone concentrations in the eastern U.S.
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
bayesian modeling, data fusion, hierarchical model, markov chain monte carlo, spatial interpolation
494-501
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Yip, Stan
87641316-820a-45c9-ba44-a28f8cb5039b
Holland, David
a9f17543-f54c-49ee-b1f3-5bbe23a2061a
January 2009
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Yip, Stan
87641316-820a-45c9-ba44-a28f8cb5039b
Holland, David
a9f17543-f54c-49ee-b1f3-5bbe23a2061a
Sahu, Sujit K., Yip, Stan and Holland, David
(2009)
Improved space-time forecasting of next day ozone concentrations in the eastern U.S.
Atmospheric Environment, 43 (3), .
(doi:10.1016/j.atmosenv.2008.10.028).
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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Published date: January 2009
Keywords:
bayesian modeling, data fusion, hierarchical model, markov chain monte carlo, spatial interpolation
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Statistics
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Local EPrints ID: 147699
URI: http://eprints.soton.ac.uk/id/eprint/147699
ISSN: 1352-2310
PURE UUID: a254027b-807a-4ce0-9275-b7bb9303c8e7
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Date deposited: 26 Apr 2010 12:14
Last modified: 14 Mar 2024 02:44
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Author:
Stan Yip
Author:
David Holland
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