High resolution space-time ozone modeling for assessing trends
High resolution space-time ozone modeling for assessing trends
This paper proposes a space-time model for daily 8-hour maximum ozone levels to provide input for regulatory activities: detection, evaluation, and analysis of spatial patterns and temporal trend in ozone summaries. The model is applied to the analysis of data from the state of Ohio which contains a mix of urban, suburban, and rural ozone monitoring sites. The proposed space-time model is auto-regressive and incorporates the most important meteorological variables observed at a collection of ozone monitoring sites as well as at several weather stations where ozone levels have not been observed. This misalignment is handled through spatial modeling. In so doing we adopt a computationally convenient approach based on the successive daily increments in meteorological variables. The resulting hierarchical model is specified within a Bayesian framework and is fitted using MCMC techniques. Full inference with regard to model unknowns as well as for predictions in time and space, evaluation of annual summaries and assessment of trends are presented.
University of Southampton
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, Alan E.
1dc59cf1-5e5f-4001-b1f9-92b0a8e2f64f
Holland, David M.
a7040f79-48c3-42f3-a449-137888cbcf28
13 February 2007
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, Alan E.
1dc59cf1-5e5f-4001-b1f9-92b0a8e2f64f
Holland, David M.
a7040f79-48c3-42f3-a449-137888cbcf28
Sahu, Sujit K., Gelfand, Alan E. and Holland, David M.
(2007)
High resolution space-time ozone modeling for assessing trends
(S3RI Methodology Working Papers, M07/01)
Southampton, GB.
University of Southampton
27pp.
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Monograph
(Working Paper)
Abstract
This paper proposes a space-time model for daily 8-hour maximum ozone levels to provide input for regulatory activities: detection, evaluation, and analysis of spatial patterns and temporal trend in ozone summaries. The model is applied to the analysis of data from the state of Ohio which contains a mix of urban, suburban, and rural ozone monitoring sites. The proposed space-time model is auto-regressive and incorporates the most important meteorological variables observed at a collection of ozone monitoring sites as well as at several weather stations where ozone levels have not been observed. This misalignment is handled through spatial modeling. In so doing we adopt a computationally convenient approach based on the successive daily increments in meteorological variables. The resulting hierarchical model is specified within a Bayesian framework and is fitted using MCMC techniques. Full inference with regard to model unknowns as well as for predictions in time and space, evaluation of annual summaries and assessment of trends are presented.
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44093-01.pdf
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Published date: 13 February 2007
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Local EPrints ID: 44093
URI: http://eprints.soton.ac.uk/id/eprint/44093
PURE UUID: 3dca2ca4-46d4-4173-bae9-f0f50bfce82f
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Date deposited: 13 Feb 2007
Last modified: 16 Mar 2024 03:15
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Author:
Alan E. Gelfand
Author:
David M. Holland
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