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.
dynamic model, forecasting/prediction, markov chain Monte carlo, misalignment, spatial variability, stationarity
1221-1234
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, Alan E.
1dc59cf1-5e5f-4001-b1f9-92b0a8e2f64f
Holland, David M.
a7040f79-48c3-42f3-a449-137888cbcf28
December 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.
Journal of the American Statistical Association, 102 (480), .
(doi:10.1198/016214507000000031).
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.
Text
sahu_jasa.pdf
- Accepted Manuscript
More information
Published date: December 2007
Keywords:
dynamic model, forecasting/prediction, markov chain Monte carlo, misalignment, spatial variability, stationarity
Organisations:
Statistics
Identifiers
Local EPrints ID: 48123
URI: http://eprints.soton.ac.uk/id/eprint/48123
ISSN: 0162-1459
PURE UUID: 695551b2-d708-40b6-8fdf-0a5342189d4e
Catalogue record
Date deposited: 29 Aug 2007
Last modified: 16 Mar 2024 03:15
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Contributors
Author:
Alan E. Gelfand
Author:
David M. Holland
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