A fast Bayesian method for updating and forecasting hourly ozone levels
A fast Bayesian method for updating and forecasting hourly ozone levels
A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows that the model predicted maps are more accurate than the maps based solely on the Eta-CMAQ forecast data for a 2 week test period. These out-of sample spatial predictions and temporal forecasts also outperform those from regression models with independent Gaussian errors. The method is fully Bayesian and is able to instantly update the map for the current hour (upon receiving monitor data for the current hour) and forecast the map for several hours ahead. In particular, the 8 h average map which is the average of the past 4 h, current hour and 3 h ahead is instantly obtained at the current hour. Based on our validation, the exact Bayesian method is preferable to more complex models in a real-time updating and forecasting environment.
bayesian inference, eta-cmaq model, space-time forecasting, hierarchical model, separable models, spatial interpolation
185-207
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Yip, Stan
87641316-820a-45c9-ba44-a28f8cb5039b
Holland, David M.
a7040f79-48c3-42f3-a449-137888cbcf28
9 November 2009
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Yip, Stan
87641316-820a-45c9-ba44-a28f8cb5039b
Holland, David M.
a7040f79-48c3-42f3-a449-137888cbcf28
Sahu, Sujit K., Yip, Stan and Holland, David M.
(2009)
A fast Bayesian method for updating and forecasting hourly ozone levels.
Environmental and Ecological Statistics, 18 (1), .
(doi:10.1007/s10651-009-0127-y).
Abstract
A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows that the model predicted maps are more accurate than the maps based solely on the Eta-CMAQ forecast data for a 2 week test period. These out-of sample spatial predictions and temporal forecasts also outperform those from regression models with independent Gaussian errors. The method is fully Bayesian and is able to instantly update the map for the current hour (upon receiving monitor data for the current hour) and forecast the map for several hours ahead. In particular, the 8 h average map which is the average of the past 4 h, current hour and 3 h ahead is instantly obtained at the current hour. Based on our validation, the exact Bayesian method is preferable to more complex models in a real-time updating and forecasting environment.
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Published date: 9 November 2009
Keywords:
bayesian inference, eta-cmaq model, space-time forecasting, hierarchical model, separable models, spatial interpolation
Organisations:
Statistics
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Local EPrints ID: 147821
URI: http://eprints.soton.ac.uk/id/eprint/147821
ISSN: 1352-8505
PURE UUID: 75e1b843-5ce2-48fc-96e6-53a1fff9123c
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Date deposited: 26 Apr 2010 13:41
Last modified: 14 Mar 2024 02:44
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Author:
Stan Yip
Author:
David M. Holland
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