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Advances in Meteorology
Volume 2012 (2012), Article ID 191575, 13 pages
http://dx.doi.org/10.1155/2012/191575
Research Article

Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone

1Finance, eBay Inc., San Jose, CA 95125, USA
2BC Cancer Agency Research Center, Vancouver, BC, Canada V5Z 4E6
3Department of Statistics, University of British Columbia, Vancouver, BC, Canada V6T 1Z2

Received 14 January 2012; Revised 24 April 2012; Accepted 27 April 2012

Academic Editor: Tareq Hussein

Copyright © 2012 Yiping Dou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper develops and empirically compares two Bayesian and empirical Bayes space-time approaches for forecasting next-day hourly ground-level ozone concentrations. The comparison involves the Chicago area in the summer of 2000 and measurements from fourteen monitors as reported in the EPA's AQS database. One of these approaches adapts a multivariate method originally designed for spatial prediction. The second is based on a state-space modeling approach originally developed and used in a case study involving one week in Mexico City with ten monitoring sites. The first method proves superior to the second in the Chicago Case Study, judged by several criteria, notably root mean square predictive accuracy, computing times, and calibration of 95% predictive intervals.