Predicting ground-level ozone concentrations by adaptive Bayesian model averaging of statistical seasonal models
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While seasonal time-varying models should generally be used to predict the daily concentration of ground-level ozone given its strong seasonal cycles, the sudden switching of models according to their designated period in an annual operational forecasting system may affect their performance, especially during the season’s transitional period in which the starting date and duration time can vary from year to year. This paper studies the effectiveness of an adaptive Bayesian Model Averaging scheme with the support of a transitional prediction model in solving the problem. The scheme continuously evaluates the probabilities of all the ozone prediction models (ozone season, nonozone season, and the transitional period) in a forecasting system, which are then used to provide a weighted average forecast. The scheme has been adopted in predicting the daily maximum of 8-h averaged ozone concentration in Macau for a period of 2 years (2008 and 2009), with results proved to be satisfactory.
KeywordsAdaptive Bayesian model averaging Kalman filter Model switching Ozone prediction Statistical model
The supports from the Science and Technology Development Fund of the Macau SAR Government under Grant No. 079/2013/A3 and the university Multi-year Research Grant MYRG2014-00038-FST of the research committee of the University of Macau are acknowledged. The Macau Meteorological and Geophysical Bureau is thanked for supplying the data.
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