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IJCNN
2008
IEEE

A comparison of bayesian and conditional density models in probabilistic ozone forecasting

13 years 10 months ago
A comparison of bayesian and conditional density models in probabilistic ozone forecasting
— Probabilistic models were developed to provide predictive distributions of daily maximum surface level ozone concentrations. Five forecast models were compared at two stations (Chilliwack and Surrey) in the Lower Fraser Valley of British Columbia, Canada, with local meteorological variables used as predictors. The models were of two types, conditional density models and Bayesian models. The Bayesian models (especially the Gaussian Processes) gave better forecasts for extreme events, namely poor air quality events defined as having ozone concentration ≥ 82 ppb.
Song Cai, William W. Hsieh, Alex J. Cannon
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IJCNN
Authors Song Cai, William W. Hsieh, Alex J. Cannon
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