DATA MINING TO INVESTIGATE THE METEOROLOGICAL DRIVERS FOR EXTREME GROUND LEVEL OZONE EVENTS

• 成果类型: Article
• 署名作者: Russell, Brook T.; Cooley, Daniel S.; Porter, William C.; Reich, Brian J.; Heald, Colette L.
• 署名单位: Clemson University; Colorado State University System; Colorado State University Fort Collins; Massachusetts Institute of Technology (MIT); North Carolina State University; Massachusetts Institute of Technology (MIT)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS954
• 发表日期: 2016
• 页码: 1673-1698
• 关键词: Optimization

摘要

This project aims to explore which combinations of meteorological conditions are associated with extreme ground level ozone conditions. Our approach focuses only on the tail by optimizing the tail dependence between the ozone response and functions of meteorological covariates. Since there is a long list of possible meteorological covariates, the space of possible models cannot be explored completely. Consequently, we perform data mining within the model selection context, employing an automated model search procedure. Our study is unique among extremes applications, as optimizing tail dependence has not previously been attempted, and it presents new challenges, such as requiring a smooth threshold. We present a simulation study which shows that the method can detect complicated conditions leading to extreme responses and resists overfitting. We apply the method to ozone data for Atlanta and Charlotte and find similar meteorological drivers for these two Southeastern US cities. We identify several covariates which help to differentiate the meteorological conditions which lead to extreme ozone levels from those which lead to merely high levels.