Open Access
September 2016 Data mining to investigate the meteorological drivers for extreme ground level ozone events
Brook T. Russell, Daniel S. Cooley, William C. Porter, Brian J. Reich, Colette L. Heald
Ann. Appl. Stat. 10(3): 1673-1698 (September 2016). DOI: 10.1214/16-AOAS954


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.


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Brook T. Russell. Daniel S. Cooley. William C. Porter. Brian J. Reich. Colette L. Heald. "Data mining to investigate the meteorological drivers for extreme ground level ozone events." Ann. Appl. Stat. 10 (3) 1673 - 1698, September 2016.


Received: 1 October 2015; Revised: 1 March 2016; Published: September 2016
First available in Project Euclid: 28 September 2016

zbMATH: 06775282
MathSciNet: MR3553240
Digital Object Identifier: 10.1214/16-AOAS954

Keywords: constrained optimization , cross-validation , multivariate regular variation , smooth threshold , tail dependence

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 3 • September 2016
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