Data mining to investigate the meteorological drivers for extreme ground level ozone events
Name
Heald_Data mining to investigate.pdf
Size
688.64 KB
Format
Adobe PDF
Checksum (MD5)
3b3c8467fbc963510118ce30e9a8d13c
Author(s)
Russell, Brook T.
Cooley, Daniel S.
Reich, Brian J.
Porter, William C
Heald, Colette L.
Date Issued
September 2016
Journal
Annals of Applied Statistics
Publisher
Institute of Mathematical Statistics
Citation
Russell, Brook T.; Cooley, Daniel S.; Porter, William C.; Reich, Brian J. and Heald, Colette L. “Data Mining to Investigate the Meteorological Drivers for Extreme Ground Level Ozone Events.” The Annals of Applied Statistics 10, no. 3 (September 2016): 1673–1698
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1214/16-AOAS954
