Data Mining to Investigate the Meteorological Drivers for Extreme Ground Level Ozone Events

TL;DR

This study develops a novel data mining approach focusing on tail dependence to identify meteorological conditions associated with extreme ground-level ozone events, applying it to data from Atlanta and Charlotte.

Contribution

It introduces a new method optimizing tail dependence for extreme event analysis, addressing challenges like smooth thresholds and model selection in meteorological data.

Findings

Identified key meteorological drivers for extreme ozone events in two US cities.

Demonstrated the method's ability to detect complex conditions leading to extremes.

Showed the approach resists overfitting in simulation studies.

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.