Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning

Members - Faculty, students, and collaborators
Research - Details about our current research projects
Theses and Dissertations - Publications and code releases for student theses and disserations
Publications - Recent technical papers and presentations
Software - Recent software releases
 

Abstract

Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.

Paper

McGovern, Amy and Gagne II, David J. and Williams, John K. and Brown, Rodger A. and Basara, Jeffrey B. (2014) Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Machine Learning. Vol 95, Issue 1, pages 27-50.

Note: we have updated the code significantly with improved documentation as of July 2014. If you downloaded it before then, please re-download.

Code release


Created by amcgovern [at] ou.edu.

Last modified July 24, 2014 4:11 PM