July 1 2008 - June 2013 (no cost extension until June 2015)
The goal of this research is to revolutionize the ability to anticipate tornadoes by developing advanced techniques for statistical pattern discovery in spatially and temporally varying relational data. These models are applied to complete fields of meteorological quantities obtained through data assimilation and simulation. Doppler radar data is limited and, while modern data assimilation techniques allow the unobserved quantities to be estimated, the resulting four-dimensional fields are too complicated for the extraction of meaningful, repeatable patterns by either humans or current data mining techniques. By studying a full field of variables, the models can identify critical interactions among high level features. The models are developed and verified in close collaboration with domain experts.
The interdisciplinary research is used to improve retention and recruitment in computer science (CS). This draws on recent evidence that underrepresented groups are not drawn to computing careers because they do not appreciate how computing can be used to solve real world problems. Introducing authentic projects into both early CS and meteorology classes will improve the number of technically trained students in both majors.
The primary broader impact of this research is to society, through the potential for reduction in loss of human life, property, and money. Models will be made available to operational meteorologists as they are verified. Another broader impact will come from increasing the number of computing oriented minors and majors through authentic projects. All data and results will be disseminated through peer reviewed publications and via open source online repositories.
McGovern, Amy and Potvin, Corey and Brown, Rodger A. (to appear) Using Large-scale Machine Learning to Improve our Understanding of the Formation of Tornadoes. Invited chapter in Large-Scale Machine Learning in the Earth Sciences.
McGovern, Amy McGovern and Balfour, Andrea and Beene, Marissa and Harrison, David (2015) Storm Evader: Using an iPad To Teach Kids about Meteorology and Technology. Bulletin of the American Meteorological Society, Volume 96, Issue 3, pages 397-404. Final pdf
McGovern, Amy and Rosendahl, Derek H. and Brown, Rodger A.(2014) Toward Understanding Tornado Formation Through Spatiotemporal Data Mining. Book chapter in Data Mining for Geoinformatics: Methods and Applications, edited by Cervone, Guid and Lin, Jessica and Waters, Nigel. 29 DOI 10.1007/978-1-4614-7669-6 2, Springer Science Business Media New York 2014. [link to a pre-print of the pdf. The officially formatted pdf is linked above.]
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. [online version, open access]
McGovern, Amy and Troutman, Nathaniel and Brown, Rodger A. and Williams, John K. and Abernethy, Jennifer. (2013) Enhanced Spatiotemporal Relational Probability Trees and Forests. Data Mining and Knowledge Discovery, Volume 26, Issue 2, pages 398-433. [online first version, open access]
Pirtle, Bradley and Kimes, Ross and McGovern, Amy and Brown, Rodger A. (2012) Using the XSEDE Supercomputing and Visualization Resources to Improve Tornado Prediction Using Data Mining. Presented at the XSEDE 2012 Conference. [extended abstract pdf]
Gagne II, David John and McGovern, Amy and and Basara, Jeffrey and Brown, Rodger A. (2012) Tornadic Supercell Environments Analyzed Using Surface and Reanalysis Data: A Spatiotemporal Relational Data Mining Approach. Journal of Applied Meteorology and Climatology. Vol. 51, No. 12, pages 2203-2217.
McGovern, Amy and Troutman, Nathaniel and Brown, Rodger A. and Williams, John K. and Abernethy, Jennifer. (2012) Enhanced Spatiotemporal Relational Probability Trees and Forests. To appear in Data Mining and Knowledge Discovery. [online first version, open access]
McGovern, Amy and Gagne II, David John and Troutman, Nathaniel and Brown, Rodger A. and Basara, Jeffrey and Williams, John. (2011) Using Spatiotemporal Relational Random Forests to Improve our Understanding of Severe Weather Processes. Statistical Analysis and Data Mining, special issue on the best of the 2010 NASA Conference on Intelligent Data Understanding. Vol 4, Issue 4, pages 407-429. [pdf preprint (1.4M), link to official online version]
McGovern, Amy and Rosendahl, D and Brown, R and Droegemeier, K. (2011) Identifying Predictive Multi-Dimensional Time Series Motifs: An application to severe weather. Data Mining and Knowledge Discovery. Volume 22, Issue 1, pages 237-258. [pdf (2.0M). Link to official springer version.]
McGovern, Amy; Supinie, Timothy; Gagne II, David John; Troutman, Nathaniel; Collier, Matthew; Brown, Rodger A.; Basara, Jeffrey; Williams, John. (2010) Understanding Severe Weather Processes through Spatiotemporal Relational Random Forests. Proceedings of the NASA Conference on Intelligent Data Understanding: CIDU 2010. pdf (500K)
Supinie, Timothy and McGovern, Amy and Williams, John and Abernethy, Jennifer. (2009) Spatiotemporal Relational Random Forests. Proceedings of the 2009 IEEE International Conference on Data Mining (ICDM) workshop on Spatiotemporal Data Mining. [pdf (272 K)]
Bodenhamer, Matthew; Bleckley, Samuel; Fennelly, Daniel; Fagg, Andrew H. and McGovern, Amy. (2009) Spatio-temporal Multi-Dimensional Relational Framework Trees. Proceedings of the 2009 IEEE International Conference on Data Mining (ICDM) workshop on Spatiotemporal Data Mining. [pdf (305 K)]
Gagne II, David J.; McGovern, Amy; Brotzge, Jerry. (2009). Classification of Convective Areas Using Decision Trees. Journal of Atmospheric and Oceanic Technology. Vol 26, Issue 7, pages 1341-1353. [pdf (1.1 MB)]
McGovern, Amy and Hiers, Nathan and Collier, Matthew and Gagne II, David J. and Brown, Rodger A. (2008). Spatiotemporal Relational Probability Trees. Proceedings of the 2008 IEEE International Conference on Data Mining, Pages 935-940. Pisa, Italy. 15-19 December 2008. [pdf (326K)]
Collier, Matthew and McGovern, Amy. (2008). Kernels for the Investigation of Localized Spatiotemporal Transitions of Drought with Support Vector Machines. Proceedings of ICDM 2008, the 8th IEEE International Conference on Data Mining Workshops. Pisa, Italy. 15-19 December 2008, pages 359-368. [pdf (400K)]
The presentations below include links to the actual talks themselves (for AMS talks) and videos and associated slides. The earlier presentations include some of the preliminary results that we used to demonstrate the feasibility of this study for the CAREER award.
This material is based upon work supported by the National Science Foundation under Grant No. IIS .0746816. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Point of contact: Amy McGovern. Last updated August 29, 2015 11:10 AM