Skip to main content
SpringerLink
Log in
Book cover

Data Mining for Geoinformatics pp 29–47Cite as

Toward Understanding Tornado Formation Through Spatiotemporal Data Mining

  • Chapter
  • First Online:

  • 2062 Accesses

Abstract

Tornadoes, which are one of the most feared natural phenomena, present a significant challenge to forecasters who strive to provide adequate warnings of the imminent danger. Forecasters recognize the general environmental conditions within which a tornadic thunderstorm, called a supercell thunderstorm, will form. They also recognize a supercell thunderstorm with its rotating updraft, or mesocyclone, when it appears on radar. However, only a minority of supercell storms produce tornadoes. Although most tornadoes are warned in advance, the majority of the tornado warnings are false alarms. In this chapter, we discuss the development of novel spatiotemporal data mining techniques for discriminating between supercell storms that produce tornadoes and those that do not. To test the novel techniques, we initially applied them to numerical models having coarse 500 meter horizontal grid spacing that did not resolve tornadoes but that did resolve the parent mesocyclones.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Allen JF (1991) Time and time again: the many ways to represent time. Int J Intell Syst 6(4): 341–355

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  • Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: In the 9th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, pp 493–498

    Google Scholar 

  • Das G, Lin K, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, New York, pp 16–22

    Google Scholar 

  • Donaldson Jr RJ, Dyer RM, Kraus MJ (1975) An objective evaluator of techniques for predicting severe weather events. In: Preprints: ninth conference on severe local storms, American Meteorological Society, Norman, pp 321–326

    Google Scholar 

  • Dwyer K, Holte R (2007) Decision tree instability and active learning. In: ECML ’07: proceedings of the 18th European conference on machine learning, Warsaw. Springer-Verlag, Berlin, pp 128–139

    Google Scholar 

  • Jensen D (2005) Proximity knowledge discovery system. http://kdl.cs.umass.edu/proximity

  • Johnson JT, MacKeen PL, Witt A, Mitchell ED, Stumpf GJ, Eilts MD, Thomas KW (1998) The storm cell identification and tracking algorithm: an enhanced WSR-88D algorithm. Weather Forecast 13(2):263–276

    Article  Google Scholar 

  • Keogh E, Lin J, Fu A (2005) HOT SAX: efficiently finding the most unusual time series subsequence. In: Proceedings of the 5th IEEE international conference on data mining (ICDM 2005), Houston, pp 226–233

    Google Scholar 

  • Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery, San Diego, pp 2–11

    Google Scholar 

  • Lin J, Keogh E, Li W, Lonardi S (2007) Experiencing SAX: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107–144

    Article  MathSciNet  Google Scholar 

  • McGovern A, Jensen D (2008) Optimistic pruning for multiple instance learning. Pattern Recognit Lett 29(9):1252–1260

    Article  Google Scholar 

  • McGovern A, Rosendahl DH, Kruger A, Beaton MG, Brown RA, Droegemeier KK (2007) Anticipating the formation of tornadoes through data mining. In: Preprints of the fifth conference on artificial intelligence and its applications to environmental sciences at the American Meteorological Society annual meeting, American Meteorological Society, San Antonio, Paper 4.3A

    Google Scholar 

  • McGovern A, Hiers N, Collier M, Gagne II DJ, Brown RA (2008) Spatiotemporal relational probability trees. In: Proceedings of the 2008 IEEE international conference on data mining, Pisa, pp 935–940

    Google Scholar 

  • McGovern A, Supinie T, Gagne II DJ, Troutman N, Collier M, Brown RA, Basara J, Williams J (2010) Understanding severe weather processes through spatiotemporal relational random forests. In: Proceedings of the 2010 NASA conference on intelligent data understanding, Mountain View, pp 213–227

    Google Scholar 

  • McGovern A, Gagne II DJ, Troutman N, Brown RA, Basara J, Williams J (2011a) Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Stat Anal Data Min 4(4):407–429

    Article  MathSciNet  Google Scholar 

  • McGovern A, Rosendahl DH, Brown RA, Droegemeier KK (2011b) Identifying predictive multi-dimensional time series motifs: an application to understanding severe weather. Data Min Knowl Discov 22(1):232–258

    Article  Google Scholar 

  • McGovern A, Troutman N, Brown RA, Williams JK, Abernethy J (2013) Enhanced spatiotemporal relational probability trees and forests. Data Min Knowl Discov 26(2):398–433

    Article  MathSciNet  Google Scholar 

  • Minnen D, Isbell C, Essa I, Starner T (2007) Detecting subdimensional motifs: an efficient algorithm for generalized multivariate pattern discovery. In: Proceedings of the 2007 seventh IEEE international conference on data mining (ICDM ’07), Omaha, pp 601–606

    Google Scholar 

  • Mueen A, Keogh E, Zhu Q, Cash S, Westover B (2009) Exact discovery of time series motifs. In: Proceedings of the SIAM international conference on data mining, Sparks, pp 473–484

    Google Scholar 

  • Neville J, Jensen D (2004) Dependency networks for relational data. In: Proceedings of the fourth IEEE international conference on data mining, Brighton, pp 170–177

    Google Scholar 

  • Neville J, Jensen D, Friedland L, Hay M (2003) Learning relational probability trees. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 625–630

    Google Scholar 

  • Noda A, Niino H (2005) Genesis and structure of a major tornado in a numerically–simulated supercell storm: importance of vertical vorticity in a gust front. Sci Online Lett Atmos 1:5–8

    Google Scholar 

  • NWS (2009) Service assessment, Mother’s Day weekend tornado in Oklahoma and Missouri, May 10, 2008. http://www.nws.noaa.gov/os/assessments/pdfs/mothers_day09.pdf

  • NWS (2011) Nws central region service assessment, Joplin, Missouri, tornado – May 22, 2011. http://www.nws.noaa.gov/os/assessments/pdfs/Joplin_tornado.pdf

  • Oates T, Cohen PR (1996) Searching for structure in multiple streams of data. In: Proceedings of the thirteenth international conference on machine learning. Morgan Kaufmann, Bari, pp 346–354

    Google Scholar 

  • Pérez JM, Muguerza J, Arbelaitz O, Gurrutxaga I, Martìn JI (2005) Consolidated trees: classifiers with stable explanation. A model to achieve the desired stability in explanation. In: Lecture notes in computer science, Springer Berlin, Heidelberg, pp 99–17

    Google Scholar 

  • Quinlan JR (1993) C4.5: programs for Machine Learning. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Rosendahl DH (2008) Identifying precursors to strong low-level rotation within numerically simulated supercell thunderstorms: a data mining approach. Master’s thesis, School of Meteorology, University of Oklahoma

    Google Scholar 

  • Schaefer JT (1990) The critical success index as an indicator of warning skill. Weather Forecast 5(4):570–575

    Article  MathSciNet  Google Scholar 

  • Shieh J, Keogh E (2009) iSAX: indexing and mining terabyte sized time series. In: Proceedings of the IEEE international conference on data mining

    Google Scholar 

  • Simmons KM, Sutter D (2011) Economic and societal impacts of tornadoes. American Meteorological Society, Boston

    Book  Google Scholar 

  • Supinie T, McGovern A, Williams J, Abernethy J (2009) Spatiotemporal relational random forests. In: Proceedings of the IEEE international conference on data mining (ICDM) workshop on spatiotemporal data mining.p electronically published

    Google Scholar 

  • Vahdatpour A, Amini N, Sarrafzadeh M (2009) Toward unsupervised activity discovery using multi-dimensional motif detection in time series. In: Proceedings of the 21st international joint conference on artificial intelligence (IJCAI’09), Pasadena, pp 1261–1266

    Google Scholar 

  • Webb GI (1995) OPUS: an efficient admissible algorithm for unordered search. J Artif Intell Res 3:431–465

    MATH  Google Scholar 

  • Wicker LJ, Wilhelmson RB (1995) Simulation and analysis of tornado development and decay within a three–dimensional supercell thunderstorm. J Atmos Sci 52(15):2675–2703

    Article  Google Scholar 

  • Xue M, Droegemeier KK, Wong V (2000) The Advanced Regional Prediction System (ARPS) - a multiscale nonhydrostatic atmospheric simulation and prediction model. Part I: model dynamics and verification. Meteorol Atmos Phys 75:161–193

    Article  Google Scholar 

  • Xue M, Droegemeier KK, Wong V, Shapiro A, Brewster K, Carr F, Weber D, Liu Y, Wang D (2001) The Advanced Regional Prediction System (ARPS) – a multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: model physics and applications. Meteorol Atmos Phys 76:143–165

    Article  Google Scholar 

  • Xue M, Wang D, Gao J, Brewster K, Droegemeier KK (2003) The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteorol Atmos Phys 82:139–170

    Article  Google Scholar 

  • Xue M, Droegemeier KK, Weber D (2007) Numerical prediction of high-impact local weather: a driver for petascale computing. In: Petascale computing: algorithms and applications. Chapman and Hall/CRC, Boca Raton, chap 18, pp 103–125

    Google Scholar 

  • Zaki MJ (2001) Spade: an efficient algorithm for mining frequent sequences. Mach Learn 42(1/2):31–60. special issue on unsupervised learning

    Google Scholar 

Download references

Acknowledgements

This material is based upon work supported by the National Science Foundation under IIS/CAREER/0746816 and corresponding REU Supplements IIS/0840956, 0938138, 1036023, 1129292, and the NSF ERC Center for Collaborative Adaptive Sensing of the Atmosphere (CASA, NSF ERC 0313747).

Author information

Authors and Affiliations

  1. School of Computer Science, University of Oklahoma, 110 W Boyd St, Norman, OK, 73019, USA

    Amy McGovern

  2. School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd. Suite 5900, Norman, OK, 73072, USA

    Derek H. Rosendahl

  3. National Severe Storms Laboratory/National Oceanic and Atmospheric Administration, 120 David L Boren Blvd, Norman, OK, 73072, USA

    Rodger A. Brown

Authors
  1. Amy McGovern
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Derek H. Rosendahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rodger A. Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amy McGovern .

Editor information

Editors and Affiliations

  1. Department of Geography and Geoinformati, George Mason University, Fairfax, Virginia, USA

    Guido Cervone

  2. Department of Computer Science, George Mason University, Fairfax, Virginia, USA

    Jessica Lin

  3. Department of Geography and Geoinformati, George Mason University, Fairfax, Virginia, USA

    Nigel Waters

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

McGovern, A., Rosendahl, D.H., Brown, R.A. (2014). Toward Understanding Tornado Formation Through Spatiotemporal Data Mining. In: Cervone, G., Lin, J., Waters, N. (eds) Data Mining for Geoinformatics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7669-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7669-6_2

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7668-9

  • Online ISBN: 978-1-4614-7669-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation