skip to main content
10.1145/2335755.2335831acmotherconferencesArticle/Chapter ViewAbstractPublication PagesxsedeConference Proceedingsconference-collections
research-article

Using the XSEDE supercomputing and visualization resources to improve tornado prediction using data mining

Published: 16 July 2012 Publication History

Abstract

In this paper we introduce the use of XSEDE resources and mathematical models for the simulation of tornadoes, as well as novel techniques for analyzing the results of these simulations.

References

[1]
J. Neville, D. Jensen, L. Friedland, and M. Hay. Learning Relational Probability Trees. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 625--630, 2003.
[2]
Kimes, Ross, 2012: Spatiotemporal Relational Data Mining of High Resolution Supercell Simulations. Capstone, University of Oklahoma, School of Meteorology.
[3]
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.
[4]
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.
[5]
Ming Xue and Donghai Wang and Jidong Gao and Keith Brewster and Kelvin K. Droegemeier. "The Advanced Regional Prediction System (ARPS) - storm-scale numerical weather prediction and data assimilation. "Meteorology and Atmospheric Physics 82 (2003): 161--193.
[6]
Ming Xue, Kevin Droegemeier, and V. Wong. "The Advanced Regional Prediction System (ARPS) - A Multiscale Nonhydrostatic Atmospheric Simulation and Prediction Model. Part 1: Model Dynamics and Verfication. "Meteorology and Atmospheric Physics 75 (2000): 161--193.
[7]
Ming Xue, Kelvin K. Droegemeier, V. Wong and A. Shapiro and Keith Brewster and Fred Carr and D. Weber and Y. Liu and D. Wang. "The Advanced Regional Prediction System (ARPS) - A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part 2: Model physics and applications. "Meteorology and Atmospheric Physics 76 (2001): 143--165.
[8]
Supinie, Timothy and McGovern, Amy and Williams, John and Abernethy, Jennifer. Spatiotemporal Relational Random Forests. Proceedings of the 2009 IEEE International Conference on Data Mining (ICDM) workshop on Spatiotemporal Data Mining, electronically published.

Cited By

View all
  • (2024)Evaluating Return on Investment for Cyberinfrastructure Using the International Integrated Reporting FrameworkSN Computer Science10.1007/s42979-024-02889-z5:5Online publication date: 17-May-2024

Index Terms

  1. Using the XSEDE supercomputing and visualization resources to improve tornado prediction using data mining

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    XSEDE '12: Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
    July 2012
    423 pages
    ISBN:9781450316026
    DOI:10.1145/2335755

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 July 2012

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. data mining
    2. relational
    3. simulations
    4. spatiotemoral

    Qualifiers

    • Research-article

    Conference

    XSEDE12

    Acceptance Rates

    Overall Acceptance Rate 129 of 190 submissions, 68%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 12 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Evaluating Return on Investment for Cyberinfrastructure Using the International Integrated Reporting FrameworkSN Computer Science10.1007/s42979-024-02889-z5:5Online publication date: 17-May-2024

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media