Anticipating the formation of tornadoes through data mining

87th AMS Annual Meeting

Volumn , Issue , 2007, Pages

  McGovern, Amy ^a   Rosendahl, Derek H ^b   Kruger, Adrianna ^a   Beaton, Meredith G ^a   Brown, Rodger A ^c   Droegemeier, Kelvin K ^b  

^a University of Oklahoma   (United States)

^b UNIVERSITY OF OKLAHOMA   (United States)

^c NATIONAL SEVERE STORMS LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77949287059     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Adlerman, E. and K. K. Droegemeier, 2005: The dependence of numerically simulated cyclic meso-cyclogenesis upon environmental vertical wind shear. Mon. Wea. Rev., 133, 3595-3623.
2. Alderman, E. J., K. K. Droegemeier, and R. P. Davies-Jones, 1999: A numerical simulation of cyclic mesocyclogenesis. Journal of Atmospheric Science, 56, 2045-2069.
3. Brown, R. A., L. R. Lemon, and D. W. Burgess, 1978: Tornado detection by pulsed doppler radar. Monthly Weather Review, 106, 29-38.
4. Burgess, D. W., V. T. Wood, and R. A. Brown, 1982: Mesocyclone evolution statistics. Preprints, 10th Conf. on Severe Local Storms, Amer. Meteor. Soc, San Antonio, TX, 84-89.
5. Davies-Jones, R., D. Burgess, and M. Foster, 1990: Test of helicity as a tornado forecast parameter. Preprints, 16th Conference on Severe Local Storms, Amer. Meteor. Soc, Kananaskis Park, AB, Canada, 588-592.
6. Dietterich, T. G., R. H. Lathrop, and T. Lozano-Perez, 1997: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89, 31-71.
7. Domingos, P. and M. Pazzani, 1997: On the optimal-ity of the simple bayesian classifier under zero-one loss. Machine Learning, 29, 103-130.
8. Donaldson, J. R., Jr., 1970: Vortex signature recognition by a doppler radar. Journal of Applied Meteorology, 9, 661-670.
9. Holte, R., 1993: Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63-90.
10. Johnson, J. T., P. L. Mackeen, A. Witt, E. D. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The storm cell identification and tracking algorithm: An enhanced wsr-88d algorithm. Weather and Forecasting, 13, 263-276.
11. Keogh, E., J. Lin, and A. Fu, 2005: HOT SAX: Efficiently finding the most unusual time series subsequence. Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), Houston, Texas, 226-233.
12. Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2006: The Warning Decision Support System - Integrated Information (WDSS-II). Weather and Forecasting, in press.
13. Lin, J., E. Keogh, S. Lonardi, and B. Chiu, 2003: A symbolic representation of time series, with implications for streaming algorithms. Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.
14. McGovern, A. and D. Jensen, 2006: Chi-squared: A simpler evaluation function for multiple-instance learning. Under Review.
15. McGovern, A., A. Kruger, D. Rosendahl, and K. Droegemeier, 2006: Open problem: Dynamic relational models for improved hazardous weather prediction, Presented at the ICML Workshop on Open Problems in Statistical Relational Learning.
16. McLaughlin, D. J., V. Chandrasekar, K. Droege-meier, S. Frasier, J. Kurose, F. Junyent, B. Philips, S. Cruz-Pol, and J. Colom, 2005: Distributed collaborative adaptive sensing (DCAS) for improved detection, understanding, and prediction of atmospheric hazards. 9th Symp. Integrated Obs. As-sim. Systems - Atmos. Oceans, Land Surface (IOAS-AOLS), Amer. Meteor. Soc, San Diego, CA.
17. Mitchell, E. D., S. V. Vasiloff, G. J. Stumpf, A. Witt, M. D. Eilts, J. Johnson, and K. W. Thomas, 1998: The National Severe Storms Laboratory tornado detection algorithm. Weather and Forecasting, 13, 352-366.
18. Oates, T. and P. R. Cohen, 1996: Searching for structure in multiple streams of data. Proceedings of the Thirteenth International Conference on Machine Learning, Morgan Kauffman, 346-354.
19. Pielke, R. and R. Carbone, 2002: Weather impacts, forecasts, and policy. Bulletin of the American Meteorological Society, 83, 393-403.
20. Ray, P., B. Johnson, K. Johnson, J. Bradberry, J. Stephens, K. Wagner, R. Wilhelmson, and J. Klemp, 1981: The morphology of several tor-nadic storms on 20 May 1977. J. Atmos. Sci, 38, 1643-1663.
21. Schapire, R. E., 2003: The boosting approach to machine learning: An overview. Nonlinear Estimation and Classification, D. D. Denison, M. H. Hansen, C. Holmes, B. Mallick, and B. Yu, eds., Springer.
22. Simmons, K. M. and D. Sutter, 2005: WSR-88D radar, tornado warnings, and tornado casualties. Weather and Forecasting, 20, 301-310.
23. Stumpf, G. J., A. Witt, E. D. Mitchell, P. L. Spencer, J. Johnson, M. D. Eilts, K. W. Thomas, and D. W. Burgess, 1998: The National Severe Storms Laboratory mesocyclone detection algorithm for the WSR-88D. Weather and Forecasting, 13, 304-326.
24. Tong, M. and M. Xue, 2005: Ensemble kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789-1807.
25. Weisman, M. and J. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Monthly Weather Review, 110, 504-520.
26. Wood, V. T. and R. A. Brown, 1997: Effects of radar sampling on single-Doppler velocity signatures of mesocyclones and tornadoes. Weather and Forecasting, 12, 928-938.
27. Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS) - a multiscale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification. Meteorology and Atmospheric Physics, 75, 161-193.
28. Xue, M., K. K. Droegemeier, V. Wong, A. Shapiro, K. Brewster, F. Carr, D. Weber, Y. Liu, and D. Wang, 2001: The Advanced Regional Prediction System (ARPS) - a multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteorology and Atmospheric Physics, 76, 134-165.
29. Xue, M., D. Wang, J. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteorology and Atmospheric Physics, 82, 139-170.
30. Zaki, M. J., 2001: Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42, 31-60, special issue on unsupervised learning.