Association between NSSL Mesocyclone Detection Algorithm-detected vortices and tornadoes

Weather and Forecasting

Volumn 19, Issue 5, 2004, Pages 872-890

  Jones, Thomas A ^a,b   McGrath, Kevin M ^a   Snow, John T ^a  

^a UNIVERSITY OF OKLAHOMA   (United States)

^b UNIVERSITY OF ALABAMA IN HUNTSVILLE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CORRELATION METHODS; DATABASE SYSTEMS; VORTEX FLOW;

DETECTION THRESHOLDS; MESOCYCLONE DETECTION ALGORITHMS (MDA); MESOCYCLONES; SPURIOUS VORTEX DETECTIONS;

TORNADOES;

DETECTION METHOD; RADAR; TORNADO; VORTEX;

EID: 13644280076     PISSN: 08828156     EISSN: None     Source Type: Journal    
DOI: 10.1175/1520-0434(2004)019<0872:ABNMDA>2.0.CO;2     Document Type: Article

References (30)

1. Brooks, H. E., C. A. Doswell III, and J. Cooper, 1994: On the environment of tornadic and nontornadic mesocyclones. Wea. Forecasting 9, 606-618.
2. Brown, R. A., J. M. Janish, and V. T. Wood, 2000: Impacts of WSR-88D scanning strategies on severe storm algorithms. Wea. Forecasting, 15, 90-102.
3. Burgess, D. W., 1976: Single Doppler radar vortex recognition: Part 1: Mesocyclone signatures. Preprints, 17th Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor Soc., 97-103.
4. Burgess, D. W., and L. R. Lemon, 1991: Characteristics of mesocyclones detected during NEXRAD test. Preprints, 25th Conf. on Radar Meteorology, Paris, France, Amer. Meteor. Soc., 39-42.
5. Burgess, D. W., and C. A. Doswell, 1993: Tornadoes and tornadic storms: A review of conceptual models. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., No. 79, Amer. Geophys. Union, 161-172.
6. Burgess, D. W., R. J. Donaldson, and P. R. Desrochers Jr., 1993: Tornado detection and warning by radar. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., No. 79, Amer. Geophys. Union, 203-221.
7. Desrochers, P. R., and R. J. Donaldson, 1992: Automatic tornado prediction with an improved mesocyclone-detection algorithm. Wea. Forecasting, 7, 373-388.
8. Droegemeier, K. D., and Coauthors, 2002: Project CRAFT: A test bed for demonstrating the real time acquisition and archival of WSR-88D level II data. Preprints, 18th Int. Conf. on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Orlando, FL, Amer. Meteor. Soc., 136-139.
9. JDOP 1979: Final report on the joint Doppler operational project. NOAA Tech. Memo. ERL NSSL-86, National Severe Storms Laboratory, Norman, OK, 84 pp.
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 (SCIT) algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263-276.
11. Jones, T. A., K. M. McGrath, and J. T. Snow, 2002: Improved methodology for correlating mesocyclone detections with tornadoes. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 166-169.
12. Kaiser, H. F., 1958: The Varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200.
13. Lakshmanan, V., 2002: An extensible, multi-source meteorological algorithm developmental interface. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 134-137.
14. Lee, R. R., and A. White, 1998: Improvement of the WSR-88D mesocyclone algorithm. Wea. Forecasting, 13, 341-351.
15. Marzban, C., and G. J. Stumpf, 1996: A neural network for tornado prediction based on Doppler radar-derived attributes. J. Appl. Meteor., 35, 617-626.
16. Marzban, C., E. D. Mitchell, and G. J. Stumpf, 1998: What are the "best predictors of tornadoes." Preprints, 19th Conf. on Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 729-732.
17. Marzban, C., E. D. Mitchell, and G. J. Stumpf, 1999: The notion of best predictors: An application to tornado prediction. Wea. Forecasting, 14, 1007-1016.
18. McGrath, K. M., T. A. Jones, and J. T. Snow, 2002: Increasing the usefulness of a mesocyclone climatology. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 162-165.
19. Mitchell, E. D., S. V. Vasiloff, G. J. Stumpf, A. Witt, M. D. Eilts, J. T. Johnson, and K. W. Thomas, 1998: The National Severe Storms Laboratory tornado detection algorithm. Wea. Forecasting, 13, 352-366.
20. Mitchell, E. D., K. L. Elmore, K. Angle, and C. Hannon, 2000: A radar signature climatology using WSR-88D level II data. Preprints, 20th Conf. on Severe Local Storms, Orlando, FL, Amer. Meteor. Soc., 92-94.
21. NSSL, 2000: WATADS version 10.2: WSR-88D algorithm testing and display system reference guide. National Severe Storms Laboratory, Norman, OK, 200 pp.
22. North, G. R., T. L. Bell, and R. F. Cahalan, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699-706.
23. Overland, J. E., and R. W. Preisendorfer, 1982: A significance test for principal components applied to cyclone climatology. Mon. Wea. Rev., 110, 1-4.
24. Richman, M. B., 1986: Rotation of principal components. J. Climatol., 6, 293-335.
25. Richman, M. B., and X. Gong, 1999: Relationships between the definition of the hyperplane width to the fidelity of principal component loading patterns. J. Climate, 12, 1557-1576.
26. Stumpf, G. J., A. Witt, E. D. Mitchell, P. L. Spencer, J. T. Johnson, M. D. Eilts, K. W. Thomas, and D. W. Burgess, 1998: The National Severe Storms Laboratory mesocyclone detection algorithm for the WSR-88D. Wea. Forecasting, 13, 304-326.
27. Trapp, R. J., and G. J. Stumpf, 2002: A reassessment of the percentage of tornadic mesocyclones. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 198-201.
28. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 237 pp.
29. Witt, A., M. D. Eilts, G. J. Stumpf, E. D. Mitchell, J. T. Johnson, and K. W. Thomas, 1998: Evaluating the performance of WSR-88D severe storm detection algorithms. Wea. Forecasting, 13, 513-518.
30. Zrnić, D. S., D. W. Burgess, and L. D. Hennington, 1985: Automatic detection of mesocyclone shear with Doppler radar. J. Atmos. Oceanic Technol., 2, 425-438.