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Article Type:
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Diagnosing the Conditional Probability of Tornado Damage Rating Using Environmental and Radar Attributes

Bryan T. Smith NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

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Richard L. Thompson NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

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Andrew R. Dean NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

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Patrick T. Marsh NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

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Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/WAF-D-14-00122.1
Page(s):
914–932
Restricted access

Abstract

Radar-identified convective modes, peak low-level rotational velocities, and near-storm environmental data were assigned to a sample of tornadoes reported in the contiguous United States during 2009–13. The tornado segment data were filtered by the maximum enhanced Fujita (EF)-scale tornado event per hour using a 40-km horizontal grid. Convective mode was assigned to each tornado event by examining full volumetric Weather Surveillance Radar-1988 Doppler data at the beginning time of each event, and 0.5° peak rotational velocity (Vrot) data were identified manually during the life span of each tornado event. Environmental information accompanied each grid-hour event, consisting primarily of supercell-related convective parameters from the hourly objective mesoscale analyses calculated and archived at the Storm Prediction Center. Results from examining environmental and radar attributes, featuring the significant tornado parameter (STP) and 0.5° peak Vrot data, suggest an increasing conditional probability for greater EF-scale damage as both STP and 0.5° peak Vrot increase, especially with supercells. Possible applications of these findings include using the conditional probability of tornado intensity as a real-time situational awareness tool.

Corresponding author address: Bryan T. Smith, NOAA/NWS/NCEP/Storm Prediction Center, 120 David L. Boren Blvd., Ste. 2300, Norman, OK 73072. E-mail: bryan.smith@noaa.gov

Abstract

Radar-identified convective modes, peak low-level rotational velocities, and near-storm environmental data were assigned to a sample of tornadoes reported in the contiguous United States during 2009–13. The tornado segment data were filtered by the maximum enhanced Fujita (EF)-scale tornado event per hour using a 40-km horizontal grid. Convective mode was assigned to each tornado event by examining full volumetric Weather Surveillance Radar-1988 Doppler data at the beginning time of each event, and 0.5° peak rotational velocity (Vrot) data were identified manually during the life span of each tornado event. Environmental information accompanied each grid-hour event, consisting primarily of supercell-related convective parameters from the hourly objective mesoscale analyses calculated and archived at the Storm Prediction Center. Results from examining environmental and radar attributes, featuring the significant tornado parameter (STP) and 0.5° peak Vrot data, suggest an increasing conditional probability for greater EF-scale damage as both STP and 0.5° peak Vrot increase, especially with supercells. Possible applications of these findings include using the conditional probability of tornado intensity as a real-time situational awareness tool.

Corresponding author address: Bryan T. Smith, NOAA/NWS/NCEP/Storm Prediction Center, 120 David L. Boren Blvd., Ste. 2300, Norman, OK 73072. E-mail: bryan.smith@noaa.gov
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  • Alexander, C. R., and Wurman J. M. , 2008: Updated mobile radar climatology of supercell tornado structures and dynamics. Preprints, 24th Conf. Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 19.4. [Available online at https://ams.confex.com/ams/pdfpapers/141821.pdf.]

  • Andra, D. L., Jr., 1997: The origin and evolution of the WSR-88D mesocyclone recognition nomogram. Preprints, 28th Conf. on Radar Meteorology, Austin, TX, Amer. Meteor. Soc., 364–365.

  • Benjamin, S. G., and Coauthors, 2004: An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev., 132, 495–518, doi:10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Blair, S. F., and Leighton J. W. , 2014: Assessing real-time tornado information disseminated through NWS products. Wea. Forecasting, 29, 591–600, doi:10.1175/WAF-D-13-00126.1.

    • Search Google Scholar
    • Export Citation

  • Bodine, D. J., Kumjian M. R. , Palmer R. D. , Heinselman P. L. , and Ryzhkov A. V. , 2013: Tornado damage estimation using polarimetric radar. Wea. Forecasting, 28, 139–158, doi:10.1175/WAF-D-11-00158.1.

    • Search Google Scholar
    • Export Citation

  • Bothwell, P. D., Hart J. A. , and Thompson R. L. , 2002: An integrated three-dimensional objective analysis scheme in use at the Storm Prediction Center. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., J117–J120. [Available online at https://ams.confex.com/ams/pdfpapers/47482.pdf.]

  • Brady, R. H., and Szoke E. J. , 1989: A case study of nonmesocyclone tornado development in northeast Colorado: Similarities to waterspout formation. Mon. Wea. Rev., 117, 843–856, doi:10.1175/1520-0493(1989)117<0843:ACSONT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., 2004: On the relationship of tornado path length and width to intensity. Wea. Forecasting, 19, 310–319, doi:10.1175/1520-0434(2004)019<0310:OTROTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brotzge, J. A., Nelson S. E. , Thompson R. L. , and Smith B. T. , 2013: Tornado probability of detection and lead time as a function of convective mode and environmental parameters. Wea. Forecasting, 28, 1261–1275, doi:10.1175/WAF-D-12-00119.1.

    • Search Google Scholar
    • Export Citation

  • Bunkers, M. J., and Baxter M. A. , 2011: Radar tornadic debris signatures on 27 April 2011. Electron. J. Oper. Meteor., 12 (7). [Available online at http://www.nwas.org/ej/2011/2011.php.]

    • Search Google Scholar
    • Export Citation

  • Burgess, D. W., Lee R. L. , Parker S. S. , Keighton S. J. , and Floyd D. L. , 1995: A study of mini supercells observed by WSR-88D radars. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 4–6.

  • Coniglio, M. C., 2012: Verification of RUC 0–1-km forecasts and SPC mesoscale analyses using VORTEX2 soundings. Wea. Forecasting, 27, 667–683, doi:10.1175/WAF-D-11-00096.1.

    • Search Google Scholar
    • Export Citation

  • Craven, J. P., and Brooks H. E. , 2004: Baseline climatology of sounding derived parameters associated with deep, moist convection. Natl. Wea. Dig., 28, 13–24.

    • Search Google Scholar
    • Export Citation

  • Crum, T. D., Alberty R. L. , and Burgess D. W. , 1993: Recording, archiving, and using WSR-88D data. Bull. Amer. Meteor. Soc., 74, 645–653, doi:10.1175/1520-0477(1993)074<0645:RAAUWD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Davies, J. M., 1993: Small tornadic supercells in the central plains. Preprints, 17th Conf. Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 305–309.

  • Davies, J. M., 2004: Estimations of CIN and LFC associated with tornadic and nontornadic supercells. Wea. Forecasting, 19, 714–726, doi:10.1175/1520-0434(2004)019<0714:EOCALA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Davies, J. M., and Fischer A. , 2009: Environmental characteristics associated with nighttime tornadoes. Electron. J. Oper. Meteor.,10 (3). [Available online at http://www.nwas.org/ej/2009/2009.php].

  • Davis, J. M., and Parker M. D. , 2014: Radar climatology of tornadic and nontornadic vortices in high-shear, low-CAPE environments in the Mid-Atlantic and southeastern United States. Wea. Forecasting, 29 ,828–853, doi:10.1175/WAF-D-13-00127.1.

    • Search Google Scholar
    • Export Citation

  • Doswell, C. A., III, Edwards R. , Thompson R. L. , Hart J. A. , and Crosbie K. C. , 2006: A simple and flexible method for ranking severe weather events. Wea. Forecasting, 21, 939–951, doi:10.1175/WAF959.1.

    • Search Google Scholar
    • Export Citation

  • Eilts, M. D., and Smith S. D. , 1990: Efficient dealiasing of Doppler velocities using local environment constraints. J. Atmos. Oceanic Technol., 7, 118–128, doi:10.1175/1520-0426(1990)007<0118:EDODVU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • French, M. M., Bluestein H. B. , PopStefanija I. , Baldi C. A. , and Bluth R. T. , 2013: Reexamining the vertical development of tornadic vortex signatures in supercells. Mon. Wea. Rev., 141, 4576–4601, doi:10.1175/MWR-D-12-00315.1.

    • Search Google Scholar
    • Export Citation

  • Grant, B. N., and Prentice R. , 1996: Mesocyclone characteristics of mini supercell thunderstorms. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., 362–365.

  • Guyer, J. L., and Hart J. A. , 2012: Examination of WSR-88D VWP data in proximity to strong tornadoes. Preprints, 26th Conf. on Severe Local Storms, Nashville, TN, Amer. Meteor. Soc., 62. [Available online at https://ams.confex.com/ams/26SLS/webprogram/Paper211652.html.]

  • Kennedy, P. C., Westcott N. E. , and Scott R. W. , 1993: Single-Doppler radar observations of a mini-supercell tornadic thunderstorm. Mon. Wea. Rev., 121, 1860–1870, doi:10.1175/1520-0493(1993)121<1860:SDROOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kingfield, D. M., LaDue J. G. , and Ortega K. L. , 2012: An evaluation of tornado intensity using velocity and strength attributes from the WSR-88D mesocyclone detection algorithm. Preprints, 26th Conf. on Severe Local Storms, Nashville, TN, Amer. Meteor. Soc., 3.2. [Available online at https://ams.confex.com/ams/26SLS/webprogram/Paper211488.html.]

  • LaDue, J. G., Ortega K. L. , Smith B. R. , Stumpf G. J. , and Kingfield D. M. , 2012: A comparison of high resolution tornado surveys to Doppler radar observed mesocyclone parameters: 2011–2012 case studies. Preprints, 26th Conf. on Severe Local Storms, Nashville, TN, Amer. Meteor. Soc., 6.3. [Available online at https://ams.confex.com/ams/26SLS/webprogram/Paper212627.html.]

  • Laflin, J. M., 2013: Verification of RAP model soundings in preconvective environments. J. Oper. Meteor., 1 (6), 66–70, doi:10.15191/nwajom.2013.0106.

    • Search Google Scholar
    • Export Citation

  • Magsig, M. A., 2008: New techniques for integrating environmental information with radar base data analysis in National Weather Service warning decision making. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., P6.10. [Available online at https://ams.confex.com/ams/24SLS/techprogram/paper_142264.htm.]

  • Mitchell, E. D., Vasiloff S. V. , Stumpf G. J. , Witt A. , Eilts M. D. , Johnson J. T. , and Thomas K. W. , 1998: The National Severe Storms Laboratory tornado detection algorithm. Wea. Forecasting, 13, 352–366, doi:10.1175/1520-0434(1998)013<0352:TNSSLT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Newman, J. F., Lakshmanan V. , Heinselman P. L. , Richman M. B. , and Smith T. M. , 2013: Range-correcting azimuthal shear in Doppler radar data. Wea. Forecasting, 28, 194–211, doi:10.1175/WAF-D-11-00154.1.

    • Search Google Scholar
    • Export Citation

  • Parker, M. D., 2014: Composite VORTEX2 supercell environments from near-storm soundings. Mon. Wea. Rev., 142, 508–529, doi:10.1175/MWR-D-13-00167.1.

    • Search Google Scholar
    • Export Citation

  • Potvin, C. K., Elmore K. L. , and Weiss S. J. , 2010: Assessing the impacts of proximity sounding criteria on the climatology of significant tornado environments. Wea. Forecasting, 25, 921–930, doi:10.1175/2010WAF2222368.1.

    • Search Google Scholar
    • Export Citation

  • Rasmussen, E. N., 2003: Refined supercell and tornado forecast parameters. Wea. Forecasting, 18, 530–535, doi:10.1175/1520-0434(2003)18<530:RSATFP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Rasmussen, E. N., and Blanchard D. O. , 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 1148–1164, doi:10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • ROC, 2014: NEXRAD coverage below 10,000 feet AGL. NOAA/Radar Operations Center, accessed 1 September 2014. [Available online at http://www.roc.noaa.gov/WSR88D/Maps.aspx.]

  • Schultz, C. J., and Coauthors, 2012a: Dual-polarization tornadic debris signatures Part I: Examples and utility in an operational setting. Electron. J. Oper. Meteor., 13 (9), 120–137. [Available online at http://www.nwas.org/ej/2012/2012.php.]

    • Search Google Scholar
    • Export Citation

  • Schultz, C. J., and Coauthors, 2012b: Dual-polarization tornadic debris signatures Part II: Comparisons and caveats. Electron. J. Oper. Meteor., 13 (10), 138–150. [Available online at http://www.nwas.org/ej/2012/2012.php.]

    • Search Google Scholar
    • Export Citation

  • Smith, B. T., Thompson R. L. , Grams J. S. , Broyles C. , and Brooks H. E. , 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part I: Storm classification and climatology. Wea. Forecasting, 27, 1114–1135, doi:10.1175/WAF-D-11-00115.1.

    • Search Google Scholar
    • Export Citation

  • Smith, T. M., and Elmore K. L. , 2004: The use of radial velocity derivatives to diagnose rotation and divergence. Preprints, 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer. Meteor. Soc., P5.6. [Available online at http://ams.confex.com/ams/pdfpapers/81827.pdf.]

  • Speheger, D. A., and Smith R. D. , 2006: On the imprecision of radar signature locations and storm path forecasts. Natl. Wea. Dig., 30, 3–10.

    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., Cortinas J. V. Jr., and Brooks H. E. , 1997: Discriminating between tornadic and nontornadic thunderstorms using mesoscale model output. Wea. Forecasting, 12, 613–632, doi:10.1175/1520-0434(1997)012<0613:DBTANT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487–1499, doi:10.1175/2009BAMS2795.1.

    • Search Google Scholar
    • Export Citation

  • Stumpf, G. J., Witt A. , Mitchell E. D. , Spencer P. L. , Johnson J. T. , Eilts M. D. , Thomas K. W. , and Burgess D. W. , 1998: The National Severe Storms Laboratory mesocyclone detection algorithm for the WSR-88D. Wea. Forecasting, 13, 304–326, doi:10.1175/1520-0434(1998)013<0304:TNSSLM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., and Vescio M. D. , 1998: The destruction potential index: A method for comparing tornado days. Preprints, 19th Conf. Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 280–282.

  • Thompson, R. L., Edwards R. , Hart J. A. , Elmore K. L. , and Markowski P. M. , 2003: Close proximity soundings within supercell environments obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 1243–1261, doi:10.1175/1520-0434(2003)018<1243:CPSWSE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., Mead C. M. , and Edwards R. , 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102–115, doi:10.1175/WAF969.1.

    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., Smith B. T. , Grams J. S. , Dean A. R. , and Broyles C. , 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part II: Supercell and QLCS tornado environments. Wea. Forecasting, 27, 1136–1154, doi:10.1175/WAF-D-11-00116.1.

    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., Smith B. T. , Dean A. R. , and Marsh P. T. , 2013: Spatial distributions of tornadic near-storm environments by convective mode. Electron. J. Severe Storms Meteor., 8 (5). [Available online at http://www.ejssm.org/ojs/index.php/ejssm/issue/view/47.]

    • Search Google Scholar
    • Export Citation

  • Torres, S. M., and Curtis C. D. , 2007: Initial implementation of super-resolution data on the NEXRAD network. Preprints, 23rd Int. Conf. on Interactive Information Processing Systems, San Antonio, TX, Amer. Meteor. Soc., 5B.10. [Available online at http://ams.confex.com/ams/pdfpapers/116240.pdf.]

  • Toth, M., Trapp R. J. , Wurman J. , and Kosiba K. A. , 2013: Comparison of mobile-radar measurements of tornado intensity with corresponding WSR-88D measurements. Wea. Forecasting, 28, 418–426, doi:10.1175/WAF-D-12-00019.1.

    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., and Weisman M. L. , 2003: Low-level mesovortices within squall lines and bow echoes. Part II: Their genesis and implications. Mon. Wea. Rev., 131, 2804–2823, doi:10.1175/1520-0493(2003)131<2804:LMWSLA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., Tessendorf S. A. , Godfrey E. S. , and Brooks H. E. , 2005: Tornadoes from squall lines and bow echoes. Part I: Climatological distribution. Wea. Forecasting, 20, 23–34, doi:10.1175/WAF-835.1.

    • Search Google Scholar
    • Export Citation

  • Wagenmaker, R., Mann G. , and Hudson M. , 2014: IBW project: Introduction and overview. NOAA/NWS, accessed 15 July 2014. [Available online at http://www.crh.noaa.gov/images/dtx/GLOMW/Presentations/2014_IBW_Project_Intro_Overview.pdf.]

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 59, Academic Press, 627 pp.

  • Wood, V. T., and Brown R. A. , 1997: Effects of radar sampling on single-Doppler velocity signatures of mesocyclones and tornadoes. Wea. Forecasting, 12, 928–938, doi:10.1175/1520-0434(1997)012<0928:EORSOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zittel, W. D., and Jing Z. , 2012: Comparison of a 2-D velocity dealiasing algorithm to the legacy WSR-88D velocity dealiasing algorithm during Hurricane Irene. Preprints, 30th Conf. on Hurricanes and Tropical Meteorology, Ponte Vedra Beach, FL, Amer. Meteor. Soc., 7C.7. [Available online at https://ams.confex.com/ams/30Hurricane/webprogram/Paper205076.html.]

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