Editorial Type:
Article
Article Type:
Research Article

WSR-88D Tornado Intensity Estimates. Part I: Real-Time Probabilities of Peak Tornado Wind Speeds

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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Douglas A. Speheger NOAA/NWS, Weather Forecast Office, Norman, Oklahoma

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

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Christopher D. Karstens NOAA/NWS/NCEP, Storm Prediction Center, Norman, Oklahoma

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Alexandra K. Anderson-Frey Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma

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Online Publication:
20 Nov 2020
Print Publication:
01 Dec 2020
DOI:
https://doi.org/10.1175/WAF-D-20-0010.1
Page(s):
2479–2492
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Abstract

The Storm Prediction Center (SPC) has developed a database of damage-surveyed tornadoes in the contiguous United States (2009–17) that relates environmental and radar-derived storm attributes to damage ratings that change during a tornado life cycle. Damage indicators (DIs), and the associated wind speed estimates from tornado damage surveys compiled in the Damage Assessment Toolkit (DAT) dataset, were linked to the nearest manual calculations of 0.5° tilt angle maximum rotational velocity Vrot from single-site WSR-88D data. For each radar scan, the maximum wind speed from the highest-rated DI, Vrot, and the significant tornado parameter (STP) from the SPC hourly objective mesoscale analysis archive were recorded and analyzed. Results from examining Vrot and STP data indicate an increasing conditional probability for higher-rated DIs (i.e., EF-scale wind speed estimate) as both STP and Vrot increase. This work suggests that tornadic wind speed exceedance probabilities can be estimated in real time, on a scan-by-scan basis, via Vrot and STP for ongoing tornadoes.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bryan T. Smith, bryan.smith@noaa.gov

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-20-0011.1.

Abstract

The Storm Prediction Center (SPC) has developed a database of damage-surveyed tornadoes in the contiguous United States (2009–17) that relates environmental and radar-derived storm attributes to damage ratings that change during a tornado life cycle. Damage indicators (DIs), and the associated wind speed estimates from tornado damage surveys compiled in the Damage Assessment Toolkit (DAT) dataset, were linked to the nearest manual calculations of 0.5° tilt angle maximum rotational velocity Vrot from single-site WSR-88D data. For each radar scan, the maximum wind speed from the highest-rated DI, Vrot, and the significant tornado parameter (STP) from the SPC hourly objective mesoscale analysis archive were recorded and analyzed. Results from examining Vrot and STP data indicate an increasing conditional probability for higher-rated DIs (i.e., EF-scale wind speed estimate) as both STP and Vrot increase. This work suggests that tornadic wind speed exceedance probabilities can be estimated in real time, on a scan-by-scan basis, via Vrot and STP for ongoing tornadoes.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bryan T. Smith, bryan.smith@noaa.gov

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-20-0011.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bluestein, H. B., K. J. Thiem, J. C. Snyder, and J. B. Houser, 2018: The multiple-vortex structure of the El Reno, Oklahoma, tornado on 31 May 2013. Mon. Wea. Rev., 146, 2483–2502, https://doi.org/10.1175/MWR-D-18-0073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bluestein, H. B., K. J. Thiem, J. C. Snyder, and J. B. Houser, 2019: Tornadogenesis and early tornado evolution in the El Reno, Oklahoma, supercell on 31 May 2013. Mon. Wea. Rev., 147, 2045–2066, https://doi.org/10.1175/MWR-D-18-0338.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bothwell, P. D., J. A. Hart, and R. L. Thompson, 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., JP3.1, https://ams.confex.com/ams/pdfpapers/47482.pdf.

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brown, R. A., and L. R. Lemon, 1976: Single Doppler radar vortex recognition. Part II: Tornadic vortex signatures. Preprints, 17th Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., 104–109.

  • Brown, R. A., L. R. Lemon, and D. W. Burgess, 1978: Tornado detection by pulsed Doppler radar. Mon. Wea. Rev., 106, 29–38, https://doi.org/10.1175/1520-0493(1978)106<0029:TDBPDR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brown, R. A., B. A. Flickinger, E. Forren, D. M. Schultz, D. Sirmans, P. L. Spencer, V. T. Wood, and C. L. Ziegler, 2005: Improved detection of severe storms using experimental fine-resolution WSR-88D measurements. Wea. Forecasting, 20, 3–14, https://doi.org/10.1175/WAF-832.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Burgess, D. W., R. J. Donaldson Jr., and P. R. Desrochers, 1993: Tornado detection and warning by radar. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 203–221.

    • Crossref
    • Export Citation

  • Burgess, D. W., M. A. Magsig, J. Wurman, D. C. Dowell, and Y. Richardson, 2002: Radar observations of the 3 May 1999 Oklahoma City tornado. Wea. Forecasting, 17, 456–471, https://doi.org/10.1175/1520-0434(2002)017<0456:ROOTMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Camp, P. J., K. Stellman, and J. Settelmaier, 2010: Utilizing mobile devices for enhanced storm damage surveys. 26th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Atlanta, GA, Amer. Meteor. Soc., 5B.4, https://ams.confex.com/ams/90annual/techprogram/paper_161540.htm.

  • Chrisman, J. N., 2011: Supplemental adaptive intra-volume low-level scan (SAILS). NOAA, 13 pp., http://www.roc.noaa.gov/wsr88d/PublicDocs/NewTechnology/SAILS_Initial_Presentation_Sep_2011.pdf.

  • Chrisman, J. N., 2014: Multiple elevation scan option for SAILS (MESO-SAILS). NOAA, 27 pp., http://www.roc.noaa.gov/wsr88d/PublicDocs/NewTechnology/MESO-SAILS_Description_Briefing_Jan_2014.pdf.

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

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Dean, A. R., R. S. Schneider, and J. T. Schaefer, 2006: Development of a comprehensive severe weather forecast verification system at the Storm Prediction Center. 23rd Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., P2.3, https://ams.confex.com/ams/23SLS/techprogram/paper_115250.htm.

  • Edwards, R., J. G. LaDue, J. T. Ferree, K. Scharfenberg, C. Maier, and W. L. Coulbourne, 2013: Tornado intensity estimation: Past, present, and future. Bull. Amer. Meteor. Soc., 94, 641–653, https://doi.org/10.1175/BAMS-D-11-00006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • French, M. M., H. B. Bluestein, I. PopStefanija, C. A. Baldi, and R. T. Bluth, 2014: Mobile, phased-array, Doppler radar observations of tornadoes at X band. Mon. Wea. Rev., 142, 1010–1036, https://doi.org/10.1175/MWR-D-13-00101.1.

    • Search Google Scholar
    • Export Citation

  • Gibbs, J. G., 2016: A skill assessment of techniques for real-time diagnosis and short-term prediction of tornado intensity using the WSR-88D. J. Oper. Meteor., 4, 170–181, https://doi.org/10.15191/nwajom.2016.0413.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gibbs, J. G., and B. R. Bowers, 2019: Techniques and thresholds of significance for using WSR-88D velocity data to anticipate significant tornadoes. J. Oper. Meteor., 7, 117–137, https://doi.org/10.15191/nwajom.2019.0709.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kingfield, D. M., and J. G. LaDue, 2015: The relationship between automated low-level velocity calculations from the WSR-88D and maximum tornado intensity determined from damage surveys. Wea. Forecasting, 30, 1125–1139, https://doi.org/10.1175/WAF-D-14-00096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

  • Marquis, J., Y. Richardson, P. Markowski, J. Wurman, K. Kosiba, and P. Robinson, 2016: An investigation of the Goshen County, Wyoming, tornadic supercell of 5 June 2009 using EnKF assimilation of mobile mesonet and radar observations collected during VORTEX2. Part II: Mesocyclone-scale processes affecting tornado formation, maintenance, and decay. Mon. Wea. Rev., 144, 3441–3463, https://doi.org/10.1175/MWR-D-15-0411.1.

    • Search Google Scholar
    • Export Citation

  • Marshall, T. P., W. Davis, and S. Runnels, 2012: Damage survey of the Joplin tornado. 26th Conf. on Severe Local Storms, Nashville, TN, Amer. Meteor. Soc., 6.1, https://ams.confex.com/ams/26SLS/webprogram/Paper211662.html.

  • 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, https://doi.org/10.1175/1520-0434(1998)013<0352:TNSSLT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Piltz, S. F., and D. W. Burgess, 2009: The impacts of thunderstorm geometry and WSR-88D beam characteristics on diagnosing supercell tornadoes. 34th Conf. on Radar Meteorology, Williamsburg, VA, Amer. Meteor. Soc., P6.18, https://ams.confex.com/ams/34Radar/techprogram/paper_155944.htm.

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

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ryzhkov, A., T. J. Schuur, D. W. Burgess, and D. S. Zrnić, 2005: Polarimetric tornado detection. J. Appl. Meteor., 44, 557–570, https://doi.org/10.1175/JAM2235.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schaefer, J. T., and R. Edwards, 1999: The SPC tornado/severe thunderstorm database. Preprints, 11th Conf. on Applied Climatology, Dallas, TX, Amer. Meteor. Soc., 603–606.

  • 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, 120–137.

    • 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, 138–150.

    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, B. T., R. L. Thompson, A. R. Dean, and P. T. Marsh, 2015: Diagnosing the conditional probability of tornado damage rating using environmental and radar attributes. Wea. Forecasting, 30, 914–932, https://doi.org/10.1175/WAF-D-14-00122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Snyder, J. C., and H. B. Bluestein, 2014: Some considerations for the use of high-resolution mobile radar data in tornado intensity determination. Wea. Forecasting, 29, 799–827, https://doi.org/10.1175/WAF-D-14-00026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Snyder, J. C., and A. V. Ryzhkov, 2015: Automated detection of polarimetric tornadic debris signatures using a hydrometeor classification algorithm. J. Appl. Meteor. Climatol., 54, 1861–1870, https://doi.org/10.1175/JAMC-D-15-0138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., and Coauthors, 2017: Tornado damage rating probabilities derived from WSR-88D data. Wea. Forecasting, 32, 1509–1528, https://doi.org/10.1175/WAF-D-17-0004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Torres, S., and C. Curtis, 2007: Initial implementation of super-resolution data on the NEXRAD network. 23rd Int. Conf. on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, San Antonio, TX, Amer. Meteor. Soc., 5B.10, https://ams.confex.com/ams/87ANNUAL/techprogram/paper_116240.htm.

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., E. D. Mitchell, G. A. Tipton, D. W. Effertz, A. I. Watson, D. L. Andra Jr., and M. A. Magsig, 1999: Descending and nondescending tornadic vortex signatures detected by WSR-88Ds. Wea. Forecasting, 14, 625–639, https://doi.org/10.1175/1520-0434(1999)014<0625:DANTVS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Van Den Broeke, M. S., and S. T. Jauernic, 2014: Spatial and temporal characteristics of polarimetric tornadic debris signatures. J. Appl. Meteor. Climatol., 53, 2217–2231, https://doi.org/10.1175/JAMC-D-14-0094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wakimoto, R. M., and Coauthors, 2016: Aerial damage survey of the 2013 El Reno tornado combined with mobile radar data. Mon. Wea. Rev., 144, 1749–1776, https://doi.org/10.1175/MWR-D-15-0367.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • WSEC, 2006: A recommendation for an enhanced Fujita scale (EF-scale). Wind Science and Engineering Center, Texas Tech University, Lubbock, TX, 95 pp., http://www.depts.ttu.edu/weweb/EFScale.pdf.

  • Wurman, J., and K. Kosiba, 2013: Finescale radar observations of tornado and mesocyclone structures. Wea. Forecasting, 28, 1157–1174, https://doi.org/10.1175/WAF-D-12-00127.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wurman, J., K. Kosiba, and P. Robinson, 2013: In situ, Doppler radar, and video observations of the interior structure of a tornado and the wind-damage relationship. Bull. Amer. Meteor. Soc., 94, 835–846, https://doi.org/10.1175/BAMS-D-12-00114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wurman, J., K. Kosiba, P. Robinson, and T. Marshall, 2014: The role of multiple-vortex tornado structure in causing storm researcher fatalities. Bull. Amer. Meteor. Soc., 95, 31–45, https://doi.org/10.1175/BAMS-D-13-00221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zrnić, D. S., and R. J. Doviak, 1975: Velocity spectra of vortices scanned with a pulse-Doppler radar. J. Appl. Meteor., 14, 1531–1539, https://doi.org/10.1175/1520-0450(1975)014<1531:VSOVSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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