Editorial Type:
Article
Article Type:
Research Article

Automated Detection of Polarimetric Tornadic Debris Signatures Using a Hydrometeor Classification Algorithm

Jeffrey C. Snyder Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Alexander V. Ryzhkov Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Print Publication:
01 Sep 2015
DOI:
https://doi.org/10.1175/JAMC-D-15-0138.1
Page(s):
1861–1870
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Abstract

Although radial velocity data from Doppler radars can partially resolve some tornadoes, particularly large tornadoes near the radar, most tornadoes are not explicitly resolved by radar owing to inadequate spatiotemporal resolution. In addition, it can be difficult to determine which mesocyclones typically observed on radar are associated with tornadoes. Since debris lofted by tornadoes has scattering characteristics that are distinct from those of hydrometeors, the additional information provided by polarimetric weather radars can aid in identifying debris from tornadoes; the polarimetric tornadic debris signature (TDS) provides what is nearly “ground truth” that a tornado is ongoing (or has recently occurred). This paper outlines a modification to the hydrometeor classification algorithm used with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) network in the United States to include a TDS category. Examples of automated TDS classification are provided for several recent cases that were observed in the United States.

Corresponding author address: Jeffrey Snyder, CIMMS/NSSL RRDD, 120 David L Boren Blvd., Norman, OK 73072. E-mail: jeffrey.snyder@noaa.gov

Abstract

Although radial velocity data from Doppler radars can partially resolve some tornadoes, particularly large tornadoes near the radar, most tornadoes are not explicitly resolved by radar owing to inadequate spatiotemporal resolution. In addition, it can be difficult to determine which mesocyclones typically observed on radar are associated with tornadoes. Since debris lofted by tornadoes has scattering characteristics that are distinct from those of hydrometeors, the additional information provided by polarimetric weather radars can aid in identifying debris from tornadoes; the polarimetric tornadic debris signature (TDS) provides what is nearly “ground truth” that a tornado is ongoing (or has recently occurred). This paper outlines a modification to the hydrometeor classification algorithm used with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) network in the United States to include a TDS category. Examples of automated TDS classification are provided for several recent cases that were observed in the United States.

Corresponding author address: Jeffrey Snyder, CIMMS/NSSL RRDD, 120 David L Boren Blvd., Norman, OK 73072. E-mail: jeffrey.snyder@noaa.gov
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Journal of Applied Meteorology and Climatology

  • Bluestein, H. B., and W. P. Unruh, 1989: Observations of the wind field in tornadoes, funnel clouds, and wall clouds with a portable Doppler radar. Bull. Amer. Meteor. Soc., 70, 1514–1525, doi:10.1175/1520-0477(1989)070<1514:OOTWFI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bluestein, H. B., J. G. LaDue, H. Stein, D. Speheger, and W. P. Unruh, 1993: Doppler radar wind spectra of supercell tornadoes. Mon. Wea. Rev., 121, 2200–2221, doi:10.1175/1520-0493(1993)121<2200:DRWSOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bluestein, H. B., M. M. French, R. L. Tanamachi, S. Frasier, K. Hardwick, F. Junyent, and A. L. Pazmany, 2007: Close-range observations of tornadoes in supercells made with a dual-polarization, X-band, mobile Doppler radar. Mon. Wea. Rev., 135, 1522–1543, doi:10.1175/MWR3349.1.

    • Search Google Scholar
    • Export Citation

  • Bluestein, H. B., J. C. Snyder, and J. B. Houser, 2015: A multiscale overview of the El Reno, Oklahoma, tornadic supercell of 31 May 2013. Wea. Forecasting, 30, 525–552, doi:10.1175/WAF-D-14-00152.1.

    • Search Google Scholar
    • Export Citation

  • Bodine, D. J., 2014: Polarimetric radar observations and numerical simulations of tornadic debris. Ph.D. dissertation, School of Meteorology, University of Oklahoma, 264 pp.

  • 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, doi:10.1175/WAF-D-11-00158.1.

    • Search Google Scholar
    • Export Citation

  • Bodine, D. J., R. D. Palmer, and G. Zhang, 2014: Dual-wavelength polarimetric radar analyses of tornadic debris signatures. J. Appl. Meteor. Climatol., 53, 242–261, doi:10.1175/JAMC-D-13-0189.1.

    • Search Google Scholar
    • Export Citation

  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principle and Applications. Cambridge University Press, 636 pp.

  • Chaney, P. L., and G. S. Weaver, 2010: The vulnerability of mobile home residents in tornado disasters: The 2008 Super Tuesday tornado in Macon County, Tennessee. Wea. Climate Soc., 2, 190–199, doi:10.1175/2010WCAS1042.1.

    • Search Google Scholar
    • Export Citation

  • Donner, W., 2007: Public warning response following tornadoes in New Orleans, LA, and Springfield, MO: A sociological analysis. Second Symp. on Policy and Socio-Economic Research, San Antonio, TX, Amer. Meteor. Soc., 2.6. [Available online at https://ams.confex.com/ams/pdfpapers/120774.pdf.]

  • French, M. M., H. B. Bluestein, L. J. Wicker, D. C. Dowell, and M. R. Kramar, 2009: An example of the use of mobile, Doppler radar data for tornado verification. Wea. Forecasting, 24, 884–891, doi:10.1175/2008WAF2222147.1.

    • Search Google Scholar
    • Export Citation

  • Giangrande, S. E., J. M. Krause, and A. V. Ryzhkov, 2008: Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar. J. Appl. Meteor. Climatol., 47, 1354–1364, doi:10.1175/2007JAMC1634.1.

    • Search Google Scholar
    • Export Citation

  • Houser, J. L., H. B. Bluestein, and J. C. Snyder, 2015: Rapid-scan, polarimetric, Doppler radar observations of tornadogenesis and tornado dissipation in a tornadic supercell: The “El Reno, Oklahoma” storm of 24 May 2011. Mon. Wea. Rev., 143, 2685–2710, doi:10.1175/MWR-D-14-00253.1.

    • Search Google Scholar
    • Export Citation

  • Hwang, Y., and T.-Y. Yu, 2013: Development and assessment of neuro-fuzzy gust front detection algorithm. 36th Conf. on Radar Meteorology, Breckenridge, CO, Amer. Meteor. Soc., 247. [Available online at https://ams.confex.com/ams/36Radar/webprogram/Paper228376.html.]

  • Istok, M. J., and Coauthors, 2009: WSR-88D dual polarization initial operating capabilities. 25th Conf. on Int. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 15.5. [Available online at https://ams.confex.com/ams/pdfpapers/148927.pdf.]

  • Johnson, N., 2013: How people respond: A meta-synthesis of post-tornado interviews and surveys. M.S. thesis, Dept. of Communication, North Carolina State University, 64 pp.

  • Kingfield, D. M., S. Degelia, K. L. Ortega, J. C. Snyder, T. M. Smith, and A. V. Ryzhkov, 2014: Development of tornadic debris signature guidance using polarimetric WSR-88D data. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 169. [Available online at https://ams.confex.com/ams/27SLS/webprogram/Handout/Paper255252/Kingfieldetal-SLS2014-Poster.pdf.]

  • Knox, J. A., and Coauthors, 2013: Tornado debris characteristics and trajectories during the 27 April 2011 Super Outbreak as determined using social media data. Bull. Amer. Meteor. Soc., 94, 1371–1380, doi:10.1175/BAMS-D-12-00036.1.

    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., and A. V. Ryzhkov, 2008: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor. Climatol., 47, 1940–1961, doi:10.1175/2007JAMC1874.1.

    • Search Google Scholar
    • Export Citation

  • Kurdzo, J. M., D. J. Bodine, B. L. Cheong, and R. D. Palmer, 2015: High-temporal resolution polarimetric X-band Doppler radar observations of the 20 May 2013 Moore, Oklahoma tornado. Mon. Wea. Rev., 143, 2711–2735, doi:10.1175/MWR-D-14-00357.1.

    • Search Google Scholar
    • Export Citation

  • Magsig, M. A., and J. T. Snow, 1998: Long-distance debris transport by tornadic thunderstorms. Part I: The 7 May 1995 supercell thunderstorm. Mon. Wea. Rev., 126, 1430–1449, doi:10.1175/1520-0493(1998)126<1430:LDDTBT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Miller, M. L., V. Lakshmanan, and T. Smith, 2013: An automated method for depicting mesocyclone paths and intensities. Wea. Forecasting, 28, 570–585, doi:10.1175/WAF-D-12-00065.1.

    • Search Google Scholar
    • Export Citation

  • NWS, 2002: La Plata, Maryland, tornado outbreak of April 28, 2002. NOAA Service Assessment, 23 pp + appendices. [Available online at http://www.nws.noaa.gov/om/assessments/pdfs/laplata.pdf.]

  • NWS, 2011a: Joplin, Missouri, tornado—May 22, 2011. NOAA National Weather Service Central Region Headquarters Service Assessment, 35 pp. [Available online at http://www.nws.noaa.gov/om/assessments/pdfs/Joplin_tornado.pdf.]

  • NWS, 2011b: The historic tornadoes of April 2011. NOAA Service Assessment, 43 pp + appendices. [Available online at http://www.nws.noaa.gov/om/assessments/pdfs/historic_tornadoes.pdf.]

  • Palmer, R. D., and Coauthors, 2011: Observations of the 10 May 2010 tornado outbreak using OU-PRIME: Potential for new science with high-resolution polarimetric radar. Bull. Amer. Meteor. Soc., 92, 871–891, doi:10.1175/2011BAMS3125.1.

    • Search Google Scholar
    • Export Citation

  • Park, H., A. V. Ryzhkov, D. S. Zrnić, and K. Kim, 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS. Wea. Forecasting, 24, 730–748, doi:10.1175/2008WAF2222205.1.

    • Search Google Scholar
    • Export Citation

  • Ryzhkov, A. V., 2007: The impact of beam broadening on the quality of radar polarimetric data. J. Atmos. Oceanic Technol., 24, 729–744, doi:10.1175/JTECH2003.1.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • 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. [Available online at http://www.nwas.org/ej/pdf/2012-EJ9.pdf.]

    • 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. [Available online at http://www.nwas.org/ej/pdf/2012-EJ10.pdf.]

    • Search Google Scholar
    • Export Citation

  • Schumacher, R. S., D. T. Lindsey, A. B. Schumacher, J. Braun, S. D. Miller, and J. L. Demuth, 2010: Multidisciplinary analysis of an unusual tornado: Meteorology, climatology, and the communication and interpretation of warnings. Wea. Forecasting, 25, 1412–1429, doi:10.1175/2010WAF2222396.1.

    • Search Google Scholar
    • Export Citation

  • Smith, R., 2014: The historic 2013 Oklahoma tornadoes: Highlights and success stories from WFO Norman. Second Symp. on Building a Weather-Ready Nation, Atlanta, GA, Amer. Meteor. Soc., 2.3. [Available online at https://ams.confex.com/ams/94Annual/webprogram/Paper242041.html.]

  • Smith, T., and K. L. Elmore, 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 https://ams.confex.com/ams/pdfpapers/81827.pdf.]

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

    • Search Google Scholar
    • Export Citation

  • Snyder, J. C., H. B. Bluestein, G. Zhang, and S. J. Frasier, 2010: Attenuation correction and hydrometeor classification of high-resolution, X-band, dual-polarized mobile radar measurements in severe convective storms. J. Atmos. Oceanic Technol., 27, 1979–2001, doi:10.1175/2010JTECHA1356.1.

    • Search Google Scholar
    • Export Citation

  • Snyder, J. C., H. B. Bluestein, V. Venkatesh, and S. J. Frasier, 2013: Observations of polarimetric signatures in supercells by an X-band mobile Doppler radar. Mon. Wea. Rev., 141, 3–29, doi:10.1175/MWR-D-12-00068.1.

    • Search Google Scholar
    • Export Citation

  • Straka, J. M., D. S. Zrnić, and A. V. Ryzhkov, 2000: Bulk hydrometeor classification and quantification using polarimetric radar data: Synthesis of relations. J. Appl. Meteor., 39, 1341–1372, doi:10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tanamachi, R. L., H. B. Bluestein, J. B. Houser, S. J. Frasier, and K. M. Hardwick, 2012: Mobile, X-band, polarimetric Doppler radar observations of the 4 May 2007 Greensburg, Kansas, tornadic supercell. Mon. Wea. Rev., 140, 2103–2125, doi:10.1175/MWR-D-11-00142.1.

    • Search Google Scholar
    • Export Citation

  • Tiefenbacher, J. P., W. Monfredo, M. Shuey, and R. J. Cecora, 2001: Examining a “near-miss” experience: Awareness, behavior, and post-disaster response among residents on the periphery of a tornado-damage path. Natural Hazards Research and Applications Information Center Quick Response Rep. 137. [Available online at http://www.colorado.edu/hazards/research/qr/qr137/qr137.html.]

  • Torres, S. M., and D. A. Warde, 2014: Ground clutter mitigation for weather radars using the autocorrelation spectral density. J. Atmos. Oceanic Technol., 31, 2049–2066, doi:10.1175/JTECH-D-13-00117.1.

    • 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, doi:10.1175/JAMC-D-14-0094.1.

    • Search Google Scholar
    • Export Citation

  • Wakimoto, R. M., N. T. Atkins, K. M. Butler, H. B. Bluestein, K. Thiem, J. Snyder, and J. Houser, 2015: Photogrammetric analysis of the 2013 El Reno tornado combined with mobile X-band polarimetric radar data. Mon. Wea. Rev., 143, 2657–2683, doi:10.1175/MWR-D-15-0034.1.

    • Search Google Scholar
    • Export Citation

  • Wurman, J., and S. Gill, 2000: Finescale radar observations of the Dimmitt, Texas (2 June 1995), tornado. Mon. Wea. Rev., 128, 2135–2164, doi:10.1175/1520-0493(2000)128<2135:FROOTD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zrnić, D. S., and M. J. Istok, 1980: Wind speeds in two tornadic storms and a tornado, deduced from Doppler spectra. J. Appl. Meteor., 19, 1405–1415, doi:10.1175/1520-0450(1980)019<1405:WSITTS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zrnić, D. S., and A. V. Ryzhkov, 1999: Polarimetry for weather surveillance radars. Bull. Amer. Meteor. Soc., 80, 389–406, doi:10.1175/1520-0477(1999)080<0389:PFWSR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zrnić, D. S., R. J. Doviak, and D. W. Burgess, 1977: Probing tornadoes with a pulse Doppler radar. Quart. J. Roy. Meteor. Soc., 103, 707–720, doi:10.1002/qj.49710343813.

    • Search Google Scholar
    • Export Citation
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