• Brown, R. A., 1998: Nomogram for aiding the interpretation of tornadic vortex signatures measured by Doppler radar. Wea. Forecasting, 13 , 505512.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. A., Lemon L. R. , and Burgess D. W. , 1978: Tornado detection by pulsed Doppler radar. Mon. Wea. Rev., 106 , 2938.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. A., Wood V. T. , and Sirmans D. , 2002: Improved tornado detection using simulated and actual WSR-88D data with enhanced resolution. J. Atmos. Oceanic Technol., 19 , 17591771.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browning, K. A., 1965: The evolution of tornadic storms. J. Atmos. Sci., 22 , 664668.

  • Burgess, D. W., 2004: High resolution analyses of the 8 May 2003 Oklahoma City storm. Part 1: Storm structure and evolution from radar data. Preprints, 22d Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., CD-ROM, 12.4.

  • Burgess, D. W., Lemon L. R. , and Brown R. A. , 1975: Tornado characteristics revealed by Doppler radar. Geophys. Res. Lett., 2 , 183184.

  • Crum, T. D., and Alberty R. L. , 1993: The WSR-88D and the WSR-88D Operational Support Facility. Bull. Amer. Meteor. Soc., 74 , 16691687.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daubechies, I., 1992: Ten Lectures on Wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 61, SIAM, 357 pp.

    • Crossref
    • Export Citation
  • Desrochers, P. R., and Donaldson R. J. Jr., 1992: Automatic tornado prediction with an improved mesocyclone-detection algorithm. Wea. Forecasting, 7 , 373388.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Desrochers, P. R., and Yee S. Y. K. , 1999: Wavelet applications for mesocyclone identification in Doppler radar observations. J. Appl. Meteor., 38 , 965980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Forbes, G. S., 1981: On the reliability of hook echoes as tornado indicators. Mon. Wea. Rev., 109 , 14571466.

  • Fujita, T., 1958: Mesoanalysis of the Illinois tornadoes of 9 April 1953. J. Meteor., 15 , 288296.

  • Lakshmanan, V., Adrianto I. , Smith T. , and Stumpf G. , 2005: A spatiotemporal approach to tornado prediction. Int. Joint Conf. on Neural Networks, Vol. 3, Montreal, QC, Canada, IEEE, 1642–1647.

  • Lee, R. R., and White A. , 1998: Improvement of the WSR-88D mesocyclone algorithm. Wea. Forecasting, 13 , 341351.

  • Liu, S., Zhang P. , Wang L. , Gong J. , and Xu Q. , 2003: Problems and solutions in real-time Doppler wind retrievals. Preprints, 31th Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., 308–309.

  • Liu, S., Xu Q. , and Zhang P. , 2005: Identifying Doppler velocity contamination caused by migrating birds. Part II: Bayes identification and probability tests. J. Atmos. Oceanic Technol., 22 , 11141121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 , 352360.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCDC, cited. 2003: Storm event. [Available online at http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwEvent~Storms.].

  • 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, Amer. Meteor. Soc., Hyannis, MA, CD-ROM, P5.6.

  • Smith, T. M., Elmore K. L. , Stumpf G. J. , and Lakshmanan V. , 2003: Detection of rotation and boundaries using two-dimensional, local, linear least squares estimates of velocity derivative. Preprints, 31st Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., 310–314.

  • Vasiloff, V. S., 2001: Improving tornado warnings with the Federal Aviation Administration’s Terminal Doppler Weather Radar. Bull. Amer. Meteor. Soc., 82 , 861874.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wieler, J. G., 1986: Real-time automated detection of mesocyclones and tornadic vortex signatures. J. Atmos. Oceanic Technol., 3 , 98113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, V. T., and Brown R. A. , 1997: Effects of radar sampling on single-Doppler velocity signatures of mesocyclones and tornadoes. Wea. Forecasting, 12 , 928938.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., Droegemeier K. K. , and Wong V. , 2000: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75 , 161193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phys., 76 , 143165.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., Tong M. , and Droegemeier K. K. , 2006: An OSSE framework based on the ensemble square-root Kalman filter for evaluating impact of data from radar networks on thunderstorm analysis and forecast. J. Atmos. Oceanic Technol., 23 , 4666.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, P., Liu S. , and Xu Q. , 2005: Identifying Doppler velocity contamination caused by migrating birds. Part I: Feature extraction and quantification. J. Atmos. Oceanic Technol., 22 , 11051113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zittel, W. D., Lee R. R. , Mitchell E. D. , and Sirmans D. , 2001: Environmental and signal processing conditions that negatively impact the performance of the WSR-88D tornado detection algorithm, Preprints, 30th Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., 310–314.

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Using Wavelet Analysis to Detect Tornadoes from Doppler Radar Radial-Velocity Observations

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  • 1 Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma, and College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
  • | 2 Center for Analysis and Prediction of Storms and School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 3 NOAA/National Severe Storms Laboratory, Norman, Oklahoma
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Abstract

A wavelet-based algorithm is developed to detect tornadoes from Doppler weather radar radial-velocity observations. Within this algorithm, a relative region-to-region velocity difference (RRVD) is defined based on the scale- and location-dependent wavelet coefficients and this difference represents the relative magnitude of the radial velocity shear between two adjacent regions of different scales. The RRVD fields of an idealized tornado and a realistic tornado from a high-resolution numerical simulation are analyzed first. It is found that the value of RRVD in the tornado region is significantly larger than those at other locations and large values of RRVD exist at more than one scale. This characteristic forms the basis of the new algorithm presented in this work for identifying tornadoes. Different from traditional tornadic vortex signature detection algorithms that typically rely on the velocity difference between adjacent velocity gate pairs at a single spatial scale, the new algorithm examines region-to-region radial wind shears at a number of different spatial scales. Multiscale regional wind shear examination not only can be used to discard a nontornadic vortex signature to reduce the false alert rate of tornado detection but also has the ability of capturing tornadic signatures at various scales for improving the detection and warning. The potential advantage of the current algorithm is demonstrated by applying it to the radar data collected by Oklahoma City, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D) on 8 May 2003 for a central Oklahoma tornado case.

Corresponding author address: Shun Liu, Environmental Modeling Center, National Centers for Environmental Prediction, Room 207, 5200 Auth Rd., Camp Springs, MD 20746. Email: Shun.Liu@noaa.gov

Abstract

A wavelet-based algorithm is developed to detect tornadoes from Doppler weather radar radial-velocity observations. Within this algorithm, a relative region-to-region velocity difference (RRVD) is defined based on the scale- and location-dependent wavelet coefficients and this difference represents the relative magnitude of the radial velocity shear between two adjacent regions of different scales. The RRVD fields of an idealized tornado and a realistic tornado from a high-resolution numerical simulation are analyzed first. It is found that the value of RRVD in the tornado region is significantly larger than those at other locations and large values of RRVD exist at more than one scale. This characteristic forms the basis of the new algorithm presented in this work for identifying tornadoes. Different from traditional tornadic vortex signature detection algorithms that typically rely on the velocity difference between adjacent velocity gate pairs at a single spatial scale, the new algorithm examines region-to-region radial wind shears at a number of different spatial scales. Multiscale regional wind shear examination not only can be used to discard a nontornadic vortex signature to reduce the false alert rate of tornado detection but also has the ability of capturing tornadic signatures at various scales for improving the detection and warning. The potential advantage of the current algorithm is demonstrated by applying it to the radar data collected by Oklahoma City, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D) on 8 May 2003 for a central Oklahoma tornado case.

Corresponding author address: Shun Liu, Environmental Modeling Center, National Centers for Environmental Prediction, Room 207, 5200 Auth Rd., Camp Springs, MD 20746. Email: Shun.Liu@noaa.gov

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