Editorial Type:
Article
Article Type:
Research Article

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

Vivek N. Mahale School of Meteorology, Advanced Radar Research Center, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Guifu Zhang School of Meteorology, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

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Ming Xue School of Meteorology, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Print Publication:
01 Aug 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0358.1
Page(s):
2017–2033
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Abstract

The three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

Corresponding author address: Vivek N. Mahale, School of Meteorology, University of Oklahoma, Ste. 5900, 120 David L. Boren Blvd., Norman, OK 73072-7307. E-mail: vmahale@ou.edu

Abstract

The three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

Corresponding author address: Vivek N. Mahale, School of Meteorology, University of Oklahoma, Ste. 5900, 120 David L. Boren Blvd., Norman, OK 73072-7307. E-mail: vmahale@ou.edu
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Journal of Applied Meteorology and Climatology

  • Bohren, C. F., and D. R. Huffman, 1983: Absorption and Scattering of Light by Small Particles. John Wiley and Sons, 530 pp.

  • Hubbert, J. C., and V. N. Bringi, 2000: The effects of three-body scattering on differential reflectivity signatures. J. Atmos. Oceanic Technol., 17, 51–61, doi:10.1175/1520-0426(2000)017<0051:TEOTBS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kumjian, M., J. Picca, S. Ganson, A. Ryzhkov, and D. Zrnić, 2010: Three-body scattering signatures in polarimetric radar data. NOAA/NSSL Rep., 12 pp. [Available online at https://www.nssl.noaa.gov/publications/wsr88d_reports/FINAL_TBSS.doc.]

  • Lemon, L. R., 1998: The radar ‘‘three-body scatter spike’’: An operational large-hail signature. Wea. Forecasting, 13, 327–340, doi:10.1175/1520-0434(1998)013<0327:TRTBSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lim, S., V. Chandrasekar, and V. N. Bringi, 2005: Hydrometeor classification system using dual-polarization radar measurements: Model improvements and in situ verification. IEEE Trans. Geosci. Remote Sens., 43, 792–801, doi:10.1109/TGRS.2004.843077.

    • Search Google Scholar
    • Export Citation

  • Lindley, T. T., and L. R. Lemon, 2007: Preliminary observations of weak three-body scattering spikes associated with low-end severe hail. Electron. J. Severe Storms Meteor.,2 (3). [Available online at http://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/12/22.]

  • Liu, H., and V. Chandrasekar, 2000: Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems, and in situ verification. J. Atmos. Oceanic Technol., 17, 140–164, doi:10.1175/1520-0426(2000)017<0140:COHBOP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Marzano, F. S., D. Scaranari, M. Celano, P. P. Alberoni, G. Vulpiani, and M. Montopoli, 2006: Hydrometeor classification from dual polarized weather radar: Extending fuzzy logic from S-band to C-band. Adv. Geosci., 7, 109–114, doi:10.5194/adgeo-7-109-2006.

    • Search Google Scholar
    • Export Citation

  • Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2013: Doppler radar meteorological observations. Part A—System concepts responsibilities and procedures. Federal Meteorological Handbook No. 11, Office of the Federal Coordinator for Meteorological Services and Supporting Research. [Available online at https://ams.confex.com/ams/pdfpapers/88063.pdf.]

  • Oye, R., C. Mueller, and S. Smith, 1995: Software for radar translation, visualization, editing, and interpolation. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 359–361.

  • Park, H. S., A. V. Ryzhkov, D. S. Zrnić, and K.-E. 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

  • Picca, J., and A. Ryzhkov, 2012: A dual-wavelength polarimetric analysis of the 16 May 2010 Oklahoma City extreme hailstorm. Mon. Wea. Rev., 140, 1385–1403, doi:10.1175/MWR-D-11-00112.1.

    • 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., 44, 557–570, doi:10.1175/JAM2235.1.

    • Search Google Scholar
    • Export Citation

  • Schuur, T. J., A. V. Ryzhkov, and P. L. Heinselman, 2003: Observations and classification of echoes with the polarimetric WSR-88D radar. NOAA/National Severe Storms Laboratory Rep., 46 pp.

  • 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

  • SPC, cited2013: Storm reports. Storm Prediction Center. [Available online at http://www.spc.noaa.gov/climo/.]

  • Straka, J. M., and D. S. Zrnić, 1993: An algorithm to deduce hydrometeor types and contents from multiparameter radar data. Preprints, 26th Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 513–516.

  • Vivekanandan, J., D. S. Zrnić, S. Ellis, D. Oye, A. V. Ryzhkov, and J. M. Straka, 1999: Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull. Amer. Meteor. Soc., 80, 381–388, doi:10.1175/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., and D. Reum, 1986: ‘‘The hail spike’’: Reflectivity and velocity signature. Preprints, 23rd Conf. on Radar Meteorology, Snowmass, CO, Amer. Meteor. Soc., 62–65.

  • Wilson, J. W., and D. Reum, 1988: The flare echo: Reflectivity and velocity signature. J. Atmos. Oceanic Technol., 5, 197–205, doi:10.1175/1520-0426(1988)005<0197:TFERAV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zrnić, D. S., 1987: Three-body scattering produces precipitation signature of special diagnostic value. Radio Sci., 22, 76–86, doi:10.1029/RS022i001p00076.

    • 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., A. V. Ryzhkov, J. M. Straka, Y. Liu, and J. Vivekanandan, 2001: Testing a procedure for the automatic classification of hydrometeor types. J. Atmos. Oceanic Technol., 18, 892–913, doi:10.1175/1520-0426(2001)018<0892:TAPFAC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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