WSR-88D Sidelobe Contamination: From a Conceptual Model to Diagnostic Strategies for Improving NWS Warning Performance

Jami B. Boettcher aCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Evan S. Bentley cNOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

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Abstract

Providing timely warnings for severe and potentially tornadic convection is a critical component of the NWS mission, and owing to the associated large reflectivity gradients, sidelobe contamination is possible. This paper focuses on elevation sidelobe contamination appearing in the low-level inflow region of supercells. A qualitative conceptual model of the Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern interacting with supercells is introduced, along with Doppler power spectrum representations of the potential mix of returned power from the main lobe and the sidelobes. These tools inform the multiple ways elevation sidelobe contamination appears in the low levels, primarily below 3 km (10 kft) of radar data. The most common manifestation is somewhat noisy data similar to particulates or biota in clear air. Trained NWS forecasters are accustomed to mentally filtering out noisy clear-air returns as less important. Elevation sidelobe contamination can be mixed with the three-body scatter spike (TBSS) artifact, though the TBSS remains the more salient feature. The most consequential form is the apparent circulation, and when it is incorrectly interpreted as valid, contributes to the false alarm ratio (FAR) for NWS tornado warnings. Quantitative results on the effect of elevation sidelobe contamination on FAR are presented. Diagnostic techniques are emphasized, and with familiarization, can be used in real-time warning operations to identify the apparent circulation as either valid or an imposter. Identification of these contaminated velocity signatures offers a unique opportunity to reduce the NWS tornado warning FAR without also reducing the probability of detection (POD).

Significance Statement

The WSR-88D weather radars provide overall high-quality data for users. However, with some severe thunderstorms, an artifact called elevation sidelobe contamination can produce what looks like a rotation signature, but it may not be real. These ambiguous velocity signatures can contribute to tornado warnings based on rotation signatures that are false circulations. This paper specifically focuses on elevation sidelobe contamination due to its impact on tornado warning decisions. Diagnostic techniques, including several examples, are presented here to aid the reader in correctly identifying elevation sidelobe contamination and why it may occur. Correct identification of an apparent circulation as an imposter due to contamination is a unique opportunity to improve NWS tornado warning performance by reducing warning false alarms.

© 2022 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: Jami B. Boettcher, jami.b.boettcher@noaa.gov

Abstract

Providing timely warnings for severe and potentially tornadic convection is a critical component of the NWS mission, and owing to the associated large reflectivity gradients, sidelobe contamination is possible. This paper focuses on elevation sidelobe contamination appearing in the low-level inflow region of supercells. A qualitative conceptual model of the Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern interacting with supercells is introduced, along with Doppler power spectrum representations of the potential mix of returned power from the main lobe and the sidelobes. These tools inform the multiple ways elevation sidelobe contamination appears in the low levels, primarily below 3 km (10 kft) of radar data. The most common manifestation is somewhat noisy data similar to particulates or biota in clear air. Trained NWS forecasters are accustomed to mentally filtering out noisy clear-air returns as less important. Elevation sidelobe contamination can be mixed with the three-body scatter spike (TBSS) artifact, though the TBSS remains the more salient feature. The most consequential form is the apparent circulation, and when it is incorrectly interpreted as valid, contributes to the false alarm ratio (FAR) for NWS tornado warnings. Quantitative results on the effect of elevation sidelobe contamination on FAR are presented. Diagnostic techniques are emphasized, and with familiarization, can be used in real-time warning operations to identify the apparent circulation as either valid or an imposter. Identification of these contaminated velocity signatures offers a unique opportunity to reduce the NWS tornado warning FAR without also reducing the probability of detection (POD).

Significance Statement

The WSR-88D weather radars provide overall high-quality data for users. However, with some severe thunderstorms, an artifact called elevation sidelobe contamination can produce what looks like a rotation signature, but it may not be real. These ambiguous velocity signatures can contribute to tornado warnings based on rotation signatures that are false circulations. This paper specifically focuses on elevation sidelobe contamination due to its impact on tornado warning decisions. Diagnostic techniques, including several examples, are presented here to aid the reader in correctly identifying elevation sidelobe contamination and why it may occur. Correct identification of an apparent circulation as an imposter due to contamination is a unique opportunity to improve NWS tornado warning performance by reducing warning false alarms.

© 2022 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: Jami B. Boettcher, jami.b.boettcher@noaa.gov
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  • American Meteorological Society, 2022: Mini-supercell. Glossary of Meteorology, https://glossary.ametsoc.org/wiki/Mini-supercell.

  • Andra, D. L., Jr., E. M. Quoetone, and W. F. Bunting, 2002: Warning decision making: The relative roles of conceptual models, technology, strategy, and forecaster expertise on 3 May 1999. Wea. Forecasting, 17, 559566, https://doi.org/10.1175/1520-0434(2002)017<0559:WDMTRR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bentley, E. S., R. L. Thompson, B. Bowers, J. Gibbs, and S. Nelson, 2021: An analysis of 2016–18 tornadoes and National Weather Service tornado warnings across the contiguous United States. Wea. Forecasting, 36, 19091924, https://doi.org/10.1175/WAF-D-20-0241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bluestein, H., C. Weiss, M. French, E. Holthaus, R. Tanamachi, S. Frasier, and A. Pazmany, 2007: The structure of tornadoes near Attica, Kansas, on 12 May 2004: High-resolution, mobile, Doppler radar observations. Mon. Wea. Rev., 135, 475506, https://doi.org/10.1175/MWR3295.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., 2004: Tornado-warning performance in the past and future: A perspective from signal detection theory. Bull. Amer. Meteor. Soc., 85, 837844, https://doi.org/10.1175/BAMS-85-6-837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., and J. Correia, Jr., 2018: Long-term performance metrics for National Weather Service tornado warnings. Wea. Forecasting, 33, 15011511, https://doi.org/10.1175/WAF-D-18-0120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brotzge, J., and W. Donner, 2013: The tornado warning process. A review of current research, challenges, and opportunities. Bull. Amer. Meteor. Soc., 94, 17151733, https://doi.org/10.1175/BAMS-D-12-00147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. A., V. T. Wood, and D. Sirmans, 2002: Improved tornado detection using simulated and actual WSR-88D data with enhanced resolution. J. Atmos. Oceanic Technol., 19, 17591771, https://doi.org/10.1175/1520-0426(2002)019<1759:ITDUSA>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, 2005a: Improved detection of severe storms using experimental fine-resolution WSR-88D measurements. Wea. Forecasting, 20, 314, https://doi.org/10.1175/WAF-832.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. A., V. T. Wood, R. M. Steadham, R. R. Lee, B. A. Flickinger, and D. Sirmans, 2005b: New WSR-88D volume coverage pattern 12: Results of field tests. Wea. Forecasting, 20, 385393, https://doi.org/10.1175/WAF848.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browning, K., 1964: Airflow and precipitation trajectories within severe local storms which travel to the right of the winds. J. Atmos. Sci., 21, 634639, https://doi.org/10.1175/1520-0469(1964)021<0634:AAPTWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chrisman, J. N., 2011: Supplemental adaptive intra-volume low-level scan (SAILS). NWS Radar Operations Center Doc., 13 pp., http://www.roc.noaa.gov/wsr88d/PublicDocs/NewTechnology/SAILS_Initial_Presentation_Sep_2011.pdf.

    • Search Google Scholar
    • Export Citation
  • Chrisman, J. N., 2014: Multiple elevation scan option for SAILS (MESO-SAILS). NWS Radar Operations Center Doc., 27 pp., http://www.roc.noaa.gov/wsr88d/PublicDocs/NewTechnology/MESO-SAILS_Description_Briefing_Jan_2014.pdf.

    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., III, and D. W. Burgess, 1993: Tornadoes and tornadic storms: A review of conceptual models. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 161172.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., 2017: A memorandum on comparisons of weather and aircraft surveillance radar requirements to determine key features for a 10-cm MPAR and SENSR. NOAA/NSSL Rep., 41 pp.

    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., and D. Zrnić, 1984: Doppler Radar and Weather Observations. Dover, 458 pp.

  • Klein, G., B. Moon, and R. R. Hoffman, 2006: Making sense of sensemaking 1: Alternative perspectives. Intell. Syst., 21, 7073, https://doi.org/10.1109/MIS.2006.75.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., 2013: Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables. J. Oper. Meteor., 1, 226242, https://doi.org/10.15191/nwajom.2013.0119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemon, L. R., 1998: The radar “three-body scatter spike”: An operational large-hail signature. Wea. Forecasting, 13, 327340, https://doi.org/10.1175/1520-0434(1998)013<0327:TRTBSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemon, L. R., and C. A. Doswell III, 1979: Severe thunderstorm evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev., 107, 11841197, https://doi.org/10.1175/1520-0493(1979)107<1184:STEAMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nai, F., J. Boettcher, C. Curtis, D. Schvartzman, and S. Torres, 2020: The impact of elevation sidelobe contamination on radar data quality for operational interpretation. J. Appl. Meteor. Climatol., 59, 707724, https://doi.org/10.1175/JAMC-D-19-0092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA, 1991: WSR-88D program. NWS Radar Operations Center, https://www.roc.noaa.gov/WSR88D/WSR88DProgram.aspx.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Saxion, D. S., and R. L. Ice, 2012: New science for the WSR-88D: Status of the dual-polarization upgrade. 28th Conf. on Interactive Information Processing Systems, New Orleans, LA, Amer. Meteor. Soc., https://ams.confex.com/ams/92Annual/webprogram/Paper197645.html.

    • Search Google Scholar
    • Export Citation
  • Simmons, K. M., and D. Sutter, 2009: False alarms, tornado warnings, and tornado casualties. Wea. Climate Soc., 1, 3853, https://doi.org/10.1175/2009WCAS1005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, B. T., R. L. Thompson, J. S. Grams, and J. C. Broyles, 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part I: Storm classification and climatology. Wea. Forecasting, 27, 11141135, https://doi.org/10.1175/WAF-D-11-00115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, R., and Coauthors, 2017: Tornado damage rating probabilities derived from WSR-88D data. Wea. Forecasting, 32, 15091528, 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.

    • Search Google Scholar
    • Export Citation
  • WDTD, 2021: Impact based warning guidance. Accessed 17 March 2021, https://training.weather.gov/wdtd/courses/woc/severe/storm-structures-hazards/tornado/twg3-now/presentation_content/external_files/30-40-50.png.

    • Search Google Scholar
    • Export Citation
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