• Andra, D. L., 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
  • Bowden, K. A., P. L. Heinselman, D. M. Kingfield, and R. P. Thomas, 2015: Impacts of phased-array radar data on forecaster performance during severe hail and wind events. Wea. Forecasting, 30, 389404, https://doi.org/10.1175/WAF-D-14-00101.1.

    • 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
  • Browning, K. A., and R. J. Donaldson, 1963: Airflow and structure of a tornadic storm. J. Atmos. Sci., 20, 533545, https://doi.org/10.1175/1520-0469(1963)020<0533:AASOAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crowe, C. C., C. J. Schultz, M. Kumjian, L. D. Carey, and W. A. Peterson, 2012: Use of dual-polarization signatures in diagnosing tornadic potential. Electron. J. Oper. Meteor., 13, 5778.

    • Search Google Scholar
    • Export Citation
  • Crum, T. D., S. D. Smith, J. N. Chrisman, R. E. Saffle, R. W. Hall, and R. J. Vogt, 2013: WSR-88D radar projects—Update 2013. Proc. 29th Conf. on Environmental Information Processing Technologies, Austin, TX, Amer. Meteor. Soc., 6B.1, https://ams.confex.com/ams/93Annual/webprogram/Paper221461.html.

  • Heinselman, P. L., and S. M. Torres, 2011: High-temporal-resolution capabilities of the National Weather Radar Testbed Phased-Array Radar. J. Appl. Meteor. Climatol., 50, 579593, https://doi.org/10.1175/2010JAMC2588.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herzegh, P. H., and A. R. Jameson, 1992: Observing precipitation through dual-polarization radar measurements. Bull. Amer. Meteor. Soc., 73, 13651374, https://doi.org/10.1175/1520-0477(1992)073<1365:OPTDPR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Illingworth, A. J., J. W. F. Goddard, and S. M. Cherry, 1987: Polarization radar studies of precipitation development in convective storms. Quart. J. Roy. Meteor. Soc., 113, 469489, https://doi.org/10.1002/qj.49711347604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johns, R. H., and C. A. Doswell III, 1992: Severe local storms forecasting. Wea. Forecasting, 7, 588612, https://doi.org/10.1175/1520-0434(1992)007<0588:SLSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knight, C. A., 2006: Very early formation of big, liquid drops revealed by ZDR in continental cumulus. J. Atmos. Sci., 63, 19391953, https://doi.org/10.1175/JAS3721.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krauss, T. W., and J. D. Marwitz, 1984: Precipitation processes within an Alberta supercell hailstorm. J. Atmos. Sci., 41, 10251035, https://doi.org/10.1175/1520-0469(1984)041<1025:PPWAAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., 2013: Principles and applications of dual-polarization weather radar. Part II: Warm and cold season applications. J. Oper. Meteor., 1, 243264, https://doi.org/10.15191/nwajom.2013.0120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and A. V. Ryzhkov, 2008: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor. Climatol., 47, 19401961, https://doi.org/10.1175/2007JAMC1874.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., A. P. Khain, N. Benmoshe, E. Ilotoviz, A. V. Ryzhkov, and V. T. J. Phillips, 2014: The anatomy and physics of ZDR columns: Investigating a polarimetric radar signature with a spectral bin microphysical model. J. Appl. Meteor. Climatol., 53, 18201843, https://doi.org/10.1175/JAMC-D-13-0354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuster, C. M., J. C. Snyder, T. J. Schuur, T. T. Lindley, P. L. Heinselman, J. C. Furtado, J. W. Brogden, and R. Toomey, 2019: Rapid-update radar observations of ZDR column depth and its use in the warning decision process. Wea. Forecasting, 34, 11731188, https://doi.org/10.1175/WAF-D-19-0024.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., and A. Witt, 1997: A fuzzy logic approach to detecting severe updrafts. AI Appl., 11, 112.

  • Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2007: The Warning Decision Support System-Integrated Information. Wea. Forecasting, 22, 596612, https://doi.org/10.1175/WAF1009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemon, L. R., 1977: New severe thunderstorm radar identification techniques and warning criteria: A preliminary report. NOAA Tech. Memo. NWS NSSFC-1, 60 pp.

  • 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
  • Liu, X., K. Zhou, Y. Lan, X. Mao, and R. J. Trapp, 2020: On the construction principle of conceptual models for severe convective weather forecasting operations in China. Wea. Forecasting, 35, 299308, https://doi.org/10.1175/WAF-D-19-0026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moller, A. R., C. A. Doswell III, M. P. Foster, and G. R. Woodall, 1994: The operational recognition of supercell thunderstorm environments and storm structures. Wea. Forecasting, 9, 327347, https://doi.org/10.1175/1520-0434(1994)009<0327:TOROST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, S. P., 1983: The influence of storm inflow structure on hail growth. J. Atmos. Sci., 40, 19651983, https://doi.org/10.1175/1520-0469(1983)040<1965:TIOSFS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA, 2013: Dual-polarization radar: Stepping stones to building a Weather-Ready Nation. NOAA, accessed 1 May 2018, https://www.weather.gov/news/130425-dualpol.

  • Picca, J. C., and A. V. Ryzhkov, 2012: A dual-wavelength polarimetric analysis of the 16 May 2010 Oklahoma City extreme hailstorm. Mon. Wea. Rev., 140, 13851403, https://doi.org/10.1175/MWR-D-11-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Picca, J. C., M. R. Kumjian, and A. V. Ryzhkov, 2010: ZDR columns as a predictive tool for hail growth and storm evaluation. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 11.3, https://ams.confex.com/ams/25SLS/webprogram/Paper175750.html.

  • Romine, G. S., D. W. Burgess, and R. B. Wilhelmson, 2008: A dual-polarization-radar-based assessment of the 8 May 2003 Oklahoma City area tornadic supercell. Mon. Wea. Rev., 136, 28492870, https://doi.org/10.1175/2008MWR2330.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., V. B. Zhuravlyov, and N. A. Rybakova, 1994: Preliminary results of X-band polarization radar studies of clouds and precipitation. J. Atmos. Oceanic Technol., 11, 132139, https://doi.org/10.1175/1520-0426(1994)011<0132:PROXBP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schvartzman, D., S. M. Torres, and Y. Tian-You, 2017: Weather radar spatiotemporal saliency: A first look at an information theory-based human attention model adapted to reflectivity images. J. Atmos. Oceanic Technol., 34, 137152, https://doi.org/10.1175/JTECH-D-16-0092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snyder, J. C., A. V. Ryzhkov, M. R. Kumjian, A. P. Khain, and J. C. Picca, 2015: A ZDR column detection algorithm to examine convective storm updrafts. Wea. Forecasting, 30, 18191844, https://doi.org/10.1175/WAF-D-15-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torres, S. M., and et al. , 2014: A demonstration of adaptive weather surveillance and multifunction capabilities on the National Weather Radar Testbed Phased Array Radar. Preprints, 2014 Int. Radar Conf., Lille, France, IEEE, 16, https://doi.org/10.1109/RADAR.2014.7060420.

    • Crossref
    • Export Citation
  • Torres, S. M., J. Boettcher, C. Curtis, F. Nai, and D. Schvartzman, 2018: Can an MPAR solution for SENSR meet all weather-surveillance mission-critical needs? Preprints, 2018 IEEE Radar Conf., Oklahoma City, OK, IEEE, 6771, https://doi.org/10.1109/RADAR.2018.8378532.

    • Crossref
    • Export Citation
  • Trapp, R. J., D. M. Wheatley, N. T. Atkins, R. W. Przybylinski, and R. Wolf, 2006: Buyer beware: Some words of caution on the use of severe wind reports in postevent assessment and research. Wea. Forecasting, 21, 408415, https://doi.org/10.1175/WAF925.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tuttle, J. D., V. N. Bringi, H. D. Orville, and F. J. Kopp, 1989: Multiparameter radar study of a microburst: Comparison with model results. J. Atmos. Sci., 46, 601620, https://doi.org/10.1175/1520-0469(1989)046<0601:MRSOAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Den Broeke, M. S., 2017: Polarimetric radar metrics related to tornado life cycles and intensity in supercell storms. Mon. Wea. Rev., 145, 36713686, https://doi.org/10.1175/MWR-D-16-0453.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Den Broeke, M. S., 2020: A preliminary polarimetric radar comparison of pretornadic and nontornadic supercell storms. Mon. Wea. Rev., 148, 15671584, https://doi.org/10.1175/MWR-D-19-0296.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, and K. W. Thomas, 1998: An enhanced hail detection algorithm for the WSR-88D. Wea. Forecasting, 13, 286303, https://doi.org/10.1175/1520-0434(1998)013<0286:AEHDAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and et al. , 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 17531766, https://doi.org/10.1175/BAMS-88-11-1753.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Using ZDR Columns in Forecaster Conceptual Models and Warning Decision-Making

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  • 1 Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma
  • | 2 NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
  • | 3 University of Oklahoma, Norman, Oklahoma
  • | 4 NOAA/National Weather Service, Norman, Oklahoma
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ABSTRACT

Research has shown that dual-polarization (dual-pol) data currently available to National Weather Service forecasters could provide important information about changes in a storm’s structure and intensity. Despite these new data being used gradually by forecasters more over time, they are still not used extensively to inform warning decisions because it is unclear how to apply dual-pol radar data to specific warning decisions. To address this knowledge gap, rapid-update (i.e., volumetric update time of 2.3 min or less) radar data of 45 storms in Oklahoma are used to examine one dual-pol signature, known as the differential reflectivity (ZDR) column, to relate this signature to warning decisions. Base data (i.e., ZDR, reflectivity, velocity) are used to relate ZDR columns to storm intensity, radar signatures such as upper-level reflectivity cores, and scientific conceptual models used by forecasters during the warning decision process. Analysis shows that 1) differences exist between the ZDR columns of severe and nonsevere storms, 2) ZDR columns develop and evolve prior to upper-level reflectivity cores, 3) rapid-update radar data provide a more complete picture of ZDR column evolution than traditional-update radar data (i.e., volumetric update time of about 5 min), and 4) ZDR columns provide a clearer and earlier indication of changes in updraft strength compared to reflectivity signatures. These findings suggest that ZDR columns can be used to inform warning decisions, increase warning confidence, and potentially increase warning lead time especially when they are integrated into existing conceptual models about a storm’s updraft and intensity.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0083.s1.

© 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: Charles Matthew Kuster, Charles.Kuster@noaa.gov

ABSTRACT

Research has shown that dual-polarization (dual-pol) data currently available to National Weather Service forecasters could provide important information about changes in a storm’s structure and intensity. Despite these new data being used gradually by forecasters more over time, they are still not used extensively to inform warning decisions because it is unclear how to apply dual-pol radar data to specific warning decisions. To address this knowledge gap, rapid-update (i.e., volumetric update time of 2.3 min or less) radar data of 45 storms in Oklahoma are used to examine one dual-pol signature, known as the differential reflectivity (ZDR) column, to relate this signature to warning decisions. Base data (i.e., ZDR, reflectivity, velocity) are used to relate ZDR columns to storm intensity, radar signatures such as upper-level reflectivity cores, and scientific conceptual models used by forecasters during the warning decision process. Analysis shows that 1) differences exist between the ZDR columns of severe and nonsevere storms, 2) ZDR columns develop and evolve prior to upper-level reflectivity cores, 3) rapid-update radar data provide a more complete picture of ZDR column evolution than traditional-update radar data (i.e., volumetric update time of about 5 min), and 4) ZDR columns provide a clearer and earlier indication of changes in updraft strength compared to reflectivity signatures. These findings suggest that ZDR columns can be used to inform warning decisions, increase warning confidence, and potentially increase warning lead time especially when they are integrated into existing conceptual models about a storm’s updraft and intensity.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0083.s1.

© 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: Charles Matthew Kuster, Charles.Kuster@noaa.gov

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