Exploring Lightning Jump Characteristics

T. Chronis * Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by T. Chronis in
Current site
Google Scholar
PubMed
Close
,
Lawrence D. Carey Department of Atmospheric Science, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by Lawrence D. Carey in
Current site
Google Scholar
PubMed
Close
,
Christopher J. Schultz Department of Atmospheric Science, University of Alabama in Huntsville, and NASA Marshall Space Flight Center, Huntsville, Alabama

Search for other papers by Christopher J. Schultz in
Current site
Google Scholar
PubMed
Close
,
Elise V. Schultz * Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by Elise V. Schultz in
Current site
Google Scholar
PubMed
Close
,
Kristin M. Calhoun Cooperative Institute for Mesoscale Meteorology Studies, Oklahoma University, and National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Kristin M. Calhoun in
Current site
Google Scholar
PubMed
Close
, and
Steven J. Goodman NOAA/National Environmental Satellite, Data, and Information Service, Greenbelt, Maryland

Search for other papers by Steven J. Goodman in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This study is concerned with the characteristics of storms exhibiting an abrupt temporal increase in the total lightning flash rate [i.e., lightning jump (LJ)]. An automated storm tracking method is used to identify storm “clusters” and total lightning activity from three different lightning detection systems over Oklahoma, northern Alabama, and Washington, D.C. On average and for different employed thresholds, the clusters that encompass at least one LJ (LJ1) last longer and relate to higher maximum expected size of hail, vertical integrated liquid, and lightning flash rates (area normalized) than do the clusters without an LJ (LJ0). The respective mean radar-derived and lightning values for LJ1 (LJ0) clusters are 80 min (35 min), 14 mm (8 mm), 25 kg m−2 (18 kg m−2), and 0.05 flash min−1 km−2 (0.01 flash min−1 km−2). Furthermore, the LJ1 clusters are also characterized by slower-decaying autocorrelation functions, a result that implies a less “random” behavior in the temporal flash rate evolution. In addition, the temporal occurrence of the last LJ provides an estimate of the time remaining to the storm’s dissipation. Depending on the LJ strength (i.e., varying thresholds), these values typically range between 20 and 60 min, with stronger jumps indicating more time until storm decay. This study’s results support the hypothesis that the LJ is a proxy for the storm’s kinematic and microphysical state rather than a coincidental value.

Corresponding author address: Themis Chronis, Earth System Science Center, University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL 35805. E-mail: themis.chronis@nsstc.uah.edu

Abstract

This study is concerned with the characteristics of storms exhibiting an abrupt temporal increase in the total lightning flash rate [i.e., lightning jump (LJ)]. An automated storm tracking method is used to identify storm “clusters” and total lightning activity from three different lightning detection systems over Oklahoma, northern Alabama, and Washington, D.C. On average and for different employed thresholds, the clusters that encompass at least one LJ (LJ1) last longer and relate to higher maximum expected size of hail, vertical integrated liquid, and lightning flash rates (area normalized) than do the clusters without an LJ (LJ0). The respective mean radar-derived and lightning values for LJ1 (LJ0) clusters are 80 min (35 min), 14 mm (8 mm), 25 kg m−2 (18 kg m−2), and 0.05 flash min−1 km−2 (0.01 flash min−1 km−2). Furthermore, the LJ1 clusters are also characterized by slower-decaying autocorrelation functions, a result that implies a less “random” behavior in the temporal flash rate evolution. In addition, the temporal occurrence of the last LJ provides an estimate of the time remaining to the storm’s dissipation. Depending on the LJ strength (i.e., varying thresholds), these values typically range between 20 and 60 min, with stronger jumps indicating more time until storm decay. This study’s results support the hypothesis that the LJ is a proxy for the storm’s kinematic and microphysical state rather than a coincidental value.

Corresponding author address: Themis Chronis, Earth System Science Center, University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL 35805. E-mail: themis.chronis@nsstc.uah.edu
Save
  • Amburn, S., and Wolf P. , 1996: VIL density as a hail indicator. Preprints, 18th Conf. on Severe Local Storms, San Francisco, CA, Amer. Meteor. Soc., 581–585.

  • Bowerman, B. L., and O’Connell R. T. , 1979: Time Series and Forecasting: An Applied Approach. Duxbury Press, 481 pp.

  • Bruning, E. C., and MacGorman D. R. , 2013: Theory and observations of controls on lightning flash size spectra. J. Atmos. Sci., 70, 40124029, doi:10.1175/JAS-D-12-0289.1.

    • Search Google Scholar
    • Export Citation
  • Carey, L. D., and Rutledge S. A. , 2000: On the relationship between precipitation and lightning in tropical island convection: A C-band polarimetric radar study. Mon. Wea. Rev., 128, 26872710, doi:10.1175/1520-0493(2000)128<2687:TRBPAL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Carey, L. D., Petersen W. A. , and Schultz C. J. , 2009: A statistical framework for the development and evaluation of a lightning jump algorithm. Preprints, Fourth Conf. on the Meteorological Applications of Lightning Data, Phoenix, AZ, Amer. Meteor. Soc., P1.13. [Available online at https://ams.confex.com/ams/89annual/techprogram/paper_150768.htm.]

  • Chronis, T., Williams E. , Anagnostou E. , Walt Petersen, 2007: Lightning as a precursor of tropical cyclogenesis. Eos, Trans. Amer. Geophys. Union, 88, 397, doi:10.1029/2007EO400001.

    • Search Google Scholar
    • Export Citation
  • Cintineo, L. J., Smith T. M. , Lakshmanan V. , Brooks H. E. , and Ortega K. L. , 2012: An objective high-resolution hail climatology of the contiguous United States. Wea. Forecasting, 27, 12351248, doi:10.1175/WAF-D-11-00151.1.

    • Search Google Scholar
    • Export Citation
  • Cintineo, L. J., Pavolonis M. J. , Sieglaff J. M. , and Lindsey D. T. , 2014: An empirical model for assessing the severe weather potential of developing convection. Wea. Forecasting,29, 639–653, doi:10.1175/WAF-D-13-00113.1.

  • Cummins, K. L., and Murphy M. J. , 2009: An overview of lightning locating systems: History, techniques, and data uses, with an in-depth look at the U.S. NLDN. IEEE Trans. Electromagn. Compat., 51, 499518, doi:10.1109/TEMC.2009.2023450.

    • Search Google Scholar
    • Export Citation
  • Cummins, K. L., Bardo E. A. , Hiscox W. L. , Pyle R. B. , and Pifer A. E. , 1995: NLDN ‘95: A combined TOA/MDF technology upgrade of the U.S. National Lightning Detection Network. Proc. Int. Aerospace and Ground Conf. on Lightning and Static Electricity, Williamsburg, VA, National Interagency Coordination Group of the National Atmospheric Electricity Hazards Protection Program, 1–15.

  • Cummins, K. L., Krider E. P. , and Malone M. , 1998: The U.S. National Detection Network and applications of cloud-to-ground lightning data by electric power utilities. IEEE Trans. Electromagn. Compat.,40, 465–480, doi:10.1109/15.736207.

  • Cummins, K. L., Cramer J. , Biagi C. , Krider E. P. , Jerauld J. , Uman M. , and Rakov V. , 2006: The U.S. National Lightning Detection Network: Post-upgrade status. Preprints, Second Conf. on Meteorological Applications of Lightning Data, Atlanta, GA, Amer. Meteor. Soc., 6.1. [Available online at https://ams.confex.com/ams/pdfpapers/105142.pdf.]

  • Deierling, W., and Petersen W. A. , 2008: Total lightning activity as an indicator of updraft characteristics. J. Geophys. Res., 113, D16210, doi:10.1029/2007JD009598.

    • Search Google Scholar
    • Export Citation
  • Emersic, C., and Saunders C. P. R. , 2010: Further laboratory investigations into the relative diffusional growth rate theory of thunderstorm electrification. Atmos. Res., 98, doi:10.1016/j.atmosres.2010.07.011.

    • Search Google Scholar
    • Export Citation
  • Gatlin, P., and Goodman S. J. , 2010: A total lightning trending algorithm to identify severe thunderstorms. J. Atmos. Oceanic Technol., 27, 322, doi:10.1175/2009JTECHA1286.1.

    • Search Google Scholar
    • Export Citation
  • Goodman, S. J., Buechler D. E. , Wright P. D. , and Rust W. D. , 1988: Lightning and precipitation history of a microburst-producing storm. Geophys. Res. Lett., 15, 11851188, doi:10.1029/GL015i011p01185.

    • Search Google Scholar
    • Export Citation
  • Goodman, S. J., and Coauthors, 2005: The North Alabama Lightning Mapping Array: Recent severe storm observations and future prospects. Atmos. Res., 76, 423437, doi:10.1016/j.atmosres.2004.11.035.

    • Search Google Scholar
    • Export Citation
  • Goodman S., and Coauthors, 2013: The GOES-R Geostationary Lightning Mapper (GLM). Atmos. Res., 125–126, 3449, doi:10.1016/j.atmosres.2013.01.006.

    • Search Google Scholar
    • Export Citation
  • Greene, D. R., and Clark R. A. , 1972: Vertically integrated liquid water—A new analysis tool. Mon. Wea. Rev., 100, 548552, doi:10.1175/1520-0493(1972)100<0548:VILWNA>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Keene, K. M., Schlatter P. T. , Hales J. E. , and Brooks H. , 2008: Evaluation of NWS watch and warning performance related to tornadic events. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., P3.19. [Available online at https://ams.confex.com/ams/pdfpapers/142183.pdf.]

  • Kolodziej Hobson, A. G. K., Lakshmanan V. , Smith T. M. , and Richman M. , 2012: An automated technique to categorize storm type from radar and near-storm environment data. Atmos. Res.,111, 104–113, doi:10.1016/j.atmosres.2012.03.004.

  • Koshak, W. J., and Coauthors, 2004: North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. J. Atmos. Oceanic Technol., 21, 543558, doi:10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Krehbiel, P. R., 2008: The DC Lightning Mapping Array. Preprints, Third Conf. on Meteorological Applications of Lightning Data, New Orleans, LA, Amer. Meteor. Soc., 3.2. [Available online at https://ams.confex.com/ams/88Annual/techprogram/paper_129095.htm.]

  • Lakshmanan, V., and Smith T. , 2009: Data mining storm attributes from spatial grids. J. Atmos. Oceanic Technol., 26, 23532365, doi:10.1175/2009JTECHA1257.1.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Smith T. , Stumpf G. J. , and Hondl K. , 2007: The Warning Decision Support System–Integrated Information. Wea. Forecasting, 22, 596612, doi:10.1175/WAF1009.1.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Hondl K. , and Rabin R. , 2009: An efficient, general-purpose technique for identifying storm cells in geospatial images. J. Atmos. Oceanic Technol., 26, 523537, doi:10.1175/2008JTECHA1153.1.

    • Search Google Scholar
    • Export Citation
  • Liu, C., and Heckman S. , 2011: The application of total lightning detection and cell tracking for severe weather prediction. Proc. Fifth Conf. on the Meteorological Applications of Lightning Data, Seattle, WA, Amer. Meteor. Soc., 8.2. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper183895.html.]

  • MacGorman, D. R., and Morgenstern C. D. , 1998: Some characteristics of cloud to ground lightning in mesoscale convective systems. J. Geophys. Res., 103, 14 01114 023, doi:10.1029/97JD03221.

    • Search Google Scholar
    • Export Citation
  • MacGorman, D. R., and Coauthors, 2008: TELEX: The Thunderstorm Electrification and Lightning Experiment. Bull. Amer. Meteor. Soc., 89, 9971013, doi:10.1175/2007BAMS2352.1.

    • Search Google Scholar
    • Export Citation
  • McCaul, E. W., Bailey J. , Hall J. , Goodman S. J. , Blakeslee R. , and Buechler D. E. , 2005: A flash clustering algorithm for North Alabama Lightning Mapping Array data. Preprints, Conf. on Meteorological Applications of Lightning Data, San Diego, CA, Amer. Meteor. Soc., 5.2. [Available online at https://ams.confex.com/ams/pdfpapers/84373.pdf.]

  • Murphy, M. J., and Nag A. , 2014: Enhanced cloud lightning performance of the U.S. National Lightning Detection Network following the 2013 upgrade. 23rd Int. Lightning Detection Conf./5th Int. Lightning Meteorology Conf., Tucson, AZ, Vaisala. [Available online at http://www.vaisala.com/en/events/ildcilmc/Pages/ILDC-2014-archive.aspx.]

  • Rudlosky, S. D., and Fuelberg H. E. , 2010: Pre- and postupgrade distributions of NLDN reported cloud-to-ground lightning characteristics in the contiguous United States. Mon. Wea. Rev.,138, 3623–3633, doi:10.1175/2010MWR3283.1.

  • Rudlosky, S. D., and Fuelberg H. E. , 2013: Documenting storm severity in the mid-Atlantic region using lightning and radar information. Mon. Wea. Rev., 141, 31863202, doi:10.1175/MWR-D-12-00287.1.

    • Search Google Scholar
    • Export Citation
  • Saunders, C. P. R., 1993: A review of thunderstorm electrification processes. J. Appl. Meteor., 32, 642655, doi:10.1175/1520-0450(1993)032<0642:AROTEP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Saunders, C. P. R., Bax-Norman H. , Emersic C. , Avila E. E. , and Castellano N. E. , 2006: Laboratory studies of the effect of cloud conditions on graupel/crystal charge transfer in thunderstorm electrification. Quart. J. Roy. Meteor. Soc., 132, 26532673, doi:10.1256/qj.05.218.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., Petersen W. A. , and Carey L. D. , 2009: Preliminary development and evaluation of lightning jump algorithms for the real-time detection of severe weather. J. Appl. Meteor. Climatol., 48, 25432563, doi:10.1175/2009JAMC2237.1.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., Petersen W. A. , and Carey L. D. , 2011: Lightning and severe weather: A comparison between total and cloud-to-ground lightning trends. Wea. Forecasting, 26, 744755, doi:10.1175/WAF-D-10-05026.1.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., Carey L. D. , Schultz E. V. , Blakeslee R. J. , and Goodman S. J. , 2014: Physical and dynamical linkages between lightning jumps and storm conceptual models. Proc. 15th Int. Conf. on Atmospheric Electricity, Norman, OK, IUGG/IAMAS. [Available online at http://www.nssl.noaa.gov/users/mansell/icae2014/preprints/Schultz_246.pdf.]

  • Stumpf, G. J., Smith T. M. , and Hocker J. , 2004: New hail diagnostic parameters derived by integrating multiple radars and multiple sensors. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., P7.8. [Available online at https://ams.confex.com/ams/pdfpapers/81451.pdf.]

  • Takahashi, T., 1978: Riming electrification as a charge generating mechanism. J. Atmos. Sci.,35, 1536–1548, doi:10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2.

  • Thomas, R., Krehbiel P. , Rison W. , Harlin J. , Hamlin T. , and Campbell N. , 2003: The LMA flash algorithm. Proc. 12th Int. Conf. on Atmospheric Electricity, Versailles, France, Int. Commission on Atmospheric Electricity, 655–656.

  • Thomas, R., Krehbiel P. , Rison W. , Hunyady S. J. , Winn W. P. , Hamlin T. , and Harlin J. , 2004: Accuracy of the Lightning Mapping Array. J. Geophys. Res., 109, D14207, doi:10.1029/2004JD004549.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., Wheatly D. M. , Atkins N. T. , and Przybylinkski R. W. , 2006: Buyer beware: Some words of caution on the use of severe wind reports in postevent assessment and research. Wea. Forecasting, 21, 408415, doi:10.1175/WAF925.1.

    • Search Google Scholar
    • Export Citation
  • Uman, M. A., 1987: The Lightning Discharge. Academic Press, 183 pp.

  • Williams, E. R., 2001: The electrification of severe storms. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 527–561.

  • Williams, E. R., and Coauthors, 1999: The behavior of total lightning activity in severe Florida thunderstorms. Atmos. Res., 51, 245265, doi:10.1016/S0169-8095(99)00011-3.

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

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1186 299 24
PDF Downloads 820 179 9