Forecast Applications of GLM Gridded Products: A Data Fusion Perspective

Kevin C. Thiel aCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNWS Storm Prediction Center, Norman, Oklahoma
cNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
dSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Kristin M. Calhoun cNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

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Anthony E. Reinhart cNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

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Abstract

The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including flash extent density (FED), minimum flash area (MFA), and total optical energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April to 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210–230 K and isothermal reflectivity at −10°C of 20–30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts.

Significance Statement

The Geostationary Lightning Mapper (GLM) is a lightning sensor on the current generation of U.S. weather satellites. This research shows how data from the space-based lightning sensor can be combined with radar, satellite imagery, and ground-based lightning networks to improve how forecasters monitor thunderstorms and issue warnings for severe weather. The rate of GLM flashes detected and the area they cover correspond well with radar and satellite signatures, especially in cases of intense and severe thunderstorms. When the GLM observes the same thunderstorm from the GOES-East and GOES-West satellites, the optical energy (brightness) of the flashes may help forecasters interpret the types of flashes observed from each sensor.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kevin Thiel, kevin.thiel@ou.edu

Abstract

The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including flash extent density (FED), minimum flash area (MFA), and total optical energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April to 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210–230 K and isothermal reflectivity at −10°C of 20–30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts.

Significance Statement

The Geostationary Lightning Mapper (GLM) is a lightning sensor on the current generation of U.S. weather satellites. This research shows how data from the space-based lightning sensor can be combined with radar, satellite imagery, and ground-based lightning networks to improve how forecasters monitor thunderstorms and issue warnings for severe weather. The rate of GLM flashes detected and the area they cover correspond well with radar and satellite signatures, especially in cases of intense and severe thunderstorms. When the GLM observes the same thunderstorm from the GOES-East and GOES-West satellites, the optical energy (brightness) of the flashes may help forecasters interpret the types of flashes observed from each sensor.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kevin Thiel, kevin.thiel@ou.edu
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  • Adler, R. F., M. J. Markus, D. D. Fenn, G. Szejwach, and W. E. Shenk, 1983: Thunderstorm top structure observed by aircraft overflights with an infrared radiometer. J. Climate Appl. Meteor., 22, 579593, https://doi.org/10.1175/1520-0450(1983)022<0579:TTSOBA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., M. J. Markus, and D. D. Fenn, 1985: Detection of severe Midwest thunderstorms using geosynchronous satellite data. Mon. Wea. Rev., 113, 769781, https://doi.org/10.1175/1520-0493(1985)113<0769:DOSMTU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bateman, M., D. Mach, and M. Stock, 2021: Further investigation into detection efficiency and false alarm rate for the geostationary lightning mappers aboard GOES-16 and GOES-17. Earth Space Sci., 8, e2020EA001237, https://doi.org/10.1029/2020EA001237.

    • Search Google Scholar
    • Export Citation
  • Bedka, K., J. Brunner, R. Dworak, W. Feltz, J. Otkin, and T. Greenwald, 2010: Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients. J. Appl. Meteor. Climatol., 49, 181202, https://doi.org/10.1175/2009JAMC2286.1.

    • Search Google Scholar
    • Export Citation
  • Bedka, K., E. M. Murillo, C. R. Homeyer, B. Scarino, and H. Mersiovsky, 2018: The above-anvil cirrus plume: An important severe weather indicator in visible and infrared satellite imagery. Wea. Forecasting, 33, 11591181, https://doi.org/10.1175/WAF-D-18-0040.1.

    • Search Google Scholar
    • Export Citation
  • Bruning, E. C., 2019: deeplycloudy/glmtools: glmtools release to accompany publication. Zenodo, accessed 5 March 2020, https://doi.org/10.5281/zenodo.2648658.

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

    • Search Google Scholar
    • Export Citation
  • Bruning, E. C., and Coauthors, 2019: Meteorological imagery for the geostationary lightning mapper. J. Geophys. Res. Atmos., 124, 14 28514 309, https://doi.org/10.1029/2019JD030874.

    • Search Google Scholar
    • Export Citation
  • Brunner, K. N., and P. M. Bitzer, 2020: A first look at cloud inhomogeneity and its effect on lightning optical emission. Geophys. Res. Lett., 47, e2020GL087094, https://doi.org/10.1029/2020GL087094.

    • Search Google Scholar
    • Export Citation
  • Buechler, D. E., and S. J. Goodman, 1990: Echo size and asymmetry: Impact on NEXRAD storm identification. J. Appl. Meteor., 29, 962969, https://doi.org/10.1175/1520-0450(1990)029<0962:ESAAIO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bunkers, M. J., B. A. Klimowski, J. W. Zeitler, R. L. Thompson, and M. L. Weisman, 2000: Predicting supercell motion using a new hodograph technique. Wea. Forecasting, 15, 6179, https://doi.org/10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Calhoun, K. M., 2018: Feedback and recommendations for the Geostationary Lightning Mapper (GLM) in severe and hazardous weather forecasting and warning operations. GOES Proving Ground Tech. Rep., 15 pp., https://hwt.nssl.noaa.gov/ewp/projects/GLM-HWT-report_2018.pdf.

  • Calhoun, K. M., 2019: Feedback and recommendations for the Geostationary Lightning Mapper (GLM) in severe and hazardous weather forecasting and warning operations. GOES Proving Ground Tech. Rep., 16 pp., https://hwt.nssl.noaa.gov/ewp/projects/GLM-HWT-report-2019.pdf.

  • Calhoun, K. M., D. R. MacGorman, C. L. Ziegler, and M. I. Biggerstaff, 2013: Evolution of lightning activity and storm charge relative to dual-Doppler analysis of a high-precipitation supercell storm. Mon. Wea. Rev., 141, 21992223, https://doi.org/10.1175/MWR-D-12-00258.1.

    • Search Google Scholar
    • Export Citation
  • Calhoun, K. M., E. R. Mansell, D. R. MacGorman, and D. C. Dowell, 2014: Numerical simulations of lightning and storm charge of the 29–30 May 2004 Geary, Oklahoma, supercell thunderstorm using EnKF mobile radar data assimilation. Mon. Wea. Rev., 142, 39773997, https://doi.org/10.1175/MWR-D-13-00403.1.

    • Search Google Scholar
    • Export Citation
  • Calhoun, K. M., E. C. Bruning, and C. J. Schultz, 2018: Principles and operational applications of geostationary lightning mapper data for severe local storms. 29th Conf. on Severe Local Storms, Stowe, VT, Amer. Meteor. Soc., 2.3, https://ams.confex.com/ams/29SLS/webprogram/Paper348316.html.

  • Calhoun, K. M., K. L. Berry, D. M. Kingfield, T. Meyer, M. J. Krocak, T. M. Smith, G. Stumpf, and A. Gerard, 2021: The experimental warning program of NOAA’s Hazardous Weather Testbed. Bull. Amer. Meteor. Soc., 102, E2229E2246, https://doi.org/10.1175/BAMS-D-21-0017.1.

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

    • Search Google Scholar
    • Export Citation
  • Carey, L. D., E. V. Schultz, C. J. Schultz, W. Deierling, W. A. Petersen, A. L. Bain, and K. E. Pickering, 2019: An evaluation of relationships between radar-inferred kinematic and microphysical parameters and lightning flash rates in Alabama storms. Atmosphere, 10, 796, https://doi.org/10.3390/atmos10120796.

    • Search Google Scholar
    • Export Citation
  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, L. Cronce, and J. Brunner, 2020: NOAA ProbSevere v2.0? ProbHail, ProbWind, and ProbTor. Wea. Forecasting, 35, 15231543, https://doi.org/10.1175/WAF-D-19-0242.1.

    • Search Google Scholar
    • Export Citation
  • Cummins, K. L., and M. J. Murphy, 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, https://doi.org/10.1109/TEMC.2009.2023450.

    • Search Google Scholar
    • Export Citation
  • Elsenheimer, C. B., and C. M. Gravelle, 2019: Introducing lightning threat messaging using the GOES-16 day cloud phase distinction RGB composite. Wea. Forecasting, 34, 15871600, https://doi.org/10.1175/WAF-D-19-0049.1.

    • Search Google Scholar
    • Export Citation
  • Elson, P., and Coauthors, 2018: SciTools/cartopy: v0.17.0. Zenodo, accessed 11 April 2020, https://doi.org/10.5281/zenodo.1490296.

  • Emersic, C., and C. Saunders, 2010: Further laboratory investigations into the relative diffusional growth rate theory of thunderstorm electrification. Atmos. Res., 98, 327340, https://doi.org/10.1016/j.atmosres.2010.07.011.

    • Search Google Scholar
    • Export Citation
  • Flournoy, M. D., M. C. Coniglio, and E. N. Rasmussen, 2021: Examining relationships between environmental conditions and supercell motion in time. Wea. Forecasting, 36, 737755, https://doi.org/10.1175/WAF-D-20-0192.1.

    • Search Google Scholar
    • Export Citation
  • Goodman, S. J., D. Mach, W. Koshak, and R. Blakeslee, 2012: GLM lightning cluster-filter algorithm. NOAA/NESDIS, 73 pp., https://www.star.nesdis.noaa.gov/goesr/documents/ATBDs/Baseline/ATBD_GOES-R_GLM_v3.0_Jul2012.pdf.

  • Goodman, S. J., and Coauthors, 2013: The GOES-R Geostationary Lightning Mapper (GLM). Atmos. Res., 125–126, 3449, https://doi.org/10.1016/j.atmosres.2013.01.006.

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

    • Search Google Scholar
    • Export Citation
  • Heidinger, A., 2013: Algorithm theoretical basis document: ABI cloud height. NOAA/NESDIS, 79 pp., https://www.star.nesdis.noaa.gov/goesr/docs/ATBD/Cloud_Height.pdf.

  • Heidinger, A., and W. Straka, 2013: Algorithm theoretical basis document: ABI cloud mask. NOAA/NESDIS, 106 pp., https://www.star.nesdis.noaa.gov/goesr/docs/ATBD/Cloud_Mask.pdf.

  • Hondl, K. D., and M. D. Eilts, 1994: Doppler radar signatures of developing thunderstorms and their potential to indicate the onset of cloud-to-ground lightning. Mon. Wea. Rev., 122, 18181836, https://doi.org/10.1175/1520-0493(1994)122<1818:DRSODT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hunter, J. D., 2007: Matplotlib: A 2D graphics environment. Comput. Sci. Eng., 9, 9095, https://doi.org/10.1109/MCSE.2007.55.

  • Kelly, D. L., J. T. Schaefer, and C. A. Doswell, 1985: Climatology of nontornadic severe thunderstorm events in the United States. Mon. Wea. Rev., 113, 19972014, https://doi.org/10.1175/1520-0493(1985)113<1997:CONSTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kuhlman, K. M., D. R. MacGorman, M. I. Biggerstaff, and P. R. Krehbiel, 2009: Lightning initiation in the anvils of two supercell storms. Geophys. Res. Lett., 36, L07802, https://doi.org/10.1029/2008GL036650.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., T. Smith, K. Hondl, G. J. Stumpf, and A. Witt, 2006: A real-time, three-dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity, and derived products. Wea. Forecasting, 21, 802823, https://doi.org/10.1175/WAF942.1.

    • Search Google Scholar
    • Export Citation
  • Light, T. E., D. M. Suszcynsky, M. W. Kirkland, and A. R. Jacobson, 2001: Simulations of lightning optical waveforms as seen through clouds by satellites. J. Geophys. Res., 106, 17 10317 114, https://doi.org/10.1029/2001JD900051.

    • Search Google Scholar
    • Export Citation
  • MacGorman, D. R., A. A. Few, and T. L. Teer, 1981: Layered lightning activity. J. Geophys. Res., 86, 9900, https://doi.org/10.1029/JC086iC10p09900.

    • Search Google Scholar
    • Export Citation
  • Mahalik, M. C., B. R. Smith, K. L. Elmore, D. M. Kingfield, K. L. Ortega, and T. M. Smith, 2019: Estimates of gradients in radar moments using a linear least squares derivative technique. Wea. Forecasting, 34, 415434, https://doi.org/10.1175/WAF-D-18-0095.1.

    • Search Google Scholar
    • Export Citation
  • Makowski, J. A., D. R. MacGorman, M. I. Biggerstaff, and W. H. Beasley, 2013: Total lightning characteristics relative to radar and satellite observations of Oklahoma mesoscale convective systems. Mon. Wea. Rev., 141, 15931611, https://doi.org/10.1175/MWR-D-11-00268.1.

    • Search Google Scholar
    • Export Citation
  • Maneewongvatana, S., and D. M. Mount, 1999: Analysis of approximate nearest neighbor searching with clustered point sets. arXiv, 9901013v1, https://doi.org/10.48550/arXiv.cs/9901013.

  • May, R. M., and Coauthors, 2022: MetPy: A meteorological Python library for data analysis and visualization. Bull. Amer. Meteor. Soc., 103, E2273E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, R. M., and L. D. Carey, 2018: Radar reflectivity and altitude distributions of lightning flashes as a function of three main storm types. J. Geophys. Res. Atmos., 123, 12 81412 828, https://doi.org/10.1029/2018JD029238.

    • Search Google Scholar
    • Export Citation
  • Mosier, R. M., C. Schumacher, R. E. Orville, and L. D. Carey, 2011: Radar nowcasting of cloud-to-ground lightning over Houston, Texas. Wea. Forecasting, 26, 199212, https://doi.org/10.1175/2010WAF2222431.1.

    • Search Google Scholar
    • Export Citation
  • Murphy, M. J., and R. K. Said, 2020: Comparisons of lightning rates and properties from the U.S. National Lightning Detection Network (NLDN) and GLD360 with GOES-16 Geostationary Lightning Mapper and advanced baseline imager data. J. Geophys. Res. Atmos., 125, e2019JD031172, https://doi.org/10.1029/2019JD031172.

    • Search Google Scholar
    • Export Citation
  • Petersen, D., and W. H. Beasley, 2013: High-speed video observations of a natural negative stepped leader and subsequent dart-stepped leader. J. Geophys. Res. Atmos., 118, 12 11012 119, https://doi.org/10.1002/2013JD019910.

    • Search Google Scholar
    • Export Citation
  • Peterson, M., 2019: Research applications for the Geostationary Lightning Mapper operational lightning flash data product. J. Geophys. Res. Atmos., 124, 10 20510 231, https://doi.org/10.1029/2019JD031054.

    • Search Google Scholar
    • Export Citation
  • Peterson, M., 2020: Modeling the transmission of optical lightning signals through complex 3-D cloud scenes. J. Geophys. Res. Atmos., 125, e2020JD033231, https://doi.org/10.1029/2020JD033231.

    • Search Google Scholar
    • Export Citation
  • Peterson, M., 2021: Holes in optical lightning flashes: Identifying poorly transmissive clouds in lightning imager data. Earth Space Sci., 8, e2020EA001294, https://doi.org/10.1029/2020EA001294.

    • Search Google Scholar
    • Export Citation
  • Peterson, M., S. Rudlosky, and D. Zhang, 2020: Changes to the appearance of optical lightning flashes observed from space according to thunderstorm organization and structure. J. Geophys. Res. Atmos., 125, e2019JD031087, https://doi.org/10.1029/2019JD031087.

    • Search Google Scholar
    • Export Citation
  • Potvin, C. K., C. Broyles, P. S. Skinner, H. E. Brooks, and E. Rasmussen, 2019: A Bayesian hierarchical modeling framework for correcting reporting bias in the U.S. tornado database. Wea. Forecasting, 34, 1530, https://doi.org/10.1175/WAF-D-18-0137.1.

    • Search Google Scholar
    • Export Citation
  • Reynolds, D. W., 1980: Observations of damaging hailstorms from geosynchronous satellite digital data. Mon. Wea. Rev., 108, 337348, https://doi.org/https://doi.org/10.1175/1520-0493(1980)108<0337:OODHFG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rison, W., R. J. Thomas, P. R. Krehbiel, T. Hamlin, and J. Harlin, 1999: A GPS-based three-dimensional lightning mapping system: Initial observations in central New Mexico. Geophys. Res. Lett., 26, 35733576, https://doi.org/10.1029/1999GL010856.

    • Search Google Scholar
    • Export Citation
  • Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562584, https://doi.org/10.1175/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rotunno, R., and J. B. Klemp, 1982: The influence of the shear-induced pressure gradient on thunderstorm motion. Mon. Wea. Rev., 110, 136151, https://doi.org/10.1175/1520-0493(1982)110<0136:TIOTSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rudlosky, S. D., 2015: Evaluating ENTLN performance relative to TRMM/LIS. J. Oper. Meteor., 3, 1120, https://doi.org/10.15191/nwajom.2015.0302.

    • Search Google Scholar
    • Export Citation
  • Rudlosky, S. D., and K. S. Virts, 2021: Dual Geostationary Lightning Mapper observations. Mon. Wea. Rev., 149, 979998, https://doi.org/10.1175/MWR-D-20-0242.1.

    • Search Google Scholar
    • Export Citation
  • Rudlosky, S. D., S. J. Goodman, K. S. Virts, and E. C. Bruning, 2019: Initial Geostationary Lightning Mapper observations. Geophys. Res. Lett., 46, 10971104, https://doi.org/10.1029/2018GL081052.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., K. A. Hilburn, A. Clayton, B. Fuchs, and S. D. Miller, 2020: Evaluating Geostationary Lightning Mapper flash rates within intense convective storms. J. Geophys. Res. Atmos., 125, e2020JD032827, https://doi.org/10.1029/2020JD032827.

    • Search Google Scholar
    • Export Citation
  • Sandmæl, T. N., B. R. Smith, J. G. Madden, J. W. Monroe, P. T. Hyland, B. A. Schenkel, and T. C. Meyer, 2023: The 2021 Hazardous Weather Testbed Experimental Warning Program radar convective applications experiment: A forecaster evaluation of the tornado probability algorithm and the new mesocyclone detection algorithm. Wea. Forecasting, 38, 11251142, https://doi.org/10.1175/WAF-D-23-0042.1.

    • Search Google Scholar
    • Export Citation
  • Schmit, T. J., M. Gunshor, G. Fu, T. Rink, K. Bah, W. Zhang, and W. Wolf, 2012: GOES-R Advanced Baseline Imager (ABI) algorithm theoretical basis document for Cloud And Moisture Imagery Product (CMIP). NOAA/NESDIS, 63 pp., https://www.star.nesdis.noaa.gov/goesr/docs/ATBD/Imagery.pdf.

  • Schmit, T. J., P. Griffith, M. M. Gunshor, J. M. Daniels, S. J. Goodman, and W. J. Lebair, 2017: A closer look at the ABI on the GOES-R series. Bull. Amer. Meteor. Soc., 98, 681698, https://doi.org/10.1175/BAMS-D-15-00230.1.

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

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., L. D. Carey, E. V. Schultz, and R. J. Blakeslee, 2015: Insight into the kinematic and microphysical processes that control lightning jumps. Wea. Forecasting, 30, 15911621, https://doi.org/10.1175/WAF-D-14-00147.1.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., L. D. Carey, E. V. Schultz, and R. J. Blakeslee, 2017: Kinematic and microphysical significance of lightning jumps versus nonjump increases in total flash rate. Wea. Forecasting, 32, 275288, https://doi.org/10.1175/WAF-D-15-0175.1.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) severe weather and aviation products: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 16171630, https://doi.org/10.1175/BAMS-D-14-00173.1.

    • Search Google Scholar
    • Export Citation
  • Stolzenburg, M., T. C. Marshall, and P. R. Krehbiel, 2015: Initial electrification to the first lightning flash in New Mexico thunderstorms. J. Geophys. Res. Atmos., 120, 11 25311 276, https://doi.org/10.1002/2015JD023988.

    • Search Google Scholar
    • Export Citation
  • Stough, S. M., L. D. Carey, C. J. Schultz, and P. M. Bitzer, 2017: Investigating the relationship between lightning and mesocyclonic rotation in supercell thunderstorms. Wea. Forecasting, 32, 22372259, https://doi.org/10.1175/WAF-D-17-0025.1.

    • Search Google Scholar
    • Export Citation
  • Strassberg, G., and M. Sowko, 2021: National Weather Service instruction 10-1605: Performance and evaluation, NWSPD 10-16 storm data preparation. NOAA NCEI Tech. Rep., 110 pp., https://www.nws.noaa.gov/directives/sym/pd01016005curr.pdf.

  • Takahashi, T., 1978: Riming electrification as a charge generation mechanism in thunderstorms. J. Atmos. Sci., 35, 15361548, https://doi.org/10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thiel, K., 2022: GOES-R and JPSS proving ground demonstration at the Hazardous Weather Testbed 2022 Spring Experiment final evaluation. Cooperative Institute for Severe and High-Impact Weather Research and Operations Tech. Rep. 47 pp., https://doi.org/10.25923/fwnq-kf73.

  • Thiel, K., and K. Calhoun, 2021: GOES-R and JPSS proving ground demonstration at the Hazardous Weather Testbed 2021 Spring Experiment final evaluation. Cooperative Institute for Severe and High-Impact Weather Research and Operations Tech. Rep., 35 pp., https://doi.org/10.25923/ksf3-js29.

  • Thiel, K., K. M. Calhoun, A. E. Reinhart, and D. R. MacGorman, 2020: GLM and ABI characteristics of severe and convective storms. J. Geophys. Res. Atmos., 125, e2020JD032858, https://doi.org/10.1029/2020JD032858.

    • Search Google Scholar
    • Export Citation
  • Thomas, R. J., P. R. Krehbiel, W. Rison, T. Hamlin, D. J. Boccippio, S. J. Goodman, and H. J. Christian, 2000: Comparison of ground-based 3-dimensional lightning mapping observations with satellite-based LIS observations in Oklahoma. Geophys. Res. Lett., 27, 17031706, https://doi.org/10.1029/1999GL010845.

    • Search Google Scholar
    • Export Citation
  • Thomson, L. W., and E. P. Krider, 1982: The effects of clouds on the light produced by lightning. J. Atmos. Sci., 39, 20512065, https://doi.org/10.1175/1520-0469(1982)039<2051:TEOCOT>2.0.CO;2.

    • Search Google Scholar
    • 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.

    • Search Google Scholar
    • Export Citation
  • Waskom, M., and Coauthors, 2020: mwaskom/seaborn: v0.10.0 (January 2020). Zenodo, accessed 11 April 2020, https://doi.org/10.5281/zenodo.3629446.

  • Weiss, S. A., D. R. MacGorman, and K. M. Calhoun, 2012: Lightning in the anvils of supercell thunderstorms. Mon. Wea. Rev., 140, 20642079, https://doi.org/10.1175/MWR-D-11-00312.1.

    • Search Google Scholar
    • Export Citation
  • Williams, E. R., 1985: Large-scale charge separation in thunderclouds. J. Geophys. Res., 90, 6013, https://doi.org/10.1029/JD090iD04p06013.

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

    • 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.

    • Search Google Scholar
    • Export Citation
  • Wolf, P., 2006: Anticipating the initiation, cessation, and frequency of cloud-to-ground lightning, utilizing WSR-88D reflectivity data. NWA Electronic J. Oper. Meteor., 19 pp., http://nwafiles.nwas.org/ej/pdf/2007-EJ1.pdf.

  • Woodard, C. J., L. D. Carey, W. A. Petersen, and W. P. Roeder, 2012: Operational utility of dual-polarization variables in lightning initiation forecasting. Electron. J. Oper. Meteor., 13, 79102.

    • Search Google Scholar
    • Export Citation
  • Zhang, D., and K. L. Cummins, 2020: Time evolution of satellite-based optical properties in lightning flashes, and its impact on GLM flash detection. J. Geophys. Res. Atmos., 125, e2019JD032024, https://doi.org/10.1029/2019JD032024.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and Coauthors, 2017: Evaluation of ENTLN performance characteristics based on the ground truth natural and rocket-triggered lightning data acquired in Florida: Evaluation of ENTLN performance. J. Geophys. Res. Atmos., 122, 98589866, https://doi.org/10.1002/2017JD027270.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y., M. Stock, J. Lapierre, and E. DiGangi, 2022: Upgrades of the Earth networks total lightning network in 2021. Remote Sens., 14, 2209, https://doi.org/10.3390/rs14092209.

    • Search Google Scholar
    • Export Citation
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