Articles | Volume 25, issue 5
https://doi.org/10.5194/nhess-25-1751-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-1751-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 May 2025
Research article |
| 27 May 2025
| 27 May 2025
Insights into thunderstorm characteristics from geostationary lightning jump and dive observations
Royal Meteorological Institute of Belgium (RMIB), Av. Circulaire 3, 1180 Uccle, Belgium
Dieter Roel Poelman
Royal Meteorological Institute of Belgium (RMIB), Av. Circulaire 3, 1180 Uccle, Belgium
Related authors
Felix Erdmann, Olivier Caumont, and Eric Defer
Nat. Hazards Earth Syst. Sci., 23, 2821–2840, https://doi.org/10.5194/nhess-23-2821-2023, https://doi.org/10.5194/nhess-23-2821-2023, 2023
Short summary
Short summary
This work develops a novel lightning data assimilation (LDA) technique to make use of Meteosat Third Generation (MTG) Lightning Imager (LI) data in a regional, convection-permitting numerical weather prediction model. The approach combines statistical Bayesian and 3-dimensional variational methods. Our LDA can promote missing convection and suppress spurious convection in the initial state of the model, and it has similar skill to the operational radar data assimilation for rainfall forecasts.
Dieter Roel Poelman, Hannes Kohlmann, and Wolfgang Schulz
EGUsphere, https://doi.org/10.5194/egusphere-2026-2100, https://doi.org/10.5194/egusphere-2026-2100, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This study evaluates the location accuracy of the EUCLID lightning detection network using lightning events near tall structures across Europe (2012–2025). A clustering approach identifies strikes likely hitting structures, enabling large-scale validation. Results show a median accuracy of ~125 m, consistent with previous studies, and reveal spatial patterns and temporal improvement due to network upgrades. The method provides a scalable proxy for performance assessment.
Dieter Roel Poelman, Hannes Kohlmann, and Wolfgang Schulz
Nat. Hazards Earth Syst. Sci., 24, 2511–2522, https://doi.org/10.5194/nhess-24-2511-2024, https://doi.org/10.5194/nhess-24-2511-2024, 2024
Short summary
Short summary
EUCLID's lightning data unveil distinctive ground strike point (GSP) patterns in Europe. Over seas, GSPs per flash surpass inland, reaching a minimum in the Alps. Mountainous areas like the Alps and Pyrenees have the closest GSP separation, highlighting terrain elevation's impact. The daily peak current correlates with average GSPs per flash. These findings could significantly influence lightning protection measures, urging a focus on GSP density rather than flash density for risk assessment.
Felix Erdmann, Olivier Caumont, and Eric Defer
Nat. Hazards Earth Syst. Sci., 23, 2821–2840, https://doi.org/10.5194/nhess-23-2821-2023, https://doi.org/10.5194/nhess-23-2821-2023, 2023
Short summary
Short summary
This work develops a novel lightning data assimilation (LDA) technique to make use of Meteosat Third Generation (MTG) Lightning Imager (LI) data in a regional, convection-permitting numerical weather prediction model. The approach combines statistical Bayesian and 3-dimensional variational methods. Our LDA can promote missing convection and suppress spurious convection in the initial state of the model, and it has similar skill to the operational radar data assimilation for rainfall forecasts.
Cited articles
Ashley, W. S., Haberlie, A. M., and Gensini, V. A.: The Future of Supercells in the United States, B. Am. Meteorol. Soc., 104, E1–E21, 2023. a
Autones, F., Claudon, M., and Moisselin, J.-M.: Algorithm Theoretical Basis Document for the Convection Product Processors of the NWC/GEO, Tech. Rep. version 2.2, Météo France, NWCSAF Tech. Rep., 61 pp., https://www.nwcsaf.org/Downloads/GEO/2018.1/Documents/Scientific_Docs/NWC-CDOP2-GEO-MFT-SCI-ATBD-Convection_v2.2.pdf (last access: 30 July 2024), 2020. a, b, c
Ávila, E. E., Bürgesser, R. E., Castellano, N. E., Collier, A. B., Compagnucci, R. H., and Hughes, A. R.: Correlations between deep convection and lightning activity on a global scale, J. Atmos. Solar-Terrest. Phys., 72, 1114–1121, https://doi.org/10.1016/j.jastp.2010.07.019, 2010. a
Bateman, M., Mach, D., and Stock, M.: 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, 2021. a
Bedka, K. M.: Overshooting cloud top detections using MSG SEVIRI Infrared brightness temperatures and their relationship to severe weather over Europe, Atmos. Res., 99, 175–189, https://doi.org/10.1016/j.atmosres.2010.10.001, 2011. a
Bedka, K. M. and Khlopenkov, K.: A Probabilistic Multispectral Pattern Recognition Method for Detection of Overshooting Cloud Tops Using Passive Satellite Imager Observations, J. Appl. Meteorol. Climatol., 55, 1983–2005, https://doi.org/10.1175/JAMC-D-15-0249.1, 2016. a, b
Blakeslee, R. J., Lang, T. J., Koshak, W. J., Buechler, D., Gatlin, P., Mach, D. M., Stano, G. T., Virts, K. S., Walker, T. D., Cecil, D. J., Ellett, W., Goodman, S. J., Harrison, S., Hawkins, D. L., Heumesser, M., Lin, H., Maskey, M., Schultz, C. J., Stewart, M., Bateman, M., Chanrion, O., and Christian, H.: Three Years of the Lightning Imaging Sensor Onboard the International Space Station: Expanded Global Coverage and Enhanced Applications, J. Geophys. Res.-Atmos., 125, e2020JD032918, https://doi.org/10.1029/2020JD032918, 2020. a
Borque, P., Vidal, L., Rugna, M., Lang, T. J., Nicora, M. G., and Nesbitt, S. W.: Distinctive Signals in 1-min Observations of Overshooting Tops and Lightning Activity in a Severe Supercell Thunderstorm, J. Geophys. Res.-Atmos., 125, e2020JD032856, https://doi.org/10.1029/2020JD032856, 020. a, b
Bourscheidt, V. and Ramos, M.-H.: On the Use of Geostationary Lightning Mapper Data as a Proxy for Precipitation, SSRN, ELSEVIER, https://doi.org/10.2139/ssrn.4594358, preprint, 2023. a
Bruning, E. C., Tillier, C. E., Edgington, S. F., Rudlosky, S. D., Zajic, J., Gravelle, C., Foster, M., Calhoun, K. M., Campbell, P. A., Stano, G. T., Schultz, C. J., and Meyer, T. C.: Meteorological Imagery for the Geostationary Lightning Mapper, J. Geophys. Res.-Atmos., 124, 14285–14309, https://doi.org/10.1029/2019JD030874, 2019. a
Chinchay, J. H. H.: Algorithm for Automatic Nowcasting Using ABI and GLM Data, in: 103rd AMS Annual Meeting, AMS, https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/418518 (last access: 13 May 2025), 2023. a
Chronis, T., Carey, L. D., Schultz, C. J., Schultz, E. V., Calhoun, K. M., and Goodman, S. J.: Exploring lightning jump characteristics, Weather Forecast., 30, 23–37, https://doi.org/10.1175/WAF-D-14-00064.1, 2015. a, b
Cintineo, J. L., Pavolonis, M. J., and Sieglaff, J. M.: ProbSevere LightningCast: A Deep-Learning Model for Satellite-Based Lightning Nowcasting, Weather Forecast., 37, 1239–1257, https://doi.org/10.1175/WAF-D-22-0019.1, 2022. a
Cummins, K. L.: On the Spatial and Temporal Variation of GLM Flash Detection and How to Manage It, 101st AMS Annual Meeting, virtual, 10–15 January 2021, extended abstract (Christoph J. Schultz, personal communication, 02 Dec 2022) and presentation (https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/382589last access: 13 May 2025), 2021. a, b, c
Curtis, N., Carey, L. D., and Schultz, C.: An Analysis of the Lightning Jump Algorithm Using Geostationary Lightning Mapper Flashes, in: 25th Int. Lightning Detection Conf./Seventh Int. Lightning Meteorology Conf., NASA, Fort Lauderdale, FL, 6 pp., https://www.vaisala.com/sites/default/files/documents (last access: 13 May 2025), 2018. a
Deierling, W. and Petersen, W. A.: Total lightning activity as an indicator of updraft characteristics, J. Geophys. Res., 113, D16210, https://doi.org/10.1029/2007JD009598, 2008. a, b
Dobber, M. and Grandell, J.: Meteosat Third Generation (MTG) Lightning Imager (LI) instrument performance and calibration from user perspective, Proc. 23rd Conf. on Characterization and Radiometric Calibration for Remote Sensing (CALCON), Logan, UT, Utah State University, 13 pp., https://digitalcommons.usu.edu/context/calcon/article/1228/filename/0/type/additional/viewcontent/CC16_PrePostLaunch_DOBBER_MANUSCRIPT.pdf (last access: 13 May 2025), 2014. a
Erdmann, F.: Préparation à l'utilisation des observations de l'imageur d'éclairs de Météosat Troisième Génération pour la prévision numérique à courte échéance (Preparation for the use of Meteosat Third Generation Lightning Imager observations in short-term numerical weather prediction), Ph.D. thesis, Université Toulouse 3 – Paul Sabatier, Toulouse, France, https://theses.fr/2020TOU30273 (last access: 13 May 2025), 2020. a
EUMETSAT: Flexible Combined Imager (FCI), 5:02 pm UTC, https://www.eumetsat.int/mtg-flexible-combined-imager-fci (last access: 17 November 2023), 2021a. a
EUMETSAT: Lightning Imager, 5:02 pm UTC, https://www.eumetsat.int/mtg-lightning-imager (last access: 17 November 2023), 2021b. a
EUMETSAT: NWCSAF General Information, 7:32 am UTC, https://www.nwcsaf.org/web/guest/nwcsaf-general-information (last access: 8 Mar 2022), 2022. a
Farnell, C. and Rigo, T.: The Lightning Jump Algorithm for Nowcasting Convective Rainfall in Catalonia, Atmosphere, 11, 397, https://doi.org/10.3390/atmos11040397, 2020. a
Farnell, C., Rigo, T., and Pineda, N.: Lightning jump as a nowcast predictor: Application to severe weather events in Catalonia, Atmos. Res., 183, 130–141, https://doi.org/10.1016/j.atmosres.2016.08.021, 2017. a, b
Feldmann, M., Hering, A., Gabella, M., and Berne, A.: Hailstorms and rainstorms versus supercells – a regional analysis of convective storm types in the Alpine region, npj Clim Atmos. Sci., 6, 19, https://doi.org/10.1038/s41612-023-00352-z, 2023. a
García-Pereda, J. and coauthors: Use of NWCSAF NWC/GEO software package with MSG, Himawari-8/9 and GOES-13/16 satellites, in: 2019 Joint EUMETSAT/AMS/NOAA Conference, Boston, USA, https://ams.confex.com/ams/JOINTSATMET/mediafile/Handout/Paper358940/EUMCONF2019Boston-JavierGarciaPereda-NWCSAFSatellites.pdf (last access: 13 May 2025), 2019. a
Gatlin, P. N. and Goodman, S. J.: A total lightning trending algorithm to identify severe thunderstorms, J. Atmos. Ocean. Technol., 27, 3–22, https://doi.org/10.1175/2009JTECHA1286.1, 2010. a
Goodman, S. J. and MacGorman, D. R.: Cloud-to-Ground Lightning Activity in Mesoscale Convective Complexes, Mon. Weather Rev., 114, 2320– 2328, https://doi.org/10.1175/1520-0493(1986)114<2320:CTGLAI>2.0.CO;2, 1986. a
Goodman, S. J., Blakeslee, R. J., Koshak, W. J., Mach, D., Bailey, J., Buechler, D., Carey, L., Schultz, C., Bateman, M., McCaul, E., and Stano, G.: The GOES-R Geostationary Lightning Mapper (GLM), Atmos. Res., 125–126, 34–49, https://doi.org/10.1016/j.atmosres.2013.01.006, 2013. a, b
Hayden, L., Liu, C., and Liu, N.: Properties of Mesoscale Convective Systems Throughout Their Lifetimes Using IMERG, GPM, WWLLN, and a Simplified Tracking Algorithm, J. Geophys. Res.-Atmos., 126, e2021JD035264, https://doi.org/10.1029/2021JD035264, 2021. a
Hulton, F. and Schultz, D. M.: Climatology of large hail in Europe: characteristics of the European Severe Weather Database, Nat. Hazards Earth Syst. Sci., 24, 1079–1098, https://doi.org/10.5194/nhess-24-1079-2024, 2024. a
Lahuerta, J. A., Lliso, L., and Ripodas, P.: Algorithm Theoretical Basis Document for the Precipitation Product Processors of the NWC/GEO, Tech. Rep. revision 0.1, AEMET, NWCSAF Tech. Rep., 75 pp., https://www.nwcsaf.org/Downloads/GEO/2018/Documents/Scientific_Docs/NWC-CDOP2-GEO-AEMET-SCI-ATBD-Precipitation_v2.1.pdf (last access: 30 July 2024), 2020. a
Lang, T. J., Ávila, E. E., Blakeslee, R. J., Burchfield, J., Wingo, M., Bitzer, P. M., Carey, L. D., Deierling, W., Goodman, S. J., Medina, B. L., Melo, G., and Pereyra, R. G.: The RELAMPAGO Lightning Mapping Array: Overview and Initial Comparison with the Geostationary Lightning Mapper, J. Atmos. Ocean. Technol., 37, 1457–1475, https://doi.org/10.1175/JTECH-D-20-0005.1, 2020. a
Leinonen, J., Hamann, U., Germann, U., and Mecikalski, J. R.: Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance, Nat. Hazards Earth Syst. Sci., 22, 577–597, https://doi.org/10.5194/nhess-22-577-2022, 2022. a
Lemon, L. R., Burgess, D. W., and Brown, R. A.: Tornadic Storm Airflow and Morphology Derived from Single-Doppler Radar Measurements, Mon. Weather Rev., 106, 48–61, https://doi.org/10.1175/1520-0493(1978)106<0048:TSAAMD>2.0.CO;2, 1978. a
Losego, J., Carl, C., and Trostel, J.: GLM Flash Trends for a Long Lived Supercell-the Megagraph, https://goes-r.nsstc.nasa.gov/home/sites/default/files/2022-10/session_4/Losego_Megagraph.pptx, last access: 19 April 2024, published in the 2022 GLM Annual Science Team Meeting (https://goes-r.nsstc.nasa.gov/home/index.php/meeting-agenda-2022, 09:50 am UTC, 2022. a
MacGorman, D. R. and Rust, W. D.: The electrical nature of storms, Oxford University Press, 198 Madison Avenue, New York, New York 10016, 1 Edn., ISBN 0-19-507337-1, 1998. a
Mach, D. M.: Geostationary Lightning Mapper Clustering Algorithm Stability, J. Geophys. Res.-Atmos., 125, e2019JD031900, https://doi.org/10.1029/2019JD031900, 2020. a, b
Marchand, M., Hilburn, K., and Miller, S. D.: Geostationary Lightning Mapper and Earth Networks Lightning Detection Over the Contiguous United States and Dependence on Flash Characteristics, J. Geophys. Res.-Atmos., 124, 11552–11567, https://doi.org/10.1029/2019JD031039, 2019. a, b
Markowski, P. M.: Hook Echoes and Rear-Flank Downdrafts: A Review, Mon. Weather Rev., 130, 852–876, https://doi.org/10.1175/1520-0493(2002)130<0852:HEARFD>2.0.CO;2, 2002. a
Mashiko, W.: A Numerical Study of the 6 May 2012 Tsukuba City Supercell Tornado. Part II: Mechanisms of Tornadogenesis, Mon. Weather Rev., 144, 3077–3098, https://doi.org/10.1175/MWR-D-15-0122.1, 2016. a
Mikuš Jurković, P., Mahović, N. S., and Počakal, D.: Lightning, overshooting top and hail characteristics for strong convective storms in Central Europe, Atmos. Res., 161–162, 153–168, https://doi.org/10.1016/j.atmosres.2015.03.020, 2015. a
Miller, P. W., Ellis, A. W., and Keighton, S. J.: The utility of total lightning trends in diagnosing single-cell thunderstorm severity: Examples from the central Appalachians region, J. Operational Meteor., 3, 82–98, https://doi.org/10.15191/nwajom.2015.0308, 2015. a
Murphy, M. J.: Preliminary Results from the Inclusion of Lightning Type and Polarity in the Identification of Severe Storms, in: Eighth Conf. on the Meteorological Application of Lightning Data, issue 7.3, https://ams.confex.com/ams/97Annual/webprogram/Paper308037.html (last access: 13 May 2025), 2017. a
Murphy, M. J. and Said, R. K.: 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, 2020. a, b, c
NASA: GOES-16 Cloud & Moisture Imagery, Earth Data [data set], https://search.earthdata.nasa.gov/search/granules?portal=idn&p=C3108204852-INPE&pg[0][v]=f&pg[0][gsk]=-start_date&fi=ABI&tl=1715560800!3!!&fst0=Atmosphere (last access: 13 May 2025), 2025. a
NCEI-NOAA: The National Centers for Environmental Information (NCEI) storm events database, https://www.ncdc.noaa.gov/stormevents/ (last access: 4 January 2023), 2020. a
Ni, X., Huang, F., Hui, W., and Xiao, H.: Lightning Evolution in Hailstorms From the Geostationary Lightning Mapper Over the Contiguous United States, J. Geophys. Res.-Atmos., 128, e2023JD038578, https://doi.org/10.1029/2023JD038578, 2023. a
Nisi, L., Hering, A., Germann, U., Schroeer, K., Barras, H., Kunz, M., and Martius, O.: Hailstorms in the Alpine region: Diurnal cycle, 4D-characteristics, and the nowcasting potential of lightning properties, Q. J. Roy. Meteorol. Soc., 146, 4170–4194, https://doi.org/10.1002/qj.3897, 2020. a, b, c
NOAA: Search – GRGLMPROD, NOAA [data set], https://www.aev.class.noaa.gov/saa/products/search?datatype_family=GRGLMPROD (last access: 13 May 2025), 2025a. a
NOAA: Storm Events Database – Bulk Data Download, NOAA [data set], https://www.ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/ (last access: 13 May 2025), 2025b. a
Pandit, S., Mishra, S., Mittal, A., and Devrani, A. K.: Nowcasting severity of thunderstorm associated with strong wind flow over Indian Subcontinent: Resource lightning surge, Atmósfera, 37, 85–98, https://doi.org/10.20937/ATM.53042, 2023. a
Pineda, N., Rigo, T., Montanyà, J., and van der Velde, O. A.: Charge structure analysis of a severe hailstorm with predominantly positive cloud-to-ground lightning, Atmos. Res., 178–179, 31–44, https://doi.org/10.1016/j.atmosres.2016.03.010, 2016. a
Rigo, T. and Farnell, C.: Characterisation of Thunderstorms with Multiple Lightning Jumps, Atmosphere, 13, 171, https://doi.org/10.3390/atmos13020171, 2022. a, b, c, d
Rudlosky, S. D. and Fuelberg, H. E.: Documenting Storm Severity in the Mid-Atlantic Region Using Lightning and Radar Information, Mon. Weather Rev., 141, 3186–3202, https://doi.org/10.1175/MWR-D-12-00287.1, 2013. a
Satrio, C. N., Bodine, D. J., Palmer, R. D., and Kuster, C. M.: Multi-Radar Analysis of the 20 May 2013 Moore, Oklahoma Supercell through Tornadogenesis and Intensification, Atmosphere, 12, 2073–4433, https://doi.org/10.3390/atmos12030313, 2021. a
Schroeter, S., Richter, H., Arthur, C., Wilke, D., Dunford, M., Wehner, M., and Ebert, E.: Forecasting the impacts of severe weather, The Aust. J. Emergency Manage., 36, 76–83, https://doi.org/10.3316/informit.766653620579430, 2021. a
Schultz, C. J., Petersen, W. A., and Carey, L. D.: Preliminary Development and Evaluation of Lightning Jump Algorithms for the Real-Time Detection of Severe Weather, J. Appl. Meteorol. Climatol., 48, 2543–2563, https://doi.org/10.1175/2009JAMC2237.1, 2009. a, b
Schultz, C. J., Petersen, W. A., and Carey, L. D.: Lightning and Severe Weather: A Comparison between Total and Cloud-to-Ground Lightning Trends, Weather Forecast., 26, 744–755, https://doi.org/10.1175/WAF-D-10-05026.1, 2011. a
Schultz, C. J., Carey, L. D., Schultz, E. V., and Blakeslee, R. J.: Kinematic and Microphysical Significance of Lightning Jumps versus Nonjump Increases in Total Flash Rate, Weather Forecast., 32, 275–288, https://doi.org/10.1175/WAF-D-15-0175.1, 2017. a
Schultz, E. V., Schultz, C. J., Carey, L. D., Cecil, D. J., and Bateman, M.: Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data, J. Oper. Meteorol., 4, 92–107, https://doi.org/10.15191/nwajom.2016.0407, 2016. a, b, c
scikit-learn developers: sklearn.cluster.DBSCAN, 4:44 pm UTC, https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html (last access: 11 March 2022), 2007–2022. a
Steiger, S. M., Orville, R. E., and Carey, L. D.: Total Lightning Signatures of Thunderstorm Intensity over North Texas. Part I: Supercells, Mon. Weather Rev., 135, 3281–3302, https://doi.org/10.1175/MWR3472.1, 2007a. a, b
Steiger, S. M., Orville, R. E., and Carey, L. D.: Total Lightning Signatures of Thunderstorm Intensity over North Texas. Part II: Mesoscale Convective Systems, Mon. Weather Rev., 135, 3303–3324, https://doi.org/10.1175/MWR3483.1, 2007b. a
Stough, S. M., Carey, L. D., Schultz, C. J., and Bitzer, P. M.: Investigating the Relationship between Lightning and Mesocyclonic Rotation in Supercell Thunderstorms, Weather Forecast., 32, 2237–2259, https://doi.org/10.1175/WAF-D-17-0025.1, 2017. a
Thiel, K. C., Calhoun, K. M., Reinhart, A. E., and MacGorman, D. R.: GLM and ABI Characteristics of Severe and Convective Storms, J. Geophys. Res.-Atmos., 125, e2020JD032858, https://doi.org/10.1029/2020JD032858, 2020. a
Thompson, R. L.: A Comparison of Right-Moving Supercell and Quasi-Linear Convective System Tornadoes in the Contiguous United States 2003–21, Weather Forecast., 38, 1497–1513, https://doi.org/10.1175/WAF-D-23-0006.1, 2023. a
Tian, Y., Yao, W., Sun, Y., Wang, Y., Liu, X., Jiang, T., Zhang, L., Meng, L., Wang, L., Sun, X., and Wang, H.: A method for improving the performance of the 2σ lightning jump algorithm for nowcasting hail, Atmos. Res., 280, 106404, https://doi.org/10.1016/j.atmosres.2022.106404, 2022. a
Wapler, K.: The life-cycle of hailstorms: Lightning, radar reflectivity and rotation characteristics, Atmos. Res., 193, 60–72, https://doi.org/10.1016/j.atmosres.2017.04.009, 2017. a, b
Williams, E., Boldi, B., Matlin, A., Weber, M., Hodanish, S., Sharp, D., Goodman, S., Raghavan, R., and Buechler, D.: The behavior of total lightning activity in severe Florida thunderstorms, Atmos. Res., 51, 245–265, https://doi.org/10.1016/S0169-8095(99)00011-3, 1999. a
Wu, F., Cui, X., and Zhang, D.-L.: A lightning-based nowcast-warning approach for short-duration rainfall events: Development and testing over Beijing during the warm seasons of 2006–2007, Atmos. Res., 205, 2–17, https://doi.org/10.1016/j.atmosres.2018.02.003, 2018. a
Zhang, D. and Cummins, K. L.: 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, 2020. a, b
Short summary
This study provides detailed insight into the thunderstorm characteristics associated with abrupt changes in the lightning activity of a thunderstorm – lightning jumps (LJs) and lightning dives (LDs) – using geostationary satellite observations. Thunderstorms exhibiting one or multiple LJs or LDs feature characteristics similar to severe thunderstorms. Storms with multiple LJs contain strong convective updrafts and are prone to produce high rain rates, large hail, or tornadoes.
This study provides detailed insight into the thunderstorm characteristics associated with...
Altmetrics
Final-revised paper
Preprint