Articles | Volume 19, issue 20
https://doi.org/10.5194/amt-19-6625-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-19-6625-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
20 Oct 2026
Research article | Highlight paper |
| 20 Oct 2026
| 20 Oct 2026
Meteosat Third Generation Lightning Imager detection performances
Bartolomeo Viticchié
CORRESPONDING AUTHOR
EUMETSAT, Eumetsat Allee 1, 64295, Darmstadt, Germany
Sven-Erik Enno
EUMETSAT, Eumetsat Allee 1, 64295, Darmstadt, Germany
David Navia
Exostaff, Riedstraße 6, 64404, Bickenbach, Germany
Janja Avbelj
EUMETSAT, Eumetsat Allee 1, 64295, Darmstadt, Germany
Jochen Grandell
EUMETSAT, Eumetsat Allee 1, 64295, Darmstadt, Germany
Related authors
Sven-Erik Enno, Bartolomeo Viticchié, David Navia, and Jochen Grandell
EGUsphere, https://doi.org/10.5194/egusphere-2026-3386, https://doi.org/10.5194/egusphere-2026-3386, 2026
Short summary
Short summary
The Meteosat Third Generation Lightning Imager (MTG-LI) is the first European space mission for monitoring lightning activity over Europe, Africa, and their surrounding areas. During its first year of observations from July 2024 to June 2025, MTG-LI detected a total of 595×106 lightning flashes. The highest lightning frequency was found in central Africa. MTG-LI is an excellent instrument for monitoring total lightning activity on operational and climatological scales.
Sven-Erik Enno, Bartolomeo Viticchié, David Navia, and Jochen Grandell
EGUsphere, https://doi.org/10.5194/egusphere-2026-3386, https://doi.org/10.5194/egusphere-2026-3386, 2026
Short summary
Short summary
The Meteosat Third Generation Lightning Imager (MTG-LI) is the first European space mission for monitoring lightning activity over Europe, Africa, and their surrounding areas. During its first year of observations from July 2024 to June 2025, MTG-LI detected a total of 595×106 lightning flashes. The highest lightning frequency was found in central Africa. MTG-LI is an excellent instrument for monitoring total lightning activity on operational and climatological scales.
Cited articles
Aich, V., Holzworth, R., Goodman, S. J., Kuleshov, Y., Price, C., and Williams, E.: Lightning: A new essential climate variable, EOS, 99, https://doi.org/10.1029/2018EO104583, 2018. a
Albrecht, R. I., Goodman, S. J., Buechler, D. E., Blakeslee, R. J., and Christian, H. J.: Where Are the Lightning Hotspots on Earth?, B. Am. Meteorol. Soc., 97, 2051–2068, https://doi.org/10.1175/BAMS-D-14-00193.1, 2016. a
Ashley, W. S. and Gilson, C. W.: A reassessment of U.S. lightning mortality, B. Am. Meteorol. Soc., 90, 1501–1518, 2009. 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, b
Bitzer, P. M., Burchfield, J. C., and Christian, H. J.: A Bayesian approach to assess the performance of lightning detection systems, J. Atmos. Oceanic Technol., 33, 563–578, 2016. a
Bližňák, V. and Sokol, Z.: First validation of the Lightning Imager aboard Meteosat Third Generation satellite with Earth Networks Total Lightning Network, I. J. Appl. Earth Observ. Geoinfo., 147, 105205, https://doi.org/10.1016/j.jag.2026.105205, 2026. a
Boccippio, D. J., Goodman, S. J., and Heckman, S.: Regional Differences in Tropical Lightning Distributions, J. Appl. Meteorol., 39, 2231–2248, https://doi.org/10.1175/1520-0450(2001)040<2231:RDITLD>2.0.CO;2, 2000a. a
Boccippio, D. J., Koshak, W. J., Blakeslee, R. J., Christian, H. J., Goodman, S. J., and Hall, J. M.: Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part I: Predicted Diurnal Variability, J. Atmos. Sci., 57, 3264–3275, https://doi.org/10.1175/1520-0426(2002)019<1318:PAOTOT>2.0.CO;2, 2000b. a
Boccippio, D. J., Cummins, K. L., Christian, H. J., and Goodman, S. J.: Combined satellite-and surface-based estimation of the intracloud–cloud-to-ground lightning ratio over the continental United States, Mon. Weather Rev., 129, 108–122, 2001. a
Boccippio, D. J., Koshak, W., Blakeslee, R., Boeck, W. L., Mach, D., Goodman, S., and Christian, H. J.: Performance assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part I: Predicted diurnal variability, J. Atmos. Ocean. Tech., 19, 1318–1332, https://doi.org/10.1175/1520-0426(2002)019<1318:PAOTOT>2.0.CO;2, 2002. a
Bozzo, A., Doutriaux-Boucher, M., Jackson, J., Spezzi, L., Lattanzio, A., and Watts, P. D.: First Release of the Optimal Cloud Analysis Climate Data Record from the EUMETSAT SEVIRI Measurements 2004–2019, Remote Sens., 16, https://doi.org/10.3390/rs16162989, 2024. a
Branick, M. L. and Doswell, Charles A., I.: An Observation of the Relationship between Supercell Structure and Lightning Ground-Strike Polarity, Weather Forecast., 7, 143–149, https://doi.org/10.1175/1520-0434(1992)007<0143:AOOTRB>2.0.CO;2, 1992. a
Cao, L., Wang, Q., Wu, L., Lu, Q., and Zhang, P.: Fengyun meteorological satellite products for earth system science, Adv. Atmos. Sci., 38, https://doi.org/10.1007/s00376-021-0425-3, 2021. a, b
Cecil, D. J., Goodman, S. J., Boccippio, D. J., Zipser, E. J., and Nesbitt, S. W.: Three Years of TRMM Precipitation Features. Part I: Radar, Radiometric, and Lightning Characteristics, Mon. Weather Rev., 133, 543–566, https://doi.org/10.1175/MWR-2876.1, 2005. a
Cecil, D. J., Buechler, D. E., and Blakeslee, R. J.: Gridded lightning climatology from TRMM-LIS and OTD: Dataset description, Atmos. Res., 135–136, 404–414, https://doi.org/10.1016/j.atmosres.2012.06.028, 2014. a
Cecil, D. J., Buechler, D. E., Lang, T. J., Virts, K. S., and Mach, D. M.: Lightning Climatology Datasets from TRMM LIS, ISS LIS, and OTD, J. Appl. Meteorol. Climatol., 65, 51–72, https://doi.org/10.1175/JAMC-D-25-0110.1, 2026. a
Chameides, W. L.: The role of lightning in the chemistry of the atmosphere, in: The Earth's Electrical Environment, 70–77, National Academy Press, Washington, D.C., 1986. a
Chen, Z., Qie, X., Sun, J., Xiao, X., Zhang, Y., Cao, D., and Yang, J.: Evaluation of Fengyun-4A Lightning Mapping Imager (LMI) Performance during Multiple Convective Episodes over Beijing, Remote Sens., 13, 1746, https://doi.org/10.3390/rs13091746, 2021. a
Cummins, K. L. and Murphy, M. J.: An overview of lightning locating systems: History, techniques, and data uses, with an in-depth look at the U.S. NLDN, IEEE T. Electromagn. C., 51, 499–518, https://doi.org/10.1109/TEMC.2009.2023450, 2009. a, b, c, d
Djalel, D., Meghni, B., and Labar, H.: Study of the lightning impact on the wind-turbine, Energy Res. J., 5, 17–25, https://doi.org/10.3844/erjsp.2014.17.25, 2014. a
Enno, S.-E., Viticchié, B., Navia, D., and Grandell, J.: First Year of Meteosat Third Generation Lightning Imager Observations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-3386, 2026. a, b, c, d
Erdmann, F., Defer, E., Caumont, O., Blakeslee, R. J., Pédeboy, S., and Coquillat, S.: Concurrent satellite and ground-based lightning observations from the Optical Lightning Imaging Sensor (ISS-LIS), the low-frequency network Meteorage and the SAETTA Lightning Mapping Array (LMA) in the northwestern Mediterranean region, Atmos. Meas. Tech., 13, 853–875, https://doi.org/10.5194/amt-13-853-2020, 2020. a
Finney, D. L., Doherty, R. M., Wild, O., and Abraham, N. L.: The impact of lightning on tropospheric ozone chemistry using a new global lightning parametrisation, Atmos. Chem. Phys., 16, 7507–7522, https://doi.org/10.5194/acp-16-7507-2016, 2016. a
Gatlin, P. N. and Goodman, S. J.: A Total Lightning Trending Algorithm to Identify Severe Thunderstorms, J. Atmos. Ocean. Tech., 27, 3–20, https://doi.org/10.1175/2009JTECHA1286.1, 2010. a
Goodman, S. J., Blakeslee, R. J., Koshak, W. J., Mach, D., Bailey, J., Buechler, D., Carey, L., Christian, H., Doswell, C., Fedor, J., McCaul, E., Peterson, W., Rudlosky, S., Squires, K., Kuhlman, K., and Mauzey, C.: The GOES-R Geostationary Lightning Mapper (GLM), Atmos. Res., 125, 34–49, https://doi.org/10.1016/j.atmosres.2013.01.006, 2013. a, b
Grebović, S., Akšamović, A., Filipović-Grčić, B., and Konjicija, S.: Investigation of lightning effects on solar power plants connected to transmission networks, Electr. Pow. Syst. Res., 238, 1108240, https://doi.org/10.1016/j.epsr.2025.112217, 2025. a
Hilburn, K. A., Kummerow, C. D., and de Graaf, I. A. M.: Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations, J. Appl. Meteorol. Climatol., 60, 3–17, https://doi.org/10.1175/JAMC-D-20-0084.1, 2021. a
Holmlund, K., Grandell, J., Schmetz, J., Stuhlmann, R., Bojkov, B., Munro, R., Lekouara, M., Coppens, D., Viticchie, B., August, T., Theodore, B., Watts, P., Dobber, M., Fowler, G., Bojinski, S., Schmid, A., Salonen, K., Tjemkes, S., Aminou, D., and Blythe, P.: Meteosat Third Generation (MTG): Continuation and Innovation of Observations from Geostationary Orbit, B. Am. Meteorol. Soc., 102, E990–E1015, https://doi.org/10.1175/BAMS-D-19-0304.1, 2021. a, b
Holmlund, K., Counet, P., Fadrique, F. M., Schmid, A., Bojkov, B., Munro, R., Grandell, J., and Obligis, E.: The EUMETSAT satellite programmes and data services, J. Eur. Meteorol. Soc., 2, 100005, https://doi.org/10.1016/j.jemets.2024.100005, 2025. a
Kaplan, J. O. and Lau, K. H.-K.: The WGLC global gridded lightning climatology and time series, Earth Syst. Sci. Data, 13, 3219–3237, https://doi.org/10.5194/essd-13-3219-2021, 2021. a
Kokou, P., Willemsen, P., Lekouara, M., Arioua, M., Mora, A., Van den Braembussche, P., Neri, E., and Aminou, D. M. A.: Algorithmic Chain for Lightning Detection and False Event Filtering Based on the MTG Lightning Imager, IEEE T. Geosci. Remote Sens., 56, 5115–5124, https://doi.org/10.1109/TGRS.2018.2808965, 2018. a
Koshak, W. J. and Solakiewicz, R. J.: A Method for Retrieving the Ground Flash Fraction and Flash Type from Satellite Lightning Mapper Observations, J. Atmos. Ocean. Tech., 32, 79–96, https://doi.org/10.1175/jtech-d-14-00085.1, 2015. a
Liaskos, C. et al.: Sensitivity of tropical tropospheric composition to lightning NOx, J. Geophys. Res.-Atmos., 120, 8512–8531, https://doi.org/10.1002/2014JD022987, 2015. a
Lyons, W. A. and Keen, C. S.: Observations of Lightning in Convective Supercells within Tropical Storms and Hurricanes, Mon. Weather Rev., 122, 1897–1916, https://doi.org/10.1175/1520-0493(1994)122<1897:OOLICS>2.0.CO;2, 1994. a
MacGorman, D. R. and Nielsen, K. E.: Cloud-to-ground lightning in a tornadic storm on 8 May 1986, Mon. Weather Rev., 119, 1557–1574, 1991. a
Mach, D. M.: Geostationary Lightning Mapper Clustering Algorithm Stability, J. Geophys. Res.-Atmos., 125, e2019JD031900, https://doi.org/10.1029/2019JD031900, 2020. a
Mäkelä, A., Jääskeläinen, S., and Kotro, J.: MTG Lightning Imager usability from a high-latitude perspective, 12th European Conference on Severe Storms, ECSS2025-104, 17–21 November 2025, Utrecht, the Netherlands, https://doi.org/10.5194/ecss2025-104, 2025. a
Marlton, G., Hare, B., Scholten, O., Protts, M., Stone, E., Twelves, S., and Devoto, F.: Mapping out lightning processes in both the VHF and VLF using LOFAR and the Met Office’s lightning detection system, LEELA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11101, https://doi.org/10.5194/egusphere-egu24-11101, 2024. a
Mitchard, C., Smith, S., Edwards, A., and Mitchell, P.: Investigation of lightning direct effects on aircraft materials, Proceedings of the International Conference on Lightning and Static Electricity, https://orca.cardiff.ac.uk/id/eprint/102939/1/102939 - Investigation of lightning ...pdf (last access: 14 October 2026), 2016. a
Montanyà, J., Van der Velde, O., Pineda, N., López, J. A., Romero, D., Solà, G., Arcanjo, M., Defer, E., Vázquez, F. G., Viticchié, B., and Enno, S.-E.: METEOSAT-12 Lightning Imager detection efficiency assessment by means of the Lightning Mapping Array in its first year of dissemination, Atmos. Meas. Tech., in preparation, 2026. 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
Murray, L. T.: Lightning NOx and Impacts on Air Quality, Curr. Pollut. Rep., 2, 115–133, https://doi.org/10.1007/s40726-016-0031-7, 2016. a
Mäkelä, A., Mäkelä, J., Haapalainen, J., and Porjo, N.: The verification of lightning location accuracy in Finland deduced from lightning strikes to trees, Atmos. Res., 172–173, 1–7, https://doi.org/10.1016/j.atmosres.2015.12.009, 2016. a
Pérez-Invernón, F. J., Gordillo-Vázquez, F. J., van der Velde, O., Montanyá, J., López Trujillo, J. A., Pineda, N., Huntrieser, H., Valks, P., Loyola, D., Seo, S., and Erbertseder, T.: Lightning-produced nitrogen oxides per flash length obtained by using TROPOMI observations and the Ebro Lightning Mapping Array, Geophys. Res. Lett., 50, e2023GL104699, https://doi.org/10.1029/2023GL104699, 2023. a
Pohjola, H. and Mäkelä, A.: The Comparison of GLD360 and EUCLID Lightning Location Systems in Europe, Atmos. Res., 123, 117–128, https://doi.org/10.1016/j.atmosres.2012.10.019, 2013. a
Price, C. and Rind, D.: Possible implications of global climate change on global lightning distributions and frequencies, J. Geophys. Res.-Atmos., 99, 10823–10831, https://doi.org/10.1029/94JD00019, 1994. a
Rakov, V. A. and Uman, M. A.: Electrical Structure of Lightning-Producing Clouds, in: Lightning: Physics and Effects, Cambridge University Press, Chap. 3, 67–107, https://doi.org/10.1017/CBO9781107340886.004, 2003. a
Reeve, N. and Toumi, R.: Lightning activity as an indicator of climate change, Q. J. Roy. Meteorol. Soc., 125, 893–903, 1999. a
Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., and Harlin, J.: A GPS‐based three‐dimensional lightning mapping system: Initial observations in central New Mexico, Geophys. Res. Lett., 26, 3573–3576, https://doi.org/10.1029/1999GL010856, 1999. a, b
Rudlosky, S. D. and Virts, K. S.: Dual Geostationary Lightning Mapper Observations, Mon. Weather Rev., 149, 979–992, https://doi.org/10.1175/MWR-D-20-0242.1, 2021. a, b
Rudlosky, S. D., Virts, K. S., Kolek, M. J., Bruning, E. C., Lojewski, S. J., and Magsig, M. A.: Initial Geostationary Lightning Mapper Observations, Geophys. Res. Lett., 46, 1097–1104, https://doi.org/10.1029/2018GL081052, 2019. a, b, c
Said, R. K. and Murphy, M. J.: GLD360 Upgrade: Performance Analysis and Applications, in: 24th International Lightning Detection Conference/Sixth International Lightning Meteorology Conference, Vaisala, San Diego, CA, https://www.vaisala.com/sites/default/files/documents/Ryan Said and Martin Murphy. GLD360 Upgrade Performance Analysis and Applications.pdf (last access: 12 October 2026), 2016. a, b
Said, R. K., Cohen, M. B., and Inan, U. S.: Highly intense lightning over the oceans: Estimated peak currents from global GLD360 observations, J. Geophys. Res.-Atmos., 118, 6905–6915, https://doi.org/10.1002/jgrd.50508, 2013. a, b, c
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
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
Schulz, W., Diendorfer, G., Pedeboy, S., and Poelman, D. R.: The European lightning location system EUCLID – Part 1: Performance analysis and validation, Nat. Hazards Earth Syst. Sci., 16, 595–605, https://doi.org/10.5194/nhess-16-595-2016, 2016. a, b, c
Schumann, U. and Huntrieser, H.: The global lightning-induced nitrogen oxides source, Atmos. Chem. Phys., 7, 3823–3907, https://doi.org/10.5194/acp-7-3823-2007, 2007. a
Shafer, M. A., MacGorman, D. R., and Carr, F. H.: Cloud-to-Ground Lightning throughout the Lifetime of a Severe Storm System in Oklahoma, Mon. Weather Rev., 128, 1798–1815, https://doi.org/10.1175/1520-0493(2000)128<1798:CTGLTT>2.0.CO;2, 2000. a
Van der Velde, O., Montanya, J., Rison, W., Aulich, G., Pineda, N., Soula, S., Romero, D., Rico, R., and Reglero, V.: First results of the Ebro Delta Lightning Mapping Array, AGU Fall Meeting Abstracts, AE33A-0290, https://ui.adsabs.harvard.edu/abs/2011AGUFMAE33A0290V (last access: 12 October 2026), 2011. a
Virts, K. S. and Koshak, W. J.: Mitigation of Geostationary Lightning Mapper Geolocation Errors, J. Atmos. Ocean. Tech., 37, 1673–1686, https://doi.org/10.1175/JTECH-D-19-0100.1, 2020. 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
Zhu, Y., Lyu, W., Cramer, J., Rakov, V., Bitzer, P., and Ding, Z.: Analysis of Location Errors of the U.S. National Lightning Detection Network Using Lightning Strikes to Towers, J. Geophys. Res.-Atmos., 125, e2020JD032530, https://doi.org/10.1029/2020JD032530, 2020. a
Zhu, Y., Stock, M., Lapierre, J., and DiGangi, E.: Upgrades of the Earth Networks Total Lightning Network in 2021, Remote Sensing, 14, 2209, https://doi.org/10.3390/rs14092209 2022. a
Editorial statement
Uniform detection of lightning as electrical activity in thunderstorm clouds has been achieved across a third of the globe, using the space-borne Lightning Imager onboard Meteosat Third Generation Imager satellites. This provides new insights into understanding and predicting severe thunderstorms, the role of lightning as a major source of NO2 in the upper troposphere, and shifts in severe thunderstorm patterns.
Uniform detection of lightning as electrical activity in thunderstorm clouds has been achieved...
Short summary
Lightning is a vital meteorological indicator, an Essential Climate Variable, and a key factor in atmospheric chemistry. EUMETSAT’s Meteosat Third Generation Lightning Imager (MTG-LI), operational since July 2024, is Europe’s first space-based geostationary lightning location system devoted to lightning diagnosis and characterization. This manuscript assesses its performance over 18 months, serving as a foundational reference for users.
Lightning is a vital meteorological indicator, an Essential Climate Variable, and a key factor...
