Bright, D. R., Wandishin, M. S., Jewell, R. E., and Weiss, S. J.: A physically based parameter for lightning prediction and its calibration in ensemble forecasts, Conf. on Meteor. Appl. of Lightning Data, Amer. Meteor. Soc., San Diego, CA, 10 January 2005, 3496, p. 30, https://www.researchgate.net/profile/David-Bright (last access: 12 February 2022), 2005.
Brimelow, J. C., Reuter, G. W., and Poolman, E. R.: Modeling maximum hail size in Alberta thunderstorms, Weather Forecast., 17, 1048–1062, https://doi.org/10.1175/1520-0434(2002)017<1048:mmhsia>2.0.co;2, 2002.
Brisson, E., Blahak, U., Lucas-Picher, P., Purr, C., and Ahrens, B.: Contrasting lightning projection using the lightning potential index adapted in a convection-permitting regional climate model, Clim. Dynam., 57, 2037–2051, https://doi.org/10.1007/s00382-021-05791-z, 2021.
Cecil, D. and Blankenship, C.: Toward a Global Climatology of Severe Hailstorms as Estimated by Satellite Passive Microwave Imagers, J. Climate, 25, 687–703, https://doi.org/10.1175/JCLI-D-11-00130.1, 2012.
Cintineo, J., Smith, T., Lakshmanan, V., Brooks, H., and Ortega, K.: An Objective High-Resolution Hail Climatology of the Contiguous United States, Weather Forecast., 27, 1235–1248, https://doi.org/10.1175/WAF-D-11-00151.1, 2012.
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.
Czernecki, B., Taszarek, M., Marosz, M., and Polrolniczak, M.: Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5, Atmos. Res., 227, 249–262, https://doi.org/10.1016/j.atmosres.2019.05.010, 2019.
Dafis, S., Fierro, A., Giannaros, T. M., Kotroni, V., Lagouvardos, K., and Mansell, E.: Performance evaluation of an explicit Lightning forecasting system., J. Geophys. Res.-Atmos., 123, 5130–5148, https://doi.org/10.1029/2017jd027930, 2018.
Dotzek, N., Groenemeijer, P., Feuerstein, B., and Holzer, A. M.: Overview of ESSL's severe convective storms research using the European Severe Weather Database ESWD, Atmos. Res., 93, 575–586, https://doi.org/10.1016/j.atmosres.2008.10.020, 2009.
ECMWF: Operational Archive, ECMWF [data set],
https://www.ecmwf.int/en/forecasts/dataset/operational-archive, last access: 10 January 2023.
Enno, S., Sugier, J., Alber, R., and Seltzer, M.: Lightning flash density in Europe based on 10 years of ATDnet data, Atmos. Res., 235, 104769, https://doi.org/10.1016/j.atmosres.2019.104769, 2020.
Feudale, L., Manzato, A., and Micheletti, S.: A cloud-to-ground lightning climatology for North-eastern Italy, Adv. Sci. Res., 10, 77–84, https://doi.org/10.5194/asr-10-77-2013, 2013.
Fluck, E., Kunz, M., Geissbuehler, P., and Ritz, S. P.: Radar-based assessment of hail frequency in Europe, Nat. Hazards Earth Syst. Sci., 21, 683–701, https://doi.org/10.5194/nhess-21-683-2021, 2021.
Gallo, B. T., Clark, A. J., and Dembek, S. R.: Forecasting tornadoes using convection-permitting ensembles, Weather Forecast., 31, 273–295, https://doi.org/10.1175/WAF-D-15-0134.1, 2016.
Gallo, B. T., Clark, A. J., Smith, B. T., Thompson, R. L., Jirak, I., and Dembek, S. R.: Blended probabilistic tornado forecasts: Combining climatological frequencies with NSSL–WRF ensemble forecasts, Weather Forecast., 33, 443–460, https://doi.org/10.1175/WAF-D-17-0132.1, 2018.
Geng, Y., Li, Q., Lin, T., Yao, W., Xu, L., Zheng, D., Zhou, X., Zheng, L., Lyu, W., and Zhang, Y.: A deep learning framework for lightning forecasting with multi-source spatiotemporal data, Q. J. Roy. Meteor. Soc., 147, 4048–4062, https://doi.org/10.1002/qj.4167, 2021.
Gensini, V. A. and Tippett, M. K.: Global Ensemble Forecast System (GEFS) predictions of Days 1–15 U.S. tornado and hail frequencies, Geophys. Res. Lett., 46, 2922–2930, https://doi.org/10.1029/2018gl081724, 2019.
Groenemeijer, P., Púčik, T., Holzer, A. M., Antonescu, B., Riemann-Campe, K., Schultz, D. M., Kühne, T., Feuerstein, B., Brooks, H. E., Doswell, C. A., Koppert, H., and Sausen, R.: Severe Convective Storms in Europe: Ten Years of Research and Education at the European Severe Storms Laboratory, B. Am. Meteorol. Soc., 98, 2641–2651, https://doi.org/10.1175/BAMS-D-16-0067.1, 2017.
Hamill, T. M., Hagedorn, R., and Whitaker, J. S.: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. part II: Precipitation, Mon. Weather Rev., 136, 2620–2632, https://doi.org/10.1175/2007mwr2411.1, 2008.
Jewell, R. and Brimelow, J.: Evaluation of Alberta hail growth model using severe hail proximity soundings from the United States, Weather Forecast., 24, 1592–1609, https://doi.org/10.1175/2009waf2222230.1, 2009.
Johnson, A. and Sugden, K.: Evaluation of Sounding-Derived Thermodynamic and Wind-Related Parameters Associated with Large Hail Events, E-Journal of Severe Storms Meteorology, 9, 1–42, https://doi.org/10.55599/ejssm.v9i5.57, 2014.
Kühne, T., Antonescu, B., Groenemeijer, P., and Púčik, T.: Lightning fatalities in Europe (2001–2020), 11th European Conference on Severe Storms, Bucharest (RO), 8 May 2023, https://doi.org/10.5194/ecss2023-146, 2023.
Kumjian, M., Lombardo, K., and Loeffler, S.: The Evolution of Hail Production in Simulated Supercell Storms, J. Atmos. Sci., 78, 3417–3440, https://doi.org/10.1175/JAS-D-21-0034.1, 2021.
Lagasio, M., Parodi, A., Procopio, R., Rachidi, F., and Fiori, E.: Lightning potential index performances in multimicrophysical cloud-resolving simulations of a back-building mesoscale convective system: The Genoa 2014 Event, J. Geophys. Res.-Atmos., 122, 4238–4257, https://doi.org/10.1002/2016jd026115, 2017.
Leinonen, J., Hamann, U., and Germann, U.: Seamless lightning nowcasting with recurrent-convolutional deep learning, Artificial Intelligence for the Earth Systems, 1, https://doi.org/10.1175/aies-d-22-0043.1, 2022.
Lepore, C., Tippett, M. K., and Allen, J. T.: ENSO-based probabilistic forecasts of March–May US tornado and hail activity, Geophys. Res. Lett., 44, 9093– 9101, https://doi.org/10.1002/2017GL074781, 2017.
Lepore, C., Tippett, M. K., and Allen, J. T.: CFSv2 monthly forecasts of tornado and hail activity, Weather Forecast., 33, 1283–1297, https://doi.org/10.1175/WAF-D-18-0054.1, 2018.
Loken, E. D., Clark, A. J., and Karstens, C. D.: Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests, Weather Forecast., 35, 1605–1631, https://doi.org/10.1175/WAF-D-19-0258.1, 2020.
Lopez, P.: A Lightning Parameterization for the ECMWF Integrated Forecasting System, Mon. Weather Rev., 144, 3057–3075, https://doi.org/10.1175/MWR-D-16-0026.1, 2016.
Martius, O., Kunz, M., Nisi, L., and Hering, A.: Conference report 1st European Hail Workshop, Meteorol. Z., 24, 441–442, https://doi.org/10.1127/metz/2015/0667, 2015.
McCaul, E. W., Goodman, S. J., LaCasse, K. M., and Cecil, D. J.: Forecasting lightning threat using cloud-resolving model simulations, Weather Forecast., 24, 709–729, https://doi.org/10.1175/2008waf2222152.1, 2009.
Mostajabi, A., Finney, D. L., Rubinstein, M., and Rachidi, F.: Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques, npj Climate and Atmospheric Science, 2, 41, https://doi.org/10.1038/s41612-019-0098-0, 2019.
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. Meteor. Soc., 146, 4170–4194, https://doi.org/10.1002/qj.3897, 2020.
Ortega, K. L., Krause, J. M., and Ryzhkov, A. V.: Polarimetric Radar Characteristics of Melting Hail. Part III: Validation of the Algorithm for Hail Size Discrimination, J. Appl. Meteorol. Clim., 55, 829–848, https://doi.org/10.1175/JAMC-D-15-0203.1, 2016.
Poręba, S., Taszarek, M., and Ustrnul, Z.: Diurnal and Seasonal Variability of ERA5 Convective Parameters in Relation to Lightning Flash Rates in Poland, Weather Forecast., 37, 1447–1470, https://doi.org/10.1175/WAF-D-21-0099.1, 2022.
Púčik, T., Groenemeijer, P., Rýva, D., and Kolář, M.: Proximity Soundings of Severe and Nonsevere Thunderstorms in Central Europe, Mon. Weather Rev., 143, 4805–4821, https://doi.org/10.1175/MWR-D-15-0104.1, 2015.
Púčik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A., Antonescu, B., and Faust, E.: Large Hail Incidence and Its Economic and Societal Impacts across Europe, Mon. Weather Rev., 147, 3901–3916, https://doi.org/10.1175/MWR-D-19-0204.1, 2019.
Rädler, A., Groenemeijer, P., Faust, E., and Sausen, R.: Detecting Severe Weather Trends Using an Additive Regressive Convective Hazard Model (AR-CHaMo), J. Appl. Meteorol. Clim., 57, 569–587, https://doi.org/10.1175/JAMC-D-17-0132.1, 2018.
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Zhang, P.: Polarimetric Radar Characteristics of Melting Hail. Part II: Practical Implications, J. Appl. Meteorol. Clim., 52, 2871–2886, https://doi.org/10.1175/JAMC-D-13-074.1, 2013.
Schmidt, M.: Improvement of hail detection and nowcasting by synergistic combination of information from polarimetric radar, model predictions, and in-situ observations, PhD dissertation, Universität Bonn, 150 pp., 2020.
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136, 1978.
Taszarek, M., Allen, J., Púčik, T., Groenemeijer, P., Czernecki, B., Kolendowicz, L., Lagouvardos, K., Kotroni, V., and Schulz, W.: A climatology of thunderstorms across Europe from a synthesis of multiple data sources, J. Climate, 32, 1813–1837, https://doi.org/10.1175/jcli-d-18-0372.1, 2019.
Taszarek, M., Allen, J., Groenemeijer, P., Edwards, R., Brooks, H., Chmielewski, V., and Enno, S.: Severe Convective Storms across Europe and the United States. Part I: Climatology of Lightning, Large Hail, Severe Wind, and Tornadoes, J. Climate, 33, 10239–10261, https://doi.org/10.1175/JCLI-D-20-0345.1, 2020a.
Taszarek, M., Allen, J., Púčik, T., Hoogewind, K., and Brooks, H.: Severe Convective Storms across Europe and the United States. Part II: ERA5 Environments Associated with Lightning, Large Hail, Severe Wind, and Tornadoes, J. Climate, 33, 10263–10286, https://doi.org/10.1175/JCLI-D-20-0346.1, 2020b.
Thompson, R. L., Edwards R., and Mead C. M.: An update to the supercell composite and significant tornado parameters, 22nd Conf. Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., P8.1,
https://ams.confex.com/ams/11aram22sls/techprogram/paper_82100.htm (last access: 10 February 2023), 2004.
Thompson, R. L., Smith, B. T., Grams, J. S., Dean, A. R., and Broyles, C.: Convective modes for significant severe thunderstorms in the contiguous United States. Part II: Supercell and QLCS tornado environments, Weather Forecast., 27, 1136–1154, https://doi.org/10.1175/WAF-D-11-00116.1, 2012.
Tsonevsky, I., Doswell, C. A., and Brooks, H. E.: Early warnings of severe convection using the ECMWF Extreme Forecast Index, Weather Forecast., 33, 857–871, https://doi.org/10.1175/waf-d-18-0030.1, 2018.
Uhlířová, I., Popová, J., and Sokol, Z.: Lightning potential index and its spatial and temporal characteristics in Cosmo NWP model, Atmos. Res., 268, 106025, https://doi.org/10.1016/j.atmosres.2022.106025, 2022.
Van Den Broeke, M. S., Schultz, D. M., Johns, R. H., Evans, J. S. and Hales, J. E.: Cloud-to-ground lightning production in strongly forced, low-instability convective lines associated with damaging wind, Weather Forecast., 20, 517–530, https://doi.org/10.1175/WAF876.1, 2005.
Vitart, F.: Evolution of ECMWF sub-seasonal forecast skill scores, Q. J. Roy. Meteor. Soc., 140, 1889–1899, https://doi.org/10.1002/qj.2256, 2013.
Wapler, K.: High-resolution climatology of lightning characteristics within Central Europe, Meteorol. Atmos. Phys., 122, 175–184, https://doi.org/10.1007/s00703-013-0285-1, 2013.
Westermayer, A., Groenemeijer, P., Pistotnik, G., Sausen, R. and Faust, E.: Identification of favorable environments for thunderstorms in reanalysis data, Meteorol. Z., 26, 59–70, https://doi.org/10.1127/metz/2016/0754, 2017.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences. 2nd Edition, International Geophysics Series, Vol. 59, Academic Press, 611 pp.,
https://sunandclimate.files.wordpress.com/2009/05/statistical-methods-in-the-atmospheric-sciences-0127519661.pdf (last access: 1 February 2022), 1995.
Wood, S. N.: Generalized additive models: an introduction with R, CRC press, https://doi.org/10.1201/9781315370279, 2017.
Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., and Llasat, M. D. C.: Predicting the potential for lightning activity in Mediterranean storms based on the weather research and forecasting (WRF) model dynamic and Microphysical Fields, J. Geophys. Res., 115, D04205, https://doi.org/10.1029/2008jd010868, 2010.
Zepka, G. S., Pinto, O., and Saraiva, A. C. V.: Lightning forecasting in southeastern Brazil using the WRF model, Atmos. Res., 135–136, 344–362, https://doi.org/10.1016/j.atmosres.2013.01.008, 2014.
