Articles | Volume 25, issue 8
https://doi.org/10.5194/nhess-25-2629-2025
© 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-25-2629-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Invited perspectives
| Highlight paper
| 
07 Aug 2025
Invited perspectives | Highlight paper |
| 07 Aug 2025
| 07 Aug 2025
Invited perspectives: Thunderstorm intensification from mountains to plains
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Pieter Groenemeijer
European Severe Storms Laboratory - Science & Training, Wiener Neustadt, Austria
European Severe Storms Laboratory e. V., Wessling, Germany
Alois Holzer
European Severe Storms Laboratory - Science & Training, Wiener Neustadt, Austria
European Severe Storms Laboratory e. V., Wessling, Germany
Monika Feldmann
Institute of Geography - Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Katharina Schröer
Department of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Francesco Battaglioli
European Severe Storms Laboratory e. V., Wessling, Germany
Lisa Schielicke
Institute for Geosciences, University of Bonn, Bonn, Germany
Department of Physics and Astronomy, The University of Western Ontario, London, Canada
Tomáš Púčik
European Severe Storms Laboratory - Science & Training, Wiener Neustadt, Austria
Bogdan Antonescu
European Severe Storms Laboratory e. V., Wessling, Germany
Faculty of Physics, University of Bucharest, Măgurele, Romania
Christoph Gatzen
Department of Civil and Environmental Engineering, The University of Western Ontario, London, Canada
Canadian Severe Storms Laboratory, London, Canada
A full list of authors appears at the end of the paper.
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George Pacey, Stephan Pfahl, and Lisa Schielicke
Weather Clim. Dynam., 6, 695–713, https://doi.org/10.5194/wcd-6-695-2025, https://doi.org/10.5194/wcd-6-695-2025, 2025
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Cold fronts are often associated with areas of intense precipitation (cells) in the warm season, but the drivers and environments of cells at different locations relative to the front are not well-understood. We show that cells ahead of the surface front have the highest amount of environmental instability and moisture. Also, low-level lifting is maximised ahead of the surface front and upper-level lifting is particularly important for cell initiation behind the front.
Monika Feldmann, Daniela I. V. Domeisen, and Olivia Martius
EGUsphere, https://doi.org/10.5194/egusphere-2025-2296, https://doi.org/10.5194/egusphere-2025-2296, 2025
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Severe thunderstorm outbreaks are a source of major damage across Europe. Using historical data, we analysed the large-scale weather patterns that lead to these outbreaks in eight different regions. Three types of regions emerge: those limited by temperature, limited by moisture and overall favourable for thunderstorms; consistent with their associated weather patterns and the general climate. These findings help explain regional differences and provide a basis for future forecast improvements.
Elena Päffgen, Lisa Schielicke, and Leonie Esters
EGUsphere, https://doi.org/10.5194/egusphere-2025-1200, https://doi.org/10.5194/egusphere-2025-1200, 2025
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Balancing academic careers and family responsibilities presents significant challenges, particularly for early-career researchers attending conferences. These events are essential for professional development but often create logistical difficulties for parents. Based on a survey of geoscientists, we show that parents require more support and non-parents largely approve of family-friendly measures. We provide practical guidelines to help conference organizers support researchers with families.
Lena Wilhelm, Cornelia Schwierz, Katharina Schröer, Mateusz Taszarek, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 3869–3894, https://doi.org/10.5194/nhess-24-3869-2024, https://doi.org/10.5194/nhess-24-3869-2024, 2024
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In our study we used statistical models to reconstruct past hail days in Switzerland from 1959–2022. This new time series reveals a significant increase in hail day occurrences over the last 7 decades. We link this trend to increases in moisture and instability variables in the models. This time series can now be used to unravel the complexities of Swiss hail occurrence and to understand what drives its year-to-year variability.
Lisa Schielicke, Yidan Li, Jerome Schyns, Aaron Sperschneider, Jose Pablo Solano Marchini, and Christoph Peter Gatzen
Weather Clim. Dynam., 5, 703–710, https://doi.org/10.5194/wcd-5-703-2024, https://doi.org/10.5194/wcd-5-703-2024, 2024
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We present course contents and results of a 2-week educational block course with a focus on Cloud Model 1 (CM1) and 3D visualization. Through hands-on experience, students gained skills in setting up and customizing the model and visualizing its output in 3D. The research aimed to bridge the gap between classroom learning and practical applications, fostering a deeper understanding of convective processes and preparing students for future careers in the field.
Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 847–872, https://doi.org/10.5194/nhess-24-847-2024, https://doi.org/10.5194/nhess-24-847-2024, 2024
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Hailstorms cause severe damage to buildings and cars, which motivates a detailed risk assessment. Here, we present a new open-source hail damage model based on radar data in Switzerland. The model successfully estimates the correct order of magnitude of car and building damages for most large hail events over 20 years. However, large uncertainty remains in the geographical distribution of modelled damages, which can be improved for individual events by using crowdsourced hail reports.
George Pacey, Stephan Pfahl, Lisa Schielicke, and Kathrin Wapler
Nat. Hazards Earth Syst. Sci., 23, 3703–3721, https://doi.org/10.5194/nhess-23-3703-2023, https://doi.org/10.5194/nhess-23-3703-2023, 2023
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Cold fronts are often associated with areas of intense precipitation (cells) and sometimes with hazards such as flooding, hail and lightning. We find that cold-frontal cell days are associated with higher cell frequency and cells are typically more intense. We also show both spatially and temporally where cells are most frequent depending on their cell-front distance. These results are an important step towards a deeper understanding of cold-frontal storm climatology and improved forecasting.
Francesco Battaglioli, Pieter Groenemeijer, Ivan Tsonevsky, and Tomàš Púčik
Nat. Hazards Earth Syst. Sci., 23, 3651–3669, https://doi.org/10.5194/nhess-23-3651-2023, https://doi.org/10.5194/nhess-23-3651-2023, 2023
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Probabilistic models for lightning and large hail were developed across Europe using lightning observations and hail reports. These models accurately predict the occurrence of lightning and large hail several days in advance. In addition, the hail model was shown to perform significantly better than the state-of-the-art forecasting methods. These results suggest that the models developed in this study may help improve forecasting of convective hazards and eventually limit the associated risks.
Lisa Schielicke and Stephan Pfahl
Weather Clim. Dynam., 3, 1439–1459, https://doi.org/10.5194/wcd-3-1439-2022, https://doi.org/10.5194/wcd-3-1439-2022, 2022
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Projected future heatwaves in many European regions will be even warmer than the mean increase in summer temperature suggests. To identify the underlying thermodynamic and dynamic processes, we compare Lagrangian backward trajectories of airstreams associated with heatwaves in two time slices (1991–2000 and 2091–2100) in a large single-model ensemble (CEMS-LE). We find stronger future descent associated with adiabatic warming in some regions and increased future diabatic heating in most regions.
Monika Feldmann, Urs Germann, Marco Gabella, and Alexis Berne
Weather Clim. Dynam., 2, 1225–1244, https://doi.org/10.5194/wcd-2-1225-2021, https://doi.org/10.5194/wcd-2-1225-2021, 2021
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Mesocyclones are the rotating updraught of supercell thunderstorms that present a particularly hazardous subset of thunderstorms. A first-time characterisation of the spatiotemporal occurrence of mesocyclones in the Alpine region is presented, using 5 years of Swiss operational radar data. We investigate parallels to hailstorms, particularly the influence of large-scale flow, daily cycles and terrain. Improving understanding of mesocyclones is valuable for risk assessment and warning purposes.
Hélène Barras, Olivia Martius, Luca Nisi, Katharina Schroeer, Alessandro Hering, and Urs Germann
Weather Clim. Dynam., 2, 1167–1185, https://doi.org/10.5194/wcd-2-1167-2021, https://doi.org/10.5194/wcd-2-1167-2021, 2021
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In Switzerland hail may occur several days in a row. Such multi-day hail events may cause significant damage, and understanding and forecasting these events is important. Using reanalysis data we show that weather systems over Europe move slower before and during multi-day hail events compared to single hail days. Surface temperatures are typically warmer and the air more humid over Switzerland and winds are slower on multi-day hail clusters. These results may be used for hail forecasting.
Carola Detring, Annette Müller, Lisa Schielicke, Peter Névir, and Henning W. Rust
Weather Clim. Dynam., 2, 927–952, https://doi.org/10.5194/wcd-2-927-2021, https://doi.org/10.5194/wcd-2-927-2021, 2021
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Stationary, long-lasting blocked weather patterns can lead to extreme conditions. Within this study the temporal evolution of the occurrence probability is analyzed, and the onset, decay and transition probabilities of blocking within the past 30 years are modeled. Using Markov models combined with logistic regression, we found large changes in summer, where the probability of transitions to so-called Omega blocks increases strongly, while the unblocked state becomes less probable.
Cited articles
Adam, S., Behrendt, A., Schwitalla, T., Hammann, E., and Wulfmeyer, V.: First assimilation of temperature lidar data into an NWP model: impact on the simulation of the temperature field, inversion strength and PBL depth, Q. J. Roy. Meteor. Soc., 142, 2882–2896, https://doi.org/10.1002/qj.2875, 2016. a
Adams-Selin, R. D., Allen, J., Gensini, V., and Heymsfield, A.: ICECHIP: Closing Critical Observational Gaps in Hail Research, North American Hail Workshop, Boulder, Colorado, https://cpaess.ucar.edu/sites/default/files/2022-09/Hail-workshop-agendaV2.pdf (last access: 5 August 2025), 2022. a
Adler, B., Kalthoff, N., Kohler, M., Handwerker, J., Wieser, A., Corsmeier, U., Kottmeier, C., Lambert, D., and Bock, O.: The variability of water vapour and pre-convective conditions over the mountainous island of Corsica, Q. J. Roy. Meteor. Soc., 142, 335–346, https://doi.org/10.1002/qj.2545, 2016. a, b
Agard, V. and Emanuel, K.: Clausius–Clapeyron scaling of peak CAPE in continental convective storm environments, J. Atmos. Sci., 74, 3043–3054, https://doi.org/10.1175/JAS-D-16-0352.1, 2017. a
Allen, J. T., Giammanco, I. M., Kumjian, M. R., Jurgen Punge, H., Zhang, Q., Groenemeijer, P., Kunz, M., and Ortega, K.: Understanding Hail in the Earth System, Rev. Geophys., 58, e2019RG000665, https://doi.org/10.1029/2019RG000665, 2020. a, b
Antonescu, B., Schultz, D. M., Holzer, A., and Groenemeijer, P.: Tornadoes in Europe: An underestimated threat, B. Am. Meteorol. Soc., 98, 713–728, https://doi.org/10.1175/BAMS-D-16-0171.1, 2017. a
Aregger, M., Martius, O., Germann, U., and Hering, A.: Differential reflectivity columns and hail – linking C-band radar-based estimated column characteristics to crowdsourced hail observations in Switzerland, arXiv [preprint], 1–21, https://doi.org/10.48550/arXiv.2410.10499, 14 October 2024. a
Auf der Maur, A. and Germann, U.: A Re-Evaluation of the Swiss Hail Suppression Experiment Using Permutation Techniques Shows Enhancement of Hail Energies When Seeding, Atmosphere, 12, 1623, https://doi.org/10.3390/atmos12121623, 2021. a
Augenstein, M., Mohr, S., and Kunz, M.: Influence of the North Atlantic Oscillation on annual spatio-temporal lightning clusters in western and central Europe, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2804, 2024. a
Bachmann, K., Keil, C., Craig, G. C., Weissmann, M., and Welzbacher, C. A.: Predictability of deep convection in idealized and operational forecasts: Effects of radar data assimilation, orography, and synoptic weather regime, Mon. Weather Rev., 148, 63–81, https://doi.org/10.1175/MWR-D-19-0045.1, 2020. a, b, c
Bagaglini, L., Ingrosso, R., and Miglietta, M. M.: Synoptic patterns and mesoscale precursors of Italian tornadoes, Atmos. Res., 253, 105503, https://doi.org/10.1016/j.atmosres.2021.105503, 2021. a
Barthlott, C., Czajka, B., Kunz, M., Saathoff, H., Zhang, H., Böhmländer, A., Gasch, P., Handwerker, J., Kohler, M., Wilhelm, J., Wieser, A., and Hoose, C.: The impact of aerosols and model grid spacing on a supercell storm from Swabian MOSES 2021, Q. J. Roy. Meteor. Soc., 150, 2005–2027, https://doi.org/10.1002/qj.4687, 2024. a
Battaglioli, F., Groenemeijer, P., Púčik, T., Taszarek, M., Ulbrich, U., and Rust, H.: Modeled Multidecadal Trends of Lightning and (Very) Large Hail in Europe and North America (1950–2021), J. Appl. Meteorol. Clim., 62, 1627–1653, https://doi.org/10.1175/JAMC-D-22-0195.1, 2023. a, b, c
Bedka, K. M., Allen, J. T., Punge, H. J., Kunz, M., and Simanovic, D.: A long-term overshooting convective cloud-top detection database over Australia derived from MTSAT Japanese Advanced Meteorological Imager Observations, J. Appl. Meteorol. Clim., 57, 937–951, https://doi.org/10.1175/JAMC-D-17-0056.1, 2018. a
Benjamin, S. G., Dévényi, D., Weygandt, S. S., Brundage, K. J., Brown, J. M., Grell, G. A., Kim, D., Schwartz, B. E., Smirnova, T. G., Smith, T. L., and Manikin, G. S.: An hourly assimilation-forecast cycle: The RUC, Mon. Weather Rev., 132, 495–518, https://doi.org/10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2, 2004. a
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. a
Blair, S. F., Deroche, D. R., Boustead, J. M., Leighton, J. W., Barjenbruch, B. L., and Gargan, W. P.: Radar-Based Assessment of the Detectability of Giant Hail, E-Journal of Severe Storms Meteorology, 6, 1–30, https://doi.org/10.55599/ejssm.v6i7.34, 2011. a
Blair, S. F., Laflin, J. M., Cavanaugh, D. E., Sanders, K. J., Currens, S. R., Pullin, J. I., Cooper, D. T., Deroche, D. R., Leighton, J. W., Fritchie, R. V., Mezeul, M. J., Goudeau, B. T., Kreller, S. J., Bosco, J. J., Kelly, C. M., and Mallinson, H. M.: High-resolution hail observations: Implications for NWS warning operations, Weather Forecast., 32, 1101–1119, https://doi.org/10.1175/WAF-D-16-0203.1, 2017. a
Bojinski, S., Blaauboer, D., Calbet, X., de Coning, E., Debie, F., Montmerle, T., Nietosvaara, V., Norman, K., Bañón Peregrín, L., Schmid, F., Strelec Mahović, N., and Wapler, K.: Towards nowcasting in Europe in 2030, Meteorol. Appl., 30, e2124, https://doi.org/10.1002/met.2124, 2023. a, b, c
Bosart, L. F., Seimon, A., LaPenta, K. D., and Dickinson, M. J.: Supercell tornadogenesis over complex terrain: The Great Barrington, Massachusetts, tornado on 29 May 1995, Weather Forecast., 21, 897–922, https://doi.org/10.1175/WAF957.1, 2006. a
Bougeault, P., Binder, P., Buzzi, A., Dirks, R., Houze, R., Kuettner, J., Smith, R. B., Steinacker, R., and Volkert, H.: The MAP Special Observing Period, B. Am. Meteorol. Soc., 82, 433–462, https://doi.org/10.1175/1520-0477(2001)082<0433:TMSOP>2.3.CO;2, 2001. a
Brennan, K. P. and Wilhelm, L.: Saharan dust linked to European hail events, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3924, 2024. a, b
Brennan, K. P., Thurnherr, I., Sprenger, M., and Wernli, H.: Insights from hailstorm track analysis in European climate change simulations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-918, 2025. a
Brimelow, J. C., Kopp, G. A., and Sills, D. M. L.: The Northern Hail Project: A renaissance in hail research in Canada, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-170, https://doi.org/10.5194/ecss2023-170, 2023. a, b
Brook, J. P., Protat, A., Soderholm, J., Carlin, J. T., McGowan, H., and Warren, R. A.: Hailtrack–improving radar-based hailfall estimates by modeling hail trajectories, J. Appl. Meteorol. Clim., 60, 237–254, https://doi.org/10.1175/JAMC-D-20-0087.1, 2021. a
Brooks, H. E., Doswell, C. A., Zhang, X., Chernokulsky, A. M. A., Tochimoto, E., Hanstrum, B., de Lima Nascimento, E., Sills, D. M. L., Antonescu, B., and Barrett, B.: A Century of Progress in Severe Convective Storm Research and Forecasting, Meteor. Mon., 59, 18.1–18.41, https://doi.org/10.1175/amsmonographs-d-18-0026.1, 2019. a
Brown, T. and Giammanco, I.: IBHS multi-faceted hail research program, RCI, 31, 5–8, 2013. a
Browning, K. A. and Foote, G. B.: Airflow and hail growth in supercell storms and some implications for hail suppression, Q. J. Roy. Meteor. Soc., 102, 499–533, https://doi.org/10.1002/qj.49710243303, 1976. a
Burlando, M., Miglietta, M. M., Avolio, E., Bechini, R., Cassola, F., De Martin, F., Lagasio, M., Milelli, M., Parodi, A., and Romanic, D.: The – WIND RISK project: nowcast and simulation of thunderstorm outflows, Bulletin of Atmospheric Science and Technology, 5, 1–10, https://doi.org/10.1007/s42865-024-00079-6, 2024. a
Calbet, X., Carbajal Henken, C., DeSouza-Machado, S., Sun, B., and Reale, T.: Horizontal small-scale variability of water vapor in the atmosphere: implications for intercomparison of data from different measuring systems, Atmos. Meas. Tech., 15, 7105–7118, https://doi.org/10.5194/amt-15-7105-2022, 2022. a
Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P. W., Trisos, C., Romero, J., Aldunce, P., Barrett, K., Blanco, G., Cheung, W. W. L., Connors, S., Denton, F., Diongue-Niang, A., Dodman, D., Garschagen, M., Geden, O., Hayward, B., Jones, C., Jotzo, F., Krug, T., Lasco, R., Lee, Y.-Y., Masson-Delmotte, V., Meinshausen, M., Mintenbeck, K., Mokssit, A., Otto, F. E. L., Pathak, M., Pirani, A., Poloczanska, E., Pörtner, H.-O., Revi, A., Roberts, D. C., Roy, J., Ruane, A. C., Skea, J., Shukla, P. R., Slade, R., Slangen, A., Sokona, Y., Sörensson, A. A., Tignor, M., van Vuuren, D., Wei, Y.-M., Winkler, H., Zhai, P., Zommers, Z., Hourcade, J.-C., Johnson, F. X., Pachauri, S., Simpson, N. P., Singh, C., Thomas, A., Totin, E., Alegría, A., Armour, K., Bednar-Friedl, B., Blok, K., Cissé, G., Dentener, F., Eriksen, S., Fischer, E., Garner, G., Guivarch, C., Haasnoot, M., Hansen, G., Hauser, M., Hawkins, E., Hermans, T., Kopp, R., Leprince-Ringuet, N., Lewis, J., Ley, D., Ludden, C., Niamir, L., Nicholls, Z., Some, S., Szopa, S., Trewin, B., van der Wijst, K.-I., Winter, G., Witting, M., Birt, A., and Ha, M.: IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Lee, H., and Romero, J., IPCC, Geneva, Switzerland, https://doi.org/10.59327/ipcc/ar6-9789291691647, 2023. a
Canepa, F., Burlando, M., and Solari, G.: Vertical profile characteristics of thunderstorm outflows, J. Wind Eng. Ind. Aerod., 206, 104332, https://doi.org/10.1016/j.jweia.2020.104332, 2020. a
Canepa, F., Romanic, D., Hangan, H., and Burlando, M.: Experimental translating downbursts immersed in the atmospheric boundary layer, J. Wind Eng. Ind. Aerod., 243, 105570, https://doi.org/10.1016/j.jweia.2023.105570, 2023. a
Carey, L. D. and Buffalo, K. M.: Environmental control of cloud-to-ground lightning polarity in severe storms, Mon. Weather Rev., 135, 1327–1353, https://doi.org/10.1175/MWR3361.1, 2007. a
Carey, L. D., Rutledge, S. A., and Petersen, W. A.: The relationship between severe storm reports and cloud-to-ground lightning polarity in the contiguous United States from 1989 to 1998, Mon. Weather Rev., 131, 1211–1228, https://doi.org/10.1175/1520-0493(2003)131<1211:TRBSSR>2.0.CO;2, 2003. a
Ceppi, A., Ravazzani, G., Salandin, A., Rabuffetti, D., Montani, A., Borgonovo, E., and Mancini, M.: Effects of temperature on flood forecasting: analysis of an operative case study in Alpine basins, Nat. Hazards Earth Syst. Sci., 13, 1051–1062, https://doi.org/10.5194/nhess-13-1051-2013, 2013. a, b
Chen, J., Dai, A., Zhang, Y., and Rasmussen, K. L.: Changes in convective available potential energy and convective inhibition under global warming, J. Climate, 33, 2025–2050, https://doi.org/10.1175/JCLI-D-19-0461.1, 2020. a
Chen, J. Y., Reinoso-Rondinel, R., Trömel, S., Simmer, C., and Ryzhkov, A.: A Radar-Based Quantitative Precipitation Estimation Algorithm to Overcome the Impact of Vertical Gradients of Warm-Rain Precipitation: The Flood in Western Germany on 14 July 2021, J. Hydrometeorol., 24, 521–536, https://doi.org/10.1175/JHM-D-22-0111.1, 2023. a, b
Chmielewski, V. C., Bruning, E. C., and Ancell, B. C.: Variations of Thunderstorm Charge Structures in West Texas on 4 June 2012, J. Geophys. Res.-Atmos., 123, 9502–9523, https://doi.org/10.1029/2018JD029006, 2018. a
Chow, F. K., Schär, C., Ban, N., Lundquist, K. A., Schlemmer, L., and Shi, X.: Crossing multiple gray zones in the transition from mesoscale to microscale simulation over complex terrain, Atmosphere, 10, 274, https://doi.org/10.3390/atmos10050274, 2019. a
Cummings, K. A., Pickering, K. E., Barth, M. C., Bela, M. M., Li, Y., Allen, D., Bruning, E., MacGorman, D. R., Ziegler, C. L., Biggerstaff, M. I., Fuchs, B., Davis, T., Carey, L., Mecikalski, R. M., and Finney, D. L.: Evaluation of Lightning Flash Rate Parameterizations in a Cloud-Resolved WRF-Chem Simulation of the 29–30 May 2012 Oklahoma Severe Supercell System Observed During DC3, J. Geophys. Res.-Atmos., 129, e2023JD039492, https://doi.org/10.1029/2023JD039492, 2024. a
Ćurić, M. and Janc, D.: Differential heating influence on hailstorm vortex pair evolution, Q. J. Roy. Meteor. Soc., 138, 72–80, https://doi.org/10.1002/qj.918, 2012. a
Ćurić, M., Janc, D., and Vučković, V.: Numerical simulation of Cb cloud vorticity, Atmos. Res., 83, 427–434, https://doi.org/10.1016/j.atmosres.2005.10.024, 2007. a
Dabernig, M., Mayr, G. J., Messner, J. W., and Zeileis, A.: Spatial ensemble post-processing with standardized anomalies, Q. J. Roy. Meteor. Soc., 143, 909–916, https://doi.org/10.1002/qj.2975, 2017. a
Dawson, D. T., Mansell, E. R., Jung, Y., Wicker, L. J., Kumjian, M. R., and Xue, M.: Low-level ZDR signatures in supercell forward flanks: The role of size sorting and melting of hail, J. Atmos. Sci., 71, 276–299, https://doi.org/10.1175/JAS-D-13-0118.1, 2014. a
De Martin, F., Davolio, S., Miglietta, M. M., and Levizzani, V.: A Conceptual Model for the Development of Tornadoes in the Complex Orography of the Po Valley, Mon. Weather Rev., 152, 1357–1377, https://doi.org/10.1175/MWR-D-23-0222.1, 2024. a, b, c, d
De Martin, F., Manzato, A., Carlon, N., Setvák, M., and Miglietta, M. M.: A dynamic and statistical analysis of giant hail environments in North-East Italy, Q. J. Roy. Meteor. Soc., e4945, https://doi.org/10.1002/qj.4945, 2025. a, b, c
Delobbe, L. and Holleman, I.: Uncertainties in radar echo top heights used for hail detection, Meteorol. Appl., 13, 361–374, https://doi.org/10.1017/S1350482706002374, 2006. a
Demortier, A., Mandement, M., Pourret, V., and Caumont, O.: Assimilation of surface pressure observations from personal weather stations in AROME-France, Nat. Hazards Earth Syst. Sci., 24, 907–927, https://doi.org/10.5194/nhess-24-907-2024, 2024. a
Doswell, C. A.: The Distinction between Large-Scale and Mesoscale Contribution to Severe Convection: A Case Study Example, Weather Forecast., 2, 3–16, https://doi.org/10.1175/1520-0434(1987)002<0003:TDBLSA>2.0.CO;2, 1987. a
Dotzek, N.: Tornadoes in Germany, Atmos. Res., 56, 233–251, https://doi.org/10.1016/S0169-8095(00)00075-2, 2001. a, b
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. a
Ducrocq, V., Davolio, S., Ferretti, R., Flamant, C., Santaner, V. H., Kalthoff, N., Richard, E., and Wernli, H.: Introduction to the HyMeX Special Issue on 'Advances in understanding and forecasting of heavy precipitation in the Mediterranean through the HyMeX SOP1 field campaign', Q. J. Roy. Meteor. Soc., 142, 1–6, https://doi.org/10.1002/qj.2856, 2016. a
Durran, D. R. and Weyn, J. A.: Thunderstorms do not get butterflies, B. Am. Meteorol. Soc., 97, 237–243, https://doi.org/10.1175/BAMS-D-15-00070.1, 2016. a
ECMWF: Destination Earth (DestinE), https://digital-strategy.ec.europa.eu/en/policies/destination-earth (last access: 10/ March 2025), 2023. a
Eirund, G. K., Drossaart van Dusseldorp, S., Brem, B. T., Dedekind, Z., Karrer, Y., Stoll, M., and Lohmann, U.: Aerosol–cloud–precipitation interactions during a Saharan dust event – A summertime case-study from the Alps, Q. J. Roy. Meteor. Soc., 148, 943–961, https://doi.org/10.1002/qj.4240, 2022. a
Emeis, S., Kalthoff, N., Adler, B., Pardyjak, E., Paci, A., and Junkermann, W.: High-resolution observations of transport and exchange processes in mountainous terrain, Atmosphere, 9, 457, https://doi.org/10.3390/atmos9120457, 2018. a
Erdmann, F. and Poelman, D. R.: Insights into thunderstorm characteristics from geostationary lightning jump and dive observations, Nat. Hazards Earth Syst. Sci., 25, 1751–1768, https://doi.org/10.5194/nhess-25-1751-2025, 2025. a
ESSL: International Fujita (IF) Scale, https://www.essl.org/cms/research-projects/international-fujita-scale/ (last access: 10 March 2025), 2023a. a
ESSL: Severe Weather Surveys and Survey Guide, http://essl.org/cms/publications/detailed-survey-reports/ (last access: 10 March 2025), 2023b. a
ESSL: ESSL news: Hail record broken again – 19cm hailstone confirmed in Italy, https://www.essl.org/cms/hail-record-broken-again-19cm-hailstone-confirmed-in-italy/ (last access: 10 March 2025), 2023c. a
European Severe Weather Database (ESWD): https://eswd.eu/, last access: 10 March 2025. a
Evaristo, R., Reinoso-Rondinel, R., Tromel, S., and Simmer, C.: Validation of Wind Fields Retrieved by Dual-Doppler Techniques Using a Vertically Pointing Radar, in: 2021 21st International Radar Symposium (IRS), Berlin, Germany, 21–22 June 2021, IEEE, 1–7, https://doi.org/10.23919/IRS51887.2021.9466173, 2021. a
Feldmann, M., Germann, U., Gabella, M., and Berne, A.: A characterisation of Alpine mesocyclone occurrence, Weather Clim. Dynam., 2, 1225–1244, https://doi.org/10.5194/wcd-2-1225-2021, 2021. a
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 Climate and Atmospheric Science, 6, 1–11, https://doi.org/10.1038/s41612-023-00352-z, 2023. a, b, c, d
Feldmann, M., Blanc, M., Brennan, K. P., Thurnherr, I., Velasquez, P., Martius, O., and Schär, C.: European supercell thunderstorms – an underestimated current threat and an increasing future hazard, arXiv [preprint], https://doi.org/10.48550/arXiv.2503.07466, 10 March 2025. a
Fencl, M., Dohnal, M., and Bareš, V.: Retrieving Water Vapor From an E-Band Microwave Link With an Empirical Model Not Requiring In Situ Calibration, Earth and Space Science, 8, e2021EA001911, https://doi.org/10.1029/2021EA001911, 2021. a
Figueras i Ventura, J., Pineda, N., Besic, N., Grazioli, J., Hering, A., van der Velde, O. A., Romero, D., Sunjerga, A., Mostajabi, A., Azadifar, M., Rubinstein, M., Montanyà, J., Germann, U., and Rachidi, F.: Analysis of the lightning production of convective cells, Atmos. Meas. Tech., 12, 5573–5591, https://doi.org/10.5194/amt-12-5573-2019, 2019. a
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. a, b
Forcadell, V., Augros, C., Caumont, O., Dedieu, K., Ouradou, M., David, C., Figueras i Ventura, J., Laurantin, O., and Al-Sakka, H.: Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks, Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, 2024. a
Fourrié, N., Nuret, M., Brousseau, P., and Caumont, O.: Data assimilation impact studies with the AROME-WMED reanalysis of the first special observation period of the Hydrological cycle in the Mediterranean Experiment, Nat. Hazards Earth Syst. Sci., 21, 463–480, https://doi.org/10.5194/nhess-21-463-2021, 2021. a
Frame, J. W. and Markowski, P.: The interaction of simulated squall lines with idealized mountain ridges, Mon. Weather Rev., 134, 1919–1941, https://doi.org/10.1175/MWR3157.1, 2006. a
French, M. M. and Kingfield, D. M.: Tornado Formation and Intensity Prediction Using Polarimetric Radar Estimates of Updraft Area, Weather Forecast., 36, 2211–2231, https://doi.org/10.1175/WAF-D-21-0087.1, 2021. a
Fuchs, B. R., Rutledge, S. A., Bruning, E. C., Pierce, J. R., Kodros, J. K., Lang, T. J., MacGorman, D. R., Krehbiel, P. R., and Rison, W.: Environmental controls on storm intensity and charge structure in multiple regions of the continental United States, J. Geophys. Res., 120, 6575–6596, https://doi.org/10.1002/2015JD023271, 2015. a
Fujita, T.: Mesoanalysis of the Illinois Tornadoes of April 1953, J. Atmos. Sci., 15, 288–296, https://doi.org/10.1175/1520-0469(1958)015<0288:MOTITO>2.0.CO;2, 1958. a
Fujita, T.: Downbursts: meteorological features and wind field characteristics, J. Wind Eng. Ind. Aerod., 36, 75–86, https://doi.org/10.1016/0167-6105(90)90294-M, 1990. a
Fujita, T. T.: The Teton-Yellowstone Tornado of 21 July 1987, Mon. Weather Rev., 117, 1913–1940, https://doi.org/10.1175/1520-0493(1989)117<1913:TTYTOJ>2.0.CO;2, 1989. a
Gallo, K., Schumacher, P., Boustead, J., and Ferguson, A.: Validation of satellite observations of storm damage to cropland with digital photographs, Weather Forecast., 34, 435–446, https://doi.org/10.1175/WAF-D-18-0059.1, 2019. a
Gatzen, C. P., Fink, A. H., Schultz, D. M., and Pinto, J. G.: An 18-year climatology of derechos in Germany, Nat. Hazards Earth Syst. Sci., 20, 1335–1351, https://doi.org/10.5194/nhess-20-1335-2020, 2020. a
Geerts, B., Andretta, T., Luberda, S. J., Vogt, J., Wang, Y., Oolman, L. D., Bikos, D., and Finch, J.: Case Study of a Long-lived Tornadic Mesocyclone in a Low-CAPE Complex-terrain Environment, E-Journal of Severe Storms Meteorology, 4, 1–29, https://doi.org/10.55599/ejssm.v4i3.20, 2009. a
Geerts, B., Parsons, D., Zieglgler, C. L., Weckwerth, T. M., Biggggerstaff, M. I., Clark, R. D., Coniglglio, M. C., Demoz, B. B., Ferrare, R. A., Gallus, W. A., Haghi, K., Hanesiak, J. M., Klein, P. M., Knupp, K. R., Kosiba, K., McFarquhar, G. M., Moore, J. A., Nehrir, A. R., Parker, M. D., Pinto, J. O., Rauber, R. M., Schumacher, R. S., Turner, D. D., Wang, Q., Wang, X., Wang, Z., and Wurman, J.: The 2015 plains elevated convection at night field project, B. Am. Meteorol. Soc., 98, 767–786, https://doi.org/10.1175/BAMS-D-15-00257.1, 2017. a
Gensini, V. A., Ashley, W. S., Michaelis, A. C., Haberlie, A. M., Goodin, J., and Wallace, B. C.: Hailstone size dichotomy in a warming climate, npj Climate and Atmospheric Science, 7, 1–10, https://doi.org/10.1038/s41612-024-00728-9, 2024. a
Germann, U., Boscacci, M., Clementi, L., Gabella, M., Hering, A., Sartori, M., Sideris, I. V., and Calpini, B.: Weather Radar in Complex Orography, Remote Sensing, 14, 503, https://doi.org/10.3390/rs14030503, 2022. a
Ghasemifard, H., Groenemeijer, P., Battaglioli, F., and Púčik, T.: Do changing circulation types raise the frequency of summertime thunderstorms and large hail in Europe?, Environmental Research: Climate, 3, 015008, https://doi.org/10.1088/2752-5295/ad22ec, 2024. a
Giordani, A., Kunz, M., Bedka, K. M., Punge, H. J., Paccagnella, T., Pavan, V., Cerenzia, I. M. L., and Di Sabatino, S.: Characterizing hail-prone environments using convection-permitting reanalysis and overshooting top detections over south-central Europe, Nat. Hazards Earth Syst. Sci., 24, 2331–2357, https://doi.org/10.5194/nhess-24-2331-2024, 2024. a
Göbel, M., Serafin, S., and Rotach, M. W.: Adverse impact of terrain steepness on thermally driven initiation of orographic convection, Weather Clim. Dynam., 4, 725–745, https://doi.org/10.5194/wcd-4-725-2023, 2023. a, b
Goger, B., Rotach, M. W., Gohm, A., Stiperski, I., Fuhrer, O., and De Morsier, G.: A new horizontal length scale for a three-dimensional turbulence parameterization in mesoscale atmospheric modeling over highly complex terrain, J. Appl. Meteorol. Clim., 58, 2087–2102, https://doi.org/10.1175/JAMC-D-18-0328.1, 2019. a
Gopalakrishnan, D., Cuervo-Lopez, C., Allen, J. T., Trapp, R. J., and Robinson, E.: A Comprehensive Evaluation of Biases in Convective Storm Parameters in CMIP6 Models over North America, J. Climate, 38, 947–971, https://doi.org/10.1175/JCLI-D-24-0165.1, 2025. a
Greene, D. R. and Clark, R. A.: Vertically Integrated Liquid Water—A New Analysis Tool, Mon. Weather Rev., 100, 548–552, https://doi.org/10.1175/1520-0493(1972)100<0548:VILWNA>2.3.CO;2, 1972. a
Groenemeijer, P.: Evaluating IASI Profiles at the ESSL Test-bed and for Selected Cases, https://www.eumetrain.org/index.php/resources/evaluating-iasi-profiles-essl-test-bed-and-selected-cases (last access: 10 March 2025), 2019. a
Guay, B., Fengler, M., and Hammerschmidt, L.: Improving Nowcasts and Forecasts via Operational Use of Meteomatics Meteodrones, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8632, https://doi.org/10.5194/egusphere-egu23-8632, 2023. a
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A., Mile, M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson, B., Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu, S., Ikuta, Y., and Fujita, T.: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres, Q. J. Roy. Meteor. Soc., 144, 1218–1256, https://doi.org/10.1002/qj.3179, 2018. a, b
Hannesen, R., Dotzek, N., Gysi, H., and Beheng, K. D.: Case study of a tornado in the Upper Rhine valley, Meteorol. Z., 7, 163–170, https://doi.org/10.1127/metz/7/1998/163, 1998. a
Hervo, M., Romanens, G., Martucci, G., Weusthoff, T., and Haefele, A.: Evaluation of an Automatic Meteorological Drone Based on a 6-Month Measurement Campaign, Atmosphere, 14, 1382, https://doi.org/10.3390/atmos14091382, 2023. a
Heymsfield, A. J., Cecchini, M. A., Detwiler, A., Honeyager, R., and Field, P.: Exploring the Composited T-28 Hailstorm Penetration Dataset to Characterize Hail Properties within the Updraft and Downdraft Regions, J. Appl. Meteorol. Clim., 62, 1803–1826, https://doi.org/10.1175/JAMC-D-23-0030.1, 2023. a, b
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
Homeyer, C. R., McAuliffe, J. D., and Bedka, K. M.: On the development of above-anvil cirrus plumes in extratropical convection, J. Atmos. Sci., 74, 1617–1633, https://doi.org/10.1175/JAS-D-16-0269.1, 2017. a
Houston, A. L., Argrow, B., Coniglio, M., Frew, E. W., Rasmussen, E. N., Weiss, C. C., and Ziegler, C. L.: Targeted Observation by Radars and UAS of Supercells (TORUS): Summary of the 2019 Field Campaign, in: 110th AMS Annual Meeting, 12–16 January 2020, Boston, MA, USA, 2020. a
Houze Jr., R. A.: Orographic effects on precipitating clouds, Rev. Geophys., 50, 1–47, https://doi.org/10.1029/2011RG000365, 2012. a, b
Houze Jr., R. A., Schmid, W., Fovell, R. G., and Schiesser, H.-H.: Hailstorms in Switzerland: Left Movers, Right Movers, and False Hooks, Mon. Weather Rev., 121, 3345–3370, https://doi.org/10.1175/1520-0493(1993)121<3345:HISLMR>2.0.CO;2, 1993. a
Imamovic, A., Schlemmer, L., and Schär, C.: Mountain volume control on deep-convective rain amount during episodes of weak synoptic forcing, J. Atmos. Sci., 76, 605–626, https://doi.org/10.1175/JAS-D-18-0217.1, 2019. a
Jensen, M. P., Flynn, J. H., Judd, L. M., Kollias, P., Kuang, C., McFarquhar, G., Nadkarni, R., Powers, H., and Sullivan, J.: A Succession of Cloud, Precipitation, Aerosol, and Air Quality Field Experiments in the Coastal Urban Environment, B. Am. Meteorol. Soc., 102, 103–105, https://doi.org/10.1175/BAMS-D-21-0104.1, 2022. a, b
Kahraman, A., Kendon, E. J., Chan, S. C., and Fowler, H. J.: Quasi-Stationary Intense Rainstorms Spread Across Europe Under Climate Change, Geophys. Res. Lett., 48, e2020GL092361, https://doi.org/10.1029/2020GL092361, 2021. a, b
Kahraman, A., Kendon, E. J., and Fowler, H. J.: Climatology of severe hail potential in Europe based on a convection-permitting simulation, Clim. Dynam., 62, 6625–6642, https://doi.org/10.1007/s00382-024-07227-w, 2024. a
Kaltenboeck, R. and Steinheimer, M.: Radar-based severe storm climatology for Austrian complex orography related to vertical wind shear and atmospheric instability, Atmos. Res., 158–159, 216–230, https://doi.org/10.1016/j.atmosres.2014.08.006, 2015. a, b
Katona, B. and Markowski, P.: Assessing the influence of complex terrain on severe convective environments in Northeastern Alabama, Weather Forecast., 36, 1003–1029, https://doi.org/10.1175/WAF-D-20-0136.1, 2021. a, b, c, d
Katona, B., Markowski, P., Alexander, C., and Benjamin, S.: The influence of topography on convective storm environments in the eastern United States as deduced from the HRRR, Weather Forecast., 31, 1481–1490, https://doi.org/10.1175/WAF-D-16-0038.1, 2016. a
Kaňák, J., Benko, M., Simon, A., and Sokol, A.: Case study of the 9 May 2003 windstorm in southwestern Slovakia, Atmos. Res., 83, 162–175, https://doi.org/10.1016/j.atmosres.2005.09.012, 2007. a
Khodayar, S., Davolio, S., Di Girolamo, P., Lebeaupin Brossier, C., Khodayar, S., Davolio, S., Di Girolamo, P., Lebeaupin Brossier, C., Flaounas, E., Fourrie, N., Lee, K.-O., Ricard, D., Vie, B., Bouttier, F., Caldas-Alvarez, A., and Ducrocq, V.: Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX, Atmos. Chem. Phys., 21, 17051–17078, https://doi.org/10.5194/acp-21-17051-2021, 2021. a
Kirshbaum, D. J.: Numerical simulations of orographic convection across multiple gray zones, J. Atmos. Sci., 77, 3301–3320, https://doi.org/10.1175/JAS-D-20-0035.1, 2020. a
Klaus, V. and Krause, J.: Investigating Hailstorm Updrafts and Nowcasting Hail Size Using a Novel Radar-Based Updraft Detection, Weather Forecast., 39, 1795–1815, https://doi.org/10.1175/WAF-D-23-0227.1, 2024. a
Klaus, V., Rieder, H., and Kaltenböck, R.: Insights in Hailstorm Dynamics through Polarimetric High-Resolution X-Band and Operational C-Band Radar: A Case Study for Vienna, Austria, Mon. Weather Rev., 151, 913–929, https://doi.org/10.1175/MWR-D-22-0185.1, 2023. a
Knight, C. A., Foote, G. B., and Summers, P. W.: Results of a Randomized Hail Suppression Experiment in Northeast Colorado. Part IX: Overall Discussion and Summary in the Context of Physical Research, J. Appl. Meteorol. Clim., 18, 1629–1639, https://doi.org/10.1175/1520-0450(1979)018<1629:ROARHS>2.0.CO;2, 1979. a
Kollias, P., McFarquhar, G. M., Bruning, E., DeMott, P. J., Kumjian, M. R., Lawson, P., Lebo, Z., Logan, T., Lombardo, K., Oue, M., Roberts, G., Shaw, R. A., van den Heever, S. C., Wolde, M., Barry, K. R., Bodine, D., Bruintjes, R., Chandrasekar, V., Dzambo, A., Hill, T. C. J., Jensen, M., Junyent, F., Kreidenweis, S. M., Lamer, K., Luke, E., Bansemer, A., McCluskey, C., Nichman, L., Nguyen, C., Patnaude, R. J., Perkins, R. J., Powers, H., Ranjbar, K., Roux, E., Snyder, J., Treserras, B. P., Tsai, P., Wales, N. A., Wolff, C., Allwayin, N., Ascher, B., Barr, J., Hu, Y., Huang, Y., Litzmann, M., Mages, Z., McKeown, K., Patil, S., Rosky, E., Tuftedal, K., Tzeng, M.-D., and Zhu, Z.: Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE), B. Am. Meteorol. Soc., 106, 310–332, https://doi.org/10.1175/bams-d-23-0014.1, 2024. a
Komjáti, K., Csirmaz, K., Breuer, H., Kurcsics, M., and Horváth, Á.: Supercell interactions with surface baroclinic zones in the Carpathian Basin, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-39, https://doi.org/10.5194/ecss2023-39, 2023. a
Kopp, J., Schröer, K., Schwierz, C., Hering, A., Germann, U., and Martius, O.: The summer 2021 Switzerland hailstorms: weather situation, major impacts and unique observational data, Weather, 78, 184–191, https://doi.org/10.1002/wea.4306, 2023. a
Kosiba, K. A., Lyza, A. W., Trapp, R. J., Rasmussen, E. N., Parker, M., Biggerstaff, M. I., Nesbitt, S. W., Weiss, C. C., Wurman, J., Knupp, K. R., Coffer, B., Chmielewski, V. C., Dawson, D. T., Bruning, E., Bell, T. M., Coniglio, M. C., Murphy, T. A., French, M., Blind-Doskocil, L., Reinhart, A. E., dward Wolff, Schneider, M. E., Silcott, M., Smith, E., Aikins, J., Wagner, M., Robinson, P., Wilczak, J. M., White, T., Bodine, D., Kumjian, M. R., Waugh, S. M., Alford, A. A., Elmore, K., Kollias, P., and Turner, D. D.: The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) Project, B. Am. Meteorol. Soc., 105, E1768–E1799, https://doi.org/10.1175/BAMS-D-22-0064.1, 2024. a
Kramer, M., Heinzeller, D., Hartmann, H., van den Berg, W., and Steeneveld, G. J.: Assessment of MPAS variable resolution simulations in the grey-zone of convection against WRF model results and observations: An MPAS feasibility study of three extreme weather events in Europe, Clim. Dynam., 55, 253–276, https://doi.org/10.1007/s00382-018-4562-z, 2020. a
Kumjian, M.: Principles and applications of dual-polarization weather radar. Part III: Artifacts, Journal of Operational Meteorology, 1, 265–274, https://doi.org/10.15191/nwajom.2013.0121, 2013. a
Kumjian, M. R. and Ryzhkov, A. V.: Polarimetric signatures in supercell thunderstorms, J. Appl. Meteorol. Clim., 47, 1940–1961, https://doi.org/10.1175/2007JAMC1874.1, 2008. a, b
Kumjian, M. R. and Ryzkhov, A. V.: Storm-relative helicity revealed from polarimetric radar measurements, J. Atmos. Sci., 66, 667–685, https://doi.org/10.1175/2008JAS2815.1, 2009. a
Kunkel, J., Hanesiak, J., and Sills, D.: The Hunt for Missing Tornadoes: Using Satellite Imagery to Detect and Document Historical Tornado Damage in Canadian Forests, J. Appl. Meteorol. Clim., 62, 139–154, https://doi.org/10.1175/JAMC-D-22-0070.1, 2023. a
Kunz, M. and Pusskeiler, M.: High-resolution assessment of the hail hazard over complex terrain from radar and insurance data, Open Access Article, 19, 427–439, https://doi.org/10.1127/0941-2948/2010/0452, 2010. a
Kunz, M., Blahak, U., Handwerker, J., Schmidberger, M., Punge, H. J., Mohr, S., Fluck, E., and Bedka, K. M.: The severe hailstorm in southwest Germany on 28 July 2013: characteristics, impacts and meteorological conditions, Q. J. Roy. Meteor. Soc., 144, 231–250, https://doi.org/10.1002/qj.3197, 2018. a, b
Kunz, M., Abbas, S. S., Bauckholt, M., Böhmländer, A., Feuerle, T., Gasch, P., Glaser, C., Groß, J., Hajnsek, I., Handwerker, J., Hase, F., Khordakova, D., Knippertz, P., Kohler, M., Lange, D., Latt, M., Laube, J., Martin, L., Mauder, M., Möhler, O., Mohr, S., Reitter, R. W., Rettenmeier, A., Rolf, C., Saathoff, H., Schrön, M., Schütze, C., Spahr, S., Späth, F., Vogel, F., Völksch, I., Weber, U., Wieser, A., Wilhelm, J., Zhang, H., and Dietrich, P.: Swabian MOSES 2021: An interdisciplinary field campaign for investigating convective storms and their event chains, Frontiers in Earth Science, 10, 1–26, https://doi.org/10.3389/feart.2022.999593, 2022. a, b
Kvak, R., Okon, L., Bližňák, V., Méri, L., and Kašpar, M.: Spatial distribution and precipitation intensity of supercells: Response to terrain asymmetry in the Western Carpathians, Central Europe, Atmos. Res., 292, 106885, https://doi.org/10.1016/j.atmosres.2023.106885, 2023. a, b
Labriola, J., Snook, N., Jung, Y., and Xue, M.: Explicit ensemble prediction of hail in 19 May 2013 Oklahoma city thunderstorms and analysis of hail growth processes with several multimoment microphysics schemes, Mon. Weather Rev., 147, 1193–1213, https://doi.org/10.1175/MWR-D-18-0266.1, 2019. a
Lainer, M., Brennan, K. P., Hering, A., Kopp, J., Monhart, S., Wolfensberger, D., and Germann, U.: Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground, Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024, 2024. a
Laiti, L., Zardi, D., de Franceschi, M., Rampanelli, G., and Giovannini, L.: Analysis of the diurnal development of a lake-valley circulation in the Alps based on airborne and surface measurements, Atmos. Chem. Phys., 14, 9771–9786, https://doi.org/10.5194/acp-14-9771-2014, 2014. a
Lakshmi, K. K. A., Sahoo, S., Biswas, S. K., and Chandrasekar, V.: Study of Microphysical Signatures Based on Spectral Polarimetry during the RELAMPAGO Field Experiment in Argentina, J. Atmos. Ocean. Tech., 41, 235–260, https://doi.org/10.1175/JTECH-D-22-0113.1, 2024. a
Lasher-Trapp, S., Orendorf, S. A., and Trapp, R. J.: Investigating a Derecho in a Future Warmer Climate, B. Am. Meteorol. Soc., 104, E1831–E1852, https://doi.org/10.1175/BAMS-D-22-0173.1, 2023. a
LeBel, L. J., Tang, B. H., and Lazear, R. A.: Examining Terrain Effects on an Upstate New York Tornado Event Utilizing a High-Resolution Model Simulation, Weather Forecast., 36, 2001–2020, https://doi.org/10.1175/WAF-D-21-0018.1, 2021. a
Leinonen, J., Hamann, U., Sideris, I. V., and Germann, U.: Thunderstorm Nowcasting With Deep Learning: A Multi-Hazard Data Fusion Model, Geophys. Res. Lett., 50, e2022GL101626, https://doi.org/10.1029/2022GL101626, 2023. a, b
Lenderink, G., Ban, N., Brisson, E., Berthou, S., Cortés-Hernández, V. E., Kendon, E., Fowler, H. J., and de Vries, H.: Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?, Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025, 2025. a
Letkewicz, C. E. and Parker, M. D.: Impact of environmental variations on simulated squall lines interacting with terrain, Mon. Weather Rev., 139, 3163–3183, https://doi.org/10.1175/2011MWR3635.1, 2011. a
Lin, Y. and Kumjian, M. R.: Influences of CAPE on Hail Production in Simulated Supercell Storms, J. Atmos. Sci., 79, 179–204, https://doi.org/10.1175/JAS-D-21-0054.1, 2022. a
Liu, J. and Niyogi, D.: Meta-analysis of urbanization impact on rainfall modification, Scientific Reports, 9, 1–14, https://doi.org/10.1038/s41598-019-42494-2, 2019. a
Loeffler, S. D., Kumjian, M. R., Jurewicz, M., and French, M. M.: Differentiating Between Tornadic and Nontornadic Supercells Using Polarimetric Radar Signatures of Hydrometeor Size Sorting, Geophys. Res. Lett., 47, e2020GL088242, https://doi.org/10.1029/2020GL088242, 2020. a
Loftus, A. M. and Cotton, W. R.: Examination of CCN impacts on hail in a simulated supercell storm with triple-moment hail bulk microphysics, Atmos. Res., 147–148, 183–204, https://doi.org/10.1016/j.atmosres.2014.04.017, 2014. a
Lombardo, K. and Bitting, M.: A Climatology of Convective Precipitation over Europe, Mon. Weather Rev., 152, 1555–1585,https://doi.org/10.1175/MWR-D-23-0156.1, 2024. a
Lombardo, K. and Kumjian, M. R.: Observations of the Discrete Propagation of a Mesoscale Convective System during RELAMPAGO–CACTI, Mon. Weather Rev., 150, 2111–2138, https://doi.org/10.1175/MWR-D-21-0265.1, 2022. a
Maddox, R. A., Hoxit, L. R., and Chappell, C. F. : A Study of Tornadic Thunderstorm Interactions with Thermal Boundaries, Mon. Weather Rev., 108, 322–336, https://doi.org/10.1175/1520-0493(1980)108<0322:ASOTTI>2.0.CO;2, 1980. a, b
Malečić, B., Telišman Prtenjak, M., Horvath, K., Jelić, D., Mikuš Jurković, P., Ćorko, K., and Mahović, N. S.: Performance of HAILCAST and the Lightning Potential Index in simulating hailstorms in Croatia in a mesoscale model – Sensitivity to the PBL and microphysics parameterization schemes, Atmos. Res., 272, 106143, https://doi.org/10.1016/j.atmosres.2022.106143, 2022. a
Mandement, M. and Caumont, O.: A numerical study to investigate the roles of former Hurricane Leslie, orography and evaporative cooling in the 2018 Aude heavy-precipitation event, Weather Clim. Dynam., 2, 795–818, https://doi.org/10.5194/wcd-2-795-2021, 2021. a
Manzato, A., Cicogna, A., Centore, M., Battistutta, P., and Trevisan, M.: Hailstone Characteristics in Northeast Italy from 29 Years of Hailpad Data, J. Appl. Meteorol. Clim., 61, 1773–1789, https://doi.org/10.1175/JAMC-D-21-0251.1, 2022a. a, b
Markowski, P. and Richardson, Y.: Mesoscale meteorology in midlatitudes, Vol. 2, John Wiley and Sons, https://doi.org/10.1002/9780470682104, 2010. a, b
Markowski, P. M. and Dotzek, N.: A numerical study of the effects of orography on supercells, Atmos. Res., 100, 457–478, https://doi.org/10.1016/j.atmosres.2010.12.027, 2011. a, b, c
Markowski, P. M., Richardson, Y. P., Richardson, S. J., and Petersson, A.: Aboveground thermodynamic observations in convective storms from balloonborne probes acting as pseudo-lagrangian drifters, B. Am. Meteorol. Soc., 99, 711–724, https://doi.org/10.1175/BAMS-D-17-0204.1, 2018. a
Marquis, J. N., Varble, A. C., Robinson, P., Nelson, T. C., and Friedrich, K.: Low-Level Mesoscale and Cloud-Scale Interactions Promoting Deep Convection Initiation, Mon. Weather Rev., 149, 2473–2495, https://doi.org/10.1175/MWR-D-20-0391.1, 2021. a, b
Marquis, J. N., Feng, Z., Varble, A., Nelson, T. C., Houston, A., Peters, J. M., Mulholland, J. P., and Hardin, J.: Near-Cloud Atmospheric Ingredients for Deep Convection Initiation, Mon. Weather Rev., 151, 1247–1267, https://doi.org/10.1175/MWR-D-22-0243.1, 2023. a
Martín, M. L., Calvo-Sancho, C., Taszarek, M., González-Alemán, J. J., Montoro-Mendoza, A., Díaz-Fernández, J., Bolgiani, P., Sastre, M., and Martín, Y.: Major Role of Marine Heatwave and Anthropogenic Climate Change on a Giant Hail Event in Spain, Geophys. Res. Lett., 51, e2023GL107632, https://doi.org/10.1029/2023GL107632, 2024. a
Marvi, M. T.: A review of flood damage analysis for a building structure and contents, Nat. Hazards, 102, 967–995, https://doi.org/10.1007/s11069-020-03941-w, 2020. a
McGinley, J.: A Diagnosis of Alpine Lee Cyclogenesis, Mon. Weather Rev., 110, 1271–1287, https://doi.org/10.1175/1520-0493(1982)110<1271:ADOALC>2.0.CO;2, 1982. a
McKeown, K. E., Davenport, C. E., Eastin, M. D., Purpura, S. M., and Riggin, R. R.: Radar Characteristics of Supercell Thunderstorms Traversing the Appalachian Mountains, Weather Forecast., 39, 639–654, https://doi.org/10.1175/waf-d-23-0110.1, 2024. a, b
Medina, B. L., Carey, L. D., Lang, T. J., Bitzer, P. M., Deierling, W., and Zhu, Y.: Characterizing Charge Structure in Central Argentina Thunderstorms During RELAMPAGO Utilizing a New Charge Layer Polarity Identification Method, Earth and Space Science, 8, e2021EA001803, https://doi.org/10.1029/2021EA001803, 2021. a
Merz, R., Tarasova, L., and Basso, S.: The flood cooking book: Ingredients and regional flavors of floods across Germany, Environ. Res. Lett., 15, 114024, https://doi.org/10.1088/1748-9326/abb9dd, 2020. a
Miglietta, M. M. and Rotunno, R.: Numerical simulations of sheared conditionally unstable flows over a mountain ridge, J. Atmos. Sci., 71, 1747–1762, https://doi.org/10.1175/JAS-D-13-0297.1, 2014. a
Miller, P. W. and Mote, T. L.: Standardizing the definition of a “pulse” thunderstorm, B. Am. Meteorol. Soc., 98, 905–913, https://doi.org/10.1175/BAMS-D-16-0064.1, 2017. a
Miyoshi, T., Kunii, M., Ruiz, J., Lien, G. Y., Satoh, S., Ushio, T., Bessho, K., Seko, H., Tomita, H., and Ishikawa, Y.: Big data assimilation revolutionizing severe weather prediction, B. Am. Meteorol. Soc., 97, 1347–1354, https://doi.org/10.1175/BAMS-D-15-00144.1, 2016. a
Mohr, S. and Kunz, M.: Recent trends and variabilities of convective parameters relevant for hail events in Germany and Europe, Atmos. Res., 123, 211–228, https://doi.org/10.1016/j.atmosres.2012.05.016, 2013. a
Mohr, S., Kunz, M., and Keuler, K.: Development and application of a logistic model to estimate the past and future hail potential in Germany, J. Geophys. Res.-Atmos., 120, 3939–3956, https://doi.org/10.1002/2014JD022959, 2015. a
Mohr, S., Wilhelm, J., Wandel, J., Kunz, M., Portmann, R., Punge, H. J., Schmidberger, M., Quinting, J. F., and Grams, C. M.: The role of large-scale dynamics in an exceptional sequence of severe thunderstorms in Europe May–June 2018, Weather Clim. Dynam., 1, 325–348, https://doi.org/10.5194/wcd-1-325-2020, 2020. a
Morgan Jr., G. M. and Towery, N. G.: On the Role of Strong Winds in Damage to Crops by Hail and Its Estimation with a Simple Instrument, Journal of Applied Meteorology (1962–1982), 15, 891–898, http://www.jstor.org/stable/26177518 (last access: 12 March 2025), 1976. a
Morrison, H., Lier‐Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S., Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud and precipitation microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a, b
Mulholland, J. P., Nesbitt, S. W., and Trapp, R. J.: A case study of terrain influences on upscale convective growth of a supercell, Mon. Weather Rev., 147, 4305–4324, https://doi.org/10.1175/MWR-D-19-0099.1, 2019. a
Mulholland, J. P., Nesbitt, S. W., Trapp, R. J., and Peters, J. M.: The influence of terrain on the convective environment and associated convective morphology from an idealized modeling perspective, J. Atmos. Sci., 77, 3929–3949, https://doi.org/10.1175/JAS-D-19-0190.1, 2020. a, b
Murillo, E. M. and Homeyer, C. R.: Severe hail fall and hailstorm detection using remote sensing observations, J. Appl. Meteorol. Clim., 58, 947–970, https://doi.org/10.1175/JAMC-D-18-0247.1, 2019. a
Murillo, E. M. and Homeyer, C. R.: What Determines Above-Anvil Cirrus Plume Infrared Temperature?, J. Atmos. Sci., 79, 3181–3194, https://doi.org/10.1175/JAS-D-22-0080.1, 2022. a
Nelson, T. C., Marquis, J., Varble, A., and Friedrich, K.: Radiosonde observations of environments supporting deep moist convection initiation during RELAMPAGO-CACTI, Mon. Weather Rev., 149, 289–309, https://doi.org/10.1175/MWR-D-20-0148.1, 2021. a
Nelson, T. C., Marquis, J., Peters, J. M., and Friedrich, K.: Environmental controls on simulated deep moist convection initiation occurring during RELAMPAGO-CACTI, J. Atmos. Sci., 79, 1941–1964, https://doi.org/10.1175/JAS-D-21-0226.1, 2022. a, b
Nesbitt, S. W., Salio, P. V., Ávila, E., Bitzer, P., Carey, L., Chandrasekar, V., Deierling, W., Dominguez, F., Dillon, M. E., Garcia, C. M., Gochis, D., Goodman, S., Hence, D. A., Kosiba, K. A., Kumjian, M. R., Lang, T., Luna, L. M., Marquis, J., Marshall, R., McMurdie, L. A., de Lima Nascimento, E., Rasmussen, K. L., Roberts, R., Rowe, A. K., Ruiz, J. J., São Sabbas, E. F., Saulo, A. C., Schumacher, R. S., Skabar, Y. G., Machado, L. A. T., Trapp, R. J., Varble, A. C., Wilson, J., Wurman, J., Zipser, E. J., Arias, I., Bechis, H., and Grover, M. A.: A storm safari in subtropical South America: Proyecto RELAMPAGO, B. Am. Meteorol. Soc., 102, E1621–E1644, https://doi.org/10.1175/BAMS-D-20-0029.1, 2021. a, b
Nisi, L., Hering, A., Germann, U., and Martius, O.: A 15-year hail streak climatology for the Alpine region, Q. J. Roy. Meteor. Soc., 144, 1429–1449, https://doi.org/10.1002/qj.3286, 2018. a, b, c, d
Nixon, C. J. and Allen, J. T.: Distinguishing between Hodographs of Severe Hail and Tornadoes, Weather Forecast, 37, 1761–1782, https://doi.org/10.1175/WAF-D-21-0136.1, 2022. a
Niyogi, D., Holt, T., Zhong, S., Pyle, P. C., and Basara, J.: Urban and land surface effects on the 30 July 2003 mesoscale convective system event observed in the southern Great Plains, J. Geophys. Res.-Atmos., 111, D19107, https://doi.org/10.1029/2005JD006746, 2006. a
Nomokonova, T., Griewank, P. J., Löhnert, U., Miyoshi, T., Necker, T., and Weissmann, M.: Estimating the benefit of Doppler wind lidars for short-term low-level wind ensemble forecasts, Q. J. Roy. Meteor. Soc., 149, 192–210, https://doi.org/10.1002/qj.4402, 2023. a
O'Neill, M. E., Orf, L., Heymsfield, G. M., and Halbert, K.: Hydraulic jump dynamics above supercell thunderstorms, Science, 373, 1248–1251, https://doi.org/10.1126/science.abh3857, 2021. a
Pacey, G., Pfahl, S., Schielicke, L., and Wapler, K.: The climatology and nature of warm-season convective cells in cold-frontal environments over Germany, Nat. Hazards Earth Syst. Sci., 23, 3703–3721, https://doi.org/10.5194/nhess-23-3703-2023, 2023. a, b
Pacey, G. P., Schultz, D. M., and Garcia-Carreras, L.: Severe Convective Windstorms in Europe: Climatology, Preconvective Environments, and Convective Mode, Weather Forecast., 36, 237–252, https://doi.org/10.1175/WAF-D-20-0075.1, 2021. a
Panosetti, D., Böing, S., Schlemmer, L., and Schmidli, J.: Idealized large-eddy and convection-resolving simulations of moist convection over mountainous terrain, J. Atmos. Sci., 73, 4021–4041, https://doi.org/10.1175/JAS-D-15-0341.1, 2016. a, b
Panziera, L., James, C. N., and Germann, U.: Mesoscale organization and structure of orographic precipitation producing flash floods in the lago maggiore region, Q. J. Roy. Meteor. Soc., 141, 224–248, https://doi.org/10.1002/qj.2351, 2015. a
Peters, J. M., Nowotarski, C. J., Mulholland, J. P., and Thompson, R. L.: The influences of effective inflow layer streamwise vorticity and storm-relative flow on supercell updraft properties, J. Atmos. Sci., 77, 3033–3057, https://doi.org/10.1175/JAS-D-19-0355.1, 2020. a
Peters, J. M., Chavas, D. R., Lebo, Z. J., and Su, C.-Y.: Cumulonimbus clouds convert a smaller fraction of CAPE into kinetic energy in a warmer atmosphere, J. Atmos. Sci., 81, 1943–1961, https://doi.org/10.1175/jas-d-24-0006.1, 2024. a
Pistotnik, G., Holzer, A. M., Kaltenböck, R., and Tschannett, S.: An F3 downburst in Austria-A case study with special focus on the importance of real-time site surveys, Atmos. Res., 100, 565–579, https://doi.org/10.1016/j.atmosres.2010.10.011, 2011. a
Pounds, L. E., Ziegler, C. L., Adams-Selin, R. D., and Biggerstaff, M. I.: Analysis of Hail Production via Simulated Hailstone Trajectories in the 29 May 2012 Kingfisher, Oklahoma, Supercell, Mon. Weather Rev., 152, 245–276, https://doi.org/10.1175/MWR-D-23-0073.1, 2024. a
Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, Atmospheric and Oceanographic Sciences Library, Springer Netherlands, ISBN 9780306481000, https://books.google.de/books?id=0MURkyjuoGMC (last access: 12 March 2025), 2010. a
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. a
Púčik, T., Groenemeijer, P., Rädler, A. T., Tijssen, L., Nikulin, G., Prein, A. F., van Meijgaard, E., Fealy, R., Jacob, D., and Teichmann, C.: Future changes in European severe convection environments in a regional climate model ensemble, J. Climate, 30, 6771–6794, https://doi.org/10.1175/JCLI-D-16-0777.1, 2017. a, b
Púčik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A. T., 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. a
Pucillo, A., Miglietta, M. M., Lombardo, K., and Manzato, A.: Application of a simple analytical model to severe winds produced by a bow echo like storm in northeast Italy, Meteorol. Appl., 27, 1–18, https://doi.org/10.1002/met.1868, 2019. a
Punge, H. J. and Kunz, M.: Hail observations and hailstorm characteristics in Europe: A review, Atmos. Res., 176–177, 159–184, https://doi.org/10.1016/j.atmosres.2016.02.012, 2016. a, b
Punge, H. J., Bedka, K. M., Kunz, M., and Reinbold, A.: Hail frequency estimation across Europe based on a combination of overshooting top detections and the ERA-INTERIM reanalysis, Atmos. Res., 198, 34–43, https://doi.org/10.1016/j.atmosres.2017.07.025, 2017. a, b
Rädler, A. T., Groenemeijer, P. H., Faust, E., Sausen, R., and Púčik, T.: Frequency of severe thunderstorms across Europe expected to increase in the 21st century due to rising instability, npj Climate and Atmospheric Science, 2, 3–7, https://doi.org/10.1038/s41612-019-0083-7, 2019. a, b
Rasmussen, E. N., Straka, J. M., Davies-Jones, R., Doswell, C. A., Carr, F. H., Eilts, M. D., and MacGorman, D. R.: Verification of the Origins of Rotation in Tornadoes Experiment: VORTEX, B. Am. Meteorol. Soc., 75, 995–1006, https://doi.org/10.1175/1520-0477(1994)075<0995:VOTOOR>2.0.CO;2, 1994. a
Raupach, T. H., Martius, O., Allen, J. T., Kunz, M., Trapp, S. L., Mohr, S., Rasmussen, K. L., and Trapp, R. J.: The effects of climate change on hailstorms, Nature Reviews Earth & Environment, 2, 213–226, https://doi.org/10.1038/s43017-020-00133-9, 2021. a
Ravazzani, G., Amengual, A., Ceppi, A., Homar, V., Romero, R., Lombardi, G., and Mancini, M.: Potentialities of ensemble strategies for flood forecasting over the Milano urban area, J. Hydrol., 539, 237–253, https://doi.org/10.1016/j.jhydrol.2016.05.023, 2016. a
Reeves, H. D. and Lin, Y. L.: The effects of a mountain on the propagation of a preexisting convective system for blocked and unblocked flow regimes, J. Atmos. Sci., 64, 2401–2421, https://doi.org/10.1175/JAS3959.1, 2007. a
Ringhausen, J., Chmielewski, V., and Calhoun, K.: Inter-Comparison of Lightning Measurements in Quasi-Linear Convective Systems, Atmosphere, 15, 309, https://doi.org/10.3390/atmos15030309, 2024. a
Ripberger, J. T., Krocak, M. J., Wehde, W. W., Allan, J. N., Silva, C., and Jenkins-Smith, H.: Measuring tornado warning reception, comprehension, and response in the United States, Weather Clim. Soc., 11, 863–880, https://doi.org/10.1175/WCAS-D-19-0015.1, 2019. a
Rombeek, N., Leinonen, J., and Hamann, U.: Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning, Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, 2024. a
Romps, D. M.: Clausius-clapeyron scaling of CAPE from analytical solutions to RCE, J. Atmos. Sci., 73, 3719–3737, https://doi.org/10.1175/JAS-D-15-0327.1, 2016. a
Rotach, M. W., Serafin, S., Ward, H. C., Arpagaus, M., Colfescu, I., Cuxart, J., De Wekker, S. F., Grubišic, V., Kalthoff, N., Karl, T., Kirshbaum, D. J., Lehner, M., Mobbs, S., Paci, A., Palazzi, E., Bailey, A., Schmidli, J., Wittmann, C., Wohlfahrt, G., and Zardi, D.: A Collaborative Effort to Better Understand, Measure, and Model Atmospheric Exchange Processes over Mountains, B. Am. Meteorol. Soc., 103, E1282–E1295, https://doi.org/10.1175/BAMS-D-21-0232.1, 2022. a, b, c, d, e, f, g
Rotunno, R. and Houze, R. A.: Lessons on orographic precipitation from the Mesoscale Alpine Programme, Q. J. Roy. Meteor. Soc., 133, 811–830, https://doi.org/10.1002/qj.67, 2007. a
Rotunno, R. and Klemp, J. B.: On the rotation and propagation of simulated supercell thunderstorms, J. Atmos. Sci., 42, 271–292, https://doi.org/10.1175/1520-0469(1985)042<0271:OTRAPO>2.0.CO;2, 1985. a
Rudlosky, S. D., Goodman, S. J., and Virts, K. S.: Chapter 16 – Lightning Detection: GOES-R Series Geostationary Lightning Mapper, in: The GOES-R Series, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 193–202, https://doi.org/10.1016/B978-0-12-814327-8.00016-0, ISBN 978-0-12-814327-8, 2020. a
Ryzhkov, A., Zhang, P., Bukovčić, P., Zhang, J., and Cocks, S.: Polarimetric Radar Quantitative Precipitation Estimation, Remote Sensing, 14, 1695, https://doi.org/10.3390/rs14071695, 2022. a
Saharia, M., Kirstetter, P. E., Vergara, H., Gourley, J. J., and Hong, Y.: Characterization of floods in the United States, J. Hydrol., 548, 524–535, https://doi.org/10.1016/j.jhydrol.2017.03.010, 2017. a, b
Saltikoff, E., Haase, G., Delobbe, L., Gaussiat, N., Martet, M., Idziorek, D., Leijnse, H., Novák, P., Lukach, M., and Stephan, K.: OPERA the radar project, Atmosphere, 10, 1–13, https://doi.org/10.3390/atmos10060320, 2019. a
Scarino, B., Itterly, K., Bedka, K., Homeyer, C. R., Allen, J., Bang, S., and Cecil, D.: Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network, Artificial Intelligence for the Earth Systems, 2, 1–30, https://doi.org/10.1175/aies-d-22-0042.1, 2023. a
Schmid, T., Portmann, R., Villiger, L., Schröer, K., and Bresch, D. N.: An open-source radar-based hail damage model for buildings and cars, Nat. Hazards Earth Syst. Sci., 24, 847–872, https://doi.org/10.5194/nhess-24-847-2024, 2024. a
Schmidli, J.: Daytime heat transfer processes over mountainous terrain, J. Atmos. Sci., 70, 4041–4066, https://doi.org/10.1175/JAS-D-13-083.1, 2013. a
Schmidt, M. L.: Improvement of hail detection and nowcasting by synergistic combination of information from polarimetric radar, model predictions, and in situ observations, PhD thesis, Rheinische Friedrich-Wilhelms-Universität Bonn, https://hdl.handle.net/20.500.11811/8701 (last access: 12 March 2025), 2020. a
Schultz, C. J., Carey, L. D., Schultz, E. V., and Blakeslee, R. J.: Insight into the kinematic and microphysical processes that control lightning jumps, Weather Forecast., 30, 1591–1621, https://doi.org/10.1175/WAF-D-14-00147.1, 2015. a
Schwartz, C. S. and Sobash, R. A.: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations, Mon. Weather Rev., 145, 3397–3418, https://doi.org/10.1175/MWR-D-16-0400.1, 2017. a
Seifert, A., Bachmann, V., Filipitsch, F., Förstner, J., Grams, C. M., Hoshyaripour, G. A., Quinting, J., Rohde, A., Vogel, H., Wagner, A., and Vogel, B.: Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle, Atmos. Chem. Phys., 23, 6409–6430, https://doi.org/10.5194/acp-23-6409-2023, 2023. a, b
Serafin, S., Rotach, M. W., Arpagaus, M., Colfescu, I., Cuxart, J., De Wekker, S. F. J., Evans, M., Grubišić, V., Kalthoff, N., Karl, T., Kirshbaum, D. J., Lehner, M., Mobbs, S., Paci, A., Palazzi, E., Raudzens Bailey, A., Schmidli, J., Wohlfahrt, G., and Zardi, D.: Multi-scale transport and exchange processes in the atmosphere over mountains, Innsbruck University Press, 42 pp., ISBN 978-3-99106-003-1, https://doi.org/10.15203/99106-003-1, 2020. a, b
Serafin, S., Schmidli, J., Goger, B., Ban, N., Westerhuis, S., Kotlarski, S., and Arpagaus, M.: TEAMx Numerical Modeling Plan, https://www.teamx-programme.org/modelling-activities/ (last access: 12 March 2025), 2024. a
Sessa, M. F. and Trapp, R. J.: Observed relationship between tornado intensity and pretornadic mesocyclone characteristics, Weather Forecast., 35, 1243–1261, https://doi.org/10.1175/WAF-D-19-0099.1, 2020. a
Sgoff, C., Acevedo, W., Paschalidi, Z., Ulbrich, S., Bauernschubert, E., Kratzsch, T., and Potthast, R.: Assimilation of crowd-sourced surface observations over Germany in a regional weather prediction system, Q. J. Roy. Meteor. Soc., 148, 1752–1767, https://doi.org/10.1002/qj.4276, 2022. a
Shikhov, A. and Chernokulsky, A.: A satellite-derived climatology of unreported tornadoes in forested regions of northeast Europe, Remote Sens. Environ., 204, 553–567, https://doi.org/10.1016/j.rse.2017.10.002, 2018. a
Šinger, M. and Púčik, T.: A challenging tornado forecast in Slovakia, Atmosphere, 11, 821, https://doi.org/10.3390/ATMOS11080821, 2020. a, b
Smith, B. T., Thompson, R. L., Grams, J. S., Broyles, C., and Brooks, H. E.: Convective modes for significant severe thunderstorms in the contiguous United States. Part I: Storm classification and climatology, Weather Forecast., 27, 1114–1135, https://doi.org/10.1175/WAF-D-11-00115.1, 2012. a
Smith, V. H., Mobbs, S. D., Burton, R. R., Hobby, M., Aoshima, F., Wulfmeyer, V., and Di Girolamo, P.: The role of orography in the regeneration of convection: A case study from the convective and orographically-induced precipitation study, Meteorol. Z., 24, 83–97, https://doi.org/10.1127/metz/2014/0418, 2014. a
Snethlage, M. A., Geschke, J., Ranipeta, A., Jetz, W., Yoccoz, N. G., Körner, C., Spehn, E. M., Fischer, M., and Urbach, D.: A hierarchical inventory of the world's mountains for global comparative mountain science, Scientific Data, 9, 149, https://doi.org/10.1038/s41597-022-01256-y, 2022a. a
Snethlage, M. A., Geschke, J., Spehn, E. M., Ranipeta, A., Yoccoz, N. G., Körner, C., Jetz, W., Fischer, M., and Urbach, D.: GMBA Mountain Inventory v2, GMBA-EarthEnv, https://doi.org/10.48601/earthenv-t9k2-1407, 2022b. a
Snyder, J. C., Ryzhkov, A. V., Kumjian, M. R., Khain, A. P., and Picca, J.: A ZDR column detection algorithm to examine convective storm updrafts, Weather Forecast., 30, 1819–1844, https://doi.org/10.1175/WAF-D-15-0068.1, 2015. a
Soderholm, B., Ronalds, B., and Kirshbaum, D. J.: The evolution of convective storms initiated by an isolated mountain ridge, Mon. Weather Rev., 142, 1430–1451, https://doi.org/10.1175/MWR-D-13-00280.1, 2014. a, b, c
Soderholm, J. S. and Kumjian, M. R.: Automating the analysis of hailstone layers, Atmos. Meas. Tech., 16, 695–706, https://doi.org/10.5194/amt-16-695-2023, 2023. a, b
Soderholm, J. S., Kumjian, M. R., McCarthy, N., Maldonado, P., and Wang, M.: Quantifying hail size distributions from the sky – application of drone aerial photogrammetry, Atmos. Meas. Tech., 13, 747–754, https://doi.org/10.5194/amt-13-747-2020, 2020. a
Sokol, Z., Minářová, J., and Novák, P.: Classification of hydrometeors using measurements of the ka-band cloud radar installed at the Milešovka Mountain (Central Europe), Remote Sensing, 10, 1674, https://doi.org/10.3390/rs10111674, 2018. a
Stanelle, T., Vogel, B., Vogel, H., Bäumer, D., and Kottmeier, C.: Feedback between dust particles and atmospheric processes over West Africa during dust episodes in March 2006 and June 2007, Atmos. Chem. Phys., 10, 10771–10788, https://doi.org/10.5194/acp-10-10771-2010, 2010. a
Tang, B., Vaughan, M., Lazear, R., Corbosiero, K., Bosart, L., Wasula, T., Lee, I., and Lipton, K.: Topographic and boundary influences on the 22 May 2014 Duanesburg, New York, tornadic supercell, Weather Forecast., 31, 107–127, https://doi.org/10.1175/WAF-D-15-0101.1, 2016. a, b
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. a
Taszarek, M., Allen, J. T., Groenemeijer, P., Edwards, R., Brooks, H. E., Chmielewski, V., and Enno, S. E.: 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. a, b, c
Taszarek, M., Allen, J. T., Púčik, T., Hoogewind, K. A., and Brooks, H. E.: 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. a, b
Taufour, M., Vié, B., Augros, C., Boudevillain, B., Delanoë, J., Delautier, G., Ducrocq, V., Lac, C., Pinty, J.-P., and Schwarzenböck, A.: Evaluation of the two-moment scheme LIMA based on microphysical observations from the HyMeX campaign, Q. J. Roy. Meteor. Soc., 144, 1398–1414, https://doi.org/10.1002/qj.3283, 2018. a
Thompson, R. L., Edwards, R., Hart, J. A., Elmore, K. L., and Markowski, P.: Close Proximity Soundings within Supercell Environments Obtained from the Rapid Update Cycle, Weather Forecast., 18, 1243–1261, https://doi.org/10.1175/1520-0434(2003)018<1243:cpswse>2.0.co;2, 2003. a
Thurnherr, I., Cui, R., Velasquez, P., Wernli, H., and Schär, C.: The effect of 3 °C global warming on hail over Europe, Authorea [preprint], https://doi.org/10.22541/au.173809555.59545480/v1, 2025. a
Trapp, R. J. and Hoogewind, K. A.: The realization of extreme tornadic storm events under future anthropogenic climate change, J. Climate, 29, 5251–5265, https://doi.org/10.1175/JCLI-D-15-0623.1, 2016. a
Trapp, R. J., Kosiba, K. A., Marquis, J. N., Kumjian, M. R., Nesbitt, S. W., Wurman, J. M., Salio, P. V., Grover, M. A., Robinson, P. T., and Hence, D. A.: Multiple-platform and multiple-doppler radar observations of a supercell thunderstorm in south america during relampago, Mon. Weather Rev., 148, 3225–3241, https://doi.org/10.1175/MWR-D-20-0125.1, 2020. a, b
Trefalt, S., Martynov, A., Barras, H., Besic, N., Hering, A. M., Lenggenhager, S., Noti, P., Röthlisberger, M., Schemm, S., Germann, U., and Martius, O.: A severe hail storm in complex topography in Switzerland - Observations and processes, Atmos. Res., 209, 76–94, https://doi.org/10.1016/j.atmosres.2018.03.007, 2018. a, b
Trömel, S., Ryzhkov, A. V., Diederich, M., Mühlbauer, K., Kneifel, S., Snyder, J., and Simmer, C.: Multisensor characterization of mammatus, Mon. Weather Rev., 145, 235–251, https://doi.org/10.1175/MWR-D-16-0187.1, 2017. a
Trömel, S., Blahak, U., Evaristo, R., Mendrok, J., Neef, L., Pejcic, V., Scharbach, T., Shrestha, P., and Simmer, C.: Fusion of radar polarimetry and atmospheric modeling, IET The Institution of Engineering and Technology, 293–344, https://doi.org/10.1049/SBRA557G, ISBN-13: 978-1-83953-624-3, 2023. a
Tuovinen, J. P., Rauhala, J., and Schultz, D. M.: Significant-hail-producing storms in Finland: Convective-storm environment and mode, Weather Forecast., 30, 1064–1076, https://doi.org/10.1175/WAF-D-14-00159.1, 2015. a
Varble, A. C., Nesbitt, S. W., Salio, P., Hardin, J. C., Bharadwaj, N., Borque, P., DeMott, P. J., Feng, Z., Hill, T. C., Marquis, J. N., Matthews, A., Mei, F., Öktem, R., Castro, V., Goldberger, L., Hunzinger, A., Barry, K. R., Kreidenweis, S. M., McFarquhar, G. M., McMurdie, L. A., Pekour, M., Powers, H., Romps, D. M., Saulo, C., Schmid, B., Tomlinson, J. M., van den Heever, S. C., Zelenyuk, A., Zhang, Z., and Zipser, E. J.: Utilizing a storm-generating hotspot to study convective cloud transitions: The CACTI experiment, B. Am. Meteorol. Soc., 102, E1597–E1620, https://doi.org/10.1175/BAMS-D-20-0030.1, 2021. a
Varble, A. C., Igel, A. L., Morrison, H., Grabowski, W. W., and Lebo, Z. J.: Opinion: A critical evaluation of the evidence for aerosol invigoration of deep convection, Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, 2023. 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
Wapler, K.: Mesocyclonic and non-mesocyclonic convective storms in Germany: Storm characteristics and life-cycle, Atmos. Res., 248, 105186, https://doi.org/10.1016/j.atmosres.2020.105186, 2021. a
Wedi, N., Bauer, P., Sandu, I., Hoffmann, J., Sheridan, S., Cereceda, R., Quintino, T., Thiemert, D., and Geenen, T.: Destination Earth: High-Performance Computing for Weather and Climate, Comput. Sci. Eng., 24, 29–37, https://doi.org/10.1109/MCSE.2023.3260519, 2022. a
Wehr, T., Kubota, T., Tzeremes, G., Wallace, K., Nakatsuka, H., Ohno, Y., Koopman, R., Rusli, S., Kikuchi, M., Eisinger, M., Tanaka, T., Taga, M., Deghaye, P., Tomita, E., and Bernaerts, D.: The EarthCARE mission – science and system overview, Atmos. Meas. Tech., 16, 3581–3608, https://doi.org/10.5194/amt-16-3581-2023, 2023. a
Wei, W. and Bai, J.: Research Progress on the Numerical Simulation at Gray-zone Scales of the Convective Boundary Layer, Advances in Earth Science, 39, 221–231, https://doi.org/10.11867/j.issn.1001-8166.2024.018, 2024. a
Wells, H. M., Hillier, J., Garry, F. K., Dunstone, N., Clark, M. R., Kahraman, A., and Chen, H.: Climatology and convective mode of severe hail in the United Kingdom, Atmos. Res., 309, 107569, https://doi.org/10.1016/j.atmosres.2024.107569, 2024. a
Whiteman, D. C. and Doran, C. J.: The relationship between overlying synoptic-scale flow and winds within a valley, J. Appl. Meteorol. Clim., 32, 1669–1682, https://doi.org/10.1175/1520-0450(1993)032<1669:TRBOSS>2.0.CO;2, 1993. a
Wieringa, J. and Holleman, I.: If cannons cannot fight hail, what else?, Meteorol. Z., 15, 659–669, https://doi.org/10.1127/0941-2948/2006/0147, 2006. a
Wilhelm, J., Mohr, S., Punge, H. J., Mühr, B., Schmidberger, M., Daniell, J. E., Bedka, K. M., and Kunz, M.: Severe thunderstorms with large hail across Germany in June 2019, Weather, 76, 228–237, https://doi.org/10.1002/wea.3886, 2021. a
Witt, A., Eilts, M. D., Stumpf, G. J., Johnson, J. T., Mitchell, E. D., and Thomas, K. W.: An enhanced hail detection algorithm for the WSR-88D, Weather Forecast., 13, 286–303, https://doi.org/10.1175/1520-0434(1998)013<0286:AEHDAF>2.0.CO;2, 1998. a
Wu, F. and Lombardo, K.: Precipitation enhancement in squall lines moving over mountainous coastal regions, J. Atmos. Sci., 78, 3089–3113, https://doi.org/10.1175/JAS-D-20-0222.1, 2021. a
Wulfmeyer, V., Behrendt, A., Kottmeier, C., Corsmeier, U., Barthlott, C., Craig, G. C., Hagen, M., Althausen, D., Aoshima, F., Arpagaus, M., Bauer, H. S., Bennett, L., Blyth, A., Brandau, C., Champollion, C., Crewell, S., Dick, G., Di Girolamo, P., Dorninger, M., Dufournet, Y., Eigenmann, R., Engelmann, R., Flamant, C., Foken, T., Gorgas, T., Grzeschik, M., Handwerker, J., Hauck, C., Höller, H., Junkermann, W., Kalthoff, N., Kiemle, C., Klink, S., König, M., Krauss, L., Long, C. N., Madonna, F., Mobbs, S., Neininger, B., Pal, S., Peters, G., Pigeon, G., Richard, E., Rotach, M. W., Russchenberg, H., Schwitalla, T., Smith, V., Steinacker, R., Trentmann, J., Turner, D. D., Van Baelen, J., Vogt, S., Volkert, H., Weckwerth, T., Wernli, H., Wieser, A., and Wirth, M.: The Convective and Orographically-induced Precipitation Study (COPS): The scientific strategy, the field phase, and research highlights, Q. J. Roy. Meteor. Soc., 137, 3–30, https://doi.org/10.1002/qj.752, 2011. a, b
Wurman, J., Dowell, D., Richardson, Y., Markowski, P., Rasmussen, E., Burgess, D., Wicker, L., and Bluestein, H. B.: The second verification of the origins of rotation in tornadoes experiment: VORTEX2, B. Am. Meteorol. Soc., 93, 1147–1170, https://doi.org/10.1175/BAMS-D-11-00010.1, 2012. a
Zardi, D. and Whiteman, C. D.: Diurnal Mountain Wind Systems, Springer Netherlands, Dordrecht, 35–119, https://doi.org/10.1007/978-94-007-4098-3_2, ISBN 978-94-007-4098-3, 2013. a
Zhao, D. and Wu, J.: Changes in urban-related precipitation in the summer over three city clusters in China, Theor. Appl. Climatol., 134, 83–93, https://doi.org/10.1007/s00704-017-2256-9, 2018. a
Zhou, Z., Zhang, Q., Allen, J. T., Ni, X., and Ng, C. P.: How Many Types of Severe Hailstorm Environments Are There Globally?, Geophys. Res. Lett., 48, e2021GL095485, https://doi.org/10.1029/2021GL095485, 2021. a, b
Executive editor
Observations suggest a clustering of severe convective storms near orography. The paper provides information on the gaps in our process understanding and the measurement techniques which are currently hampering an improved prediction of the events and their associated hazards. It provides an outline of a multinational coordinated European field campaign on Thunderstorm Intensification from Mountains to Plains.
Observations suggest a clustering of severe convective storms near orography. The paper provides...
Short summary
Strong thunderstorms have been studied mainly over flat terrain in the past. However, they are particularly frequent near European mountain ranges, so observations of such storms are needed. This article gives an overview of our existing knowledge on this topic and presents plans for a large European field campaign with the goals to fill the knowledge gaps, validate tools for thunderstorm warnings, and improve numerical weather prediction near mountains.
Strong thunderstorms have been studied mainly over flat terrain in the past. However, they are...
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