Articles | Volume 24, issue 11
https://doi.org/10.5194/nhess-24-3869-2024
© Author(s) 2024. 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-24-3869-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Nov 2024
Research article |
| 12 Nov 2024
| 12 Nov 2024
Reconstructing hail days in Switzerland with statistical models (1959–2022)
Institute of Geography, Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Cornelia Schwierz
Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland
Katharina Schröer
Institute of Environmental Social Sciences and Geography, University of Freiburg, Freiburg, Germany
Mateusz Taszarek
Department of Meteorology and Climatology, Adam Mickiewicz University, Poznan, Poland
Olivia Martius
Institute of Geography, Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
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Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
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Jérôme Kopp, Agostino Manzato, Alessandro Hering, Urs Germann, and Olivia Martius
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S. Mubashshir Ali, Matthias Röthlisberger, Tess Parker, Kai Kornhuber, and Olivia Martius
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Kathrin Wehrli, Fei Luo, Mathias Hauser, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, Dim Coumou, Wilhelm May, Philippe Le Sager, Frank Selten, Olivia Martius, Robert Vautard, and Sonia I. Seneviratne
Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, https://doi.org/10.5194/esd-13-1167-2022, 2022
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The ExtremeX experiment was designed to unravel the contribution of processes leading to the occurrence of recent weather and climate extremes. Global climate simulations are carried out with three models. The results show that in constrained experiments, temperature anomalies during heatwaves are well represented, although climatological model biases remain. Further, a substantial contribution of both atmospheric circulation and soil moisture to heat extremes is identified.
Alexandre Tuel, Bettina Schaefli, Jakob Zscheischler, and Olivia Martius
Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, https://doi.org/10.5194/hess-26-2649-2022, 2022
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River discharge is strongly influenced by the temporal structure of precipitation. Here, we show how extreme precipitation events that occur a few days or weeks after a previous event have a larger effect on river discharge than events occurring in isolation. Windows of 2 weeks or less between events have the most impact. Similarly, periods of persistent high discharge tend to be associated with the occurrence of several extreme precipitation events in close succession.
Daniel Steinfeld, Adrian Peter, Olivia Martius, and Stefan Brönnimann
EGUsphere, https://doi.org/10.5194/egusphere-2022-92, https://doi.org/10.5194/egusphere-2022-92, 2022
Preprint archived
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We assess the performance of various fire weather indices to predict wildfire occurrence in Northern Switzerland. We find that indices responding readily to weather changes have the best performance during spring; in the summer and autumn seasons, indices that describe persistent hot and dry conditions perform best. We demonstrate that a logistic regression model trained on local historical fire activity can outperform existing fire weather indices.
Lisa-Ann Kautz, Olivia Martius, Stephan Pfahl, Joaquim G. Pinto, Alexandre M. Ramos, Pedro M. Sousa, and Tim Woollings
Weather Clim. Dynam., 3, 305–336, https://doi.org/10.5194/wcd-3-305-2022, https://doi.org/10.5194/wcd-3-305-2022, 2022
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Atmospheric blocking is associated with stationary, self-sustaining and long-lasting high-pressure systems. They can cause or at least influence surface weather extremes, such as heat waves, cold spells, heavy precipitation events, droughts or wind extremes. The location of the blocking determines where and what type of extreme event will occur. These relationships are also important for weather prediction and may change due to global warming.
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.
Timothy H. Raupach, Andrey Martynov, Luca Nisi, Alessandro Hering, Yannick Barton, and Olivia Martius
Geosci. Model Dev., 14, 6495–6514, https://doi.org/10.5194/gmd-14-6495-2021, https://doi.org/10.5194/gmd-14-6495-2021, 2021
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When simulated thunderstorms are compared to observations or other simulations, a match between overall storm properties is often more important than exact matches to individual storms. We tested a comparison method that uses a thunderstorm tracking algorithm to characterise simulated storms. For May 2018 in Switzerland, the method produced reasonable matches to independent observations for most storm properties, showing its feasibility for summarising simulated storms over mountainous terrain.
Alexandre Tuel and Olivia Martius
Nat. Hazards Earth Syst. Sci., 21, 2949–2972, https://doi.org/10.5194/nhess-21-2949-2021, https://doi.org/10.5194/nhess-21-2949-2021, 2021
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Extreme river discharge may be triggered by large accumulations of precipitation over short time periods, which can result from the successive occurrence of extreme-precipitation events. We find a distinct spatiotemporal pattern in the temporal clustering behavior of precipitation extremes over Switzerland, with clustering occurring on the northern side of the Alps in winter and on their southern side in fall. Clusters tend to be followed by extreme discharge, particularly in the southern Alps.
Jérôme Kopp, Pauline Rivoire, S. Mubashshir Ali, Yannick Barton, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 5153–5174, https://doi.org/10.5194/hess-25-5153-2021, https://doi.org/10.5194/hess-25-5153-2021, 2021
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Episodes of extreme rainfall events happening in close temporal succession can lead to floods with dramatic impacts. We developed a novel method to individually identify those episodes and deduced the regions where they occur frequently and where their impact is substantial. Those regions are the east and northeast of the Asian continent, central Canada and the south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and north of Argentina and south of Bolivia.
Regula Muelchi, Ole Rössler, Jan Schwanbeck, Rolf Weingartner, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 3577–3594, https://doi.org/10.5194/hess-25-3577-2021, https://doi.org/10.5194/hess-25-3577-2021, 2021
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This study analyses changes in magnitude, frequency, and seasonality of moderate low and high flows for 93 catchments in Switzerland. In lower-lying catchments (below 1500 m a.s.l.), moderate low-flow magnitude (frequency) will decrease (increase). In Alpine catchments (above 1500 m a.s.l.), moderate low-flow magnitude (frequency) will increase (decrease). Moderate high flows tend to occur more frequent, and their magnitude increases in most catchments except some Alpine catchments.
Regula Muelchi, Ole Rössler, Jan Schwanbeck, Rolf Weingartner, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 3071–3086, https://doi.org/10.5194/hess-25-3071-2021, https://doi.org/10.5194/hess-25-3071-2021, 2021
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Runoff regimes in Switzerland will change significantly under climate change. Projected changes are strongly elevation dependent with earlier time of emergence and stronger changes in high-elevation catchments where snowmelt and glacier melt play an important role. The magnitude of change and the climate model agreement on the sign increase with increasing global mean temperatures and stronger emission scenarios. This amplification highlights the importance of climate change mitigation.
Jakob Zscheischler, Philippe Naveau, Olivia Martius, Sebastian Engelke, and Christoph C. Raible
Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, https://doi.org/10.5194/esd-12-1-2021, 2021
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Compound extremes such as heavy precipitation and extreme winds can lead to large damage. To date it is unclear how well climate models represent such compound extremes. Here we present a new measure to assess differences in the dependence structure of bivariate extremes. This measure is applied to assess differences in the dependence of compound precipitation and wind extremes between three model simulations and one reanalysis dataset in a domain in central Europe.
Cited articles
Akaike, H.: A new look at the statistical model identification, IEEE Trans. Automat. Control, 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974. a
Allen, J., Karoly, D., and Mills, G.: A severe thunderstorm climatology for Australia and associated thunderstorm environments, Aust. Meteorol. Oceanogr. J., 61, 143–158, https://doi.org/10.22499/2.6103.001, 2011. a
Allen, J. T., Tippett, M. K., and Sobel, A. H.: An empirical model relating U.S. monthly hail occurrence to large-scale meteorological environment, J. Adv. Model. Earth Syst., 7, 226–243, https://doi.org/10.1002/2014MS000397, 2015. a, b, c
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
Andersson, T., Andersson, M., Jacobsson, C., and Nilsson, S.: Thermodynamic indices for forecasting thunderstorms in southern Sweden, Meteorol. Mag., 118, 141–146, 1989. a
Applequist, S., Gahrs, G. E., Pfeffer, R. L., and Niu, X.-F.: Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting, Weather Forecast., 17, 783–799, https://doi.org/10.1175/1520-0434(2002)017<0783:COMFPQ>2.0.CO;2, 2002. a
Augenstein, M., Mohr, S., and Kunz, M.: Trends of thunderstorm activity and relation to large-scale atmospheric conditions in western and central Europe, other, display, ESSL https://doi.org/10.5194/ecss2023-98, 2023. a
BAFU: Umgang mit Naturgefahren in der Schweiz – Bericht des Bundesrats in Erfuellung des Postulats 12.4271 Darbellay vom 14.12.2012, Technischer Bericht, BAFU – Bundesamt fuer Umwelt, https://www.bafu.admin.ch/bafu/de/home/themen/naturgefahren/publikationen-studien/publikationen/umgang-mit-naturgefahren-in-der-schweiz.html (last access: 6 November 2024), 2012. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barras, H., Martius, O., Nisi, L., Schroeer, K., Hering, A., and Germann, U.: Multi-day hail clusters and isolated hail days in Switzerland – large-scale flow conditions and precursors, Weather Clim. Dynam., 2, 1167–1185, https://doi.org/10.5194/wcd-2-1167-2021, 2021. a, b, c, d
Battaglioli, F., Groenemeijer, P., Púčik, T., Taszarek, M., Ulbrich, U., and Rust, H.: Modelled 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, 2023a. a, b, c, d, e, f, g, h, i, j
Battaglioli, F., Groenemeijer, P., Tsonevsky, I., and Púčik, T.: Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts, Nat. Hazards Earth Syst. Sci., 23, 3651–3669, https://doi.org/10.5194/nhess-23-3651-2023, 2023b. a
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, S., Bidlot, J., Haimberger, L., Woollen, J., Buontempo, C., and Thépaut, J.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteorol. Soc., 147, 4186–4227, https://doi.org/10.1002/qj.4174, 2021. a
Betschart, M. and Hering, A.: Automatic Hail Detection at MeteoSwiss – Verification of the radar based hail detection algorithms POH, MESHS and HAIL, Arbeitsberichte der MeteoSchweiz, 238, 59 pp., https://www.meteoschweiz.admin.ch/service-und-publikationen/service.html (last access: 6 November 2024), 2012. a
Billet, J., DeLisi, M., Smith, B. G., and Gates, C.: Use of Regression Techniques to Predict Hail Size and the Probability of Large Hail, Weather Forecast., 12, 154–164, https://doi.org/10.1175/1520-0434(1997)012<0154:UORTTP>2.0.CO;2, 1997. a, b
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-J. Sev. Storms Meteorol., 6, 1–30, https://doi.org/10.55599/ejssm.v6i7.34, 2021. a
Brooks, H. E., Lee, J. W., and Craven, J. P.: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data, Atmos. Res., 67-68, 73–94, https://doi.org/10.1016/S0169-8095(03)00045-0, 2003. a, b
Budikova, D.: Role of Arctic sea ice in global atmospheric circulation: A review, Global Planet. Change, 68, 149–163, https://doi.org/10.1016/j.gloplacha.2009.04.001, 2009. a
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, b
Cheng, K., Harris, L., Bretherton, C., Merlis, T. M., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S., and Fueglistaler, S.: Impact of Warmer Sea Surface Temperature on the Global Pattern of Intense Convection: Insights From a Global Storm Resolving Model, Geophys. Res. Lett., 49, e2022GL099796, https://doi.org/10.1029/2022GL099796, 2022. a
Copernicus Climate Change Service, Climate Data Store: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023a. a
Copernicus Climate Change Service, Climate Data Store: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.bd0915c6, 2023b. a
Craven, J. P. and Brooks, H.: Baseline climatology of sounding derived parameters associated with deep moist convection, Natl. Weather Dig., 28, 13–24, 2004. a
Czernecki, B., Taszarek, M., Marosz, M., Półrolniczak, M., Kolendowicz, L., Wyszogrodzki, A., and Szturc, J.: 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. a, b
Dennis, E. J. and Kumjian, M. R.: The Impact of Vertical Wind Shear on Hail Growth in Simulated Supercells, J. Atmos. Sci., 74, 641–663, https://doi.org/10.1175/JAS-D-16-0066.1, 2017. a
Dessens, J., Berthet, C., and Sanchez, J.: Change in hailstone size distributions with an increase in the melting level height, Atmos. Res., 158–159, 245–253, https://doi.org/10.1016/j.atmosres.2014.07.004, 2015. a
Doswell, C. A., Brooks, H. E., and Maddox, R. A.: Flash Flood Forecasting: An Ingredients-Based Methodology, Weather Forecast., 11, 560–581, https://doi.org/10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2, 1996. 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 Clima. Atmos. Sci., 6, 19, https://doi.org/10.1038/s41612-023-00352-z, 2023. 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
Foote, B., Krauss, T. W., and Makitov, V.: Hail metrics using conventional radar, in: Proceedings of the 16th Conference on Planned and Inadvertent Weather Modification, 10 January 2005, San Diego, CA, USA, https://ams.confex.com/ams/Annual2005/techprogram/paper_86773.htm (last access: 6 November 2024), 2005. a
Gaal, R. and Kinter, J. L.: Soil Moisture Influence on the Incidence of Summer Mesoscale Convective Systems in the U.S. Great Plains, Mon. Weather Rev., 149, 3981–3994, https://doi.org/10.1175/MWR-D-21-0140.1, 2021. a
Galway, J. G.: The Lifted Index as a Predictor of Latent Instability, B. Am. Meteorol. Soc., 37, 528–529, https://doi.org/10.1175/1520-0477-37.10.528, 1956. a
García-Ortega, E., Merino, A., López, L., and Sánchez, J.: Role of mesoscale factors at the onset of deep convection on hailstorm days and their relation to the synoptic patterns, Atmos. Res., 114-115, 91–106, https://doi.org/10.1016/j.atmosres.2012.05.017, 2012. a
Gensini, V. A., Converse, C., Ashley, W. S., and Taszarek, M.: Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings, Weather Forecast., 36, 2143–2160, https://doi.org/10.1175/WAF-D-21-0056.1, 2021. a
George, J.: Weather forecasting for aeronautics, Q. J. Roy. Meteorol. Soc., 87, 120–120, https://doi.org/10.1002/qj.49708737120, 1961. a
Giaiotti, D., Nordio, S., and Stel, F.: The climatology of hail in the plain of Friuli Venezia Giulia, Atmos. Res., 67-68, 247–259, https://doi.org/10.1016/S0169-8095(03)00084-X, 2003. a
Gladich, I., Gallai, I., Giaiotti, D., and Stel, F.: On the diurnal cycle of deep moist convection in the southern side of the Alps analysed through cloud-to-ground lightning activity, Atmos. Res., 100, 371–376, https://doi.org/10.1016/j.atmosres.2010.08.026, 2011. 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. and van Delden, A.: Sounding-derived parameters associated with large hail and tornadoes in the Netherlands, Atmos. Res., 83, 473–487, https://doi.org/10.1016/j.atmosres.2005.08.006, 2007. a
Hastie, T. and Tibshirani, R.: Generalized Additive Models: Some Applications, J. Am. Stat. Assoc., 82, 371–386, https://doi.org/10.1080/01621459.1987.10478440, 1987. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hoogewind, K. A., Baldwin, M. E., and Trapp, R. J.: The Impact of Climate Change on Hazardous Convective Weather in the United States: Insight from High-Resolution Dynamical Downscaling, J. Climate, 30, 10081–10100, https://doi.org/10.1175/JCLI-D-16-0885.1, 2017. a, b
Hosmer, D. W. and Lemeshow, S.: Applied Logistic Regression, in: 1st Edn., Wiley, ISBN 978-0-471-35632-5, ISBN 978-0-471-72214-4, https://doi.org/10.1002/0471722146, 2000. a
Huntrieser, H., Schiesser, H. H., Schmid, W., and Waldvogel, A.: Comparison of Traditional and Newly Developed Thunderstorm Indices for Switzerland, Weather Forecast., 12, 108–125, https://doi.org/10.1175/1520-0434(1997)012<0108:COTAND>2.0.CO;2, 1997. a, b
Jeong, J.-H., Fan, J., Homeyer, C. R., and Hou, Z.: Understanding Hailstone Temporal Variability and Contributing Factors over the U.S. Southern Great Plains, J. Climate, 33, 3947–3966, https://doi.org/10.1175/JCLI-D-19-0606.1, 2020. a
Johns, R. H. and Doswell, C. A.: Severe Local Storms Forecasting, Weather Forecast., 7, 588–612, https://doi.org/10.1175/1520-0434(1992)007<0588:SLSF>2.0.CO;2, 1992. a
Johnson, A. W. and Sugden, K. E.: Evaluation of Sounding-Derived Thermodynamic and Wind-Related Parameters Associated with Large Hail Events, E-J. Sev. Storms Meteorol., 9, 1–42, https://doi.org/10.55599/ejssm.v9i5.57, 2021. a, b
Kaltenböck, 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
Kaltenböck, R., Diendorfer, G., and Dotzek, N.: Evaluation of thunderstorm indices from ECMWF analyses, lightning data and severe storm reports, Atmos. Res., 93, 381–396, https://doi.org/10.1016/j.atmosres.2008.11.005, 2009. 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
Kopp, J., Hering, A., Germann, U., and Martius, O.: Verification of weather-radar-based hail metrics with crowdsourced observations from Switzerland, Atmos. Meas. Tech., 17, 4529–4552, https://doi.org/10.5194/amt-17-4529-2024, 2024. a, b
Kumjian, M. R. and Lombardo, K.: A Hail Growth Trajectory Model for Exploring the Environmental Controls on Hail Size: Model Physics and Idealized Tests, J. Atmos. Sci., 77, 2765–2791, https://doi.org/10.1175/JAS-D-20-0016.1, 2020. a, b
Kumjian, M. R., 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. 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. Meteorol. Soc., 144, 231–250, https://doi.org/10.1002/qj.3197, 2018. a, b
Li, F., Chavas, D. R., Reed, K. A., and Dawson Ii, D. T.: Climatology of Severe Local Storm Environments and Synoptic-Scale Features over North America in ERA5 Reanalysis and CAM6 Simulation, J. Climate, 33, 8339–8365, https://doi.org/10.1175/JCLI-D-19-0986.1, 2020. a
Lock, N. A. and Houston, A. L.: Empirical Examination of the Factors Regulating Thunderstorm Initiation, Mon. Weather Rev., 142, 240–258, https://doi.org/10.1175/MWR-D-13-00082.1, 2014. a
Lugauer, M. and Winkler, P.: Thermal circulation in South Bavaria climatology and synoptic aspects, Meteorol. Z., 14, 15–30, https://doi.org/10.1127/0941-2948/2005/0014-0015, 2005. a
Mansfield, E. R. and Helms, B. P.: Detecting Multicollinearity, Am. Stat., 36, 158–160, https://doi.org/10.2307/2683167, 1982. a
Manzato, A.: Hail in Northeast Italy: Climatology and Bivariate Analysis with the Sounding-Derived Indices, J. Appl. Meteorol. Clim., 51, 449–467, https://doi.org/10.1175/JAMC-D-10-05012.1, 2012. a
Manzato, A., Serafin, S., Miglietta, M. M., Kirshbaum, D., and Schulz, W.: A Pan-Alpine Climatology of Lightning and Convective Initiation, Mon. Weather Rev., 150, 2213–2230, https://doi.org/10.1175/MWR-D-21-0149.1, 2022. a
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. a
Melcón, P., Merino, A., Sánchez, J. L., López, L., and García-Ortega, E.: Spatial patterns of thermodynamic conditions of hailstorms in southwestern France, Atmos. Res., 189, 111–126, https://doi.org/10.1016/j.atmosres.2017.01.011, 2017. 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, b, c, d
Mohr, S., Kunz, M., and Geyer, B.: Hail potential in Europe based on a regional climate model hindcast: Hail Potential In Europe, Geophys. Res. Lett., 42, 10,904–10,912, https://doi.org/10.1002/2015GL067118, 2015a. a, b, c
Mohr, S., Kunz, M., and Keuler, K.: Development and application of a logistic model to estimate the past and future hail potential in Germany: Logistic Model Estimating Hail Potential, J. Geophys. Res.-Atmos., 120, 3939–3956, https://doi.org/10.1002/2014JD022959, 2015b. a, b, c
Moncrieff, M. W. and Miller, M. J.: The dynamics and simulation of tropical cumulonimbus and squall lines, Q. J. Roy. Meteorol. Soc., 102, 373–394, https://doi.org/10.1002/qj.49710243208, 1976. a
Morgan, G. M.: A General Description of the Hail Problem in the Po Valley of Northern Italy, J. Appl. Meteorol., 12, 338–353, https://doi.org/10.1175/1520-0450(1973)012<0338:AGDOTH>2.0.CO;2, 1973. a
Müller, S. and Schmutz, M.: Nationales Hagelprojekt. Schlussbericht: Aufbereitung historische Hagel-Daten, Tech. rep., Meteotest AG, Bern, 2021. a
Nisi, L., Hering, A., Germann, U., and Martius, O.: A 15‐year hail streak climatology for the Alpine region, Q. J. Roy. Meteorol. Soc., 144, 1429–1449, https://doi.org/10.1002/qj.3286, 2018. a, b
Nisi, L., Hering, A., Germann, U., Schroeer, K., Barras, H., Kunz, M., and Martius, O.: Hailstorms in the Alpine region: Diurnal cycle, characteristics, and the nowcasting potential of lightning properties, Q. J. Roy. Meteorol. Soc., 146, 4170–4194, https://doi.org/10.1002/qj.3897, 2020. a
Nixon, C. J., Allen, J. T., and Taszarek, M.: Hodographs and Skew Ts of Hail-Producing Storms, Weather Forecast., 38, 2217–2236, https://doi.org/10.1175/WAF-D-23-0031.1, 2023. a, b, c
Pilguj, N., Taszarek, M., Allen, J. T., and Hoogewind, K. A.: Are Trends in Convective Parameters over the United States and Europe Consistent between Reanalyses and Observations?, J. Climate, 35, 3605–3626, https://doi.org/10.1175/JCLI-D-21-0135.1, 2022. a, b, c
Piper, D. and Kunz, M.: Spatiotemporal variability of lightning activity in Europe and the relation to the North Atlantic Oscillation teleconnection pattern, Nat. Hazards Earth Syst. Sci., 17, 1319–1336, https://doi.org/10.5194/nhess-17-1319-2017, 2017. 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, 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
Punge, H. 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
Punge, H. J., Bedka, K. M., Kunz, M., Bang, S. D., and Itterly, K. F.: Characteristics of hail hazard in South Africa based on satellite detection of convective storms, Nat. Hazards Earth Syst. Sci., 23, 1549–1576, https://doi.org/10.5194/nhess-23-1549-2023, 2023. a
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 Clim. Atmos. Sci., 2, 30, https://doi.org/10.1038/s41612-019-0083-7, 2019. a
Rasmussen, E. N.: Refined Supercell and Tornado Forecast Parameters, Weather Forecast., 18, 530–535, https://doi.org/10.1175/1520-0434(2003)18<530:RSATFP>2.0.CO;2, 2003. a
Raupach, T. H., Soderholm, J. S., Warren, R. A., and Sherwood, S. C.: Changes in hail hazard across Australia: 1979–2021, npj Clim. Atmos. Sci., 6, 143, https://doi.org/10.1038/s41612-023-00454-8, 2023b. a, b
Roebber, P. J.: Visualizing Multiple Measures of Forecast Quality, Weather Forecast., 24, 601–608, https://doi.org/10.1175/2008WAF2222159.1, 2009. a
Rohrer, M., Brönnimann, S., Martius, O., Raible, C. C., and Wild, M.: Decadal variations of blocking and storm tracks in centennial reanalyses, Tellus A, 71, 1586236, https://doi.org/10.1080/16000870.2019.1586236, 2019. a
Sánchez, J. L., Marcos, J. L., Dessens, J., López, L., Bustos, C., and García-Ortega, E.: Assessing sounding-derived parameters as storm predictors in different latitudes, Atmos. Res., 93, 446–456, https://doi.org/10.1016/j.atmosres.2008.11.006, 2009. a, b
Schmeits, M. J., Kok, K. J., and Vogelezang, D. H. P.: Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics, Weather Forecast., 20, 134–148, https://doi.org/10.1175/WAF840.1, 2005. a, b
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
Schröer, K., Trefalt, S., Hering, A., Germann, U., and Schwierz, C.: Hagelklima Schweiz: Daten, Ergebnisse und Dokumentation: Fachbericht MeteoSchweiz No. 283, Tech. rep., MeteoSchweiz, https://doi.org/10.18751/PMCH/TR/283.HAGELKLIMA, 2023. a, b, c
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–464, 1978. a
Showalter, A. K.: A Stability Index for Thunderstorm Forecasting, B. Am. Meteorol. Soc., 34, 250–252, https://doi.org/10.1175/1520-0477-34.6.250, 1953. a
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, 2020a. a, b
Taszarek, M., Pilguj, N., Allen, J. T., Gensini, V., Brooks, H. E., and Szuster, P.: Comparison of convective parameters derived from ERA5 and MERRA2 with rawinsonde data over Europe and North America, J. Climate, 34, 3211–3237, https://doi.org/10.1175/JCLI-D-20-0484.1, 2020b. a
Taszarek, M., Allen, J. T., Brooks, H. E., Pilguj, N., and Czernecki, B.: Differing Trends in United States and European Severe Thunderstorm Environments in a Warming Climate, B. Am. Meteorol. Soc., 102, E296–E322, https://doi.org/10.1175/BAMS-D-20-0004.1, 2021. a, b
Taylor, C. M.: Detecting soil moisture impacts on convective initiation in Europe, Geophys. Res. Lett., 42, 4631–4638, https://doi.org/10.1002/2015GL064030, 2015. a
Tippett, M. K., Allen, J. T., Gensini, V. A., and Brooks, H. E.: Climate and Hazardous Convective Weather, Curr. Clim. Change Rep., 1, 60–73, https://doi.org/10.1007/s40641-015-0006-6, 2015. a
Trefalt, S., Germann, U., Hering, A., Clementi, L., Boscacci, M., Schröer, K., and Schwierz, C.: Hail Climate Switzerland Operational radar hail detection algorithms at MeteoSwiss: quality assesssment and improvement: Fachbericht MeteoSchweiz No. 284, Tech. rep., MeteoSchweiz, https://doi.org/10.18751/PMCH/TR/284.HAILCLIMATE, 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, b
Van Delden, A.: The synoptic setting of thunderstorms in western Europe, Atmos. Res., 56, 89–110, https://doi.org/10.1016/S0169-8095(00)00092-2, 2001. a
Varga, A. J. and Breuer, H.: Evaluation of convective parameters derived from pressure level and native ERA5 data and different resolution WRF climate simulations over Central Europe, Clim. Dynam., 58, 1569–1585, https://doi.org/10.1007/s00382-021-05979-3, 2022. a
Waldvogel, A., Federer, B., and Grimm, P.: Criteria for the Detection of Hail Cells, J. Appl. Meteorol., 18, 1521–1525, https://doi.org/10.1175/1520-0450(1979)018<1521:CFTDOH>2.0.CO;2, 1979. a
Weisman, M. L. and Klemp, J. B.: The Dependence of Numerically Simulated Convective Storms on Vertical Wind Shear and Buoyancy, Mon. Weather Rev., 110, 504–520, https://doi.org/10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2, 1982. a
Weisman, M. L. and Klemp, J. B.: The Structure and Classification of Numerically Simulated Convective Stormsin Directionally Varying Wind Shears, Mon. Weather Rev., 112, 2479–2498, https://doi.org/10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2, 1984. a
Wiese, W.: Polareis Und Atmosphärische Schwankungen, Geograf. Ann., 6, 273–299, https://doi.org/10.1080/20014422.1924.11881099, 1924. a
Willemse, S.: A statistical analysis and climatological interpretation of hailstorms in Switzerland, PhD Thesis, ETH Zurich, https://doi.org/10.3929/ETHZ-A-001486581, 1995. a, b
Wu, J., Guo, J., Yun, Y., Yang, R., Guo, X., Meng, D., Sun, Y., Zhang, Z., Xu, H., and Chen, T.: Can ERA5 reanalysis data characterize the pre-storm environment?, Atmos. Res., 297, 107108, https://doi.org/10.1016/j.atmosres.2023.107108, 2024. a
Short summary
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.
In our study we used statistical models to reconstruct past hail days in Switzerland from...
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