Articles | Volume 4, issue 4
https://doi.org/10.5194/wcd-4-905-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-4-905-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2023
Research article |
| 03 Nov 2023
| 03 Nov 2023
Exploring hail and lightning diagnostics over the Alpine-Adriatic region in a km-scale climate model
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Nikolina Ban
Department of Atmospheric and Cryospheric Sciences (ACINN), University of Innsbruck, Innsbruck, Austria
Marie-Estelle Demory
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Wyss Academy for Nature, University of Bern, Bern, Switzerland
Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Raffael Aellig
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
now at: Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Oliver Fuhrer
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
Jonas Jucker
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Xavier Lapillonne
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
Christoph Schär
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
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Xavier Lapillonne, Daniel Hupp, Fabian Gessler, André Walser, Andreas Pauling, Annika Lauber, Benjamin Cumming, Carlos Osuna, Christoph Müller, Claire Merker, Daniel Leuenberger, David Leutwyler, Dmitry Alexeev, Gabriel Vollenweider, Guillaume Van Parys, Jonas Jucker, Lukas Jansing, Marco Arpagaus, Marco Induni, Marek Jacob, Matthias Kraushaar, Michael Jähn, Mikael Stellio, Oliver Fuhrer, Petra Baumann, Philippe Steiner, Pirmin Kaufmann, Remo Dietlicher, Ralf Müller, Sergey Kosukhin, Thomas C. Schulthess, Ulrich Schättler, Victoria Cherkas, and William Sawyer
Geosci. Model Dev., 19, 755–772, https://doi.org/10.5194/gmd-19-755-2026, https://doi.org/10.5194/gmd-19-755-2026, 2026
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The ICON climate and numerical weather prediction model was fully ported to Graphical Processing Units (GPUs) using OpenACC compiler directives, covering all components required for operational weather prediction. The GPU port together with several performance optimizations led to a speed-up of 5.6× when comparing to traditional Central Processing Units (CPUs) . Thanks to this adaptation effort, MeteoSwiss became the first national weather service to run the ICON model operationally on GPUs.
Anurag Dipankar, Mauro Bianco, Mona Bukenberger, Till Ehrengruber, Nicoletta Farabullini, Oliver Fuhrer, Abishek Gopal, Daniel Hupp, Andreas Jocksch, Samuel Kellerhals, Clarissa A. Kroll, Xavier Lapillonne, Matthieu Leclair, Magdalena Luz, Christoph Müller, Chia Rui Ong, Carlos Osuna, Praveen Pothapakula, Andreas Prein, Matthias Röthlin, William Sawyer, Christoph Schär, Sebastian Schemm, Giacomo Serafini, Hannes Vogt, Ben Weber, Robert C. Jnglin Wills, Nicolas Gruber, and Thomas C. Schulthess
Geosci. Model Dev., 19, 713–729, https://doi.org/10.5194/gmd-19-713-2026, https://doi.org/10.5194/gmd-19-713-2026, 2026
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Climate models are becoming more detailed and accurate by simulating weather at scales of just a few kilometers. Simulating at km-scale is computationally demanding requiring powerful supercomputers and efficient code. This work presents a refactored dynamical core of a state-of-the-art climate model using a Python-based approach. The refactored code has passed through a sequence of verification and validation demonstrating its usability in performing km-scale global simulations.
Jan Niklas Richter, Anselm Arndt, Nikolina Ban, Nicolas Gampierakis, Fabien Maussion, Rainer Prinz, Matthias Scheiter, Nikolaus Umlauf, and Lindsey Nicholson
EGUsphere, https://doi.org/10.5194/egusphere-2025-6249, https://doi.org/10.5194/egusphere-2025-6249, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025, https://doi.org/10.5194/hess-29-1201-2025, 2025
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Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and the Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
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Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Roman Brogli, Christoph Heim, Jonas Mensch, Silje Lund Sørland, and Christoph Schär
Geosci. Model Dev., 16, 907–926, https://doi.org/10.5194/gmd-16-907-2023, https://doi.org/10.5194/gmd-16-907-2023, 2023
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Qinggang Gao, Christian Zeman, Jesus Vergara-Temprado, Daniela C. A. Lima, Peter Molnar, and Christoph Schär
Weather Clim. Dynam., 4, 189–211, https://doi.org/10.5194/wcd-4-189-2023, https://doi.org/10.5194/wcd-4-189-2023, 2023
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We developed a vortex identification algorithm for realistic atmospheric simulations. The algorithm enabled us to obtain a climatology of vortex shedding from Madeira Island for a 10-year simulation period. This first objective climatological analysis of vortex streets shows consistency with observed atmospheric conditions. The analysis shows a pronounced annual cycle with an increasing vortex shedding rate from April to August and a sudden decrease in September.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
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This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Christian R. Steger, Benjamin Steger, and Christoph Schär
Geosci. Model Dev., 15, 6817–6840, https://doi.org/10.5194/gmd-15-6817-2022, https://doi.org/10.5194/gmd-15-6817-2022, 2022
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Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain.
Christian Zeman and Christoph Schär
Geosci. Model Dev., 15, 3183–3203, https://doi.org/10.5194/gmd-15-3183-2022, https://doi.org/10.5194/gmd-15-3183-2022, 2022
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Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.
Roman Brogli, Silje Lund Sørland, Nico Kröner, and Christoph Schär
Weather Clim. Dynam., 2, 1093–1110, https://doi.org/10.5194/wcd-2-1093-2021, https://doi.org/10.5194/wcd-2-1093-2021, 2021
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In a warmer future climate, climate simulations predict that some land areas will experience excessive warming during summer. We show that the excessive summer warming is related to the vertical distribution of warming within the atmosphere. In regions characterized by excessive warming, much of the warming occurs close to the surface. In other regions, most of the warming is redistributed to higher levels in the atmosphere, which weakens the surface warming.
Daniel Regenass, Linda Schlemmer, Elena Jahr, and Christoph Schär
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-426, https://doi.org/10.5194/hess-2021-426, 2021
Manuscript not accepted for further review
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Weather and climate models need to represent the water cycle on land in order to provide accurate estimates of moisture and energy exchange between the land and the atmosphere. Infiltration of water into the soil is often modeled with an equation describing water transport in porous media. Here, we point out some challenges arising in the numerical solution of this equation and show the consequences for the representation of the water cycle in modern weather and climate models.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
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We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Christian Zeman, Nils P. Wedi, Peter D. Dueben, Nikolina Ban, and Christoph Schär
Geosci. Model Dev., 14, 4617–4639, https://doi.org/10.5194/gmd-14-4617-2021, https://doi.org/10.5194/gmd-14-4617-2021, 2021
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Kilometer-scale atmospheric models allow us to partially resolve thunderstorms and thus improve their representation. We present an intercomparison between two distinct atmospheric models for 2 summer days with heavy thunderstorms over Europe. We show the dependence of precipitation and vertical wind speed on spatial and temporal resolution and also discuss the possible influence of the system of equations, numerical methods, and diffusion in the models.
Jeremy McGibbon, Noah D. Brenowitz, Mark Cheeseman, Spencer K. Clark, Johann P. S. Dahm, Eddie C. Davis, Oliver D. Elbert, Rhea C. George, Lucas M. Harris, Brian Henn, Anna Kwa, W. Andre Perkins, Oliver Watt-Meyer, Tobias F. Wicky, Christopher S. Bretherton, and Oliver Fuhrer
Geosci. Model Dev., 14, 4401–4409, https://doi.org/10.5194/gmd-14-4401-2021, https://doi.org/10.5194/gmd-14-4401-2021, 2021
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FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run fast, but this makes it hard to add features if you do not (or even if you do) know Fortran. We have written a Python interface to FV3GFS that lets you import the Fortran model as a Python package. We show examples of how this is used to write
modelscripts, which reproduce or build on what the Fortran model can do. You could do this same wrapping for any compiled model, not just FV3GFS.
Cited articles
Adams-Selin, R. D.: A Three-Dimensional Hail Trajectory Clustering Technique, Mon. Weather Rev., 151, 2361–2375, https://doi.org/10.1175/mwr-d-22-0345.1, 2023. a
Adams-Selin, R. D., Clark, A. J., Melick, C. J., Dembek, S. R., Jirak, I. L., and Ziegler, C. L.: Evolution of WRF-HAILCAST during the 2014–16 NOAA/Hazardous Weather Testbed Spring Forecasting Experiments, Weather Forecast., 34, 61–79, https://doi.org/10.1175/waf-d-18-0024.1, 2019. a, b, c
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, b
Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belušić, D., Berthou, S., Brisson, E., Cardoso, R. M., Chan, S. C., Christensen, O. B., Fernández, J., Fita, L., Frisius, T., Gašparac, G., Giorgi, F., Goergen, K., Haugen, J. E., Hodnebrog, Ø., Kartsios, S., Katragkou, E., Kendon, E. J., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H.-J., Raffa, M., Remedio, A. R., Schär, C., Soares, P. M. M., Srnec, L., Steensen, B. M., Stocchi, P., Tölle, M. H., Truhetz, H., Vergara-Temprado, J., de Vries, H., Warrach-Sagi, K., Wulfmeyer, V., and Zander, M. J.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w, 2021. a, b
Barras, H., Hering, A., Martynov, A., Noti, P. A., Germann, U., and Martius, O.: Experiences with > 50 000 crowdsourced hail reports in Switzerland, B. Am. Meteorol. Soc., 100, 1429–1440, https://doi.org/10.1175/BAMS-D-18-0090.1, 2019. a, b, c
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
Belušić, A., Prtenjak, M. T., Güttler, I., Ban, N., Leutwyler, D., and Schär, C.: Near-surface wind variability over the broader Adriatic region: insights from an ensemble of regional climate models, Clim. Dynam., 50, 4455–4480, https://doi.org/10.1007/s00382-017-3885-5, 2018. a
Betz, H. D., Schmidt, K., Laroche, P., Blanchet, P., Oettinger, W. P., Defer, E., Dziewit, Z., and Konarski, J.: LINET-An international lightning detection network in Europe, Atmos. Res., 91, 564–573, https://doi.org/10.1016/j.atmosres.2008.06.012, 2009 (data available at: https://www.nowcast.de/en/solutions/linet-data) a, b, c
Brimelow, J. C., Reuter, G. W., and Poolman, E. R.: Modeling Maximum Hail Size in Alberta Thunderstorms, Weather Forecast., 17, 1048–1062, https://doi.org/10.1175/1520-0434(2002)017<1048:MMHSIA>2.0.CO;2, 2002. a
Carlson, T. N., Benjamin, S. G., Forbes, G. S., and Li, Y.-F.: Elevated Mixed Layers in the Regional Severe Storm Environment: Conceptual Model and Case Studies, Mon. Weather Rev., 111, 1453–1474, https://doi.org/10.1175/1520-0493(1983)111<1453:emlitr>2.0.co;2, 1983. a
Cintineo, J. L., Smith, T. M., Lakshmanan, V., Brooks, H. E., and Ortega, K. L.: An objective high-resolution hail climatology of the contiguous United States, Weather Forecast., 27, 1235–1248, https://doi.org/10.1175/WAF-D-11-00151.1, 2012. a
Croatian Meteorological and Hydrological Service: DHMZ, DHMZ [data set], https://meteo.hr/proizvodi_e.php?section=proizvodi_usluge¶m=services, last access: 20 October 2023.
FEON: Hydrological Yearbook of Switzerland 2013, https://www.bafu.admin.ch/dam/bafu/en/dokumente/hydrologie/uz-umwelt-zustand/hydrologisches_jahrbuchderschweiz2013.pdf (last access: 13 September 2023), 2013. a
Fierro, A. O., Mansell, E. R., MacGorman, D. R., and Ziegler, C. L.: The Implementation of an Explicit Charging and Discharge Lightning Scheme within the WRF-ARW Model: Benchmark Simulations of a Continental Squall Line, a Tropical Cyclone, and a Winter Storm, Mon. Weather Rev., 141, 2390–2415, https://doi.org/10.1175/mwr-d-12-00278.1, 2013. a
Foote, B., Krauss, T. W., and Makitov, V.: Hail matrics using convectional radar, in: Proceedings of 16th Conference on Planned and Inadvertent Weather Modification, American Meteorological Society, San Diego, CA, https://ams.confex.com/ams/pdfpapers/86773.pdf (last access: 20 October 2023), 2005. 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
Grams, C. M., Binder, H., Pfahl, S., Piaget, N., and Wernli, H.: Atmospheric processes triggering the central European floods in June 2013, Nat. Hazards Earth Syst. Sci., 14, 1691–1702, https://doi.org/10.5194/nhess-14-1691-2014, 2014. a
GVZ: Geschäftsbericht 2013, Gebäudeversicherung Kanton Zürich, https://www.gvz.ch/_file/146/gvz-geschaeftsbericht-2013.pdf, (last access: 13 September 2023), 2013. a
Hentgen, L., Ban, N., Kröner, N., Leutwyler, D., and Schär, C.: Clouds in Convection-Resolving Climate Simulations Over Europe, J. Geophys. Res.-Atmos., 124, 3849–3870, https://doi.org/10.1029/2018JD030150, 2019. 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., 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., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020 (data available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview). a, b
Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, GES DISC [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/06, 2019. a
James, R. P. and Markowski, P. M.: A Numerical Investigation of the Effects of Dry Air Aloft on Deep Convection, Mon. Weather Rev., 138, 140–161, https://doi.org/10.1175/2009mwr3018.1, 2010. a
Jelić, D., Megyeri, O. A., Malečić, B., Belušić Vozila, A., Strelec Mahović, N., and Telišman Prtenjak, M.: Hail Climatology Along the Northeastern Adriatic, J. Geophys. Res.-Atmos., 125, e2020JD032749, https://doi.org/10.1029/2020JD032749, 2020. a
Jelić, D., Prtenjak, M. T., Malečić, B., Vozila, A. B., Megyeri, O. A., and Renko, T.: A new approach for the analysis of deep convective events: Thunderstorm intensity index, Atmosphere, 12, 908, https://doi.org/10.3390/atmos12070908, 2021. a
Joe, P., Burgess, D., Potts, R., Keenan, T., Stumpf, G., and Treloar, A.: The S2K Severe Weather Detection Algorithms and Their Performance, Weather Forecast., 19, 43–63, https://doi.org/10.1175/1520-0434(2004)019<0043:tsswda>2.0.co;2, 2004. 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
Kalthoff, N., Adler, B., Barthlott, C., Corsmeier, U., Mobbs, S., Crewell, S., Träumner, K., Kottmeier, C., Wieser, A., Smith, V., and Di Girolamo, P.: The impact of convergence zones on the initiation of deep convection: A case study from COPS, Atmos. Res., 93, 680–694, https://doi.org/10.1016/j.atmosres.2009.02.010, 2009. a
Keil, C., Heinlein, F., and Craig, G. C.: The convective adjustment time-scale as indicator of predictability of convective precipitation, Q. J. Roy. Meteor. Soc., 140, 480–490, https://doi.org/10.1002/qj.2143, 2013. a, b
Lac, C., Chaboureau, J.-P., Masson, V., Pinty, J.-P., Tulet, P., Escobar, J., Leriche, M., Barthe, C., Aouizerats, B., Augros, C., Aumond, P., Auguste, F., Bechtold, P., Berthet, S., Bielli, S., Bosseur, F., Caumont, O., Cohard, J.-M., Colin, J., Couvreux, F., Cuxart, J., Delautier, G., Dauhut, T., Ducrocq, V., Filippi, J.-B., Gazen, D., Geoffroy, O., Gheusi, F., Honnert, R., Lafore, J.-P., Lebeaupin Brossier, C., Libois, Q., Lunet, T., Mari, C., Maric, T., Mascart, P., Mogé, M., Molinié, G., Nuissier, O., Pantillon, F., Peyrillé, P., Pergaud, J., Perraud, E., Pianezze, J., Redelsperger, J.-L., Ricard, D., Richard, E., Riette, S., Rodier, Q., Schoetter, R., Seyfried, L., Stein, J., Suhre, K., Taufour, M., Thouron, O., Turner, S., Verrelle, A., Vié, B., Visentin, F., Vionnet, V., and Wautelet, P.: Overview of the Meso-NH model version 5.4 and its applications, Geosci. Model Dev., 11, 1929–1969, https://doi.org/10.5194/gmd-11-1929-2018, 2018. a
Lagasio, M., Parodi, A., Procopio, R., Rachidi, F., and Fiori, E.: Lightning Potential Index performances in multimicrophysical cloud-resolving simulations of a back-building mesoscale convective system: The Genoa 2014 event, J. Geophys. Res.-Atmos., 122, 4238–4257, https://doi.org/10.1002/2016jd026115, 2017. a
Lanicci, J. M. and Warner, T. T.: A Synoptic Climatology of the Elevated Mixed-Layer Inversion over the Southern Great Plains in Spring. Part I: Structure, Dynamics, and Seasonal Evolution, Weather Forecast., 6, 181–197, https://doi.org/10.1175/1520-0434(1991)006<0181:ASCOTE>2.0.CO;2, 1991. a
Leutwyler, D., Fuhrer, O., Lapillonne, X., Lüthi, D., and Schär, C.: Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19, Geosci. Model Dev., 9, 3393–3412, https://doi.org/10.5194/gmd-9-3393-2016, 2016. a, b
Leutwyler, D., Lüthi, D., Ban, N., Fuhrer, O., and Schär, C.: Evaluation of the convection-resolving climate modeling approach on continental scales, J. Geophys. Res.-Atmos., 122, 5237–5258, https://doi.org/10.1002/2016JD026013, 2017. a
Luo, L., Xue, M., and Zhu, K.: The Initiation and Organization of a Severe Hail-Producing Mesoscale Convective System in East China: A Numerical Study, J. Geophys. Res.-Atmos., 125, 1–23, https://doi.org/10.1029/2020JD032606, 2020. a
Lüthi, S., Ban, N., Kotlarski, S., Steger, C. R., Jonas, T., and Schär, C.: Projections of Alpine Snow-Cover in a High-Resolution Climate Simulation, Atmosphere, 10, 463, https://doi.org/10.3390/atmos10080463, 2019. a
Lynn, B. and Yair, Y.: Prediction of lightning flash density with the WRF model, Adv. Geosci., 23, 11–16, https://doi.org/10.5194/adgeo-23-11-2010, 2010. a, b
Malečić, B., Prtenjak, M. T., Horvath, K., Jelić, D., Jurković, P. M., 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, b
Malečić, B., Cui, R., Demory, M. E., Horvath, K., Jelić, D., Schär, C., Prtenjak, M. T., Velasquez, P., and Ban, N.: Simulating Hail and Lightning Over the Alpine Adriatic Region – A Model Intercomparison Study, J. Geophys. Res.-Atmos., 128, e2022JD037989, https://doi.org/10.1029/2022jd037989, 2023. a, b
Markowski, P. and Richardson, Y.: Mesoscale Meteorology in Midlatitudes, Wiley, https://doi.org/10.1002/9780470682104, 2010. a
MeteoSwiss: MeteoSwiss Contact Form, https://www.meteoswiss.admin.ch/about-us/contact/contact-form.html, last access: 20 October 2023.
NCCS: Hail Climate Switzerland – National hail hazard maps, https://www.nccs.admin.ch/nccs/en/home/the-nccs/priority-themes/hail-climate-switzerland/brochure-technical-report.html (last access: 13 September 2023), 2021. a
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
Panosetti, D., Schlemmer, L., and Schär, C.: Convergence behavior of idealized convection-resolving simulations of summertime deep moist convection over land, Clim. Dynam., 55, 215–234, https://doi.org/10.1007/s00382-018-4229-9, 2018. a
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P., Alias, A., Belušić, D., Berthou, S., Caillaud, C., Cardoso, R. M., Chan, S., Christensen, O. B., Dobler, A., de Vries, H., Goergen, K., Kendon, E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz, H.-J., Schär, C., Soares, P. M. M., Truhetz, H., and Vergara-Temprado, J.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation, Clim. Dynam., 56, 3581–3602, https://doi.org/10.1007/s00382-021-05657-4, 2021. a
Počakal, D., Večenaj, Ž., and Štalec, J.: Hail characteristics of different regions in continental part of Croatia based on influence of orography, Atmos. Res., 93, 516–525, https://doi.org/10.1016/j.atmosres.2008.10.017, 2009. a, b, c
Poolman, E.: Die voorspelling van haelkorrelgroei in Suid-Afrika (The forecasting of hail growth in South Africa), Master's thesis, University of Pretoria, 1992. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
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. 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, c
Reinhardt, T. and Seifert, A.: A three-category ice scheme for LMK, COMSO Newsletter, 6, 115–120, http://www.cosmo-model.org/content/model/documentation/newsLetters/newsLetter06/newsLetter_06.pdf (last access: 20 October 2023), 2006. a
Ritter, B. and Geleyn, J.-F.: A Comprehensive Radiation Scheme for Numerical Weather Prediction Models with Potential Applications in Climate Simulations, Mon. Weather Rev., 120, 303–325, https://doi.org/10.1175/1520-0493(1992)120<0303:acrsfn>2.0.co;2, 1992. a
Romps, D. M., Seeley, J. T., Vollaro, D., and Molinari, J.: Projected increase in lightning strikes in the united states due to global warming, Science, 346, 851–854, https://doi.org/10.1126/science.1259100, 2014. a
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-Scale Climate Models: Prospects and Challenges, B. Am. Meteorol. Soc., 101, E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020. a
Schemm, S., Nisi, L., Martinov, A., Leuenberger, D., and Martius, O.: On the link between cold fronts and hail in Switzerland, Atmos. Sci. Lett., 17, 315–325, https://doi.org/10.1002/asl.660, 2016. a
Schmid, W., Schiesser, H. H., and Waldvogel, A.: The Kinetic Energy of Hailfalls. Part IV: Patterns of Hailpad and Radar Data, J. Appl. Meteorol., 31, 1165–1178, https://doi.org/10.1175/1520-0450(1992)031<1165:tkeohp>2.0.co;2, 1992. a
Schumann, U.: Influence of Mesoscale Orography on Idealized Cold Fronts, J. Atmos. Sci., 44, 3423–3441, https://doi.org/10.1175/1520-0469(1987)044<3423:iomooi>2.0.co;2, 1987. a
Seeley, J. T. and Romps, D. M.: The Effect of Global Warming on Severe Thunderstorms in the United States, J. Climate, 28, 2443–2458, https://doi.org/10.1175/jcli-d-14-00382.1, 2015. a
Takahashi, T.: Riming Electrification as a Charge Generation Mechanism in Thunderstorms, J. Atmos. Sci., 35, 1536–1548, https://doi.org/10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2, 1978. a
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
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
Treloar, A.: Vertically integrated radar reflectivity as an indicator of hail size in the greater Sydney region of Australia, in: Proceedings of 19th Conference on Severe Local Storms, American Meteorological Society, Minneapolis, MN, 14–18 September 1998, 48–51, 1998. a
Vergara-Temprado, J., Ban, N., Panosetti, D., Schlemmer, L., and Schär, C.: Climate models permit convection at much coarser resolutions than previously considered, J. Climate, 33, 1915–1933, https://doi.org/10.1175/JCLI-D-19-0286.1, 2020. 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
Wernli, H., Paulat, M., Hagen, M., and Frei, C.: SAL – A novel quality measure for the verification of quantitative precipitation forecasts, Mon. Weather Rev., 136, 4470–4487, https://doi.org/10.1175/2008MWR2415.1, 2008. a, b
Wüest, M., Frei, C., Altenhoff, A., M.Hagen, Litschi, M., and Schär, C.: A gridded hourly precipitation dataset for Switzerland using rain-gauge analysis and radar-based disaggregation, Int. J. Climatol., 30, 1764–1775, 2010. a
Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., and del Carmen Llasat, M.: Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields, J. Geophys. Res., 115, D04205, https://doi.org/10.1029/2008jd010868, 2010. a, b
Zimmer, M., Craig, G. C., Keil, C., and Wernli, H.: Classification of precipitation events with a convective response timescale and their forecasting characteristics, Geophys. Res. Lett., 38, L05802, https://doi.org/10.1029/2010gl046199, 2011. a
Short summary
Our study focuses on severe convective storms that occur over the Alpine-Adriatic region. By running simulations for eight real cases and evaluating them against available observations, we found our models did a good job of simulating total precipitation, hail, and lightning. Overall, this research identified important meteorological factors for hail and lightning, and the results indicate that both HAILCAST and LPI diagnostics are promising candidates for future climate research.
Our study focuses on severe convective storms that occur over the Alpine-Adriatic region. By...
