Articles | Volume 24, issue 7
https://doi.org/10.5194/nhess-24-2331-2024
© 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-24-2331-2024
© 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 Jul 2024
Research article |
| 12 Jul 2024
| 12 Jul 2024
Characterizing hail-prone environments using convection-permitting reanalysis and overshooting top detections over south-central Europe
Antonio Giordani
CORRESPONDING AUTHOR
Department of Physics and Astronomy (DIFA) “Augusto Righi”, University of Bologna, Bologna, Italy
ARPAE-SIMC Emilia Romagna, Bologna, Italy
Michael Kunz
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Kristopher M. Bedka
NASA Langley Research Center, Science Directorate, Climate Science Branch, Hampton, VA, USA
Heinz Jürgen Punge
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Tiziana Paccagnella
ARPAE-SIMC Emilia Romagna, Bologna, Italy
Valentina Pavan
ARPAE-SIMC Emilia Romagna, Bologna, Italy
Ines M. L. Cerenzia
ARPAE-SIMC Emilia Romagna, Bologna, Italy
Silvana Di Sabatino
Department of Physics and Astronomy (DIFA) “Augusto Righi”, University of Bologna, Bologna, Italy
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Cited articles
Adams-Selin, R. D. and Ziegler, C. L.: Forecasting hail using a one-dimensional hail growth model within WRF, Mon. Weather Rev., 144, 4919–4939, 2016. a
Adler, R. F., Markus, M. J., and Fenn, D. D.: Detection of severe Midwest thunderstorms using geosynchronous satellite data, Mon. Weather Rev., 113, 769–781, 1985. a
Allen, J. T. and Allen, E. R.: A review of severe thunderstorms in Australia, Atmos. Res., 178, 347–366, 2016. a
Baldi, M., Ciardini, V., Dalu, J. D., De Filippis, T., Maracchi, G., and Dalu, G.: Hail occurrence in Italy: Towards a national database and climatology, Atmos. Res., 138, 268–277, 2014. a
Bang, S. D. and Cecil, D. J.: Constructing a multifrequency passive microwave hail retrieval and climatology in the GPM domain, J. Appl. Meteorol. Clim., 58, 1889–1904, 2019. a
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, 2023. a, b
Bica, B., Knabl, T., Steinacker, R., Ratheiser, M., Dorninger, M., Lotteraner, C., Schneider, S., Chimani, B., Gepp, W., and Tschannett, S.: Thermally and dynamically induced pressure features over complex terrain from high-resolution analyses, J. Appl. Meteorol. Clim., 46, 50–65, 2007. a
Brimelow, J. C., Burrows, W. R., and Hanesiak, J. M.: The changing hail threat over North America in response to anthropogenic climate change, Nat. Clim. Change, 7, 516–522, 2017. a
Brooks, H. E.: Proximity soundings for severe convection for Europe and the United States from reanalysis data, Atmos. Res., 93, 546–553, 2009. 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, 73–94, 2003. a
Bryan, G. H., Wyngaard, J. C., and Fritsch, J. M.: Resolution requirements for the simulation of deep moist convection, Mon. Weather Rev., 131, 2394–2416, 2003. a
Cecil, D. J.: Passive microwave brightness temperatures as proxies for hailstorms, J. Appl. Meteorol. Clim., 48, 1281–1286, 2009. a
Cecil, D. J. and Blankenship, C. B.: Toward a global climatology of severe hailstorms as estimated by satellite passive microwave imagers, J. Climate, 25, 687–703, 2012. a
Cerenzia, I. M. L., Giordani, A., Paccagnella, T., and Montani, A.: Towards a convection-permitting regional reanalysis over the Italian domain, Meteorol. Appl., 29, e2092, https://doi.org/10.1002/met.2092, 2022. a, b
Changnon, S. A. and Burroughs, J.: The tristate hailstorm: The most costly on record, Mon. Weather Rev., 131, 1734–1739, 2003. a
Changnon Jr, S. A.: Hailstreaks, J. Atmos. Sci., 27, 109–125, 1970. 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, 2020. a
Chen, Y., Paschalis, A., Kendon, E., Kim, D., and Onof, C.: Changing spatial structure of summer heavy rainfall, using convection-permitting ensemble, Geophys. Res. Lett., 48, e2020GL090903, https://doi.org/10.1029/2020GL090903, 2021. a
Clark, A. J., Gao, J., Marsh, P. T., Smith, T., Kain, J. S., Correia Jr., J., Xue, M., and Kong, F.: Tornado pathlength forecasts from 2010 to 2011 using ensemble updraft helicity, Weather Forecast., 28, 387–407, 2013. a
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.: Convection-permitting models: a step-change in rainfall forecasting, Meteorol. Appl., 23, 165–181, 2016. a
Cooney, J. W., Bedka, K. M., Bowman, K. P., Khlopenkov, K. V., and Itterly, K.: Comparing Tropopause-Penetrating Convection Identifications Derived From NEXRAD and GOES Over the Contiguous United States, J. Geophys. Res.-Atmos., 126, e2020JD034319, https://doi.org/10.1029/2020JD034319, 2021. a, b, c
Davini, P., Bechini, R., Cremonini, R., and Cassardo, C.: Radar-based analysis of convective storms over northwestern Italy, Atmosphere, 3, 33–58, 2012. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, 2011. a
De Martin, F., Carlon, N., Pavan, F., Carpentari, S., Giazzi, M., Peressutti, G., Miglietta, M. M., and Davolio, S.: Toward a dedicated warning system of severe storms in Italy: the PRETEMP project, Poster presented at the 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-18, https://doi.org/10.5194/ecss2023-18, 2023. a
Ebert, E. E.: Fuzzy verification of high-resolution gridded forecasts: a review and proposed framework, Meteorol. Appl., 15, 51–64, 2008. a
Elliott, M. S., MacGorman, D. R., Schuur, T. J., and Heinselman, P. L.: An analysis of overshooting top lightning mapping array signatures in supercell thunderstorms, in: Proc. 22nd Int. Lightning Detection Conf., 2–3 April 2012, Broomfield, Colorado, USA, https://www.vaisala.com/sites/default/files/documents/An Analysis of Overshooting Top Lightning Mapping Array (last access: 10 July 2024), 2012. a, b
Flaounas, E., Davolio, S., Raveh-Rubin, S., Pantillon, F., Miglietta, M. M., Gaertner, M. A., Hatzaki, M., Homar, V., Khodayar, S., Korres, G., Kotroni, V., Kushta, J., Reale, M., and Ricard, D.: Mediterranean cyclones: current knowledge and open questions on dynamics, prediction, climatology and impacts, Weather Clim. Dynam., 3, 173–208, https://doi.org/10.5194/wcd-3-173-2022, 2022. 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
Fujita, T. T.: Memoirs of an Effort to Unlock the Mystery of Severe Storms: During the 50 Years, 1942–1992, University of Chicago, https://cir.nii.ac.jp/crid/1130572417839208210 (last access: 10 July 2024), 1992. a
Gagne, D. J., McGovern, A., Haupt, S. E., Sobash, R. A., Williams, J. K., and Xue, M.: Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles, Weather Forecast., 32, 1819–1840, 2017. a
Gascón, E., Merino, A., Sánchez, J., Fernández-González, S., García-Ortega, E., López, L., and Hermida, L.: Spatial distribution of thermodynamic conditions of severe storms in southwestern Europe, Atmos. Res., 164, 194–209, 2015. a
Gensini, V. A., Converse, C., Ashley, W. S., and Taszarek, M.: Machine learning classification of significant tornadoes and hail in the United States using ERA5 proximity soundings, Weather Forecast., 36, 2143–2160, 2021. a
Giordani, A.: hail-analysis, GitHub [code], https://github.com/agiord/hail-analysis (last access: 10 July 2024), 2024. a
Giordani, A., Cerenzia, I. M. L., Paccagnella, T., and Di Sabatino, S.: SPHERA, a new convection-permitting regional reanalysis over Italy: Improving the description of heavy rainfall, Q. J. Roy. Meteorol. Soc., 149, 781–808, https://doi.org/10.1002/qj.4428, 2023. a, b
Giovannini, L., Davolio, S., Zaramella, M., Zardi, D., and Borga, M.: Multi-model convection-resolving simulations of the October 2018 Vaia storm over Northeastern Italy, Atmos. Res., 253, 105455, https://doi.org/10.1016/j.atmosres.2021.105455, 2021. a
Gobbo, S., Ghiraldini, A., Dramis, A., Dal Ferro, N., and Morari, F.: Estimation of hail damage using crop models and remote sensing, Remote Sens., 13, 2655, https://doi.org/10.3390/rs13142655, 2021. a
Groenemeijer, P., Púčik, T., Holzer, A. M., Antonescu, B., Riemann-Campe, K., Schultz, D. M., Kühne, T., Feuerstein, B., Brooks, H. E., Doswell III, C. A., Koppert, H. J., and Sausen, R.: Severe convective storms in Europe: Ten years of research and education at the European Severe Storms Laboratory, B. Am. Meteorol. Soc., 98, 2641–2651, 2017. a
Gunturi, P. and Tippett, M.: Managing severe thunderstorm risk: Impact of ENSO on US tornado and hail frequencies, Technical Rep., WillisRe, Minneapolis, https://www.columbia.edu/~mkt14/files/WillisRe_Impact_of_ENSO_on_US_Tornado_and_Hail_frequencies_Final.pdf (last access: 10 July 2024), 2017. 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.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, 2020. a
Hitchens, N. M. and Brooks, H. E.: Evaluation of the Storm Prediction Center's convective outlooks from day 3 through day 1, Weather Forecast., 29, 1134–1142, 2014. a
Hohl, R., Schiesser, H.-H., and Knepper, I.: The use of weather radars to estimate hail damage to automobiles: an exploratory study in Switzerland, Atmos. Res., 61, 215–238, 2002. 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, 2017. a
Houze Jr., R. A.: Cloud dynamics, Academic Press, https://doi.org/10.1016/B978-0-12-374266-7.09984-4, 2014. 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, b, c, d
Johns, R. H. and Doswell III, C. A.: Severe local storms forecasting, Weather Forecast., 7, 588–612, 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, 2014. a
Jurković, P. M., Mahović, N. S., and Počakal, D.: Lightning, overshooting top and hail characteristics for strong convective storms in Central Europe, Atmos. Res., 161, 153–168, 2015. a
Knight, C. A. and Knight, N. C.: Hailstorms, in: Severe convective storms, Springer, 223–254, https://doi.org/10.1007/978-1-935704-06-5_6, 2001. 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, 2022. a
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, 2020. a
Kunz, M.: The skill of convective parameters and indices to predict isolated and severe thunderstorms, Nat. Hazards Earth Syst. Sci., 7, 327–342, https://doi.org/10.5194/nhess-7-327-2007, 2007. a, b
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, 2018. a
Kunz, M., Wandel, J., Fluck, E., Baumstark, S., Mohr, S., and Schemm, S.: Ambient conditions prevailing during hail events in central Europe, Nat. Hazards Earth Syst. Sci., 20, 1867–1887, https://doi.org/10.5194/nhess-20-1867-2020, 2020. a, b, c
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, 2019. a
Laviola, S., Levizzani, V., Ferraro, R. R., and Beauchamp, J.: Hailstorm detection by satellite microwave radiometers, Remote Sens., 12, 621 https://doi.org/10.3390/rs12040621, 2020. a
Li, M., Zhang, D.-L., Sun, J., and Zhang, Q.: A statistical analysis of hail events and their environmental conditions in China during 2008–15, J. Appl. Meteorol. Clim., 57, 2817–2833, 2018. a
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F., Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis, D., Gutmann, E., Kurkute, S., Li, Y., Thompson G., and Yates, D.: Continental-scale convection-permitting modeling of the current and future climate of North America, Clim. Dynam., 49, 71–95, 2017. a
Löffler-Mang, M., Schön, D., and Landry, M.: Characteristics of a new automatic hail recorder, Atmos. Res., 100, 439–446, 2011. a
Lupo, K. M., Torn, R. D., and Yang, S.-C.: Evaluation of stochastic perturbed parameterization tendencies on convective-permitting ensemble forecasts of heavy rainfall events in New York and Taiwan, Weather Forecast., 35, 5–24, 2020. a
Malečić, B., Cui, R., Demory, M.-E., Horvath, K., Jelic, D., Schär, C., Prtenjak, M. T., Velasquez, P., and Ban, N.: Simulating Hail and Lightning over the Alpine Adriatic Region – A model Intercomparison Study, Authorea Preprints, https://doi.org/10.1029/2022JD037989, 2022. a
Manzato, A.: Hail in northeast Italy: Climatology and bivariate analysis with the sounding-derived indices, J. Appl. Meteorol. Clim., 51, 449–467, 2012. a
Manzato, A., Riva, V., Tiesi, A., and Marcello Miglietta, M.: Observational analysis and simulations of a severe hailstorm in northeastern Italy, Q. J. Roy. Meteorol. Soc., 146, 3587–3611, 2020. 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, 1779–1795, 2022a. 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, 2022b. a, b
Marcos, J., Sánchez, J., Merino, A., Melcón, P., Mérida, G., and García-Ortega, E.: Spatial and temporal variability of hail falls and estimation of maximum diameter from meteorological variables, Atmos. Res., 247, 105142, https://doi.org/10.1016/j.atmosres.2020.105142, 2021. a
Marion, G., Trapp, R. J., and Nesbitt, S. W.: Using overshooting top area to discriminate potential for large, intense tornadoes, Geophys. Res. Lett., 46, 12520–12526, 2019. a
Markowski, P. and Richardson, Y.: Mesoscale meteorology in midlatitudes, in: Vol. 2, John Wiley & Sons, https://doi.org/10.1002/9780470682104, 2011. a, b
Marsigli, C., Ebert, E., Ashrit, R., Casati, B., Chen, J., Coelho, C. A. S., Dorninger, M., Gilleland, E., Haiden, T., Landman, S., and Mittermaier, M.: Review article: Observations for high-impact weather and their use in verification, Nat. Hazards Earth Syst. Sci., 21, 1297–1312, https://doi.org/10.5194/nhess-21-1297-2021, 2021. a
Mecikalski, J. R., Sandmæl, T. N., Murillo, E. M., Homeyer, C. R., Bedka, K. M., Apke, J. M., and Jewett, C. P.: A random-forest model to assess predictor importance and nowcast severe storms using high-resolution radar–GOES satellite-lightning observations, Mon. Weather Rev., 149, 1725–1746, 2021. a, b
Melcón, P., Merino, A., Sánchez, J. L., López, L., and Hermida, L.: Satellite remote sensing of hailstorms in France, Atmos. Res., 182, 221–231, 2016. a
Mikuš, P., Prtenjak, M. T., and Mahović, N. S.: Analysis of the convective activity and its synoptic background over Croatia, Atmos. Res., 104, 139–153, 2012. 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, 2015. a
Morel, C. and Senesi, S.: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology, Q. J. Roy. Meteorol. Soc., 128, 1953–1971, 2002. a
Murillo, E. M. and Homeyer, C. R.: Severe hail fall and hailstorm detection using remote sensing observations, J. Appl. Meteorol. Clim., 58, 947–970, 2019. a
Nisi, L., Martius, O., Hering, A., Kunz, M., and Germann, U.: Spatial and temporal distribution of hailstorms in the Alpine region: a long-term, high resolution, radar-based analysis, Q. J. Roy. Meteorol. Soc., 142, 1590–1604, 2016. 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, 2018. a
Ortega, K. L.: Evaluating multi-radar, multi-sensor products for surface hailfall diagnosis, E-J. Sever. Storms Meteorol., 13, 1–36, https://doi.org/10.55599/ejssm.v13i1.69, 2018. a
Palencia, C., Giaiotti, D., Stel, F., Castro, A., and Fraile, R.: Maximum hailstone size: Relationship with meteorological variables, Atmos. Res., 96, 256–265, 2010. a
Paterson, D. A. and Sankaran, R.: Hail impact on building envelopes, J. Wind Eng. Indust. Aerodynam., 53, 229–246, 1994. 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 L.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, 2015. a, b
Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P., and Holland, G. J.: The future intensification of hourly precipitation extremes, Nat. Clim. Change, 7, 48–52, 2017. a
Proud, S. R.: Analysis of overshooting top detections by Meteosat Second Generation: a 5-year dataset, Q. J. Roy. Meteorol. Soc., 141, 909–915, 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, 2017. a
Púčik, T., Groenemeijer, P., Taszarek, M., and Battaglioli, F.: Pre-storm environments and storm-scale properties of the major hailstorms of 2021 and 2022 in Europe, in: 11th European Conference on Severe Storms, 8–12 May 2023, Bucharest, Romania, ECSS2023-124, https://doi.org/10.5194/ecss2023-124, 2023. 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, b, c, d
Puskeiler, M., Kunz, M., and Schmidberger, M.: Hail statistics for Germany derived from single-polarization radar data, Atmos. Res., 178, 459–470, 2016. a
Rädler, A. T., Groenemeijer, P., Faust, E., and Sausen, R.: Detecting severe weather trends using an additive regressive convective hazard model (AR-CHaMo), J. Appl. Meteorol. Clim., 57, 569–587, 2018. a
Reynolds, D. W.: Observations of damaging hailstorms from geosynchronous satellite digital data, Mon. Weather Rev., 108, 337–348, 1980. a
Riemann-Campe, K., Fraedrich, K., and Lunkeit, F.: Global climatology of convective available potential energy (CAPE) and convective inhibition (CIN) in ERA-40 reanalysis, Atmos. Res., 93, 534–545, 2009. a
Sánchez, J., Gil-Robles, B., Dessens, J., Martin, E., Lopez, L., Marcos, J., Berthet, C., Fernández, J., and García-Ortega, E.: Characterization of hailstone size spectra in hailpad networks in France, Spain, and Argentina, Atmos. Res., 93, 641–654, 2009. a
Sartori, G.: Eventi meteorologici estremi, Dati e valutazioni sulla radicalizzazione del clima in Veneto, Rapporto Tecnico, Consiglio Regionale del Veneto, https://www.yumpu.com/it/document/view/15065933/eventi-meteorologici-estremi-consiglio-regionale-veneto (last access: 10 July 2024), 2012. a
Schättler, U., Doms, G., and Schraff, C.: A description of the nonhydrostatic regional COSMO-model. Part VII: user's guide, Rep. COSMO-Model, Deutscher Wetterdienst, p. 195, https://doi.org/10.5676/DWD_pub/nwv/cosmo-doc_6.00_VII, 2018. a
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to Meteosat Second Generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, 2002. a
Seneviratne, S., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S., Wehner, M., and Zhou, B.: Weather and Climate Extreme Events in a Changing Climate, in: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021. a
Setvák, M., Bedka, K., Lindsey, D. T., Sokol, A., Charvát, Z., Št'ástka, J., and Wang, P. K.: A-Train observations of deep convective storm tops, Atmos. Res., 123, 229–248, 2013. a
Sherwood, S. C., Chae, J.-H., Minnis, P., and McGill, M.: Underestimation of deep convective cloud tops by thermal imagery, Geophys. Res. Lett., 31, L11102, https://doi.org/10.1029/2004GL019699, 2004. a
Svabik, O., Meyer, V., Tüchler, L., and Zenkl, G.: Hail risk areas in Austria, on the basis of reports 1971–2011 and weather radar images 2002–2011, in: 7th European Conference on Severe Storms, 3–7 June 2013, Helsinki, Finland, p. 2, https://www.essl.org/ECSS/2013/programme/abstracts/50.pdf (last access: 10 July 2024), 2013. a
Tang, B. H., Gensini, V. A., and Homeyer, C. R.: Trends in United States large hail environments and observations, NPJ Clim. Atmos. Sci., 2, 1–7, 2019. a
Taszarek, M., Brooks, H. E., Czernecki, B., Szuster, P., and Fortuniak, K.: Climatological aspects of convective parameters over Europe: A comparison of ERA-Interim and sounding data, J. Climate, 31, 4281–4308, 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, 2003. a
Tiesi, A., Mazzà, S., Conte, D., Ricchi, A., Baldini, L., Montopoli, M., Picciotti, E., Vulpiani, G., Ferretti, R., and Miglietta, M. M.: Numerical simulation of a Giant-hail-bearing Mediterranean supercell in the Adriatic Sea, Atmosphere, 13, 1219, https://doi.org/10.3390/atmos13081219, 2022. a
Tippett, M. K., Sobel, A. H., Camargo, S. J., and Allen, J. T.: An empirical relation between US tornado activity and monthly environmental parameters, J. Climate, 27, 2983–2999, 2014. a
Torralba, V., Hénin, R., Cantelli, A., Scoccimarro, E., Materia, S., Manzato, A., and Gualdi, S.: Modelling hail hazard over Italy with ERA5 large-scale variables, Weather Clim. Extrem., 39, 100535, https://doi.org/10.1016/j.wace.2022.100535, 2023. a, b, c, d
Trapp, R. J. and Hoogewind, K. A.: The realization of extreme tornadic storm events under future anthropogenic climate change, J. Climate, 29, 5251–5265, 2016. a
Trapp, R. J., Diffenbaugh, N. S., Brooks, H. E., Baldwin, M. E., Robinson, E. D., and Pal, J. S.: Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing, P. Natl. Acad. Sci. USA, 104, 19719–19723, 2007. a
Wellmann, C., Barrett, A. I., Johnson, J. S., Kunz, M., Vogel, B., Carslaw, K. S., and Hoose, C.: Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail, Atmos. Chem. Phys., 20, 2201–2219, https://doi.org/10.5194/acp-20-2201-2020, 2020. 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, 2021. a
Wilhelmson, R. B. and Wicker, L. J.: Numerical modeling of severe local storms, in: Severe convective storms, Springer, 123–166, https://doi.org/10.1007/978-1-935704-06-5_4, 2001. a
Wu, C.-M. and Arakawa, A.: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part II, J. Atmos. Sci., 71, 2089–2103, 2014. a
Xie, B., Zhang, Q., and Wang, Y.: Trends in hail in China during 1960–2005, Geophys. Res. Lett., 35, L13801, https://doi.org/10.1029/2008GL034067, 2008. a
Yeo, S., Leigh, R., and Kuhne, I.: The April 1999 Sydney Hailstorm, Aust. J. Emerg. Manage., 14, 23–25, 1999. a
Zhou, J., Pavek, M. J., Shelton, S. C., Holden, Z. J., and Sankaran, S.: Aerial multispectral imaging for crop hail damage assessment in potato, Comput. Elect. Agricult., 127, 406–412, 2016. a
Short summary
To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports is tested and presented. Hail likelihood peaks in mid-summer at 15:00 UTC over northern Italy and shows improved agreement with observations compared to previous estimates. By separating ambient signatures based on hail severity, enhanced appropriateness for large-hail occurrence is found.
To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency...
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