Articles | Volume 24, issue 7
https://doi.org/10.5194/nhess-24-2331-2024
https://doi.org/10.5194/nhess-24-2331-2024
Research article
 | 
12 Jul 2024
Research article |  | 12 Jul 2024

Characterizing hail-prone environments using convection-permitting reanalysis and overshooting top detections over south-central Europe

Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, Valentina Pavan, Ines M. L. Cerenzia, and Silvana Di Sabatino

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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., Giammanco, I., Kumjian, M., 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, c, d, e, f, g
Allen, J. T. and Allen, E. R.: A review of severe thunderstorms in Australia, Atmos. Res., 178, 347–366, 2016. a
Allen, J. T., Tippett, M. K., and Sobel, A. H.: An empirical model relating US monthly hail occurrence to large-scale meteorological environment, J. Adv. Model. Earth Syst., 7, 226–243, 2015. a, b
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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.
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