Articles | Volume 17, issue 22
https://doi.org/10.5194/amt-17-6707-2024
https://doi.org/10.5194/amt-17-6707-2024
Research article
 | 
26 Nov 2024
Research article |  | 26 Nov 2024

Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks

Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka

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Cited articles

Ackermann, L., Soderholm, J., Protat, A., Whitley, R., Ye, L., and Ridder, N.: Radar and environment-based hail damage estimates using machine learning, Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024, 2024. a, b
Al-Sakka, H., Boumahmoud, A.-A., Fradon, B., Frasier, S. J., and Tabary, P.: A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars, J. Appl. Meteorol. Climatol., 52, 2328–2344, https://doi.org/10.1175/JAMC-D-12-0236.1, 2013. a, b, c, d, e, f, g, h, i
Amburn, S. A. and Wolf, P. L.: VIL Density as a Hail Indicator, Weather Forecast., 12, 473–478, https://doi.org/10.1175/1520-0434(1997)012<0473:VDAAHI>2.0.CO;2, 1997. a
ANELFA: ANELFA hailpad database, ANELFA, https://www.anelfa.asso.fr/-Reseaux-.html (last access: 18 November 2024), 2024. a
Barnes, S. L.: A Technique for Maximizing Details in Numerical Weather Map Analysis, J. Appl. Meteorol. Climatol., 3, 396–409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2, 1964. a
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Short summary
This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
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