Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4529-2024
https://doi.org/10.5194/amt-17-4529-2024
Research article
 | 
30 Jul 2024
Research article |  | 30 Jul 2024

Verification of weather-radar-based hail metrics with crowdsourced observations from Switzerland

Jérôme Kopp, Alessandro Hering, Urs Germann, and Olivia Martius

Related authors

Double-moment normalization of hail size number distributions over Switzerland
Alfonso Ferrone, Jérôme Kopp, Martin Lainer, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 17, 7143–7168, https://doi.org/10.5194/amt-17-7143-2024,https://doi.org/10.5194/amt-17-7143-2024, 2024
Short summary
Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024,https://doi.org/10.5194/amt-17-2539-2024, 2024
Short summary
How observations from automatic hail sensors in Switzerland shed light on local hailfall duration and compare with hailpad measurements
Jérôme Kopp, Agostino Manzato, Alessandro Hering, Urs Germann, and Olivia Martius
Atmos. Meas. Tech., 16, 3487–3503, https://doi.org/10.5194/amt-16-3487-2023,https://doi.org/10.5194/amt-16-3487-2023, 2023
Short summary
A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods
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
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Solar background radiation temperature calibration of a pure rotational Raman lidar
Vasura Jayaweera, Robert J. Sica, Giovanni Martucci, and Alexander Haefele
Atmos. Meas. Tech., 18, 1461–1469, https://doi.org/10.5194/amt-18-1461-2025,https://doi.org/10.5194/amt-18-1461-2025, 2025
Short summary
Exploring commercial Global Navigation Satellite System (GNSS) radio occultation (RO) products for planetary boundary layer studies in the Arctic
Manisha Ganeshan, Dong L. Wu, Joseph A. Santanello, Jie Gong, Chi Ao, Panagiotis Vergados, and Kevin J. Nelson
Atmos. Meas. Tech., 18, 1389–1403, https://doi.org/10.5194/amt-18-1389-2025,https://doi.org/10.5194/amt-18-1389-2025, 2025
Short summary
Research on atmospheric temperature fine measurements from the near surface to 60 km altitude based on an integrated lidar system
Zhangjun Wang, Tiantian Guo, Xianxin Li, Chao Chen, Dong Liu, Luoyuan Qu, Hui Li, and Xiufen Wang
Atmos. Meas. Tech., 18, 1405–1414, https://doi.org/10.5194/amt-18-1405-2025,https://doi.org/10.5194/amt-18-1405-2025, 2025
Short summary
Testing ground-based observations of wave activity in the (lower and upper) atmosphere as possible (complementary) indicators of streamer events
Michal Kozubek, Lisa Kuchelbacher, Jaroslav Chum, Tereza Sindelarova, Franziska Trinkl, and Katerina Podolska
Atmos. Meas. Tech., 18, 1373–1388, https://doi.org/10.5194/amt-18-1373-2025,https://doi.org/10.5194/amt-18-1373-2025, 2025
Short summary
Quality assessment of YUNYAO radio occultation data in the neutral atmosphere
Xiaoze Xu, Wei Han, Jincheng Wang, Zhiqiu Gao, Fenghui Li, Yan Cheng, and Naifeng Fu
Atmos. Meas. Tech., 18, 1339–1353, https://doi.org/10.5194/amt-18-1339-2025,https://doi.org/10.5194/amt-18-1339-2025, 2025
Short summary

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, 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, c, d
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. Clim., 52, 2328–2344, https://doi.org/10.1175/JAMC-D-12-0236.1, 2013. a
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, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. a, b
Download
Short summary
We present a verification of two products based on weather radars to detect the presence of hail and estimate its size.  Radar products are remote detection of hail, so they must be verified against ground-based observations. We use reports from users of the Swiss Weather Services phone app to do the verification. We found that the product estimating the presence of hail provides fair results but that it should be recalibrated and that estimating the hail size with radar is more challenging.
Share