Articles | Volume 17, issue 8
https://doi.org/10.5194/amt-17-2539-2024
© 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/amt-17-2539-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 May 2024
Research article |
| 02 May 2024
| 02 May 2024
Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground
Martin Lainer
CORRESPONDING AUTHOR
Federal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerland
Killian P. Brennan
Federal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerland
now at: Institute for Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland
Alessandro Hering
Federal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerland
Jérôme Kopp
Oeschger Centre for Climate Change Research, Bern, Switzerland
Institute of Geography, University of Bern, Bern, Switzerland
Samuel Monhart
CORRESPONDING AUTHOR
Federal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerland
Daniel Wolfensberger
Federal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerland
Urs Germann
Federal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerland
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Short summary
This study uses deep learning (the Mask R-CNN model) on drone-based photogrammetric data of hail on the ground to estimate hail size distributions (HSDs). Traditional hail sensors' limited areas complicate the full HSD retrieval. The HSD of a supercell event on 20 June 2021 is retrieved and contains > 18 000 hailstones. The HSD is compared to automatic hail sensor measurements and those of weather-radar-based MESHS. Investigations into ground hail melting are performed by five drone flights.
This study uses deep learning (the Mask R-CNN model) on drone-based photogrammetric data of hail...
