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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 13, issue 2
Atmos. Meas. Tech., 13, 747–754, 2020
https://doi.org/10.5194/amt-13-747-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 747–754, 2020
https://doi.org/10.5194/amt-13-747-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 17 Feb 2020

Research article | 17 Feb 2020

Quantifying hail size distributions from the sky – application of drone aerial photogrammetry

Joshua S. Soderholm et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Mirena Feist-Polner on behalf of the Authors (09 Jan 2020)  Author's response    Manuscript
ED: Publish as is (10 Jan 2020) by Brian Kahn
AR by Joshua Soderholm on behalf of the Authors (17 Jan 2020)  Author's response    Manuscript
Publications Copernicus
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Short summary
Collecting measurements of hail size and shape is difficult due to the infrequent and dangerous nature of hailstorms. To improve upon this, a new technique called HailPixel is introduced for measuring hail using aerial imagery collected by a drone. A combination of machine learning and computer vision methods is used to extract the shape of thousands of hailstones from the aerial imagery. The improved statistics from the much larger HailPixel dataset show significant benefits.
Collecting measurements of hail size and shape is difficult due to the infrequent and dangerous...
Citation