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

Soderholm, Joshua S.; Kumjian, Matthew R.; McCarthy, Nicholas; Maldonado, Paula; Wang, Minzheng

doi:https://doi.org/10.5194/amt-13-747-2020

Articles | Volume 13, issue 2

https://doi.org/10.5194/amt-13-747-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-747-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 2

Research article

| Highlight paper

|

17 Feb 2020

Research article | Highlight paper |

| 17 Feb 2020

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

Joshua S. Soderholm, Matthew R. Kumjian, Nicholas McCarthy, Paula Maldonado, and Minzheng Wang

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Final revised paper (published on 17 Feb 2020)
Preprint (discussion started on 02 Sep 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of Quantifying Hail Size Distributions from the Sky', Anonymous Referee #2, 19 Sep 2019
- AC1: 'Reply to RC1', Joshua Soderholm, 29 Oct 2019
RC2: 'Review of amt-2019-281', Tomeu Rigo, 11 Dec 2019
- AC2: 'Reply to RC2', Joshua Soderholm, 06 Jan 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Joshua Soderholm on behalf of the Authors (06 Jan 2020)

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

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.