Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-747-2020
https://doi.org/10.5194/amt-13-747-2020
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

Viewed

Total article views: 4,483 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,607 821 55 4,483 59 53
  • HTML: 3,607
  • PDF: 821
  • XML: 55
  • Total: 4,483
  • BibTeX: 59
  • EndNote: 53
Views and downloads (calculated since 02 Sep 2019)
Cumulative views and downloads (calculated since 02 Sep 2019)

Viewed (geographical distribution)

Total article views: 4,483 (including HTML, PDF, and XML) Thereof 4,076 with geography defined and 407 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Mar 2024
Download
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.