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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Cited articles

Bemis, S. P., Micklethwaite, S., Turner, D., James, M. R., Akciz, S., Thiele, S. T., and Bangash, H. A.: Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology, J. Struct. Geol., 69, 163–178,, 2014. a
Brown, T. M., Giammanco, I. M., and Kumjian, M. R.: IBHS Hail Field Research Program: 2012–2014, in: 27th Conference on Severe Local Storms, November, 2012–2014, American Meteorological Society, Madison, WI, 2014. a
Changnon, S. A., Changnon, D., Ray Fosse, E., Hoganson, D. C., Roth, R. J., and Totsch, J. M.: Effects of Recent Weather Extremes on the Insurance Industry: Major Implications for the Atmospheric Sciences, B. Am. Meteorol. Soc., 78, 425–435,<0425:EORWEO>2.0.CO;2, 1997. a
Cheng, H., Jiang, X., Sun, Y., and Wang, J.: Color image segmentation: advances and prospects, Pattern Recogn., 34, 2259–2281,, 2001. a
Cheng, L. and English, M.: A Relationship Between Hailstone Concentration and Size, J. Atmos. Sci., 40, 204–213,<0204:arbhca>;2, 1983. a
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.