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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-281
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-281
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Sep 2019

Submitted as: research article | 02 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Quantifying Hail Size Distributions from the Sky: Application of Drone Aerial Photogrammetry

Joshua S. Soderholm1, Matthew R. Kumjian2, Nicholas McCarthy3, Paula Maldonado4, and Minzheng Wang5 Joshua S. Soderholm et al.
  • 1University of Bonn, Meteorological Institute, Germany
  • 2The Pennsylvania State University, Department of Meteorology and Atmospheric Science, USA
  • 3The University of Queensland, Australia
  • 4University of Buenos Aires, Argentina
  • 5Northraine PTY. LTD.

Abstract. A new technique, named "HailPixel," is introduced for measuring the maximum dimension and intermediate dimension of hailstones from aerial imagery. The photogrammetry procedure applies a convolutional neural network for robust detection of hailstones against complex backgrounds and an edge detection method for measuring the shape of identified hailstones. This semi-automated technique is capable of measuring many thousands of hailstones within a single survey, which is several orders of magnitude larger (e.g., 10 000 or more hailstones) than population sizes from existing sensors (e.g., a hail pad). Comparison with a co-located hail pad for an Argentinan hailstorm event during the RELAMPAGO project demonstrates the larger population size of the HailPixel survey significantly improves the shape and tails of the observed hail size distribution. When hailfall is sparse, such as during large and giant hail events, the large survey area of this technique is especially advantageous for resolving the hail size distribution.

Joshua S. Soderholm et al.
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Joshua S. Soderholm et al.
Data sets

HailPixel Survey Data and Analysis from 26 November 2018, San Rafael, Argentina J. Soderholm https://doi.org/10.5281/zenodo.3383227

Joshua S. Soderholm et al.
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Latest update: 13 Nov 2019
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Short summary
Collecting measurements of hail size and shape are 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 are used to extract the shape of thousands of hailstones from the aerial imagery. The improved statistics from the much larger HailPixel dataset shows significant benefit.
Collecting measurements of hail size and shape are difficult due to the infrequent and dangerous...
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