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Quantifying hail size distributions from the sky – application of drone aerial photogrammetry
| Content Provider | Semantic Scholar |
|---|---|
| Author | Soderholm, Joshua Kumjian, Matthew R. McCarthy, Nicholas Maldonado, Paula Acosta Wang, Minzheng |
| Copyright Year | 2019 |
| 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. 10000 or more hailstones) than population sizes from existing sensors (e.g. a hail pad). Comparison with a co-located hail pad for an Argentinian 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 hail fall 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. |
| File Format | PDF HTM / HTML |
| DOI | 10.5194/amt-2019-281 |
| Alternate Webpage(s) | https://www.atmos-meas-tech.net/13/747/2020/amt-13-747-2020.pdf |
| Alternate Webpage(s) | https://www.atmos-meas-tech-discuss.net/amt-2019-281/amt-2019-281.pdf |
| Alternate Webpage(s) | https://www.atmos-meas-tech-discuss.net/amt-2019-281/amt-2019-281-RC1-print.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
