Quantifying hail size distributions from the sky - Application of drone aerial photogrammetry
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/143889 |
| Acceso en línea: | http://hdl.handle.net/11336/143889 |
| Access Level: | acceso abierto |
| Palabra clave: | HAIL SIZE DISTRIBUTION PHOTOGRAMMETRY RELAMPAGO https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| Sumario: | 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 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. |
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