Articles | Volume 10, issue 4
https://doi.org/10.5194/amt-10-1335-2017
https://doi.org/10.5194/amt-10-1335-2017
Research article
 | 
06 Apr 2017
Research article |  | 06 Apr 2017

Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

Christophe Praz, Yves-Alain Roulet, and Alexis Berne

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Cited articles

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Bernauer, F., Hürkamp, K., Rühm, W., and Tschiersch, J.: Snow event classification with a 2D video disdrometer – A decision tree approach, Atmos. Res., 172, 186–195, 2016.
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016.
Bishop, C. M.: Pattern Recognition and Machine Learning, Springer, 2006.
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Short summary
The Multi-Angle Snowflake Camera (MASC) provides high-resolution pictures of individual falling snowflakes and ice crystals. A method is proposed to automatically classify these pictures into six classes of snowflakes as well to estimate the degree of riming and to detect whether or not the particles are melting. Multinomial logistic regression is used with a manually classified reference set. The evaluation demonstrates the good and reliable performance of the proposed technique.