Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1829-2025
https://doi.org/10.5194/gmd-18-1829-2025
Development and technical paper
 | 
17 Mar 2025
Development and technical paper |  | 17 Mar 2025

Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)

Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer

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Cited articles

Avanzi, F., De Michele, C., Ghezzi, A., Jommi, C., and Pepe, M.: A processing-modeling routine to use SNOTEL hourly data in snowpack dynamic models, Adv. Water Resour., 73, 16–29, https://doi.org/10.1016/j.advwatres.2014.06.011, 2014. a
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Blandini, G., Avanzi, F., Gabellani, S., Ponziani, D., Stevenin, H., Ratto, S., Ferraris, L., and Viglione, A.: A random forest approach to quality-checking automatic snow-depth sensor measurements, The Cryosphere, 17, 5317–5333, https://doi.org/10.5194/tc-17-5317-2023, 2023. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010950718922, 2001. a
Cho, K., van Merriënboer, B., Bahdanau, D., and Bengio, Y.: On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, in: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 103–111, https://doi.org/10.3115/v1/W14-4012, 2014. a
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Short summary
Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
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