The Cryosphere
The Cryosphere
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Articles | Volume 19, issue 10
Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4759-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-4759-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 10
Research article
 | 
21 Oct 2025
Research article |  | 21 Oct 2025

Learning to filter: snow data assimilation using a Long Short-Term Memory network

Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris

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Short summary
Reliable snow water equivalent and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we propose a Long Short-Term Memory neural network for operational use in snow hydrology. Once trained, it reduces computational cost by 70 percent compared to the Ensemble Kalman Filter, with a slight decrease in performances. This deep learning approach provides a scalable, efficient, and cost-effective modeling solution for hydrology.
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