The Cryosphere
The Cryosphere
TC
Articles | Volume 20, issue 4
Articles | Volume 20, issue 4
https://doi.org/10.5194/tc-20-2295-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-2295-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 4
Research article
 | 
21 Apr 2026
Research article |  | 21 Apr 2026

Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning

Gabiel Stachura and Zbigniew Ustrnul

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Short summary
Reanalyses still struggle to accurately estimate snow depth, mostly because their horizontal resolution is beyond the spatial scale of snow variability. A comparison of two Copernicus reanalyses ERA5 and ERA5-Land reveals systematic errors and highlights the importance of data assimilation. A Random Forests model is able to reduce the systematic error by around a half. Spatial downscaling in complex terrain reflects mainly elevation dependence but also shadowing effect of surrounding topography.
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