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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2026
Research article |
| 21 Apr 2026
| 21 Apr 2026
Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, 31-007, Poland
Department of Climatology, Institute of Geography and Spatial Management, Jagiellonian University, Krakow, 31-007 Poland
Institute of Meteorology and Water Management – National Research Institute, Krakow, 30-215, Poland
Zbigniew Ustrnul
Department of Climatology, Institute of Geography and Spatial Management, Jagiellonian University, Krakow, 31-007 Poland
Institute of Meteorology and Water Management – National Research Institute, Krakow, 30-215, Poland
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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.
Reanalyses still struggle to accurately estimate snow depth, mostly because their horizontal...
