The Cryosphere
The Cryosphere
TC
Articles | Volume 20, issue 1
Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-737-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-737-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 1
Research article
 | 
28 Jan 2026
Research article |  | 28 Jan 2026

Ensemble-based snow depth data assimilation for a multi-layer snow scheme over the European Arctic

Åsmund Bakketun, Jostein Blyverket, and Malte Müller

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Aas, K. S., Gisnås, K., Westermann, S., and Berntsen, T. K.: A Tiling Approach to Represent Subgrid Snow Variability in Coupled Land Surface–Atmosphere Models, Journal of Hydrometeorology, 18, https://doi.org/10.1175/JHM-D-16-0026.1, 2017. a
Alonso-González, E., Gascoin, S., Arioli, S., and Picard, G.: Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment, The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, 2023. a, b
Arduini, G., Balsamo, G., Dutra, E., Day, J. J., Sandu, I., Boussetta, S., and Haiden, T.: Impact of a Multi-Layer Snow Scheme on Near-Surface Weather Forecasts, Journal of Advances in Modeling Earth Systems, 11, 4687–4710, https://doi.org/10.1029/2019MS001725, 2019. a
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K.-I., Lenderink, G., Niemelä, S., Nielsen, K. P., Onvlee, J., Rontu, L., Samuelsson, P., Muñoz, D. S., Subias, A., Tijm, S., Toll, V., Yang, X., and Køltzow, M. Ø.: The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System, Monthly Weather Review, 145, 1919–1935, https://doi.org/10.1175/MWR-D-16-0417.1, 2017. a
Blyverket, J., Hamer, P. D., Bertino, L., Albergel, C., Fairbairn, D., and Lahoz, W. A.: An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US, Remote Sensing, 11, 478, https://doi.org/10.3390/rs11050478, 2019. a
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Obtaining accurate estimates of seasonal snow conditions requires a combination of observations and numerical models. We use a model accounting for the vertical structure of the snow, and a data assimilation method representing varying uncertainty of the model in time and space. Compared to existing products, neglecting these considerations, our system produced improved estimates of seasonal snow conditions. Snow mass estimates suggest a potential impact on derived hydrological applications.
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