Articles | Volume 12, issue 1
https://doi.org/10.5194/tc-12-247-2018
© Author(s) 2018. 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-12-247-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jan 2018
Research article |
| 23 Jan 2018
| 23 Jan 2018
Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites
Kristoffer Aalstad
CORRESPONDING AUTHOR
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Sebastian Westermann
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Thomas Vikhamar Schuler
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Julia Boike
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Telegrafenberg A43, 14473 Potsdam, Germany
Laurent Bertino
Nansen Environmental and
Remote Sensing Center, Thormøhlens gate 47, Bergen 5006, Norway
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- Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment E. Alonso-González et al. 10.5194/tc-17-3329-2023
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- Impact of forcing data and land surface properties on snow simulation in a regional climate model: a case study over the Tianshan Mountains, Central Asia Q. Li et al. 10.1007/s11629-020-6621-2
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- Operational snow-hydrological modeling for Switzerland R. Mott et al. 10.3389/feart.2023.1228158
- The state and future of the cryosphere in Central Asia M. Barandun et al. 10.1016/j.wasec.2020.100072
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
- Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models B. Cluzet et al. 10.5194/tc-18-5753-2024
- The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere S. Westermann et al. 10.5194/gmd-16-2607-2023
- Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay M. Kronenberg et al. 10.5194/tc-16-5001-2022
- Simulating the effect of subsurface drainage on the thermal regime and ground ice in blocky terrain in Norway C. Renette et al. 10.5194/esurf-11-33-2023
- Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering J. Fiddes et al. 10.5194/hess-23-4717-2019
- Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection C. Tso et al. 10.1016/j.jconhyd.2020.103679
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al. 10.3389/feart.2020.00325
- Country-scale spatio-temporal monitoring of soil erosion in Iran using the G2 model S. Mohammadi et al. 10.1080/17538947.2021.1919230
- Understanding wind-driven melt of patchy snow cover L. van der Valk et al. 10.5194/tc-16-4319-2022
- A Downscaling Intercomparison Study: The Representation of Slope- and Ridge-Scale Processes in Models of Different Complexity B. Kruyt et al. 10.3389/feart.2022.789332
- Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco M. Baba et al. 10.3390/rs10121982
- Reconciling Svalbard Glacier Mass Balance T. Schuler et al. 10.3389/feart.2020.00156
- Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model J. Hou et al. 10.1029/2020WR029010
- Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography K. Aalstad et al. 10.1016/j.rse.2019.111618
- Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
- Enhancing SWAT’s snow module for multivariate Elevation-dependent snow and streamflow data assimilation M. Bayat et al. 10.1016/j.jhydrol.2025.133153
- Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations B. Cluzet et al. 10.1016/j.coldregions.2019.102918
- An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook M. Taheri & A. Mohammadian 10.3390/su141811395
- Inferring surface energy fluxes using drone data assimilation in large eddy simulations N. Pirk et al. 10.5194/amt-15-7293-2022
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Latest update: 13 Jul 2025
Short summary
We demonstrate how snow cover data from satellites can be used to constrain estimates of snow distributions at sites in the Arctic. In this effort, we make use of data assimilation to combine the information contained in the snow cover data with a simple snow model. By comparing our snow distribution estimates to independent observations, we find that this method performs favorably. Being modular, this method could be applied to other areas as a component of a larger reanalysis system.
We demonstrate how snow cover data from satellites can be used to constrain estimates of snow...
