Articles | Volume 20, issue 6
https://doi.org/10.5194/tc-20-3345-2026
© 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-20-3345-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jun 2026
Research article |
| 10 Jun 2026
| 10 Jun 2026
Assessing the impact of meteorological forcing and its uncertainty on snow modeling and reanalysis
Haorui Sun
Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
Steven A. Margulis
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
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Short summary
Estimating Snow Water Equivalent (SWE) has large uncertainties from meteorological data, with no single dataset being universally superior. Our multi-forcing approach, which combines datasets, yields more accurate SWE estimates than single-forcing methods by mitigating bias. Even after data assimilation corrects for prior errors, the multi-forcing ensemble improves accuracy and uncertainty characterization, offering a more robust and reliable strategy for water resource management.
Estimating Snow Water Equivalent (SWE) has large uncertainties from meteorological data, with no...
