Articles | Volume 20, issue 6
https://doi.org/10.5194/tc-20-3345-2026
https://doi.org/10.5194/tc-20-3345-2026
Research article
 | 
10 Jun 2026
Research article |  | 10 Jun 2026

Assessing the impact of meteorological forcing and its uncertainty on snow modeling and reanalysis

Haorui Sun and Steven A. Margulis

Data sets

Reanalysis Output and ASO Verification Data for Assessing the Impact of Meteorological Forcing and Its Uncertainty on Snow Modeling and Reanalysis (1.0.0) H. Sun https://doi.org/10.5281/zenodo.20533477

Model code and software

sunhaorui/meteorological-forcing-impact-analysis: Initial Code Release (v1.0.0) H. Sun https://doi.org/10.5281/zenodo.20546684

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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.
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