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This is a Cohort Study study (also classified as: Quantitative Study).

October 1, 2025Open Access

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

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Prior snow water equivalent estimates exhibit significant variability, largely due to differences in cumulative snowfall.
Errors in snow water equivalent are primarily governed by bias from the various meteorological forcing datasets used.
A multi-forcing ensemble that integrates multiple meteorological inputs enhances prior snow water equivalent accuracy.
Assimilation of lidar estimates substantially reduces errors in snow water equivalent and enhances overall model reliability.

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This study assesses meteorological forcing datasets' impact on snow water equivalent, indicating improved accuracy and uncertainty characterization.

Sun et al. (2025) studied this question.

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This study assesses meteorological forcing datasets' impact on snow water equivalent, indicating improved accuracy and uncertainty characterization.