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• W3207046961 abstract "Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave-based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point-based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE-and of corresponding changes to other snowpack properties-to the microwave-based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships." @default.
• W3207046961 created "2021-10-25" @default.
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• W3207046961 date "2021-11-01" @default.
• W3207046961 modified "2023-10-16" @default.
• W3207046961 title "Solving Challenges of Assimilating Microwave Remote Sensing Signatures With a Physical Model to Estimate Snow Water Equivalent" @default.
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• W3207046961 doi "https://doi.org/10.1029/2021wr030119" @default.
• W3207046961 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8597594" @default.
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• W3207046961 hasPublicationYear "2021" @default.
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