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.

中文翻译：

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用物理模型解决同化微波遥感特征以估计雪水当量的挑战

在过去的几十年中，季节性雪水当量 (SWE) 的全球监测取得了显着进展。然而，从被动和主动微波特征估计 SWE 时仍然存在挑战，因为 SWE 检索需要雪特性的先验表征。数值实验表明，利用物理雪模型获取积雪特征可以潜在地改进基于微波的 SWE 反演。本研究旨在确定用物理雪模型同化主动和被动微波特征的挑战，并研究应对这些挑战的解决方案。在基于点的研究观察的指导下，我们设计了一个敏感性实验来量化物理建模的 SWE 变化以及其他积雪特性的相应变化对基于微波的 SWE 反演的影响。结果表明，用物理雪模型同化微波特征面临着与 SWE 和雪微结构之间的物理关系相关的一些关键挑战。我们证明如果微波算法考虑到这些关系，这些挑战是可以克服的。