Solving Challenges of Assimilating Microwave Remote Sensing Signatures With a Physical Model to Estimate Snow Water Equivalent. Issue 11 (4th November 2021) - Merkouriadi, Ioanna; Lemmetyinen, Juha; Liston, Glen E.; Pulliainen, Jouni
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. Key Points: Simulated snow properties from SnowModel are used in microwave‐based snow water equivalent (SWE) retrievals by MEMLS3&a Biases in physically modeled SWE can induce larger biases in microwave‐based SWE retrievals The challenges can be mitigated when microwave algorithms account for the physical relationship of snow properties