Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments

• Published in: The Cryosphere
• Main Authors: Cho, Eunsang, Vuyovich, Carrie M., Kumar, Sujay V., Wrzesien, Melissa L., Kim, Rhae Sung
• Format: Text
• Language: English
• Published: 2023
• Subjects: Tundra
• Online Access: https://doi.org/10.5194/tc-17-3915-2023; https://tc.copernicus.org/articles/17/3915/2023/

Satellite-based synthetic aperture radar (SAR) sensors have the potential to provide the first global measure of snow water equivalent (SWE), with key advantages compared to existing satellite observations (e.g., passive microwave sensors) such as high spatial resolution and capability in mountainous areas. While recent studies have shown some capability in challenging conditions, such as deep snow and forested areas, there is still work to be done to understand the limitations and benefits of these observations in an assimilation system. In this study, we develop an observing system simulation experiment (OSSE) to characterize the expected error levels of active microwave-based volume-scattering SWE retrievals over a western Colorado domain. We found that for a hypothetical SAR snow mission, the root mean square error (RMSE) of SWE improves by about 20 % in the mountainous environment if the retrieval algorithm can estimate SWE up to 600 mm and the tree cover fraction up to 40 %. Results also demonstrate that the potential SWE retrievals have larger improvements in the tundra (43 %) snow class, followed by boreal forest (22 %) and montane forest (17 %). Even though active microwave sensors are known to be limited by liquid water in the snowpack, they still reduced errors by up to 6 %–16 % of domain-averaged SWE in the melting period, suggesting that the SWE retrievals can add value to meltwater estimations and hydrological applications. Overall, this work provides a quantitative benchmark of the utility of a potential snow mission concept in a mountainous domain, helping to prioritize future algorithm development and field validation activities.