Evaluating the Utility of Active Microwave Observations as a Snow Mission Concept Using Observing System Simulation Experiments

• Main Authors: Cho, Eunsang, Vuyovich, Carrie M., Kumar, Sujay V., Wrzesien, Melissa L., Kim, Rhae Sung
• Format: Text
• Language: English
• Published: 2022
• Subjects: Tundra
• Online Access: https://doi.org/10.5194/tc-2022-220; https://tc.copernicus.org/preprints/tc-2022-220/

As a future satellite mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) with advantages including finer spatial resolution and improved capabilities in deeper snowpack and forest-covered areas as compared to existing missions (e.g., passive microwave sensors). In mountainous regions, however, the potential utility of spaceborne active microwave sensors for SWE retrievals particularly under deep snow and forest cover has not been evaluated yet. In this study, we develop an observing system simulation experiment (OSSE) that includes the characterization of expected error levels of the active microwave-based volume-scattering SWE retrievals and realistic orbital configurations over a western Colorado domain. We found that active microwave sensors can improve a root mean square error (RMSE) of SWE by about 20 % in the mountainous environment if the active microwave signals with a mature retrieval algorithm can estimate SWE up to 600 mm of deep SWE and up to 40 % of tree cover fraction (TCF). Results also demonstrated that the potential SWE retrievals have larger improvements in 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-average 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 prioritize future algorithm development and field validation activities.