A Snow Water Equivalent Retrieval Framework Coupling 1D Hydrology and Passive Microwave Radiative Transfer Models

The retrieval of continuous snow water equivalent (SWE) directly from passive microwave observations is hampered by ambiguity, which can potentially be mitigated by incorporating knowledge on snow hydrological processes. In this paper, we present a data assimilation (DA)-based SWE retrieval framework coupling the QCA-Mie scattering (DMRT-QMS) model (a dense medium radiative transfer (RT) microwave scattering model) and a one-dimensional column-based multiple-layer snow hydrology model. The snow hydrology model provides realistic estimates of the snowpack physical parameters required to drive the DMRT-QMS model. This paper devises a strategy to specify those internal parameters in the snow hydrology and RT models that lack observational records. The modeled snow depth is updated by assimilating brightness temperatures (Tbs) from the X, Ku, and Ka bands using an ensemble Kalman filter (EnKF). The updated snow depth is then used to predict the SWE. The proposed framework was tested using the European Space Agency's Nordic Snow Radar Experiment (ESA NoSREx) dataset for a snow field experiment from 2009 to 2012 in Sodankyl & auml;, Finland. The achieved SWE retrieval root mean square error of 34.31 mm meets the requirements of NASA and ESA snow missions and is about 70% less than the open-loop SWE. In summary, this paper introduces a novel SWE retrieval framework that leverages the combined strengths of a snow hydrology model and a radiative transfer model. This approach ensures physically realistic retrievals of snow depth and SWE. We investigated the impact of various factors on the framework's performance, including observation time intervals and combinations of microwave observation channels. Our results demonstrate that a one-week observation interval achieves acceptable retrieval accuracy. Furthermore, the use of multi-channel and multi-polarization Tbs is preferred for optimal SWE retrieval performance.