'THE AMSR2 SATELLITE-BASED MICROWAVE SNOW ALGORITHM (SMSA): A NEW ALGORITHM FOR ESTIMATING GLOBAL SNOW ACCUMULATION

Moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches are not yet available and so the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach for estimating snow depth (SD) and snow water equivalent (SWE) is presented called the Satellite-based Microwave Snow Algorithm (SMSA). Using the Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations the approach leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval. The SD and SWE retrieval approach minimizes the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2012) and AMSR2 Tb observations. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2018-19 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.