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Since the launch of the Scanning Multichannel Microwave Radiometer (SMMR) in 1978, several studies have demonstrated the capability of spaceborne passive microwave sensors for mapping global snow water equivalent (SWE). Currently, SWE values are estimated operationally from microwave brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and distributed through the National Snow and Ice Data Center (NSIDC). In this study, we report results regarding the comparison between AMSR-E SWE and SWE/snow depth values distributed by the Snow Data Assimilation System (SNODAS) product of the NOAA's National Operational Hydrologic Remote Sensing Center and snow depth measured by automatic weather stations of the World Meteorological Organization. Generally, we found poor correlation between the AMSR-E and SNODAS SWE/snow depth values. The algorithm performance improves when considering WMO data, though the number of samples used for the analysis might play a role in this sense. We discuss algorithm-related sources of error and uncertainties, such as vegetation and grain size. Moreover, we report results aimed at evaluating whether replacing the linear approach with a nonlinear one and not using the brightness temperatures and ancillary data sets combined as in the current approach but taken separately as inputs to the algorithm might improve the performance of the algorithm.