Although snowfall is an important component of global precipitation in extratropical regions, satellite snowfall estimate is still in an early developmental stage, and existing satellite remote sensing techniques do not yet provide reliable estimates of snowfall over higher latitudes. Toward the goal of developing a global snowfall algorithm, in this study, a Bayesian technique has been tested for snowfall retrieval over land using highfrequency microwave satellite data. In this algorithm, observational data from satelliteand surface-based radars and in situ aircraft measurements are used to build the a priori database consisting of snowfall profiles and corresponding brightness temperatures. The retrieval algorithm is applied to the Advanced Microwave Sounding Unit-B data for snowfall cases that occurred over the Great Lakes region, and the results are compared with the surface radar data and daily snowfall data collected from National Weather Service stations. Although the algorithm is still at an ad hoc stage, the results show that the satellite retrievals compare well with surface measurements in the early winter season, when there is no accumulated snow on ground. However, for the late winter season, when snow constantly covers the ground, the snowfall retrievals become very noisy and show overestimation. Therefore, it is concluded that developing methods to efficiently remove surface snow cover contamination will be the major task in the future to improve the accuracy of satellite snowfall retrieval over land.