A snowfall retrieval algorithm based on Bayes' theorem is developed using high-frequency microwave satellite data. In this algorithm, observational data from both airborne and surface-based radars are used to construct an a priori database of snowfall profiles. These profiles are then used as input to a forward radiative transfer model to obtain brightness temperatures at high microwave frequencies. In the radiative transfer calculations, two size distributions for snowflakes and ten observed atmospheric sounding profiles are used with snowfall profiles from observations. In addition, the scattering properties of the snowflakes are calculated on the basis of realistic nonspherical shapes using discrete dipole approximation. The algorithm is first verified by airborne microwave and radar observations and then applied to the Advanced Microwave Sounding Unit-B (AMSU-B) satellite data. The retrieved snowfall rates using AMSU-B data from three snowfall cases in the vicinity of Japan show reasonable agreement with surface radar observations with correlation coefficients of about 0.8, 0.6, and 0.96 for the three cases, respectively. The comparison results also suggest the algorithm performs better for dry and heavy snow cases, but is less accurate for wet and weak snow cases.