The retrievals of cold land atmospheric and surface parameters from space continue to be one of the more challenging and increasingly important areas of satellite research. Of particular interest to hydrologists, atmospheric scientists and climatologists are the regional and global retrievals of snow cover parameters (extent, water equivalent and depth) and cold season precipitation rates (frozen rain and snowfall). Snow cover affects both the global water and energy budgets at regional and global scales. Snowfall, on the other hand, represents a substantial portion of global precipitation in many parts of the world and is coupled to snow cover. On a regional scale, snowfall retrievals are particularly important for winter weather forecasting and water resource management.

The retrievals of winter precipitation from satellites (visible, infrared and microwave) have proven to be extremely difficult primarily due to the unique characteristics associated with such precipitation. Unlike summer rainfall which is often the result of strong atmospheric convection, cold precipitation systems are mostly stratiform, and thus difficult to discriminate against non-precipitating winter clouds. In addition, snowfall is often associated with snow cover on the ground, which can be another significant source of confusion for the observing satellite.

Recent research using measurements from the Advanced Microwave Sounding Unit (AMSU) at NOAA/NESDIS has shown that winter precipitation- and snow cover-sized particles respond to microwave radiation at specific microwave frequencies. The discrimination between snowfall, rain and snow cover signatures can be made successfully utilizing the unique combination of the AMSU frequency channels in the atmospheric window (23, 31, 89, 150 GHz), opaque water vapor (183±1, ±3, ±7 GHz) and oxygen absorption (50-60 GHz) bands. This unique combination of channels has led to important extensions of the existing retrieval base: the extension of the rain rate operational product to include snowfall detection, and the extension of the snow cover extent product to include snow water equivalent retrievals. The objective of this paper is to describe these AMSU-based snow products in more detail. Specifically, the paper will describe each algorithm and discuss its main retrieval concepts, techniques and future challenges. Validation examples and case studies results will also be presented.