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Terrestrial snow processes are important for energy management, water management, and disaster management applications of national priority because of the timing of snowmelt and the subsequent fate of melted water play an extremely important role in the hydrological response of catchments. In the global water cycle, terrestrial snow is a dynamic fresh-water reservoir that stores precipitation and delays runoff. On average, over 60% of the northern hemisphere land surface has snow cover in midwinter, and over 30% of Earth's total land surface has seasonal snow (Robinson et al., 1993). NOAA has operational decision support tools (DST) in place to provide snow information to a wide variety of end-users and applications. NASA has conducted considerable research and developed advanced measurement and modeling tools to improve snow information. These tools are sufficiently mature to transfer them to NOAA¿s primary snow decision support framework, SNODAS. This crosscutting Integrated Systems Solutions (ISS) project will therefore transfer, demonstrate and enable the use of snow-related NASA observational and modeling research results in NOAA's operational Snow Data Assimilation System (SNODAS) to improve water management, disaster management and agricultural efficiency decision support. The SNODAS is a major part of the National Snow Analyses (, a critically important Decision Support Tool (DST) which is widely used to make operational decisions on agricultural production, water resource management, flood, drought, weather and climate prediction, hazard mitigation and mobility assessment. -- In the summer of 2010, NOHRSC began work toward the long-term objective of comparing/combining LIS LSM model states with satellite snow observations. First, the global spin-up characteristics of the operative LIS LSMs (CLM 2.0 and NOAH 2.7.1) were explored. More focused spin-up studies were also performed at higher resolution over the Alaska and Central Asia domains. Based on the results of that work, a long (7-year) spin-up followed by a retrospective analysis for the years 2003-2009 was performed for the Central Asia domain, to explore the ability of the system to reproduce the broad snow cover characteristics (e.g., MODIS 8-day maximum snow extent) available in the satellite record. That effort showed that even in the absence of data assimilation, LIS-CLM was capable of reproducing the seasonal evolution of the snowpack in the Central Asia region, as well as the sometime significant differences in this evolution from one year to another. This finding implies that even without data assimilation, the operational system currently in place can provide answers to basic questions in real time, such as how the snowpack compares with other years in a short-term (~7-year) climatology.