The seasonal snowpack plays a key role in many ecological and hydrological processes worldwide. Due to its high albedo and insulating capabilities the extensive snow-covered area of the northern hemisphere influences the entire climate system. Also in mountain regions, the seasonal snowpack is often the main source of runoff.
Despite its importance, monitoring the snowpack remains challenging. Neither remote sensing observations nor in situ measurements are sufficient to provide an accurate description of snowpack properties. Recent studies suggest that data assimilation (DA) of remotely sensed products is the path forward to estimate the spatial distribution of relevant snowpack characteristics. Among all the potential variables, the snow surface temperature (SST), fractional snow cover (FSCA), and albedo are especially favourable for improving snowpack simulations because they reflect different facets of the snowpack energy and mass budgets.
There are some satellites that can provide snow informations from the regional (e.g. Sentinel 3, MODIS) to the high-resolution scales (Landsat). Given the agenda of the space agencies, SST-DA will become increasingly available in the next years. For example, the TRISHNA mission (CNES) will provide SST and albedo measurements at 60 m resolution every 3 days, and it is planned for launch in 2024.
We have developed the Multiscale Snow data Assimilation system (MuSA). MuSA is able to fuse observations with snow simulations generated from the Flexible Snowpack Model (FSM2). To date, we have used MuSA to assimilate drone snow depth retrievals at 5m resolution, and develop single cell joint assimilation experiments. We plan to use MuSA, to run distirbuted joint assimilation experiments fusing FSCA, SST and albedo with FSM2, becoming MuSA the world's most sophisticated freely available snow DA toolbox.