Sentinel-3 for Science, Land Study 1: Snow

This SEOM study is to develop, implement and validate algorithms for deriving several key snow parameters from Sentinel 3 optical satellite data, appropriate for addressing ESA’s Cryosphere challenge (Seasonal snow, lake/river ice and land ice, their effect on the climate system, water resources, energy and carbon cycles: the representation of terrestrial cryosphere in land surface, atmosphere and climate models).
This study takes a step toward achieving GCOS snow observation goals, effectively linking snow cover and albedo essential climate variables (ECVs) while developing capacity to extend snow climate data records (CDRs). This work aims to assimilate satellite optical data in a snow model by pushing data assimilation capabilities to the near real time frame and thus serving operational models to improve hydrological and weather forecasting skill and e.g. flood and avalanche hazard management.