Continental-scale Assimilation of Remotely Sensed Snow Observations
Paul R. Houser Chaojiao Sun and Jeffrey P. Walker
Snow plays an important role in governing both the global energy and water budgets, as a result of its high albedo, thermal properties, and being a medium-term water store. However, the problem of accurately forecasting snow in regional and global atmospheric and hydrologic models is difficult, as a result of snow related features that display variability at scales below those resolved by the models and errors in model forcing data. Hence, any Land Surface Model (LSM) snow initialization based on model spin-up will be affected by these errors. By assimilating snow observation products into the LSM the effects of these errors may be offset, but special care must be taken to avoid erroneous systematic influences on the water budget as a result of the assimilation. We have developed Kalman Filter based methods for the assimilation of relevant microwave (SSM/I) and visible (MODIS) remotely sensed snow observation products into the catchment-based LSM that is being used by the NASA Seasonal-to-Interannual Prediction Project (NSIPP). This work is focused on a retrospective study of North America, using the uncoupled NSIPP LSM, with a perspective of eventual coupled global implementation.