A Global Land Data Assimilation System (GLDAS) has been developed at NASA’s Goddard Space Flight Center. The goal of GLDAS is to produce optimal output fields of land surface states and fluxes by making use of data from advanced remote and ground-based observing systems. GLDAS has its heritage in the multiple-institution North American Land Data Assimilation Systems (LDAS). GLDAS can run Mosaic, the Community Land Model (CLM), and NOAA’s NOAH land surface model globally at 0.25° and coarser resolutions. A vegetation-based “tiling” approach is used to simulate sub-grid scale variability. Forcing options include observation-based precipitation and radiation fields as well as the best available global atmospheric forecast model output.

Recently, an optional routine was installed in GLDAS to make use of snow cover fields derived from observations made by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite. The routine corrects GLDAS modeled snow variables based on the existence of snow in the MODIS fields. Results from recent test simulations will be presented and discussed.