The goal of the Global Land Data Assimilation System (GLDAS) is to generate optimal fields of land surface states and fluxes by integrating satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques (Rodell et al., 2004).  GLDAS drives multiple, offline (not coupled to the atmosphere) land surface models, integrates a huge quantity of observation based data, and executes globally at high resolutions (2.5° to 1 km), enabled by the Land Information System (LIS) software package (Kumar et al., 2006). 

A vegetation-based “tiling” approach is used to simulate sub-grid scale variability, with a 1 km global vegetation dataset as its basis.  Soil and elevation parameters are derived from high resolution global datasets.  Observation-based precipitation and downward radiation products and the best available analyses from atmospheric data assimilation systems are employed to force the models.

Intercomparison and validation of these products is being performed with the aim of identifying an optimal forcing scheme.  Data assimilation techniques for incorporating satellite based hydrological products, including snow cover and water equivalent, soil moisture, surface temperature, and leaf area index, are now being tested and implemented. The output fields support several current and proposed weather and climate prediction, water resources applications, and water cycle investigations.  GLDAS has resulted in a massive archive of modeled and observed, global, surface meteorological data, parameter maps, and output which includes 1° and 0.25° resolution 1979-present simulations of the Noah, CLM, Mosaic, and VIC land surface models.  The project is funded by NASA's Energy and Water Cycle Study (NEWS) Initiative.  More information is available at the Land Data Assimilation Systems (LDAS) and Land Information System (LIS) web sites.

Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L. Lighty, J. L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood, and J. Sheffield, 2006: Land Information System - An interoperable framework for high resolution land surface modeling, Environ. Modelling and Software, 21, 1402-1415.

Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin, J. P. Walker, D. Lohmann, and D. Toll, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85 (3), 381–394.