Remotely sensed forcing data and the Global Land Data Assimilation System

A high-resolution, near-real-time Global Land Data Assimilation System (GLDAS) that integrates information from relevant satellite- and ground-based observations within a land data assimilation framework is being developed at NASA´s Goddard Space Flight Center and NOAA´s National Centers for Environmental Prediction (NCEP). The GLDAS framework enables it to serve as a powerful tool for various types of research and applications. Here, we examine the impact of replacing model-based forcing fields with observation-derived fields on Mosaic land surface model (LSM) simulations. Due to a paucity of recent land surface state observations over large domains, GLDAS-forced Mosaic LSM simulations will be evaluated against finer-resolution North American LDAS-forced results.