Assimilation of Satellite Soil Moisture Data Products in the NCEP Global Forecast System

Global soil moisture data products have been or will be continuously generated from existing and planned satellite microwave sensors. They could be used for initialization of soil moisture state variables in numerical weather, climate and hydrological forecast models. A global Soil Moisture Operational Product System (SMOPS) has been developed at NOAA-NESDIS to continuously provide global soil moisture data products to meet NOAA-NCEP's soil moisture data needs. To assimilate the soil moisture data products in improving forecasts of the NCEP Global Forecast System (GFS), the Ensemble Kalman Filter (EnKF) data assimilation algorithm has been implemented in the GFS. In this paper, the quality of the soil moisture data products from SMOPS is examined against in situ measurements. The biases of the soil moisture retrievals from the Noah land surface model simulations in GFS are corrected before assimilating the retrievals into the model. The sequentially assimilating soil moisture data experiments with full cycle runs of the EnKF-GFS and NCEP Gridpoint Statistical Interpolation (GSI) analysis system were performed. The impact on GFS simulations of soils moisture and energy fluxes, 2-meter surface air temperature and humidity, and precipitation forecast was investigated. Results from this investigation together with the SMOPS and EnKF-GFS systems will be presented.