Impacts of assimilating land observational data products on NCEP numerical weather prediction models

Several land data products have become operationally available from various satellite platforms at NESDIS Office of Satelliteand Product Operations (OSPO) in the past years. Land surface temperature (LST) data sets are operationally available from GOES LST (GLST) and the GOES Surface and Insolation Product (GSIP) systems using the operational GOES-East and GOES-West satellites. New LST data products are or will be available from Suomi-NPP, and future JPSS, GOES-R satellites. The NESDIS Soil Moisture Operational Product System (SMOPS) is currently making global soil moisture retrievals from ESA SMOS, EUMETSAT ASCAT, Naval Research Lab's WindSat and AMSR2 of JAXA's GCOM-W1 satellites operationally available. Other land surface satellite data products operationally available include surface type, albedo, vegetation indices and green vegetation fraction (GVF), fire and smoke, land surface emissivity, snow cover, snow depth and snow water equivalent (SWE), etc. It is well documented that these land surface observations have significant impacts on numerical weather prediction models. However, none of those near real time operationally available land surface data products has been utilized in NCEP NWP models. This study has examined the impact of assimilating soil moisture, NRT albedo, GVF, surface type, solar insolation into the Noah land surface model or the global forecast system (GFS) of NCEP. This paper will present the results of the impact study. Plan for integrating most satellite land data products into NCEP NWP models will be discussed.