Developing Land Surface Temperature Product from Goes Imager Data

Information on land surface temperature (LST) is important for understanding climate change, modeling the hydrological and biogeochemical cycles, and is a prime candidate parameter for Numerical Weather Prediction assimilation models. In particular, the LST data derived from geostationary operational environmental satellite (GOES) provides unique opportunity for studying LST diurnal variation. Current GOES LST at the U.S. NOAA NESDIS is an intermediate product derived from sounder data which is limited in resolution and accuracy. High resolution GOES LSTs with better accuracy are required from users such as the U.S. NCEP Weather forecast and data assimilation model. Further, the LSTs derived from GOES-East (centered at 75 W) and GOES-West (centered at 135 W) may be significantly different because of algorithm inconsistency. In order to support the NOAA mission goals in climate, weather, and water, we are developing a consistent LST product derived from GOES satellite imagers. This paper introduces some of results obtained from this project.