Developing Operational Land Surface Temperature Product for the U.S. GOES Satellites

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 750 W) and GOES-West (centered at 1350 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 an operational LST product derived from measurements of the current GOES Imagers. Some features of the GOES LST product include 1) a consistent regression algorithm with coefficients determined through a regression tree approach, 2) high temporal resolution (30 minutes), 3) full Imager spatial resolution (4 km), 4) emissivity explicit formula, 5) the product will be available in full disk and CONUS scan modes. In this presentation we are showing the product development details and some validation results.