Land Surface Skin Temperature Errors in the NCEP Models over CONUS

The accurately predicted land surface skin temperature (LST) in numerical models is regarded not only as an essential product of numerical weather and climate prediction, but also as a critical surface field for the assimilation of satellite radiance observations, especially observations in so-called window channels that are sensitive to the state of the earth surface. Nevertheless, it is still a challenging task owing to the multiplicity of the related physical processes and their complex interactions. This study focuses on the Continental United States (CONUS). The derived LST products from the ground measurements, the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Geostationary Operational Environmental Satellites (GOES) are used to assess the LST predicted from NOAA National Centers for Environmental Prediction (NECP) operational forecast models such as the Global Forecast System (GFS), the North American Mesoscale Forecast System (NAM) and the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS-2). The assessment can help to evaluate how realistic these models predict the LST over different regions and different types of surfaces, understand the land-atmosphere interactions and the role of different processes in contributing to errors, and thus improve the model physics. Results from this investigation will be presented.