The objective of this research is to derive land surface
temperature from GOES data at hourly intervals for atmospheric
model assimilation to improve short range weather forecasts and
nowcasting applications. The rate of change of LST is sensitive
to the characteristics of the land surface such as soil
moisture, land use and vegetation. Regions of high soil moisture
content or dense vegetation which has access to a a source of
moisture exhibit cooler LST than dry soil or vegetation which is
stressed because a lack of available soil moisture. This is
illustrated in the figures to the right. The retrieval of land
surface temperature (LST) from GOES measurements is accomplished
with a physical split window algorithm and the 11 and 12
micrometer channels of either the imager or the sounder. The
technique is derived from a perturbation form of the radiative
transfer equation that is simplified through parameterization to
retrieve the surface parmater corrected for atmospheric water
vapor effects. The physical appr