Abstract:
Satellite retrievals of land surface temperature (LST, also referred to as skin temperature) are available from a variety of polar orbiting and geostationary platforms. Assimilating such LST retrievals into a land surface model (that is either driven by observed meteorological forcing data or coupled to an atmospheric model) should improve estimates of land surface conditions. However, LST data from retrievals and models typically exhibit very different climatologies for a variety of reasons. For example, LST modeling is fraught with numerical stability problems, because the heat capacity associated with LST is very small. This requires land modelers to approximate the heat capacity as zero, or else be limited to a surface temperature prognostic variable that represents several centimeters of soil. LST retrievals from satellite, on the other hand, are strongly affected by the look-angle, a problem that is particularly acute with geostationary satellites. Moreover, satellites observe a radiometric temperature that is difficult to compare to the model's LST because the emissivity of the land surface is not well known. Finally, LST retrievals from different satellite platforms already exhibit different climatologies due to different sensor characteristics.

The assimilation of LST retrievals into a land surface model may thus benefit from a scaling approach whereby the LST retrievals from each sensor are scaled to the model's LST climatology before they are assimilated into the land model. After assimilation, the merged LST product may be scaled back into the climatology of the LST retrievals if the application calls for it. A simple and effective scaling method is to utilize a mapping between the cumulative distribution functions of the satellite and model data. Because of the strong seasonal and diurnal cycle of LST, scaling parameters must be derived separately for each 3-hour interval and for each month.

In our paper, we demonstrate the feasibility of this approach by assimilating LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) into the NASA Catchment land surface model as it is driven with surface meteorological forcing data from the Global Soil Wetness Project 2 (GSWP-2) for the period 1986 through 1995.