Reichle, R. H., R. D. Koster, M. G. Bosilovich, and S. Mahanama:
"Biases and scaling in soil moisture and temperature data assimilation"
Invited Presentation, AGU Spring Meeting, Baltimore, MD, USA, 2006.

Abstract:
Satellite remote sensing observations of soil moisture and land surface temperature (LST) may be assimilated into a land surface model (that is either driven by observed meteorological forcing data or coupled to an atmospheric model). The hope is, of course, that the assimilation provides superior estimates of land surface conditions that can subsequently be used in the initialization of weather or seasonal climate forecasts.

There are, however, serious obstacles to success in land data assimilation. Assimilating satellite data into the land model without adequate treatment of the sometimes severe biases between the land model fields and the satellite data creates serious imbalances in the model-generated mass and energy fluxes.

In this paper, we investigate the biases between the land model fields and satellite retrievals from different platforms for soil moisture and LST. We also demonstrate how such biases can be addressed through a scaling approach. In particular, the retrievals from each sensor are scaled to the land model's climatology before they are assimilated into the land model. After assimilation, the merged land surface product may be scaled back into the climatology of the satellite retrievals if the application calls for it.


Home

NASA-GSFC / GMAO / Rolf Reichle