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United States Department of Agriculture

Agricultural Research Service

Title: Prospects for Improving Land Surface Model Performance Via the Assimilation of Remote Sensing Products

Authors
item Crow, Wade
item Wood, E - PRINCETON UNIVERSITY
item Kustas, William

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: March 1, 2003
Publication Date: June 1, 2003
Citation: Crow, W.T., Wood, E.F., Kustas, W.P. 2003. Prospects for improving land surface model performance via the assimilation of remote sensing products [abstract]. In: Eos. Trans. 83(47), American Geophysical Union Fall Meeting Supplement.

Technical Abstract: An ongoing challenge for hydrologists and remote sensing scientist is the design of experiments to demonstrate the value - in any - of remote sensing observations for efforts to monitor and/or predict surface hydrologic processes at large scales. The need is especially pressing for remote observations of surface geophysical state variables like soil moisture and skin temperature. One efficient utilization of remote surface state observations is within the context of a data assimilation system designed to merge surface state predictions from numerical models with remote observations of the land surface. Such systems contain at least three components: a numerical land surface model, an emission model to convert land surface model predictions into observable quantities (e.g. brightness temperature), and an assimilation algorithm. The "value" of remote sensing observations therefore depends on a myriad of factors including the quality of non-updated open-loop model predictions, the optimality of the data assimilation approach, and the accuracy of the observational model. One basic benchmark for data assimilation approaches should be the accuracy of model predictions (e.g. evapotranspiration) obtainable from non-updated open-loop model simulations. This talk will address some of the basic issues surrounding such evaluations and examine ways in which remote sensing observations can add skill or value to land surface model predictions.

Last Modified: 11/26/2014
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