|Stochastic Systems Group|
Prof. Dennis McLaughlin
Parsons Lab, MIT
In this talk we describe a soil moisture data assimilation procedure based on the ensemble Kalman filter. This procedure is illustrated with an application to the Southern Great Plains 1997 (SGP97) field experiment. It uses land surface and radiative transfer models to derive soil moisture estimates from airborne L band microwave observations and ground-based measurements of micrometeorological variables, soil texture, and vegetation type. The ensemble filter approach is appealing because (1) it can accommodate a wide range of standard "community" land surface models, (2) it provides a very flexible way to include input and measurement uncertainties, (3) it provides information on the accuracy of its estimates, and (4) it is relatively efficient, making moderate-scale applications feasible. Overall, the results from this field test indicate that the ensemble Kalman filter is an accurate, efficient, and flexible data assimilation procedure that can extract useful information from remote sensing measurements. However, the question remains whether this or any competing approach can solve very large (continental to global scale) data assimilation problems.
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