Data Assimilation of Remotely Sensed Measurements in Large-Dimensional and Nonlinear Environmental Models
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Noisy measurements and uncertain model output are merged in the process of data assimilation. In environmental applications with remotely sensed measurements the problem is constrained by large dimensionality and nonlinearity of the model. In a number of application examples candidate approaches for dealing with the constraints will be presented.
Dara Entekhabi received the Ph.D. degree in civil engineering from the Massachusetts Institute of Technology, Cambridge, in 1990. He is currently a Professor in the Department of Civil and Environmental Engineering and in the Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge. He is a Fellow of the American Meteorological Society and a Fellow of the American Geophysical Union. He is the HYDROS Principal Investigator.