Optimal interpolation and the Kalman filterThe estimation theory of stochastic-dynamic systems is described and used in a numerical study of optimal interpolation. The general form of data assimilation methods is reviewed. The Kalman-Bucy, KB filter, and optimal interpolation (OI) filters are examined for effectiveness in performance as gain matrices using a one-dimensional form of the shallow-water equations. Control runs in the numerical analyses were performed for a ten-day forecast in concert with the OI method. The effects of optimality, initialization, and assimilation were studied. It was found that correct initialization is necessary in order to localize errors, especially near boundary points. Also, the use of small forecast error growth rates over data-sparse areas was determined to offset inaccurate modeling of correlation functions near boundaries.
Document ID
19830033191
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Cohn, S. (New York Univ. New York, NY, United States)
Isaacson, E. (New York University New York, NY, United States)
Ghil, M. (NASA Goddard Space Flight Center Laboratory for Atmospheric Sciences, Greenbelt, MD; New York University, New York, NY, United States)