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• W2098552783 abstract "Abstract This paper introduces a new approximate solution of the optimal nonlinear filter suitable for nonlinear oceanic and atmospheric data assimilation problems. The method is based on a local linearization in a low-rank kernel representation of the state’s probability density function. In the resulting low-rank kernel particle Kalman (LRKPK) filter, the standard (weight type) particle filter correction is complemented by a Kalman-type correction for each particle using the covariance matrix of the kernel mixture. The LRKPK filter’s solution is then obtained as the weighted average of several low-rank square root Kalman filters operating in parallel. The Kalman-type correction reduces the risk of ensemble degeneracy, which enables the filter to efficiently operate with fewer particles than the particle filter. Combined with the low-rank approximation, it allows the implementation of the LRKPK filter with high-dimensional oceanic and atmospheric systems. The new filter is described and its relevance demonstrated through applications with the simple Lorenz model and a realistic configuration of the Princeton Ocean Model (POM) in the Mediterranean Sea." @default.
• W2098552783 created "2016-06-24" @default.
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• W2098552783 date "2008-01-01" @default.
• W2098552783 modified "2023-10-13" @default.
• W2098552783 title "A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography" @default.
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• W2098552783 doi "https://doi.org/10.1175/2007mwr1927.1" @default.
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