Tidal flow forecasting using reduced rank square root filters
type: Item
instance of: scholarly article
title: Tidal flow forecasting using reduced rank square root filters
zbMATH Open document ID: 0911.76076
DOI: 10.1007/BF02427924
published in: Stochastic Hydrology and Hydraulics
publication date: 1999-05-09T00:00:00Z
review text: We present an approximation to the Kalman filter algorithm which is based on a reduced rank approximation of the error covariance using a square root factorization. The use of the factorization ensures that the error covariance matrix remains positive semi-definite at all times, while the smaller rank reduces the number of computations and storage requirements. The number of computations and storage required depend on the problem at hand, but will typically be orders of magnitude smaller than for the full Kalman filter without significant loss of accuracy. The algorithm is applied to a model based on a linearized version of the two-dimensional shallow water equations for the prediction of tides and storm surges.
Mathematics Subject Classification ID: 76M35
Mathematics Subject Classification ID: 86A05
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 1186737
zbMATH Keywords: Kalman filter algorithm
zbMATH Keywords: reduced rank approximation
zbMATH Keywords: error covariance
zbMATH Keywords: two-dimensional shallow water equations
author: M. Verlaan
author: A. W. Heemink
describes a project that uses: SparseMatrix
MaRDI profile type: Publication
cites work: Q3993534
cites work: Factorization methods for discrete sequential estimation
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cites work: Application of the distributed parameter filter to predict simulated tidal induced shallow water flow
cites work: Data assimilation for non-linear tidal models
cites work: Stochastic models, estimation, and control. Vol. 1
cites work: Some new algorithms for recursive estimation in constant, linear, discrete-time systems
cites work: Q3693078
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description: scientific article; zbMATH DE number 1186737