Two-dimensional shallow water flow identification

DOI10.1016/0307-904X(88)90002-9zbMath0644.76014OpenAlexW2081775626MaRDI QIDQ1102791

Arnold W. Heemink

Publication date: 1988

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0307-904x(88)90002-9

zbMATH Keywords

Chandrasekhar-type algorithmdiscrete time-invariant Kalman filterlinear two-dimensional shallow water equationsNorth Seasix-hours-ahead prediction of storm surgessystem noise processtwo-dimensional shallow water flow

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Hydrology, hydrography, oceanography (86A05) Water waves, gravity waves; dispersion and scattering, nonlinear interaction (76B15) Identification in stochastic control theory (93E12) Basic methods in fluid mechanics (76M99)

Related Items (7)

Application of the distributed parameter filter to predict simulated tidal induced shallow water flow ⋮ Data assimilation in transport models ⋮ Data Assimilation of Water Elevation in Shallow Water Flow Based on the Extended Kalman Filter FEM Using Measurement Data from Image Analysis ⋮ Parameter identification in tidal models with uncertain boundary conditions ⋮ Estimation of Wave Propagation using a Kalman Filter ⋮ Design of a fixed-interval smoother using covariance information based on the innovations approach in linear discrete-time stochastic systems ⋮ A computationally efficient Kalman smoother for the evaluation of the \(\text{CH}_4\) budget in Europe

Cites Work

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• Dynamic meteorology: data assimilation methods
• Pratical aspects of stochastic dynamic tidal modelling
• Stochastic models, estimation, and control. Vol. 1
• Stochastic processes and filtering theory
• Numerical aspects of different Kalman filter implementations
• Some new algorithms for recursive estimation in constant, linear, discrete-time systems

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