Wave data assimilation with the Kalman filter

Voorrips, A. C.; Heemink, A.W.; Komen, G. J.

doi:10.1016/s0924-7963(98)00094-3

Cited by 29 publications

(16 citation statements)

References 26 publications

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“…The most common technique in DA systems is the Optimum Interpolation (OI). Other DA methods for ocean waves have also been examined, e.g., an efficient low-rank approximation to the Kalman filter (KF) presented by Voorrips et al (1999).…”

Section: Introductionmentioning

confidence: 99%

A new methodology for using buoy measurements in sea wave data assimilation

2010

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One of the main drawbacks in modern sea wave data assimilation models is the limited temporal and spatial improvement obtained in the final forecasting products. This is mainly due to deviations coming either from the relevant atmospheric input or from the dynamics of the wave model, resulting to systematic errors of the forecasted fields of numerical wave models, when no observation is available for assimilation. A potential solution is presented in this work, based on a combination of advanced statistical techniques, data assimilation systems, and wave models. More precisely, Kalman filtering algorithms are implemented into the wave model WAM and the results are assimilated by an Optimum Interpolation Scheme, in order to extend the beneficial influence of the latter in time and space. The case studied concerns a 3-month period in an open sea area near the SouthWest coast of the USA (Pacific Ocean).

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Section: Introductionmentioning

confidence: 99%

A new methodology for using buoy measurements in sea wave data assimilation

2010

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“…Some authors (Voorrips et al, 1999;Heemink et al, 2001) report the use of ≈ 1. This can bring the algorithm beyond a purely tangent-linear forecast, but it is no more defined as a finite difference approximation and would require an ensemble interpretation.…”

Section: Re-initializationmentioning

confidence: 99%

A comparison of error subspace Kalman filters

Nerger

Hiller

Schröter

2005

Tellus A: Dynamic Meteorology and Oceanography

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Three advanced filter algorithms based on the Kalman filter are reviewed and presented in a unified notation. They are the well known Ensemble Kalman filter (EnKF), the Singular Evolutive Extended Kalman (SEEK) filter, and the less common Singular Evolutive Interpolated Kalman (SEIK) filter. For comparison, the mathematical formulations of the filters are reviewed in relation to the extended Kalman filter as error subspace Kalman filters. The algorithms are presented in their original form and possible variations are discussed. The comparison of the algorithms shows their theoretical capabilities for efficient data assimilation with large-scale nonlinear systems.In particular, problems of the analysis equations are apparent in the original EnKF algorithm due to the Monte Carlo sampling of ensembles. Theoretically, the SEIK filter appears to be a numerically very efficient algorithm with high potential for use with nonlinear models. The superiority of the SEIK filter is demonstrated on the basis of identical twin experiments using a shallow water model with nonlinear evolution. Identical initial conditions for all three filters allow for a consistent comparison of the data assimilation results. These show how choices of particular state ensembles and assimilation schemes lead to significant variations of the filter performance. This is related to different qualities of the predicted error subspaces as is demonstrated in a examination of the predicted state covariance matrices.

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“…Assimilation schemes based on the optimal interpolation method of a limited number of spectral parameters and Kalman filtering (Voorrips 1999;Voorrips et al, 1999) are currently under development for the DAUT wave model. Similar schemes will be developed for assimilation of current and temperature data into the hydrodynamic model, as well as for assimilation of winds and air temperature close to the surface into the weather model.…”

Section: Future Plansmentioning

confidence: 99%

Advancement of Operational Oceanography in Greece: The Case of the Poseidon System

Soukissian

Chronis

Nittis

et al. 2002

Journal of Atmospheric & Ocean Science

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Operational Oceanography in Greece has become today a necessity, since it can provide the means and the solutions on societal, economic, environmental and scientific problems related with the coastal environment. A rational approach to these problems can be based on integrated coastal zone management, which, in turn, is supported by the systematic and continuous operation of marine monitoring systems. A real-time monitoring and forecasting system (called the ''POSEIDON'' system) is currently in operation in the Aegean Sea. The three main components of the POSEIDON system are: (a) an integrated marine monitoring network consisting of oceanographic and wave buoys (POSEIDON network), (b) a telecommunication system for real-time data transmission and (c) the Aegean Operational Forecasting System (AOFOS) including a meteorological model, an offshore wave model, a general circulation ocean model, a surface pollutant dispersion model and a shallow water wave model. Three years of experience has shown that the POSEIDON system is an essential and necessary tool for developing Operational Oceanography in Greece, being also the only integrated and multipurpose system of this nature in the whole Mediterranean Sea.

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Wave data assimilation with the Kalman filter

Cited by 29 publications

References 26 publications

A new methodology for using buoy measurements in sea wave data assimilation

A new methodology for using buoy measurements in sea wave data assimilation

A comparison of error subspace Kalman filters

Advancement of Operational Oceanography in Greece: The Case of the Poseidon System

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