An operational coupled hybrid wave prediction model

Janssen, Peter A. E. M.; Komen, G. J.; Voogt, W.J.P. de

doi:10.1029/jc089ic03p03635

Cited by 56 publications

(14 citation statements)

References 14 publications

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“…Cases were selected in which the waves were pure wind sea. The observed spectra were used to calibrate the parametric GONO model (Janssen et al, 1984). In both cases, the surface winds were measured.…”

Section: Parametric Relationsmentioning

confidence: 99%

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Friction velocity scaling in wind wave generation

Janssen

Komen

Voogt³

1987

Boundary-Layer Meteorol

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This note is devoted to the problem ofthe appropriate scaling ofparameters relevant for sea waves, such as wave height, peak frequency, duration, and fetch. In the past, the growth of sea waves has often been analysed in terms ofthe wind velocity at a fixed height, despite the fact that many authors have stressed the importance of scaling with the friction velocity. This problem would be immaterial if the ratio between the friction velocity and the wind speed at a fixed height were a constant. There is, however, ample evidence that this ratio increases with wind speed (Smith and Banke, 1975;Smith, 1980), in agreement with dimensional considerations by Chamock (1955) on the friction height. As a result, the scaling problem is an important one. In this note we conjecture that the correct procedure is to scale wave parameters with friction velocity, and we discuss experimental evidence for the correctness of this conjecture. Comparing two independent datasets ('JONSWAP' and 'KNMI'), we find some evidence supporting our ideas. Further confirmation remains desirable, however, and suggestions are made as to how this might be obtained.

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Section: Parametric Relationsmentioning

confidence: 99%

“…Most wave models, e.g., those of the hybrid parametrical type (Gunther et al, 1979;Janssen et al, 1984) use these fetch laws for the prediction of the wind sea state.…”

Section: Introductionmentioning

confidence: 99%

Friction velocity scaling in wind wave generation

Janssen

Komen

Voogt³

1987

Boundary-Layer Meteorol

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“…In the assimilation of wave data into numerical models, method (d) is commonly employed, mainly because it is easily implemented. Komen (1985) applied a simple updating technique to assimilate the wave buoy data into a coupled hybrid wave model GONO (Janssen et al 1984) in the prediction of swell propagation. Thomas (1988) developed a scheme to update the wave spectrum using the buoy data of SWH and wind speed.…”

Section: Examples Of This Type Of Model Include Wam (Wamdimentioning

confidence: 99%

Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting

Zhang

et al. 2006

Journal of Hydroinformatics

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Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters. The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period, by definition. To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model. This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.

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“…• The nonlinear interactions are performed so that the windsea is reshaped to resemble the Kruseman spectrum (Janssen et al 1984). …”

Section: B Swmmentioning

confidence: 99%

A Case Study of the 9 August 1988 South Atlantic Storm: Numerical Simulations of the Wave Activity

Innocentini¹,

Neto²

1996

Wea. Forecasting

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During 9-11 August 1988, a cyclone developed over Uruguay in the lee of the Andes Mountains and moved over the South Atlantic Ocean, where it redeveloped into an intense storm. This storm was responsible for unusual wave activity along the Brazilian shoreline from 22Њ to 32ЊS. The Brazilian news media reported the loss of at least one life, waves of 3 m and higher, and the disappearance of a drainage pipe, which weighed 8000 kg, off the shores of Rio de Janeiro.In this paper, the evolution of this intense storm and the associated ocean wave response is studied through European Centre for Medium-Range Weather Forecasts analyses, a hydrostatic limited-area meteorological model, and a second-generation prognostic wave model.The atmospheric model results indicated the presence of a long-lived and large fetch with surface wind velocities higher than 12 m s 01 directed toward the coast. Some areas with velocities of 20 m s 01 were embedded in the fetch. The wave model forced by this wind field was able to simulate waves with a significant height of 8 m far from the coast and about 4 m in regions very close to the Brazilian coast in agreement with the occurrence reported at Rio de Janeiro. The swell propagation toward the coast of Rio de Janeiro was obstructed by a northeastward 10-m wind during the first 24-h period of the model's integration. During the second 24-h period, the fetch was still large and strong, but the obstacle was removed by a counterclockwise rotation of wind direction favoring the swell and windsea propagation toward the Rio de Janeiro coast.

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An operational coupled hybrid wave prediction model

Cited by 56 publications

References 14 publications

Friction velocity scaling in wind wave generation

Friction velocity scaling in wind wave generation

Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting

A Case Study of the 9 August 1988 South Atlantic Storm: Numerical Simulations of the Wave Activity

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