Accurate Representation of Arbitrary Depth Source Terms in Coastal Wave Prediction Models

1999 ◽  
Author(s):  
Robert E. Jensen ◽  
Donald T. Resio
Keyword(s):  
Prediction Models ◽  
Source Terms ◽  
Accurate Representation ◽  
Wave Prediction

scholarly journals Numerical and Physical Diffusion: Can Wave Prediction Models Resolve Directional Spread?

2005 ◽  
Vol 22 (7) ◽  
pp. 886-895 ◽  
Author(s):  
F. Ardhuin ◽  
T. H. C. Herbers
Keyword(s):  
Continental Shelf ◽  
Prediction Models ◽  
Spectral Band ◽  
Wave Spectrum ◽  
Single Frequency ◽  
Propagation Time ◽  
Time Step ◽  
Numerical Diffusion ◽  
Wave Prediction ◽  
Lagrangian Scheme

Abstract A new semi-Lagrangian advection scheme called multistep ray advection is proposed for solving the spectral energy balance equation of ocean surface gravity waves. Existing so-called piecewise ray methods advect wave energy over a single time step using “pieces” of ray trajectories, after which the spectrum is updated with source terms representing various physical processes. The generalized scheme presented here allows for an arbitrary number N of advection time steps along the same rays, thus reducing numerical diffusion, and still including source-term variations every time step. Tests are performed for alongshore uniform bottom topography, and the effects of two types of discretizations of the wave spectrum are investigated, a finite-bandwidth representation and a single frequency and direction per spectral band. In the limit of large N, both the accuracy and computation cost of the method increase, approaching a nondiffusive fully Lagrangian scheme. Even for N = 1 the semi-Lagrangian scheme test results show less numerical diffusion than predictions of the commonly used first-order upwind finite-difference scheme. Application to the refraction and shoaling of narrow swell spectra across a continental shelf illustrates the importance of controlling numerical diffusion. Numerical errors in a single-step (Δt = 600 s) scheme implemented on the North Carolina continental shelf (typical swell propagation time across the shelf is about 3 h) are shown to be comparable to the angular diffusion predicted by the wave–bottom Bragg scattering theory, in particular for narrow directional spectra, suggesting that the true directional spread of swell may not always be resolved in existing wave prediction models, because of excessive numerical diffusion. This diffusion is effectively suppressed in cases presented here with a four-step semi-Lagrangian scheme, using the same value of Δt.

Performance of Formulations for Whitecapping in Wave Prediction Models

2002 ◽  
Author(s):  
Gerbrant Ph. van Vledder ◽  
David P. Hurdle
Keyword(s):  
Prediction Models ◽  
Wave Spectra ◽  
Third Generation ◽  
Wave Prediction

This paper describes work currently being carried out to examine possible methods to improve the computation of the dissipation by whitecapping in third generation wave prediction models. Such alternatives are needed to avoid unphysical dissipation behavior in the case of double-peaked wave spectra. First, an overview is given of the problems associated with the formulation for whitecapping that is now widely used in wave prediction models. Second, a summary is given of existing suggestions to improve the whitecapping formulation. Third, a number of examples are given with the new formulations to illustrate the potential improvements.

Validation of Operational Global Wave Prediction Models with Spectral Buoy Data

2002 ◽  
Author(s):  
K. M. Wingeart ◽  
T. H. C. Herbers ◽  
W. C. O'Reilly ◽  
P. A. Wittmann ◽  
R. E. Jensen ◽  
...  
Keyword(s):  
Prediction Models ◽  
Wave Prediction ◽  
Buoy Data

scholarly journals Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1257
Author(s):  
Chih-Chiang Wei
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Prediction Models ◽  
Wind Wave ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Surface Wind ◽  
Wind Fields ◽  
Forecast Horizon ◽  
Wave Prediction

Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data.

A semi-operational comparison of two parametrical wave prediction models

1984 ◽  
Vol 37 (3) ◽  
pp. 89-106 ◽  
Author(s):  
Heinz Günther ◽  
Gerbrand J. Komen ◽  
Wolfgang Rosenthal
Keyword(s):  
Prediction Models ◽  
Wave Prediction
Sign in / Sign up

Export Citation Format

Share Document