Performance evaluation of SWAN ST6 physics forced by ERA5 wind fields for wave prediction in an enclosed basin

2021 ◽  
Vol 240 ◽  
pp. 109936
Author(s):  
Burak Aydoğan ◽  
Berna Ayat
1982 ◽  
Vol 22 (05) ◽  
pp. 764-774
Author(s):  
D.T. Resio ◽  
C.L. Vincent

Abstract In recent years, the number of wave prediction models has increased greatly. These models range from relatively simple parameterizations of significant wave height as a function of wind, duration, and fetch to rather sophisticated solutions for the generation, propagation, and dissipation of two-dimensional (2D) wave spectra. It sometimes is suggested that any wave model will provide reasonable answers when properly applied, and that provide reasonable answers when properly applied, and that most of the deviations between measured waves and predicted waves can be explained by discrepancies predicted waves can be explained by discrepancies between actual and estimated wind fields. Although much of the error in wave prediction almost certainly is related to problems in determining a wind field, this paper examines the specific question of whether there are differences among these models such that even if the wind field were specified perfectly, there would remain significant deviations among predicted waves. First, wave generation under uniform wind fields is compared by use of nondimensional parameters. Then the models are compared again under conditions of time-varying, space-varying wind fields and with irregular fetch boundaries. We concluded that, in the open ocean with a long-duration, slowly varying weather system, most models produce similar results; however, near a coast or in produce similar results; however, near a coast or in regions with rapidly varying weather systems, marked differences can be expected from the use of different models. Introduction The need for wave data has led increasingly to the use of wave hindcast techniques to produce wave climates, and a number of major hindcast efforts are under way in the U.S. alone. Numerous techniques are available, ranging from significant wave techniques in which wave parameters can be estimated from nomograms, to parameters can be estimated from nomograms, to directional spectral models, which usually are run on large-core, high-speed digital computers. Table 1 lists some of these techniques. A common underlying assumption of practicing engineers is that each of the techniques will practicing engineers is that each of the techniques will produce similar results when properly applied with produce similar results when properly applied with correct wind input. This paper demonstrates that this is not always the case. Instead, various models can be shown to have theoretical differences that in climatological as well as specific applications might lead to significant discrepancies in estimates of sea state.Since all wave hindcasts begin with reconstruction of past wind fields from historical records, a baseline error past wind fields from historical records, a baseline error present in all wave estimates comes from inaccuracies in present in all wave estimates comes from inaccuracies in available meteorological data. Often it seems as though investigators tacitly assume that the wind error dominates the total error term in hindcast studies and, hence, that the absolute accuracy of the wave model is not that important. A consequence of this might be that, where available meteorological data are high-quality, a wave model of high quality should be used; but where available meteorological data are low-quality (or sparse in time and space), a simple wave model will suffice. This logic assumes that any errors introduced by the wave model should be of comparable magnitude to those implicit in the meteorological input. It is not clear, however, that this is a reasonable argument with respect to errors, since they tend to be additive. Thus, the root mean square error will increase by the square root of 2 when a wave model with independent error characteristics of equal magnitude to the meteorological data is applied. If the error is already large, adding 40% to it could be detrimental to the final results. SPEJ p. 764


2021 ◽  
Vol 9 (11) ◽  
pp. 1257
Author(s):  
Chih-Chiang Wei

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.


Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Provat K. Saha ◽  
Allen L. Robinson ◽  
...  

1981 ◽  
Author(s):  
Ross L. Pepper ◽  
Robert S. Kennedy ◽  
Alvah C. Bittner ◽  
Steven F. Wiker

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