Abstract Changes in waves and wind caused by climate change would induce changes in the cross‐shore distribution of the longshore sediment transport rate, which would lead to morphological changes on the updrift and downdrift sides of coastal structures. Therefore, the impacts of climate change on the cross‐shore distributions of the longshore sediment transport rate and the longshore current velocity, which induces sediment transport, were examined at a sandy beach in Japan using a one‐dimensional numerical model and 9‐year wave and wind data simulated at 2‐hr intervals for the present and future climates. Both the present‐climate distributions had northward and southward predominant values near the shore and offshore, respectively, as a result of the combination of the southerly and northerly waves. Under the RCP8.5 scenario, the distributions shifted southward in the nearshore region, even though the mean wave direction did not change. This occurred because the significant wave height of the southerly waves decreased more than that of the northerly waves under this scenario. In the offshore region, northward longshore sediment transport became predominant because the number of large southerly waves increased. The results obtained using the peak wave directions differed from those obtained using the mean wave directions. There was a significant shift in the distributions to the south, and the bimodal distributions became unimodal. Future changes in the distributions can be estimated using 1‐day interval data instead of 2‐hr interval data with an error of 30% in the nearshore region.

Wind speed and wind fetch are two critical factors influencing the development of wind waves. However, due to the challenges in acquiring accurate wind fetch, most existing empirical models for retrieving significant height of wind waves (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i>) primarily consider wind speed while neglecting the influence of wind fetch, resulting in relatively low inversion accuracy. Given the strong correlation between wind wave mean period and wind fetch, this study indirectly accounted for the impact of wind fetch on wind wave development by incorporating wind wave mean period, and developed two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i> inversion models based on the Elfouhaily spectrum and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i>, wind speeds and wind wave mean periods provided by ECMWF, which are defined as Model 1 (M1) and Model 2 (M2), respectively. The two developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i> inversion models were evaluated using multiple datasets, including sea surface data provided by ECMWF and NDBC, GNSS buoy measurements, wind speeds provided by meteorological station, and airborne SAR imagery. The results demonstrate that both models achieve high inversion accuracy for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i> in most scenarios, with M2 exhibiting particularly robust stability, and the correlation coefficients between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i> retrieved by M2 and the reference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i> are greater than 0.95, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMSE</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAE</i> are approximately 0.19m and 0.13m, respectively. Furthermore, when developing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i> inversion model, this study enhanced the inversion accuracy by incorporating the wind wave mean period to indirectly account for wind fetch effects on wind wave development, thereby providing a novel research direction for obtaining high-precision <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SH_ww</i>.

Accurate prediction of significant wave height is of great reference value for wave energy generation. However, due to the non-linearity and non-stationarity of significant wave height, traditional algorithms face difficulties in achieving satisfactory prediction results. In this study, a hybrid CEEMDAN-VMD-TimesNet model is proposed for non-stationary significant wave height prediction. Based on the significant wave height in the South Sea of China, the performance of the SVM model, the GRU model, the LSTM model, the TimesNet model, the CEEMDAN-TimesNet model and the CEEMDAN-VMD-TimesNet model are compared in terms of multi-step prediction. It is found that the prediction accuracy of the TimesNet model is higher than that of the SVM model, the GRU model and the LSTM model. The non-stationarity of significant wave height is reduced by CEEMDAN decomposition. Thus, the CEEMDAN-TimesNet model performs better than the TimesNet model in predicting significant wave height. The prediction accuracy of the CEEMDAN-VMD-TimesNet model is further improved by employing VMD for the secondary decomposition of components with high and moderate complexity. Additionally, the CEEMDAN-VMD-TimesNet model can accurately predict trends and extreme values of significant wave height with minimal phase shifts even during typhoon periods. The results demonstrate that the CEEMDAN-VMD-TimesNet model exhibits superiority in predicting significant wave height.

This paper proposes to utilise a new adaptive kernel density estimation (KDE) methodology based on linear diffusion processes for predicting the probability distribution tails of sea-state parameters. A key conclusion was reached that the proposed new methodology can lead to more accurate prediction results than traditional methods based on fittings to a measured significant wave height data set at National Data Buoy Center station 46014. This proposed methodology was subsequently utilised for deriving an accurate 50-year environmental contour line that was used in the dynamic analysis of a two-body point absorber wave energy converter. After systematically analysing the calculation results, another key conclusion was drawn that it is advantageous to use a more reliable contour line derived using the proposed new methodology for long-term dynamic analysis of wave energy converters. In summary, the proposed new adaptive KDE methodology is recommended to be utilised and to be refined continuously in future research work in the field of long-term reliability analysis of marine sustainable energy systems.

High-resolution wave modeling of the Southwestern Nigerian coastal shelf: Implications on geomorphic contrasts between barrier-lagoon and mud coasts

Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height

Abstract The dynamics of typhoon-induced waves in semienclosed seas become an interesting topic with the increase of typhoon intensity. Based on the calibrated Simulating Waves Nearshore (SWAN) model, wave dynamics were investigated under distinct typhoon tracks [e.g., Matmo (2014), Rumbia (2018), and Lekima (2019)] in the Bohai Sea. Distributions of significant wave heights (SWHs) are affected by the typhoon wind fields and are directly related to the typhoon tracks. The classical JONSWAP wave spectra were adopted for the analysis of sea states (e.g., wind seas or swells) to further explain variations in wave heights. Results indicate that the dominant sea state with higher energy experiences significant spatiotemporal variability under distinct tracks. For typhoons passing through the central part of the Bohai Sea (e.g., Rumbia), high-energy waves are observed under swell-dominated and mixed sea states, which are subjected to the fetch limitation in the semienclosed sea and rapid changes in typhoon winds. The high energy waves induced by other typhoons passing along the edges of the Bohai Sea correspond to the wind-sea-dominated sea state. Spatiotemporal variability of the sea state exhibits a high correlation with its position relative to the typhoon center. Therefore, a reference frame based on the radius of the maximum wind speed was established to discuss the sea states in this semienclosed sea. Further investigations reveal that swells (wind seas) dominate the regions within the radius of the maximum wind speed (elsewhere), and the double-peaked wave spectra tend to appear in the left quadrants.

Abstract A high‐resolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long short‐term memory to simulate large‐scale, high‐resolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the data‐driven model has root‐mean‐square errors below 6 cm for daily mean significant wave height and 1 s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our data‐driven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models.

Global concern on extreme events is increasing the need for real time monitoring of the wave fields in coastal areas. High Frequency (HF) radars, a remote sensing technology widely applied to measure near real time surface coastal currents with demonstrated accuracy, can also play a major role in the operational monitoring of waves height, period and direction. However, the ability of HF radar to measure waves can be jeopardized by specific ocean-meteorological and environmental conditions. Thus, a case-to-case analysis and parameterization is necessary to ensure the best data in each study area. In the southeastern (SE) Bay of Biscay, the EuskOOS HF radar network, composed by two compact HF radar stations provides hourly surface waves data in near real time. In this work, we analyze the effects of wind and noise levels on the radar skills for wave measurement, compared with existing in-situ data obtained by an offshore buoy. Then, the HF radar wave measurements for 2022 are analyzed with special focus on the most energetic observed wave events. The analysis performed versus in-situ data shows that both stations present reliable and accurate data for waves over 1.5 m, in agreement to what can be expected for a 4.46 MHz radar. The highest correlations are observed for waves &amp;gt; 4 m significant wave height, which demonstrates the capabilities for monitoring highly energetic events. Interference and noise detected on very precise time slots significantly reduced the availability and reliability of the measurements. Also, local winds blowing from land direction were found to affect the agreement between radar and in-situ measurements. Recommendations extracted from the analysis are provided, with the aim that they can be extended to other HF networks for more accurate wave monitoring.

Wave energy is regarded as one of the powerful renewable energy sources and depends on the assessment of significant wave height (Hs) for feasibility. Hence, this study explores the potential of wave energy by assessing and predicting Hs for two study sites in Queensland (Emu Park and Townsville), Australia. Assessment and prediction of Hs is extremely important for reliable planning, cost management and implementation of wave energy projects. The study utilized oceanic datasets based on wave measurements obtained from buoys along coastal regions of Queensland that are transmitted to nearby receiver stations. The parameters of the datasets include maximum wave height, zero up crossing wave period, peak energy wave period and sea surface temperature to accurately predict Hs. A new hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short Term (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) is developed which is benchmarked by Multi-Layer Perceptron (MLP), Random Forest (RF) and Categorical Boosting (CatBoost) to compare the performance. All models attain relatively high-performance results. The MVMD-CNN-BiLSTM attains slightly better performance values for both study sites among all developed models with highest correlation values of 0.9957 and 0.9986 for Emu Park and Townsville, respectively. Other performance evaluation metrics were also higher for MVMD-CNN-BiLSTM with lowest error values in comparison to the benchmark models. The annual mean of Hs is also computed to compare and obtain an insight with a linear projection. There is a greater ocean wave energy potential for Emu Park for a 10-year period with a projected mean Hs of 0.865 m in comparison to Townsville where the projected mean was of 0.665 m.

Abstract This article presents a joint statistical model, which is needed in probabilistic design and structural risk assessment, that has been fitted to data of wind and wave conditions for an offshore location off South Brittany. The data are from a numerical model and contain hourly values for several wind and wave variables over a period of 32 years. The joint distribution presented in this article considers the variables wind direction, mean wind speed, significant wave height, wave direction, and peak period. A conditional model for turbulence given wind speed is introduced to yield an additional variable for the joint model. The joint model is constructed as a product of marginal and conditional models for the various variables. Additionally, the fitted models will be used to construct environmental contours for some of the variables. For significant wave height, various models are used to obtain different extreme value estimates, illustrating the uncertainties involved in extrapolating statistical models beyond the support of the data, and a discussion on the use of nonparametric copulas for the joint distribution is presented. Moreover, bootstrap has been performed to estimate the uncertainty in estimated model parameters from sampling variability. The effect of changing which variable to model as the marginal in a conditional model is illustrated by switching from wind speed to significant wave height. Such joint distribution models are important inputs for design of offshore structures, and in particular for offshore wind turbines, and the influence of the joint model in design is illustrated by a simple case study. This article is an extension of the conference paper by Vanem et al. (2023, “A Joint Probability Distribution Model for Multivariate Wind and Wave Conditions,” 42nd International Conference on Ocean, Offshore and Arctic Engineering).

Capture mechanism of a multi-dimensional wave energy converter with a strong coupling parallel drive

Impact of operations and maintenance on the energy production of floating offshore wind farms across the North Sea and the Iberian Peninsula

Comparison of wave spectrum assimilation and significant wave height assimilation based on Chinese-French oceanography satellite observations

Multivariate GRU and LSTM models for wave forecasting and hindcasting in the southern Caspian Sea

This paper examines metocean data from NORA3, a state-of-the-art wind and wave hindcast dataset for Northern Europe. Two offshore Norwegian areas, Utsira Nord (UN) and Sørlige Nordsjø II (SN2), are investigated. Both areas offer significant potential for the offshore wind sector. UN is situated in deep-sea water, suitable for floating offshore wind turbines. In contrast, SN2 lies in intermediate waters and ranks among the North Sea’s most promising regions allocated for offshore wind. Data from NORA3, originally on a 3-km resolution grid, are resampled into unstructured grids spanning from 1982 to 2022. This refined dataset offers a climatology time scale with superior spatial and temporal resolution compared to most other hindcast and reanalysis databases. The study examines mean wind speed and direction across seven levels, ranging from 10 m to 750 m above the surface. Analyses of extreme wind and wave conditions have been conducted. Results reveal that UN experiences higher extreme wave heights than SN2 while the extreme wind speeds may be substantially larger at SN2 than UN. Moreover, this study establishes joint distribution models that encompass several parameters, including mean wind speed, significant wave height, wave spectral peak period, and direction difference between wind and waves. Thus, this metocean data is valuable for designing and analyzing floating wind farms over their lifecycles.

Since its first launching, the ability of satellite Altimetry in providing reliable and accurate ocean geophysical information of the sea surface height (SSH), significant wave height (SWH), and wind speed has been proven by numerous researchers, as it was designed for observing the ocean dynamics through nadir range measurement between satellite and the sea surface. However, to achieve high level accuracy, environmental and geophysical effects on the range measurement must be accurately determined and corrected, particularly the effects from the atmospheric water vapor which can divert altimeter range up to 3–45 cm. Thus, satellite Altimetry is originally equipped with the on-board microwave radiometer to measure the water vapour content for correcting the range measurement. To our knowledge, no one has attempted to apply the on-board radiometer for atmospheric studies. In this present work, we attempt to optimize the on-board radiometer data for studying the atmosphere variability due to the El Niño–Southern Oscillation (ENSO) phenomena. We convert the on-board water vapor data into the precipitable water vapour (PWV), and we then investigate whether the derived PWV can capture the variability of ocean–atmosphere phenomena due to ENSO as accurate as the conventional Altimetry-derived sea level anomaly (SLA). Based on our analysis using the empirical orthogonal function (EOF), the results show convincing argument that Altimetry-derived PWV are reliable in examining the atmospheric fluctuation as the correlation of its primary principal component time series (PC1) with Oceanic Nino Index (ONI) is higher (0.87) than SLA (0.80). These results may reinforce the confidence in the ability of satellite Altimetry for ocean–atmospheric studies.Graphical

Feasibility study of a method for tuning wave energy converters

A global study of extreme value (1 in 100-year return period) tropical cyclone generated waves is conducted across all tropical cyclone basins. The study uses a 1000 year tropical cyclone synthetic track database to force a validated parametric wave model. The resulting distributions of extreme significant wave height show that values in the North Atlantic and Western Pacific basins are the largest globally. This is partly due to the relative intensities and frequencies of occurrence of storms in these basins but also because the typical velocities of forward movement of storms are larger and hence can sustain the generation of larger waves. These larger values of velocity of forward movement tend to occur at higher latitudes. As a result, in both of these basins the largest extreme waves occur at higher latitudes than the maximum tropical cyclone winds. In all other tropical cyclone basins, storms tend to propagate more east–west and hence the maximum values of extreme significant wave height and wind speed occur at comparable latitudes.

STGWN: Enhanced spatiotemporal wave forecasting using multiscale features