Search Results (118)

In high-frequency surface wave radar (HFSWR) systems, the retrieval of the directional wave spectrum has remained challenging, especially in the case of echoes from long ranges with a low signal-to-noise ratio (SNR). Therefore, a quadratic programming algorithm based on the regularization technique is proposed with an empirical criterion for estimating the optimal regularization parameter, which minimizes the effect of noise to obtain more accurate inversion results. The reliability of the inversion method is preliminarily verified using simulated Doppler spectra under different wind speeds, wind directions, and SNRs. The directional wave spectra inverted from a radar network with two multiple-input multiple-output (MIMO) systems are basically consistent with those from the ERA5 data, while there is a limitation for the very concentrated directional distribution due to the truncated second order in the Fourier series. Further, in the field experiment during a storm that lasted three days, the wave parameters are calculated from the inverted directional spectra and compared with the ERA5 data. The results are shown to be in reasonable agreement at four typical locations in the core detection area. In addition, reasonable performance is also obtained under the condition of low SNRs, which further verifies the effectiveness of the proposed inversion algorithm. Full article

This study assesses the wave energy potential and spectral variability in the Väinameri—a semi-sheltered, island-filtered basin on Estonia’s west coast—by combining six months of high-resolution in situ wave spectra with deep learning-enhanced satellite retrievals. Directional spectra were recorded at Rohuküla Harbor using a wave-following LainePoiss buoy from June to December 2024. In parallel, one-dimensional wave spectra were reconstructed from Sentinel-1 SAR imagery using a long short-term memory (LSTM) neural network trained on more than 71,000 collocations with NORA3 WAM hindcasts. Spectral pairs matched within a ±1 h window exhibited strong agreement in the dominant 0.2–0.4 Hz frequency band, while systematic underestimation at higher frequencies reflected both the radar resolution limits and the short-period, wind–sea-dominated nature of the Baltic Sea. Our results confirm that LSTM-enhanced SAR retrievals enable robust bulk and spectral wave characterizations in data-sparse nearshore regions, and offer a practical basis for the site evaluation, device tuning, and survivability testing of pilot-scale wave energy converters under both typical and storm-driven forcing conditions. Full article

Characterizing the coastal wave environment, typically composed of wind-driven waves and boat wakes, and its interaction with built infrastructure is essential for planning sustainable and resilient shoreline development and protection. Objectively identifying and measuring non-stationary wave features, particularly boat wakes, in longer data records remains a challenge. A wave gauge array of four pressure sensors was deployed for several weeks in the northernmost section of urbanized Tampa Bay, FL, a sheltered, shallow (mean depth 1.2 m) region with frequent recreational small-boat activity. New methods for analyzing these measurements were explored. The array had a square geometry, allowing the calculation of directional spectra. Most prior studies of boat wakes could only examine amplitude spectra. A nearby seawall was found to be a significant source of wave reflection. Additionally, a novel empirical method for identifying wakes, distinguishing them from wind-driven waves, and providing an estimate of their duration and amplitude was developed. The method was found to reliably identify most primary wakes but not reflected wakes. Reflected boat wakes were identified manually, and only during times of relatively high water levels when the shoreline in front of the seawall was flooded. Full article

This study introduces a data-driven and machine learning (ML)-based methodology for converting the encounter wave frequency spectrum to the original wave spectrum, a critical process for navigating vessels with forward speed in various control and adjustment missions. The spectral conversion from the encounter- to original-frequency domain faces challenges under certain wave conditions due to the non-uniqueness of the inverse problem. To resolve these challenges, the authors developed an artificial neural network (ANN) model that transforms the encounter-frequency spectrum into the original wave spectrum at a given vessel speed and wave direction. The model was trained and validated using a large dataset mapped from various JONSWAP wave spectra to the corresponding encounter-frequency spectra for various vessel speeds and wave parameters. The hyperparameters of the ANN model were subsequently tested and optimized. The results demonstrate that the ANN model can effectively predict the original wave spectrum with high accuracy, as evidenced by a favorable R2 value and error distribution analysis. This approach not only enhances the reliability of wave spectrum estimation during maritime navigation but also broadens the capability of real-time operational controls and adjustments. Full article

This study builds on the early brain dynamics work of Erol Başar, focusing on the human electroencephalogram (EEG) in relation to the generation of event-related potentials (ERPs) and behaviour. Scalp EEG contains not only oscillations but non-wave noise elements that may not relate to functional brain activity. These require identification and removal before the true impacts of brain oscillations can be assessed. We examined EEG/ERP/behaviour linkages in young adults during an auditory equiprobable Go/No-Go task. Forty-seven university students participated while continuous EEG was recorded. Using the PaWNextra algorithm, valid estimates of pink noise (PN) and white noise (WN) were obtained from each participant’s prestimulus EEG spectra; within-participant subtraction revealed noise-free oscillation spectra. Frequency principal component analysis (f-PCA) was used to obtain noise-free frequency oscillation components. Go and No=Go ERPs were obtained from the poststimulus EEG, and separate temporal (t)-PCAs obtained their components. Exploratory multiple regression found that alpha and beta prestimulus oscillations predicted Go N2c, P3b, and SW1 ERP components related to the imperative Go response, while PN impacted No-Go N1b and N1c, facilitating early processing and identification of the No-Go stimulus. There were no direct effects of prestimulus EEG measures on behaviour, but the EEG-affected Go N2c and P3b ERPs impacted Go performance measures. These outcomes, derived via our mix of novel methodologies, encourage further research into natural frequency components in the noise-free oscillations immediately prestimulus, and how these affect task ERP components and behaviour. Full article

The frequency shift of multipath sound rays induced by the motion of a sound source in an ocean waveguide environment is a crucial factor affecting the detection capabilities of both active and passive sonar systems, as well as the quality of underwater communication. Therefore, investigating the sound field characteristics of a moving sound source in the SOFAR channel is of significant importance. By comparing the spectra of continuous-wave (CW) signals with pulse widths of 1 s and 15 s received by a vertical array in SOFAR channel, it was observed that the sound field of the moving source exhibits a stable spectral splitting characteristic. Two frequency shift bright lines in the vertical direction were identified, corresponding to two sets of sound ray paths. One set of sound ray paths corresponds to the direct sound and the first surface-reflected sound, and the other set of sound ray paths corresponds to the first seabed-reflected sound and the first surface- and seabed-reflected sound. This study revealed that the spectral splitting of the moving sound source’s sound field displays a distribution trend in a depth direction similar to that of the multipath delay structure. A multipath sound ray frequency shift calculation model, based on ray theory, was developed to explain and predict the vertical distribution pattern of spectral splitting in the sound field of a moving sound source. By combining the model with measured data, it was found that the spectral splitting arises from the frequency shift differences corresponding to multipath sound ray paths. Additionally, the frequency shifts for the D&S and B&SB ray paths are generally proportional to the cosine values of the initial grazing angles of the sound waves at the emission source and the cosine values of the horizontal azimuthal angle between the source motion direction and the receiver. Full article

This study presents the development and validation of a complex-valued neural network model to predict wave conditions—such as wave direction, height, and period—surrounding floating structures. Accurate wave predictions are crucial for optimizing the design, operation, and maintenance of offshore platforms and floating offshore wind turbines, particularly in the context of digital twins. The proposed methodology leverages motion data obtained through numerical simulations of a floating structure to train the prediction model, enabling it to predict both the amplitude and phase information of the surrounding waves. The model successfully addresses the challenges of representing wave direction data in polar coordinates and capturing phase differences between motion components, which are difficult for traditional real-valued neural networks. The performance of the model was validated through various test cases, with the maximum prediction error found to be less than 10% and most predictions showing an error of less than 5%. Wave direction predictions demonstrated high accuracy, with errors consistently below 2%. While the model was trained using pseudo-measurement data, the results suggest that high-accuracy predictions can be achieved using real-world measurement data. This work contributes to enhancing wave prediction models for floating structures and is expected to improve the safety, performance, and long-term stability of such systems. Full article

Typhoon-induced waves significantly threaten marine transportation and safety, often leading to catastrophic marine disasters. Accurate wave simulations are vital for effective disaster prevention. However, traditional studies have primarily focused on significant wave height (SWH) and heavily relied on resource-intensive numerical simulations while often neglecting wave spectra, which are essential for understanding the distribution of wave energy across various frequencies and directions. Addressing this gap, our study introduces an LSTM–Self Attention–Dense model that comprehensively simulates both SWH and wave frequency spectra. The model was rigorously trained and validated on three years of global typhoon data and exhibited accuracy in forecasting both SWH and wave spectra. Furthermore, our analysis identifies optimal input data windows and underscores wind speed and central pressure as critical predictive features. This novel approach not only enhances marine risk assessment but also offers a swift and efficient forecasting tool for managing extreme weather events, thereby contributing to the advancement of disaster management strategies. Full article

This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions. Full article

The mean dissipation rate of turbulent energy reaches a constant value at high Taylor–Reynolds numbers ($R_{\lambda }$). This value is associated with the well-scaling dissipation spectrum in Kolmogorov units, where the maximum corresponds to the bottleneck peak. Even the scalar dissipation rate at the high $R_{\lambda }$ considered in the present direct numerical simulations attains a constant value as $Sc$ increases. In this scenario, the maximum of the scalar dissipation spectra reaches its peak within the bottleneck, starting at $Sc>0.5$. A qualitative explanation for the formation of the two bottlenecks is related to the blockage of energy transfer from large to small scales in the inertial ranges. Within the bottleneck, the self-similar, ribbon-like structures transition into the rod-like structures characteristic of the exponential decay range. Investigating the viscous dependence of the bottleneck’s amplitude may be aided by examining the evolution of a passive scalar. As $Sc$ decreases, the scalar spectra undergo changes across the wave number k range. The bottleneck is dismantled, and at very low $Sc$ values, the spectrum tends towards Batchelor’s theoretical prediction, diminishing proportionally to $k^{-17/3}$. To comprehend the flow structures responsible for the bottleneck, visualizations of $\theta {\nabla }^2\theta$ and probability density functions at various $Sc$ values are presented and compared with those of $u_i{\nabla }^2{u}_i$. The numerical method employed for generating three-dimensional spectra and quantities such as energy and scalar variance dissipation in physical space must be accurate, particularly in resolving small scales. This paper additionally demonstrates that the second-order finite difference scheme conserving kinetic energy and scalar variance in the inviscid limit in viscous simulations accurately predicts the exponential decay range in one-dimensional and three-dimensional turbulent kinetic energy and scalar variance spectra. Full article

Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements and realizing cost-effective large-scale deployment. Unlike traditional approaches, this buoy uses the kinematic extension of the variometric approach for displacement analysis stand-alone engine (Kin-VADASE) velocity measurement method, thus eliminating the need for additional high-precision measurement units and an expensive complement of satellite orbital products. Through testing in the South China Sea and Laoshan Bay, the results showed good consistency in significant wave height and main wave direction between the novel buoy and a Datawell DWR-G4, even under mild wind and wave conditions. However, wave mean period disparities were observed partially because of sampling frequency differences. To validate this idea, we used Joint North Sea Wave Project (Jonswap) spectral waves as input signals, the bias characteristics of the mean periods of the spectral calculations were compared under conditions of identical input signals and gradient-distributed wind speeds. Results showed an average difference of 0.28 s between the sampling frequencies of 1.28 Hz and 5 Hz. The consequence that high-frequency signals have considerable effects on the mean wave period calculations indicates the necessity of the buoy’s high-frequency operation mode. This GNSS drifting buoy offers a cost-effective, globally deployable solution for ocean wave measurement. Its potential for large-scale networked ocean wave observation makes it a valuable oceanic research and monitoring instrument. Full article

A third-generation wave model is driven by the synthetic wind field combined with the revised Holland wind and surface wind product from the National Centers for Environmental Prediction (NCEP). The temporal and spatial characteristics of the wind waves and swell during the typhoon are studied, as well as the responses of their wave energy spectra to the source terms. The results show that the typhoon waves have a more complicated asymmetric structure than the wind field, and the maximum significant wave height is always located on the right side of the direction along which the typhoon is moving, where wind waves are dominant, due to the extended fetch. The nonlinear wave–wave interaction helps to redistribute the energy of the wind seas at a high frequency to the remotely generated swells at a low frequency, ensuring that the typhoon wave’s energy spectrum remains unimodal. This process occurs in regions without extended fetch, and a similar continued downshift in frequency as the wave–wave interaction occurs for the wind input as well when the waves outrun the typhoon, due to the nonlinear coupling between the wind and growing swells. Full article

The uncertainty in estimating the wave spectrum from the records of wave elevation by heave–pitch–roll buoys is studied, considering the effects of the estimation method and the spectral resolution adopted in the process. This investigation utilizes measurements from a wave buoy moored in deep water in the South Atlantic Ocean. First, the spectra are computed using the autocorrelation function and the direct Fourier method. Second, the spectral resolution is tested in terms of degrees of freedom. The degrees of freedom are varied, and the resulting spectra and integrated parameters are computed, showing significant variability. A simple and robust methodology for determining the wave spectrum is suggested, which involves calculating the average energy density in each frequency band. The results of this methodology reduce the variability of the estimated parameters, improving overall accuracy while preserving frequency resolution, which is crucial in complex sea states. Additionally, to demonstrate the feasibility of the implemented approach, the final spectrum is fitted using an empirical model ideal for that type of spectrum. Finally, the performance and the goodness of the fit process for the final averaged curve are checked by widely used statistical metrics, such as R² = 0.97 and root mean square error = 0.49. Full article

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively. Full article

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on. Full article