Wave Prediction Research Articles

Electromagnetic frequency prediction pattern for electrical circuited shell based on FSTT theory using electromagnetic wave prediction method

This research focuses on electromagnetic analysis of electrical circuited shell to predict possible failure using wave prediction method. The equation is established considering axial and lateral hydrostatic pressure based on first order shear deformation theory of shell motion using the wave propagation approach and classic Flügge shell equations. For validation of accuracy of this research method, a comparison carried out with experimental data. With this method the effects of circuit parameters as well as different t power-law exponent on the frequencies are investigated. The results showed fantastic agreement between the present method and experimental ones.

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

AbstractAccurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

Nonlinear model predictive dynamic positioning of a remotely operated vehicle with wave disturbance preview

Exploiting preview information of disturbances within robot control is an effective method of handling state perturbations. For underwater vehicles operating in wave-dominated environments, disturbances significantly influence vehicle response and pose a threat to operational safety. In consideration of this, a complete end-to-end control architecture is developed in this work for disturbance rejection during station keeping tasks under wave perturbations, encompassing a nonlinear model predictive controller (NMPC) combined with a deterministic sea wave predictor (DSWP). The wave predictor exploits a continuous measurement of the wave elevation at a location upstream of the vehicle to form a forecast of the temporal evolution of the wave elevation at the vehicle location. The predicted wave parameters are then used to estimate the impending wave-induced hydrodynamic loading, enabling explicit consideration of accurate short-term disturbance forecasts within the controller, ensuring this preview is incorporated in the optimised control sequence. Experimental testing confirms the validity of the wave predictor, producing RMSE as low as 0.017 m. The proposed strategy is then simulated under various wave conditions, optimising control actions inclusive of the preview information. The NMPC outperforms a similar disturbance mitigating feed-forward controller, with average improvements of up to 52% and is found to perform best even with noisy, lower accuracy wave predictions, as well as with communication time delays, providing a high degree of robustness. This demonstrates the potential of the proposed control framework to effectively tackle disturbance mitigation of underwater vehicles subject to large magnitude wave disturbances, expanding the operational envelop of autonomous systems towards forbidding ocean environments.

Prediction of impulse waves generated by partially submerged landslides with a low Froude number based on prototype physical experiments

Impulse waves generated by landslides are a potential threat to reservoirs. Wave prediction formulas that can quickly assess the hazards and extent of landslide-induced waves are an important means for early warning and disaster prevention and mitigation. Partially submerged landslides often generate landslide waves with low Froude numbers. There is limited research on prediction formulas for such waves, and most studies focused on specific wave propagation stages rather than forming a comprehensive formula system. In this study, three typical low Froude number submerged landslides that occurred in the Baihetan reservoir were selected as prototypes, and a large-scale three-dimensional (3D) physical model experiment field with dimensions of 30 × 29.5 × 1.5 m3 was constructed. A total of 95 experiments were performed. The entire process of impulse wave generation in the reservoir area was investigated by dividing the waves into four successive stages: initiation, rapid circular attenuation propagation, progressive attenuation propagation along the channel, and wave run-up. Based on a large amount of physical experimental data, formulas were derived for the maximum wave amplitude, propagation wave amplitude considering the degree of landslide submergence, and impulse wave run-up considering the shore slope orientation and ravine angle. These formulas were combined to form a comprehensive formula system to calculate the whole process of the impulse waves generated by the landslide in the narrow river channel with a wide influence range. The comprehensive formula system was applied to typical representative landslide experiments, and its accuracy was analyzed; the prediction accuracy ranged from 56% to 89.5%. This study can serve as a reference for assessing the risk of impulse waves generated by landslides in reservoir areas.

Statistical distribution of surface elevation by using quadratic forms of normal random variables

This paper is concerned with the statistical properties of surface elevation, which is crucial for the design and operation of marine and coastal structures and is helpful to the prediction of rogue waves. A semi-analytic quadratic model is proposed to describe the statistical distribution of surface elevation by using the theory of quadratic forms of normal random variables, in which the characteristic function and cumulants are given in analytical form, and the probability density is obtained by the numerical inversion of the characteristic function using the fast Fourier transform algorithm. Compared with Monte Carlo simulations, the quadratic model presents excellent performance in describing the probability density function of surface elevation, especially at the tails, for varying degrees of steepness, bandwidth, and directional spreading in both finite and infinite water depth. The effect of these parameters that characterize sea states on the statistical properties of surface elevation is also investigated. Through a comparative study, the quadratic model is superior to previously existing theoretical models.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

Data Assimilation and Parameter Identification for Water Waves Using the Nonlinear Schrödinger Equation and Physics-Informed Neural Networks

The measurement of deep water gravity wave elevations using in situ devices, such as wave gauges, typically yields spatially sparse data due to the deployment of a limited number of costly devices. This sparsity complicates the reconstruction of the spatio-temporal extent of surface elevation and presents an ill-posed data assimilation problem, which is challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN) to reconstruct physically consistent wave fields between two elevation time series measured at distinct locations within a numerical wave tank. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schrödinger equation (NLSE) into the PINN’s loss function. We first showcase a data assimilation task by employing constant NLSE coefficients predetermined from spectral wave properties. However, due to the relatively short duration of these measurements and their possible deviation from the narrow-band assumptions inherent in the NLSE, using constant coefficients occasionally leads to poor reconstructions. To enhance this reconstruction quality, we introduce the base variables of frequency and wavenumber, from which the NLSE coefficients are determined, as additional neural network parameters that are fine tuned during PINN training. Overall, the results demonstrate the potential for real-world applications of the PINN method and represent a step toward improving the initialization of deterministic wave prediction methods.

Real-time phase-resolved ocean wave prediction in directional wave fields: Second-order Lagrangian wave models

The Improved Choppy Wave Model (ICWM) was established to achieve a second-order Lagrangian expansion of surface waves. The second-order Lagrangian nonlinear interaction terms were found to be negligible and were therefore discarded. However, these interactions in directional wave fields remained unexplored. ICWM was successfully used, especially for floating offshore wind turbines, but it was noted that its accuracy in predicting directional sea states needed improvement. Hence, we formulated the Complementary ICWM (CICWM) with the nonlinear terms for free surface elevation in directional waves. Detailed formulations for data assimilation and wave propagation were provided, enabling real-time wave forecasting for directional seas using a simplified assimilation method. Comparing the model performances against tank-scale experiments showed that CICWM enhances the surface elevation description in directional seas. Ultimately, it was confirmed that the second-order Lagrangian model can reduce the prediction error of the linear wave model by 90% in directional seas for the experimental setups and sea states investigated in this study.

Enhancing typhoon wave hindcasting with random forests and BP neural networks in the SWAN model

Forecasting typhoon waves during typhoons is crucial. In this paper, the numerical wave model SWAN was enhanced through integration with two machine learning methods: the Back Propagation Neural Network and Random Forest. This integration facilitated the development of two distinct models, namely SWAN-BP and SWAN-Tree. Through correlation analysis, key input features were identified for the machine learning models. The forecasts from the SWAN model were subsequently utilized as inputs to enhance further wave prediction. These hybrid models were validated using data from Typhoon Doksuri (2023) and Typhoon Nesat (2017). The results indicated significant improvements in predicting typhoon-induced wave heights with both the SWAN-BP and SWAN-Tree models compared to the original SWAN model. Specifically, the SWAN-BP model demonstrated a 33% improvement in accuracy for the Typhoon Doksuri, whereas the SWAN-Tree model exhibited a 24% improvement. For Typhoon Nesat, the accuracy improvements were 23% for the SWAN-BP model and 21% for the SWAN-Tree model. These findings demonstrate that integrating wave numerical models with machine learning techniques can significantly enhance the predictive accuracy of numerical models. This approach offers a cost-effective means to improve the existing wave forecasting database. Traditionally, the direct use of meteorological and oceanographic data for typhoon wave prediction might be compromised by biases inherent in the numerical wave models. However, the SWAN-BP and SWAN-Tree models effectively reduce these biases, thereby providing more accurate and robust predictions. In conclusion, this paper enhances the predictive accuracy of the SWAN model and establishes a crucial foundation for more precise typhoon wave forecasting through the application of machine learning techniques.

An assessment of the impact of boundary conditions in dynamical downscaling techniques for fetch-limited waves

ABSTRACT The handling of boundary conditions (BCs) is a key issue for the development of dynamical downscaling techniques in which nearshore wave models require the definition of the sea conditions at the offshore boundaries. This study addresses the implications of using simplified BCs represented by bulk or partitioned wave parameters with respect to using the complete information from directional wave spectra. As a large-scale model, we used WAVEWATCH III (WW3) running over the Mediterranean Sea providing the wave BCs for the small-scale SWAN model. One year of measured directional wave spectra from three deepwater buoys allowed the assessment of the WW3 model accuracy. The nested SWAN model simulations propagated the wave field over three nearshore areas. The accuracy of SWAN simulations was assessed through the comparison of computed results with wave measurements from nearshore buoys. Being the wave conditions dominated by unimodal seas, modest differences of error metrics are found between the datasets forced by different types of BCs. Conversely, focusing the attention on multimodal seas, an accurate definition of wave BCs achieved by the use of the directional wave spectra or, in alternative, by the reconstruction of wave spectra from partitioned wave parameters becomes crucial for nearshore wave predictions.

Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA

Abstract We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System (UFS) at the National Oceanic and Atmospheric Administration (NOAA), part of the US National Weather Service (NWS) operational forecasting suite. The operational implementation of the atmosphere-wave coupled Global Ensemble Forecast System version 12 (GEFSv12) in September 2020 was a critical step in NOAA’s transition to the broader community-based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact events. The integration of a coupled wave component with higher spatial and temporal resolution and optimized physics parameterizations notably enhanced forecast skill and predictability, particularly benefiting winter storm predictions of wave heights and peak wave periods. This successful endeavor encountered challenges that were addressed by the simultaneous development of new features that enhanced wave model forecast skill and product quality and facilitated by a multidisciplinary team collaborating with NOAA’s operational forecasting centers. The GEFSv12 upgrade marks a pivotal shift in NOAA’s global forecasting capabilities, setting a new standard in wave prediction. We also describe the coupled GEFSv12-Wave component impacts on NOAA operational forecasts, and ongoing experimental enhancements, which altogether represent a substantial contribution to NOAA’s transition to the fully-coupled UFS framework.

Possible influence of the large-scale environment on extreme waves over the Yellow Sea in boreal spring

The Yellow Sea (YS) is exposed to various weather systems, such as typhoons, monsoon activities, and extratropical cyclones, which can pose a major threat to the adjacent coastal regions through the development of energetic oceanic surface waves. Unusually severe surface wave events in the YS occur with considerable frequency during the boreal spring (March-April-May), but have received less attention compared to winter and summer. This study focuses on the characteristics of spring extreme wave height (EWH) events in the YS during 1979-2022, based on observational and long-term reanalysis datasets. Our analysis shows that extreme waves, i.e. daily maximum heights in the top 5% of all spring days, take about 12 h to build up to the peaks, while they decay more slowly after the peaks. During the extreme events, the Siberian High is found to extend anomalously eastward compared to spring climatology. Such an anomalous extension contributes to the increase of the sea level pressure gradient and the intensification of the surface wind speed in the YS. Meanwhile, in the range of 6 ∼ 24 h following the peaks of the EWH events in the YS, swells induced by strong northerly winds begin to have an impact in the YS. These swells contribute to maintaining higher wave energy levels in the YS for longer after the atmospheric source has been removed. We further explore the large-scale environmental conditions that could provide the predictability of extreme waves in the basin developed by these findings. This study presents implications for assessing the risks associated with extreme waves in coastal regions and for improving coastal management strategies in the YS.

Energy Maximization Absorption of Wave Energy Converter Based on Fourier Pseudo-Spectral Method and Adaptive Dynamic Programming

In this paper, a novel noncausal control framework to address the energy maximization problem of wave energy converters (WECs) with physical constraints is proposed. The energy maximization problem of WECs is a constrained optimal control problem. The proposed control framework converts this problem into a reference trajectory-tracking problem through the Fourier pseudo-spectral method (FPSM) and utilizes the online tracking adaptive dynamic programming (OTADP) algorithm to realize real-time trajectory tracking for practical use in the ocean environment. Using the wave prediction technique, the optimal trajectory is generated online through a receding horizon (RH) implementation. A critic neural network (NN) is applied to approximate the optimal cost value function and calculate the error-tracking control by solving the associated Hamilton-Jacobi-Bellman (HJB) equation. The proposed WEC control framework improves computational efficiency and makes it feasible for the online control of the WEC in practice. Simulation results show the effects of the receding-horizon implementation of FPSM with different window lengths and window functions, while verifying the performances of tracking control and energy absorption of WECs in two different sea conditions.

Analysis and extreme event prediction of waves in the eastern shelf seas of China based on a 44-year hindcast

Analysis and extreme event prediction of regional waves are crucial for the design, spatial layout and protection of ocean engineering structures. In this study, the regional wave characteristic is analysed in the eastern shelf seas of China based on a 44-year wave hindcast. The spatial variation of peak period is smaller than that of significant wave height, which may be affected by swell propagation, and the seasonal distribution differences of these parameters can generally be explained by changes in the East Asian monsoon and are significantly affected by tropical cyclones. Both local wind wave and remotely generated swell should be considered when designing ocean structures. In addition, the joint distribution characteristics of wave period and height reveal a natural regional classification of representative wave states, which can be used to avoid deploying structures near classification boundaries. The extreme wave events are characterized by their number, duration and intensity, highlighting areas susceptible to extreme waves. Meanwhile, the extreme event is predicted, and its return patterns are highly consistent with weather system characteristics such as tropical cyclone tracks. The results can provide a significant basis for the design and protection of ocean engineering structures.

Research on numerical modeling of two-dimensional freak waves and prediction of freak wave heights based on LSTM deep learning networks

Freak waves have posed a serious threat to the safety of marine structures. Thus, the accurate simulation and prediction of freak waves are crucial for maritime safety. This paper proposes a nonlinear numerical method based on phase modulation. This method achieves precise time and location simulation of two-dimensional freak waves, while preserving the key statistical characteristics of the wave sequence, thereby obtaining more accurate two-dimensional wave height information. Additionally, this paper constructs a Long Short-Term Memory (LSTM) deep neural network integrated with a Sequence-to-Sequence (seq2seq) framework for predicting sequences of freak wave heights. In this study, we conduct an in-depth predictive analysis of the experimental data for sequences of freak wave heights. And this paper provides a comprehensive assessment of the performance of Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks in the task of freak wave prediction. The experimental results indicate that the seq2seq-LSTM model demonstrates significant performance advantages in predicting the wave heights of freak waves, particularly for long-term predictions. In summary, the numerical simulation method based on phase modulation and the improved seq2seq-LSTM deep learning model are of significant practical value for enhancing the safety of offshore platforms and ships.

Hybrid framework of deep extreme learning machine (DELM) based on sparrow search algorithm for non-stationary wave prediction

Accurate and trustworthy wave height forecast is essential for the efficient exploitation of wave energy. However, the non-linearity and non-stationarity of waves present a challenge task when using the conventional statistical models in the ocean wave forecasting. Aiming at these problems, this paper proposes a short-term wave height prediction method based on the Variational Mode Decomposition (VMD), Deep Extreme Learning Machine (DELM) and Sparrow Search Algorithm (SSA). Specifically, the original wave height data is initially decomposed into numerous sub-series from high to low frequency by VMD technique. Subsequently, the SSA method is employed to optimize the DELM parameters to enhance the functionality of the basic DELM model. Three wave height datasets located in the North Pacific Ocean are employed as cases in this paper. In comparison with other benchmark models, the RMSE parameters of VMD-SSA-DELM model are reduced by 35.59%–73.33% even at 10-h lead time by analyzing different forecast durations, which demonstrate the proposed model's superiority in short-term wave height prediction.

An Attention-Based Deep Learning Model for Phase-Resolved Wave Prediction

Abstract Phase-resolved wave prediction capability, even if only over two wave periods in advance, is of value for optimal control of wave energy converters, resulting in a dramatic increase in power generation efficiency. Previous studies on wave-by-wave predictions have shown that an artificial neural network (ANN) model can outperform the traditional linear wave theory-based model in terms of both prediction accuracy and prediction horizon when using synthetic wave data. However, the prediction performance of ANN models is significantly reduced by the varying wave conditions and buoy positions that occur in the field. To overcome these limitations, a novel wave prediction method is developed based on the neural network with an attention mechanism. This study validates the new model using wave data measured at sea. The model utilizes past time histories of three Sofar Spotter wave buoys at upwave locations to predict the vertical motion of a Datawell Waverider-4 at a downwave location. The results show that the attention-based neural network model is capable of capturing the slow variation in the displacement of the buoys, which reduces the prediction error compared to a standard ANN and long short-term memory model.

Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model

IntroductionThe precise forecasting of Significant wave height(SWH) is vital to ensure the safety and efficiency of aquatic activities such as ocean engineering, shipping, and fishing.MethodsThis paper proposes a deep learning model named SAC-ConvLSTM to perform 24-hour prediction with the SWH in the South China Sea. The long-term prediction capability of the model is enhanced by using the attention mechanism and context vectors. The prediction ability of the model is evaluated by mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and Pearson correlation coefficient (PCC).ResultsThe experimental results show that the optimal input sequence length for the model is 12. Starting from 12 hours, the SAC-ConvLSTM model consistently outperforms other models in predictive performance. For the 24-hour prediction, this model achieves RMSE, MAE, and PCC values of 0.2117 m, 0.1083 m, and 0.9630, respectively. In addition, the introduction of wind can improve the accuracy of wave prediction. The SAC-ConvLSTM model also has good prediction performance compared to the ConvLSTM model during extreme weather, especially in coastal areas.DiscussionThis paper presents a 24-hour prediction of SWH in the South China Sea. Through comparative validation, the SAC-ConvLSTM model outperforms other models. The inclusion of wind data enhances the model's predictive capability. This model also performs well under extreme weather conditions. In physical oceanography, variables related to SWH include not only wind but also other factors such as mean wave period and sea surface air pressure. In the future, additional variables can be incorporated to further improve the model's predictive performance.

Free Infragravity Waves on the Inner Shelf: Observations and Parameterizations at Two Southern California Beaches

AbstractNumerical predictions of nearshore waves and shoreline runup are usually initialized on the inner shelf, seaward of the surfzone, with sea‐swell (SS) waves from local wave buoys or regional wave models. Lower frequency infragravity (IG) waves are not reliably measured by buoys or included in regional models. Here, co‐located pressure and velocity observations are used to characterize IG waves in 10–15 m depth in southern California. Shoreward propagating IG waves are often dominated by free waves, with the boundwave energy fraction <30% for moderate and low energy incident SS waves. Only 5% of records, with energetic long swell, show primarily bound waves. The shoreline slope of concave beaches increases by ∼3 between spring high and low tides, and free seaward and shoreward IG energy in 10–15 m vary tidally. The observed linear dependency of free IG energy on SS energy and period is consistent with Ardhuin et al. (2014, https://doi.org/10.1016/j.ocemod.2014.02.006)'s parameterization (R2 = 0.71). Including the tide level as a proxy for beach slope and modifying the SS frequency dependency increases R2 to 0.91. The ratio of free seaward to shoreward propagating IG energy suggests between 50 and 100% of the energy radiated seaward in depths of 10–15 m is trapped offshore and redirected shoreward. Free (random phase) and bound (phase‐coupled) IG waves are combined to initialize the SWASH numerical model. SWASH predicted runup is only weakly influenced by waves at the offshore boundary. Nonlinear IG generation and dissipation in the shoaling and surfzone overwhelm the effects of shoreward propagating waves observed at the offshore boundary.

Muzzle bubble dynamics characterization of underwater launching

To comprehensively understand the dynamic behavior of muzzle bubbles during underwater launching, an emptying process aligned with the muzzle flow characteristics is established and an evaporative condensation mechanism is modeled according to the high temperature and pressure properties of the propellant gas. Utilizing the spherical bubble theory, which comprises the inflation process and evaporative condensation effects, the dynamics of muzzle bubbles and their corresponding pressure waves are investigated. The numerical simulation results well agree with the experimental observations in terms of bubble radius and near-field pressure waves. Furthermore, the influence of two key factors on the bubble dynamics is examined: underwater launching depth and initial muzzle pressures. The results illustrate that the inflation process needs to be accurately described for precise pressure wave predictions. Using the evaporation condensation model, the bubble radius and frequency can be accurately characterized. Moreover, the launching depth influences the free expansion radius and oscillation frequency mostly due to the increase in hydrostatic pressure, which decreases by 33% and increases by 150% in the 1–20 m range, respectively. The initial muzzle pressure affects the initial expansion velocity and initial shock wave mainly due to the increase in the mass flow rate, which increase by 56% and 82% in the 35–65 MPa range, respectively.