Wave Prediction Research Articles

Spectra of solar shallow-water waves from bright point observations

Rossby waves, large-scale meandering patterns drifting in longitude, detected in the Sun, were recently shown to a play a crucial role in understanding “seasons” of space weather. Unlike Earth's purely classical atmospheric Rossby waves, the solar counterparts are strongly magnetized and most likely originate in the tachocline. Because of their deeper origin, detecting these magnetized Rossby waves is a challenging task that relies on careful observations of long-lived longitudinally drifting magnetic patterns at the surface and above. Here, we have utilized 3 years of global, synchronous observations of coronal bright point densities to obtain empirical signatures of dispersion relations that can be attributed to the simulated waves in the tachocline. By tracking the bright point densities at selected latitudes, we computed their wave-numbertimes frequency spectra. Wave-numbertimes frequency spectra were computed utilizing the Wheeler-Kiladis method. This method has been extensively used in the identification of equatorial waves in Earth's atmosphere by highlighting spectral peaks in the wave-numbertimes frequency space. Our results are compatible with the predictions of magneto-Rossby waves with typical periods of several months and inertio-gravity waves with typical periods of a few weeks, depending on the background magnetic field's strength and stratification at the convection zone base. Our analysis suggests that magnetized Rossby waves originate from the tachocline toroidal field of lesssim 15 kG. Global observations of bright points over extended periods will allow us to better constrain the stratification and magnetic field strength in the tachocline.

Real-time ocean wave prediction in time domain with autoregression and echo state networks

This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). The performance of these models is evaluated on time series data at different water depths and wave conditions, including both measured and simulated data with a focus on real-time prediction of ocean waves at a given location without resolving for the surrounding ocean surface, in other words, short-time single-point forecasting. The work presented includes training the models on historical wave data and testing their ability to predict phase-resolved future surface wave patterns for short-time forecasts. Additionally, this study discusses the feasibility of deploying these models for extended time intervals. It provides valuable insights into the trade-offs between accuracy and practicality in the real-time implementation of predictive models for wave elevation, which are needed in wave energy converters to optimise the control algorithm.

Primordial acoustic turbulence: Three-dimensional simulations and gravitational wave predictions

Primordial acoustic turbulence: Three-dimensional simulations and gravitational wave predictions

Mixed neural operator learning on the solitary wave propagation over slope topography and inverse problem

This study proposes the mixed neural operator (MNO) learning framework, which further combines with the particle swarm optimization (PSO) to address challenges of solitary wave propagation over topography. The forward problem is defined as the evolution prediction of the solitary wave propagating over topography, while the inverse problem is defined as an optimization to identify the topography parameter based on the solitary wave elevation. Both the forward and inverse problems can be considered within a single framework and the dataset are provided by the classical Korteweg–de Vries (KdV) equation. The MNO framework is shown to simulate the evolution of solitary waves over topography, accurately capturing the wave elevation under different topographical conditions. By comparing with different neural operators, it is found that the U-shape neural operator is the most suitable for the KdV equation simulation. The coefficient of determination for the inverse problem based on the combination of MNO and PSO can reach 0.992, showing great potential of the approach in topography recognition. Finally, the proposed learning framework is preliminary applied to the prediction of the tsunami runup onto a complex beach, and a good agreement is also achieved between the direct simulation and the learning framework prediction.

Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety

The fishing industry has a large role in the Indonesian economy, with potential profits in 2020 of around US$ 1.338 billion. Tuban Regency is one of the regions in East Java that contributes to the fisheries sector. Fisheries relate to the work of fishermen. Accidents in shipping are still a major concern. One of the natural factors that influence shipping accidents is the height of the waves. Fisherman safety regulations have been established by the Ministry of Maritime Affairs and Fisheries and the Meteorology, Climatology and Geophysics Agency. Apart from regulations, the results of wave height predictions using the Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) methods can help fishermen determine shipping departures, thereby reducing the risk of accidents. In this study, the Grid Search hyperparameter tuning process was used for both methods which were carried out on four location coordinates. Based on the analysis results, LSTM is superior in predicting wave height for the next 30 days because it can predict wave height at all three locations, with results at the first location (RMSE 0.045; MAE 0.029; MAPE 8.671%), second location (RMSE 0.051; MAE 0.035; MAPE 10.64%), and third location (RMSE 0.044; MAE 0.027; MAPE 7.773%), while XGBoost only has the best value at fourth location (RMSE 0.040; MAE 0.025; MAPE 7.286%).Hyperparameter tuning with gridsearch is used in LSTM and XGBoost to obtain optimal accuracyLSTM outperforms in three locations, while XGBoost outperforms in the fourth location.Advanced prediction techniques such as LSTM and XGBoost improve fishermen's safety by providing accurate wave height estimates, thereby reducing the possibility of shipping accidents.

Prediction of impulse waves generated by the potential failure of large deposits in the Rumei Reservoir, Lancang River, China

The impulse wave generated by the reservoir landslide seriously endangers the surrounding safety. In this paper, the failure mode of a large deposit in Rumei Reservoir, China was first explored by centrifuge test. Thereafter, a physical model based on Froude similarity (scale 1:200) and a numerical model based on fluid‒solid coupling were constructed. The failure motion process and the generated impulse waves of the deposits at high (2895 m) and low (2750 m) water levels are studied in detail. The results of the two models are similar. The velocity and wave height of the deposits at the 2750 m water level are greater than those at the 2895 m water level. The maximum wave heights generated in the river under both conditions exceed 46 m and 22 m, respectively, and the maximum run-ups formed on the opposite bank are 71 m and 30 m, respectively. This also suggests that landslides in the near field area are dangerous. Waves at both water levels will not overtop the dam, but attention should be given to the effect of the first wave at the 2895 m water level on the dynamic water pressure in the key area of the dam.

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.

Analysis of the Principle of Gravitational Wave Detection and State- of-art Results

Over one hundred years after Einstein’s first theoretical prediction of gravitational waves, the recent observations of gravitational waves by interferometric detectors have risen excitement in the science community with the light from a new era of directly observing perturbations in spacetime itself. With the large quantity of new research in the gravitational wave detection area, this study provides a brief summary of the current achievements of the field. This paper first gives a short introduction of the field, then offers a summarize of the theories and principles of gravitational wave detection. Afterwards, it demonstrates modern detectors using the advanced LIGO detectors as an example, and presents its groundbreaking results, signal GW150914 and GW151226. Finally, the limitations and prospects of current observations are proposed. By summarizing fundamental concepts and major achievements in the field of gravitational wave detection, it is hoped to provide future students and researchers with a basic idea of the field and thus promote new ideas and advancements in the rising era of gravitational astronomy.

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.