Peak Wave Period Research Articles

Improvement of Numerical Simulation Using Applied Statistical Properties of Directional Waves by Typhoons

Kim, D.-H.; Yoo, C.-I.; Jeong, Y.-H.; Lee, S.-Y., and Kim, H.-J., 2021. Improvement of numerical simulation using applied statistical properties of directional waves by typhoons. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 231–235. Coconut Creek (Florida), ISSN 0749-0208. Typhoon-generated wave fields are of importance both scientifically for understanding coastal zone protection and operationally for predicting potentially hazardous conditions for coastal regions. However, the effects of directional wave-to-wave characteristics near bays and estuaries during typhoons are poorly understood due to a strong lack of wave field data. To improve the accuracy of wave height design, this study investigated a highly accurate numerical simulation using the statistical properties of directional waves. Directional wave data were collected using the directional wave recorder MIDAS-DWR, the Geoje and Haeundae floating buoys, and the Gwangan light beacon on typhoons Ma-on and Muifa in July and August, 2011. Moreover, numerical simulation results from STWAVE based on the wave action balance equation were compared with significant wave heights and periods of collected in-situ measurement data. Model input parameters included significant wave height and peak period, as well as wave direction either from wave gauge observations (Case 1) or wave direction statistical properties (Cases 2 and 3). Numerical simulations using the mean and standard deviation of principal wave direction observation data in Mode ± 45° for the largest wave height (Case 3) showed excellent agreement. Results show that wave characteristics can be accurately analyzed using statistical analysis of wave direction rather than actual wave direction inputs, which fluctuates highly in the outer sea. Further, a numerical experiment considering the influence of local wind and flow was performed, showing that the influence of these factors on wave characteristics during typhoons can be studied continuously.

Influence of natural climate variability on extreme wave power over Indo-Pacific Ocean assessed using ERA5

In recent decades, wave power (WP) energy from the ocean is one of the cleanest renewable energy sources associated with oceanic warming. In Indo-Pacific Ocean, the WP is significantly influenced by natural climate variabilities, such as El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO). In this study, the impact of major climate variability modes on seasonal extreme WP is examined over the period 1979–2019 using ERA5 reanalysis data and the non-stationary generalized extreme value analysis is applied to estimate the climatic extremes. Independent ENSO influence after removing the IOD impact (ENSO|IOD) on WP are evident over the northeast and central Pacific during December–February, and March–May, respectively, which subsequently shifts towards the western Pacific in June–August (JJA) and September–November (SON). The ENSO|PDO impact on WP exhibits similar yet weaker intensity year round compared to ENSO. Extreme WP responses due to the IOD|ENSO include widespread decreases over the tropical and eastern Indian Ocean, with localized increases only over South China and Philippine seas and Bay of Bengal during JJA, and the Arabian Sea during SON. Lastly, for the PDO|ENSO, the significant increases in WP are mostly confined to the Pacific, and most prominent in the North Pacific. Composite analysis of different phase combinations of PDO (IOD) with El Niño (La Niña) reveals stronger (weaker) influences year-round. The response patterns in significant wave height, peak wave period, sea surface temperatures, and sea level pressure help to explain the seasonal variations in WP.

Numerical study on wave attenuation of extreme waves by emergent rigid vegetation patch

Attributed to the processes of wave focusing, extreme waves often occur unexpectedly with huge wave height in both deep and shallow water regions, which can cause serious damages to the coastal infrastructures, and threaten the intactness of coastal communities. Field observations after extreme weather events have revealed that as a type of natural barrier, coastal vegetations can effectively attenuate the intensity of extreme surges and waves and thus play a good role in naturally protecting the coast. Usually, focused wave is used as a typical extreme wave model to study the great impact of extreme waves on the coast. To numerically evaluate the wave attenuation of extreme waves by the emergent rigid vegetation patch, a high-resolution numerical wave tank has been established by applying the nonhydrostatic numerical wave model (NHWAVE), where both 2nd-order crest and trough focused waves can be generated by considering wave-wave interactions. The efficiencies of the rigid emergent vegetation patch in mitigating the maximum wave amplitude and total wave energy of both crest and trough focused waves as function significant wave height, peak wave period, water depth, vegetation density and width have been well analyzed and discussed. The results indicate that the emergent rigid vegetation patch can effectively reduce the maximum wave amplitude of focused wave by averagely 31% and the total wave energy by averagely 45%, thus, showing that coastal vegetation can effectively protect the coast from extreme waves. It is hoped that the research findings drawn from this study can further broaden our understanding on the wave attenuation of extreme waves by coastal vegetations.

Strong extratropical cyclone generated ocean swells in the Northern Pacific from modeling and in-situ observations on the Hawaiian Islands

The Northern Pacific Ocean swell environment is generated by extratropical cyclones originating at mid-latitude regions that travel thousands of miles, unimpeded, across the Pacific Ocean. Some of these massive swells reach the Hawaiian Islands causing damage to the shoreline and coastal infrastructure. Understanding the cause and relation between extratropical (EX) cyclone trends and associated swells that affect the Hawaiian Islands is essential towards coastal mitigation from destructive flooding. The strong EX-cyclogenesis events are detected from the atmospheric reanalysis dataset during 1979–2017 and are used to hindcast swells in the North Pacific domain using modeling and in situ observations. Our results show a rise in the number and intensity of strong EX-cyclones during 1979–2017, and this increase accelerates during 2007–2017. The deseasonalized and Thiel–Sen trends show that poleward and westward movement and increment in the frequency of strong EX-cyclones cause a significant rise in swell generation and intensity during 2007–2017. Almost one-third of the strong EX-cyclone-generated waves reach the Hawaiian Islands during 1979–2017 and one-quarter of it during 2007–2017.​ The deseasonalized maximum daily significant wave height and peak period trend slightly decreased during 1979–2017; however, it significantly increased during 2007–2017. These trends show similarity with El Niño-Southern Oscillation events, which affect the EX-cyclone pattern.

Coupled Delft3D-Object Model to Predict Mobility of Munition on Sandy Seafloor

The coupled Delft3D-object model has been developed to predict the mobility and burial of objects on sandy seafloors. The Delft3D model is used to predict seabed environmental factors such as currents, waves (peak wave period, significant wave height, wave direction), water level, sediment transport, and seabed change, which are taken as the forcing term to the object model consisting of three components: (a) physical parameters such as diameter, length, mass, and rolling moment; (b) dynamics of the rolling cylinder around its major axis; (c) an empirical sediment scour model with re-exposure parameterization. The model is compared with the observational data collected from a field experiment from 21 April to 13 May 2013 off the coast of Panama City, Florida. The experimental data contain both object mobility using sector scanning sonars and maintenance divers as well as simultaneous environmental time series data of the boundary layer hydrodynamics and sediment transport conditions. Comparison between modeled and observed data clearly shows the model’s capabilities and limitations.

Limiting Wave Conditions for the Safe Maintenance of Floating Wind Turbines

This paper investigates the limiting wave conditions at which a wind turbine technician can complete maintenance activities safely and effectively on a 15MW floating offshore wind turbine. Through linear, frequency-domain statistical analysis of floating turbine motion and applying acceptable motion limits for technician working, significant wave height and peak wave period limits are investigated. It was found that over the range of wave conditions considered, the turbine nacelle motion did not exceed the motion limits for technician working considered in this analysis. Further analysis found that the turbine nacelle motion increased with increasing significant wave height and was also significantly influenced by peak wave period. The impact of differing wave characteristics is also investigated through the use of different wave energy spectra and also found to have an impact on turbine nacelle motion.

Assessment of global wave forces and moments on porous vertical barriers in random wave fields

Experimental investigations were carried out to assess the global wave forces and wave induced moments on slotted vertical barriers (SVB). Fourty two different wave barrier configurations (5%, 10%, 20%, 30%, 40%, 50% and 60% porosities and 1 to 6 number of slotted walls) were tested in random wave fields of JONSWAP spectra for wide range of significant wave heights and peak periods. It is found that the wave force is very sensitive to the change in porosity of the SVB. It is also found that relatively long waves and low porosity on SVB results in the highest wave force and short waves and high porosity on the SVB results in the lowest wave force. For most of the conditions, the wave force on SVB is less than the wave force on a single impervious vertical wall and force reduction to an extent of 20% to 80% is possible for the range of porosity and number of porous walls studied. A predictive equation to estimate the wave induced significant moment is provided with high regression coefficient. The average lever arm for assessing the wave induced moment is 0.6145 times the local water depth.

Positive relationship between seasonal Indo-Pacific Ocean wave power and SST

The influence of increasing sea surface temperatures (SSTs), in response to greenhouse warming, on wave power (WP) remains uncertain. Here, seasonal relationships between SST anomalies and mean and extreme WP over the Indo-Pacific Ocean are examined. Overall, seasonal WP has significantly increased over much of the Pacific, Indian, and Southern Ocean by 1.21–3.10 kW/m dec−1 over 1979–2019. Contributions from wave characteristics, namely significant wave height (SWH) and peak wave period (PWP), to changes in WP show that SWH contributes most in extra-tropical regions, and PWP most in tropical regions. Further, seasonal relationships between SST anomalies and WP indicate that increases in WP are also seen during strong El Niño years in December–February, and in-phase combinations of El Niño and positive Indian Ocean Dipole (IOD) events during June–August and September–November. Results highlight both long-term increasing SSTs and climate variability roles for inducing large-scale seasonal WP changes throughout the Indo-Pacific.

Surrogate modeling of time-dependent metocean conditions during hurricanes

Metocean conditions during hurricanes are defined by multiple parameters (e.g., significant wave height and surge height) that vary in time with significant auto- and cross-correlation. In many cases, the nature of the variation of these characteristics in time is important to design and assess the risk to offshore structures, but a persistent problem is that measurements are sparse and time history simulations using metocean models are computationally onerous. Surrogate modeling is an appealing approach to ease the computational burden of metocean modeling; however, modeling the time-dependency of metocean conditions using surrogate models is challenging because the conditions at one time instant are dependent on not only the conditions at that instant but also on the conditions at previous time instances. In this paper, time-dependent surrogate modeling of significant wave height, peak wave period, peak wave direction, and storm surge is explored using a database of metocean conditions at an offshore site. Three types of surrogate models, including Kriging, multilayer perceptron (MLP), and recurrent neural network with gated recurrent unit (RNN-GRU), are evaluated, with two different time-dependent structures considered for the Kriging model and two training set sizes for the MLP model, resulting in a total of five models evaluated in this paper. The performance of the models is compared in terms of accuracy and sensitivity toward hyperparameters, and the MLP and RNN-GRU models are demonstrated to have extraordinary prediction performance in this context.

Evaluation of long-term power capture performance of a bistable point absorber wave energy converter in South China Sea

Nowadays, extensive research has been conducted to generate electricity from ocean waves, but there are not too many studies on the capture of wave energy in a specific ocean area. As the largest sea area in China's offshore waters, the South China Sea contains a large amount of wave energy and is regarded as a natural wave energy power plant. A comparative study was conducted to investigate the effects of wave energy converter (WEC) parameters on the long-term power capture performance of a bistable point absorber WEC installed in the South China Sea in this paper. Combined with the joint probability distribution of significant wave height Hs and peak wave period Tp in South China Sea, a mathematical model was developed for the evaluation of long-term power performance of WECs. The governing equations of the point absorber WEC were established using the hybrid frequency-time domain method. The dynamic response and captured power can be obtained after numerically solving the equations of motion with the 4th order Runge-Kutta Method. The results show that the bistable mechanism has great advantages in the long-term captured power of WEC. In general, a circular truncated cone-shaped buoy WEC with a larger buoy size and smaller nonlinear parameter tend to result in better long-term power capture performance. The insights given in this study provide a motivation and foundation for developing a complete set of performance optimization methods of point absorber WECs installed in South China Sea.

Optimization of SWAN Wave Model to Improve the Accuracy of Winter Storm Wave Prediction in the East Sea

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen’s formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA’s) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts’ newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA’s) meso-scale forecasting data. We analyzed the accuracy of each model’s results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

Power maximising control of a heaving point absorber wave energy converter

Control systems play a critical role in improving the economic viability of wave energy converters (WEC). This paper presents a framework for a causal power-maximising feedback controller inspired by Phi method. Quadratic damper control and PID control are combined to collectively achieve the optimal power absorption conditions for the peak wave periods, which results in a nonlinear controller being suboptimal in irregular waves. The proposed controller is tested on a fully submerged heaving spherical point absorber in regular and irregular wave conditions in time domain. The tuning of the PID gains are investigated in frequency-domain from a stability and control bandwidth perspective. The modelled WEC dynamics includes linear hydrodynamic forces (excitation and radiation) and the nonlinear viscous drag force. The main benefits of the proposed controller are (i) it does not require any time-consuming optimisation either online or offline; (ii) has a wider bandwidth and better power absorption performance as compared to the conventional spring-damper (PI) controller; and (iii) easy to implement in practice like standard PID control.

Spectral peak wave period climatology and its relationship with natural climate variability over the Bay of Bengal

Spectral peak wave period climatology and its relationship with natural climate variability over the Bay of Bengal

Methodology for developing a response-based correction factor (alpha-factor) for allowable sea state assessment of marine operations considering weather forecast uncertainty

The concept of the alpha-factor, a correction factor on the significant wave height limit, was developed by DNV to consider the effect of weather forecast uncertainty in planning and executing marine operations. In this paper, a new defined response-based correction factor, called the response-based alpha-factor αR, is proposed to account for the forecast uncertainty of both significant wave height Hs and peak wave period Tp, and quantify their effect on dynamic response of offshore structures. A general methodology for developing αR for the use in assessing allowable sea states for marine operations is presented, with emphasis on considering the effect of the weather forecast uncertainty. It consists of uncertainty quantification of the sea state forecast, statistical analysis of dynamic responses of the coupled system for marine operations and allowable sea state assessment using response-based criteria. Based on the methodology, αR for an operation can be derived, in terms of the ratio between the characteristic values of the operational limiting response parameter in the condition with or without the weather forecast uncertainty. Then, the allowable sea states can be assessed in terms of the forecast lead time, to include the effect of the weather forecast uncertainty on the operation decision-making. The workable weather windows can finally be identified and selected through a comparison between the allowable sea states and weather forecasts in the execution phase. Followed by the detailed description of the proposed methodology, a case study dealing with single blade installation for offshore wind turbine using a semi-submersible crane vessel is conducted. The crane tip motion is regarded as the operational limiting response parameter to illustrate the methodology based on frequency-domain response analysis approach. The uncertainty in sea state forecasts generated by two machine learning-based forecasting methods is considered in this paper. Results show that in addition to the forecast uncertainty of Hs, that of Tp also requires to be addressed for marine operations with floating structures (such as the semi-submersible crane vessel). The proposed method provides an efficient way to incorporate the weather forecast uncertainty into the allowable sea states assessment for marine operations. The obtained allowable sea states can provide a good reference for the decision-making of the operation in the execution phase.

Predicting Lake Erie wave heights and periods using XGBoost and LSTM

Waves in large lakes put coastal communities and vessels under threat, and accurate wave predictions are needed for early warnings. While physics-based numerical wave models such as WAVEWATCH III (WW3) are useful to provide spatial information to supplement in situ observations, they require intensive computational resources. An attractive alternative is machine learning (ML) methods, which can potentially improve the performance of numerical wave models, while only requiring a small fraction of the computational cost. In this study, we applied novel ML methods based on XGBoost and a Long Short-Term Memory (LSTM) recurrent neural network for predicting wave height and period under the near-idealized wave growth conditions of Lake Erie. Data sets of significant wave height (H), peak wave period (Tp) and surface wind from two offshore buoys from 1994 to 2017 were processed for model training and testing. We trained and validated the ML models with the data sets from 1994 to 2015, and then used the trained models to predict significant wave height and peak period for 2016 and 2017. The XGBoost model yielded the best overall performance, with Mean Absolute Percentage Error (MAPE) values of 15.6%–22.9% in H and 8.3%–13.4% in Tp. The LSTM model yielded MAPE values of 23.4%–30.8% in H and 9.1%–13.6% in Tp. An unstructured grid WW3 applied to Lake Erie yielded MAPE values of 15.3%–21.0% in H and 12.5%–19.3% in Tp. However, WW3 underestimated H and Tp during strong wind events, with relative biases of -11.76% to -14.15% in H and -15.59% to -19.68% in Tp. XGBoost and LSTM improve on these predictions with relative biases of -2.56% to -10.61% in H and -8.08% to -10.13% in Tp. An ensemble mean of these three models yielded lower scatter scores than the members, with MAPE values of 13.3%–17.3% in H and 8.0%–13.0% in Tp, although it did not improve the bias. The ML models ran significantly faster than WW3: For this 2-year run on the same computing environment, WW3 needed 24 h with 60 CPUs, whereas the trained LSTM needed 0.24 s on 1 CPU, and the trained XGBoost needed only 0.03 s on 1 CPU.

An early warning system for wave-driven coastal flooding at Imperial Beach, CA

Waves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.

Effect of wave climate change on longshore sediment transport in Southwestern Black Sea

As a result of long-term changes of the wave climate, changes in spatial and temporal scales occur in coastal regions and cause coastal morphology to change. This study investigates the effect of changes in wave climate (wave height, wave period, wave direction) on longshore sediment transport (LST) at Karasu coast, located on the southwestern coast of the Black Sea. European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim wind data covering the period of 1979–2018 was used to obtain the wave climate of the Southwestern Black Sea coasts and to understand the dynamics of LST throughout the region. Net and gross sediment transport rates were calculated according to three different empirical and one numerical methods by using hourly significant wave heights, peak wave periods and incident wave angles. According to the 40-year wave climate data in the region, the average annual significant wave height is 0.92 m, the peak wave period is 5.53 s and the angle of wave incidence is between N-NNE sector. Waves are composed of two components; swell waves and wind waves and as a result of the study, it has been observed that swell waves have an order of magnitude larger contribution to the LST along the coast than wind waves for the respective research area, by an average factor of 12 and 17 for the net and gross LST rate, respectively. At the end of the 40-year period between 1979 and 2018, according to three different empirical and one numerical LST methods, the average net LST rate along the Karasu coast was 65,000 ± 5,000 m3/year, while the gross LST rate was 190,000 ± 20,000 m3/year. When the change of gross LST during the study period was examined, it was observed that an average decrease of 5.6% occurred. This reduction in the LST rate was observed to be associated with a 4.1% decrease in the height of the swell waves at the end of 40 years.

Prediction of the height and period of sea waves in the coastal waters of Meulaboh, Aceh Province, Indonesia

This research examines the prediction of sea waves in the coastal waters of Meulaboh. The sea wave prediction are based on the 2015-2019 monsoons in the coastal waters of Meulaboh. The method used is an empirical equation that consist of wave parameters, i.e., significant wave height Hmo and peak period Tp . The sea wave prediction generate an average of the height and period of the sea waves of 0.7 m; 5 s in the Southwest direction. Based on the prediction, it shows that the sea waves in Meulaboh coastal waters are influenced by the Southwest wind that blows towards the coast from the Indian Ocean.

Extreme wave value analysis under uncertainty of climate change scenarios off Iberian Peninsula coast

Extreme wave value analysis under uncertainty scenarios was developed to estimate wave climate characteristics at 17 stations in southwestern European coast off the Iberian Peninsula. A comprehensive wave dataset downscaled with the Wave Watch III (WWIII) model by Meteogalicia under MarRisk Project was used considering results of models from the Coupled Model Intercomparison Project 5 (CMIP5). Descriptive statistics for significant wave height (Hs), peak wave period (Tp), and mean and peak wave direction were performed for historical data (1960–2005), and for projected data in two twenty-year time periods under two Representative Concentration Pathway (RCP) scenarios (2026–2045 and 2081–2100). Hs and Tp extreme values for the study area were obtained using the Gumbel, Fréchet and Weibull probability distributions for the 10-, 50-, and 100-year return period. Obtained results showed that: historical Hs values decrease from North to South and are higher than those calculated in any of the RCP future scenarios; mean Tp values appear to be constant in all stations; and means for peak and mean direction have higher frequency of occurrence in Q4 (270°–360°). Moreover, Hs and Tp values for 100-year return period were computed, which can be used as design criteria for structural analyses in maritime works.

Investigation on Spectrum Estimation Methods for Bimodal Sea State Conditions.

The reliable monitoring of sea state parameters is a key factor for weather forecasting, as well as for ensuring the safety and navigation of ships. In the current analysis, two spectrum estimation techniques, based on the Welch and Thomson methods, were applied to a set of random wave signals generated from a theoretical wave spectrum obtained by combining wind sea and swell components with the same prevailing direction but different combinations of significant wave heights, peak periods, and peak enhancement factors. A wide benchmark study was performed to systematically apply and compare the two spectrum estimation methods. In this respect, different combinations of wind sea spectra, corresponding to four grades of the Douglas Scale, were combined with three swell spectra corresponding to different swell categories. The main aim of the benchmark study was to systematically investigate the effectiveness of the Welch and Thomson methods in terms of spectrum restitution and the assessment of sea state parameters. The spectrum estimation methods were applied to random wave signals with different durations, namely 600 s (short) and 3600 s (long), to investigate how the record length affected the assembled sea state parameters, which, in turn, were assessed by the nonlinear least square method. Finally, based on the main outcomes of the benchmark study, some suggestions are provided to select the most suitable spectrum reconstruction method and increase the effectiveness of the assembled sea state parameters.