Extreme Wave Heights Research Articles

Trend Analysis of Extreme Wind and Wave Height at Key Port Locations Along the Indian Coastline

Trend Analysis of Extreme Wind and Wave Height at Key Port Locations Along the Indian Coastline

Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events

Providing an accurate prediction of Significant Wave Height (SWH), and specially of extreme SWH events, is crucial for coastal engineering activities and holds major implications in several sectors as offshore renewable energy. With the aim of overcoming the challenge of skewness and imbalance associated with the prediction of these extreme SWH events, a fuzzy-based cascade ensemble of regression models is proposed. This methodology allows to remarkably improve the predictive performance on the extreme SWH values, by using different models specialised in different ranges on the target domain. The method’s explainability is enhanced by analysing the contribution of each model, aiding in identifying those predictor variables more characteristic for the detection of extreme SWH events. The methodology has been validated tackling a long-term SWH prediction problem, considering two case studies over the southwest coast of the United States of America. Both reanalysis data, providing information on various meteorological factors, and SWH measurements, obtained from the nearby stations and the station under examination, have been considered. The goodness of the proposed approach has been validated by comparing its performance against several machine learning and deep learning regression techniques, leading to the conclusion that fuzzy ensemble models perform much better in the prediction of extreme events, at the cost of a slight deterioration in the rest of the samples. The study contributes to advancing the SWH prediction field, specially, to understanding the behaviour behind extreme SWH events, critical for various sectors reliant on oceanic conditions.

Assessing extreme significant wave height in China’s coastal waters under climate change

Accurately estimating the return values of significant wave height is essential for marine and coastal infrastructure, particularly as climate change intensifies the frequency and intensity of extreme wave events. Traditional models, which assume stationarity in wave data, often underestimate future risks by neglecting the impacts of climate change on wave dynamics. Combining time series decomposition and recurrence analysis, the research develops a nonstationary framework to predict significant wave height. The stochastic component is modelled using a stationary probability distribution, while the deterministic component is predicted based on sea surface temperature projections from CMIP6 climate scenarios. The model evaluation demonstrates strong predictive capability for both stochastic and deterministic components. Application of the model to China’s coastal waters reveals significant discrepancies between stationary and nonstationary return value estimates. Compared to conventional distribution models, the nonstationary model predicts substantial increases in extreme wave heights. These findings underscore the importance of adopting nonstationary models to more accurately assess future risks posed by extreme wave events in a changing climate.

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.

Applying the multivariate Gaidai reliability method in combination with an efficient deconvolution scheme to prediction of extreme ocean wave heights

Applying the multivariate Gaidai reliability method in combination with an efficient deconvolution scheme to prediction of extreme ocean wave heights

Universality of finite-time ray focusing statistics in two-dimensional and one-dimensional potential flows.

In the investigation of extreme density or wave height statistics in a disordered medium, of special interest is the search for universal or fundamental properties shared by different types of disorder. In previous work [Chen and Kaplan, Entropy 25, 161 (2023)1099-430010.3390/e25010161] we have established a direct connection between the degree of stretching or focusing of ray trajectories and the density distribution. Here we demonstrate the universality of this connection for different physical contexts, and both analytically and numerically show a universal scaling relationship for the stretching exponent distribution in weak, small-angle scattering at finite times for different dispersion relations. We observe that the mean, skewness, and kurtosis of the stretching exponent all display universal nonmonotonic behavior on timescales comparable to the time of first caustic formation, corresponding to the first generation of hot spots in the density profile. In particular, the mean stretching exponent attains negative values before beginning its linear rise at large times. Using the correspondence between two-dimensional small-angle scattering and a one-dimensional kicked model, we show how higher moments of the distribution of the second derivative of the potential affect the statistics of the stretching exponents.

Single-instant spatial wave height forecast using machine learning: An image-to-image translation approach based on generative adversarial networks

An innovative single-instant approach for spatial significant wave height forecast based on a Generative Adversarial Networks (GANs) model is presented. The model's most distinctive feature is its ability to provide spatial wave height forecast with only a single snapshot of wind field input, eliminating the need for continuous wind field sequences while simultaneously maintaining high accuracy and enhanced efficiency. The proposed model architecture comprises an image-to-image translation generator and a patch-style discriminator, working in tandem to forecast high-quality wave height data. The model is trained using a 20-year ECMWF Reanalysis v5 (ERA5) wind and wave dataset. A vast sea area covering 15°-40°N, 105°-130°E in West Pacific is used for this study. The presented model is carefully tested against a 10-year period historical dataset and the results are discussed. The model exhibits outstanding overall performances with Mean Absolute Error (MAE) of 0.16 m, Root Mean Square Error (RMSE) of 0.24 m, and Mean Absolute Percentage Error (MAPE) of 13.4 %. The model proves particularly effective in forecasting extreme wave heights during tropical cyclones, capturing the structure of typhoon-driven waves and providing wave heights with favorable accuracy. It is also proficient at delivering satisfactory statistical wave height forecasts for long-term periods.

Trends of extreme waves around Hainan Island during typhoon processes

Extreme waves mostly occur during typhoon processes in China Seas. This study statistically shows the trends of extreme wave height over the past several decades around Hainan Island, based on the reconstruction of historical wave processes during the typhoon events from 1949 to 2022. Totally 807 events in the tropical cyclone (TC) best track datasets are numerically simulated by the widely used wave model SWAN. The driving wind fields are reconstructed as a combination of an empirical cyclone wind model with the reanalysis wind field data, in order to better simulate waves near cyclone centers with higher spatial resolution. Based on the reconstructed wave data, the spatial distribution of wave characteristics around Hainan Island has been provided. The Mann-Kendall test shows a descending trend of extreme wave height in the southeast side of Hainan Island, where the maximum descending rate could reach up to 5 cm/yr. The influence of the long-term trend on the design extreme wave height is also discussed. Comparing to the variation of the design extreme wave height with sampling period, the long-term trend is also a significant impact factor.

A Comparative Study on Load Assessment Methods for Offshore Wind Turbines Using a Simplified Method and OpenFAST Simulations

Simplified methods are often used for load estimations during the initial design of the foundations of offshore wind turbines (OWTs). However, the reliability of simplified methods for designing different OWTs needs to be studied. This paper provides a comparative study to evaluate the reliability of simplified approaches. The foundation loads are calculated for OWTs at the mudline level using a simplified approach and OpenFAST simulations and compared. Three OWTs, NREL 5 MW, DTU 10 MW, and IEA 15 MW, are used as reference models. An Extreme Turbulence Model wind load at a rated wind speed, combined with a 50-year Extreme Wave Height (EWH) and Extreme Operating Gust (EOG) wind load and a 1-year maximum wave height are used as the load combinations in this study. In addition, the extreme loads are calculated using both approaches for various metocean data from five different wind farms. Further, the pile penetration lengths calculated using the mudline loads via two methods are compared. The results show that the simplified method provides conservative results for the estimated loads compared to the OpenFAST results, where the extent of conservativism is studied. For example, the bending moment and shear force at the mudline using the simplified approach are 23% to 69% and 32% to 53% higher compared to the OpenFAST results, respectively. In addition, the results show that the simplified approach can be effectively used during the initial phases of monopile foundation design by using factors such as 1.5 and 2 for the shear force and bending moment, respectively.

Metocean conditions at two Norwegian sites for development of offshore wind farms

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

Global tropical cyclone extreme wave height climatology

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

Characteristics and distribution model of wind and wave in Shengsi mussel culture area under the impacts of tropical cyclones

Abstract The primary industry in Shengsi County, Zhejiang Province, China, is the raft-based cultivation of Mytilus coruscus, which is highly sensitive to wind and wave disturbances. This study aims to understand the characteristics of tropical cyclones along Zhejiang's coast, their relation to climate change, and the impact of wind and waves on Shengsi mussel farming during cyclones. Analyzing data from 2000 to 2018, including tropical cyclone paths, wind and wave data, and mussel farming statistics, the research identifies four cyclone path types, with the peak prevalence from July to September. Annual cyclone energy fluctuates, peaking in August. In the cyclone impact zone, the average wind speed is 24.59 m/s. Type D cyclones have the most significant impact. A linear relationship exists between cyclone center distance and wind speed/wave height. Extreme wind speed and wave height follow the Gumbel joint probability distribution. Since 2000, the Shengsi mussel farming area experienced an 11.20-year return period for wind and waves from the strongest cyclone, Muifa. Given the recurrence period of Muifa intensity cyclones in 2020 and the following years, the study recommends disaster preparedness for the Shengsi mussel farming area and emphasizes the role of mutual aid insurance.

Understanding sea wave height conditions in sumatra waters

Ocean waves play a crucial role in influencing a wide range of marine and fisheries activities. In this research study, we aim to analyze the climatological conditions of sea wave heights in Sumatran waters over a span of 83 years (1940-2022). We utilize three-dimensional Copernicus numerical model simulations to examine seasonal patterns of sea wave heights and assess the likelihood of extreme wave events. Our findings reveal that high waves, reaching heights of 4-5 meters, are predominantly observed in the western and southern zones off the coast of Sumatra, particularly in the vicinity of smaller islands. Conversely, in enclosed or semi-enclosed waters, such as the lee side of Sumatra and the Malacca Strait, sea wave heights tend to be relatively lower. Seasonally, our study indicates that extreme wave heights are more likely to occur during the west season as opposed to the east season. This research provides valuable insights into the dynamic ocean wave conditions in Sumatran waters, which can have significant implications for various sectors reliant on the ocean environment.

Wave Modulation in a Strong Tidal Current and Its Impact on Extreme Waves

Abstract Accurate estimates of extreme waves are central for maritime activities, and stochastic wave models are the best option available for practical applications. However, the way currents influence the statistics of space–time extremes in spectral wave models has not been properly assessed. Here we demonstrate impacts of the wave modulation caused by one of the world’s strongest open ocean tidal currents, which reaches speeds of at least 3 m s−1. For a bimodal swell and wind sea state, we find that most intense interactions occur when the wind sea opposes the tidal current, with an increase in significant wave height and spectral steepness up to 45% and 167%, respectively. The steepness modulation strengthens the second-order Stokes contribution for the normalized extreme crests, which increases between 5% and 14% during opposing wind sea and current. The normalized extreme wave heights have a strong dependence on the narrow-bandedness parameter, which is sensitive to the variance distribution in the bimodal spectrum, and we find an increase up to 12% with currents opposing the wind sea. In another case of swell opposing a tidal jet, we find the spectral steepness to exceed the increase predicted by a simplified modulation model. We find support in single-point observations that using tidal currents as forcing in wave models improves the representation of the expected maximum waves, but that action must be taken to close the gap of measurements in strong currents. Significance Statement The purpose of this study is to investigate how a very strong tidal current affects the surface wave field, and how it changes the stochastic extreme waves formulated for a space–time domain. Our results suggest that the expected maximum waves become more realistic when tidal currents are added as forcing in wave models. Here, the expected extremes exceed traditional model estimates, i.e., without current forcing, by more than 10%. These differences have implications for maritime operations, both in terms of planning of marine structures and for navigational purposes. However, there is a significant lack of observations in environments with such strong currents, which are needed to further verify our results.

Extreme waves in the Caribbean Sea: spatial regionalization and long-term analysis

The extreme wave height distribution in the Caribbean Sea is studied using a new method based on the maximum basin-wide aggregate of significant wave height, Hs, values per month. Besides, by means of the Self-Organizing Maps (SOM) technique, we identify coherent geographical regions with similar extreme wave height variability in the Caribbean Sea. Our findings revealed three primary regions: the eastern side with comparatively lower values, the central region with intermediate values, and the western side with the highest extreme wave heights. The study also examines the wind forcing conditions driving the spatial and temporal variability of the extreme waves, highlighting the influence of the low-pressure belt dynamics as well as the role played by the Caribbean Low-Level Jet (CLLJ) index, and the impact of cold fronts and hurricanes on extreme wave heights. Additionally, we explore the relationship between the extreme wave height distribution and climatic indices, such as the Atlantic Multidecadal Oscillation (AMO), the Atlantic Meridional Mode (AMM), the North Atlantic Oscillation (NAO) and the Oceanic Niño (ONI). The results reveal that the spatial distribution of extreme wave heights in the Caribbean Sea is mostly ruled by the influence of the CLLJ, with correlations close to 80%. In addition, significant correlations were observed between the extreme wave heights and the ENSO in the central Caribbean, as well as positive correlations between the extreme wave heights and NAO in the eastern part of the basin, and significant values of correlation with the negative phases of AMO and AMM in the whole basin. We show that, unlike conventional (or broadly used) methods deployed to identify extreme wave height, such as percentile 99th, Hs99, our methodology allows a further assessment of the wind and climate forcing conditions associated with the extreme wave events. Although, we acknowledge that the method here presented has limitations to capture extreme wave height outliers, it has the advantage of being used concomitant with the wind forcing to develop multivariate wave climate analysis at basin scale, and could be extended to a more local scale when studying coastal processes.

Long-term assessment of wave conditions and wave energy resource in the Arctic Ocean

It was recently shown that the Arctic has been warming much faster than the rest of the globe during the last decades. This warming has reduced the ice extent significantly, which will strongly impact the wave climate in the Arctic regions, thus affecting the design of marine structures, operations, and energy resources. This study focuses on the higher latitudes, and uses the advanced wave hindcast NORA3, which covers a big part of the North Atlantic and the whole Arctic Ocean, to analyze the spatio–temporal properties of wave height and wave energy flux during the last three decades. The most energetic waves in the Arctic Ocean are observed in the Greenland Sea and the Barents Sea. The study shows that the substantial diminishing of sea ice in the Arctic induces local and regional changes in both mean and extreme wave conditions. In the Arctic Ocean the changes in extreme wave height are more pronounced compared to changes in mean wave conditions. The results also indicate a strong positive trend in the extreme wave heights in the Arctic regions of the Barents Sea, the Kara Sea, the Laptev Sea, the East Siberian Sea, the Chukchi Sea, and the Beaufort Sea.

Long-term statistics of atmospheric conditions over the Baltic Sea and meteorological features related to wind wave extremes in the Gulf of Gdańsk

The goal of this study is to describe wind wave climate and wave extremes of the Gulf of Gdańsk in the southern Baltic Sea and associated meteorological conditions over the Baltic Sea. We obtain the characteristic features of 34 severe historical storms in the Gulf of Gdańsk during the period 1958–2001 and link them with extreme significant wave heights hindcast for five grid points in this gulf. The long-term statistics of atmospheric pressure systems over central and northern Europe, and the north-eastern Atlantic Ocean are derived from a 44-year REMO reanalysis database. A link between the mean, minimum and variability range of atmospheric pressure has been quantified. In general, the higher the mean pressure the smaller its variability and vice versa. Long-term characteristic features of winds over the Baltic Sea have been estimated from the REMO database. Strong winds directions vary from W, WSW to SW in the southern Baltic to more southerly SSW directions in the northern part of the Baltic Sea. The Empirical Orthogonal Functions (EOF) analysis shows that more than 50% of the variability in the atmospheric pressure in the Baltic Sea can be explained by the first EOF mode. The first four EOF modes can reproduce above 90% variability of the hindcast pressure time series. Statistical properties of the hindcast significant wave height over the Gulf of Gdańsk are computed based on the 44-year HIPOCAS database. All the computed statistics of wave heights reveal a very strong sheltering effect caused by the Hel Peninsula.

Contributions of Nonlinear Spectral Components to the Probability Distribution of Rogue Waves Based on the Results of Numerical Simulation of the Euler Equations

The contributions of various wave components (second, third and difference harmonics) to the formation of probability distributions of extreme wave heights, as well as amplitudes of crests and troughs, are evaluated based on the direct numerical simulation of irregular nonlinear deep water waves within three-dimensional potential equations of hydrodynamics. The simulation results taking into account 4- and 5-wave nonlinear interactions are analyzed. Different non-linear harmonics participate in the formation of the probability distributions in nontrivial way, essentially not satisfying the principles of linear superposition and ordering of the contribution by the parameter of weak nonlinearity.

Observations of Extreme Waves and Wave Spectra during Hurricane Dorian (2019)

Due to the sparse distribution of buoys in the ocean, direct observations of extreme wave activity as excited by tropical cyclones (TCs) are few; but nonetheless, these buoys provide rare opportunities to learn more about how weather systems interact with the ocean wave field. In this study, National Oceanic and Atmospheric Administration National Data Buoy Center buoy observations of one-dimensional (1D) and two-dimensional (2D) wave spectra during the translation speed of Hurricane Dorian (2019) through the Atlantic Ocean were used to examine extreme wave events in the days before and after its September 1, 2019 landfall in The Bahamas. Observations of wave properties during storm intensification and decay showed that although significant wave height naturally rose and fell, the dominant wave period remained virtually constant. At the height of the storm, a lethal combination formed, with the highest significant wave heights being recorded at over 8 m (more than four times the mean) and dominant wave periods exceeded 12 s. Wave direction also varied widely as wave regimes shifted from wind-sea to swell, with spectral wave energy peaking at over 120 m2/Hz, six times higher than pre-Hurricane Dorian wave states. This study provides the first in situ characterization of extreme wave heights as induced by Hurricane Dorian. The study recommends that a number of observational platforms be funded, developed, and deployed throughout the Lucayan Archipelago.

Contribution of tropical cyclone induced waves to the mean and extreme wave climatology in the Bay of Bengal

This paper examines the contribution of tropical cyclones (TCs) to the mean wave and the extreme wave climatology in the Bay of Bengal (BoB). To assess the impact of TCs on the wave activity in the BoB, we compare 40-years long WAVEWATCH III (WW3) numerical simulation with a twin experiment, where TC signatures are filtered out from the wind forcing dataset. Our experimental strategy of twin simulations, with and without TCs, allow us to separate the TC-induced waves from the background wave activity and therefore allow us to quantify their contribution. The simulation samples more than 200 TCs in the BoB and is extensively validated against the available buoy observations that measure 39 TCs in the BoB. Results show that TCs contribute up to ∼80 % and ∼40 % to the extreme significant wave height (SWH) climatology defined as the maximum SWH and highest 1 % SWH and up to ∼9 % to the mean SWH climatology during the post-monsoon season with slightly lesser contributions during the pre-monsoon season. This contribution is largest in the northern BoB and along the east coast of India during the post-monsoon season and in the eastern BoB and along the Bangladesh and Myanmar coast during the pre-monsoon season.