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Significant wave height plays a crucial role in influencing marine ecosystems, ocean shipping, and other maritime activities. The distribution of buoy observation data tends to be sparse. Gridded wave data obtained through numerical simulation typically offer broader applicability, albeit with higher computational demands. In this paper, a deep learning model based on Full Connected and Convolutional Neural Networks is proposed, utilizing sparse buoy observation data as input to reconstruct the distribution of significant wave height in the sea area. The model reconstruction results are validated using ERA5 data, demonstrating excellent performance. Additionally, we explore the influence of the model's spatial boundaries and the number of input buoys on reconstruction accuracy, as well as the adaptability of the model to different sea areas. This study provides a novel method and approach for the rapid and cost-effective retrieval of regional significant wave height. • A significant wave height reconstruction model based on deep learning method is proposed. • The model utilizes sparse buoy data to reconstruct the distribution of significant wave height in the sea area. • The model shows rapid and accurate computational performance, with excellent adaptability to the sea area. [ABSTRACT FROM AUTHOR]

In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (Hs) and wave period (Ts) from X-band marine radar images. Since the shore-based radar image in this study is interfered with by other radar radial noise lines and solid target objects, to ensure that the proposed convolutional neural network (CNN) extracts the image features accurately, it is necessary to pre-process the radar image to eliminate interference. Firstly, a pre-trained GoogLeNet is used to extract multi-scale depth space features from the radar images to estimate Hs and Ts. Since CNN-based models cannot analyze the temporal behavior of wave features in radar image sequences, self-attention is connected after the deep convolutional layer of the CNN to construct a convolutional self-attention (CNNSA)-based model that generates spatial-temporal features for Hs and Ts estimation. Simultaneously, Hs and Ts measured by nearby buoys are used for model training and reference. The experimental results show that the proposed CNNSA model reduces the RMSD by 0.24 m and 0.11 m, respectively, in Hs estimation compared to the traditional SNR-based and CNN-based methods. In Ts estimation, the RMSD is reduced by 0.3 s and 0.08 s, respectively.

The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China's offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites' SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors. [ABSTRACT FROM AUTHOR]

Wind speed and significant wave height are the most relevant metocean variables that support a wide range of engineering and economic activities. Their characterization through remote sensing estimations is required to compensate for the shortage of in situ observations. This study demonstrates the value of satellite altimetry to identify typical spatial patterns of wind speed and significant wave height in the northeastern region of the United States. Data from five altimetry satellite missions were evaluated against the available in situ observations with a 10 km sampling radius and a 30 min time window. An objective analysis of the collective altimeter dataset was performed to create aggregated composite maps of the wind speed and significant wave height. This asynchronous compositing of multi-mission altimeter data is introduced to compile a sufficient sampling of overpasses over the area of interest. The results of this approach allow for quantifying spatial patterns for the wind speed and significant wave height in the summer and winter seasons. The quality of altimeter estimations was assessed regarding the distance from the coast and the topography. It was found that while the altimeter data are highly accurate for the two variables, bias increases near the coast. The average minimum and maximum wind speed values detected in buoy stations less than 40 km from the coast were not matched by the aggregated altimeter time series. The method exposes the spatial and time gaps to be filled using data from future missions. The challenges of the objective analysis near the coast, especially in semi-enclosed areas, and the implications of the altimeter estimations due to the land contamination are explained. The results indicate that the combination of altimetry data from multiple satellite missions provides a significant complementary information resource for nearshore and coastal wind and wave regime estimations. [ABSTRACT FROM AUTHOR]

The wave spectrometer operated by the China–France Oceanography Satellite (CFOSAT) can provide global ocean wave observation data. Although a lot of work on calibration and verification has been carried out in the open oceans dominated by swells, the quality of the data in the relatively enclosed sea area with complex terrain still lacks sufficient examination. The objective of this study is to assess the performance of the significant wave height data of the CFOSAT in the South China Sea (SCS), a unique sea area characterized by semi-enclosed basin and multi-reef terrain, and to recognize the environmental factors affecting the data quality. Compared against the long-term observations from five mooring or buoy sites, we find that the data is well performed in the relatively open and deep areas of the SCS, with an average correlation coefficient as high as 0.87, and a low average root-mean-square error of 0.47 m. However, the combined effects of complex topography, monsoons, and swell proportion variation will affect the performance of data. In the southern deep areas, the waves may be affected by a large number of dotted reefs, leading to wave deformations and energy dissipation in different seasons. In the northern nearshore areas, waves tend to be sheltered by the land or distorted by the shallow topography effects. These processes make it difficult for the swell to fully develop as in the open oceans. The low proportion of swell is a disadvantage for the CFOSAT to correctly observe the wave data and may lead to possible errors. Our results emphasize the importance of more verification when applying the CFOSAT data in certain local seas, and the necessity to adjust the algorithm of inverting wave spectra according to specific environmental factors. [ABSTRACT FROM AUTHOR]

Estimating reflector heights at stationary GNSS sites with interferometric reflectometry (IR) is a well-established technique in ocean remote sensing. Additionally, IR offers the possibility to estimate the significant wave height (SWH) with a linear model using the damping coefficient from an inverse modelling applied to GNSS signal-to-noise ratio (SNR) observations. Such a linear model serves as a benchmark in the present study, where an alternative approach for the estimation of both SWH and reflector height is presented that is based on kernel regression and clustering techniques. In this alternative approach, the reflector height is estimated by analyzing local extrema occurring in the interference pattern that is present in GNSS SNR observations. Various predictors are derived from clustering statistics and the estimated reflector heights. These predictors are used for the SWH determination with supervised machine learning. Linear models, bagged regression trees, and artificial neural networks are applied and respective results are compared for various predictor sets. In a second step, damping coefficients obtained from the inverse modelling mentioned above are additionally taken into account as predictors. The usability of the alternative approach is demonstrated. Compared to the benchmark, a significant improvement in terms of accuracy is found for an artificial neural network with predictors from both the alternative and the inverse modelling approach. [ABSTRACT FROM AUTHOR]

The wave is one of the fundamental parameters in the evolution of a coastline. The knowledge of its detailed behavior, as it depends on different stochastic variables, is very complex. However, through the analysis of time series it is possible to have an idea of the wave conditions in a coastal area. This paper describes and analyzes the swell conditions in Santa Elena Bay, in the sector between Salinas and Salango beaches, in the period 2016-2020. Different wave conditions were simulated for each month of the study period, considering the variables significant wave height, period and wave direction, using the DELFT3D model, WAVE module. The results were compared with the Global Ocean Physics Reanalysis simulation, from two virtual buoys near the coast. According to the results, near the coast of Santa Elena Bay, wave conditions are found with heights from 0.60 m to 0.93 m, approach direction from 221.1° to 270.1° and periods from 11.5 s to 16.0 s. The results obtained allow us to have a continuous spatial grid of reference on the wave behavior in Santa Elena, which could be improved with field data over time. [ABSTRACT FROM AUTHOR]

Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterization can help in the design of ocean structures such as wave energy converters and sea dikes. Therefore, engineers need long time series of ocean wave parameters. Numerical models are a valuable source of ocean wave data; however, they are computationally expensive. Consequently, statistical and data-driven approaches have gained increasing interest in recent decades. This work investigates the spatiotemporal relationship between North Atlantic wind and significant wave height (Hs) at an off-shore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks to extract the spatial features that contribute to Hs. Then, long short-term memory is used to learn the long-term temporal dependencies between wind and waves. [ABSTRACT FROM AUTHOR]

The main goal of this study is to investigate if the publicly available sea state forecasts for the Aran Islands region in the Republic of Ireland can be improved. This improvement is achieved by using the combination of local scale sea state forecasts and Bayesian Model Averaging techniques. The question of a good forecast has been around since the start of forecasting. With current state-of-the-art numerical models, computational power, and vast data availability, we consider whether it is possible to improve model forecasts only by using the combination of publicly available forecasts, free open-source software, and very moderate computational power. It is shown that it is possible to improve the sea state forecast by at least 1%, and in some cases up to 8%. The reduction of error is between 6% and 48%. With a more careful and specific selection of training parameters, it is possible to improve the forecast accuracy even more. The possibility of extending this local improvement to the whole coastal area around the island of Ireland is explored. Unfortunately, it is currently impossible, due to a lack of live data buoys in the coastal waters. Nonetheless, it is shown that the proposed process is simple and can be implemented by anyone whose livelihood depends on an accurate sea state forecast. It does not require large computational power, model forecasts are publicly available, and there is minimal to no training in forecasting and statistics required to enable one to perform such improvements for one’s area of interest, provided one has access to live wave data. [ABSTRACT FROM AUTHOR]

The extreme significant wave height predictions often neglect within-year wave climate variability and wave directionality. Depending on a geographical region, local wind patterns and year climate variability could have an influence on the long-term prediction of waves. The Adriatic Sea having two dominant wind patterns of different characteristics, Bura and Jugo, is a great example for the case study. The 23-year hindcast wave data used in the presented study is extracted from the WorldWaves database. Based on wind and wave data, annual extreme significant wave heights generated by different wind patterns and for different months are fitted by Gumbel distribution using maximum likelihood estimation. Combined long-term extremes are then predicted by calculating system probability. It was found that considering the wave directionality, and especially the seasonality of wave climate, leads to a larger prediction of extreme significant wave heights. The extreme value prediction considering wave directionality on average yields 4% larger significant wave heights, while considering within-year climate variability leads to, on average, 8% larger extremes compared to the predictions when both effects are neglected. [ABSTRACT FROM AUTHOR]

Quantifying the spatially varying nearshore wave characteristics and energy dissipation mechanisms is of utmost importance for several coastal management and engineering applications as well as for flood hazard assessment. This study presents the ArcGIS Wave Transformation toolbox (ArcWaT), a model-based GIS toolbox for estimating wave transformation from wave magnitude and direction model outputs. In order to assess the ArcWaT capabilities, a case study was developed using ADCIRC+SWAN model outputs from a highly-resolved numerical mesh developed for the nearshore areas of the state of Maryland and forced with wind and pressure fields from Hurricane Irene. Results show that several wave transformation processes, i.e., wave generation, shoaling, refraction, wave breaking, diffraction, and wave attenuation due to bottom friction, are more easily identified when using ArcWaT if compared to the non-processed wave magnitude and direction model outputs. Among these processes wave attenuation due to bottom friction is found to be the most significant. [ABSTRACT FROM AUTHOR]

The propagation of waves in shallow waters is affected by the bottom topography unlike deep water waves of the coastal environment. Due to the interaction of the wave with bed topography, the wave transformation processes occur. Refraction, diffraction, shoaling, and breaking are the wave transformation processes that occur in the coastal environment. The significant wave height over rugged topography is a standardized statistics to denote the characteristic height of the random waves in a sea state. Therefore, the objective of the present study is to predict the significant wave height over rugged topography. The SWAN standalone and SWAN DHH platform are used to predict significant wave height over rugged topography in Mehamn harbour, Norway. The SWAN model results are almost similar to the lab data of Vold and Lothe (2009) for all the 22 scenarios at all the output locations. Further, the four cases reported by Taehun (2011) and lab data from Vold and Lothe (2009) for that four cases are compared with the SWAN model results. It is observed that the SWAN model results are much closer to the lab data of Vold and Lothe (2009).

As new energy technologies boom in recent years, marine renewable energy, especially wave power is one potential trend. However, few relevant studies focus on extreme sea conditions. In this paper, a numerical model of typhoon waves in the Taiwan Strait is established based on the third-generation ocean wave model SWAN and then calculated by the wave energy empirical equation. Typhoon No. 200808 Fung-wong, strong typhoon No. 200815 Jangmi and strong typhoon No. 201808 Maria are used for verification and analysis. Finally, the results show that most concentrated wave energy values are more than 300 kW/m for typhoon and more than 900 kW/m for strong typhoons, over 60 times and 180 times the annual average (5 kW/m) in the Chinese sea area, respectively. In terms of other locations, corresponding values are more than 50 kW/m and over 100 kW/m. Therefore, typhoons’ wave energy is certainly a huge asset if fully utilized.

Significant wave height (SWH) prediction can effectively improve the safety of marine activities and reduce the occurrence of maritime accidents, which is of great significance to national security and the development of the marine economy. In this study, we comprehensively analyzed the SWH prediction performance of the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) by considering different input lengths, prediction lengths, and model complexity. The experimental results show that (1) the input length impacts the prediction results of SWH, but it does not mean that the longer the input length, the better the prediction performance. When the input length is 24h, the prediction performance of RNN, LSTM, and GRU models is better. (2) The prediction length influences the SWH prediction results. As the prediction length increases, the prediction performance gradually decreases. Among them, RNN is not suitable for 48h long-term SWH prediction. (3) The more layers of the model, the better the SWH prediction performance is not necessarily. When the number of layers is set to 3 or 4, the model’s prediction performance is better.

In preparation for validation of the swath-based altimetry mission (Surface Water Oceanography Topography, SWOT), we developed a buoy array, equipped with Global Navigation Satellite System/Inertial Navigation System, capable of accurately observing sea surface height (SSH), wave information and tropospheric delay. Here we present results from an 8-day trial deployment at five locations along a Sentinel-6 Michael Freilich (S6MF) ground track in Bass Strait. A triplet buoy group including two new buoys (Mk-VI) and a single predecessor (Mk-IV) were deployed in proximity to the historic Jason-series comparison point. SSH solutions compared against an in-situ mooring suggest the new buoys were working at an equivalent precision of ~1.5 cm to the previous design (MK-IV). At 10-km spacing along the S6MF track, the buoy array was shown to observe the progression of oceanographic and meteorological phenomena. Tidal analysis of the buoy array indicated moderate spatial variability in the shallow water tidal constituents, with differences in the instantaneous tidal height of up to ~0.2 m across the 40-km track. Further, tidal resonance within Bass Strait was observed to vary, most probably modulated by atmospheric conditions, yet only partially captured by an existing dynamic atmospheric correction product. A preliminary investigation into the spatial scale of the buoy error based on observed/inferred geostrophic currents with our present buoy array configuration suggests that the signal-noise ratio of the array became significant at 20-km spacing in Bass Strait. Finally, as an illustrative comparison between the buoy array and high resolution S6MF data, a single cycle was compared. The wet tropospheric delay observed by the S6MF radiometer exhibited some potential land contamination in the deployed area, while the 1-Hz and 20-Hz significant wave height from S6MF appeared within mission requirements. Generally good agreement between buoy and altimeter SSH was observed. However, subtle differences between the altimeter and the buoy sea level anomaly series warrants further investigation with additional cycles from a sustained deployment in the area. We conclude that the buoy array offers a useful geodetic tool to help quantify and understand intra-swath variability in the context of the SWOT mission.

Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterization can help in the design of ocean structures such as wave energy converters and sea dikes. Therefore, engineers need long time series of ocean wave parameters. Numerical models are a valuable source of ocean wave data; however, they are computationally expensive. Consequently, statistical and data-driven approaches have gained increasing interest in recent decades. This work investigates the spatiotemporal relationship between North Atlantic wind and significant wave height ( $ {H}_s $ ) at an off-shore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks to extract the spatial features that contribute to $ {H}_s $ . Then, long short-term memory is used to learn the long-term temporal dependencies between wind and waves.

The main goal of this study is to investigate if the publicly available sea state forecasts for the Aran Islands region in the Republic of Ireland can be improved. This improvement is achieved by using the combination of local scale sea state forecasts and Bayesian Model Averaging techniques. The question of a good forecast has been around since the start of forecasting. With current state-of-the-art numerical models, computational power, and vast data availability, we consider whether it is possible to improve model forecasts only by using the combination of publicly available forecasts, free open-source software, and very moderate computational power. It is shown that it is possible to improve the sea state forecast by at least $ 1\% $ , and in some cases up to $ 8\% $ . The reduction of error is between $ 6\% $ and $ 48\% $ . With a more careful and specific selection of training parameters, it is possible to improve the forecast accuracy even more. The possibility of extending this local improvement to the whole coastal area around the island of Ireland is explored. Unfortunately, it is currently impossible, due to a lack of live data buoys in the coastal waters. Nonetheless, it is shown that the proposed process is simple and can be implemented by anyone whose livelihood depends on an accurate sea state forecast. It does not require large computational power, model forecasts are publicly available, and there is minimal to no training in forecasting and statistics required to enable one to perform such improvements for one’s area of interest, provided one has access to live wave data.

The study used the SCHISM ocean model combined with the WWM III wind wave model to quantify the interaction between wind waves and tides in the coastal zone of the Changjiang River Estuary and its adjacent areas. The wave and storm surge during Typhoon Ampil, which made landfall in July 2018 in Shanghai, were simulated by using the Climate Forecast System Version 2 (CFSv2) and Medium-Range Weather Forecasts (ECMWF) latest reanalysis (ERA5) wind dataset from 1 July to 31 July. Model results with CFSv2 forcing show better performance in terms of significant wave height and storm surge than those with ERA5 forcing. To investigate the interactions between waves, water levels, currents, and their combined effects on significant wave and surge variations, six numerical sensitivity experiments were designed according to the different coupling methods between SCHISM and WWMIII. The research shows that in coastal areas with water depths of less than 10 m, waves are affected by water levels and currents. The differences in the effect on significant wave height between wave-tide coupling and one-way coupling with water levels and currents are negligible. Wave setup is an important physical term which cannot be ignored during the variations of storm surge caused by Typhoon Ampil. The contributions of wave set up were concentrated in coastal areas with water depths less than 10 m. The peak wave setup occurred in the Changjiang River Estuary, reaching 0.15 m. In Xiangshan Bay and Sanmen Bay, wave radiation stress makes the proportion of wave setup to the total surge reached more than 30%. The consideration of wave-tide interaction can effectively improve the accuracy of numerical wave and storm surge simulations, which can provide more accurate hindcasts of wave and storm surge variations in the Changjiang River Estuary and its adjacent coastal areas.

Predicting sea wave parameters such as significant wave height (SWH) has recently been identified as a critical requirement for maritime security and economy. Earth observation satellite missions have resulted in a massive rise in marine data volume and dimensionality. Deep learning technologies have proven their capabilities to process large amounts of data, draw useful insights, and assist in environmental decision making. In this study, a new deep-learning-based hybrid feature selection approach is proposed for SWH prediction using satellite Synthetic Aperture Radar (SAR) mode altimeter data. The introduced approach integrates the power of autoencoder deep neural networks in mapping input features into representative latent-space features with the feature selection power of the principal component analysis (PCA) algorithm to create significant features from altimeter observations. Several hybrid feature sets were generated using the proposed approach and utilized for modeling SWH using Gaussian Process Regression (GPR) and Neural Network Regression (NNR). SAR mode altimeter data from the Sentinel-3A mission calibrated by in situ buoy data was used for training and evaluating the SWH models. The significance of the autoencoder-based feature sets in improving the prediction performance of SWH models is investigated against original, traditionally selected, and hybrid features. The autoencoder–PCA hybrid feature set generated by the proposed approach recorded the lowest average RMSE values of 0.11069 for GPR models, which outperforms the state-of-the-art results. The findings of this study reveal the superiority of the autoencoder deep learning network in generating latent features that aid in improving the prediction performance of SWH models over traditional feature extraction methods. [ABSTRACT FROM AUTHOR]

In this paper, a high-resolution Sentinel-3 synthetic aperture radar altimeter is used to observe ISWs in the Sulu Sea. Based on the advantages of the simultaneous observation of Sentinel-3 OLCI and SRAL, the changes in σ 0 , SWH, and SSHA caused by the ISWs are quantitatively analyzed. The results show that σ 0 decreases and then increases after being modulated by the ISWs in the altimeter operation direction; SWH shows a large change; and the change trend of SSHA is the same as that of σ 0 . Because of the angle between the propagation direction of the ISWs and the SRAL trajectory, the actual position corresponding to the peak power in the waveform detects the ISWs before the nadir, at which time σ 0 is already modulated by ISWs, resulting in the deviation of σ 0 . In addition, the sea surface roughness within the SRAL footprint in this case is no longer uniform, which violates the assumption of retracking and leads to the incorrect estimation of geophysical parameters such as SWH and SSHA. With a view to correcting these errors, the effect of ISWs on the retracker must therefore be considered and the model for waveform modified accordingly. [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ECs) produce comparable wave heights and hazardous sea states to those under hurricanes. With strong wind speeds and considerably larger diameters, the impact of ECs on the global wave climate is substantial. Because ECs occur more frequently, move more quickly, and exist in a large variety of sizes and shapes, these storms are not as well documented. Here, we present findings from a global EC tracking algorithm aimed at creating an EC database of wave producing storms. We focus on the major ocean basins of extratropical cyclone activity: The North Atlantic, North Pacific, and Southern Oceans. Our tracking algorithm uses mean sea level pressure fields from the European Centre for Medium‐Range Weather Forecasts (ECMWF) high resolution, advanced data assimilating, ERA5 reanalysis over the years 1979–2020. The 42 year‐long data set of thousands of cyclone tracks allows seasonal patterns and longer‐term trends of EC activity to be analyzed in these ocean regions. In addition, we employ 36 years of satellite radar altimeter‐derived significant wave height (Hs) data from 14 inter‐calibrated satellite missions to study the wave climate induced by the identified ECs in each basin. The resulting wave climatology compares well with previous results in the mid to high latitudes. We then analyze average wave heights under different EC characteristics, including cyclone translational speed and maximum winds, which have strong effects on wave height variability. Finally, a brief analysis of EC and wave climate under positive and negative phases of the North Atlantic Oscillation is presented. Plain Language Summary: Large storms which occur outside of the tropics, known as extratropical cyclones (ECs), create very large waves that can surpass that of tropical cyclones. These extreme sea states are responsible for coastal hazards including erosion and surge induced flooding. While tropical cyclones, or hurricanes, are well documented by devoted agencies, ECs are more difficult to track given their high occurrence, fast movement, and inconsistency of size and shape. Here we present the results from a tracking algorithm, applied over a 42 year period, aimed at documenting the many ECs which occur over the global oceans with specific focus placed on wave development within these storms. We use satellite measurements of wave heights within the tracked ECs to investigate wave growth and overall wave climate within these storms. In addition to studying the seasonality of ECs and associated wave activity, we show the regional influence of longer time‐scale climate variability on the same. Key Points: A new extratropical cyclone tracker is applied to 42 years of global reanalysis to investigate climatology and seasonality of stormsOcean basin composite analyses of satellite‐derived significant wave height observations show wave dependence on cyclones characteristicsThe North Atlantic Oscillation climate modes show strong influence on cyclone activity and induced wave climate in the North Atlantic [ABSTRACT FROM AUTHOR]

Understanding wave characteristics is crucial for coastal activities, coastal infrastructure, navigation safety, and coastline management policies. Although wave characteristics in several regions of the Arabian Gulf are relatively studied, until now, wave features in Iraq marine water are not well recognized. In this study, we analyzed the wave parameters measured off Iraq marine water, northwest coast of the Arabian Gulf during 2017–2021 to identify the features associated with different wind systems. However, the results indicated that the averages of wind speed are higher in summer than in winter. The peak wind speeds are generally due to the northwest wind, and the maximum wind speed recorded during the study period reaches 19.9 m/s. Additionally, the results revealed that wave heights in summer are generally lower than that in winter. The highest significant and maximum wave heights during the study period reach 0.78 and 1.95 m, respectively. The highest wave heights were recorded during the southeast wind, although its speed is generally lower than the northwest wind. This mainly could be attributed to a large fetch available for the southeast wind relative to a limited fetch available to the northwest wind. Waves with long periods were mainly recognized during winter, spring, and fall seasons. The maximum significant wave period and zero-crossing wave period reach 6.84 and 5.4 s, respectively. The results obtained could be beneficial for overall coastal and near shore activities in the region. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the influence of tidal flat reclamations on storm surges in the West Korean Bay during the Typhoon Bavi (2020). The simulation results have shown that during the typhoon, there were some changes in wave characteristics by the tidal flat reclamation at the measuring points along the natural coastal areas where the dikes would not be constructed. The significant wave height (Swh), peak wave period (Tp) and wave power (Wp) at most of measuring points were increased after each reclamation stage, for example, with the largest increase of 36.17% in the maximum Swh value at the measuring point S7 after the reclamation stage 2. Furthermore, the wave characteristics at seaward side of dikes were significantly increased as the reclamation plans were implemented. After the stage 1, the maximum Swh values increased by 23.95%, 29.72% and 15.79% for the regions A, B and C, respectively, while 55.34% and 62.39% for the regions A and B after the stage 2, respectively. The results have also shown that the Swh and Wp during the typhoon became smaller and more closely related to the tides as the water depth at measuring points decreased. It can be concluded that the wave impacts during storm events would become more severe, especially in front of dikes, as the reclamation projects are implemented in the West Korean Bay. [ABSTRACT FROM AUTHOR]

Though numerical wave models have been applied widely to significant wave height prediction, they consume massive computing memory and their accuracy needs to be further improved. In this paper, a two-dimensional (2D) significant wave height (SWH) prediction model is established for the South and East China Seas. The proposed model is trained by Wave Watch III (WW3) reanalysis data based on a convolutional neural network, the bidirectional long short-term memory and the attention mechanism (CNNBiLSTM-Attention). It adopts the convolutional neural network to extract spatial features of original wave height to reduce the redundant information input into the BiLSTM network. Meanwhile, the BiLSTM model is applied to fully extract the features of the associated information of time series data. Besides, the attention mechanism is used to assign probability weight to the output information of the BiLSTM layer units, and finally, a trainingmodel is constructed. Up to 24-h prediction experiments are conducted under normal and extreme conditions, respectively. Under the normal wave condition, for 3-, 6-, 12- and 24-h forecasting, the mean values of the correlation coefficients on the test set are 0.996, 0.991, 0.980, and 0.945, respectively. The corresponding mean values of the root mean square errors are measured at 0.063 m, 0.105 m, 0.172 m, and 0.281 m, respectively. Under the typhoon-forced extreme condition, the model based on CNN-BiLSTM-Attention is trained by typhooninduced SWH extracted from the WW3 reanalysis data. For 3-, 6-, 12- and 24-h forecasting, the mean values of correlation coefficients on the test set are respectively 0.993, 0.983, 0.958, and 0.921, and the averaged root mean square errors are 0.159 m, 0.257 m, 0.437 m, and 0.555 m, respectively. The model performs better than that trained by all the WW3 reanalysis data. The result suggests that the proposed algorithm can be applied to the 2D wave forecast with higher accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Wind and waves are the main factors of environmental loading on ships and offshore structures. Thus, detailed understanding of wind and wave conditions can improve the design and maintenance of these structures. This paper developed a validated long-term wind and wave hindcast database covering the recent 32 years from 1988 to 2019. The spatial distribution of wind and wave characteristics for the whole Xisha Islands' domain were analyzed. Frequency and directional distributions of wind speeds and significant wave heights were investigated at several locations around typical islands. Extreme value models were used to estimate the wind speed for 100-year return levels, whereas environmental contour approaches were utilized to establish the extreme sea-state parameters for 50- and 100-year return periods. It was found that the Weibull distribution was better fitted to the significant wave heights of the Xuande Atoll's sites in the open sea, while the exponential Weibull distribution provided a better fit at the Yongle Atoll's sites where waves are sheltered. [ABSTRACT FROM AUTHOR]

The particular structure and configuration of the Venice lagoon represents a paramount case study concerning coastal flooding which affects natural, historical/cultural properties, together with industrial, commercial, economical and port activities. In order to defend Venice (and other sites) within the lagoon from severe floods, the Italian Government promoted the construction of a complex hydraulic/maritime system, including a movable storm surge barrier named Experimental Electromechanical Module (MoSE), to be activated when specific water levels occur. When the MoSE barriers are raised, the only access to the lagoon for commercial and cruise ships is represented by the Malamocco lock gate, provided that suitable safety conditions (involving the significant wave height) are satisfied. In addition, the Italian Government has recently established that, in the near future, large ships will always have to enter/exit the lagoon only through the Malamocco entrance. In turn, the navigation within the Venice lagoon is (will be) controlled by the combined MoSE-Malamocco system, ruled by both univariate and bivariate paradigms/guidelines. As a novelty, in the present work, for the first time, the statistics of significant wave heights and water levels in the Venice lagoon (both univariate and bivariate ones) are investigated: in particular, these variables turn out to be dependent, and their joint occurrence (statistically modeled via Copulas) can determine the stop of ship navigation, yielding significant economic losses. Here, univariate and bivariate Return Periods and Failure Probabilities are used to thoroughly model the statistical behavior of significant wave heights and water levels, in order to provide useful quantitative indications for the management of the tricky hydraulic, maritime and economical system of the Venice lagoon. [ABSTRACT FROM AUTHOR]

A small-aperture receiving array occupying a small area provides convenience for the layout of high-frequency (HF) radars. However, this kind of antennas has a wide beam, which adversely affects the wave measurements. To solve this problem, a method for extracting wave parameters from wide-beam radar echoes, which models current variations across the beam, is proposed in this article. The nondirectional wave spectrum is extracted from the echoes of a small-aperture HF radar, and wave parameters such as the significant wave height, peak, and mean wave period are then calculated from it. Simulation is carried out to assess the performance of the method for different current velocities, radar frequencies, and wind conditions. The proposed method is then applied to a nine-day dataset collected by a multifrequency HF radar with a circular receiving array (MHF-C) radar for further validation via comparisons between the radar-estimated and the buoy-measured wave parameters. Some factors that may affect the performance of wave height measurements are analyzed, such as the signal-to-noise ratio and radial current velocity. The correlation coefficient (CC) between the radar-estimated and the buoy-measured significant wave height is 0.90, and the root mean square difference (RMSD) is 0.51 m. For the mean wave period, the CC is 0.61 and the RMSD is 0.67 s. The results demonstrate that the significant wave height and the mean wave period extracted by the proposed method are consistent with the buoy-measured values, and the proposed method can overcome the broadening influence of the Doppler spectrum caused by ocean currents. [ABSTRACT FROM AUTHOR]

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.

In the future, shifts in wind storms across the North and Baltic Seas are highly unpredictable, challenging the projection of wave conditions for managing coastal hazards. Moreover, regional sea level rise (SLR), with very large uncertainty, complicates the situation for stakeholders seeking recommendations for climate adaptation plans. The purpose of this study is to examine the change of the storm surge and wind wave components of the water level due to climate change in a low tidal range Køge Bay near the entrance of the Baltic Sea. Under a high greenhouse gas emission scenario RCP8.5, we employed a regional climate model (HIRHAM) forced wave model (WAM) and focused on the wave model results during the “storm surge conditions” (exceeding 20 years storm surge events) and “stormy conditions” (exceeding 90th percentile of wave heights). We find that the change in both wave height and period in the future is negligible under “stormy conditions”. Nevertheless, under “storm surge conditions” when considering SLR, the simulated wave height is projected to double in the near future (mid-century) under RCP 8.5, and the wave period may also increase by about 1.5 seconds. This is because some high significant wave height events in the future are associated with the storm surge events when considering SLR. The findings suggest that the combined effects of mean sea level rise, storm surge and waves are likely to increase the risk to a bay with geography and exposure comparable to Køge Bay. As a result, the future plan for climate engineering protection should place a premium on the additional wave energy protection associated with storm surges.

In the Mediterranean Sea, coastal extreme sea levels are mainly caused by storm surges driven by atmospheric pressure and surface winds from extratropical cyclones. In addition, wind-waves generated by the same atmospheric perturbations may also contribute to coastal extremes through wave setup (temporal rise above the mean sea level due to dissipation and breaking of waves in shallow waters close to the shore). This study investigates the spatial and temporal variability of coastal extreme sea levels in the Mediterranean basin, using a new ocean hindcast generated with a coupled hydrodynamic-wave model that simulates storm surges and wind-waves. The numerical simulation covers the period 1950-2021 with high temporal sampling (1h) and at unprecedented spatial resolution for a basin scale analysis, that reaches 200 m along the coastlines. Coastal storm surges and wave heights are validated with available observations (tide gauges, waves buoys and satellites). Comparison against tide gauges shows an average RMSE of 7.5 cm (7.7 cm for extreme events) and mean linear correlation of 0.64 for the whole period. Similarly, comparison of simulated and observed significant wave height shows good agreement, with RMSE lower than 0.25 m and a coefficient correlation as high as 0.95. The results confirm that coastal extreme sea levels are more likely to be located in regions with wide continental shelves favouring the wind contribution to storm surges along with shallow waters that favour wave setup induced by depth-breaking. The contribution of waves to coastal extreme sea levels has been quantified, using the hindcast in combination with an uncoupled simulation and has been shown to be significant, with an assessed wave setup spatial footprint at regional scale and observed maximum sea levels increased by up to 120% in the presence of waves.

To estimate significant wave height of ocean wave, a machine learning framework is developed. Significant wave height and period can be used by supervised training of machine learning to predict ocean conditions. In this paper we proposed a method to predict significant wave height using Support vector regression (SVR). Buoy dataset taken from the Queensland government open data portal the input from which were aggregated into supervised learning test and training data sets, which were supplied to machine learning models. The SVR model replicated significant wave height with a root-mean-squared-error of 0.044 and performed on the test data with 95% accuracy. Comparing to forecasting with the physics-based model the Machine learning SVR model requires only a fraction (< 1=1200th) of the computation time, to predict Significant wave height.

The Hellenic National Meteorological Service (HNMS) issues marine weather bulletins for the Mediterranean and the Black Seas as part of the Global Maritime Distress and Safety System, with wind forecasts being of high importance. This study evaluates the accuracy of HNMS marine forecasts during Wind Warning (WW) events when weather warnings were also issued for Greece. The analysis focuses on events that occurred over the Ionian and the Aegean Seas from September 2019 to February 2023. Remote sensing data are used to objectively verify the forecasted wind speed and sea state. An evaluation of the accuracy of numerical weather prediction products against satellite data during the weather system ‘Barbara’ (February 2023), that was also a WW event, is included.

This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period.

The lack of observations and biases of winds and bathymetry in the coastal region result in significant biases in the numerical modeling of sea waves. In this study, we establish a two-module bias correction model based on the Long-Short Term Memory neural network (denoted as TM-BCM) which accounts for the data imbalance by separately training the neural network for low and high sea states. The in-situ observations in 2020 and 2021 are used to model training and testing, respectively. The test results show that TM-BCM significantly reduces the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of modeled sea waves by 52.9% and 65.5%, respectively. In addition, TM-BCM outperforms the single-module model (denoted as SM-BCM) that does not account for the imbalance of training data. Our results highlight the great value of deep learning in correcting biases for numerical models and the importance of accounting for data imbalance in training the neural network. • The two-module bias correction model for Sea wave hindcasting based on the LSTM accounts for the data imbalance of low and high sea states. • The new bias correction model can significantly reduce the biases of the numerical wave model in the coastal region. • The two-module bias correction model performs well not only under monsoon conditions but also in high sea states. [ABSTRACT FROM AUTHOR]

Extreme value analysis (EVA) uses data to estimate long-term extreme environmental conditions for variables such as significant wave height and period, for the design of marine structures. Together with models for the short-term evolution of the ocean environment and for wave–structure interaction, EVA provides a basis for full probabilistic design analysis. Alternatively, environmental contours provide an approximate approach to estimating structural integrity, without requiring structural knowledge. These contour methods also exploit statistical models, including EVA, but avoid the need for structural modelling by making what are believed to be conservative assumptions about the shape of the structural failure boundary in the environment space. These assumptions, however, may not always be appropriate, or may lead to unnecessary wasted resources from over design. We demonstrate a methodology for efficient fully probabilistic analysis of structural failure. From this, we estimate the joint conditional probability density of the environment (CDE), given the occurrence of an extreme structural response. We use CDE as a diagnostic to highlight the deficiencies of environmental contour methods for design; none of the IFORM environmental contours considered characterise CDE well for three example structures. • Methodology for full probabilistic analysis of fluid loading presented with linear kinematics. • Methodology incorporates conditional simulation of time series and importance sampling. • Conditional density of environment (CDE) given extreme loading estimated for simple structures. • Performance of different environmental contour methods in characterising CDE quantified. • Parametric form underlying IFORM-type methods shown to be important source of variability. [ABSTRACT FROM AUTHOR]

Accurate predictions of significant wave heights are crucial in the field of marine and ocean engineering. This paper presents two hybrid intelligent models, EN-1 and EN-2, that combine random forest (RF) and extreme learning machine (ELM) methods through direct linear weighting and gene expression programming-based nonlinear weighting methods, respectively, for the prediction of weekly mean significant wave heights. The performances of the EN-1 and EN-2 models were compared with those of 11 reference models. The results showed that the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) of the EN-1 and EN-2 models were 0.9850, 0.0567, 0.0736 and 3.7107%, 0.9940, 0.0335, 0.0487 and 2.2373%, respectively, for the training datasets and 0.9802, 0.0627, 0.0847 and 4.1461%, 0.9754, 0.0707, 0.0966 and 4.2614%, respectively, for the testing datasets, indicating that the EN-1 and EN-2 models have good predictive performance and can be effectively used for the prediction of weekly mean significant wave heights. In addition, a sensitivity analysis was conducted to investigate the influence of the input variables on the model performance. • Two hybrid models are proposed for the prediction of weekly mean significant wave heights. • The differences of the two weighting methods in improving the performance of the model are compared. • Sensitivity analysis is conducted to investigate the influence of input variables on the model performance. [ABSTRACT FROM AUTHOR]

Accurate and rapid ocean surface wave simulations are essential for navigation safety, marine activity, and climate change research. However, traditional numerical wave models incur substantial computational costs, especially in long-term simulations for climatology research. Deep-learning methods have shown promise in enabling faster simulations with fewer computational resources. However, the cumulative error in current deep-learning wave models limits their use as surrogate component models in climate modeling. In this work, a deep-learning model, named the Global Wave Surrogate Model for Climate simulation (GWSM4C), was developed based on a convolutional architecture, which accounts for the physical processes (wind waves and swells) of waves. Because GWSM4C can automatically generate initial conditions for each simulation moment depending only on historical wind fields, the accumulation of errors from previous simulated wave states can be avoided. The experimental results demonstrated that simulated global significant wave height produced by GWSM4C exhibited a correlation coefficient of 0.94 and a root-mean-square error of 0.21 m compared with the traditional numerical wave model. Furthermore, the generalization test of the GWSM4C indicates that it is feasible to use GWSM4C for long-term wave simulation in climatology-related studies or to integrate it into a coupled climate model as a surrogate wave component model. • GWSM4C is a cutting-edge cost-effective deep-learning model for climate models. • STE architectures are designed to extract wave propagation features. • GWSM4C produces global wave heights without cumulative errors using wind fields. [ABSTRACT FROM AUTHOR]

In this study, we present EBANO (Ensemble BAsed on uNimodal Ordinal classifiers), which is a novel ensemble approach of ordinal classifiers that includes four soft labelling approaches along with an ordinal logistic regression model. These models are integrated within the ensemble using a new aggregation methodology that automatically weights each individual classifier using a randomised search algorithm. In addition, the proposed EBANO methodology is applied to tackle short-term prediction of Significant Wave Height (SWH). Thus, we employ EBANO using a diverse set of eight datasets derived from reanalysis data and buoy-recorded SWH measurements. To approach the problem from an ordinal classification perspective, the SWH values are discretised into five ordered classes by applying hierarchical clustering. EBANO is compared with each of the individual classifiers integrated in the proposed ensemble along with a different ensemble technique termed HESCA. Both the average results and the ranks obtained show the superiority of EBANO over the compared methodologies, being more pronounced in the metrics that account for the imbalance present in the datasets considered. Finally, a statistical analysis is performed, confirming the statistical significance of the observed differences in all comparisons. This analysis underscores the effectiveness of EBANO in addressing the problem of SWH prediction, showcasing its excellence. [ABSTRACT FROM AUTHOR]

Information on wave direction and height is an important input to the coastal engineers. The availability of measured data at every location in the ocean makes maritime operations smoother. However, the practical impossibility makes it to look for alternative datasets like ERA5 reanalysis data. In this study, we compare the significant wave height and mean wave direction in the ERA5 with the buoy-measured data available at the nearest locations in the coastal waters of India. Even though the ERA5 overestimates the measured significant wave heights at certain instances, they both are in good agreement at most of the locations. The correlation coefficient varies from 0.82 to 0.99, with the RMSE falling between 0.15 and 0.31 m. However, the ERA5 wave direction deviates significantly from the measured buoy data at certain locations due to the substantial difference between the measured and ERA5 mean direction of wind-seas. The ERA5 dataset matches the measured mean wave direction when swell dominates i.e., during the southwest monsoon for the locations in the Arabian Sea and during post-monsoon season for the locations in the western Bay of Bengal. • Compared ERA5 significant wave height and mean wave direction with buoy data. • Significant wave heights are in good agreement with ERA5 at most of the locations. • ERA5 wave direction deviates significantly from the buoy data in nearshore waters when wind-seas dominates. [ABSTRACT FROM AUTHOR]

Ocean wave parameters (OWPs), including wave propagation direction (WPD), significant wave height (SWH), and main wave wavelength (MWW) can be typically retrieved using an interferometric imaging radar altimeter (InIRA). However, the inversion accuracy of ocean waves in Ku (15.8 GHz) and Ka (35.8 GHz) bands has not yet been evaluated due to the lack of field observation data. In this paper, to assess the inversion accuracy of OWPs in Ku and Ka bands, an airborne observation experiment using simultaneous Ku and Ka bands was carried out for the first time in Rizhao, Shandong Province, China. A dual-band InIRA (DInIRA) was configured with small incidence angles (4°–18°) and a Global Navigation Satellite System (GNSS) buoy; a mobile weather station was placed at the intersection of the plane routes for validation. Afterward, the WPD, SWH, and MWW were retrieved based on the imaging of sea surface height. As compared with the field in situ data, the WPD inversion results of main wind wave were found to be consistent with the measurement environmental wind direction. The SWH inversion biases, retrieved by the Ku and Ka bands, were 0.38 m and 0.27 m; the MWW inversion biases for the swells were equal to 16.75 m and 3.67 m; and the MWW inversion biases about the wind waves were 2.32 m and 0.57 m. Ultimately, it was established that the OWPs could be effectively retrieved by the DInIRA, and the inversion accuracy of the SWH and the MWW in the Ka band outperformed that in the Ku band. [ABSTRACT FROM AUTHOR]

A formulation to assess a breakwater damage probability and to select the optimal maintenance strategy is presented and illustrated with a case study in Mexico. The formulation includes the calculation of annual and lifetime exceedance damage probabilities, the cost of damage/failure consequences, epistemic uncertainty and information on port logistics. The limit-state corresponds to the stability of the breakwater core (armour) units and the cost of failure consequences includes the explicit consideration of costs of fatalities, loss of armour units, damage and loss of property to port facilities, and economic loss due to business interruption in the port. The Van der Meer approach is followed to describe the damage state and the number of waves is modelled as lognormal instead of normal. A curve of tetrapod's weight versus wave height is proposed as an approximation to the tetrapod's fragility. FOSM (First Order Second Moments) and MCS (Monte Carlo simulation) techniques are implemented. The wave height is identified, through the calculation of gradients, as the most sensitive parameter for epistemic uncertainty. The optimal maintenance is selected after assessing three maintenance alternatives by balancing costs and safety. A breakwater located in Mexico called Tampico Port is used as case study to illustrate the formulation. [ABSTRACT FROM AUTHOR]

The rational assessment of regional energy distribution provides a scientific basis for the selection and siting of power generation units. This study, which focused on the Bohai Sea, set 31 research coordinate points in the Bohai sea for assessing the potential/trends of wave energy flux (WEF). We applied a point-to-point time series prediction method which modelled the different geographical coordinate points separately. Subsequently, we evaluated the performance of three traditional machine learning methods and three automated machine learning methods. To estimate WEF, the best model was applied to each research coordinate points, respectively. Then, the WEF was calculated and predicted based on the data of MWP, SWH, and water depth. The results indicate that, for all coordinates in the Bohai Sea, the H2O-AutoML algorithm is superior to the other five algorithms. Gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and stacked ensemble models yielded the best performance out of the H2O algorithms. The significant wave height (SWH), the mean wave period (MWP), and the WEF in the Bohai Sea tended to be concentrated in the center of the sea and dispersed in the nearshore areas. In the year 2000, 2010, 2020, and 2030, the maximum annual average WEF at each research coordinate in the Bohai Sea is around 1.5 kW/m, with a higher flux in autumn and winter. In summary, the results provide ocean parameter characterization for the design and deployment of wave energy harvesting devices. Moreover, the automated machine learning introduced herein has potential for use in more applications in ocean engineering. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the scale of deep sea net-cage use in the nearshore area of Hainan Island, tropical cyclone-induced wave hazard assessment is urgently needed. In this study, the wind-wave-current coupled ADCIRC + SWAN model, which considers the effects of tidal and storm surges, was used to simulate tropical cyclone events over the last 33 years. This model adopts an unstructured high-resolution grid with a nearshore resolution of up to 100 m. The compared simulated results and observations during typhoons JEBI (2013), HAIYAN (2013) and KALMAEGI (2014) were in agreement. This study statistically analyzed maximum significant wave heights on the basis of a large set of simulated storm wave level maps to derive the wave heights of different return periods. Then, the results of nearshore wave hazard classification were obtained by applying the affinity propagation (AP) clustering method to dozens of nearshore profiles. The results demonstrate that the risk at any point in the nearshore area of Hainan Island is dominated by the wave hazard type and water depth condition. The wave hazard assessment method developed for Hainan Island will be significant in assisting government decision-making in the rational planning of deep sea net-cage aquaculture. [ABSTRACT FROM AUTHOR]

The purpose of these comments and discussion has been to demonstrate how wave statistics can be incorporated into future applications of the proposed empirical equations to estimate the maximum relative uplift pressure under positive and negative freeboards on berm revetment with Seabee slope. This is demonstrated by using a joint distribution of significant wave height and surf parameter as well as by giving examples of the results. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of wave parameters of the European Centre for Medium-Range Weather Forecasting Reanalysis v5 (ERA5) in the South China Sea (SCS) is systematically examined with field measured data of two buoys at offshore sites and a subsea-based platform at a nearshore site, which has a total observational period of nearly three years. It is suggested that the wave parameters provided by ERA5, such as significant wave height (Hs) and mean wave period (Tm), are in good agreement with the observational data of the three sites. Compared with the in situ data, the correlation coefficient of ERA5 Hs is in the range of 0.87–0.93, and the root mean square error is only in the range of 0.22–0.57 m. The error of standard deviation does not exceed 0.29 m and is even as low as 0.04 m at two sites. The wave propagation directions of the ERA5 and in situ data are also basically the same. However, when the data are applied in engineering, some wave parameters extracted from ERA5 may deviate from the measured statistics. It is possible to significantly overestimate the average duration of a particular Hs. Further analysis shows that the possible errors of ERA5 wave parameters may be due to insufficient description of topographic conditions, which shows that the error value changes obviously with the wave direction. The results preliminarily confirm the validity of ERA5 data in the SCS, but also indicate that it is necessary to calibrate and validate the data adequately when applying the global model and its reanalysis data to specific ocean areas. [ABSTRACT FROM AUTHOR]

In this study, the azimuth cut-off method, typically used for SAR moderate wind speed estimation purposes, is analyzed under high wind regimes. Firstly, the importance of the pixel spacing, the size of the boxes selected for Synthetic Aperture Radar (SAR) image partitioning and the image texture in terms of homogeneities are discussed by considering their influence on the azimuth cut-off (λc) estimation. Secondly, a quality control analysis of the reliability of λc is carried out by evaluating the distance between the autocorrelation functions (ACF) and their correspondent fittings. This analysis points out the importance of filtering out the unreliable and unfeasible λc values in order to improve the wind speed estimation. The quality control procedure is based on a x2 test, applied on a large Sentinel-1A dataset. The soundness of the test is verified by an increment in terms of correlation between λc estimations and wind speed values. This approach is, then, applied under high wind regimes, i.e., tropical cyclones.

It is known that densely populated coastal areas may be adversely affected as a result of the climate change effects. In this respect, for coastal protection, utilization, and management it is critical to understand the changes in wind speed (WS) and significant wave height (SWH) in coastal areas. Innovative approaches, which are one of the trend analysis methods used as an effective way to examine these changes, have started to be used very frequently in many fields in recent years, although not in coastal and marine engineering. The Innovative Polygon Trend Analysis (IPTA) method provides to observe the one-year behavior of the time series by representing the changes between consecutive months as well as determining the trends in each individual month. It is not also affected by constraints such as data length, distribution type or serial correlation. Therefore, the main objective of this study is to investigate whether using innovative trend methods compared to the traditional methods makes a difference in trends of the climatological variables. For this goal, trends of mean and maximum WS and SWH series for each month at 33 coastal locations in Black Sea coasts were evaluated. Wind and wave parameters WS and SWH were obtained from 42-year long-term wave simulations using Simulating Waves Nearshore (SWAN) model forced by the Climate Forecast System Reanalysis (CFSR). Monthly mean and maximum WS and SWH ​​were calculated at all locations and then trend analyses using both traditional and innovative methods were performed. Low occurrence of trends were detected for mean SWH, maximum SWH, mean WS, and maximum WS according to the Mann-Kendall test in the studied months. The IPTA method detected more trends, such as the decreasing trend of the mean SWH at most locations in May, July and November December. The lowest (highest) values were seen in summer (winter), according to a one-year cycle on the IPTA template for all variables. According to both methods, most of the months showed a decreasing trend for the mean WS at some locations in the inner continental shelf of the southwestern and southeastern Black Sea. The IPTA method can capture most of the trends detected by the Mann-Kendall method, and more missed by the latter method.