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Significant wave height (SWH) is an important parameter to reflect wave state, which is of great significance in ocean engineering. However, the current wave observation methods have limitations in capturing wave field data with high spatial resolution. In this study, to generate the SWHs field over the Northeast Pacific and Northwest Atlantic, multi-source satellite altimeter data (CRYOSAT-2, SARAL, JASON-3, SENTINEL-3A, SENTINEL-3B, HY-2B and CFOSAT) are fused with a spatial resolution of 0.125° x 0.125° and a temporal resolution of 1 day. We employ the Inverse Distance Weighting (IDW) method and the IDW-based spatiotemporal (IDW-ST) method for data fusion. The fusion results exhibit a consistent spatial distribution characteristic, but the results of the IDW method display the visible trajectory. Moreover, the IDW-ST method, which incorporates time factors, shows great agreement between the fused SWH and buoy data. However, when the water depth change near the grid point has a great influence on the fusion, the complexity of bathymetric topography makes the traditional two-dimensional spatial fusion methods inadequate. Therefore, an improved method is proposed based on the IDW-ST fusion method, which introduces the water depth factor and significantly enhances fusion accuracy in regions where bathymetric variations greatly affect fusion results. The proposed method can be used to generate reliable SWH fields, especially in complex bathymetric topography conditions, and provide significant support for marine infrastructure design, ocean energy utilization and marine disaster protection. [ABSTRACT FROM AUTHOR]

X-band marine radar captures the signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information about wind wave parameters. Traditional methods of significant wave height (SWH) estimation rely on physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN based approaches necessitate costly in situ data. In this study, as a viable alternative, we propose generating synthetic radar images with specified wave parameters using Fourier-based approach and Pierson- Moskowitz wave spectrum. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of the reconstruction ANN. After that, we train the regression ANN based on the previous convolutional part to obtain SWH back from the synthetic images. Then, we apply preliminary trained weights for the regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in SWH estimation accuracy from radar images with preliminary training on synthetic data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Statistical descriptions of extreme met-ocean conditions are essential for the safe and reliable design and operation of structures in marine environments. The significant wave height ( H S ) is one of the most essential wave parameters for coastal and offshore structural design. Recent studies have reported that a time-varying component exists globally in the H S . Therefore, the non-stationary behavior of an annual maximum series of H S is important for various ocean engineering applications. This study aims to analyze the frequency of H S over the northern Indian Ocean by modeling the non-stationarity in the H S series using a non-stationary Generalized Extreme Value (GEV) distribution. The hourly maximum H S data (with a spatial resolution of 0.5° longitude × 0.5° latitude) collected from the global atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for the study. To model the annual maximum series of H S using a non-stationary GEV distribution, two physical covariates (El-Ni n ~ o Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) and time covariates are introduced into the location and scale parameters of the GEV distribution. The return levels of various frequencies of H S are estimated under non-stationary conditions. From the results, average increases of 13.46%, 13.66%, 13.85%, and 14.02% are observed over the study area for the 25-year, 50-year, 100-year, and 200-year return periods, respectively. A maximum percentage decrease of 33.3% and a percentage increase of 167% are observed in the return levels of various return periods. The changes in the non-stationary return levels over time highlight the importance of modeling the non-stationarity in H S . [ABSTRACT FROM AUTHOR]

Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes. Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial coverage and high operational costs. With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results. This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval. We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave. Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model. The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data. These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research. [ABSTRACT FROM AUTHOR]

This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model's predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model's forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model's prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen. [ABSTRACT FROM AUTHOR]

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively. [ABSTRACT FROM AUTHOR]

Predicting nearshore significant wave heights (SWHs) with high accuracy is of great importance for coastal engineering activities, marine and coastal resource studies, and related operations. In recent years, the prediction of SWHs in twodimensional fields based on deep learning has been gradually emerging. However, predictions for nearshore areas still suffer from insufficient resolution and poor accuracy. This paper develops a NS (NearShore) model based on the GWSM4C model (Global Wave Surrogate Model for Climate simulations). In the training area, the GWSM4C -NS model achieved a correlation coefficient (CC) of 0.977, with a spatial Root Mean Square Error (RMSE), annual mean spatial relative error (MAPE), and annual mean spatial absolute error (MAE) of 0.128 m, 10.7%, and 0.103 m, respectively. Compared to the GWSM4C model's predictions, the RMSE and MAE decreased by 59% and 60% respectively, demonstrating the model's effectiveness in enhancing nearshore SWH predictions. Additionally, applying this model to untrained sea areas to further validate its learning capability in wave energy propagation resulted in a CC of 0.951, with RMSE, MAPE, and MAE of 0.161m, 12.9%, and 0.137m, respectively. The RMSE and MAE were 43% and 39% lower than the GWSM4C model's interpolated predictions. The results shown above suggest that the newly proposed model can effectively improve the performance of GWSM4C in nearshore areas. [ABSTRACT FROM AUTHOR]

Significant wave height (SWH) prediction is crucial for marine safety and navigation. A slow failure particle swarm optimization for long short-term memory (SFPSO-LSTM) is proposed to enhance SWH prediction accuracy. This study utilizes data from four locations within the EAR5 dataset, covering 1 January to 31 May 2023, including variables like wind components, dewpoint temperature, sea level pressure, and sea surface temperature. These variables predict SWH at 1-h, 3-h, 6-h, and 12-h intervals. SFPSO optimizes the LSTM training process. Evaluated with R2, MAE, RMSE, and MAPE, SFPSO-LSTM outperformed the control group in 13 out of 16 experiments. Specifically, the model achieved an optimal RMSE of 0.059, a reduction of 0.009, an R2 increase to 0.991, an MAE of 0.045, and an MAPE of 0.032. Our results demonstrate that SFPSO-LSTM provides reliable and accurate SWH predictions, underscoring its potential for practical applications in marine and atmospheric sciences. [ABSTRACT FROM AUTHOR]

This paper describes a calibration procedure for a non-optimally configured High Frequency Radar (HFR) for the period 1 April 2021, to 31 March 2022, to assess sea waves characteristics. The HFR system, a 16.5 MHz WEllen RAdar (WERA), is part of an innovative network for monitoring the state of the sea. The system is installed in the western part of Sicily (Italy) where a wave buoy is positioned. HFR data underestimate the spectral significant wave heights (Hm0), in particular for Hm0 > 2 m, highlighting the need for calibration of the HFR system to ensure its optimal performance for operational purposes. The calibration was performed with both in-situ and modelled data provided by the Copernicus Marine Service. The best results were obtained when the buoy data were used as reference. Encouraging results were achieved as demonstrated by the improvement of the quantitative metrics after the calibration. Indeed, the RMSE decreased from 0.60 to 0.36 m; the correlation R increased slightly from 0.86 to 0.88, the slope from 0.48 to 0.8; whereas intercept from 0.11 to 0.31 m. Moreover, waves higher than > 2 m are well reproduced by the calibrated HFR time series with the RMSE decreasing from 1.3 to 0.53 m. [ABSTRACT FROM AUTHOR]

The Copernicus Sentinel-6 Michael Freilich (S6-MF) and Jason-3 (J3) Tandem Experiment (S6-JTEX) provided over 12 months of closely collocated altimeter sea state measurements, acquired in "low-resolution" (LR) and synthetic aperture radar "high-resolution" (HR) modes onboard S6-MF. The consistency and uncertainties associated with these measurements of sea state are examined in a region of the eastern North Pacific. Discrepancies in mean significant wave height (Hs, 0.01 m) and root-mean-square deviation (0.06 m) between J3 and S6-MF LR are found to be small compared to differences with buoy data (0.04, 0.29 m). S6-MF HR data are found to be highly correlated with LR data (0.999) but affected by a nonlinear sea state-dependent bias. However, the bias can be explained robustly through regression modelling based on Hs. Subsequent triple collocation analysis (TCA) shows very little difference in measurement error (0.18 ± 0.03 m) for the three altimetry datasets, when analysed with buoy data (0.22 ± 0.02 m) and ERA5 reanalysis (0.27 ± 0.02 m), although statistical precision, limited by total collocations (N = 535), both obscures interpretation and motivates the use of a larger dataset. However, we identify uncertainties in the collocation methodology, with important consequences for methods such as TCA. Firstly, data from some commonly used buoys are found to be statistically questionable, possibly linked to erroneous buoy operation. Secondly, we develop a methodology based on altimetry data to show how statistically outlying data also arise due to sampling over local sea state gradients. This methodology paves the way for accurate collocation closer to the coast, bringing larger collocation sample sizes and greater statistical robustness. [ABSTRACT FROM AUTHOR]

Substantial effort has gone into identifying threats to the African oystercatcher Haematopus moquini, a species of seabird native to the mainland coasts and offshore islands of southern Africa. Winter storms represent a possible further threat owing to the potential to exclude an obligate intertidal forager from its feeding areas for periods of days. Using data from the Slangkop wave buoy off the Cape Peninsula of South Africa during surveys of the number of oystercatchers on Robben Island, and arguments based on body-mass records, the tentative conclusion is that winter storms do not currently represent a threat to the African oystercatcher but this may become a factor in the future. [ABSTRACT FROM AUTHOR]

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China's offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China's offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions. [ABSTRACT FROM AUTHOR]

Typhoon waves possess significant destructive potential, and their numerical simulation relies on accurate sea surface wind fields. An evaluation of different combinations of the radial air pressure distribution coefficient B and the radius of maximum wind speed (Rmax) in the Holland wind field (HWF) model was conducted to determine the optimal configuration. The HWF and the ERA5 wind field (EWF) were used as input wind fields to drive the typhoon wave model for China’s coastal waters. Validation results indicated that neither wind field accurately reflected real conditions; therefore, a hybrid wind field (HBWF) was created by combining HWF and EWF using weighting coefficients that vary with the radius of wind speed to enhance accuracy. Simulation results showed that the HBWF improved the accuracy of significant wave heights (SWHs), with a mean relative error of 25.29%, compared to 32.48% for HWF and 27.94% for EWF. Additionally, HBWF also demonstrated the best performance in terms of root mean square error (RMSE) and consistency index. Overall, the HBWF enhances the simulation accuracy of typhoon waves in China's coastal waters.

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption.

Significant wave height (SWH) is an important parameter to reflect wave state, which is of great significance in ocean engineering. However, the current wave observation methods have limitations in capturing wave field data with high spatial resolution. In this study, to generate the SWHs field over the Northeast Pacific and Northwest Atlantic, multi-source satellite altimeter data (CRYOSAT-2, SARAL, JASON-3, SENTINEL-3A, SENTINEL-3B, HY-2B and CFOSAT) are fused with a spatial resolution of 0.125° x 0.125° and a temporal resolution of 1 day. We employ the Inverse Distance Weighting (IDW) method and the IDW-based spatiotemporal (IDW-ST) method for data fusion. The fusion results exhibit a consistent spatial distribution characteristic, but the results of the IDW method display the visible trajectory. Moreover, the IDW-ST method, which incorporates time factors, shows great agreement between the fused SWH and buoy data. However, when the water depth change near the grid point has a great influence on the fusion, the complexity of bathymetric topography makes the traditional two-dimensional spatial fusion methods inadequate. Therefore, an improved method is proposed based on the IDW-ST fusion method, which introduces the water depth factor and significantly enhances fusion accuracy in regions where bathymetric variations greatly affect fusion results. The proposed method can be used to generate reliable SWH fields, especially in complex bathymetric topography conditions, and provide significant support for marine infrastructure design, ocean energy utilization and marine disaster protection.

X-band marine radar captures the signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information about wind wave parameters. Traditional methods of significant wave height (SWH) estimation rely on physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN based approaches necessitate costly in situ data. In this study, as a viable alternative, we propose generating synthetic radar images with specified wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of the reconstruction ANN. After that, we train the regression ANN based on the previous convolutional part to obtain SWH back from the synthetic images. Then, we apply preliminary trained weights for the regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in SWH estimation accuracy from radar images with preliminary training on synthetic data.

This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state. [ABSTRACT FROM AUTHOR]

Using shallow-water buoy observations, reanalysis data, and numerical models, this study analyzes the variations in sea temperature and significant wave height (SWH) caused by two sequential tropical cyclones (TCs) 'Lionrock' and 'Kompasu' in October 2021 in the northeast Beibu Gulf, South China Sea. The results show that the sea surface temperature (SST) cooling of the nearshore waters was larger than the offshore water in the basin of the gulf, with the cooling amplitude and rate decreasing and the cooling time lagging behind wind increasing from coast to offshore. The near-surface temperature at the buoy station had a maximum decrease of 2.8 °C after 'Lionrock', and the decrease increased slightly to 3 °C after the stronger wind of 'Kompasu'. The total decrease of 4.6 °C indicates that the sequential TCs had a superimposed effect on the cooling of the Beibu Gulf. The heat budget analysis revealed that the sea surface heat loss and the Ekman pumping rate in the nearshore waters during 'Kompasu' (−535 W/m2 and 5.8 × 10−4 m/s, respectively) were significantly higher than that (−418 W/m2 and 4 × 10−4 m/s) during 'Lionrock'. On the other hand, the SST cooling (−1.2 °C) during the second TC is smaller than (−1.6 °C) the first weaker TC in the gulf basin, probably due to the deepening of the mixed layer. During the observation period, the waves in the Beibu Gulf were predominantly wind-driven. The maximum SWHs reached 1.58 m and 2.3 m at the bouy station near shore during the two TCs, and the SWH variation was highly correlated to the wind variation with a correlation of 0.95. The SWH increases from the nearshore to offshore waters during the TCs. The SAWN and ARCIRC coupled model results suggest that wave variations in the Beibu Gulf are primarily influenced by water depth, bottom friction, and whitecapping. Two days after the TCs, sea surface cooling and high waves appeared again due to a cold air event. [ABSTRACT FROM AUTHOR]

Studies on the variability in ocean wave climate provide engineers and policy makers with information to plan, develop, and control coastal and offshore activities. Ocean waves bear climatic imprints through which the global climate system can be better understood. Using the recently updated ERA5 dataset, this study evaluated the spatiotemporal distribution and variability in significant wave height (SWH) in the Eastern Tropical Atlantic (ETA). The short-term trends and rates of change were obtained using the Mann–Kendall trend test and the Theil–Sen slope estimator, respectively, and decadal trends were assessed using wavelet transformation. Significant, positive monthly and yearly trends and a prevailing decadal trend were observed across the domain. Observed trends suggest that stronger waves are getting closer to the coast and are modulated by the Southern and Northern Atlantic mid-latitude storm fields. These observations have implications for the increasing coastal erosion rates on the eastern coast of the Tropical Atlantic. [ABSTRACT FROM AUTHOR]

Accurate significant wave height (SWH) forecasting is essential for various marine activities. While traditional numerical and mathematical-statistical methods have made progress, there is still room for improvement. This study introduces a novel transformer-based approach called the 2D-Geoformer to enhance SWH forecasting accuracy. The 2D-Geoformer combines the spatial distribution capturing capabilities of SWH numerical models with the ability of mathematical-statistical methods to identify intrinsic relationships among datasets. Using a comprehensive long time series of SWH numerical hindcast datasets as the numerical forecasting database and ERA5 reanalysis SWH datasets as the observational proxies database, with a focus on a 72-hour forecasting window, the 2D-Geoformer is designed. By training the potential connections between SWH numerical forecasting fields and forecasting errors, we can retrieve SWH forecasting errors for each numerical forecasting case. The corrected forecasting results can be obtained by subtracting the retrieved SWH forecasting errors from the original numerical forecasting fields. During longterm validation periods, this method consistently and effectively corrects numerical forecasting errors for almost every case, resulting in a significant reduction in root mean square error compared to the original numerical forecasting fields. Further analysis reveals that this method is particularly effective for numerical forecasting fields with higher errors compared to those with relatively smaller errors. This integrated approach represents a substantial advancement in SWH forecasting, with the potential to improve the accuracy of operational SWH forecasts. The 2D-Geoformer combines the strengths of numerical models and mathematical-statistical methods, enabling better capture of spatial distributions and intrinsic relationships in the data. The method's effectiveness in correcting numerical forecasting errors, particularly for cases with higher errors, highlights its potential for enhancing SWH forecasting accuracy in operational settings. [ABSTRACT FROM AUTHOR]

As an emerging observational method, spaceborne global navigation satellite system-reflectometry (GNSS-R) has been applied recently for significant wave height (SWH) retrieval. However, the complexity of the sea surface and the influence of multiple potential factors have been constraining the accuracy of SWH retrieval. This study verified the effect of sea surface temperature (SST), sea surface salinity (SSS), and seasonal variation on cyclone-GNSS (CYGNSS) observables for the first time. After controlling for the SWH, the CYGNSS observables exhibit a dependence on SST and SSS, where the dependence on SST dominates. The correlation coefficient (R) between SST and CYGNSS observables is the highest in 3.5–4 m, which is 0.53. In addition, the geographical distribution of retrieval bias exhibits seasonality. Therefore, seasonal factors can provide an additional contribution to SWH retrieval. SWH retrieval is based on the multilayer perceptron. The European center for medium-range weather forecast reanalysis 5th Generation SWH data were used as the reference for the computation of retrieval performance metrics. The results show that after considering SST, salinity, and season, the root mean square error (RMSE) of the retrieved SWH decreases from 0.65 to 0.48 m and the R increases from 0.66 to 0.83. The retrievals were compared to the ground truth measurements from the National Data Buoy Center buoys; the RMSE decreased from 0.52–1.07 m to 0.30–0.61 m, and the R increased from 0.44–0.71 to 0.60–0.78. [ABSTRACT FROM AUTHOR]

We utilized data from the numerical wave model WAVEWATCH-III and examined the spatial variability of waves considering 30 nearshore locations in the eastern Arabian Sea. The wave parameters from the model compare well with the buoy data (correlation coefficient ~ 0.98 and bias ~ 0.17 m). During monsoon, wave heights in the central-eastern Arabian Sea are higher than those in the southern and northern parts due to the influence of the Findlater jet and intermediate-period waves are dominating the entire area. The significant wave height is less than 1.5 m in non-monsoon and reaches 5 m in July. Variation in wave height between two nearby locations is highest in the northeastern Arabian Sea along the Gujarat coast. For a distance of 388 km from central Kerala to Karnataka, there is no significant spatial variability in wave height. Eastern Arabian Sea experiences a higher peak period in the non-monsoon due to reduction in the local wind speed. The integral period does not show significant spatial variability similar to wave height. The maximum (minimum) wave heights were found in 2013 (2015) and the variations are linked to the monsoon intensity. [ABSTRACT FROM AUTHOR]

The lack of automatic wave measurement equipment leads to a decrease in the number of observations. Which, in turn, leads to a decrease in the quality of numerical forecasting. This paper presents an algorithm for obtaining wind wave parameters (significant height, period and direction of the main wave system) using a marine radar. This approach has the potential to significantly expand the coverage and increase the number of observations. The methods utilized in this research involve spectral analysis of time series images of the sea surface. The field measurements of waves in the drift and on the move of the ship obtained during the AI57, AI58 and AI63 sea expeditions conducted between 2021 and 2022 aboard a research vessel AkademikIoffe. The accuracy of the algorithm is confirmed through validation against data acquired from wave buoys, altimetry satellites, and visual observations. [ABSTRACT FROM AUTHOR]

Similar in strength to hurricanes, Extratropical Cyclones (ECs) are responsible for innavigable sea states, coastal inundation and erosion, and subsequent destruction to coastal infrastructure. Across modern operational wave models, there exists a known systematic underestimation of wave heights during these extreme events. Using a global database of EC storm tracks and 36 years of satellite altimeter data, we examine EC structure and assess model performance through storm centered composite analyses of significant wave height (${H_s}$ H s ) and U10 wind speed ($|U10|$ | U 10 | ). Through the collocation of satellite altimeter observations with a state-of-the-art reanalysis product (ERA5), we investigate model performance with respect to $|U10|$ | U 10 | and ${H_s}$ H s within ECs of varying intensities. By rotating our data reference frames, we align all storm directions to account for asymmetry in EC wind fields and subsequent increased wave growth This rotation results in the organization of the strongest wind speed and ${H_s}$ H s , as well as trends in the ERA5 performance. A characteristic EC radii is calculated and used to normalize data coordinates in the storm centered reference frame, which highlights the organization of EC structures. Performance metrics are then compared within different EC quadrants to explore the relationship between wind forcing accuracy and underestimation of ${H_s}$ H s . [ABSTRACT FROM AUTHOR]

The applicability of high-frequency (HF) radar systems for wave measurement in an estuary was explored by extracting the significant wave height (${H_{sr}}$ H sr ) using a traditional Barrick equation from the Doppler spectra observed by three radar systems installed in Ise Bay, Japan. The minimum value of ${H_{sr}}$ H sr estimated around each grid point was relatively consistent with the wave height observed with a wave gauge/buoy, except for a deterioration of wave measurement accuracy caused by a decrease in seawater conductivity from the freshwater inflow after flooding. Furthermore, the relationship between the accuracy and the signal-to-noise ratios for the first- and second-order peaks (SNR1 and SNR2, respectively) highlighted the difficulty in determining the threshold values of SNRs in the bay. Thus, we suggest the use of ${H_{sr}}$ H sr as a criterion for quality control when applying a nonlinear inversion method for estimating ocean wave spectra based on the Bayesian possibility theorem (BIM). Our suggestion is to select the appropriate Doppler spectra and increase the acquisition rates of wave data with low relative error compared to BIMs using SNR1 and SNR2. These results can promote the applicability of the nonlinear inversion in estuary regions. [ABSTRACT FROM AUTHOR]

Satellite radar altimeters (SA) have been providing ocean wind and wave measurements for over 35 years. These data have been used for modelling data assimilation, improving wind and wave climatology, and determining long-term trends of the oceanic wave parameters. Fixed observational sites (in situ locations), such as buoys, have provided reliable wave observations since the early 1970s. However, their positioning is inhomogeneous, mainly in the Northern Hemisphere, and only provides point measurements. SA significant wave height (SWH) measurements have been proven as accurate as in situ observations, particularly in the open ocean. Progress in coastal altimetry sensors, upgraded data corrections, and new extraction algorithms have recently improved the quality of SA measurements closer to the coast. This study evaluates the performance of 12 SA missions from 1985 to 2020, particularly in nearshore areas. The SA SWH along-track measurements are compared with observations from 402 in situ locations, distributed worldwide within 25 km of the coastline. Results indicate a slight overestimation from the 12 SA missions, mainly for lower sea states (under 2 m high) and closer to the coast (0 to 10 km). The Sentinel-3 mission showed the highest percentages of valid measurements near the coast and presented 72.66% of collocated in situ data. This SA mission has shown the best overall performance closer to the coast, with biases, correlation coefficient, and root-mean-squared error of 0.23 m, 0.85 m, and 0.50 m, respectively. SA undersampling in coastal areas is present and can lead to underestimation during extreme wave events. The cross-validation of the wave data in two regional analyses conducted during periods of severe wave conditions is evaluated for the new altimeters' generation. [ABSTRACT FROM AUTHOR]

Significant wave height (SWH) is a key parameter for wave energy extraction, ship navigation, oil and gas extraction, coastal structure construction, etc. Direct measurements of SWH using buoys are expensive and accurate over a limited area while numerical models are computationally expensive, inaccurate, with limited generalizability. Thus, this work focuses on developing generalizable machine learning models for predicting SWH from wind parameters (speed, direction, and gust) and atmospheric parameters (temperature and pressure). Two deep learning models (Artificial Neural Network (ANN) and Self Normalizing Neural Network (SNN) and two gradient boosting tree-based models (XGBoost and LightGBM) have been used in this study. Three different data sets were collected from the National Data Buoy Center: Data Set-1 (DS1): 12 years of data from 47 stations; Data Set-2 (DS2): 14 months of additional data from 6 stations randomly selected from DS1; Data Set-3 (DS3): 13 years data from completely 6 new stations. DS1 was split into training, testing, and validation datasets. Training and hyper-parameter tuning was done on the train and validation dataset, while the performance of the models was evaluated on the DS1 test dataset, DS2, and DS3, employing three error metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-square score (R2). The collected data underwent data cleaning, preprocessing, and exploratory data analysis before modeling. The deep learning models have demonstrated superior fitting capacity to the tree-based models, achieving the lowest MSE (0.047), MAE (0.153), and highest R2 score (0.953) on test data. However, the gradient boosting models demonstrate better generalizing capacity than the deep learning models on DS2 and DS3. This study helps further Sustainable Development Goal 7 (SDG 7) by allowing fast and cheap assessment of wave height for ocean energy site development.

Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes. Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial coverage and high operational costs. With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results. This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval. We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave. Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model. The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data. These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research.

This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model’s forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model’s prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen.

High waves and surges associated with storms pose threats to the coastal communities around the Great Lakes. Numerical wave models, such as WAVEWATCHIII, are commonly used to predict the wave height and direction for the Great Lakes. These predictions help determine risks and threats associated with storm events. To verify the reliability and accuracy of the wave model outputs, it is essential to compare them with observed wave conditions (e.g., significant wave height), many of which come from buoys. However, in the Great Lakes, most of the buoys are retrieved before those lakes are frozen; therefore, winter wave measurements remain a gap in the Great Lakes' data. To fill the data gap, we utilize data from the Inland Water Surface Height product of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as complements. In this study, the data quality of ICESat-2 is evaluated by comparing with wave conditions from buoy observations in the Great Lakes. Then, we evaluate the model quality of NOAA's Great Lakes Waves-Unstructured Forecast System version 2.0 (GLWUv2) by comparing its retrospective forecast simulations for significant wave height with the significant wave height data from ICESat-2, as well as data from a drifting Spotter buoy that was experimentally deployed in the Great Lakes. The study indicates that the wave measurements obtained from ICESat-2 align closely with the in situ buoy observations, displaying a root-mean-square error (RMSE) of 0.191 m, a scatter index (SI) of 0.46, and a correlation coefficient of 0.890. Further evaluation suggests that the GLWUv2 tends to overestimate the wave conditions in high wave events during winter. The statistics show that the RMSE in 0–0.8 m waves is 0.257 m, while the RMSE in waves higher than 1.5 m is 0.899 m. [ABSTRACT FROM AUTHOR]

Statistical evaluation of sea’s state is essential for the design and managements of marine structures, operations, shipping and navigation security. For the purpose of coastal navigation, little to no research has been done on the state of the sea along the Nigerian coast. Understanding the local sea’s state can help to lessen the frequency of incidents brought on by ships capsizing. In order to notify users of probable areas where vessel overturning may occur, this study therefore employs the warning and prediction services system created and developed by the Indian National Centre for Ocean Information Services (INCOIS). The system bases its warning advisory on the Boat Safety Index (BSI), a newly developed index with a sufficient threshold. Furthermore, this study investigates the statistical properties of the sea’s state along the Nigerian coast, using daily significant wave height (SWH) and 10 m wind speed (u) data spanning 37 years (1980–2016). The Douglas smooth (wavelet) sea’s state classification prevailed in both cases in the sea. It occurred more in the dry season than in the rainy season. The Douglas smooth (wavelets) sea also prevailed in all months of the year except in August, when the Douglas slight sea had higher occurrences. A spatial analysis using SWH showed that Douglas calm seas are predominant in the eastern waters of the study region. The majority of the study area's waters are dominated by the Douglas smooth (wavelets) sea, which is especially prevalent in the area around Lagos Lagoon. In the western coast of the study area, the Douglas slight sea had the highest frequencies. Very low occurrences of the Douglas moderate and rough seas were observed over the research area. Additionally, a spatial analysis using u revealed that the Douglas smooth (wavelets) sea prevailed in the eastern coast of the study area. The Douglas light and moderate seas increased offshore. In the far western coast of the study location, the Douglas rough to high seas showed very rare occurrences. In all, very little or no occurrence of the Douglas rough to phenomenal seas was observed. Insignificant trends exist for the Douglas smooth (wavelets) sea over the study area. The Boat Safety Index used to evaluate the research location showed that it is considered safe for coastal sailing. [ABSTRACT FROM AUTHOR]

In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height ( H s ) and wave period ( T s ) 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 H s and T s . 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 H s and T s estimation. Simultaneously, H s and T s 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 H s estimation compared to the traditional SNR-based and CNN-based methods. In T s estimation, the RMSD is reduced by 0.3 s and 0.08 s, respectively. [ABSTRACT FROM AUTHOR]

To obtain new information about surface waves, it is proposed to use an underwater acoustic wave gauge, and an assessment of its effectiveness can be performed using a numerical simulation and field experiment. A new device, an underwater acoustic wave gauge named "Kalmar", was developed by the Institute of Applied Physics of the Russian Academy of Sciences for long-term, all-weather monitoring of wind waves. The instrument uses ultrasound to probe the water surface from underwater and can be used to verify remote sensing data. In this work, the capabilities of the device are tested and compared with ADCP data. Two independent methods for processing underwater acoustic wave gauge data are discussed and compared. One of them is completely new for acoustic measurements and is based on the analysis of the shape of the reflected acoustic pulse averaged over space and time. The other allows processing individual reflected pulses and calculating the time implementation of the distance to the water surface. It is shown that two independent methods of significant wave height retrieval from the acoustic wave gauge measurements are highly correlated. The "Kalmar" acoustic wave gauge and the RDI WH-600 acoustic Doppler current profiler operated simultaneously at the test site in Gelendzhik from 1 February to 10 February 2020. The significant wave heights measured by the two instruments are in good agreement. [ABSTRACT FROM AUTHOR]

The lack of automatic wave measurement equipment leads to a decrease in the number of observations. Which, in turn, leads to a decrease in the quality of numerical forecasting. This paper presents an algorithm for obtaining wind wave parameters (significant height, period and direction of the main wave system) using a marine radar. This approach has the potential to significantly expand the coverage and increase the number of observations. The methods utilized in this research involve spectral analysis of time series images of the sea surface. The field measurements of waves in the drift and on the move of the ship obtained during the AI57, AI58 and AI63 sea expeditions conducted between 2021 and 2022 aboard a research vessel AkademikIoffe. The accuracy of the algorithm is confirmed through validation against data acquired from wave buoys, altimetry satellites, and visual observations. [ABSTRACT FROM AUTHOR]

Marine X-band radar is an important navigational tool that records signals reflected from the sea surface. Theoretical studies show that the initial unfiltered signal contains information about the sea surface state, including wind wave parameters. Physical laws describing the intensity of the signal reflected from the rough surface are the basis of the classical approaches for significant wave height (SWH) estimation. Nevertheless, the latest research claims the possibility of SWH approximation using machine learning models. Both classical and AI-based approaches require in situ data collected during expensive sea expeditions or with wave monitoring systems. An alternative to real data is generation of synthetic radar images with certain wind wave parameters. This Fourier-based approach is capable of modelling the sea clutter images for wind waves of any given height. Assuming a fully-developed sea, we generate synthetic images from the Pierson–Moskowitz wave spectrum. After that, we apply an unsupervised learning using synthetic radar images to train the convolutional part of the neural network as the encoding part of the autoencoder. In this study, we demonstrate how the accuracy of SWH estimation based on radar images changes when the neural network is pretrained on synthetic data. [ABSTRACT FROM AUTHOR]

Marine radars are vital for safe navigation at sea, detecting vessels and obstacles. Sea clutter, caused by Bragg scattering, is usually filtered out as noise. It becomes detectable in unfiltered radar images, acquired using SeaVision hardware package, when wind speed and wave height exceed certain thresholds. The parameters of wind-induced ocean waves can be determined using these images; however, traditional spectral methods for obtaining wave characteristics face limitations in improving accuracy. Deep learning techniques offer advantages in image processing tasks, being more robust and able to handle noisier data, yet delivering the results without Fourier transformations and not necessarily requiring long series of radar imagery. In our study, we present the method exploiting convolutional neural networks (CNNs) for estimating wave characteristics from shipborne radar data captured using SeaVision package. In particular, we train our CNN to infer significant wave height using estimates provided by the Spotter buoy as ground truth. Our CNN-based method has an advantage over the classical methods due to the low requirements for radar image data since we process just one SeaVision snapshot, whereas classical method requires more than 20 min of radar images. [ABSTRACT FROM AUTHOR]

HF radar systems form part of many operational coastal monitoring systems providing near-real-time surface currents for many useful applications. Although wave measurements have been possible with these systems for many years, they have not yet been adopted widely for operational monitoring because they have not been thought to be sufficiently accurate or reliable. However, the value of such data is beginning to be appreciated, and this is motivating more work on wave measurement with HF radar systems with many more papers on accuracy assessment and data availability appearing in the literature. In this paper, the wave measurement capability, limitations, and differences between different radar types are reviewed, and methods to assess accuracy are discussed and applied to phased array HF radar data obtained from the University of Plymouth WERA radars using the Seaview Software inversion method during April and November 2012 compared with directional buoy data. Good accuracy over a range of different wave parameters will be demonstrated. Newly available single-radar inversions are shown to be less accurate than dual-radar inversions, although they still provide useful data, and ways to improve performance are discussed. Swell and wind–sea components in the directional spectra are identified, and qualitative agreement with buoy peak parameters is demonstrated. Recommendations are given on statistical methods for the validation of wave parameters. [ABSTRACT FROM AUTHOR]

Growth and expansion in construction has increased recently and especially in coastal areas. In Alexandria, Egypt, mega projects such as El-Max Port Project (Middle Port), Port of ABU QIR (EG AKI), hotels, and restaurants were spread along the coastal lines, thus, it will need a high electrical energy. Although, the great economic benefits of such projects, it will have some negative impacts, such as overloading on the present grid. According to recommendations of COP 27, Egypt is one of the countries targeting to increase the dependency on green energy to minimize the production of greenhouse gases. This study is interested in wave energy as a renewable source of energy. Using a machine learning model that predicts wave height and wave period through the year 2030 in three separate places (Alamein, Alexandria, and Mersa-Matruh), this study will try to estimate the future amount of wave energy along Egypt's coast. Hourly measurements of the significant height and the mean wave period for the period 1979–2023 have been utilized for this. An extractor for wave energy can also be built on the Overtopping Breakwater for Energy Conversion (OBREC) in order to use this energy to fill the hole in the electric grid. The machine learning model was developed using hourly wave height and period data from three buoys, and as a result, the results have a root mean square error (RMSE) of 0.52. The amount of energy taken, wave power, and system efficiency at each place were then fully determined using a mathematical model for each of the three locations. The area along the coast of Alamein had the highest energy extraction rates, followed by Alexandria and Mersa-Matruh in that order. The results of the mathematical model indicate that the yearly power generation for Alamein, Alexandria, and Mersa-Matruh is 25287 MWhr, 14713 MWhr, and 4865 MWhr, respectively.

Significant wave height (SWH) prediction is crucial for marine safety and navigation. A slow failure particle swarm optimization for long short-term memory (SFPSO-LSTM) is proposed to enhance SWH prediction accuracy. This study utilizes data from four locations within the EAR5 dataset, covering 1 January to 31 May 2023, including variables like wind components, dewpoint temperature, sea level pressure, and sea surface temperature. These variables predict SWH at 1-h, 3-h, 6-h, and 12-h intervals. SFPSO optimizes the LSTM training process. Evaluated with R2, MAE, RMSE, and MAPE, SFPSO-LSTM outperformed the control group in 13 out of 16 experiments. Specifically, the model achieved an optimal RMSE of 0.059, a reduction of 0.009, an R2 increase to 0.991, an MAE of 0.045, and an MAPE of 0.032. Our results demonstrate that SFPSO-LSTM provides reliable and accurate SWH predictions, underscoring its potential for practical applications in marine and atmospheric sciences.

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively.

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China’s offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China’s offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions.

Microseism noise, which occurs in the period range of 2–20 s, is the most energetic band in the earth's background spectra. In the present study, we examined the amplitude spectra and directional characteristics of microseism in the Indian Ocean. We use the data from ten openly accessible land stations located all around the Indian Ocean. The probability power spectral density was used to characterize the microseism. To characterize the microseism, we employ the frequency dependent polarization approach, which is governed by the Eigen value decomposition of the 3 × 3 spectral covariance matrix. The spatial and temporal variation of microseism was investigated in order to better understand its distribution in the Indian Ocean region, which is regarded as a global source of microseism. For some stations, we observe the splitting of double frequency microseism into short period (2–5 s) and long period (6–10 s) microseism. The polarization analysis reveals the dominant sources of the microseism are located in the Southern Ocean. We also correlated the spatio-temporal variation of significant wave heights (swh) with the power spectral densities at each station. We observe a remarkable correlation between power spectral density with the significant wave height (swh) in both spatially and temporally in secondary microseism band. We also characterize the dominant surface wave types in the microseism band. In long period band Rayleigh waves are dominant and Love waves are prominent in the short period band.

The terrain, such as Taiwan Island, have been shown to have complex effects on typhoons and the associated typhoon waves. Terrain effects change with typhoon tracks. In this study, three types of typhoon tracks (northern, middle and southern) were defined according to the relationship between the typhoon tracks and Taiwan Island. Typhoons on these three tracks and typhoon waves were simulated using the Weather Research and Forecasting–Simulating Waves Nearshore model. In each type of typhoon, a control case without the Taiwan topography was simulated to compare with real cases. The results showed that typhoons on different tracks were affected by the terrain of Taiwan Island in different ways. Taiwan Island had weakening, decelerating and deflective effects on typhoons. The ranking for the weakening effect was middle track (81%) > southern track (69%) > northern track (3%). The decelerating effect was 7% in the northern track and 25% in the southern track. The deflective effect of the terrain makes typhoons on the northern (southern) track deflect toward the south (north). When a typhoon on a middle track passed over Taiwan Island, a new center of low pressure would replace the former center and make the track discontinuous. In addition, the influence of typhoons on regions near Taiwan Island changed with the typhoon's tracks. The influence ranking of typhoons in Taiwan Island was the middle track > southern track > northern track, which was consistent with that of typhoons in the Taiwan Strait and opposite to that of typhoons in Fujian Province. The influence ranking of typhoons on the Taiwan Strait was the opposite of the typhoon intensity ranking, which suggests that the intensities of wind and waves in the strait were more related to typhoon tracks than typhoon intensity. The variations in the significant wave height were similar to those of the wind speed, but there was a time lag (2 h) between them due to the wave growth process and swells. In addition, the significant wave height distribution sometimes differed from the wind speed distribution under the influence of swells and terrain. [ABSTRACT FROM AUTHOR]

The Arctic is experiencing the greatest increase in air temperature on Earth. This significant climatic change is leading to a significant positive trend of increasing wave heights and greater coastal erosion. This in turn effects local economies and ecosystems. Increasing wave energy is one of the main drivers of this alarming trend. However, the data on spatial and temporal patterns of wave heights in the Arctic are either coarse, interpolated or limited to point measurements. The aim of this study is to overcome this shortcoming by using remote sensing data. In this study, the Synthetic Aperture Radar (SAR) satellite TerraSAR-X (TS-X) and TanDEM-X (TD-X) imagery are used to obtain sea state information with a high spatial resolution in Arctic nearshore waters in the Canadian Beaufort Sea. From the entire archive of the TS-X/TD-X StripMap mode with coverage around 30 km × 50 km acquired between 2009 and 2020 around Herschel Island, Qikiqtaruk (HIQ), all the ice-free scenes were processed. The resulting dataset of 175 collocated scenes was used to map the significant wave height ( H s ) and to link spatial and temporal patterns to local coastal processes. Sea state parameters are estimated in raster format with a 600 m step using the empirical algorithm CWAVE_EX. The statistics of the H s were aggregated according to spatial variability, seasonality and wind conditions. The results show that the spatial wave climate is clearly related to the dominant wind regime and seasonality. For instance, the aggregation of all the scenes recorded in July between 2009 and 2020 results in an average of 0.82 m H s , while in October the average H s is almost 0.40 m higher. The analysis by wind direction shows that fetch length and wind speed are likely the most important variables influencing the spatial variability. A larger fetch under NW conditions results in a mean wave height of 0.92 m, while waves generated under ESE conditions are lower at 0.81 m on average. [ABSTRACT FROM AUTHOR]

This article presents an extreme value analysis on data of significant wave height based on time-series simulation. A method to simulate time series with given marginal distribution and preserving the autocorrelation structure in the data is applied to significant wave height data. Then, extreme value analysis is performed by simulating from the fitted time-series model that preserves both the marginal probability distribution and the autocorrelation. In this way, the effect of serial correlation on the extreme values can be taken into account, without subsampling and de-clustering of the data. The effect of serial correlation on estimating extreme wave conditions have previously been highlighted, and failure to account for this effect will typically lead to an overestimation of extreme conditions. This is demonstrated by this study, which compares extreme value estimates from the simulated times-series model with estimates obtained directly from the marginal distribution assuming that 3-h significant wave heights are independent and identically distributed. A dataset of significant wave height provided as part of a second benchmark exercise on environmental extremes that was presented at OMAE 2021 has been analyzed. This article is an extension of a study presented at OMAE 2022 (OMAE2022-78795) and includes additional preprocessing of the data to account for seasonality and new results. [ABSTRACT FROM AUTHOR]

To analyze the macroscopic distribution characteristics of global wave, based on the FHDI-GNWM global wave data from 2011 to 2015, a study was conducted on the global significant wave height Hs, spectral peak period Tp, and main wave direction Dmain. The study shows that the global average of significant wave height Hs generally exhibits a characteristic of being greater in the southern hemisphere than in the northern hemisphere, and in the southern hemisphere, there is an obvious high- value area of significant wave height Hs with an average value greater than 3.6 m, which corresponds to the Antarctic Circumpolar Current region. There are significant differences in the macroscopic distribution characteristics of wave height for the major oceans worldwide, such as the Northwest Pacific, East Pacific, North Atlantic, South Atlantic, and Indian Ocean. The average spectral peak period Tp in most of the world's oceans is greater than 12 s, and Tp on the west coast of each continent is significantly greater than on the east coast. The distribution pattern of the primary wave direction worldwide is not obvious, and there are significant differences in the main wave direction in different sea areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As wave height is an important parameter in marine climate measurement, its accurate prediction is crucial in ocean engineering. It also plays an important role in marine disaster early warning and ship design, etc. However, challenges in the large demand for computing resources and the improvement of accuracy are currently encountered. To resolve the above mentioned problems, sequence-to-sequence deep learning model (Seq-to-Seq) is applied to intelligently explore the internal law between the continuous wave height data output by the model, so as to realize fast and accurate predictions on wave height data. Simultaneously, ensemble empirical mode decomposition (EEMD) is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition (EMD), and then improves the prediction accuracy. A significant wave height forecast method integrating EEMD with the Seq-to-Seq model (EEMD-Seq-to-Seq) is proposed in this paper, and the prediction models under different time spans are established. Compared with the long short-term memory model, the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors. The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term (3-h, 6-h, 12-h and 24-h forecast horizon) and long-term (48-h and 72-h forecast horizon) predictions. [ABSTRACT FROM AUTHOR]