Search Results (55)

This study assesses the wave energy potential and spectral variability in the Väinameri—a semi-sheltered, island-filtered basin on Estonia’s west coast—by combining six months of high-resolution in situ wave spectra with deep learning-enhanced satellite retrievals. Directional spectra were recorded at Rohuküla Harbor using a wave-following LainePoiss buoy from June to December 2024. In parallel, one-dimensional wave spectra were reconstructed from Sentinel-1 SAR imagery using a long short-term memory (LSTM) neural network trained on more than 71,000 collocations with NORA3 WAM hindcasts. Spectral pairs matched within a ±1 h window exhibited strong agreement in the dominant 0.2–0.4 Hz frequency band, while systematic underestimation at higher frequencies reflected both the radar resolution limits and the short-period, wind–sea-dominated nature of the Baltic Sea. Our results confirm that LSTM-enhanced SAR retrievals enable robust bulk and spectral wave characterizations in data-sparse nearshore regions, and offer a practical basis for the site evaluation, device tuning, and survivability testing of pilot-scale wave energy converters under both typical and storm-driven forcing conditions. Full article

A modeling technique for a nearshore hybrid wind–wave energy converter system (HWWECS) is presented in this research. The model consists of the buoy, wind system, and generator, allowing simulation of the HWWECS’s behavior in response to varied wave circumstances, such as different wave heights and periods. The HWWECS is made up of two buoy units and a wind system that work together to power a generator. The Wave Analysis at Massachusetts Institute of Technology (WAMIT) software is used to calculate the hydrodynamic forces. A variable inertia hydraulic flywheel is used to bring the system into resonance with incident wave frequencies in order to improve power production. Full article

As nearshore aquaculture spaces become saturated, the development of fisheries aquaculture for deep sea has become an inevitable trend. This paper proposes an innovative deep-sea aquaculture platform that incorporates a vessel-shaped main structure and a single-point mooring system. The potential flow theory and the Morison equation are utilized to calculate the hydrodynamic loads on the main structure and the netting and mooring systems, respectively. The deformation and force of the netting in current are simulated, and the accuracy of the analytical methods used is validated based on experimental results. The influences of the netting system on the hydrodynamic characteristics of the platform are analyzed. Optimization on the single-point mooring system is conducted under static and dynamic conditions, considering the influences of various mooring parameters, including mooring line length, buoyancy of buoys, and mass of sinkers. The patterns of changes in motion response, mooring line tension, and minimum touchdown length under different mooring parameters are calculated and analyzed. The results indicate that changes in mooring line length have minimal impact on the dynamic response of the platform and mooring system. The addition of appropriate buoys or sinkers can reduce the motion response of the platform and the tension in the mooring lines. Moreover, compared to adding buoys, incorporating sinkers more effectively enhances the overall safety and stability of the platform system. Full article

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. Full article

In the face of global ocean warming, monitoring essential climate variables from space is necessary for understanding regional trends in ocean dynamics and their subsequent impacts on ecosystem health. Analysis Ready Data (ARD), being preprocessed satellite-derived products such as Sea Surface Temperature (SST), allow for easy synoptic analysis of temperature conditions given the consideration of regional biases within a dynamic range. This is especially true for SST retrieval in thermally complex coastal zones. In this study, we assessed the accuracy of 30 m resolution Landsat ARD Surface Temperature products to measure nearshore SST, derived from Landsat 8 TIRS, Landsat 7 ETM+, and Landsat 5 TM thermal bands over a 37-year period (1984–2021). We used in situ lighthouse and buoy matchup data provided by Fisheries and Oceans Canada (DFO). Excellent agreement (R² of 0.94) was found between Landsat and spring/summer in situ SST at the farshore buoy site (>10 km from the coast), with a Landsat mean bias (root mean square error) of 0.12 °C (0.95 °C) and a general pattern of SST underestimation by Landsat 5 of −0.28 °C (0.96 °C) and overestimation by Landsat 8 of 0.65 °C (0.98 °C). Spring/summer nearshore matchups revealed the best Landsat mean bias (root mean square error) of −0.57 °C (1.75 °C) at 90–180 m from the coast for ocean temperatures between 5 °C and 25 °C. Overall, the nearshore image sampling distance recommended in this manuscript seeks to capture true SST as close as possible to the coastal margin—and the critical habitats of interest—while minimizing the impacts of pixel mixing and adjacent land emissivity on satellite-derived SST. Full article

The wave period is a wave parameter that is significantly influenced by factors such as wind speed and bottom topography. Previous research on wave period parameterization has primarily focused on wind-dominated sea areas and may not be applicable to certain regions, such as the equatorial calm or coastal areas dominated by swell waves. To address this limitation, this paper utilizes the third-generation wave numerical model SWAN to perform wave numerical simulations for a portion of the Northwest Pacific Ocean. The simulation incorporates observational data from nearshore stations, buoys, and satellite altimeters for error analysis. To develop a new wave parameterization scheme (WS-23), we employ extensive NDBC buoy data and incorporate the exponential rate and wave age characteristics that were previously established by predecessors. Our scheme introduces a judgement mechanism to distinguish between wind waves, swell waves, and mixed waves. The resulting ocean wave factor enhances the mean wave period values calculated using the model and other parameterization schemes. The experimental results demonstrate that our new parameterization scheme effectively improves the abnormal peak of the fitting data. Comparing the output values of the mean wave period element output of the SWAN model with our new parameterization scheme, we observe a reduction in the mean values of $E_a$, $E_c$, and $RMSE$ by 0.231, 1.94%, and 0.162, respectively, while increasing the average $r$ by 0.05. Full article

While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noise, typically after an extreme event occurrence or some accidental damage to the sensors. Such data artifacts, if not handled diligently prior to modeling, can significantly impact the reliability of any further predictive analysis. Therefore, we present a framework that integrates data reconstruction of key sea state variables and multi-step-ahead forecasting of current speed from the reconstructed time series for 19 depth levels simultaneously. Using multivariate chained regressions, the reconstruction algorithm rigorously tests from an ensemble of tree-based models (fed only with surface characteristics) to impute gaps in the vertical profiles of the sea state variables down to 20 m deep. Subsequently, a deep encoder–decoder model, comprising multi-head convolutional networks, extracts high-level features from each depth level’s multivariate (reconstructed) input and feeds them to a deep long short-term memory network for 24 h ahead forecasts of current speed profiles. In this work, we utilized Viking buoy data, and demonstrated that with limited training data, we could explain an overall 80% variation in the current speed profiles across the forecast period and the depth levels. Full article

The Chinese-French Oceanography SATellite (CFOSAT) jointly developed by the Chinese National Space Agency (CNSA) and the Centre National d’Etudes Spatiales (CNES) of France carries a wave spectrometer (Surface Waves Investigation and Monitoring, SWIM). SWIM has one nadir and five off-nadir beams to measure ocean surface waves. These near-nadir beams range from 0° to 10° at an interval of 2°. In this work, we investigated the performance of wave parameters derived from wave spectra measured by SWIM at off-nadir beams during the period 2020 to December 2022, e.g., incidence angles of 6°, 8° and 10°, which were collocated with the wave simulated by Simulating Waves Nearshore (SWAN). The validation of SWAN-simulated significant wave heights (SWHs) against National Data Buoy Center (NDBC) buoys of National Oceanic and Atmospheric Administration (NOAA) exhibited a 0.42 m root mean square error (RMSE) in the SWH. Our results revealed a RMSE of 1.02 m for the SWIM-measured SWH in the East Pacific Ocean compared with the SWH simulated by SWAN, as well as a 0.79 correlation coefficient (Cor) and a 1.17 squared error (Err) for the wave spectrum at an incidence angle of 10°, which are better than those (i.e., the RMSEs were > 1.1 m with Cors < 0.76 and Errs > 1.2) achieved at other incidence angles of SWH up to 14 m. This analysis indicates that the SWIM product is a relevant resource for wave monitoring over global seas. The collocated wave retrievals for more than 300 cases from Gaofen-3 (GF-3) synthetic aperture radar (SAR) images in China Seas were also used to verify the accuracy of SWIM-measured wave spectra. The energy of the SWIM-measured wave spectra represented by SWH was found to decrease with an increasing incidence angle in a case study. Moreover, the SWIM-measured wave spectra were most consistent with the SAR-derived wave spectra at an incidence angle of 10°, yielding a 0.77 Cor and 1.98 Err between SAR-derived and SWIM wave spectra under regular sea state conditions (SWH < 2 m). The error analysis indicates that the difference in SWH between SWIM at an incidence angle of 10° and SWAN has an increasing tendency with the growth in sea surface wind and sea state and it stabilizes to be 0.6 m at SWH > 4 m; however, the current and sea level have less influence on the uncertainties of the SWIM product. Full article

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models. Full article

Despite recent advancements in ocean–wave observations, how a tropical cyclone’s (TC’s) track, intensity, and translation speed affect the directional wave spectra evolution is poorly understood. Given the scarcity of available wave spectral observations during TCs, there are few studies about the performance of spectral wave models, such as Simulating Waves Nearshore (SWAN), under various TC scenarios. We combined the National Data Buoy Center observations and numerical model hindcasts to determine the linkages between wave spectrum evolution and TC characteristics during hurricanes Matthew 2016, Dorian 2019, and Isaias 2020. Five phases were identified in the wave spectrogram based on the normalized distance to the TC, the sea–swell separation frequency, and the peak wave frequency, indicating how the wave evolution relates to TC characteristics. The wave spectral structure and SWAN model’s performance for wave energy distribution within different phases were identified. The TC intensity and its normalized distance to a buoy were the dominant factors in the energy levels and peak wave frequencies. The TC heading direction and translation speed were more likely to impact the durations of the phases. TC translation speeds also influenced the model’s performance on swell energy. The knowledge gained in this work paves the way for improving model’s performance during severe weather events. Full article

Wave buoy measurements were carried out near the northeastern Black Sea coast at the natural reserve Utrish in 2020–2021. In total, about 11 months of data records were collected during two stages of the experiment at 600 and 1500 m offshore and depths of 18 and 42 m. The measured waves propagate almost exclusively from the seaward directions. Generally, the waves do not follow the local wind directions, thus, implying a mixed sea state. Nevertheless, dimensionless wave heights and periods appears to be quite close to the previously established empirical laws for the wind-driven seas. The results of the wave turbulence theory are applied for estimates of spectral energy fluxes and their correspondence to the energy flux from the turbulent wind pulsations. These estimates are consistent with today’s understanding of wind–wave interaction. It is shown that the main fraction of the wind energy flux is sent to the direct Kolmogorov–Zakharov cascade to high wave frequencies and then dissipates in small amounts. Less than $1\%$ of the wind energy flux is directed to the low frequency band (the so-called inverse Kolmogorov–Zakharov cascade), thus, providing wave energy growth. Full article

This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating $ZephI{R}^{\mathrm{TM}}$ 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed. Full article

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. Full article

The hybrid form of wave energy converter (WEC) is a recent advancement in research concerning harvesting energy from the ocean. This study investigates the effect of size and position of the point absorber integrated with a backward bent duct buoy. The aim of this optimisation is to maximise the WEC-absorbed power and heave response amplitude operators (RAO) at a specific sea site. The optimisation process was applied based on the data collected over a one-year period about sea characteristics for a nearshore region of the Mantanani Island. We present a methodology for optimising the Hybrid BBDB-PA based on a statistical analysis and the hydrodynamics of the system in the frequency and time domain. We used the ANSYS/AQWA software for the hydrodynamic diffraction analysis, and the design of experiments method was applied through the statistical software to determine the optimised parameters. We found that the diameter and gap length between PA and BBDB were found to significantly influence two characteristics, namely, heave RAO and maximum power absorption of PA. This observation shows that the PA size was directly proportional to the performance because a higher diameter has more contact with the ocean’s wet surface area with the ocean and absorbed higher wave energy. Moreover, the gap length between the PA and BBDB was directly correlated with a wavelength, which followed the theoretical value for peak-to-trough length, where the maximum wave height occurs. Despite the condition parameter, we discovered that the WEC position and arrangement were responsible for the highest value of the power, regardless of the PA position used in the experiment. The results of this research provide recommendations for optimising the ocean energy harvesting in order to fully utilise ocean space for energy. Full article

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. Full article