Estimation Of Significant Wave Height Research Articles

A deep hybrid network for significant wave height estimation

The influence of weather conditions on sea state, and in particular on the dynamic evolution of waves, is an important issue that affects several areas, including maritime traffic and the planning of coastal works. To collect relevant data, buoys are used to set up distributed sensor networks along coastal areas. However, unfavourable weather conditions can lead to downtime, which can be extended due to maintenance issues. The ability to improve the robustness of these sensor systems using predictive models, i.e. digital twins, to interpolate and extrapolate missing data is an important and growing area of research. To accomplish such a task, models must be found that can account for both the spatial and temporal dynamics of the input data to correctly estimate the variables of interest. In this work, a deep learning architecture is proposed to realize a digital twin for the monitoring buoy for significant wave height estimation using spatial and temporal information about the wind field in the area of interest. The proposed methodology was applied to a case study using wave height data from an Italian Sea Monitoring Network buoy installed near the coast of Sicily and wind field data from the Copernicus Climate Change Service ERA5 reanalysis. The reported results show that the use of a multi-block hybrid deep neural network consisting of convolutional layers for spatial feature extraction and short-term memory layers for modelling the involved dynamics, which takes into account the buoy surrounding area, outperforms other empirical, numerical, machine learning and deep learning methods used in the literature.

Comparison of BPNN and Dual-Branch CNN for Significant Wave Height Estimation From Polarimetric Gaofen-3 SAR Wave Mode Data

Comparison of BPNN and Dual-Branch CNN for Significant Wave Height Estimation From Polarimetric Gaofen-3 SAR Wave Mode Data

Evaluating Wave Potential and Assessing the Economic Viability of Wave Energy Converters in the South Java Seas

As an archipelagic nation, Indonesia holds substantial potential for wave energy as a renewable resource. Certain coastlines of islands facing the Indian Ocean, particularly in the western and southern regions, exhibit significant wave energy throughout the year. To identify suitable locations for Wave Energy Converter (WEC) installation, it is essential to assess wave hindcast data. This study utilizes NOAA and ERA5 reanalysis wave data to analyze wave characteristics in Indonesia from 2008 to 2018. Data processing with Ocean Data View is employed to estimate key wave parameters at various locations, including significant wave height, mean wave period, and mean wave direction. Two locations in the South Java seas were identified for WEC installation based on this research. The average values for the period 2008 to 2018 indicate a significant wave height of around 2m, with a maximum height of 5m, a wave period of 10–14s, and a wave direction of 195–210 degrees. Notably, NOAA data suggests a higher estimation of significant wave height compared to ERA5 data. The average annual wave power potential based on ERA5 and NOAA is 164.43 MW/m and 252.15 MW/m, respectively. Furthermore, this study incorporates an economic simulation for the construction of a multi-point absorber WEC. The objective is to offer insights into the Levelized Cost of Electricity (LCOE) and compare it with other WEC technologies. Assuming a WEC capacity of 130 kW, the total construction cost is estimated at $2,093,725, resulting in a Levelized Cost of Energy (LCOE) of $91/MWh.

Significant wave height estimation from shipborne marine radar data using convolutional and self-attention network

Significant wave height estimation from shipborne marine radar data using convolutional and self-attention network

Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images

Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images

Measurements of nearshore ocean-surface kinematics through coherent arrays of free-drifting buoys

Abstract. Surface gravity wave breaking occurs along coastlines in complex spatial and temporal patterns that significantly impact erosion, scalar transport, and flooding. Numerical models are used to predict wave breaking and associated processes but many lack sufficient evaluation with observations. To fill the need for more nearshore wave measurements, we deployed coherent arrays of small-scale, free-drifting buoys named microSWIFTs. The microSWIFT is a small buoy equipped with a GPS module to measure the buoy's position, horizontal velocities, and an inertial measurement unit (IMU) to directly measure the buoy's rotation rates, accelerations, and heading. Measurements were collected over a 27 d field experiment in October 2021 at the US Army Corps of Engineers Field Research Facility in Duck, NC. The microSWIFTs were deployed as a series of coherent arrays, meaning they all sampled simultaneously with a common time reference, leading to a rich spatial and temporal dataset during each deployment. Measurements spanned offshore significant wave heights ranging from 0.5 to 3 m and peak wave periods ranging from 5 to 15 s over the entire experiment. The completed dataset consists of 67 deployment files that contain 971 drift tracks that contain all associated data. We use an attitude and heading reference system (AHRS) 9-degrees-of-freedom Kalman filter to rotate the measured accelerations from the reference frame of the buoy to the Earth reference frame. We then use the corrected accelerations to compute the vertical velocity and sea-surface elevation. We give example evaluations of wave spectral energy density estimates from individual microSWIFTs compared with a nearby acoustic wave and current (AWAC) sensor. A zero-crossing algorithm is applied to each buoy time series of sea-surface elevation to extract realizations of measured surface gravity waves, yielding 116 307 wave realizations throughout the experiment. We also compute significant wave height estimates from the aggregate wave realizations and compare these estimates with the nearby AWAC estimates. An example of spatial variability in cross-shore velocity and vertical acceleration is explored. Wave-breaking events, detected by high-intensity vertical acceleration peaks, are explored, and the cross-shore distribution of all breaking events detected in the experiment is shown. A total of 3419 wave-breaking events were detected across the entire experiment. These data are available at https://doi.org/10.5061/dryad.hx3ffbgk0 (Rainville et al., 2023) and will be used to investigate nearshore wave kinematics, transport of buoyant particles, and wave-breaking processes.

Polarization-Enhancement Effects for the Retrieval of Significant Wave Heights from Gaofen-3 SAR Wave Mode Data

In order to investigate the impact of utilizing multiple pieces of polarization information on the performance of significant wave height (SWH) estimation from Gaofen-3 SAR data, the extreme gradient boosting (XGBoost) models were developed, validated, and compared across 9 single-polarizations and 39 combined-polarizations based on the collocated datasets of Gaofen-3 SAR wave mode imagettes matched with SWH data from ERA5 reanalysis as well as independent SWH observations from buoys and altimeters. The results show that the performance of our SWH inversion models varies across the nine different single-polarizations. The co-polarizations (HH, VV, and RL) and hybrid-polarizations (45° linear, RH, and RV) generally exhibit superior performance compared to the cross-polarizations (HV, VH, and RR) at low to moderate sea states, while the cross-polarizations are more advantageous for high SWH estimation. The combined use of multiple pieces of polarization information does not always improve the model performance in retrieving SWH from Gaofen-3 SAR. Only the polarization combinations that incorporate cross-polarization information have the potential to enhance the model performance. In these cases, the performance of our models consistently improves with the incorporation of additional polarization information; however, this improvement diminishes gradually with each subsequent polarization and may eventually reach a saturation point. The optimal estimation of SWH is achieved with the polarization combination of HV + VH + RR + RH + RV + 45° linear, which shows consistently lower RMSEs compared to ERA5 SWH (0.295 m), buoy SWH (0.273 m), Cryosat-2 SWH (0.109 m), Jason-3 SWH (0.414 m), and SARAL SWH (0.286 m). Nevertheless, it still exhibits a slight overestimation at low sea states and a slight underestimation at high sea states. The inadequate distribution of data may serve as a potential explanation for this.

Joint Return Value Estimation of Significant Wave Heights and Wind Speeds with Bivariate Copulas

Joint Return Value Estimation of Significant Wave Heights and Wind Speeds with Bivariate Copulas

Evaluation of Spectral Wave Models Physics as Applied to a 100‐Year Southern Hemisphere Extra‐Tropical Cyclone Sea State

AbstractExtreme significant wave height estimates, and their probability of exceedance, are fundamental offshore and coastal engineering design parameters. These estimates are characterized by uncertainty due to an incomplete understanding of the atmosphere‐ocean energy and momentum exchanges during intense storms. This particularly affects extreme wave statistics of ocean regions exposed to large and frequent synoptic disturbances such as Extra‐Tropical Cyclones (ETCs). In this work, we assessed the performance of global phase‐averaged spectral wave models in representing the 1 in 100‐year sea state generated by a Southern Ocean ETC in April 2021. We collected in situ and remote sensing observations, from the storm generation region to its decaying phase and the impact on South‐East Australian coastlines. We compared the observations with a suite of reanalysis and hindcast global wave model data sets. While comparing well for wind speed up to 20 m/s, the models presented differences in solving the air‐sea momentum exchange between the atmosphere and the ocean for wind speed velocities between 20 and 35 m/s, which are a distinctive characteristic of ETCs. Despite marked differences in the storm generation region, the models converged to a similar representation of the swell systems impacting the South‐East Australian coastlines, as demonstrated by a comparison with deep‐water buoy observations close to the coastlines. Furthermore, we found that the energy of the ERA5 reanalysis, which assimilates satellite wave height measurements is quickly dispersed and, as such, of little advantage in representing the 9.9 m significant wave heights that impacted the South‐East Australian coastlines on 10 April 2021.

Comparative Performance of Radar, Laser, and Waverider Buoy Measurements of Ocean Waves. Part II: Time-Domain Analysis

Abstract Accurate measurements of ocean waves underpin efficient offshore operations and optimal offshore structure design, helping to ensure the offshore industry can operate both safely and economically. Popular instruments used by the offshore industry are the Rosemount WaveRadar (Radar) and the Waverider Buoy. The Optech Laser has been used at some locations for specific studies. Recent reports indicate systematic differences of order 10% among the wave measurements made by these instruments. This paper examines the relative performance of these instruments based upon various time-domain comparisons, including results from a quality control (QC) procedure, capabilities of measuring the wave surface profile (skewness), and crest heights for varying wind sea and swell conditions. The QC check provides good-quality data that can be further investigated with an assurance of error-free data, suggesting that the Waverider produces the best-quality data with the lowest failure rate compared to the Laser and Radar. A significant number of the Waverider surface elevation records have negative skewness, particularly at higher sea states, affecting its crest height measurements, which are lower than those from the Laser and Radar. Additionally, the significant wave height (H1/3) estimates of the Radar are lower than the Laser and Waverider, but its zero-crossing wave periods (TZ), on average, are longer than the Laser and the Waverider. The significant heights (H1/3) of Laser and Waverider are in good agreement for all three datasets, but the Waverider’s zero-crossing wave period (TZ) estimates are significantly longer than the Laser.

A Non-Stationary and Directional Probabilistic Analysis of Coastal Storms in the Greek Seas

The variability of coastal storms over the years and direction is considered in a unified, innovative approach, providing crucial information for a wide variety of coastal engineering studies and wave energy applications under the impact of climatic change. Specifically, an alternative easy-to-apply technique is presented and applied to consider the storms’ direction as a covariate. This technique enables the probabilistic representation of coastal storms in every direction over the directional domain and is efficiently incorporated into a non-stationary directional extreme value analysis. The developed methodology is applied to six locations in the Greek Seas. Based on the derived results, the most likely and most extreme significant wave height estimates present, in general, a bimodal behavior with pronounced maxima. In particular, the first peak is observed before the twenty-first century, while the second peak is likely to occur around the middle of the twenty-first century. Furthermore, coastal storms coming from directions of large fetches are the most severe storms, presenting though a drop in their intensity at the end of the twenty-first century. On the contrary, coastal storms of fetch-limited directions may present minor variations in their probability distributions over the years.

Spatial Probability Characteristics of Waves Generated by Polar Lows in Nordic and Barents Seas

Polar lows (PLs) are mesoscale, up to 1000 km, rather short lifetime (less than 15–30 h) cyclonic atmospheric systems formed in polar latitudes and associated with cold outbreak events. Strong winds, higher than 15 m/s, can then generate high surface waves which may pose danger to marine and coastal infrastructures. To investigate the probability of high waves generated by PLs in the Nordic and Barents Seas, analysis can be performed using available PL statistical distributions obtained from satellite passive microwave data, MODIS infrared imagery and ASCAT scatterometer data. Classical self-similar laws for wind waves development based on the extended duration concept are used to obtain first-guess estimates of significant wave height and the wavelength of waves generated by PL. All possible combinations of PL parameters (maximum wind speed, lifetime, diameter, translation velocity and direction of propagation) are considered to obtain the occurrence of waves exceeding specified levels, ranging from 2 to 15 m for significant wave height and from 100 to 500 m for wavelength. Particularly, PL-generated waves higher than 4 m occur up to 6 times a year, higher than 8 m occur up to 2–3 times a year, higher than 10 m occur up to once a year, the probability of 12 m waves is one event in several years and 15 m SWHs occur less than once in a decade. The area most affected by strong waves from PLs is the near shore zone around the Scandinavian peninsula, northward from the North Cape. The relative contribution of PLs in the formation of the waves field in the Nordic and Barents Seas is discussed.

Flippin’ χSOLO, an Upper-Ocean Autonomous Turbulence-Profiling Float

Abstract A new autonomous turbulence profiling float has been designed, built, and tested in field trials off Oregon. Flippin’ χSOLO (FχS) employs a SOLO-II buoyancy engine that not only changes but also shifts ballast to move the center of mass to positions on either side of the center of buoyancy, thus causing FχS to flip. FχS is outfitted with a full suite of turbulence sensors—two shear probes, two fast thermistors, and pitot tube, as well as a pressure sensor and three-axis linear accelerometers. FχS descends and ascends with turbulence sensors leading, thereby permitting measurement through the sea surface. The turbulence sensors are housed antipodal from communication antennas so as to eliminate flow disturbance. By flipping at the sea surface, antennas are exposed for communications. The mission of FχS is to provide intensive profiling measurements of the upper ocean from 240 m and through the sea surface, particularly during periods of extreme surface forcing. While surfaced, accelerometers provide estimates of wave height spectra and significant wave height. From 3.5 day field trials, here we evaluate (i) the statistics from two FχS units and our established shipboard profiler, Chameleon, and (ii) FχS-based wave statistics by comparison to a nearby NOAA wave buoy. Significance Statement The oceanographic fleet of Argo autonomous profilers yields important data that define the state of the ocean’s interior. Continued deployments over time define the evolution of the ocean’s interior. A significant next step will be to include turbulence measurements on these profilers, leading to estimates of thermodynamic mixing rates that predict future states of the ocean’s interior. An autonomous turbulence profiler that employs the buoyancy engine, mission logic, and remote communication of one particular Argo float is described herein. The Flippin’ χSOLO is an upper-ocean profiler tasked with rapid and continuous profiling of the upper ocean during weather conditions that preclude shipboard profiling and that includes the upper 10 m that is missed by shipboard turbulence profilers.

Benefits of fully focused SAR altimetry to coastal wave height estimates: A case study in the North Sea

Estimating the three geophysical variables significant wave height (SWH), sea surface height, and wind speed from satellite altimetry continues to be challenging in the coastal zone because the received radar echoes exhibit significant interference from strongly reflective targets such as sandbanks, sheltered bays, ships etc. Fully focused SAR (FF-SAR) processing exhibits a theoretical along-track resolution of up to less than half a metre. This suggests that the application of FF-SAR altimetry might give potential gains over unfocused SAR (UF-SAR) altimetry to resolve and mitigate small-scale interferers in the along-track direction to improve the accuracy and precision of the geophysical estimates.The objective of this study is to assess the applicability of FF-SAR-processed Sentinel-6 Michael Freilich (S6-MF) coastal altimetry data to obtain SWH estimates as close as possible to the coast.We have developed a multi-mission FF-SAR processor and applied the coastal retracking algorithm CORALv2 to estimate SWH. We assess different FF-SAR and UF-SAR processing configurations, as well as the baseline Level-2 product from EUMETSAT, by comparison with the coastal, high-resolution SWAN-Kuststrook wave model from the Deltares RWsOS North Sea operational forecasting system. This includes the evaluation of the correlation, the median offset, and the percentage of cycles with high correlation as a function of distance to the nearest coastline. Moreover, we analyse the number of valid records and the L2 noise of the records. The case study comprises five coastal crossings of S6-MF that are located along the Dutch coast and the German coast along the East Frisian Islands in the North Sea.We observe that accurate and precise SWH records can be estimated in the nearshore zone within 1–3 km from the coast using satellite SAR altimetry. We find that the FF-SAR-processed dataset with a Level-1b posting rate of 140 Hz shows the greatest similarity with the wave model. We achieve a correlation of ∼0.8 at 80% of valid records and a gain in precision of up to 29% of FF-SAR vs UF-SAR for 1–3 km from the coast. FF-SAR shows, for all cycles, a high correlation of greater than or equal to 0.8 for 1–3 km from the coast. We estimate the decay of SWH from offshore at 30 km to up to 1 km from the coast to amount to 26.4% ± 3.1%.

Evaluation of Multi-Incidence Angle Polarimetric Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval

Significant wave height (SWH) is one of the most important descriptors for ocean wave fields. The polynomial regression (PolR) and Gaussian process regression (GPR) models are implemented to explore the effects of polarization and incidence angles on the SWH estimation from multi-incidence angle quad-polarization Gaofen-3 SAR wave mode data, based on the collocated data set of approximately 12,000 Gaofen-3 wave mode imagettes, matched with SWH from the fifth generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the model performance improves, as long as polarimetry information increases. The hybrid polarizations perform stronger than the co-polarizations or cross-polarizations alone, and they show better performance over the low to high seas. The lower incidence angles are more favorable for SAR SWH inversion. It is superior to introduce incidence angle in piecewise way, rather than to include it as an independent variable in the models. Then, the final PolR and GPR models, with the superior input scheme that includes the quad-polarized features and introduces the incidence angle in piecewise way, are assessed independently through a comparison with observations from altimeter and buoys. The accuracies of our SWH estimates are comparable or even higher than other published results. The GPR model outperforms the PolR model, due to the superiority of the added nonlinearity of GPR.

Response component analysis for sea state estimation using artificial neural networks and vessel response spectral data

The use of the ‘ship as a wave buoy analogy’ (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel response spectral data to statistical wave properties for a small uninhabited surface vessel. Results showed a strong correlation between heave responses and significant wave height estimates, whilst the accuracy of mean wave period and wave heading predictions were observed to improve considerably when data from multiple vessel degrees of freedom (DOFs) was utilized. Overall, 3-DOF (heave, pitch and roll) NNs for SSE were shown to perform well when compared to existing SSE approaches that use similar simulation setups. One advantage of using small vessels for SAWB was shown as SSE accuracy was reasonable even when motion responses were low (in high-frequency, low wave height sea states). Given the information-dense statistical representation of vessel motion responses in spectral form, as well as the ability of NNs to effectively model complex relationships between variables, the designed SSE method shows promise for future adaptation to mobile SSE systems using the SAWB approach.

Improved CycleGAN-based shadow estimation for ocean wave height inversion from marine X-band radar images

A novel algorithm is developed to estimate the shadowing ratio for the significant wave height (SWH) inversion of the ocean wave fields imaged by horizontal polarized X-band nautical radar intelligently and conveniently. To solve the problem that the accuracy of the calculated ratio of shadowing in local image areas is not ideal, and the high resolution radar images will lead to time-consuming in estimation of root mean square slope and angle-blurred for sea surface image edge detection, a shadow estimation model from marine X-band radar images based on Convolutional Neural Network (CNN) is established. The model applies the improved CycleGAN to SWH estimation using the geometric shadow effect, which is visible on the marine X-band radar sea surface images due to the presence of the modulation effect of the rough surface. The neural network model can be successfully trained from simulation-based data and then applied to real measured data, and the algorithm does not require any reference measurements. Compared with the traditional shadow-based method, the SWH derived by using this proposed method matches well with that measured by an in-situ buoy nearby, which indicates the goodness of our proposal.

Interference-sensitive coastal SAR altimetry retracking strategy for measuring significant wave height

Satellite altimetry is a radar remote sensing technology for the precise observation of the ocean surface and its changes over time. Its measurements allow the determination of geometric and physical parameters such as sea level, significant wave height or wind speed. This work presents a novel coastal retracking algorithm for SAR altimetry to estimate the significant wave height. The concept includes an adaptive interference masking scheme to sense and mitigate spurious interfering signals that typically arise from strongly reflective targets in the coastal zone. The described procedure aims at increasing the number of valid records in the coastal zone. The effectiveness of the novel retracking algorithm is validated using the methodology recently developed in the framework of the European Space Agency Sea State Climate Change Initiative project. Several different metrics were extracted as functions of sea state and distance to the nearest coast: outliers, number of valid records, intrinsic noise, power spectral density, and correlation statistics for the comparison with wave model and in-situ data. Two coastal case study scenarios complement the validation. The results show that with the presented novel retracking algorithm, the number of valid 20-Hz records in the near coastal zone of less than 5 km off the coast is increased by more than 25% compared to the best competing coastal retracking algorithm with no degradation of quality of the estimated records. We emphasise the importance of the correct choice of the quality flag that is provided together with the significant wave height. Our findings suggest that the strategy for the significant wave height quality flag of the official baseline Level-2 product of the Sentinel-3 mission can be redefined to obtain more robust significant wave height estimates in the coastal zone.

An effective estimation strategy for significant wave height via independent component analysis

In this paper, an effective significant wave height (SWH) estimation method of X-band radar image sequences based on independent component analysis (ICA) is proposed. Radial direction data is first selected from the radar image, and then the original components of ocean waves are obtained via the ICA theory. Considering the formation mechanism of ocean waves, the relationship between SWH and energy of ocean waves is explored, which is propitious for a more effective estimation of wave height. Finally, by adopting the fitted relationship, SWH is estimated by calculating the energy of waves. The proposed method is tested by the simulated data and the real data. The results show that the proposed method achieves better correlation coefficient between the real value and the estimation value, which is more than 92%. The root mean squared error (RMSE) is lower than the EOF-based method and the EEMD-based method. The results indicate that the performance of the proposed method has been improved in terms of significant wave height estimation.

Mitigation of Rain Effect on Wave Height Measurement Using X-Band Radar Sensor

The presence of rain can negatively affect the performance of many sensors such as X-band radar. In this paper, an effective approach is proposed to mitigate the effect of rain on significant wave height ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H}_{s}$ </tex-math></inline-formula> ) estimation from X-band radar sensor data along with a machine-learning (ML)-based method. First of all, the haze removal algorithm is applied to rain-contaminated radar images as pre-processing. Then, three different features are extracted from the processed radar images. Different combinations of these three features are utilized to estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H}_{s}$ </tex-math></inline-formula> under the rain condition by using support vector regression (SVR)-based and temporal convolutional network (TCN)-based regression methods. It is found that the root-mean-square-errors (RMSEs) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H}_{s}$ </tex-math></inline-formula> estimation results using two typical methods (signal-to-noise ratio (SNR)-based and ensemble empirical mode decomposition (EEMD)-based linear fitting methods) are decreased by 0.14 m and 0.48 m after introducing the haze removal algorithm, respectively. Also, a relatively high accuracy can be achieved using the SVR-based regression method with the combination of SNR and gray level co-occurrence matrix (GLCM) features. Compared to the SNR-based and EEMD-based linear regression methods, the proposed SVR-based method further improves the estimation accuracy, with reductions of RMSE by 0.19 m and 0.82 m, respectively.