Ocean Wave Data Research Articles

Evaluation of Microseismic Motion at the KAGRA Site Based on Ocean Wave Data

Abstract Microseismic motion, ambient ground vibration caused by ocean waves, affects ground-based gravitational wave detectors. In this study, characteristics of the ocean waves including seasonal variations and correlation coefficients were investigated to obtain the significant wave heights at 13 coasts in Japan. The relationship between the ocean waves and the microseismic motion at the KAGRA site was also evaluated. As a result, it almost succeeded in explaining the microseismic motion at the KAGRA site by the principal components of the ocean wave data. One possible application of this study is microseismic forecasting, an example of which is also presented.

Ensemble hindcasting of winds and waves for the coastal and oceanic region of Southern Brazil

When hindcasting wave fields of storm events with wave models, the quality of the results strongly depends on several factors such as the computational grid resolution and the accuracy of the atmospheric forcing. In an effort to minimize the uncertainties involved in this process, three ocean wave and surface wind ensemble hindcast systems were established using the Simulating WAves Nearshore (SWAN) model and Weather Research and Forecasting Model (WRF) with atmospheric data from ERA5 Ensemble of Data Assimilation (EDA) as well as deterministic high-resolution systems. We established three ensemble systems to tackle this: SWN-ERA5EDA using ERA5-EDA global reanalyses winds, SWN-WRFERA5 employing WRF downscaling of ERA5-EDA, and SWN-WRFPPar incorporating WRF multi-physics runs for dynamical downscaling. This study focuses on extreme events in southern Brazil during an austral winter, highlighting the importance of increasing the resolution of ocean wave and surface wind data to provide more accurate and reliable forecasts for coastal and marine activities. Our analyses revealed that atmospheric downscaling performed with WRF not only increased the ensemble spread by significant amounts but also enhanced the sharpness of the wave ensemble hindcast compared with those based solely on the ERA5 EDA. Specifically, for the Rio Grande buoy location, the significant wave height (Hs) from the SWN-WRFERA5 system showed an increase of 0.5 over SWN-ERA5, and Hs from the SWN-WRFPPar system increased by 0.6. Additionally, the wave peak period (Tp) for both SWN-WRFERA5 and SWN-WRFPPar systems experienced an increase of 1.2 compared to SWN-ERA5. Additionally, the ensemble produced with the WRF multi-physics approach captured peaks in the significant wave height registered by the buoy that were not reproduced by other ensemble systems, demonstrating an improvement in predictive accuracy, despite presenting a smaller correlation between spread and strong localized wave variations. Besides quantifying the hindcast error, the methodology presented in this work also offers a way to generate alternative and improved representations of past extreme events. This approach significantly contributes to our ability to sample recent climatic conditions and expand the dataset for statistical analyses, which is especially valuable for ocean and coastal engineering projects. This study underscores the critical role of enhancing computational and methodological approaches in wave modeling for better understanding and mitigating the impacts of extreme weather events on coastal and oceanic regions.

Australian Ocean surface waves dataset from SAR

AbstractIn this article, a regional ocean surface waves dataset from Sentinel‐1 A and B Synthetic Aperture Radar (SAR) satellites has been described. The ocean wave data have been extracted from the Sentinel‐1 level‐2 OCN (ocean) product as provided by the European Space Agency and downloadable for this region from the Copernicus Australasia regional data hub. The source OCN data have been produced by evolving versions of Sentinel‐1 Instrument Processing Facility (IPF). The structure of the source OCN NetCDF files changes over time and presents a challenge in performing long duration, time series analyses, including the examination of potential inconsistencies in OCN wave data, due to employment of different IPF versions over the duration of the satellite missions. Here, the input OCN wave data have been homogenized to a single, easily usable standard format after applying a quality assurance and control procedure that removes various inconsistencies in variables, coordinates, dimensions and land flag, and through the addition of new auxiliary variables. The new format has the desirable properties of being compact in size, consistent in structure, and scalable in temporal and spatial coverage. It is also convenient to use and offers opportunities to perform fast, multi‐year regional processing and analysis for calibration and validation studies and scientific applications. No re‐processing of Sentinel‐1 level‐1 data has been carried out in this work.

Kajian Karakteristik Gelombang Laut di Wilayah Pantai Pulau Merah, Kabupaten Banyuwangi


 
 
 
 Sea wave parameter information is used in maritime-related activities such as sea transportation, offshore exploration, fisheries, port construction, shipping safety, coastal area development, and coastal mitigation. This study described the characteristics of the sea waves in Pulau Merah Beach, Banyuwangi Regency. Wind reanalysis data of 10 meters was processed using the Sverdrup Munk Bretschneider (SMB) method using the Easywave algorithm. The analyzed and processed the sea wave model's assimilation data from Marine Copernicus pointwise by WRPLOT. The results of wind and ocean wave data processing showed that the direction, height, and period of sea waves on Pulau Merah Beach are strongly influenced by the Monsoon Wind. The sea wave height in the Pulau Merah Beach region reaches its peak value during the East season, which is 1.5 meters. In contrast, the value of the period of sea waves in the Pulau Merah Beach area reached its peak value during the Transitional Season I period, which was 8.3 seconds.
 
 
 

A novel quality control method of time-series ocean wave observation data combining deep-learning prediction and statistical analysis

Quality control (QC) of marine data is a critical aspect in ensuring the usability of oceanic data. In this paper, we propose a novel QC method for time-series ocean wave data, which combines deep learning prediction and statistical analysis. Our method first realizes multi-element LSTM prediction of the time-series ocean wave observation data, capturing both temporal consistency and physical relationships between the multi-element inputs. Then, it applies peak detection on the regional mean difference ratio derived from the predicted and true values of the ocean wave data, and finally labels the anomalous data points based on peak detection results. We conducted experiments on the time-series wave data of four sites from the National Marine Science Center, China, and compared our proposed method with traditional QC method that is currently used for operational marine data quality control, as well as classic anomaly detection models including Isolation Forest and VAE-LSTM. The results show a significant improvement in the Precision, Recall, and F1 Score for erroneous samples using our proposed method. Our proposed method takes advantages of both deep learning and statistical analysis and considers physical correlation of multiple elements of marine data, effectively addressing the problem of erroneous discrimination of abnormal sea conditions in the traditional method, and providing valuable insights for the study of marine time-series observational data.

Deep learning approach for downscaling of significant wave height data from wave models

The generation of high-resolution wave data remains a challenging task due to limitations in measurement techniques and computational resources. In the present study, we attempt to address the downscaling of ocean wave data from wave models using deep learning model, specifically convolutional neural networks. We conducted a comprehensive performance evaluation by comparing traditional methods, including bilinear interpolation, bicubic interpolation, and Kriging interpolation, against an improved Wave-Super-Resolution Convolutional Neural Network (W_SRCNN) in terms of high-resolution processing of significant wave height. Evaluation metrics including peak signal-to-noise ratio, root-mean-square error, structural similarity, cosine similarity, and no-reference image quality assessment were employed to assess the effectiveness of the methods. The results suggest that W_SRCNN outperforms other approaches in terms of overall performance, local detail preservation, and computational efficiency. Our findings demonstrate the successful application of deep learning techniques to achieve high-resolution processing of ocean data. The obtained results underscore the significance of tailored deep learning architectures to the analysis of ocean wave data, and thereby encouraging future investigations into their potential for high-fidelity reconstruction of sea surface geophysical fields from multi-source data, including ocean models, satellite observations, and buoy measurements.

Marine Environmental Impact on CFAR Ship Detection as Measured by Wave Age in SAR Images

Satellite synthetic aperture radar (SAR) images are recognized as one of the most efficient tools for day/night, all weather and large area monitoring of ships at sea. However, false alarms discrimination is still one key problem on SAR ship detection. While many discrimination techniques have been proposed for the treatment of false alarms, not enough emphasis has been targeted to explore how obtained false alarms are related to the changing ocean environmental conditions. To this end, we combined a large set of Sentinel-1 SAR images with ocean surface wind and wave data into one dataset. SAR images were separated into three distinct groups according to wave age (WA) conditions present during image acquisition: young wind sea, old wind sea, and swell. A constant false alarm rate (CFAR) ship detection algorithm was implemented based on the generalized gamma distribution (GΓD). Kolmogorov–Smirnov distance was used to analyze the distribution goodness-of-fit among distinct ocean environments. A backscattering analysis of different sizes of ship targets and sea clutter was further performed using the OpenSARShip and automatic identification system (AIS) datasets to assess its separability. We derived a discrimination threshold adjustment based on WA conditions and showed its efficacy to drastically reduce false alarms. To our present knowledge, the use of WA as part of the CFAR and for the adjustment of the threshold of detection is a novelty which could be tested and evaluated for different SAR sensors.

Historical global ocean wave data simulated with CMIP6 anthropogenic and natural forcings

This dataset presents historical ocean wave climate during 1960–2020, simulated using the numerical model WAVEWATCH III (WW3) forced by Coupled Model Intercomparison Project phase 6 (CMIP6) simulations corresponding to natural-only (NAT), greenhouse gas-only (GHG), aerosol-only (AER) forcings, combined forcing (natural and anthropogenic; ALL), and pre-industrial control conditions. Surface wind at 3-hourly temporal resolution, and sea-ice area fraction at monthly frequency, from a CMIP6 model - MRI-ESM2.0 are used to force WW3 over the global ocean. Model calibration and validation of the significant wave height are carried out using inter-calibrated multi-mission altimeter data produced by the European Space Agency Climate Change Initiative, with additional corroboration using ERA-5 reanalysis. The simulated dataset is assessed for its skill to represent mean state, extremes, trends, seasonal cycle, time consistency, and spatial distribution over time. Numerically simulated wave parameters for different individual external forcing scenario is not available yet. This study produces a novel database particularly useful for detection and attribution analysis to quantify the relative contributions of natural and anthropogenic forcings to historical changes.

The Impacts of the Application of the Ensemble Optimal Interpolation Method in Global Ocean Wave Data Assimilation

The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China’s offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China’s ocean satellite data in ocean wave data assimilation and prediction.

Learning the spatiotemporal relationship between wind and significant wave height using deep learning

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

A proposed method for predicting extreme design forces of wave energy converters

This paper proposes to utilize a Generalized Gamma distribution for fitting the significant wave height data in order to more accurately calculate the sea state parameter distribution tails. The accuracy of the proposed method has been demonstrated based on the fittings to two ocean wave data sets. This proposed method has subsequently been utilized for deriving a 50-year environmental contour line. The engineering significance of using a more reliable contour line such as the one derived using the proposed method for predicting the long-term design force values for wave energy converters has been highlighted.

Pan-Arctic ocean wind and wave data by spaceborne SAR

ABSTRACT The Arctic is one of the most significant changing areas on the Earth under the climate change scenario. More regions in the Arctic are becoming ice-free oceans in the melting season or through the whole year. Therefore, ocean wind and wave, as the two most important parameters in the air–sea interface, are drawing significant attention to the Arctic Ocean. Scatterometer and radar altimeter are the two traditional remote sensing instruments for ocean wind and wave observations, while the former is limited by coarse spatial resolution and the latter has small spatial coverage. Wind and wave data in high spatial resolution and wide coverage by synthetic aperture radar (SAR) are currently lacking in the Arctic Ocean. We developed an ocean wind and wave dataset by Sentinel-1 SAR in the pan-Arctic Ocean (above 60°N), covering January 2017 to May 2021. By comparing with sea surface wind speed data of scatterometer, the SAR-retrieved wind data achieve an accuracy of 1.23 m/s, in terms of root mean square error (RMSE). Compared with significant wave height data of radar altimeter, the SAR retrievals have an RMSE of 0.66 m. The data records are in the standard NetCDF-4 format. The dataset is publicly available at: http://www.dx.doi.org/10.11922/sciencedb.00834.

Bivariate kernel density estimation for environmental contours at two offshore sites

ABSTRACT This paper proposes a novel environmental contour line approach based on measured ocean wave data at two offshore sites. For implementing the novel environmental contour line approach, we propose the use of bivariate kernel density estimation with rigorous bandwidth selection based on the Scott-Tapia-Thompson estimation method. The environmental contours obtained by using the proposed novel approach have been compared with those obtained by using the traditional Inverse-First-Order Reliability Method (IFORM) with Rosenblatt transformation or principal component analysis, and the effectiveness of our proposed novel approach have been clearly substantiated. The research results in this paper demonstrate that our proposed novel approach can be utilised as an effective tool for generating environmental contours if one has sufficient data extending beyond the return period of interest.

Analysis of linkage type sea wave power plant design through motion study and 3D printed modelling

Sustainable energy generation is becoming increasingly important because of the expected limitations in current energy resources and for reducing pollution. Wave energy generation has seen significant developments in recent years. This research has produced a linkage-type PLTGL design that utilizes ocean waves to raise and lower the float lever and move the linkage mechanism which then causes a back and forth movement of the rack gear which will rotate the pinion and cause the dynamo drive shaft to rotate. Using ocean wave data in the waters of Tanjung Berikat, Bangka Tengah, and with the help of the SolidWorks application, this design is then simulated to determine the resulting movement and rotation. The rotation generated through the simulation can reach 532 Rpm with the assumption that it does not involve friction forces and material loads that exist in the PLTGL design. This design was made in 3D using the CR-10 V3 type 3D printing machine which produced a model with a scale of 25% of the original design. The model is used as a discussion material for designers to improve the design.

Effect of Spatial and Temporal Resolution Data on Design and Power Capture of a Heaving Point Absorber

For a heaving point absorber to perform optimally, it has to be designed to resonate to the prevailing ocean wave period. Hence, it is important to make the ocean wave data analysis to be as accurate as possible. In this study, existing wave condition data is used to investigate the effect of the temporal resolution (daily vs. hourly) of wave data on the design of the device and power capture. The temporal resolution effect on the estimation of ocean wave resource theoretical potential is also investigated. Results show that the temporal resolution variation of the ocean wave data affects the design of the device and its power capture, but the theoretical power resource assessment is not significantly affected. The device designed for the Gulf of Mexico is also analyzed with wave condition in Oregon, which has about 40 times the wave resource theoretical potential compared to the Gulf of Mexico. The results confirmed that a device should be designed for a specific location as the device performed better in the Gulf of Mexico, which has much less ocean wave resource theoretical potential. At last, the effect of the design, diameter and season (summer and winter) on the power output of the device is also investigated using statistical hypothesis testing methods. The results show that the power capture of a device is significantly affected by these parameters.

A novel environmental contour method for predicting long-term extreme wave conditions

Abstract This paper proposes a novel environmental contour line method based on measured ocean wave data for predicting the long-term extreme wave conditions. For implementing the proposed environmental contour line method, a bivariate kernel density estimation approach is used along with over-smoothed bandwidth selection. The environmental contours obtained using the proposed method have been compared with those obtained using the Gaussian copula (Nataf transformation) method and another parameterized model, and the effectiveness and superiority of the proposed method have been clearly substantiated. The research results in this paper demonstrate that the proposed method can be utilized as an effective tool for predicting the long-term extreme wave conditions.

Validation of CODAR 25MHz High-Frequency Radar derived Waves in Donghea, Korea

In order to examine the possibility of wave observation of CODAR HF radar, which is mostly used in Korea for surface ocean current observation, CODAR HF radar was installed on the coast to observe real-time waves. Unlike other HF radars, CODAR HF radars are easy to install because they are integrated with transmission and reception. and It is suitable for the domestic coastal environment where the coastline was developed. The CODAR HF radar used in this study transmits and receives waves using a center frequency of 25.4 MHz and a bandwidth of 150 kHz to observe the ocean wave. From the center of the observation point, a circular area of 140 degrees excluding land is observed, and ocean wave data is observed every 10 minutes from the total observation point to the shells 1 to 31 through the spatial average in units of 1 km from 31 km. According to the specifications of the 25Mhz CODAR HF radar, it is said that it is possible to observe a wave height of 4m, but the observation shows that it can be observed up to 6m.The observed CODAR HF radar wave data was compared and validated with the wave data observed by three wave measurement equipments (WAVE rider, AWAC, KMA wave buoy). In the case of the result of comparison of significant wave height and wave period in each installed shell region 1) WAVE rider was Corr. 0.76, 0.87 and RMSE 0.57 m, 0.65 s 2) AWAC was Corr. 0.92, 0.91 and RMSE 0.46 m, 0.45 s 3) KMA wave buoy was Corr. 0.80, 0.89 RMSE 0.65 m, 0.55 s, and all three types of underwater wave observation equipment confirmed high similarity to the wave result observed in HF radar. However, since the underwater wave observation equipment was located only at the 2 and 3 shells (2 to 3 km from the HF radar), additional verification is necessary through the installation of the underwater wave observation equipment at a remote shell location. Also, it is necessary to technically solve that the wave data acquisition rate of HF radar is lower than that of the radial vector. However, HF radar s wide-area wave observation data is expected to compensate for the disadvantages of underwater wave observation data (fixed point observation, difficulty in management and installation, and high maintenance cost). The wave observation data of HF radar could contribute greatly to ocean wave research.

Centuries of monthly and 3-hourly global ocean wave data for past, present, and future climate research

Ocean surface waves are essential to navigation safety, coastal activities, and climate systems. Numerical simulations are still the primary methods used in wave climate research, especially in future climate change scenarios. Recently, First Institute of Oceanography-Earth System Model version 2.0 (FIO-ESM v2.0), a global climate model coupled with an ocean wave model, was carried out the Coupled Model Intercomparison Project phase 6 (CMIP6) experiments. Here, we present the global monthly-mean and 3-hourly instantaneous wave parameter dataset from the FIO-ESM v2.0 CMIP6 experiments, including 700-year piControl, 165-year historical, three 86-year future scenarios (ssp125, ssp245, and ssp585, respectively), and two 150-year climate sensitive experiments (1pctCO2 and abrupt-4xCO2) simulations. Historical results show that the model can capture the basic wave climate features under climate change. These unique centuries of global wave data are from a fully coupled system and can provide the community with a vital long-term data source for scientific and engineering applications, such as wave climate research, wave-related process studies and parameterizations, as well as coastal and near-shore industry designs.

Assessment of extreme waves in the North Atlantic Ocean by regional frequency analysis

Regional frequency analysis is applied to North Atlantic Ocean wave data to determine the regions that can be considered homogenous from the point of view of extreme distributions and to obtain the extreme regional frequency distributions able to characterize those regions. The entire information is used on a regional scale to obtain an accurate estimation of the probability of occurrence of extreme events at a given location. Regional frequency analysis is implemented using 43 years (1958-2000) of hindcast data extracted from the HIPOCAS database. The domain of this area covers the latitudes 20.0∘N to 68.0∘N and the longitudes -50.0∘W to - 4.0∘W, with a grid resolution of 1∘ x 1∘. This approach can identify sites that lie close to each other but belong to different regions and in this way defines the areas in which the North Atlantic Ocean can be divided from a statistical point of view.

Conceptual Design and Simulation of a Self-Adjustable Heaving Point Absorber Based Wave Energy Converter

Different concepts and methods have been proposed and developed by many researchers to harvest ocean wave energy. In this paper, a new self-adjustable wave energy converter concept is presented, which changes its inertia through ballasting and de-ballasting using sea water. The trigger of ballasting and de-ballasting is controlled by the critical wave period. Therefore, the self-adjustable wave energy converter is able to interact at resonance with the ocean waves at two different resonant bandwidths. Ten years real wave data with hourly resolution from a selected location in Gulf of Mexico was used in this paper to decide the critical wave period and other parameters of the wave energy converter. The annual energy performance of the self-adjustable wave energy converter was also estimated and compared with non-adjustable wave energy converter with similar dimensions. Structural analysis including both static and fatigue analysis was performed on the self-adjustable wave energy converter to determine its survivability with the real ocean wave data. The results show that the self-adjustable wave energy converter is able to capture more energy than non-adjustable wave energy converter, and is able to survive during the hash ocean wave conditions.