Search Results (45)

Oceanographic buoys are essential platforms for marine observation, supporting real-time monitoring, climate studies, operational oceanography, coastal management, and offshore activities. This review maps the scientific literature on oceanographic buoys and identifies the main research sectors that structure the field. A Scopus search using TITLE-ABS-KEY((oceanographic OR metocean OR “ocean observing” OR “ocean observation”) AND (buoy OR buoys)) retrieved 1876 documents published between 1938 and 2026. The records were processed using a network-based sector detection and visualization workflow. Five thematic sectors were identified: platform engineering, sensors, moorings, and autonomous systems; marine wind, waves, metocean, and offshore energy applications; physical oceanography, sea-surface temperature (SST), currents, and environmental variability; coastal and ocean observing networks and operational services; and drifting buoys, floats, and global ocean datasets. The results show that buoy research has evolved from isolated measurement systems to integrated observing infrastructures that combine in situ sensing, satellite validation, autonomous operation, telemetry, and decision-support services. By linking bibliometric structure and thematic interpretation, this review provides a sector-based map showing where scientific observation, engineering design, operational services, and autonomous technologies converge. Full article

This study presents a hydrodynamic assessment of a toroidal wave energy converter (WEC) operating under low-energy conditions of the west coast of Mexico. Performance analysis incorporates the coupling surge, heave, and pitch motions. To investigate mooring–device interaction, two mooring configurations were examined: (A) a single catenary system and (B) a catenary system with a surface-floating buoy. The WEC was evaluated under operational conditions, operational conditions with a constant surface current, and extreme seas. The results show that under operational conditions, the WEC-mooring B configuration achieves higher energy capture than the WEC-mooring A configuration, with performance peaks at 13 s and 11 s, respectively. The presence of a surface current does not significantly influence absorbed power. Under extreme conditions, mooring B reduces mooring-line stresses but causes greater horizontal foundation forces and increased floater drift compared to mooring A. When mooring effects are included, mooring A’s performance is advantageous because it shifts peak energy capture toward the dominant sea states at the study site. This maintains better station-keeping capability and achieves a maximum capture width ratio (CWR) of approximately 0.5. Full article

Drifting fish aggregating devices (DFADs) are central to tropical tuna purse-seine fisheries, yet their hydrodynamic performance under realistic seas has not been adequately addressed, particularly for emerging eco-friendly designs. A three-dimensional framework based on computational fluid dynamics is developed to assess the motion response and mooring loads of full-scale DFADs comprising raft buoys, biodegradable cotton rope, and iron sinkers, using four buoy layouts (Models A to D). Unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are performed with a realizable k–ε closure, volume of fluid (VOF) free-surface capturing, the Euler overlay method, dynamic overset meshes, and catenary mooring coupling. Regular waves representative of operational conditions (T = 1.40 to 2.40 s, H = 0.10 to 0.40 m) are imposed via a VOF wave-forcing technique, and mesh/time-step sensitivity analyses demonstrate the accurate reproduction of the first-order wave elevation (error < 0.8%). Surge drift per cycle and heave response amplitude operators, with the relative mooring force, are evaluated as functions of the relative wavelength (λ/L_a) and wave steepness (H/λ). The results reveal that the buoy layout exerts first-order control on DFAD dynamics, whereas short, steep waves dominate motion and line loads. The intermediate end-point sinker mass achieves a favorable balance between motion suppression and mooring load control, whereas distributing a fixed total sinker mass along the rope reduces heave response and mooring force by improving the tension redistribution and overall stability. Across all sea states, Models A and D reduced motion envelopes and mooring forces, indicating their suitability as robust, low-impact configurations. The proposed framework and design recommendations provide quantitative guidance for optimizing eco-DFAD geometry and deployment strategies, supporting safer and more sustainable DFAD-based tuna fisheries. Full article

Stokes drift is the net displacement of ocean surface water particles caused by nonlinear surface waves. Its estimation typically relies on sea surface wave spectra, and truncation of the high-frequency spectral tail can significantly affect accuracy. This study uses directional wave spectrum data from the SWIM instrument onboard CFOSAT. By introducing a wind-speed-dependent parameterization scheme for the transition wavenumber (k_n) between the equilibrium and saturation ranges, as well as a cutoff wavenumber (k_m), we constructed a model to supplement the high-frequency tail of the wave spectrum combined with mask filtering to optimize spectrum reconstruction. The Stokes drift calculated with this model shows a better correlation (R = 0.699) with buoy observations than the widely used ERA5 reanalysis (R = 0.613). Analysis reveals pronounced regional differences in the contribution of high-frequency waves to surface Stokes drift, exceeding 80% in equatorial low-wind regions while dropping below 10% in the high-wind Southern Ocean due to enhanced breaking dissipation. The global Stokes drift distribution exhibits clear hemispheric asymmetry and seasonal evolution, with peak values (>0.12 m/s) in the Antarctic Circumpolar Current region. The proposed method provides a reliable, observation-based approach for improving global Stokes drift estimation, with direct implications for modelling ocean transport, Langmuir turbulence, and air–sea interactions. Full article

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the $\tau$-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the $\tau$-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments. Full article

Ocean surface radial current velocities can be derived from synthetic aperture radar (SAR) Doppler shift observations using the Doppler centroid technique and a recently developed Doppler velocity model. However, comprehensive evaluations of the accuracy and reliability of these retrievals remain limited. To address this gap, we analyzed 6341 Sentinel-1 SAR scenes acquired over the South China Sea (SCS) between December 2017 and October 2023, in conjunction with drifting buoy observations, to systematically validate the retrieved radial current velocities. A linear fitting method and the dual co-polarization Doppler velocity (DPDop) model were applied to correct for the influence of non-geophysical factors and sea state effects. The validation against the drifter data yielded a bias of 0.01 m/s, a root mean square error (RMSE) of 0.18 m/s, and a mean absolute error (MAE) of 0.16 m/s. Further comparisons with the Surface and Merged Ocean Currents (SMOC) dataset revealed bias, RMSE, and MAE values of 0.07 m/s, 0.14 m/s, and 0.12 m/s in the Beibu Gulf, and −0.06 m/s, 0.23 m/s, and 0.19 m/s in the Kuroshio intrusion area. These results demonstrate that SAR Doppler measurements have a strong potential to complement existing ocean observations in the SCS by providing high-resolution (1 km) ocean surface current maps. Full article

Satellite-retrieved sea-surface skin temperature (SST_skin) is essential for many Near-Real-Time studies. This study aimed to assess the potential to improve the accuracy of satellite-based SST_skin retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. The benefits and shortcomings of various ML methods were assessed through comparisons with withheld in situ measurements. The results demonstrate that the ML-based algorithms achieve promising accuracy, with mean biases within 0.07 K when compared with the buoy data and ranging from −0.107 K to 0.179 K relative to the ship-derived SST_skin data. Notably, both XGBoost and RF stand out for their superior correlation and efficacy in the statistical results of validation. The improved SST_skin derived using the ML-based algorithms could enhance our understanding of vital oceanic and atmospheric characteristics and have the potential to reduce uncertainty in oceanographic, meteorological, and climate research. Full article

Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements and realizing cost-effective large-scale deployment. Unlike traditional approaches, this buoy uses the kinematic extension of the variometric approach for displacement analysis stand-alone engine (Kin-VADASE) velocity measurement method, thus eliminating the need for additional high-precision measurement units and an expensive complement of satellite orbital products. Through testing in the South China Sea and Laoshan Bay, the results showed good consistency in significant wave height and main wave direction between the novel buoy and a Datawell DWR-G4, even under mild wind and wave conditions. However, wave mean period disparities were observed partially because of sampling frequency differences. To validate this idea, we used Joint North Sea Wave Project (Jonswap) spectral waves as input signals, the bias characteristics of the mean periods of the spectral calculations were compared under conditions of identical input signals and gradient-distributed wind speeds. Results showed an average difference of 0.28 s between the sampling frequencies of 1.28 Hz and 5 Hz. The consequence that high-frequency signals have considerable effects on the mean wave period calculations indicates the necessity of the buoy’s high-frequency operation mode. This GNSS drifting buoy offers a cost-effective, globally deployable solution for ocean wave measurement. Its potential for large-scale networked ocean wave observation makes it a valuable oceanic research and monitoring instrument. Full article

In order to address the issue of limited common coverage and high cost in mapping ocean surface vector current by two (or more) high-frequency surface wave radars, this paper proposes a single-station surface wave radar vector current inversion algorithm. The feasibility of this algorithm has been validated in the Taiwan Strait. Based on the ocean dynamic characteristics of the Taiwan Strait, the algorithm utilizes the radial current obtained from a high-frequency surface wave radar (HFSWR) in Fujian Province to invert the ocean surface vector current. The surface vector current can be decomposed into three primary dynamic components: tidal currents, wind-driven currents, and geostrophic currents. Firstly, tidal current forecasting models and Ekman and Stokes theories are used to calculate the tidal and wind-driven currents in the Taiwan Strait, respectively. Subsequently, the directions of geostrophic currents in the Taiwan Strait are determined with sea surface height data, and the magnitudes of the geostrophic currents are constrained using the radial current from the single HFSWR. Finally, the three components are added together to obtain the vector current. Comparative results demonstrate that the efficacy of the algorithm has been validated through field experiments (with two HFSWRs and two drifting buoys) conducted in the southwestern of the Taiwan Strait. Further research is needed on the applicability of this algorithm to other sea areas and monitoring systems. Full article

To reduce costs associated with the detection and population assessment of Antarctic krill and diversify the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting sonar buoy fulfills its primary functions and meets the requirements for krill detection in designated marine areas. The initial experiment lasted seven days, during which the buoy collected 157 records of speed and location data as well as 82 records of sea surface temperature and acoustic data, demonstrating its potential for krill detection. The experiment also revealed shortcomings in the initial design of the drifting sonar buoy, leading to proposed improvements. The paper further compares the advantages and disadvantages of the drifting sonar buoy and traditional fishing vessels in krill detection with the buoy offering unique benefits in low-cost deployment, labor savings, broad monitoring range, and continuous real-time data monitoring. The drifting sonar buoy serves as an excellent complement to fishing vessels in krill detection. Full article

Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathematical model for trajectory prediction is developed, transforming the challenge of predicting trajectory points into predicting velocities in eastward and northward directions. Subsequently, a four-layer trajectory prediction calculation framework (FLTPCF) is established, outlining a complete workflow for the real-time online training of marine environment data and SDBs’ trajectory prediction. Thirdly, for facilitating accurate long-term trajectory prediction, a hybrid artificial neural network trajectory prediction model, named CNN–BiGRU–Attention, integrates a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism (AM), tuned for spatiotemporal feature extraction and extended time-series reasoning. Extensive experiments, including ablation studies, comparative analyses with state-of-the-art models like BiLSTM and Transformer, evaluations against numerical methods, and adaptability tests, were conducted for justifying the CNN–BiGRU–Attention model. The results highlight the CNN–BiGRU–Attention model’s excellent convergence, accuracy, and generalization capabilities in predicting 24, 48, and 72 h trajectories for SDBs with varying drogue statuses and under different sea conditions. This work has great potential to promote the intelligent degree of marine environmental monitoring. Full article

In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment including permanent GNSS reference stations (PGSs), acoustic tide gauges (ATGs), and dedicated GNSS buoys (DGB), etc. placed on Zhi’wan, Wai’ling’ding, Dan’gan, and Miao’Wan islands of the WSCS. The PGSs data of Zhi’wan and Wai’ling’ding islands were processed and analyzed using the GAMIT/GLOBK (Version 10.7) and Hector (Version 1.9) software to define the datum for Cal/Val of altimeters in WSCS. The DGB was used to transfer the datum from the PGSs to the ATGs of Zhi’wan, Wai’ling’ding, and Dan’gan islands. Separately, the tidal and mean sea surface (MSS) corrections are needed in the Cal/Val of altimeters. We evaluated the global/regional tide models of FES2014, HAMTIDE12, DTU16, NAO99jb, GOT4.10, and EOT20 using the three in situ tide gauge data of WSCS and Hong Kong tide gauge data (No. B329) derived from the Global Sea Level Observing System. The HAMTIDE12 tide model was chosen to be the most accurate one to maintain the tidal difference between the locations of the ATGs and the altimeter footprints. To establish the sea surface connections between the ATGs and the altimeter footprints, a GPS towing body and a highly accurate ship-based SSH measurement system (HASMS) were used to measure the sea surface of this area in 2018 and 2022, respectively. The global/regional mean sea surface (MSS) models of DTU 2021, EGM 2008 (mean dynamic topography minus by CLS_MDT_2018), and CLS2015 were accurately evaluated using the in situ measured data and HY-2A altimeter, and the CLS2015 MSS model was used for Cal/Val of altimeters in WSCS. The data collected by the equipment of WSCS, related auxiliary models mentioned above, and the sea level data of the hydrological station placed on Dan’gan island were used to accomplish the Cal/Val of HY-2B, HY-2C, Jason-3, and Sentinel-3A (S3A) altimeters. The bias of HY-2B (Pass No. 375) was −16.7 ± 45.2 mm, with a drift of 0.5 mm/year. The HY-2C biases were −18.9 ± 48.0 mm with drifts of 0.0 mm/year and −5.6 ± 49.3 mm with −0.3 mm/year drifts for Pass No. 170 and 185, respectively. The Jason-3 bias was −4.1 ± 78.7 mm for Pass No. 153 and −25.8 ± 85.5 mm for Pass No. 012 after it has changed its orbits since April 2022, respectively. The biases of S3A were determined to be −16.5 ± 46.3 mm with a drift of −0.6 mm/year and −9.8 ± 30.1 mm with a drift of 0.5 mm/year for Pass No. 260 and 309, respectively. The calibration results show that the WSCS can commercialize the satellite altimeter calibration. We also discussed the calibration potential for a wide swath satellite altimeter of WSCS. Full article

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. Full article

The Array for Real-time Geostrophic Oceanography (Argo) program provides valuable data for maritime research and rescue operations. This paper is based on Argo historical and satellite observations, and inverted sea surface and submarine drift trajectories. A neural network method was developed to predict the position of Argo buoys, improving target tracking and emergency support capabilities. Based on a deep learning framework using a Simple Recurrent Unit (SRU), a new Time–Space Feature Fusion Method based on an Attention Mechanism (TSFFAM) model was constructed. The TSFFAM mechanism can predict the target trajectory more accurately, avoiding the disadvantages of traditional Long Short-Term Memory (LSTM) models, which are time consuming and difficult to train. The TSFFAM model is able to better capture multi-scale ocean factors, leading to more accurate and efficient buoy trajectory predictions. In addition, it aims to shed light on the mechanism of the joint multi-element and multi-scale effects of laminar and surface currents on multi-scale ocean factors, thereby deepening our understanding of the multi-element and multi-scale interactions in different spatio-temporal regimes of the ocean. Experimental verification was conducted in the Pacific Ocean using buoy trajectory data, and the experimental results showed that the buoy trajectory prediction models proposed in this paper can achieve high prediction accuracy, with the TSFFAM model improving the accuracy rate by approximately 20%. This research holds significant practical value for the field of maritime studies, precise rescue operations, and efficient target tracking. Full article

The NOAA in situ Sea Surface Temperature (SST) Quality Monitor (iQuam) online system collects in situ SSTs from various sources, performs quality control (QC), and provides QC’ed data to users. Like many other in situ QCs, the iQuam QC employs comparisons with Level 4 SST analysis. However, the current daily L4 analyses do not capture the diurnal cycle, nor do they resolve the fine structure of SST in dynamic areas. As a result, high-quality in situ SSTs significantly deviating from the L4 SST may be rejected. This paper discusses the new Diurnal Reference Check (DRC), which addresses overscreening for buoys whose sampling frequency is sufficient for resolving the diurnal cycle. The DRC separates records from individual buoys into 24-h segments and characterizes each segment with the median nighttime (MNT) SST and the amplitude of the diurnal signal (ADS). The segment is rejected if the ADS is unrealistically large or if the difference between the MNT and L4 SST exceeds a geographically dependent threshold. The outliers are further screened out by comparison of individual in situ SSTs with the MNT. All thresholds are determined from the analysis of matchups with reprocessed NOAA SSTs from multiple low-orbiting satellites. The satellite matchups are also used to validate the QC results. The DRC minimizes the overscreening, increases the number of high-quality in situ data by ~5%, and reduces the QC reliance on the L4 analysis. In addition, a new retrospective satellite-based quality check is introduced to identify matchups, which are most useful for training SST algorithms and validation of reprocessed satellite data. Full article