Search Results (65)

This study focuses on how mesoscale eddies at the Kuroshio boundary in the East China Sea modulate underwater acoustic propagation. Using high-resolution reanalysis data from the Hybrid Coordinate Ocean Model (HYCOM) and validated acoustic ray-tracing simulations, the OW + SLA method is employed for eddy identification and classification. Statistical analysis of 120 eddy events from 2015 to 2020 clarifies their seasonal variation characteristics. Warm eddies shift the convergence zone 15–30 km away from the sound source and broaden it by 20–40%, while cold eddies shift it 10–25 km toward the source and narrow it by 15–35%. A linear relationship exists between eddy amplitude and acoustic transmission loss (TL = 72.4 + 0.42 h, R² = 0.61), where TL is the transmission loss in decibels (dB) and h is the eddy amplitude in meters (m), and there are depth-dependent transmission loss modulation effects. These results provide practical guidance not only for sonar system design and acoustic communication optimization but also for error correction in underwater acoustic navigation systems operating in eddy-prone environments. Full article

Tropical cyclones typically weaken rapidly during poleward propagation due to decreasing sea surface temperatures and increasing vertical wind shear. Super Typhoon Oscar (1995) deviated from this pattern by maintaining Category-5 intensity at an anomalously high latitude. This study investigates the oceanic mechanisms driving this resilience by integrating satellite SST data with atmospheric (ERA5) and oceanic (HYCOM) reanalysis products. Our analysis shows that the storm track intersected a persistent marine heatwave (MHW) characterized by a deep thermal anomaly extending to approximately 150 m. This elevated heat content formed a strong stratification barrier at the base of the mixed layer (~32 m) that prevented the typical entrainment of cold thermocline water. Instead, storm-induced turbulence mixed warm subsurface water upward to effectively mitigate the negative cold-wake feedback. This process sustained extreme upward enthalpy fluxes exceeding 210 W m⁻² and generated a regime of thermodynamic compensation that enabled the storm to maintain its structure despite an unfavorable atmospheric environment with moderate-to-strong vertical wind shear (15–20 m s⁻¹). These results indicate that the three-dimensional ocean structure acts as a more reliable predictor of typhoon intensity than SST alone in regions affected by MHWs. As MHWs deepen under climate warming, this cold-wake mitigation mechanism is likely to become a significant factor influencing future high-latitude cyclone hazards. Full article

Seabed topography exerts a profound influence on underwater acoustic propagation, and the coupling effect between bottom acoustic properties and the source–receiver geometric configuration remains insufficiently quantified, particularly in seamount shielding scenarios. To address this gap, in this study, the BELLHOP ray model was integrated with Earth topography 1 (ETOPO1) topographic data and Hybrid Coordinate Ocean Model (HYCOM) hydrological data for seamounts east of Taiwan. Transmission loss (TL) of 300 Hz sound waves was simulated across four typical bottom types (rock, coarse sand, silt, and clay) under varying source depths (50–1000 m) and receiver depths (50–500 m). The maximum sonar detection range was delineated using an 80 dB TL threshold as the criterion for effective detection. The key findings reveal that the bottom properties are the primary factors that reduce the detection range: the maximum detection range over rock bottom exceeds that over clay by more than 8-fold. Notably, a shallow source–shallow receiver configuration mitigates the acoustic shadow effect induced by seamounts, whereas deep receiver deployment (≥500 m) diminishes the discriminative impact of bottom types on the propagation behavior. Furthermore, a segmented empirical prediction formula was established, which reconciles both the physical mechanisms (e.g., bottom reflection-absorption and seamount shielding) and engineering applicability. This formula provides a robust theoretical basis for evaluating sonar performance in complex seabed topography settings, thereby facilitating optimized underwater detection strategies in seamount-dominated marine environments. Full article

The present study has applied a probabilistic oil spill modeling framework to assess the potential risks associated with offshore oil spills in the Foz do Amazonas sedimentary basin, a region of exceptional ecological importance and increasing geopolitical and socio-environmental relevance. By integrating a large ensemble of simulations with validated hydrodynamic, atmospheric and wave-driven forcings, the analysis of said simulations has provided a robust and seasonally resolved assessment of oil drift and beaching patterns along the Guianas and the Brazilian Equatorial Margin. The model has presented a total of 47,500 simulations performed on 95 drilling sites located across the basin, using the Lagrangian Spill, Transport and Fate Model (STFM) and incorporating a six-year oceanographic and meteorological variability. The simulations have included ocean current fields provided by HYCOM, wind forcing provided by GFS and Stokes drift provided by ERA5. Model performance has been evaluated by comparisons with satellite-tracked surface drifters using normalized cumulative Lagrangian separation metrics and skill scores. Mean skill scores have reached 0.98 after 5 days and 0.95 after 10 days, remaining above 0.85 up to 20 days, indicating high reliability for short to intermediate forecasting horizons and suitability for probabilistic applications. Probabilistic simulations have revealed a pronounced seasonal effect, governed by the annual migration of the Intertropical Convergence Zone (ITCZ). During the JFMA period, shoreline impact probabilities have exceeded 40–50% along extensive portions of the French Guiana and Amapá state (Brazil) coastlines, with oil reaching the coast typically within 10–20 days. In contrast, during the JASO period, beaching probabilities have decreased to below 15%, accompanied by a substantial reduction in impact along the coastline and higher variability in arrival times. Although coastal exposure has been markedly reduced during JASO, a residual probability of approximately 2% of oil intrusion into the Amazonas river mouth has persisted. Full article

The retrieval of radial ocean surface current from Synthetic Aperture Radar (SAR) data is important for ocean current research and effective ocean remote sensing. Existing algorithms, primarily based on the Average Cross-Correlation Coefficient (ACCC) method, suffer from drawbacks, including low Doppler frequency-shift estimation accuracy and susceptibility to azimuth ambiguity, hindering accurate measurements. To address these limitations, this paper proposes a method for high-resolution radial current velocity estimation. This approach employs Maximum A Posteriori (MAP) estimation based on signal modeling of the local Doppler power spectrum. This method achieves better Doppler frequency shift estimation accuracy than ACCC and effectively mitigates the azimuth ambiguity, substantially enhancing the precision of radial ocean surface velocity estimation. The algorithm was validated using raw Sentinel-1 Strip-map mode real data and HYCOM data acquired over the Seychelles Islands on 23 April 2023, and the central Indian Ocean (south of the equator) on 20 May 2023. Compared with the Sentinel-1 Level 2 ocean Surface Radial Velocity (RVL) product, the method demonstrates the improvements in both spatial resolution and retrieval accuracy. Specifically, the quantitative comparison with HYCOM data showed a reduction in Root Mean Square Error (RMSE) of up to 34.3% and an improvement in Mean Absolute Error (MAE) of up to 32.1%. Moreover, its ability to suppress the azimuth Doppler ambiguity is demonstrated in the real-data experiment. Full article

Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article

A cyclonic ocean eddy (COE) exhibited an extraordinarily prolonged response to sequential typhoons Hinnamnor (1 September 2022) and Muifa (11 September 2022), reaching its peak strength 20 days post-typhoon (1 October 2022), almost double the typical 7–14-day latency for mesoscale eddies. In this study, we use a functional analysis apparatus, namely the multiscale window transform (MWT) and the MWT-based theory of canonical transfer and multiscale energetics analysis, to investigate the dynamics underlying this phenomenon. The original fields, which are obtained from HYCOM reanalysis data, are initially decomposed into three parts in three different scale windows, respectively, with the eddy-scale window (or COE window) lying in between. By examining the evolution of eddy kinetic energy (EKE), the response can be divided into two stages. From the energetic diagnosis, the COE’s response is not only visible at the surface but was even strengthened through interactions between the subsurface and surface, with vertical transport playing a crucial role. This response can be categorized into two stages: The energetics of the long-delayed response is in the first stage due to the storage of the eddy-scale available potential energy (EAPE) from the high-frequency scale window, where the typhoon injects energy through an inverse canonical transfer. The resulting EAPE is transported downward to the sub-surface. In the second stage, the subsurface EKE is carried upward to the surface via pressure work, leading to an explosive growth of the COE. These findings illuminate the significance of subsurface–surface interactions in modulating long-delayed eddy responses. Full article

The evolution of the three-dimensional thermohaline structure of mesoscale eddies is crucial for assessing energy and mass transfer during their long-distance propagation in the ocean. However, the understanding and quantitative evaluation of the role that mesoscale eddies play in driving variations of thermohaline in the deep sea remains constrained due to the scarcity of in situ observations, particularly in marginal seas such as the South China Sea (SCS). In this study, we propose an artificial intelligence (AI)–physics-based deep learning model that integrates satellite measurements and Argo data from 2003 to 2021 to reconstruct the three-dimensional thermohaline structure of mesoscale eddies in the SCS. Besides utilizing basic sea surface hydrodynamic parameters obtained from satellite data for model training, an additional branch incorporating eddy physical parameters was introduced to optimize the model. The results demonstrate that the model effectively reconstructs thermohaline properties within mesoscale eddies in the SCS. Compared to Argo observations, the average root mean square error (RMSE) for temperature (salinity) within anticyclonic eddies was 0.34 °C (0.036 PSU), while it was 0.36 °C (0.032 PSU) within cyclonic eddies in the upper 1500 m. Further validation using high-resolution glider observations tracking an anticyclonic eddy originating in the SCS confirms the model’s efficiency, achieving an RMSE of 0.2962 °C (0.0138 PSU) for temperature (salinity). The accuracy of our proposed model significantly outperforms that of HYCOM and GLORYS simulations, with the RMSE reduced by 40% to 60%. The distinctive capabilities provide valuable insights into understanding the fine-scale structures of mesoscale eddies, especially in regions with limited in situ data. Full article

In this study, a novel algorithm to retrieve the current speed along the range direction under extreme sea states is developed from C-band synthetic aperture radar imagery. To this aim, a Sentinel-1 (S-1) dual-polarized synthetic aperture radar (SAR) dataset consisting of 2300 images is collected during 200 tropical cyclones (TCs). The dataset is complemented with collocated wave simulations from the Wavewatch-III (WW3) model and reanalysis currents from the HYbrid Coordinate Ocean Model (HYCOM). The corresponding TC winds are officially released by IFRMER, while the Stokes drift following the wave propagation direction is estimated from the waves simulated by WW3. In this study, first the dependence of wind, Stokes drift, and range current on the Doppler centroid anomaly is investigated, and then the extreme gradient boosting (XGBoost) machine learning model is trained on 87% of the S-1 dataset for range current retrieval purposes. The rest of the dataset is used for testing the retrieval algorithm, showing a root mean square error (RMSE) and a correlation coefficient (r) of 0.11 m/s and 0.97, respectively, with the HYCOM outputs. A validation against measurements collected from two high-frequency (HF) phased-array radars is also performed, resulting in an RMSE and r of 0.12 m/s and 0.75, respectively. Those validation results are better than the 0.22 m/s RMSE and 0.28 r achieved by the empirical CDOP model. Hence, the experimental results confirm the soundness of the XGBoost, exhibiting a certain improvement over the empirical model. Full article

Ocean satellite data are often impeded by intrinsic limitations in resolution and accuracy. However, conventional data reconstruction approaches encounter substantial challenges when facing the nonlinear oceanic system and high-resolution fusion of variables. This research presents a Discrete Satellite Gridding Neural Network (DSGNN), a new machine learning method that processes satellite data within a discrete grid framework. By transforming the positional information of grid elements into a standardized vector format, the DSGNN significantly elevates the accuracy and resolution of data fusion through a neural network model. This method’s innovative aspect lies in its discretization and fusion technique, which not only enhances the spatial resolution of oceanic data but also, through the integration of multi-element datasets, better reflects the true physical state of the ocean. A comprehensive analysis of the reconstructed datasets indicates the DSGNN’s consistency and reliability across different seasons and oceanic regions, especially in its adept handling of complex nonlinear interactions and small-scale oceanic features. The DSGNN method has demonstrated exceptional competence in reconstructing global ocean datasets, maintaining small error variance, and achieving high congruence with in situ observations, which is almost equivalent to 1/12° hybrid coordinate ocean model (HYCOM) data. This study offers a novel and potent strategy for the high-resolution reconstruction and fusion of ocean satellite datasets. Full article

This study addresses the issue of vortex current fields near the islands and reefs in China’s straits, which pose significant challenges to engineering construction and navigation safety in the surrounding waters. To monitor these vortex fields, the study proposes an innovative method utilizing acoustic signals. The study utilizes the numerical simulation and phase feature extraction of acoustic signals in the vortex current field, based on ray acoustic theory and time-reversal mirror technology. The study successfully monitored the central position of the vortex core and characteristic radius of the vortex current field near Barley Straw Reef using HYCOM data for the first time. Furthermore, the performance of the method was analyzed under different acoustic phase perturbations and signal-to-noise ratios. The numerical simulation results demonstrate that the acoustic method is effective in monitoring near-shore vortex fields, and the time-reversal mirror technique is useful in extracting phase difference information from acoustic signals generated by vortex currents. Full article

Whale mortality and strandings have increased in recent years, with deceased whales often brought to landfill. However, the disposal of whale remains offshore holds significant ecological importance and can be a culturally and ethically sensitive approach. Moreover, offshore disposal mitigates potential risks associated with onshore whale remains disposal, such as the spread of diseases and the logistical challenges of managing large carcasses. A challenge with offshore disposal is defining the best release location to avoid the remains drifting ashore or into shipping channels. Here we compared the drift model outputs using a drift forecast model (SARMAP) for a 14 m whale carcass that was moved offshore in southeast Queensland, Australia, and fitted with a satellite tracker over an observation period of 150 h until positioning signal ceased. The modelling was conducted using different ocean products (BLUElink, HYCOM, and Copernicus), which showed a good agreement with the tracked whale carcass, albeit with changing wind conditions and contrasting currents flowing northward along the coast and, further offshore, flowing south. This case study illustrated that wind was the foremost driver of carcass drift due to the surface area above the water surface. The drift forecast simulations allowed for a reliable prediction of the floating whale drift that can assist authorities with decision making. Offshore disposal of whale carcasses is a sustainable practice but requires good planning and scientific assessment. Full article

During the period of 2018–2022, there were six named Loop Current Eddy (LCE) shedding events in the central Gulf of Mexico (GoM). LCEs form when a large anticyclonic eddy (AE) separates from the main Loop Current (LC) and propagates westward. In doing so, each LCE traps and advects warmer, saltier waters with lower Chlorophyll-a (Chl-a) concentrations than the surrounding Gulf waters. This difference in water mass permits the study of the effectiveness of using Chl-a from satellite-derived ocean color to identify LCEs in the GoM. In this work, we apply an eddy-tracking algorithm to Chl-a to detect LCEs, which we have validated against the traditional sea surface height-(SSH) based eddy-tracking approach with three datasets. We apply a closed-contour eddy-tracking algorithm to the SSH of two model products (HYbrid Coordination Ocean Model; HYCOM and Nucleus for European Modelling of the Ocean; NEMO) and absolute dynamic topography (ADT) from altimetry, as well as satellite-derived Chl-a data to identify the six named LCEs from 2018 to 2022. We find that Chl-a best characterizes LCEs in the summertime due to a basin-wide increase in the horizontal gradient of Chl-a, which permits a more clearly defined eddy edge. This study demonstrates that Chl-a can be effectively used to identify and track LC and LCEs in the GoM, serving as a promising source of information for regional data assimilative models. Full article

This study investigates the effects of the gulf stream (GS) on sea-level oscillations across various time scales and assesses the performance of a coastal and estuarine model nested within a global model in simulating these variations. It aims to improve boundary conditions to simulate sea-level oscillations more accurately by considering the influence of GS flow. An inverse correlation is observed between observed sea-level oscillation and GS flow, which becomes more pronounced over longer time scales. Using Delft3D, a high-resolution coastal and estuarine model is developed to simulate circulation dynamics in the central Indian River Lagoon (IRL), FL, and adjacent coastal areas on the Florida east coast. The model is nested into the HYCOM (Hybrid Coordinate Ocean Model), and meteorological forcings are derived from the NARR (North American Regional Reanalysis) model. The model demonstrates satisfactory performance across key parameters, including tide, salinity, water temperature, and currents. However, there remains a noticeable difference between the modeled and observed data. To address this, the model is executed with modified flow boundary conditions at eastern boundary nodes, integrating HYCOM tide, and observing low-frequency sea-level variations. The implementation of the new boundary conditions results in an improved simulation of sea-level oscillations. This study presents the conceptual framework and detailed methodologies employed in the creation of a high-resolution model tailored for estuarine and coastal areas nested into global models capable of satisfactorily simulating sea-level oscillations even when the global model does not represent GS effects. Full article

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