Search Results (47)

The Haiyang-2B (HY-2B) satellite, launched on 25 October 2018, carries both active and passive microwave sensors, including a scanning microwave Radiometer (SMR), to deliver high-precision, all-weather global observations. Sea surface temperature (SST) is among its key products. We evaluated the HY-2B SMR Level-4A (L4A) SST (25 km resolution) over the North Pacific (0–60°N, 120°E–100°W) for the period 1 October 2023 to 31 March 2025 using the extended triple collocation (ETC) and dual-pairing methods. These comparisons were made against the Remote Sensing System (RSS) microwave and infrared (MWIR) fused SST product and the National Oceanic and Atmospheric Administration (NOAA) in situ SST Quality Monitor (iQuam) observations. Relative to iQuam, HY-2B SST has a mean bias of –0.002 °C and a root mean square error (RMSE) of 0.279 °C. Compared to the MWIR product, the mean bias is 0.009 °C with an RMSE of 0.270 °C, indicating high accuracy. ETC yields an equivalent standard deviation (ESD) of 0.163 °C for HY-2B, compared to 0.157 °C for iQuam and 0.196 °C for MWIR. Platform-specific ESDs are lowest for drifters (0.124 °C) and tropical moored buoys (0.088 °C) and highest for ship and coastal moored buoys (both 0.238 °C). Both the HY-2B and MWIR products exhibit increasing ESD and RMSE toward higher latitudes, primarily driven by stronger winds, higher columnar water vapor, and elevated cloud liquid water. Overall, HY-2B SST performs reliably under most conditions, but incurs larger errors under extreme environments. This analysis provides a robust basis for its application and future refinement. Full article

The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high-resolution observations of sea-surface height. However, their direct use in ocean circulation studies is complicated by the presence of small-scale unbalanced motion signals and instrumental noise, which hinder accurate estimation of geostrophic velocities. To address these limitations, we developed an adaptive convolutional neural network (CNN)-based filtering technique that refines SWOT-derived sea-level observations. The network includes multi-head attention layers to exploit information on concurrent wind fields and standard altimetry interpolation errors. We train the model with a custom loss function that accounts for the differences between geostrophic velocities computed from SWOT sea-surface topography and simultaneous in-situ drifter velocities. We compare our method to existing filtering techniques, including a U-Net-based model and a variational noise-reduction filter. Our adaptive-filtering CNN produces accurate velocity estimates while preserving small-scale features and achieving a substantial noise reduction in the spectral domain. By combining satellite and in-situ data with machine learning, this work demonstrates the potential of an adaptive CNN-based filtering approach to enhance the accuracy and reliability of SWOT-derived sea-level and velocity estimates, providing a valuable tool for global oceanographic applications. 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

Eddy identification and tracking are essential for understanding ocean dynamics. This study employed the elliptical Gaussian function (EGF) simulations and the py-eddy-tracker (PET) algorithm, validated by Surface Velocity Program (SVP) drifter data, to track eddies in the western North Pacific Ocean. The PET method effectively identified large- and mesoscale eddies but struggled with submesoscale features, indicating areas for improvement. Simulated satellite altimetry by EGF, mirroring Surface Water and Ocean Topography (SWOT)’s high-resolution observations, confirmed PET’s capability in processing fine-scale data, though accuracy declined for submesoscale eddies. Over 22 years, 1,188,649 eddies were identified, mainly concentrated east of Taiwan. Temporal analysis showed interannual variability, more cyclonic than anticyclonic eddies, and a seasonal peak in spring, likely influenced by marine conditions. Short-lived eddies were uniformly distributed, while long-lived ones followed major currents, validating PET’s robustness with SVP drifters. The launch of the SWOT satellite in 2022 has enhanced fine-scale ocean studies, enabling the detection of submesoscale eddies previously unresolved by conventional altimetry. SWOT observations reveal intricate eddy structures, including small cyclonic features in the northwestern Pacific, demonstrating its potential for improving eddy tracking. Future work should refine the PET algorithm for SWOT’s swath altimetry, addressing data gaps and unclosed contours. Integrating SWOT with in situ drifters, numerical models, and machine learning will further enhance eddy classification, benefiting ocean circulation studies and climate modeling. Full article

High-frequency (HF) ocean radars have become essential tools for monitoring surface currents, offering real-time, wide-area coverage with cost-effectiveness. This study compares the compact CODAR system (MABT, 13 MHz) and the square-array phased-array radar (KNTN, 8 MHz) deployed at Cape Maobitou, Taiwan. Radial velocity measurements were evaluated against data from the Global Drifter Program (GDP), and a quality control (QC) mechanism was applied to improve the data’s reliability. The results indicated that KNTN provides broader spatial coverage, whereas MABT demonstrates higher precision in radial velocity measurements. Baseline velocity comparisons between MABT and KNTN revealed a correlation coefficient of 0.77 and a root-mean-square deviation (RMSD) of 0.23 m/s, which are consistent with typical values reported in previous radar performance evaluations. Drifter-based velocity comparisons showed an initial correlation of 0.49, with an RMSD of 0.43 m/s. In more stable oceanic regions, the correlation improved to 0.81, with the RMSD decreasing to 0.24 m/s. To clarify, this study does not include multiple environmental scenarios but focuses on cases where both radar systems operated simultaneously and where surface drifter data were available within the overlapping area. Comparisons are thus limited by these spatiotemporal conditions. Radar data may still be affected by environmental or human factors, such as ionospheric variations, interference from radio frequency management issues, or inappropriate parameter settings, which could reduce the accuracy and consistency of the observations. International ocean observing programs have developed quality management procedures to enhance data reliability. In Taiwan, the Taiwan Ocean Research Institute (TORI) has established a data quality management mechanism based on international standards for data filtering, noise reduction, and outlier detection, improving the accuracy and stability of radar-derived velocity measurements.To eliminate the effects caused by different center frequencies between MABT and KNTN, this study used the same algorithms and parameter settings as much as possible in all steps, from Doppler spectra processing to radial velocity calculation, ensuring the comparability of the data. This study highlights the strengths and limitations of compact and phased-array HF radar systems based on co-observed cases under consistent operational conditions. Future research should explore multi-frequency radar integration to enhance spatial coverage and measurement precision, improving real-time coastal current monitoring and operational forecasting. Full article

The Southeastern Tropical Indian Ocean (SETIO) forms part of the global ocean conveyor belt and thermohaline circulation that has a significant influence in controlling the global climate. This region of the ocean has very few observations using surface drifters, and this study presents, for the first time, paths of satellite tracked drifters released in the Timor Sea (123.3° E, 13.8° S). The drifter data were used to identify the ocean dynamics, forcing mechanisms and connectivity in the SETIO region. The data set has high temporal (~5 min) and spatial (~120 m) resolution and were collected over an 8-month period between 17 September 2020 and 25 May 2021. At the end of 250 days, drifters covered a region separated by ~8000 km (83–137° E, 4–21° S) and transited through several forcing mechanisms including semidiurnal and diurnal tides, submesoscale and mesoscale eddies, channel and headland flows, and inertial currents generated by tropical storms. Initially, all the drifters moved as a single cluster, and at 120° E longitude they entered a region of high eddy kinetic energy defined here as the ‘SETIO Mixing Zone’ (SMZ), and their movement was highly variable. All the drifters remained within the SMZ for periods between 3 and 5 months. Exiting the SMZ, drifters followed the major ocean currents in the system (either South Java or South Equatorial Current). Two of the drifters moved north through Lombok and Sape Straits and travelled to the east as far as Aru Islands. The results of this study have many implications for connectivity and transport of buoyant materials (e.g., plastics), as numerical models do not have the ability to resolve many of the fine-scale physical processes that contribute to surface transport and mixing in the ocean. Full article

The main goal of this study is to develop a method for finding the joint probability distribution of the state of the characteristics of the NEMO (Nucleus for European Modeling of the Ocean) ocean dynamics model with data assimilation using the Generalized Kalman filter (GKF) method developed earlier by the authors. The method for finding the joint distribution is based on the Karhunen–Loeve decomposition of the covariance function of the joint characteristics of the ocean. Numerical calculations of the dynamics of ocean currents, surface and subsurface ocean temperatures, and water salinity were carried out, both with and without assimilation of observational data from the Argo project drifters. The joint probability distributions of temperature and salinity at individual points in the world ocean at different depths were obtained and analyzed. The Atlantic Meridional Overturning Circulation (AMOC) system was also simulated using the NEMO model with and without data assimilation, and these results were compared to each other and analyzed. Full article

Drifter observations can provide high-resolution surface velocity data (Lagrangian data), commonly used to reconstruct Eulerian velocity fields. Gaussian Process Regression (GPR), a machine learning method based on Gaussian probability distributions, has been widely applied for velocity field interpolation due to its ability to provide interpolation error estimates and handle separations between particles. However, its evaluation has primarily relied on cross-validation, which approximates temporal and spatial correlations but does not fully capture their dependencies, limiting the comprehensiveness of performance assessment. Moreover, GPR has not been rigorously tested on model datasets with reference velocity fields to evaluate its overall accuracy and the reliability of the error estimate. This study addresses these gaps by (1) assessing the accuracy of GPR-reconstructed fields and their error estimates, (2) evaluating GPR performance across temporal and spatial dimensions, and (3) analyzing the relationship between training data density and prediction accuracy. Using six metrics, GPR predictions are evaluated on a double-gyre model and a Navy Coastal Ocean Model (NCOM). Results show that GPR achieves high accuracy, contingent on sampling density and velocity magnitude, while validating the posterior covariance matrix as a reliable error predictor. These findings provide critical insights into the strengths and limitations of GPR in oceanographic applications. Full article

Due to the geostrophic balance, horizontal divergence-free is often assumed when analyzing large-scale oceanic flows. However, the geostrophic balance is a leading-order approximation. We investigate the statistical feature of weak horizontal compressibility in the Gulf of Mexico by analyzing drifter data (the Grand LAgrangian Deployment (GLAD) experiment and the LAgrangian Submesoscale ExpeRiment (LASER)) based on the asymptotic probability density function of the angle between velocity and acceleration difference vectors in a strain-dominant model. The results reveal a notable divergence at scales between 10 km and 300 km, which is stronger in winter (LASER) than in summer (GLAD). We conjecture that the divergence is induced by wind stress with its curl parallel to the Earth’s rotation. Full article

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future. Full article

Wave-induced mixing can enhance vertical mixing in the upper ocean, facilitating the exchange of heat and momentum between the surface and deeper layers, thereby influencing ocean circulation and climate patterns. Building on previous research, this study proposes a wave-induced mixing parameterization scheme (referred to as EXP3) specifically designed for typhoon periods. This scheme was integrated into the fully coupled ocean–wave–atmosphere model COAWST and applied to analyze Typhoon In-Fa (2021) as a case study. The simulation results were validated against publicly available data, demonstrating a good overall match with observed phenomena. Subsequently, a comparative analysis was conducted between the EXP3 scheme, the previous scheme (EXP2) and the original model scheme (EXP1). Validation against Argo and Drifter buoy data revealed that both EXP2 and EXP3, which include wave-induced mixing effects, resulted in a decrease in the simulated mixed layer depth (MLD) and mixed layer temperature (MLT), with EXP3 showing closer alignment with the observed data. Compared to the other two experiments, EXP3 enhanced vertical motion in the ocean due to intensified wave-induced mixing, leading to increased upper-layer water divergence and upwelling, a decrease in sea surface temperature and accelerated rightward deflection of surface currents. This phenomenon not only altered the temperature structure of the ocean surface layer but also significantly impacted the regional ocean dynamics. Full article

High-frequency radars (HFRs) provide remote information on ocean surface velocity in extended coastal areas at high resolutions in space ($\mathcal{O}$(km)) and time ($\mathcal{O}$(h)). They directly produce radial velocities (in the radar antenna’s direction) combined to provide total vector velocities in areas covered by at least two radars. HFRs are a key element in ocean observing systems, with several important environmental applications. Here, we provide an assessment of the HFR-TirLig network in the NW Mediterranean Sea, including results from the gap-filling open-boundary modal analysis (OMA) using in situ velocity data from drifters. While the network consists of three radars, only two were active during the assessment experiment, so the test also includes an area where the radial velocities from only one radar system were available. The results, including several metrics, both Eulerian and Lagrangian, and configurations, show that the network performance is very satisfactory and compares well with the previous results in the literature in terms of both the radial and total combined vector velocities where the coverage is adequate, i.e., in the area sampled by two radars. Regarding the OMA results, not only do they perform equally well in the area sampled by the two radars but they also provide results in the area covered by one radar only. Even though obviously deteriorated with respect to the case of adequate coverage, the OMA results can still provide information regarding the velocity structure and speed as well as virtual trajectories, which can be of some use in practical applications. A general discussion on the implications of the results for the potential of remote sensing velocity estimation in terms of HFR network configurations and complementing gap-filling analysis is provided. Full article

Evaluating sea surface temperature (SST) products is essential before their application in marine environmental monitoring and related studies. SSTs from the in situ SST Quality Monitor (iQuam) system, Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Global Change Observation Mission 1st-Water, and the Microwave Radiation Imager (MWRI) aboard the Chinese Fengyun-3D satellite are intercompared utilizing extended triple collocation (ETC) and direct comparison methods. Additionally, error characteristic variations with respect to time, latitude, SST, sea surface wind speed, columnar water vapor, and columnar cloud liquid water are analyzed comprehensively. In contrast to the prevailing focus on SST validation accuracy, the random errors and the capability to detect SST variations are also evaluated in this study. The result of ETC analysis indicates that iQuam SST from ships exhibits the highest random error, above 0.83 °C, whereas tropical mooring SST displays the lowest random error, below 0.28 °C. SST measurements from drifters, tropical moorings, Argo floats, and high-resolution drifters, which possess random errors of less than 0.35 °C, are recommended for validating remotely sensed SST. The ability of iQuam, AMSR2, and MWRI to detect SST variations diminishes significantly in ocean areas between 0°N and 20°N latitude and latitudes greater than 50°N and 50°S. AMSR2 and iQuam demonstrate similar random errors and capabilities for detecting SST variations, whereas MWRI shows a high random error and weak capability. In comparison to iQuam SST, AMSR2 exhibits a root-mean-square error (RMSE) of about 0.51 °C with a bias of −0.05 °C, while MWRI shows an RMSE of about 1.26 °C with a bias of −0.14 °C. Full article

We present a review of in situ observations regarding the interactions between tropical cyclones and the ocean in the western North Pacific for the year 2023. A total of at least 13 tropical cyclones occurred during this period. According to the Japan Meteorological Agency, Typhoon Mawar recorded the yearly minimum pressure at 900 hPar. On average, each tropical cyclone captured 7.4 surface drifters and 25.2 Argo floats when the search radius is 300 km. During Guchol, the maximum in situ Lagrangian current reached 1.23 m/s, with sustained wind speeds of the tropical cyclone up to 31.7 m/s and a relative position of 174 km. Additionally, several Argo floats were active during tropical cyclones, with maximum sea surface temperature cooling reaching 0.66 °C. This annual review provides a comprehensive summary of the current state of in situ observations regarding tropical cyclone–ocean interaction. These findings serve as valuable references for both scientific research and operational forecasting. Full article

Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing. Full article