Search Results (26)

Accurate forecasting of significant wave height (WVHT) is essential for marine disaster prevention, offshore operations, and coastal management. However, WVHT time series typically exhibit strong nonlinearity and non-stationarity, which pose significant challenges for reliable prediction, especially under complex sea conditions. To address these issues, a hybrid forecasting framework based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the iTransformer model is proposed for the North Atlantic Ocean. In the proposed method, the original WVHT time series is first decomposed into multiple intrinsic mode functions using CEEMDAN to alleviate non-stationarity and reveal multi-scale characteristics. Subsequently, the iTransformer model is employed to capture the temporal dependencies of each decomposed component, and the final prediction is obtained through reconstruction. Experiments are conducted using multi-variable buoy observations from the North Atlantic, incorporating meteorological and oceanographic factors. Results demonstrate that the proposed CEEMDAN-iTransformer model significantly improves forecasting accuracy and stability compared with baseline models across multiple prediction horizons. The framework shows strong capability in handling complex wave dynamics and provides an effective solution for high-precision WVHT forecasting. Full article

With the rapid development and widespread application of multibeam echo-sounding systems, large-scale and high-resolution seafloor topography can be efficiently acquired, enabling precise mapping of seabed terrain. However, due to complex oceanographic conditions, instrumental noise, and acoustic interferences, the acquired multibeam data often contain outliers that deviate from the true seafloor surface. These outliers can distort the representation of seafloor topography, adversely affecting subsequent geological analysis and engineering applications. To address this issue, a hybrid outlier detection method combining CUBE filtering with the Isolation Forest (IForest) algorithm, termed CUBE-IForest, is proposed. The method first employs CUBE filtering to remove gross outliers based on local uncertainty estimation, followed by the application of IForest to identify subtle anomalies in the refined data, achieving hierarchical detection of outliers. Experimental results based on in situ multibeam bathymetric data from the northeastern Pacific demonstrate that compared with traditional filtering methods the CUBE-IForest approach significantly improves detection accuracy and reduces both false positive and false negative rates by approximately 30%, confirming its efficiency and reliability in seafloor mapping and analysis. 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

Underwater towed systems (UTSs) are widely used in underwater exploration and oceanographic data acquisition. However, the heave motion information of the towing ship is usually affected by the measurement transmitting delay, sensor noise and surface waves, which will result in uncontrolled depth variation of the towed vehicle, so as to adversely affect the monitoring performance and mechanical robustness of the UTS. To resolve this problem, a heave motion prediction approach based on sparse Bayesian learning (SBL) incorporated with empirical mode decomposition (EMD) for the UTS is proposed in this paper. With the proposed approach, a heave motion model of the towing ship with random waves is firstly developed based on strip theory. Meanwhile, the EMD is employed to eliminate the high-frequency noise of the measurement data to restore low-frequency towing ship motion. And then, the SBL is utilized to train the weight parameters in the built model to predict the heave motion, which not only reconstruct the heave motion from non-stationary sensor signals with noise but also prevent overfitting. Furthermore, the depth compensation of the towed vehicle is then performed using the predicted heave motion. Finally, experimental results demonstrate that the proposed EMD-SBL method significantly improves both the prediction accuracy and model adaptability under various sea conditions, and it also guarantees that the maximum prediction depth error of the heave motion does not exceed 1 cm. Full article

In the field of marine science, the measurement of ocean currents is essential for the conduction of marine surveys, the understanding of ocean dynamics, and also the interpretation of oceanic climate change. The expendable current profiler (XCP) is an equipment employed in oceanographic research, capable of providing detailed profiles of oceanic flow by measuring the velocity and direction of currents at various depths when it falls from surface to bottom. The performance of the XCP largely relies upon the precision and stability of its electrodes. Silver/silver chloride (Ag-AgCl) electrodes, renowned for their superior electrochemical stability and low-noise characteristics, are frequently selected as the electrode material for XCP. This paper focuses on four pairs of Ag-AgCl electrodes, designated as Electrodes I, II, III, and IV, where Electrodes I and II are custom-made from a company, Electrode III is a self-developed electrode, and Electrode IV is an improved self-developed electrode. A detailed description of the fabrication process of Electrode III is provided in this study. Multiple experiments were conducted on these four pairs of electrodes to investigate their self-noise, power spectral density, and frequency response under various experimental conditions. The experimental results indicate that, in the absence of an external electric field, the power spectral density at 1 Hz for Electrodes I, II, and III is in the tens of nanovolts per square root hertz (nV/√Hz) of magnitude. The performance of Electrode IV is superior, with a power spectral density of only a few nV/√Hz at 1 Hz when without an external electric field, and its frequency response within the 13–18 Hz range, which is of utmost concern to XCP, is also fundamentally stable, meeting the requirements for sea trial utilization of XCP. Full article

Underwater gliders are extensively employed in oceanographic observation and detection. The structural characteristics of thin-wall shells are more susceptible to vibrations from internal mechanical components; this noise emission becomes more complex with the presence of water surfaces. The finite element method (FEM) is introduced to discuss the dynamic performance of cylindrical shells with different lengths. The acoustic-structure coupling, together with the effect of the water surface, is validated by comparisons with experimental or analytical solutions under three cases: half-filled, half-submerged, and partially submerged in fluid. Compared to the verification result, the relative error of the eigenfrequency derived from the numerical result is less than 3%, and then the mesh division and boundary conditions are adjusted to calculate the vibroacoustic response of a disc-type glider. During its water entry process, there are six distinct bright curves in frequency–depth spectra of sound pressure radiated from a partially immersed disc-type glider. The first curve is continuous, while the remaining five curves display discontinuities around a region where the geometric curvature changes gradually. As the submerged depth increases, this causes a shift in the resonance frequencies, evidenced by the curves transitioning from higher to lower frequencies. Full article

Monitoring the oceans and establishing a global ocean observing system is a task of paramount importance for topics as diverse as the study of climate change, the management of marine environments, and the safety of coastal areas and marine traffic. These systems must be based on long-term observations that allow the correct modeling of the behavior of the seas and the proper environmental management of them. Despite the logical present trend toward automation, in situ measurements from oceanographic vessels are still needed at present, especially when dealing with biogeochemical variables or when seeking information from the subsurface or deep layers of the sea. Long-term measurements by oceanographic vessels can be carried out at one single fixed oceanographic station with a high sampling frequency (typically once a month) or across a grid of stations. In the latter case a larger geographical area is usually covered, but the cost is a reduction of sampling frequency. The question that arises is: what objectives can be achieved, and what questions can be answered according to the sampling frequency and the spatial coverage of the monitoring program? In this work, we analyze the influence of the sampling frequency on the capacity of observing programs to capture the temporal variability of ocean variables at different time scales and to estimate average seasonal cycles and long-term trends. This analysis is conducted through the study of sea surface chlorophyll concentrations in the Western Mediterranean. The trade-off between sampling frequency and spatial coverage is addressed. For this purpose, a monitoring program in the Spanish Mediterranean waters is used as a case study. We show that monthly and fortnightly intervals are the best sampling frequencies for describing the temporal variability of ocean variables as well as their average seasonal cycles. Quarterly sampling could also be appropriate for estimating such seasonal cycles. Surprisingly, the limitations of these low frequency samplings do not arise from the high frequency variability of ocean variables but from the shape of the seasonal cycles. Both high and low frequency sampling designs could be suitable for detecting long-linear trends, depending on the variance of the noise and that of the trend. In the case of quarterly sampling, we show that some statistics improve with the length of the time series, whereas others do not. Although some results may be related to the dynamics of this region, the results are generally applicable to any other marine monitoring system. Full article

Investigating underwater environments using Multi-Beam Echo Sounder (MBES) point cloud registration technology is a critical yet underdeveloped area in oceanographic research. This paper presents a fast, deterministic Branch-and-Bound (BnB) method with four degrees of freedom, which combines Inertial Measurement Unit (IMU) data with MBES point cloud data for precise registration. Given the prevalence of outliers and noise in underwater acoustic measurements, the BnB method is employed to provide globally deterministic solutions. However, due to the exponential convergence speed of the BnB method with respect to the dimensionality of the solution space, searching within a six-degree-of-freedom parameter space (three rotational and three translational degrees of freedom) can be extremely time-consuming. To this end, the Z-axis of the point cloud is aligned with the gravitational direction of the IMU, reducing the rotational degrees of freedom from three to one, specifically concerning yaw. Additionally, an outlier exclusion strategy is introduced to eliminate mismatches, significantly reducing the number of key-point correspondences and thereby improving registration efficiency. Experiments conducted on both public and real-world lake datasets demonstrate that the proposed method achieves a favorable balance between speed and accuracy, outperforming other tested methods and meeting the demands of contemporary research. Full article

With the accumulation of synthetic aperture radar (SAR) altimetry data and advancements in retracking algorithms, the improved along-track spatial resolution and signal-to-noise ratio have significantly enhanced the availability and precision of sea surface height (SSH) measurements, particularly in challenging environments such as coastal areas, ocean currents, and polar regions. These improvements have refined the accuracy and reliability of mean sea surface (MSS) models, which in turn have enhanced the precision of mean dynamic topography (MDT) and geostrophic current models. However, in-depth research is required to quantify the specific contributions of SAR altimetry to these critical regions and their impacts on the MSS, MDT, and geostrophic currents. Given that DTU21MSS (Technical University of Denmark MSS 2021) incorporates a substantial amount of SAR altimetry data, this study utilized independent Sentinel-3A altimetric observations to evaluate the signal improvements of DTU21MSS compared with DTU15MSS, with a focus on its performance in polar, coastal, and current regions. In addition, a least-squares-based approach was employed to assess the impact of the improved MSS model on the deduced MDT and geostrophic current signals. The numerical results revealed that DTU21MSS achieved an accuracy improvement of ~8% within 20 km offshore compared with DTU15MSS. In the polar regions within 100 km offshore, DTU21MSS exhibited a maximum signal enhancement of ~0.1 m, with overall improvements of 10–20%. The DTU21MSS-derived MDT solution demonstrates better consistency with validation data, reducing the standard deviation of misfits from 0.058 m to 0.054 m. Signal enhancements of maximumly 0.1 m were observed in the polar regions and the Mediterranean/Red Sea. Furthermore, improvements in the MSS and its error information could directly enhance the deduced MDT models, highlighting its foundational role in precise oceanographic modeling. Full article

Direct numerical simulation (DNS) has been employed with success in a variety of oceanographic applications, particularly for investigating the internal dynamics of Kelvin–Helmholtz (KH) billows. However, it is difficult to relate these results directly with observations of ocean turbulence due to the significant scale differences involved (ocean shear layers are typically on the order of tens to hundreds of meters in thickness, compared to DNS studies, with layers on the order of one to tens of centimeters). As efforts continue to inform our understanding of geophysical-scale turbulence by extrapolating DNS results, it is important to understand the impact of model setup and initial conditions on the resulting turbulent quantities. Given that geophysical-scale measurements, whether through microstructures or other techniques, can only provide estimates of averaged TKE quantities (e.g., TKE dissipation or buoyancy flux), it may be necessary to compare mean turbulent quantities derived from DNS (i.e., across one or more complete billow evolutions) with ocean measurements. In this study, we analyze the effect of domain length and initial velocity noise on resulting turbulent quantities. Domain length is important, as dimensions that are not integer multiples of the natural KH billow wavelength may compress or stretch the billows and impact their energetics. The addition of random noise in the initial velocity field is often used to trigger turbulence and suppress secondary instabilities; however, the impact of noise on the resulting turbulent energetics is largely unknown. In this study, we conclude that domain lengths on the order of 1.5 times the natural wavelength or less can affect the resulting turbulent energetics by a factor of two or more. We also conclude that increasing the amplitude of random initial velocity noise decreases the resulting turbulent energetics, but that different realizations of the random noise field may have an even greater impact than amplitude. These results should be considered when designing a DNS experiment. Full article

To support the application of ocean surface radiance data from the ultraviolet imager (UVI) payload of the HY-1C oceanographic satellite and to improve the quantification level of ocean observation technology, the authenticity check study of ocean surface radiance data from the UVI payload was conducted to provide a basis for the quantification application of data products. The UVI load makes up for the lack of detection capabilities of modern ocean remote sensing satellites in the ultraviolet band. The UVDRAMS (Ultra-Violet Dual-band RadiAnce Measurement System) was used to verify the surface radiance data collected at 16 stations in the study area and the pupil radiance data collected by the UVI payload to establish an effective radiative transfer model and to identify the model parameters using the seeker optimization algorithm (SOA). The study of the UVDRAMS measurement system based on the SOA algorithm and the validation of the sea surface radiance of the UVI payload of the HY-1C satellite shows that 97.2% of the incident pupil radiance of the UVI payload is contributed by the atmospheric reflected radiance, and only 2.8% is from the real radiation of the water surface, while the high signal-to-noise ratio of the UVI payload of the HY-1C ocean satellite can effectively distinguish the reflectance of the water body. The high signal-to-noise ratio of the UVI payload of the HY-1C ocean satellite can effectively distinguish the amount of standard deviation in the on-satellite radiation variation, which meets the observation requirements and provides a new way of thinking and technology for further quantitative research in the future. Full article

The temporal variation of received clutter and noise at a pair of oceanographic high frequency radars (HFR) operating near the geomagnetic equator in the Republic of Palau is investigated. Oceanographic HFRs process range-gated Doppler spectra from groundwave signals that are backscattered from the ocean’s surface to derive maps of ocean currents. The range performance of the radars exhibited a regular diurnal signal which is determined to be a result of both ionospheric clutter and noise. The increased Clutter plus Noise Floor (C+NF) decreases the Signal to Clutter plus Noise Ratio (SCNR) which, in turn, reduces the range and quality of ocean surface current measurement. Determining the nature and origin of this degradation is critical to QA/QC of existing HFR deployments as well as performance predictions of future installations. Nighttime impacts are most severe and negatively affect ocean surface current measurements as low SCNR is found to extend across the Doppler spectra at all ranges, challenging the ability of HFR to map the ocean surface current. Daytime degradation is less severe and presents itself in a way consistent with independent observations of ionospheric clutter, specifically the diurnal temporal pattern and range where the C+NF features occur. A timeseries analysis of SCNR and C+NF is pursued to understand this relationship using received range-dependent Doppler spectra and C+NF features using image segmentation techniques. Clutter plus noise features are classified into daytime, nighttime, and no-noise feature types. The diurnal structure and variability of these features are examined, and the occurrences of each feature type are calculated. The occurrences are compared with space weather indices including a measure of geomagnetic activity, namely the EE (Equatorial Electro Jet) index (determined from magnetometers measuring the earth’s magnetic field), as well as solar impacts using the F10.7 solar radio clutter index to assess the relationship of ionospheric conditions with HFR ocean surface current measurement. Full article

X-band radars are in growing use for various oceanographic purposes, providing spatial real-time information about sea state parameters, surface elevations, currents, and bathymetry. Therefore, it is very appealing to use such systems as operational aids to harbour management. In an installation of such a remote sensing system in Haifa Port, consistent radially aligned spikes of brightness randomly distributed with respect to azimuth were identified. These streak noise patterns were found to be interfering with the common approach of oceanographic analysis. Harbour areas are regularly frequented with additional electromagnetic transmissions from other ship and land-based radars, which may serve as a source of such interference. A new approach is proposed for the filtering of such undesirable interference patterns from the X-band radar images. It was verified with comparison to in-situ measurements of a nearby wave buoy. Regardless of the actual source of the corresponding pseudo-wave energy, it was found to be crucial to apply such filtration in order to improve the performance of the standard oceanographic parameter retrieval algorithm. This results in better estimation of the mean sea state parameters towards lower values of the significant wave height. For the commercial WaMoSII system this enhancement was clearly apparent in the improvement of the built-in quality control criteria marks. The developed prepossessing procedure improves the robustness of the directional spectra estimation practically eliminating pseudo-wave energy components. It also extends the system’s capability to measure storm events earlier on, a fact that is of high importance for harbour operational decision making. Full article

Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD. Full article

Understanding the environmental effects of marine energy (ME) devices is fundamental for their sustainable development and efficient regulation. However, measuring effects is difficult given the limited number of operational devices currently deployed. Numerical modeling is a powerful tool for estimating environmental effects and quantifying risks. It is most effective when informed by empirical data and coordinated with the development and implementation of monitoring protocols. We reviewed modeling techniques and information needs for six environmental stressor–receptor interactions related to ME: changes in oceanographic systems, underwater noise, electromagnetic fields (EMFs), changes in habitat, collision risk, and displacement of marine animals. This review considers the effects of tidal, wave, and ocean current energy converters. We summarized the availability and maturity of models for each stressor–receptor interaction and provide examples involving ME devices when available and analogous examples otherwise. Models for oceanographic systems and underwater noise were widely available and sometimes applied to ME, but need validation in real-world settings. Many methods are available for modeling habitat change and displacement of marine animals, but few examples related to ME exist. Models of collision risk and species response to EMFs are still in stages of theory development and need more observational data, particularly about species behavior near devices, to be effective. We conclude by synthesizing model status, commonalities between models, and overlapping monitoring needs that can be exploited to develop a coordinated and efficient set of protocols for predicting and monitoring the environmental effects of ME. Full article