The influence of station-to-station line orientation on sea current speed observations using Coastal Acoustic Tomography (CAT) was quantitatively investigated. For this purpose, we conducted CAT experiments at five stations in Yeosu Bay, South Korea. Through these experiments, the sea current speeds were estimated along a total of six tomographic observation lines with different orientations, and the results were compared with current speeds measured simultaneously by an Acoustic Doppler Current Profiler (ADCP). The comparison showed that the concordance between tomography-estimated sea current speed and ADCP-measured sea current speed tended to decrease as the acute angle between the predominant tidal current direction in Yeosu Bay and a tomographic observation line increased. This tendency is interpreted as arising because the smaller the difference between the two one-way travel times obtained during tomographic observations, the greater the effect of the travel time measurement error whose magnitude is relatively direction-independent. This interpretation was supported by a simple numerical simulation. Furthermore, quantitative analysis of these simulation results indicated that a smaller acute angle between the predominant sea current direction in the survey area and a tomographic observation line enhances the robustness of sea current speed estimation against travel time measurement errors. The results show that the station-to-station line in CAT should be arranged considering the predominant sea current direction in the survey area, which can provide an important guideline for selecting station locations.
Ruth G. Curry, Michael W. Lomas, Megan R. Sullivan
et al.
Despite decades of ship-based observations at the Bermuda Atlantic Timeseries Study (BATS) site, ambiguities linger in our understanding of the region's annual carbon cycle. Difficulties reconciling geochemical estimates of annual net community production (ANCP) with direct measurements of nutrient delivery and carbon exports (EP) have implied either an insufficient understanding of these processes, and/or that they are playing out on shorter time and spatial scales than resolved by monthly sampling. We address the latter concern using autonomous underwater gliders equipped with biogeochemical sensors to quantify ANCP from mass balances of oxygen (O2) and nitrate (NO3) over a full annual cycle. The timing, amplitude and distribution of O2 production, consumption, and NO3 fluxes reaffirm ideas about strong seasonality in physical forcing and trophic structure creating a dual system: i.e. production fueled by NO3 supplied to the photic zone from deeper layers in the first half of the year, versus being recycled within the upper ocean during the second half. The evidence also supports recently proposed hypotheses regarding the production and recycling of carbon with non-Redfield characteristics, deplete in nitrogen and phosphorus, to explain observed patterns of high NCP in the absence of significant NO3 supply. It further identifies significant contributions to ANCP and EP potentially linked to vertically migrating communities of salps in spring after all convective activity has ceased. The improved resolution of the datasets, combined with more precise definitions of photic and subphotic integration depths, brings the estimates of ANCP and EP into better alignment with each other.
Tilemachos Bolioudakis, Theodoros Diamantopoulos, Peter J. Diamessis
et al.
The shoaling of high-amplitude Internal Solitary Waves (ISWs) of depression in the South China Sea (SCS) is examined through large-scale parallel turbulence-resolving high-accuracy/resolution simulations. A select, near-isobath-normal, bathymetric transect of the gentle SCS continental slope is employed together with stratification and current profiles obtained by in-situ measurements. Three simulations of separate ISWs with initial deep-water amplitudes in the range [136m, 150m] leverage a novel wave-tracking capability for a propagation distance of 80km and accurately reproduce key features of in-situ-observed phenomena with significantly higher spatiotemporal resolution. The interplay between convective and shear instability and the associated turbulence formation and evolution, as a function of deep-water ISW amplitude are further studied in-part revealing processes previously not observed in the field. Across all three waves, the convective instability develops in a similar fashion. Heavier water entrained from the wave rear plunges into its interior, giving rise to transient, yet distinct, subsurface vortical structures. Ultimately, a gravity current is triggered which horizontally advances through the wave interior and mixes it down to pycnocline's base. Although the waveform remains distinctly symmetric, Kelvin-Helmholtz billows emerge near the well-mixed ISW trough, disturb the wave's trailing edge and give rise to an active wake. The evolution of the kinetic energy associated with finer-scale perturbations to the ISW-induced velocity field shows two different growth regimes, each dominated by either convective or shear instability. The wake's perturbation kinetic energy is nonlinearly dependent on deep-water wave amplitude and can become a sizable fraction of the kinetic energy of the deep-water ISW.
Young Ho Kim, Yuri V. Lvov, Leslie M. Smith
et al.
In this work, we study a three-wave kinetic equation with resonance broadening arising from the theory of stratified ocean flows. Unlike Gamba-Smith-Tran(On the wave turbulence theory for stratified flows in the ocean, Math. Models Methods Appl. Sci. 30 (2020), no.1, 105--137), we employ a different formulation of the resonance broadening, which makes the present model more suitable for ocean applications. We establish the global existence and uniqueness of strong solutions to the new resonance broadening kinetic equation.
Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model’s structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine’s structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine’s health.
Deep-sea corals are critical to global exploration of deep-sea biodiversity, but research on them in the Indian Ocean is very limited. In this study, we report the first discovery of two new species of red corals at a depth of 1697 m in the western Indian Ocean. The species were identified through detailed morphological analysis, including microscopic examination of colony structure, branches and autozooids (assessing size, abundance and spacing), as well as scanning electron microscopy (SEM) of sclerite morphology and quantity. Two species for the genus Hemicorallium Gray, 1867 were found as new species, designated Hemicorallium indicodensum sp. nov. and Hemicorallium jiaolongensis sp. nov. H. indicodensum sp. nov. is characterised by densely branched structures and numerous globular double-club sclerites, distinguishing it from other species. Meanwhile, H. jiaolongensis sp. nov. features yellowish-white colonies with short rods with sharp, large radial projections in the tentacles. Four mitochondrial regions were used to reveal the phylogenetic relationship in Coralliidae, supporting the taxonomic placement of these two new species. This study provides significant new insights into the biodiversity of deep-sea corals in the Indian Ocean, enriching the known species pool. Additionally, a more comprehensive key to the genus Hemicorallium is provided, further enhancing our understanding of the group’s taxonomy.
The Southeast Asian monsoon is characterized by many features that are distinct from those of the East Asian monsoon, including monsoon intensity and evolution. They are also influenced differently by external factors and affect global climate in diverse ways. Studies that consider these factors should yield a better understanding of both monsoon components.
Noémie Letellier, Caitlin G. Jones-Ngo, Michael W. Cheung
et al.
Background: As the health burden attributable to wildfire activity increases under climate change, it is crucial to determine which subgroups face heightened vulnerability to wildfire smoke. Marginalized communities may experience disproportionate risk from overlapping individual and community vulnerability factors. We leverage recent developments in machine learning methods for high-dimensional settings to construct detailed profiles of California communities disproportionately impacted by wildfire smoke across 27 potential effect modifiers. Methods: We used daily 2006–2019 data on hospital admissions and emergency department (ED) visits for cardio-respiratory diseases in California. We applied a time-stratified case-crossover study design to analyze the effect of wildfire-specific fine particulate matter (PM2.5) on cardio-respiratory diseases. Then, we investigated heterogeneous effects using a generalized random forest approach, which can handle a large set of individual-level (age, sex, race/ethnicity) and area-level (e.g., poverty level, racial/ethnic segregation) factors to construct vulnerability profiles for each Air Basin, representing areas with similar meteorological and geographic conditions. Results: A 10 µg/m3 increase in wildfire PM2.5 concentration (2-day moving average) was associated with an increased risk of hospital admissions and ED visits related to respiratory diseases (OR = 1.014, 95 % confidence interval = 1.012–1.016). No association was found for cardiovascular diseases. Associations between exposure to wildfire PM2.5 and respiratory diseases varied strongly by individual- (age, sex, race/ethnicity) and area-level factors (such as A/C prevalence, Black/White dissimilarity index). The importance of these effect modifiers, and vulnerability profiles, changed across Air Basins. Conclusions: Machine learning can characterize the complex heterogeneity in wildfire smoke-related health impacts.
Goulven Monnier, Benjamin Camus, Yann-Hervé Hellouvry
In this paper, we detail the high-performance implementation of our spaceborne radar simulator for satellite oceanography. Our software simulates the sea surface and the signal to imitate, as far as possible, the measurement process, starting from its lowest level mechanisms. In this perspective, raw data are computed as the sum of many illuminated scatterers, whose time-evolving properties are related to the surface roughness, topography, and kinematics. To achieve efficient performance, we intensively use GPU computing. Moreover, we propose a fast simulation mode based on the assumption that the instantaneous Doppler spectrum within a range gate varies on a timescale significantly larger than the PRI. The sea surface can then be updated at a frequency much smaller than the PRF, drastically reducing the computational cost. When the surface is updated, Doppler spectra are computed for all range gates. Signals segments are then obtained through 1D inverse Fourier transforms and pondered to ensure a smooth time evolution between surface updates. We validate this fast simulation mode with a radar altimeter simulation case of the Sentinel-3 SRAL instrument, showing that simulated raw data can be focused and retrieved using state-of-the-art algorithms. Finally, we show that, using a modest hardware configuration, our simulator can generate enough data in one day to compute the SWH and SSH spectra of a scene. This demonstrate that we achieved an important state-of-the-art speed-up.
The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.
To study the rapid estimation method and characteristics of low-frequency ambient sea noise generated by far-field ships, firstly, based on the reciprocity principle of sound fields and the fact that the number of noise sources significantly exceeds the number of receiving array elements, the positions of noise sources and receiving array elements were swapped to effectively reduce the sound field estimates and the running time. Secondly, a vertical directionality analysis method for ambient noise generated by ship noise was derived. And lastly, the ambient sea noise generated by ship noise in the Philippine Sea was estimated and analyzed, and the validity of the estimation method was verified based on measured data in the region. The estimation method presented in this paper can be used to predict the level and directionality of ambient noise generated by ship noise in a large area of sea, and acts as technical support for the meaningful use of sonar arrays in the actual marine environment.
Field determination and ecological risk assessment of dissolved lead (Pb) were performed at two Yellow Sea sites in China using a continuous automated electrochemical system (CAEDS). This CAEDS instrument includes an automatic triple filter sampler and an electrochemical detection water quality analyzer, which might be operated automatically four times daily. The dissolved Pb concentrations varied from 0.29 to 1.57 μg/L in the South Yellow Sea over 16 days and from 0.32 to 2.28 μg/L in the North Yellow Sea over 13 days. During the typhoon and algal bloom periods, the Pb concentration was as high as ten times greater than usual. According to the calculation of contamination factors (C<sub>f</sub>) and subsequent analysis, seawater quality was classified as Grade II. Through species sensitivity distribution (SSD) method experiments and ecological risk analysis, an average risk quotient (RQ) below 1 for both areas was obtained, indicating a low-to-moderate ecological risk. This system will be helpful for Pb monitoring and assessment in seawater and contribute to the biogeochemical cycling study of Pb.
Spatially-indexed multivariate data appear frequently in geostatistics and related fields including oceanography and environmental science. To take full advantage of this data structure, cross-covariance functions are constructed to describe the dependence between any two component variables at different spatial locations. Modeling of multivariate spatial random fields requires these constructed cross-covariance functions to be valid, which often presents challenges that lead to complicated restrictions on the parameter space. The purpose of this work is to present techniques using multivariate mixtures for establishing validity that are simultaneously simplified and comprehensive. In particular, cross-covariances are constructed for the recently-introduced confluent hypergeometric (CH) class of covariance functions, which has slow (polynomial) decay in the tails of the covariance that better handles large gaps between observations in comparison with other covariance models. In addition, the spectral density of the confluent hypergeometric covariance is established and used to construct new valid cross-covariance models. The approach leads to valid multivariate cross-covariance models that inherit the desired marginal properties of the confluent hypergeometric model and outperform the multivariate Matérn model in out-of-sample prediction under slowly-decaying correlation of the underlying multivariate random field. The model captures heavy tail decay and dependence between variables in an oceanography dataset of temperature, salinity and oxygen, as measured by autonomous floats in the Southern Ocean.
Recently, as humans have become increasingly interested in ocean resources, underwater vehicle-manipulator systems (UVMSs) have played an increasingly important role in ocean exploitation. To realize precise operation in underwater narrow spaces, the fly arm underwater vehicle manipulator system (FAUVMS) is proposed with manipulators as its core. However, this system suffers severe dynamic coupling effects due to the combination of small vehicle and big manipulators. To resolve this issue, we propose a robust adaptive controller that contains two parts. In the first part, the extended Kalman filter (EKF) is designed to estimate the system states and predicts external disturbances to achieve adaptive control. In the second part, a chattering-free sliding mode control (SMC) is designed to converge the tracking errors to zero, thus guaranteeing the robustness of the controller. We constructed the simulation platform based on the geometric model of FAUVMS, and various simulations are carried out under different situations. Compared to the traditional methods, the proposed method has a faster convergent speed, a better robustness and adaptiveness to external disturbances, and the tracking errors of positions of the vehicle and each end-effector are much smaller.
Abstract Many changes took place in physical oceanography over the last 60 years, as the science encountered the global‐scale problems of climate and in the availability of numerous new technologies. Changes have been in the known science, and in the intellectual culture in which it is pursued.
Balamurugan Sadaiappan, Preethiya Balakrishnan, Vishal CR
et al.
Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.
The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with uncertainties. Since the system parameters are uncertain and system performances are influenced by disturbances and noises, the robustness cannot be satisfied by approximating the nonlinear theory by a linear theory. In this paper, a learning-based model predictive controller (LB-MPC) is designed for the position control of an electro-hydraulic cylinder system. In order to reduce the influence of uncertainty of the hydraulic system caused by the model mismatch, the Gaussian process (GP) is adopted, and also the real-time input and output data are used to improve the model. A comparative simulation of GP-MPC and MPC is performed assuming that the interference and uncertainty terms are bounded. Consequently, the proposed control strategy can effectively improve the piston position quickly and precisely with multiple constraint conditions.
Wind wave-induced artifact surface velocity (WASV) is an important component of the sea surface motions detected by synthetic aperture radar (SAR) systems. Understanding the characteristics of the interference of WASV on SAR current velocity estimates is necessary to improve the accuracy of retrievals. In this study, we assessed and analyzed the sensitivity of WASV in C-band along-track interferometric (ATI) SAR to radar configuration, wind field, swell field, and a wave spectrum model. Results showed that the influence of wind speed on WASV increased with the current velocity. The swell also affected WASV, especially at higher wind speeds; WASV was more strongly influenced by swell amplitude than by swell wavelength. In terms of radar configurations, results showed that VV polarization was more suitable than HH polarization in the estimation of WASV. The interference of WASV was minimal at moderate incidence angles (around 40°), and an appropriate ATI baseline selection was also given. The WASV was more strongly influenced by sea states than by the wave spectrum model or by a spreading function. The findings of this study improve our understanding of WASV and provide a reference for the design of future ATI SAR current measurement instruments and projects.
The maritime industry is trying to utilize new technology for enhancing its competitiveness to overcome today’s severe economic situation, and some interact effects, or potentially emergent effects, will emerge during the introduction of these technologies. In this study, various simulations that relate to marine logistics and shipping were performed. By contrast, a detailed method that can reproduce emergent effects is required to some extent. This study utilized a Monte Carlo simulation for uncertainties, such as market and failure uncertainties. To evaluate and explore the emergent effect correctly and accurately when multiple technologies are introduced, an evaluation methodology was developed, which can evaluate the interact effect from the perspective of profit improvement and CO<sub>2</sub> reduction during the transportation period. As a case study, decision making for introducing 28 technology combinations to the maritime industry was conducted, and the utility of the proposed methodology was assessed.