Hasil untuk "Oceanography"

E. Sebille, StefanoAliani, K. L. Law et al.

Marine plastic debris floating on the ocean surface is a major environmental problem. However, its distribution in the ocean is poorly mapped, and most of the plastic waste estimated to have entered the ocean from land is unaccounted for. Better understanding of how plastic debris is transported from coastal and marine sources is crucial to quantify and close the global inventory of marine plastics, which in turn represents critical information for mitigation or policy strategies. At the same time, plastic is a unique tracer that provides an opportunity to learn more about the physics and dynamics of our ocean across multiple scales, from the Ekman convergence in basin-scale gyres to individual waves in the surfzone. In this review, we comprehensively discuss what is known about the different processes that govern the transport of floating marine plastic debris in both the open ocean and the coastal zones, based on the published literature and referring to insights from neighbouring fields such as oil spill dispersion, marine safety recovery, plankton connectivity, and others. We discuss how measurements of marine plastics (both in situ and in the laboratory), remote sensing, and numerical simulations can elucidate these processes and their interactions across spatio-temporal scales.

K. Caldeira, M. Wickett

W. Emery, R. Thomson

Chapter and section headings: Preface. Acknowledgments. Data Acquisition and Recording. Introduction. Basic sampling requirements. Temperature. Salinity. Depth or pressure. Sea-level measurement. Eulerian currents. Lagrangian current measurements. Wind. Precipitation. Chemical tracers. Transient chemical tracers. Data Processing and Presentation. Introduction. Calibration. Interpolation. Data presentation. Statistical Methods and Error Handling. Introduction. Sample distributions. Probability. Moments and expected values. Common probability density functions. Central limit theorem. Estimation. Confidence intervals. Selecting the sample size. Confidence intervals for altimeter bias estimators. Estimation methods. Linear estimation (regression). Relationship between regression and correlation. Hypothesis testing. Effective degrees of freedom. Editing and despiking techniques: the nature of errors. Interpolation: filling the data gaps. Covariance and the covariance matrix. Bootstrap and jackknife methods. The Spatial Analyses of Data Fields. Traditional block and bulk averaging. Objective analysis. Empirical orthogonal functions. Normal mode analysis. Inverse methods. Time-series Analysis Methods. Basic concepts. Stochastic processes and stationarity. Correlation functions. Fourier analysis. Harmonic analysis. Spectral analysis. Spectral analysis (parametric methods). Cross-spectral analysis. Wavelet analysis. Digital filters. Fractals. Appendices. References. Index. 8 illus., 135 line drawings.

R. Preisendorfer, C. Mobley

M. Tomczak, J. S. Godfrey

Introduction - what drives the ocean currents? temperature, salinity, density and the oceanic pressure field the Coriolis force, geostropy, Rossby waves and the westwood intensification Ekman layer transports, Ekman pumping and the Sverdrup balance water mass formation, subduction and the oceanic heat budget Antarctic oceanography Arctic oceanography -the path of North Atlantic Deep Water the Pacific Ocean hydrology of the Pacific Ocean adjacent seas of the Pacific Ocean the Indian Ocean hydrology of the Indian Ocean adjacent seas of the Indian Ocean and the Australasian Mediterranean Sea the Atlantic Ocean hydrology of the Atlantic Ocean aspects of advanced regional oceanography the oceans and the world's mean climate El Nino and the Southern oscillation (ENSO) the ocean and climate change.

K. Thyng, C. Greene, R. Hetland et al.

J. Dudley, G. Genty, A. Mussot et al.

Over a decade ago, an analogy was drawn between the generation of large ocean waves and the propagation of light fields in optical fibres. This analogy drove numerous experimental studies in both systems, which we review here. In optics, we focus on results arising from the use of real-time measurement techniques, whereas in oceanography we consider insights obtained from analysis of real-world ocean wave data and controlled experiments in wave tanks. This Review of the work in hydrodynamics includes results that support both nonlinear and linear interpretations of rogue wave formation in the ocean, and in optics, we also provide an overview of the emerging area of research applying the measurement techniques developed for the study of rogue waves to dissipative soliton systems. We discuss the insights gained from the analogy between the two systems and its limitations in modelling real-world ocean wave scenarios that include physical effects that go beyond a one-dimensional propagation model.An analogy between wave propagation in hydrodynamics and in optics has yielded new insights into the mechanisms leading to the formation of giant rogue waves on the ocean. We review experimental progress and field measurements in this area.Key pointsAn analogy between wave propagation on the ocean and in optical fibres has provided new insights into the physical mechanisms and dynamical features that underpin the occurrence of rogue waves.Real-time measurement techniques studying instabilities in fibre optics have highlighted the emergence of localized breather structures associated with nonlinear focusing, a scenario confirmed in wave-tank experiments.The experimental techniques developed for rogue wave measurement in optics have also yielded improved understanding of transient dynamics and dissipative soliton structures in lasers.Advanced analysis and hindcasting of real-world ocean wave data have revealed the central role of directionality and the superposition of random wave trains in the formation of ocean rogue waves.The emergence of oceanic rogue waves in the general case is likely to arise from both linear and nonlinear mechanisms to different degrees depending on the prevalent wind and sea state conditions.Machine learning could play a key role in future efforts to forecast and predict ocean rogue waves and to identify new areas of physical analogy and overlap between optics and hydrodynamics.

Wan-Gu Kim, Byoung-Nam Kim, Yohan Chweh

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.

Renfei Kuang, Jinhai Zhao, Tuo Zhang et al.

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.

Xuying Hu, Qian Zhang, Meiling Ge et al.

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.

Song Yang

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.

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.

Qianlong Zhao, Shiqiu Peng, Jingzhe Wang et al.

Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It proves to achieve comparable or even more accurate results than traditional methods in a more flexible manner in existing applications in various fields. In the field of physical oceanography, an important scientific field of oceanography, the abundance of ocean surface data and high dynamic complexity pave the way for an extensive application of deep learning. Moreover, researchers have already conducted a great deal of work to innovate traditional approaches in ocean circulation, ocean dynamics, ocean climate, ocean remote sensing and ocean geophysics, leading oceanographic studies into the “AI ocean era”. In our study, we categorize numerous research topics in physical oceanography into four aspects: surface elements, subsurface elements, typical ocean phenomena, and typical weather and climate phenomena. We review the cutting-edge applications of deep learning in physical oceanography over the past three years to provide comprehensive insights into its development. From the perspective of three application scenarios, namely spatial data, temporal data and data generation, three corresponding deep learning model types are introduced, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs), and also their principal application tasks. Furthermore, this study discusses the current bottlenecks and future innovative prospects of deep learning in oceanography. Through summarizing and analyzing the existing research, our aim is to delve into the potential and challenges of deep learning in physical oceanography, providing reference and inspiration for researchers in future oceanographic studies.

B. Wirtz

Yuanqing Wang, Jinghao Zhang, Yongchao Zheng et al.

The Brillouin scattering spectrum has been used to investigate the properties of a liquid medium. Here, we propose an improved method based on the double-edge technique to obtain the Brillouin spectrum of a liquid. We calculated the transmission ratios and deduced the Brillouin shift and linewidth to construct the Brillouin spectrum by extracting the Brillouin edge signal through filtered double-edge data. We built a detection system to test the performance of this method and measured the Brillouin spectrum for distilled water at different temperatures and compared it with the theoretical pre-diction. The observed difference between the experimental and theoretical values for Brillouin shift and linewidth is less than 4.3 MHz and 3.2 MHz, respectively. Moreover, based on the double-edge technique, the accuracy of the extracted temperatures and salinity is approximately 0.1 °C and 0.5%, respectively, indicating significant potential for application in water detection and oceanography.

C. Dong, Guangjun Xu, Guoqing Han et al.

With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.

Changui Lee, Seojeong Lee

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.