Hasil untuk "Oceanography"

R. Feely, S. Doney, S. Cooley

Author Posting. © Oceanography Society, 2009. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 22 no. 4 (2009): 36-47.

Dennis A. Hansell, C. Carlson, D. Repeta et al.

Author Posting. © Oceanography Society, 2009. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 22 no. 4 (2009): 202-211.

A. Longhurst, S. Sathyendranath, T. Platt et al.

C. Zender, H. Bian, D. Newman

Yue Wu, Yulin Pan

Induced diffusion (ID), an important mechanism of spectral energy transfer in the internal gravity wave (IGW) field, plays a significant role in driving turbulent dissipation in the ocean interior. In this study, we revisit the ID mechanism to elucidate its directionality and role in ocean mixing under varying IGW spectral forms, with particular attention to deviations from the standard Garrett-Munk (GM) spectrum. The original interpretation of ID as an action diffusion process, as proposed by McComas et al., suggests that ID is inherently bidirectional, with its direction governed by the vertical-wavenumber spectral slope $σ$ of the IGW action spectrum, $n \propto m^σ$. In contrast, by evaluating the wave kinetic equation, we reveal a more complete depiction of ID, comprising both diffusive and scale-separated transfers that are rooted in energy conservation within wave triads. Although the action diffusion may reverse direction depending on the sign of $σ$ (i.e., between red and blue spectral cases), the combined ID transfer consistently leads to a forward energy cascade at the dissipation scale, thereby contributing positively to turbulent dissipation. This supports the viewpoint of ID as a dissipative mechanism in physical oceanography. This study presents a physically grounded overview of ID and offers insights into the specific types of wave-wave interactions responsible for turbulent dissipation.

Rui-Yang Zhang, Henry B. Moss, Lachlan Astfalck et al.

We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine science, and ocean engineering -- using a physics-informed spatio-temporal Gaussian process surrogate model. The majority of existing placement campaigns either follow standard `space-filling' designs or relatively ad-hoc expert opinions. A key challenge to applying principled active learning in this setting is that Lagrangian observers are continuously advected through the vector field, so they make measurements at different locations and times. It is, therefore, important to consider the likely future trajectories of placed observers to account for the utility of candidate placement locations. To this end, we present BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories. We observe noticeable benefits of BALLAST-aided sequential observer placement strategies on both synthetic and high-fidelity ocean current models. In addition, we developed a novel GP inference method -- the Vanilla SPDE Exchange (VaSE) -- to boost the GP posterior sampling efficiency, which is also of independent interest.

Angeliki Xenaki, Peter Gerstoft, Ethan Williams et al.

Extensive monitoring of acoustic activities is important for many fields, including biology, security, oceanography, and Earth science. Distributed acoustic sensing (DAS) is an evolving technique for continuous, wide-coverage measurements of mechanical vibrations, which is suited to ocean applications. DAS illuminates an optical fiber with laser pulses and measures the backscattered wave due to small random variations in the refractive index of the material. External stimuli, such as mechanical strain due to acoustic wavefields impinging on the fiber-optic cable, modulate the backscattered wave. Continuous measurement of the backscattered signal provides a distributed sensing modality of the impinging wavefield. Considering the potential use of existing telecommunication fiber-optic cables deployed across the oceans, DAS has emerged as a promising technology for monitoring the underwater soundscape. This review presents advances in DAS in the last decade and details the underlying physics from electromagnetic to mechanical and eventually acoustic quantities. To guide the use of DAS for ocean applications, the effect of DAS acquisition parameters in signal processing is explained. Finally, DAS is demonstrated on data from the Ocean Observatories Initiative Regional Cabled Array for the detection of sound sources, such as whales, ships, and earthquakes.

Victor Trappler, Arthur Vidard

Data Assimilation is the process in which we improve the representation of the state of a physical system by combining information coming from a numerical model, real-world observations, and some prior modelling. It is widely used to model and to improve forecast systems in Earth science fields such as meteorology, oceanography and environmental sciences. One key aspect of Data assimilation is the analysis step, where the output of the numerical model is adjusted in order to account for the observational data. In Variational Data Assimilation and under Gaussian assumptions, the analysis step comes down to solving a high-dimensional non-linear least-square problem. In practice, this minimization involves successive inversions of large, and possibly ill-conditioned matrices constructed using linearizations of the forward model. In order to improve the convergence rate of these methods, and thus reduce the computational burden, preconditioning techniques are often used to get better-conditioned matrices, but require either the sparsity pattern of the matrix to inverse, or some spectral information. We propose to use Deep Neural Networks in order to construct a preconditioner. This surrogate is trained using some properties of the singular value decomposition, and is based on a dataset which can be constructed online to reduce the storage requirements.

Peyman Fahimi, Andrew J. Irwin, Michael Lynch

The motility skills of phytoplankton have evolved and persisted over millions of years, primarily in response to factors such as nutrient and light availability, temperature and viscosity gradients, turbulence, and predation pressure. Phytoplankton motility is broadly categorized into swimming and buoyancy regulation. Despite studies in the literature exploring the motility costs and benefits of phytoplankton, there remains a gap in our integrative understanding of direct and indirect energy expenditures, starting from when an organism initiates movement due to any biophysical motive, to when the organism encounters intracellular and environmental challenges. Here we gather available pieces of this puzzle from literature in biology, physics, and oceanography to paint an overarching picture of our current knowledge. The characterization of sinking and rising behavior as passive motility has resulted in the concept of sinking and rising internal efficiency being overlooked. We define this efficiency based on any energy dissipation associated with processes of mass density adjustment, as exemplified in structures like frustules and vacuoles. We propose that sinking and rising are active motility processes involving non-visible mechanisms, as species demonstrate active and rapid strategies in response to turbulence, predation risk, and gradients of nutrients, light, temperature, and viscosity. Identifying intracellular buoyancy-regulating dissipative processes offers deeper insight into the motility costs relative to the organism's total metabolic rate.

Juan Yu, Jia-Wei Gao, Ke Cao et al.

ABSTRACT Flavobacteriaceae is the major participant in the degradation of algal polysaccharides. With diverse polysaccharide utilization loci (PULs) and specific carbohydrate-active enzymes (CAZymes), Flavobacteriaceae strains appear to have different abilities in algal polysaccharide degradation and therefore change their roles in the bacterial community. Here, we identified two novel isolates as two novel species of genus Flagellimonas with the names Flagellimonas alginolytica sp. nov. and Flagellimonas cixiensis sp. nov. Furthermore, the comprehensive genomic comparison of 41 Flagellimonas genomes revealed that Flagellimonas strains were diverse in the CAZymes and PUL profiles and exhibited a preference for polysaccharides derived from brown algae. The evolutionary analysis of alginate utilization loci (AUL) in this genus illuminated that the function genes in AULs, that is, PL7 and PL17, were more reliant on the stable inheritance from ancestors associated with gene duplication and loss rather than horizontal gene transfer (HGT) from outside, and the AUL structures exhibited a trend of simplification which resulted in the incidental decrease in alginate degradation ability. This study highlights the important role of vertical inheritance in the evolution of AULs and proves that the discrepancy in AUL structure can arouse phenotypic differences, providing a new perspective on the evolution of AUL and the niche adaptation mechanism of Flavobacteriaceae strains.IMPORTANCEFlavobacteriaceae play an important role in the marine carbon cycle with their noteworthy ability in algal polysaccharides degradation, which is primarily reliant on diverse polysaccharide utilization loci (PULs). Our study highlights the crucial role of vertical inheritance in the evolution of alginate utilization loci (AUL) in Flagellimonas strains and reveals the AUL structural simplification found in Flagellimonas strains that will lead to the reduction of alginate degradation ability. These insights advance understanding of niche adaptation strategy and related evolutionary mechanisms of Flavobacteriaceae strains.

Qing Huang, Xiaoyan Zhang, Anqi Jin et al.

Many studies have used various time-frequency feature extraction methods to convert ship-radiated noise into three-dimensional (3D) data suitable for computer vision (CV) models, which have shown good results in public datasets. However, traditional feature engineering (FE) has been enhanced to interface matching–feature engineering (IM-FE). This approach requires considerable effort in feature design, larger sample duration, or a higher upper limit of frequency. In this context, this paper proposes a one-dimensional network design for underwater acoustic target recognition (UATR-ND1D), only combined with fast Fourier transform (FFT), which can effectively alleviate the problem of IM-FE. This method is abbreviated as FFT-UATR-ND1D. FFT-UATR-ND1D was applied to the design of a one-dimensional network, named ResNet1D. Experiments were conducted on two mainstream datasets, using ResNet1D in 4320 and 360 tests, respectively. The lightweight model ResNet1D_S, with only 0.17 M parameters and 3.4 M floating point operations (FLOPs), achieved average accuracies were 97.2% and 95.20%. The larger model, ResNet1D_B, with 2.1 M parameters and 5.0 M FLOPs, both reached optimal accuracies, 98.81% and 98.42%, respectively. Compared to existing methods, those with similar parameter sizes performed 3–5% worse than the methods proposed in this paper. Additionally, methods achieving similar recognition rates require more parameters of 1 to 2 orders of magnitude and FLOPs.

Jiada Guan, Huaguo Zhang, Tong Han et al.

High-resolution water depth data are of great significance in island research and coastal ecosystem monitoring. However, the acquisition of high-resolution imagery has been a challenge due to the difficulties and high costs associated with obtaining such data. To address this issue, this study proposes a water depth inversion method based on Gaofen-1 (GF-1) satellite data, which integrates multi-source satellite data to obtain high-resolution bathymetric data. Specifically, the research utilizes bathymetric data derived from Sentinel-2 and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as prior information, combined with high-resolution imagery obtained from the GF-1 satellite constellation (GF-1B/C/D). Then, it employs a scale-invariant effect to map bathymetry with a spatial resolution of 2 m, applied to four study areas in the Pacific Islands. The results are further evaluated using ICESat-2 data, which demonstrate that the water depth inversion results from this study possess high accuracy, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values exceeding 0.85, root mean square error (RMSE) ranging from 0.56 to 0.90 m, with an average of 0.7125 m, and mean absolute error (MAE) ranging from 0.43 to 0.76 m, with an average of 0.55 m. Additionally, this paper discusses the applicability of the scale-invariant assumption in this research and the improvements of the quadratic polynomial ratio model (QPRM) method compared to the classical linear ratio model (CLRM) method. The findings indicate that the integration of multi-source satellite remote sensing data based on the scale-invariant effect can effectively obtain high-precision, high-resolution bathymetric data, providing significant reference value for the application of GF-1 satellites in high-resolution bathymetry mapping.

M. Paglione, Y. Hao, S. Decesari et al.

<p>Understanding the chemical composition and sources of organic aerosol (OA) in the Arctic is critical given its importance for particle climate-relevant properties. This study presents a year-long analysis (May 2019–June 2020) of PM<span class="inline-formula">₁</span> filter samples collected in Ny-Ålesund, Svalbard. A multi-instrumental approach is employed to characterize the comprehensive chemical composition of PM<span class="inline-formula">₁</span>, with a specific focus on its water-soluble organic fraction depicted combining proton nuclear magnetic resonance spectroscopy (H-NMR) and high-resolution time-of-flight aerosol mass spectrometry (HR-TOF-AMS), which provide complementary insights into the nature and structure of the organic aerosol classes characterizing the bulk OA mixture. Positive matrix factorization (PMF) source apportionment identifies consistent OA sources from the H-NMR and AMS datasets, showing a pronounced seasonality in their relative contributions to total OA mass. Winter–spring aerosol is dominated by long-range transport of Eurasian anthropogenic pollution (up to 70 %), while summer is characterized by biogenic aerosols from marine sources (up to 44 %), including sulfur compounds, amines, and fatty acids. Occasional summertime high OA loadings are associated with wildfire aerosols enriched in levoglucosan and humic-like substances (HULIS; averagely 27 %–28 %). Eventually, about 28 %–40 % of the OA mass is attributed to an unresolved mixture of extremely oxidized compounds of difficult specific source attribution. This integrated approach provides valuable insights into the seasonal dynamics of OA sources in the Arctic.</p>

Yongfeng Yang, Yunfei Zhang, Qiang Liu et al.

Solid Earth tide represents the response of solid Earth to the lunar (solar) gravitational force. The yielding solid Earth due to the force has been thought to be a prolate ellipsoid since the time of Lord Kelvin, yet the ellipsoid's geometry such as major semi-axis's length, minor semi-axis's length, and flattening remains unresolved. Additionally, the tidal displacement of reference point is conventionally resolved through a combination of expanded potential equations and given Earth model. Here we present a geometric model in which both the ellipsoid's geometry and the tidal displacement of reference point can be resolved through a rotating ellipse with respect to the Moon (Sun). We test the geometric model using 23-year gravity data from 22 superconducting gravimeter (SG) stations and compare it with the current model recommended by the IERS (International Earth Rotation System) conventions (2010), the average Root Mean Square (RMS) deviation of the gravity change yielded by the geometric model against observation is 6.47 μGal (equivalent to 2.07 cm), while that yielded by the current model is 30.77 μGal (equivalent to 9.85 cm). The geometric model will greatly contribute to many application fields such as geodesy, geophysics, astronomy, and oceanography.

Alexander Lobbe, Dan Crisan, Oana Lang

Data assimilation plays a crucial role in numerical modeling, enabling the integration of real-world observations into mathematical models to enhance the accuracy and predictive capabilities of simulations. This approach is widely applied in fields such as meteorology, oceanography, and environmental science, where the dynamic nature of systems demands continuous updates to model states. However, the calibration of models in these high-dimensional, nonlinear systems poses significant challenges. In this paper, we explore a novel calibration methodology using diffusion generative models. We generate synthetic data that statistically aligns with a given set of observations (in this case the increments of the numerical approximation of a solution of a partial differential equation). This allows us to efficiently implement a model reduction and assimilate data from a reference system state modeled by a highly resolved numerical solution of the rotating shallow water equation of order 104 degrees of freedom into a stochastic system having two orders of magnitude less degrees of freedom. To do so, the new samples are incorporated into a particle filtering methodology augmented with tempering and jittering for dynamic state estimation, a method particularly suited for handling complex and multimodal distributions. This work demonstrates how generative models can be used to improve the predictive accuracy for particle filters, providing a more computationally efficient solution for data assimilation and model calibration.

Iacopo Tirelli, Miguel Alfonso Mendez, Andrea Ianiro et al.

Complex phenomena can be better understood when broken down into a limited number of simpler "components". Linear statistical methods such as the principal component analysis and its variants are widely used across various fields of applied science to identify and rank these components based on the variance they represent in the data. These methods can be seen as factorisations of the matrix collecting all the data, assuming it consists of time series sampled from fixed points in space. However, when data sampling locations vary over time, as with mobile monitoring stations in meteorology and oceanography or with particle tracking velocimetry in experimental fluid dynamics, advanced interpolation techniques are required to project the data onto a fixed grid before the factorisation. This interpolation is often expensive and inaccurate. This work proposes a method to decompose scattered data without interpolating. The approach employs physics-constrained radial basis function regression to compute inner products in space and time. The method provides an analytical and mesh-independent decomposition in space and time, demonstrating higher accuracy. Our approach allows distilling the most relevant "components" even for measurements whose natural output is a distribution of data scattered in space and time, maintaining high accuracy and mesh independence.

Nicolae Adrian Visan, Dan Catalin Niculescu, Radu Ionescu et al.

The impact of fossil fuel utilisation in different combustion systems on climate change due to greenhouse gas accumulation in the atmosphere is rather evident. A part of these gases comes from the large engines used for propulsion in marine applications. In the continuous global effort made by engine manufacturers to mitigate this negative impact, one way is represented by the utilisation of alternative fuels such as biodiesel and methanol, based on dedicated research to fulfil the more stringent regulations concerning pollutant emissions issued by piston heat engines. In this study, a numerical investigation was conducted on a four-stroke large marine diesel engine (ALCO 16V 251C) at several engine speeds and full load conditions. Different blends of diesel–methanol and biodiesel B20–methanol with methanol mass fractions of 10% and 20% were considered for theoretical analysis in two techniques of methanol supply: direct injection mode of a blend of base fuel diesel/biodiesel B20 with methanol and injection of methanol after the intercooler, and direct injection of the base fuel. The results show that, if 10% in power loss can be acceptable, then for diesel–methanol 10%, in the direct injection technology, the NOx emission can be reduced up to 19%, but with a compromise of an 8% increase in SOOT emission, while for biodiesel B20–methanol 10%, with the same direct injection method, the NOx emissions increase by up to 58% with the benefit of reducing SOOT by up to 23% relative to the original diesel fuel operation. For a 20% methanol fraction in blend fuel, the drop in power is more than 10% regardless of the method of methanol supply and the base fuel, diesel, or B20 used.

Kue-Hong Chen, Jeng-Hong Kao, Yi-Hui Hsu

In this manuscript, we will apply the regularized meshless method, coupled with an error estimation technique, to tackle the challenge of modeling oblique incident waves interacting with multiple cylinders. Given the impracticality of obtaining an exact solution in many real engineering problems, we introduce an error estimation technique designed to achieve reliable solutions. This technique excels in providing dependable solutions that closely approximate analytical solutions. An additional advantage is its capacity to identify the optimal number of points for both source and collocating points, thereby enhancing computational efficiency. The validity of the proposed method will be demonstrated through three numerical cases, presenting results that exhibit substantial agreement.

Wenquan Zhang, Huameng Ge, Chengbing Song et al.

The Bohai Sea is a semi-enclosed shallow water that is influenced by both natural and anthropogenic stressors. However, the microeukaryotic communities and environmental factors that affect them in different regions remain largely unclear. We investigated microeukaryotic communities in surface sediments from five geographic regions using high-throughput sequencing of the 18S rDNA gene. The Miaodao Archipelago, Yellow River Estuary, and Central Bohai Sea had the highest Shannon and Simpson indices of the eukaryotic communities, while the Yellow River Estuary exhibited the highest Chao1 index. The microeukaryotic communities in surface sediments were mainly composed of Dinoflagellata, Bacillariophyta, Ciliophora, Cercozoa, and Protalveolata. <i>Thalassiosira</i> has a relatively high abundance at the Liaodong Bay and Central Bohai Sea, possessing the proportion of 41.70% and 38.10%, respectively, while <i>Gonyaulax</i> was the most abundant taxa in the Bohai Bay, occupying a proportion of 57.77%. Moreover, a negative correlation between diatoms and dinoflagellates was observed. Phosphorus, nitrogen, salinity, temperature, and silicate were major environmental determinants of microeukaryotic composition. Microeukaryotic communities in the surface sediments, especially for the composition and ratio of diatoms to dinoflagellates, reflected the environmental quality of marine ecosystems. Overall, these microeukaryotic community compositions provide a reliable indicator for monitoring the level of marine eutrophication in the Bohai Sea.

Chenjing Shang, Li Sihui, Chunyuan Li et al.

Abstract Background Salt Overly Sensitive 1 (SOS1), a plasma membrane Na+/H+ exchanger, is essential for plant salt tolerance. Salt damage is a significant abiotic stress that impacts plant species globally. All living organisms require copper (Cu), a necessary micronutrient and a protein cofactor for many biological and physiological processes. High Cu concentrations, however, may result in pollution that inhibits the growth and development of plants. The function and production of mangrove ecosystems are significantly impacted by rising salinity and copper contamination. Results A genome-wide analysis and bioinformatics techniques were used in this study to identify 20 SOS1 genes in the genome of Kandelia obovata. Most of the SOS1 genes were found on the plasma membrane and dispersed over 11 of the 18 chromosomes. Based on phylogenetic analysis, KoSOS1s can be categorized into four groups, similar to Solanum tuberosum. Kandelia obovata's SOS1 gene family expanded due to tandem and segmental duplication. These SOS1 homologs shared similar protein structures, according to the results of the conserved motif analysis. The coding regions of 20 KoSOS1 genes consist of amino acids ranging from 466 to 1221, while the exons include amino acids ranging from 3 to 23. In addition, we found that the 2.0 kb upstream promoter region of the KoSOS1s gene contains several cis-elements associated with phytohormones and stress responses. According to the expression experiments, seven randomly chosen genes experienced up- and down-regulation of their expression levels in response to copper (CuCl2) and salt stressors. Conclusions For the first time, this work systematically identified SOS1 genes in Kandelia obovata. Our investigations also encompassed physicochemical properties, evolution, and expression patterns, thereby furnishing a theoretical framework for subsequent research endeavours aimed at functionally characterizing the Kandelia obovata SOS1 genes throughout the life cycle of plants.