Статті в журналах: "Ocean field"

1

O'Dor, Ron, and Víctor Ariel Gallardo. "How to Census Marine Life: ocean realm field projects." Scientia Marina 69, S1 (June 30, 2005): 181–99. http://dx.doi.org/10.3989/scimar.2005.69s1181.

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2

Irrgang, C., J. Saynisch, and M. Thomas. "Impact of variable seawater conductivity on motional induction simulated with an ocean general circulation model." Ocean Science 12, no. 1 (January 15, 2016): 129–36. http://dx.doi.org/10.5194/os-12-129-2016.

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Abstract. Carrying high concentrations of dissolved salt, ocean water is a good electrical conductor. As seawater flows through the Earth's ambient geomagnetic field, electric fields are generated, which in turn induce secondary magnetic fields. In current models for ocean-induced magnetic fields, a realistic consideration of seawater conductivity is often neglected and the effect on the variability of the ocean-induced magnetic field unknown. To model magnetic fields that are induced by non-tidal global ocean currents, an electromagnetic induction model is implemented into the Ocean Model for Circulation and Tides (OMCT). This provides the opportunity to not only model ocean-induced magnetic signals but also to assess the impact of oceanographic phenomena on the induction process. In this paper, the sensitivity of the induction process due to spatial and temporal variations in seawater conductivity is investigated. It is shown that assuming an ocean-wide uniform conductivity is insufficient to accurately capture the temporal variability of the magnetic signal. Using instead a realistic global seawater conductivity distribution increases the temporal variability of the magnetic field up to 45 %. Especially vertical gradients in seawater conductivity prove to be a key factor for the variability of the ocean-induced magnetic field. However, temporal variations of seawater conductivity only marginally affect the magnetic signal.

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3

Shang, E. C., and Y. Y. Wang. "Ocean acoustic field simulations for monitoring large-scale ocean structures." Computer Physics Communications 65, no. 1-3 (April 1991): 238–45. http://dx.doi.org/10.1016/0010-4655(91)90177-m.

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4

Bo, Li, Zhong Yi Li, and Yue Jin Zhang. "Ocean Surface Modeling in Vary Wind Field." Key Engineering Materials 480-481 (June 2011): 1452–56. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.1452.

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In ocean surface modeling a popular method of wave modeling is making use of ocean wave spectrum, which is a physical wave model and based on linear wave theories. The ocean waves produced in this way can reflect the statistical characteristics of the real ocean well. However, few investigations of ocean simulation have been focused on turbulent fluid under vary wind field in this way, while all ocean wave models are built with the same wind parameters. In order to resolve the problem of traditional method, we proposed a new method of dividing the ocean surface into regular grids and generating wave models with different parameters of wind in different location of view scope. The method not only preserves the fidelity of statistical characteristics, but also can be accelerated with the processing of GPU and widely used in VR applications.

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5

Small, J., L. Shackleford, and G. Pavey. "Ocean feature models − their use and effectiveness in ocean acoustic forecasting." Annales Geophysicae 15, no. 1 (January 31, 1997): 101–12. http://dx.doi.org/10.1007/s00585-997-0101-7.

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Abstract. The aim of this paper is to test the effectiveness of feature models in ocean acoustic forecasting. Feature models are simple mathematical representations of the horizontal and vertical structures of ocean features (such as fronts and eddies), and have been used primarily for assimilating new observations into forecasts and for compressing data. In this paper we describe the results of experiments in which the models have been tested in acoustic terms in eddy and frontal environments in the Iceland Faeroes region. Propagation-loss values were obtained with a 2D parabolic-equation (PE) model, for the observed fields, and compared to PE results from the corresponding feature models and horizontally uniform (range-independent) fields. The feature models were found to represent the smoothed observed propagation-loss field to within an rms error of 5 dB for the eddy and 7 dB for the front, compared to 10–15-dB rms errors obtained with the range-independent field. Some of the errors in the feature-model propagation loss were found to be due to high-amplitude 'oceanographic noise' in the field. The main conclusion is that the feature models represent the main acoustic properties of the ocean but do not show the significant effects of small-scale internal waves and fine-structure. It is recommended that feature models be used in conjunction with stochastic models of the internal waves, to represent the complete environmental variability.

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6

Timmermans, Mary-Louise, and Steven R. Jayne. "The Arctic Ocean Spices Up." Journal of Physical Oceanography 46, no. 4 (April 2016): 1277–84. http://dx.doi.org/10.1175/jpo-d-16-0027.1.

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AbstractThe contemporary Arctic Ocean differs markedly from midlatitude, ice-free, and relatively warm oceans in the context of density-compensating temperature and salinity variations. These variations are invaluable tracers in the midlatitudes, revealing essential fundamental physical processes of the oceans, on scales from millimeters to thousands of kilometers. However, in the cold Arctic Ocean, temperature variations have little effect on density, and a measure of density-compensating variations in temperature and salinity (i.e., spiciness) is not appropriate. In general, temperature is simply a passive tracer, which implies that most of the heat transported in the Arctic Ocean relies entirely on the ocean dynamics determined by the salinity field. It is shown, however, that as the Arctic Ocean warms up, temperature will take on a new role in setting dynamical balances. Under continued warming, there exists the possibility for a regime shift in the mechanisms by which heat is transported in the Arctic Ocean. This may result in a cap on the storage of deep-ocean heat, having profound implications for future predictions of Arctic sea ice.

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7

Marks, K. M. "Southern Ocean gravity field image available." Eos, Transactions American Geophysical Union 73, no. 12 (1992): 130. http://dx.doi.org/10.1029/91eo00108.

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8

Tolstoy, A., and B. Sotirin. "Ocean tomography via matched‐field processing." Journal of the Acoustical Society of America 97, no. 5 (May 1995): 3249. http://dx.doi.org/10.1121/1.411711.

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9

Voosen, Paul. "Ocean geoengineering scheme aces field test." Science 378, no. 6626 (December 23, 2022): 1266–67. http://dx.doi.org/10.1126/science.adg3935.

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10

Sushkevich, Tamara, Sergey Strelkov, and Svetlana Maksakova. "“Future Earth”: Nigmatulin Hypothesis and Dynamic Model of Radiation Field of Ocean-Atmosphere System." EPJ Web of Conferences 248 (2021): 01014. http://dx.doi.org/10.1051/epjconf/202124801014.

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The United Nations has proclaimed a Decade of Ocean Science for Sustainable Development (2021-2030) to support efforts to reverse the cycle of decline in ocean health and gather ocean stakeholders worldwide behind a common framework that will ensure ocean science can fully support countries in creating improved conditions for sustainable development of the Ocean. The marine realm is the largest component of the Earth’s system that stabilizes climate and support life on Earth and human well-being. Scientific understanding of the ocean’s responses to pressures and management action is fundamental for sustainable development. Planet Earth is a natural example of a dynamic system with nonlinear processes that is in continuous change. The Earth’s radiation field is a single physical field (electromagnetic radiation) and the unifying factor of the Earth dynamical system. The Earth’s climate system is a natural environment that includes the atmosphere, the hydrosphere (oceans, seas, lakes, rivers), the cryosphere (land surface, snow, sea and mountain ice, etc.), and the biosphere that unites all living things. According to the hypothesis of R.I. Nigmatulin “Ocean is a dictator of climate”. H2O and CO2 are competing climate influences. In this article, we propose original author’s mathematical models for radiation blocks with hyperspectral data on absorption by atmospheric components. The new models are based on the development of the theory of the optical transfer operator and the method of influence functions in the theory of radiation transfer and Boltzmann equations, as well as the iterative method of characteristics with iteration convergence accelerations.

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11

Van Sumeren, Hans, Liesl Hotaling, Ed Bailey, and Jason Slade. "Ocean Technology Field Academy—Empowering Ocean Stakeholders for a Sustainable Future." Marine Technology Society Journal 55, no. 3 (May 1, 2021): 100–101. http://dx.doi.org/10.4031/mtsj.55.3.36.

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Abstract Data generated from ocean observing, or ocean-atmosphere coupled observing, permeates every facet of ocean research, ocean sustainability efforts, and the blue economy and offers workforce opportunities for all education levels. The Decade of Ocean Science for Sustainable Development and the Seabed 2030 effort will capitalize on the data availability and place a spotlight on the increased need for a workforce capable of analyzing and applying these data to generate solutions for sustainable ocean uses.Although most jobs will not require advanced degrees in engineering or science, the preparation of the 21st century ocean technology workforce demands an understanding of marine science and other disciplines, an ability to resolve complex environmental issues, and the ability to communicate complex ideas to a broad audience. Fostering these critical abilities will require a new set of learning opportunities. Developing and maintaining such a workforce will rely on innovative and flexible educational programs that break through the traditional “siloed” approach to education, while offering multiple and rapid pathways for degree and certification attainment.In response, we propose a program to prepare a workforce with the ability to utilize sensors, sensor platforms, sensor networks, crewed and uncrewed surface/underwater vehicles, sonar systems, and data processing capabilities.

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12

Lilley,, F. E. M. (Ted), Antony White, and Graham S. Heinson. "Earth's magnetic field: ocean current contributions to vertical profiles in deep oceans." Geophysical Journal International 147, no. 1 (September 2001): 163–75. http://dx.doi.org/10.1046/j.1365-246x.2001.00514.x.

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13

Haines, Steven. "The Evolving Law of the Sea." Journal of Navigation 38, no. 02 (May 1985): 244–57. http://dx.doi.org/10.1017/s0373463300031362.

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The ocean environment within which navigators operate is not only physical in character; it is an economic, political and legal environment as well. One of the most significant factors influencing general environmental development in recent years has been the rapid advance and expansion in the field of ocean and ocean-related technology. Much of this technological evolution, while helping to reduce the restrictions imposed by the physical characteristics of the oceans, has created new challenges of an economic, political and legal nature.

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14

Zieliński, Tymon, Tomasz Kijewski, Aleksandra Koroza, Paulina Pakszys, and Izabela Kotynska-Zielinska. "Ocean zmian - innowacyjne działania edukacyjne w zakresie Ocean Literacy." Forum Filologiczne Ateneum, no. 1(10)2022 (December 31, 2022): 335–40. http://dx.doi.org/10.36575/2353-2912/1(10)2022.335.

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15

Semedo, Alvaro, Kay Sušelj, Anna Rutgersson, and Andreas Sterl. "A Global View on the Wind Sea and Swell Climate and Variability from ERA-40." Journal of Climate 24, no. 5 (March 1, 2011): 1461–79. http://dx.doi.org/10.1175/2010jcli3718.1.

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Abstract In this paper a detailed global climatology of wind-sea and swell parameters, based on the 45-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) wave reanalysis is presented. The spatial pattern of the swell dominance of the earth’s oceans, in terms of the wave field energy balance and wave field characteristics, is also investigated. Statistical analysis shows that the global ocean is strongly dominated by swell waves. The interannual variability of the wind-sea and swell significant wave heights, and how they are related to the resultant significant wave height, is analyzed over the Pacific, Atlantic, and Indian Oceans. The leading modes of variability of wind sea and swell demonstrate noticeable differences, particularly in the Pacific and Atlantic Oceans. During the Northern Hemisphere winter, a strong north–south swell propagation pattern is observed in the Atlantic Ocean. Statistically significant secular increases in the wind-sea and swell significant wave heights are found in the North Pacific and North Atlantic Oceans.

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16

Wu, Zhiyuan, and Naire Mohamad Alshdaifat. "Simulation of Marine Weather during an Extreme Rainfall Event: A Case Study of a Tropical Cyclone." Hydrology 6, no. 2 (May 24, 2019): 42. http://dx.doi.org/10.3390/hydrology6020042.

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The ocean is the largest source of water vapor on the planet, while precipitation is the greatest in tropical oceans and coastal areas. As a strong convective weather, typhoons bring not only strong winds but also strong precipitations. The accurate prediction of rainfall and precipitation induced by typhoons is still difficult because of the nonlinear relationship between typhoon precipitation and physical processes such as typhoon dynamics, heat, cloud microphysics, and radiation. In order to fully describe the interaction between sea and air, we simulated rainfall distribution under the influence of a typhoon using a state-of-the-art, atmosphere–ocean-wave model considering a real typhoon over the South China Sea as a case study. The typhoon wind field, pressure field, and spatial and temporal distribution of rainfall were simulated on the basis of this coupled atmosphere–ocean-wave model. The spatial asymmetry distribution characteristics of typhoon wind field, pressure field, and rainfall were revealed by the simulation. The reasons for this asymmetric distribution were elaborated through a diagnostic analysis.

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17

Sampson, Charles R., James Cummings, John A. Knaff, Mark DeMaria, and Efren A. Serra. "An Upper Ocean Thermal Field Metrics Dataset." Meteorology 1, no. 3 (September 1, 2022): 327–40. http://dx.doi.org/10.3390/meteorology1030021.

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The upper ocean provides a source of thermal energy for tropical cyclone development and maintenance through a series of complex interactions. In this work, we develop a seventeen-year dataset of upper ocean thermal field metrics for use in tropical cyclone studies and development of tropical cyclone intensity prediction models. These metrics include the surface temperature, two different measures of vertically integrated heat content, and four different measures of vertically averaged temperature. Some metrics have been used to study upper-ocean energy response to tropical cyclone passage, while others have been employed to improve operational tropical cyclone intensity prediction models. The vertically integrated ocean heat content has been used to improve tropical cyclone intensity forecasts at U.S. tropical cyclone forecast centers and is an integral part of several operational intensity forecast models. A static 2005–2021 dataset that includes all twelve metrics described within is available on the Naval Research Laboratory web server, and a subset of six metrics have been produced in real-time at Fleet Numerical Meteorology and Oceanography Center and provided to the public via the GODAE server since 2021.

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18

Vermeersen, B. "Effects of ice-melt induced gravity changes and solid earth deformation in the Netherlands." Netherlands Journal of Geosciences - Geologie en Mijnbouw 87, no. 3 (September 2008): 215. http://dx.doi.org/10.1017/s0016774600023295.

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Determining sea-level change caused by continental ice mass variations is a far more complicated matter than one might think. Even if effects like induced changes in ocean currents or thermal expansion of ocean water are neglected, melt water does not redistribute uniformly and homogeneously over the world’s oceans. If land ice melts, the gravity field of the earth changes due to the redistribution of the ice and melt water masses.

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19

Sokov, A. V. "TO THE 80th ANNIVERSARY OF BYSHEV – A MEMBER OF THE POLYGON–70 EXPEDITION." Journal of Oceanological Research 48, no. 3 (October 30, 2020): 236–43. http://dx.doi.org/10.29006/1564-2291.jor-2020.48(3).14.

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The article is dedicated to the 80th anniversary of Dr. Vladimir I. Byshev – mathematician, oceanologist, Head of the Laboratory of large-scale variability of hydrophysical fields of the Shirshov Institute of Oceanology of Russian Academy of Sciences. Vladimir Byshev is a major scientist in the study of the temporal and spatial variability of oceanological and meteorological characteristics in a wide range of scales, features of the interaction of the ocean and atmosphere, large-scale disturbances of the climate system, an active direct participant in two dozen scientific expeditions, including such large ocean projects as Polygon–70, POLYMODE, Megapolygon, Atlantex–90, as well as a number of expeditions to the regions of the western boundary currents of the Atlantic Ocean and the equatorial region of the Indian Ocean, in which new, previously unknown elements of the circulation of the World Ocean were discovered. He is an expert in the field of climate, a member of the Editorial boards of several scientific journals and the author of over 200 scientific publications. He is a co-author of the Atlas POLYMODE (1986), the largest international oceans research project, and the author of the well-known monograph “Synoptic and large-scale variability of the ocean and atmosphere”.

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20

Piontkovski, SA, YN Tokarev, EP Bitukov, R. Williams, and DA Kiefer. "The bioluminescent field of the Atlantic Ocean." Marine Ecology Progress Series 156 (1997): 33–41. http://dx.doi.org/10.3354/meps156033.

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21

Bostock, M. G., and A. M. Trehu. "Wave-Field Decomposition of Ocean Bottom Seismograms." Bulletin of the Seismological Society of America 102, no. 4 (August 1, 2012): 1681–92. http://dx.doi.org/10.1785/0120110162.

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22

Baxley, Paul A. "Matched‐field processing in a wedgelike ocean." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3047. http://dx.doi.org/10.1121/1.418628.

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23

Li, Zhongyi, and Hao Wang. "Ocean Wave Simulation Based on Wind Field." PLOS ONE 11, no. 1 (January 25, 2016): e0147123. http://dx.doi.org/10.1371/journal.pone.0147123.

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24

Chen, Kai, Ming Deng, Xianhu Luo, and Zhongliang Wu. "A micro ocean-bottom E-field receiver." GEOPHYSICS 82, no. 5 (September 1, 2017): E233—E241. http://dx.doi.org/10.1190/geo2016-0242.1.

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Ocean-bottom electromagnetic (EM) receivers are used to record EM signals for marine magnetotelluric and controlled-source EM offshore data acquisition. These marine EM data are used for offshore gas hydrate and petroleum exploration. Although many conventional receivers are used for offshore data acquisition, they have deficiencies, such as a large size, high cost, and low operational efficiency. To address these limitations, we have developed a micro ocean-bottom E-field (micro-OBE) receiver. It reduces costs and deck-space use, while providing improved horizontal resolution and operational efficiency. Based on conventional receiver specifications, including low noise levels, low power consumption, and low clock-drift error, we reduced the receiver size, provided a low-cost release mechanism, integrated an acoustic telemetry module, improved the operational efficiency, and reduced the field acquisition cost. The resulting micro-OBE is comprised of a compact nylon frame, 17 inch glass sphere, logging system, batteries, recovery beacon, burn-wire release mechanism, transducer, electrode, red flag, and anchor (which eliminates the heavy and expensive acoustic release). It has no alloy aluminum press case, and it contains a minimal number of glass spheres, watertight cables, and connectors. Its size is [Formula: see text] (not including the electrode arm, red flag, and anchor). Its weight in air is 70 kg. It has a noise level of [Formula: see text] (Hz) at 8 Hz, and it provides 33 days of battery lifetime. Offshore data acquisition was performed to evaluate the micro-OBE field performance during an offshore gas-hydrate experiment. Our results indicate that the receiver effectively functioned throughout the operation and offshore data acquisition. Micro-OBE was thus verified as providing a low cost, compact size, and high operational efficiency.

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25

Zhang, Taoye, Ziqiang Wang, HanWang, Wen Luo, and Dongshuang Li. "Visualization Method for Mesoscale Eddies Characteristics in Ocean Flow Fields Based on Variable Particle Systems." Journal of Asian Geography 3, no. 2 (September 24, 2024): 27–36. http://dx.doi.org/10.36777/jag2024.3.2.3.

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Visualization of ocean flow fields is a major area of interest in marine science. Existing visualization methods based on randomly generated particles tend to exhibit a uniform distribution. While this approach effectively represents ocean flow fields, it inadequately conveys the characteristic structures within the flow in an intuitive manner. Ocean eddies, as crucial components of ocean dynamics (Patrizio & Thompson, 2021), are key features in visualizing ocean flow fields. Mesoscale eddies, which contain 90% of the ocean's kinetic energy, play a vital role in transporting this energy. These eddies significantly influence local marine environments and are instrumental in understanding the kinetic energy of ocean flow fields, providing valuable insights for fields such as oceanography, meteorology, and climate research. The aim of this study is to establish a method based on variable particle systems. Initially, a feature set for mesoscale eddies and a parameter set for variable particles are defined. By mapping the feature set to the parameter set, we express the characteristic structure of mesoscale eddies dynamically. Using ocean flow field data, we demonstrate the method's ability to effectively highlight the characteristic structures of mesoscale eddies within the flow field through qualitative and quantitative evaluation metrics. By analyzing and visualizing ocean flow fields, we can gain a deeper understanding of their spatiotemporal evolution, thereby uncovering the patterns and regularities of their movement. This research approach not only facilitates the effective development, utilization, and sustainable management of marine resources but also aligns with the demands of digital ocean technology for visualizing marine spatial information.

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26

Isern-Fontanet, Jordi, Joaquim Ballabrera-Poy, Antonio Turiel, and Emilio García-Ladona. "Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated." Nonlinear Processes in Geophysics 24, no. 4 (October 17, 2017): 613–43. http://dx.doi.org/10.5194/npg-24-613-2017.

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Abstract. Ocean currents play a key role in Earth's climate – they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

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27

Qiao, Fangli, Yeli Yuan, Jia Deng, Dejun Dai, and Zhenya Song. "Wave–turbulence interaction-induced vertical mixing and its effects in ocean and climate models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2065 (April 13, 2016): 20150201. http://dx.doi.org/10.1098/rsta.2015.0201.

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Heated from above, the oceans are stably stratified. Therefore, the performance of general ocean circulation models and climate studies through coupled atmosphere–ocean models depends critically on vertical mixing of energy and momentum in the water column. Many of the traditional general circulation models are based on total kinetic energy (TKE), in which the roles of waves are averaged out. Although theoretical calculations suggest that waves could greatly enhance coexisting turbulence, no field measurements on turbulence have ever validated this mechanism directly. To address this problem, a specially designed field experiment has been conducted. The experimental results indicate that the wave–turbulence interaction-induced enhancement of the background turbulence is indeed the predominant mechanism for turbulence generation and enhancement. Based on this understanding, we propose a new parametrization for vertical mixing as an additive part to the traditional TKE approach. This new result reconfirmed the past theoretical model that had been tested and validated in numerical model experiments and field observations. It firmly establishes the critical role of wave–turbulence interaction effects in both general ocean circulation models and atmosphere–ocean coupled models, which could greatly improve the understanding of the sea surface temperature and water column properties distributions, and hence model-based climate forecasting capability.

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28

Xiong, Xiong, Ri Jie Yang, and Kang Le Miao. "Simulation of Ocean Wave-Generated Magnetic Field Disturbance Observed above Sea-Surface Based on Directional Spectrum." Advanced Materials Research 791-793 (September 2013): 1139–44. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1139.

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Ocean wave have a magnetic field disturbance, caused by the motional induction of sea water moving in the steady main field of Earth. Mass experiment indicates ocean wave-generated magnetic field disturbance can be a major limitation on the performance of airborne magnetic anomaly detection. To check the character of such disturbance observed above sea-surface, a harmonic ocean wave-generated magnetic field disturbance mathematical model based on Weavers monochromatic wave-generated magnetic field model and ocean wave directional spectrum is proposed. Algorithm is presented for real-time simulation of ocean wave-generated magnetic field disturbance corresponding to the proposed mathematical model. Numerical simulations of ocean wave-generated magnetic field disturbance are sampled above sea-surface by a stationary magnetometer and an airborne magnetometer moving steadily along a rectilinear path. Spectrum analysis of the samples is performed. Simulations results indicate that the proposed harmonic ocean wave magnetic field disturbance mathematical model can well-simulate the real sea conditions. Numerical simulations also reveal that there is a Doppler frequency shift with the increase of magnetometer flight speed. Moreover, energy of the magnetic field disturbance is more dispersed and frequency band is wider with the increase of magnetometer flight speed.

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29

Yoon, Seunghyun, Yongsung Park, Peter Gerstoft, and Woojae Seong. "Predicting ocean pressure field with a physics-informed neural network." Journal of the Acoustical Society of America 155, no. 3 (March 1, 2024): 2037–49. http://dx.doi.org/10.1121/10.0025235.

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Ocean sound pressure field prediction, based on partially measured pressure magnitudes at different range-depths, is presented. Our proposed machine learning strategy employs a trained neural network with range-depth as input and outputs complex acoustic pressure at the location. We utilize a physics-informed neural network (PINN), fitting sampled data while considering the additional information provided by the partial differential equation (PDE) governing the ocean sound pressure field. In vast ocean environments with kilometer-scale ranges, pressure fields exhibit rapidly fluctuating phases, even at frequencies below 100 Hz, posing a challenge for neural networks to converge to accurate solutions. To address this, we utilize the envelope function from the parabolic-equation technique, fundamental in ocean sound propagation modeling. The envelope function shows slower variations across ranges, enabling PINNs to predict sound pressure in an ocean waveguide more effectively. Additional PDE information allows PINNs to capture PDE solutions even with a limited amount of training data, distinguishing them from purely data-driven machine learning approaches that require extensive datasets. Our approach is validated through simulations and using data from the SWellEx-96 experiment.

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30

Zhou, Jianbo, Shengchun Piao, Yiwang Huang, Shizhao Zhang, and Ke Qu. "A spatial correlation model for the horizontal non-isotropic ocean ambient noise vector field." Journal of Low Frequency Noise, Vibration and Active Control 36, no. 2 (June 2017): 124–37. http://dx.doi.org/10.1177/0263092317711984.

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The ocean ambient noise is one of interference fields of underwater acoustic channel. The design and use of any sonar system are bound to be affected by ocean ambient noise, so to research the spatial correlation characteristics of noise field is of positive significance to improving the performance of sonar system. Only wind-generated noise is considered in most existing ambient noise models. In this case, the noise field is isotropic in horizontal direction. However, due to those influencing factors, like rainfall, ships and windstorm, etc. for a real ocean environment, noise field becomes anisotropic horizontally and the spatial structure of ambient field also changes correspondingly. This paper presents a spatial correlation of the acoustic vector field of anisotropic field by introducing Von Mises probability distribution to describe horizontal directivity. Closed-form expressions are derived which relate the cross-correlation among the sound pressure and three orthogonal components of vibration velocity, besides, the influence of the non-uniformity of noise field on the correlation characteristics of noise vector field was analysed. The model presented in this paper can provide theoretical guidance for the design and application of vector sensors array. Furthermore, the achievement could be applied to front extraction, Green’s function extraction, inversion for ocean bottom parameters, and so on.

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31

TOLSTOY, A. "REVIEW OF MATCHED FIELD PROCESSING FOR ENVIRONMENTAL INVERSE PROBLEMS." International Journal of Modern Physics C 03, no. 04 (August 1992): 691–708. http://dx.doi.org/10.1142/s0129183192000439.

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Matched Field Processing (MFP) is a signal processing technique that has enjoyed much recent success in the Underwater Acoustics community, mainly as a consequence of the high accuracy currently achievable in predictions of non-isotropic, non-planar ocean acoustic fields. MFP is applied to acoustic fields measured on arrays of hydrophones and has been used primarily to solve the inverse source problem, i.e., to determine the unknown range, depth, and bearing of acoustic sources in a known ocean environment. However, the MFP approach has also been applied to the environmental inverse problem, i.e., to determine the characteristics of an unknown ocean environment. This paper will review the work done in this latter area.

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32

Wang, Ling. "Dake Chen: unraveling the secrets of ocean–climate interaction." National Science Review 4, no. 1 (January 1, 2017): 136–39. http://dx.doi.org/10.1093/nsr/nww100.

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Abstract The ocean is a complex and mysterious system that attracts scientists around the world to unravel its secrets. Dake Chen, a distinguished physical oceanographer and an academician of the Chinese Academy of Sciences, is one of them. Since the mid-1980s, he has been studying ocean dynamics and ocean–atmosphere interaction, and has made seminal contributions to the understanding and prediction of short-term climate variability, especially the El Niño phenomenon. In a recent interview with NSR, Professor Dake Chen says that China has made significant progress in recent years in ocean research, but, in order to make breakthroughs in the field of oceanography, China needs to further expand the scope of research programs from coastal seas to open oceans, to greatly increase the investment in global ocean-observing systems and to pay more attention to fundamental scientific problems in addition to practical applications. He also calls for a better-defined national strategic plan for ocean science and technology.

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33

Xu, Yan, Jun Xu, Wei Hua Zhu, Xia Feng, and Hai Yan Xie. "3-D Modeling the Magnetic Field due to Ocean Tidal Flow O1." Advanced Materials Research 658 (January 2013): 471–74. http://dx.doi.org/10.4028/www.scientific.net/amr.658.471.

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The tidal motion of the ocean water through the ambient magnetic field, generates secondary electric and magnetic field. The magnetic fields generated by the diurnal (O1) ocean flow can be clearly detected. We simulate the magnetic signals for tidal constituents –diurnal (O1) tides. The idea of exploiting tidal signals for EM studies of the Earth is not new, but so far it was used only for interpretation of inland and transoceanic magnetic field data due to O1. Emphasis in this work is made on a discussion of sea bottom electric field of the same origin.

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34

Irrgang, C., J. Saynisch, and M. Thomas. "Impact of variable sea-water conductivity on motional induction simulated with an OGCM." Ocean Science Discussions 12, no. 4 (August 19, 2015): 1869–91. http://dx.doi.org/10.5194/osd-12-1869-2015.

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Abstract. Carrying high concentrations of dissolved salt, ocean water is a good electrical conductor. As sea-water flows through the Earth's ambient geomagnetic field, electric fields are generated, which in turn induce secondary magnetic fields. In current models for oceanic induced magnetic fields, a realistic consideration of sea-water conductivity is often neglected and the effect on the variability of the oceanic induced magnetic field unknown. To model magnetic fields that are induced by non-tidal global ocean currents, an electromagnetic induction model is implemented into the Ocean Model for Circulation and Tides (OMCT). This provides the opportunity to not only model oceanic induced magnetic signals, but to assess the impact of oceanographic phenomena on the induction process. In this paper, the sensitivity of the induction process due to spatial and temporal variations in sea-water conductivity is investigated. It is shown that assuming an ocean-wide uniform conductivity is insufficient to accurately capture the temporal variability of the magnetic signal. Using instead a realistic global sea-water conductivity distribution increases the temporal variability of the magnetic field up to 45 %. Especially vertical gradients in sea-water conductivity prove to be a key factor for the variability of the oceanic induced magnetic field. However, temporal variations of sea-water conductivity only marginally affect the magnetic signal.

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35

Irrgang, Christopher, Jan Saynisch-Wagner, and Maik Thomas. "Depth of origin of ocean-circulation-induced magnetic signals." Annales Geophysicae 36, no. 1 (January 29, 2018): 167–80. http://dx.doi.org/10.5194/angeo-36-167-2018.

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Abstract. As the world ocean moves through the ambient geomagnetic core field, electric currents are generated in the entire ocean basin. These oceanic electric currents induce weak magnetic signals that are principally observable outside of the ocean and allow inferences about large-scale oceanic transports of water, heat, and salinity. The ocean-induced magnetic field is an integral quantity and, to first order, it is proportional to depth-integrated and conductivity-weighted ocean currents. However, the specific contribution of oceanic transports at different depths to the motional induction process remains unclear and is examined in this study. We show that large-scale motional induction due to the general ocean circulation is dominantly generated by ocean currents in the upper 2000 m of the ocean basin. In particular, our findings allow relating regional patterns of the oceanic magnetic field to corresponding oceanic transports at different depths. Ocean currents below 3000 m, in contrast, only contribute a small fraction to the ocean-induced magnetic signal strength with values up to 0.2 nT at sea surface and less than 0.1 nT at the Swarm satellite altitude. Thereby, potential satellite observations of ocean-circulation-induced magnetic signals are found to be likely insensitive to deep ocean currents. Furthermore, it is shown that annual temporal variations of the ocean-induced magnetic field in the region of the Antarctic Circumpolar Current contain information about sub-surface ocean currents below 1000 m with intra-annual periods. Specifically, ocean currents with sub-monthly periods dominate the annual temporal variability of the ocean-induced magnetic field. Keywords. Electromagnetics (numerical methods) – geomagnetism and paleomagnetism (geomagnetic induction) – history of geophysics (transport)

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36

Beron-Vera, Francisco J., María J. Olascoaga, and Gustavo J. Goni. "Surface Ocean Mixing Inferred from Different Multisatellite Altimetry Measurements." Journal of Physical Oceanography 40, no. 11 (November 1, 2010): 2466–80. http://dx.doi.org/10.1175/2010jpo4458.1.

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Abstract Two sea surface height (SSH) anomaly fields distributed by Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) Altimetry are evaluated in terms of the effects that they produce on mixing. One SSH anomaly field, tagged REF, is constructed using measurements made by two satellite altimeters; the other SSH anomaly field, tagged UPD, is constructed using measurements made by up to four satellite altimeters. Advection is supplied by surface geostrophic currents derived from the total SSH fields resulting from the addition of these SSH anomaly fields to a mean SSH field. Emphasis is placed on the extraction from the currents of Lagrangian coherent structures (LCSs), which, acting as skeletons for patterns formed by passively advected tracers, entirely control mixing. The diagnostic tool employed to detect LCSs is provided by the computation of finite-time Lyapunov exponents. It is found that currents inferred using UPD SSH anomalies support mixing with characteristics similar to those of mixing produced by currents inferred using REF SSH anomalies. This result mainly follows from the fact that, being more easily characterized as chaotic than turbulent, mixing as sustained by currents derived using UPD SSH anomalies is quite insensitive to spatiotemporal truncations of the advection field.

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37

Tenzer, Robert, and Peter Vajda. "A global correlation of the step-wise consolidated crust-stripped gravity field quantities with the topography, bathymetry, and the CRUST 2.0 Moho boundary." Contributions to Geophysics and Geodesy 39, no. 2 (January 1, 2009): 133–47. http://dx.doi.org/10.2478/v10126-009-0006-4.

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A global correlation of the step-wise consolidated crust-stripped gravity field quantities with the topography, bathymetry, and the CRUST 2.0 Moho boundaryWe investigate globally the correlation of the step-wise consolidated cruststripped gravity field quantities with the topography, bathymetry, and the Moho boundary. Global correlations are quantified in terms of Pearson's correlation coefficient. The elevation and bathymetry data from the ETOPO5 are used to estimate the correlation of the gravity field quantities with the topography and bathymetry. The 2×2 arc-deg discrete data of the Moho depth from the global crustal model CRUST 2.0 are used to estimate the correlation of the gravity field quantities with the Moho boundary. The results reveal that the topographically corrected gravity field quantities have the highest absolute correlation with the topography. The negative correlation of the topographically corrected gravity disturbances with the topography over the continents reaches -0.97. The ocean, ice and sediment density contrasts stripped and topographically corrected gravity field quantities have the highest correlation with the bathymetry (ocean bottom relief). The correlation of the ocean, ice and sediment density contrasts stripped and topographically corrected gravity disturbances over the oceans reaches 0.93. The consolidated crust-stripped gravity field quantities have the highest absolute correlation with the Moho boundary. In particular, the global correlation of the consolidated crust-stripped gravity disturbances with the Moho boundary is found to be -0.92. Among all the investigated gravity field quantities, the consolidated crust-stripped gravity disturbances are thus the best suited for a refinement of the Moho density interface by means of the gravimetric modeling or inversion.

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38

Qiu, Shi Hui, and Qi Wang. "The Application of Wind and Solar Power Generation System in the Ocean Marginal Oil Field Development." Advanced Materials Research 282-283 (July 2011): 731–34. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.731.

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Facing the growing energy needs, ocean marginal fields more and more attention. But the supply is one of the constraints of ocean marginal oil field development. Therefore research and development in power supply system of local installation, low cost, easy maintenance and management, resource conservation, environmental-friendly is critical. This paper investigates the application of wind and solar energy development, as against the existing marginal oil field development project, through designing wind and solar power generation system to discuss the development of marginal oil fields in the feasibility of the application to make constructive comments.

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39

Michalopoulou, Zoi-Heleni, and Peter Gerstoft. "Inversion in an uncertain ocean using Gaussian processes." Journal of the Acoustical Society of America 153, no. 3 (March 2023): 1600–1611. http://dx.doi.org/10.1121/10.0017437.

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Gaussian processes (GPs) can capture correlation of the acoustic field at different depths in the ocean. This feature is exploited in this work for pre-processing acoustic data before these are employed for source localization and environmental inversion using matched field inversion (MFI) in an underwater waveguide. Via the application of GPs, the data are denoised and interpolated, generating densely populated acoustic fields at virtual arrays, which are then used as data in MFI. Replicas are also computed at the virtual receivers at which field predictions are made. The correlations among field measurements at distinct spatial points are manifested through the selection of kernel functions. These rely on hyperparameters, that are estimated through a maximum likelihood process for optimal denoising and interpolation. The approach, employing Gaussian and Matérn kernels, is tested on synthetic and real data with both an exhaustive search and genetic algorithms and is found to be superior to conventional beamformer MFI. It is also shown that the Matérn kernel, providing more degrees of freedom because of an increased number of hyperparameters, is preferable over the frequently used Gaussian kernel.

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40

Tishchenko, Pavel. "Electric Field of the Ocean Induced by Diffusion." Journal of Electromagnetic Analysis and Applications 07, no. 01 (2015): 10–18. http://dx.doi.org/10.4236/jemaa.2015.71002.

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41

Jie, Shi. "Numerical Method for Simulating Ocean Ambient Noise Field." Information Technology Journal 12, no. 24 (December 1, 2013): 8370–76. http://dx.doi.org/10.3923/itj.2013.8370.8376.

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42

Yoneyama, Kunio, Yukio Masumoto, Yoshifumi Kuroda, Masaki Katsumata, Keisuke Mizuno, Yukari N. Takayabu, Masanori Yoshizaki, et al. "MISMO FIELD EXPERIMENT IN THE EQUATORIAL INDIAN OCEAN." Bulletin of the American Meteorological Society 89, no. 12 (December 2008): 1889–904. http://dx.doi.org/10.1175/2008bams2519.1.

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43

Minassian, George R. "Matched‐field source localization in a random ocean." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3048. http://dx.doi.org/10.1121/1.418685.

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44

Harrison, Brian F., Richard J. Vaccaro, and Donald W. Tufts. "Robust matched-field localization in uncertain ocean environments." Journal of the Acoustical Society of America 103, no. 6 (June 1998): 3721–24. http://dx.doi.org/10.1121/1.423091.

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45

Zheleznyak, L. K., and V. N. Koneshov. "Studying the gravitational field of the world ocean." Herald of the Russian Academy of Sciences 77, no. 3 (June 2007): 217–26. http://dx.doi.org/10.1134/s1019331607030021.

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46

Adams, Eric, Makoto Akai, Guttorm Alendal, Lars Golmen, Peter Haugan, Howard Herzog, Stephen Masutani, et al. "Letter: International Field Experiment on Ocean Carbon Sequestration." Environmental Science & Technology 36, no. 21 (November 2002): 399A. http://dx.doi.org/10.1021/es022442b.

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47

Migliaccio, Maurizio, Lanqing Huang, and Andrea Buono. "SAR Speckle Dependence on Ocean Surface Wind Field." IEEE Transactions on Geoscience and Remote Sensing 57, no. 8 (August 2019): 5447–55. http://dx.doi.org/10.1109/tgrs.2019.2899491.

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48

Winks, Lewis, Mark Ward, Joseph Zilch, and Ewan Woodley. "Residential marine field-course impacts on ocean literacy." Environmental Education Research 26, no. 7 (April 28, 2020): 969–88. http://dx.doi.org/10.1080/13504622.2020.1758631.

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49

ZHU, Yongqiang, Lihu JIA, Chunming DUAN, Xiaoyan SUN, and Wenrui GUO. "Status of testing field for ocean energy generation." Journal of Modern Power Systems and Clean Energy 5, no. 2 (September 26, 2015): 160–68. http://dx.doi.org/10.1007/s40565-015-0152-9.

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50

Hogg, Andrew McC, Michael P. Meredith, Don P. Chambers, E. Povl Abrahamsen, Chris W. Hughes, and Adele K. Morrison. "Recent trends in the Southern Ocean eddy field." Journal of Geophysical Research: Oceans 120, no. 1 (January 2015): 257–67. http://dx.doi.org/10.1002/2014jc010470.

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