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Готові списки джерел за темами / Ocean data

Добірка наукової літератури з теми "Ocean data"

Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Ocean data"

1

Anonymous. "Ocean data." Eos, Transactions American Geophysical Union 75, no. 42 (1994): 490. http://dx.doi.org/10.1029/eo075i042p00490-04.

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2

Bellingham, James G., and Mike Godin. "Exploring ocean data." ACM SIGMOD Record 37, no. 2 (June 2008): 78–82. http://dx.doi.org/10.1145/1379387.1379410.

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3

Zhuang, Yueting, Yaoguang Wang, Jian Shao, Ling Chen, Weiming Lu, Jianling Sun, Baogang Wei, and Jiangqin Wu. "D-Ocean: an unstructured data management system for data ocean environment." Frontiers of Computer Science 10, no. 2 (October 20, 2015): 353–69. http://dx.doi.org/10.1007/s11704-015-5045-6.

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4

Brown, Murray. "Ocean Data View 4.0." Oceanography 11, no. 2 (1998): 19–21. http://dx.doi.org/10.5670/oceanog.1998.04.

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5

Barisits, Martin, Fernando Barreiro, Thomas Beermann, Karan Bhatia, Kaushik De, Arnaud Dubreuil, Johannes Elmsheuser, et al. "The Data Ocean Project." EPJ Web of Conferences 214 (2019): 04020. http://dx.doi.org/10.1051/epjconf/201921404020.

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Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.

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6

Denner, Warren W., and Christopher N. K. Mooers. "Archived ocean data bases." Eos, Transactions American Geophysical Union 68, no. 45 (1987): 1580. http://dx.doi.org/10.1029/eo068i045p01580-03.

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7

Edwards, Christopher A., Andrew M. Moore, Ibrahim Hoteit, and Bruce D. Cornuelle. "Regional Ocean Data Assimilation." Annual Review of Marine Science 7, no. 1 (January 3, 2015): 21–42. http://dx.doi.org/10.1146/annurev-marine-010814-015821.

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8

Olsen, Are, Robert M. Key, Steven van Heuven, Siv K. Lauvset, Anton Velo, Xiaohua Lin, Carsten Schirnick, et al. "The Global Ocean Data Analysis Project version 2 (GLODAPv2) – an internally consistent data product for the world ocean." Earth System Science Data 8, no. 2 (August 15, 2016): 297–323. http://dx.doi.org/10.5194/essd-8-297-2016.

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Abstract. Version 2 of the Global Ocean Data Analysis Project (GLODAPv2) data product is composed of data from 724 scientific cruises covering the global ocean. It includes data assembled during the previous efforts GLODAPv1.1 (Global Ocean Data Analysis Project version 1.1) in 2004, CARINA (CARbon IN the Atlantic) in 2009/2010, and PACIFICA (PACIFic ocean Interior CArbon) in 2013, as well as data from an additional 168 cruises. Data for 12 core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have been subjected to extensive quality control, including systematic evaluation of bias. The data are available in two formats: (i) as submitted but updated to WOCE exchange format and (ii) as a merged and internally consistent data product. In the latter, adjustments have been applied to remove significant biases, respecting occurrences of any known or likely time trends or variations. Adjustments applied by previous efforts were re-evaluated. Hence, GLODAPv2 is not a simple merging of previous products with some new data added but a unique, internally consistent data product. This compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 6 µmol kg−1 in total alkalinity, 0.005 in pH, and 5 % for the halogenated transient tracers.The original data and their documentation and doi codes are available at the Carbon Dioxide Information Analysis Center (http://cdiac.ornl.gov/oceans/GLODAPv2/). This site also provides access to the calibrated data product, which is provided as a single global file or four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under the doi:10.3334/CDIAC/OTG.NDP093_GLODAPv2. The product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This paper documents the GLODAPv2 methods and products and includes a broad overview of the secondary quality control results. The magnitude of and reasoning behind each adjustment is available on a per-cruise and per-variable basis in the online Adjustment Table.

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9

Adhikary, Subhrangshu, and Saikat Banerjee. "Improved Large-Scale Ocean Wave Dynamics Remote Monitoring Based on Big Data Analytics and Reanalyzed Remote Sensing." Nature Environment and Pollution Technology 22, no. 1 (March 2, 2023): 269–76. http://dx.doi.org/10.46488/nept.2023.v22i01.026.

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Oceans and large water bodies have the potential to generate a large amount of green and renewable energy by harvesting the ocean surface properties like wind waves and tidal waves using Wave Energy Converter (WEC) devices. Although the oceans have this potential, very little ocean energy is harvested because of improper planning and implementation challenges. Besides this, monitoring ocean waves is of immense importance as several ocean-related calamities could be prevented. Also, the ocean serves as the maritime transportation route. Therefore, a need exists for remote and continuous monitoring of ocean waves and preparing strategies for different situations. Remote sensing technology could be utilized for a large scale low-cost opportunity for monitoring entire ocean bodies and extracting several important ocean surface features like wave height, wave time period, and drift velocities that can be used to estimate the ideal locations for power generation and find locations for turbulent waters so that maritime transportation hazards could be prevented. To process this large volume of data, Big Data Analytics techniques have been used to distribute the workload to worker nodes, facilitating a fast calculation of the reanalyzed remote sensing data. The experiment was conducted on Indian Coastline. The findings from the experiment show that a total of 1.86 GWh energy can be harvested from the ocean waves of the Indian Coastline, and locations of turbulent waters can be predicted in real-time to optimize maritime transportation routes.

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10

Peres Teixeira, Carlos Eduardo. "THE DATA WE NEED FOR THE OCEAN WE WANT TO PREDICT: A BRAZILIAN PERSPECTIVE." Arquivos de Ciências do Mar 55, Especial (March 18, 2022): 292–97. http://dx.doi.org/10.32360/acmar.v55iespecial.78513.

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A Predicted Ocean is one of the UN Ocean Decade goals. Ocean observations and numerical simulations of the ocean circulation are at the heart of this outcome. Numerical models are used to understand the present and predict future ocean states, but also the human impact on it, among many other uses. However, its results are only a representation of reality, and we need to validate the numerical model outputs with observational data before using them. Considering its coast extension and the marine economic importance, Brazil does not collect enough physical ocean data and we have only a few real-time observation systems. Unfortunately, due to the COVID and the current national science budget crisis, the number of real-time observations has been further reduced. From a positive perspective, I must believe that this situation will change. We need to be prepared to convince the stakeholders of the importance of observing systems to our society and secure a budget in that regard. This is the way to better predict our oceans. Keywords: ocean modeling, observation systems, Ocean Decade, numerical model validation.

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Дисертації з теми "Ocean data"

1

Woodgate, Rebecca A. "Data assimilation in ocean models." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359566.

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2

Moore, A. M. "Data assimilation in ocean models." Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375276.

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3

Anderson, Timothy A. "Visualization and assessment of Global Ocean Data assimulation experiment profile data for the Pacific Ocean." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA395809.

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4

Lguensat, Redouane. "Learning from ocean remote sensing data." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0050/document.

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Reconstruire des champs géophysiques à partir d'observations bruitées et partielles est un problème classique bien étudié dans la littérature. L'assimilation de données est une méthode populaire pour aborder ce problème, et se fait par l'utilisation de techniques classiques, comme le filtrage de Kalman d’ensemble ou des filtres particulaires qui procèdent à une évaluation online du modèle physique afin de fournir une prévision de l'état. La performance de l'assimilation de données dépend alors fortement de du modèle physique. En revanche, la quantité de données d'observation et de simulation a augmenté rapidement au cours des dernières années. Cette thèse traite l'assimilation de données d'une manière data-driven et ce, sans avoir accès aux équations explicites du modèle. Nous avons développé et évalué l'assimilation des données par analogues (AnDA), qui combine la méthode des analogues et des méthodes de filtrage stochastiques (filtres Kalman, filtres à particules, chaînes de Markov cachées). Des applications aux modèles chaotiques simplifiés et à des études de cas de télédétection réelle (température de surface de lamer, anomalies du niveau de la mer), nous démontrons la pertinence d'AnDA pour l'interpolation de données manquantes des systèmes dynamiques non linéaires et à haute dimension à partir d'observations irrégulières et bruyantes.Motivé par l'essor du machine learning récemment, la dernière partie de cette thèse est consacrée à l'élaboration de modèles deep learning pour la détection et de tourbillons océaniques à partir de données de sources multiples et/ou multi temporelles (ex: SST-SSH), l'objectif général étant de surpasser les approches dites expertes
Reconstructing geophysical fields from noisy and partial remote sensing observations is a classical problem well studied in the literature. Data assimilation is one class of popular methods to address this issue, and is done through the use of classical stochastic filtering techniques, such as ensemble Kalman or particle filters and smoothers. They proceed by an online evaluation of the physical modelin order to provide a forecast for the state. Therefore, the performanceof data assimilation heavily relies on the definition of the physical model. In contrast, the amount of observation and simulation data has grown very quickly in the last decades. This thesis focuses on performing data assimilation in a data-driven way and this without having access to explicit model equations. The main contribution of this thesis lies in developing and evaluating the Analog Data Assimilation(AnDA), which combines analog methods (nearest neighbors search) and stochastic filtering methods (Kalman filters, particle filters, Hidden Markov Models). Through applications to both simplified chaotic models and real ocean remote sensing case-studies (sea surface temperature, along-track sea level anomalies), we demonstrate the relevance of AnDA for missing data interpolation of nonlinear and high dimensional dynamical systems from irregularly-sampled and noisy observations. Driven by the rise of machine learning in the recent years, the last part of this thesis is dedicated to the development of deep learning models for the detection and tracking of ocean eddies from multi-source and/or multi-temporal data (e.g., SST-SSH), the general objective being to outperform expert-based approaches

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5

Zika, Jan David Climate &amp Environmental Dynamics Laboratory Faculty of Science UNSW. "Quantifying ocean mixing from hydrographic data." Awarded by:University of New South Wales. Climate & Environmental Dynamics Laboratory, 2010. http://handle.unsw.edu.au/1959.4/44872.

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The relationship between the general circulation of the ocean and, along-isopycnal and vertical mixing is explored. Firstly, advection down isopycnal tracer gradients is related to mixing in specific regions of the ocean. Secondly, a general inverse method is developed for estimating both mixing and the general circulation. Two examples of down gradient advection are explored. Firstly the region of Mediterranean outflow in the North Atlantic. Given a known transport of warm salty water out of the Mediterranean Sea and the mean hydrography of the eastern North Atlantic, the vertical structure of the along-isopycnal mixing coefficient, K, and the vertical mixing coefficient, D, is revealed. Secondly, the Southern Ocean Meridional Overturning Circulation, SMOC, is investigated. There, relatively warm salty water is advected southward, along-isopycnals, toward fresher cooler surface waters. The strength and structure of the SMOC is related to K and D by considering advection down along-isopycnal gradients of temperature and potential vorticity. The ratio of K to D and their magnitudes are identified. A general tool is developed for estimating the ocean circulation and mixing; the \textit{tracer-contour inverse method}. Integrating along contours of constant tracer on isopycnals, differences in a geostrophic streamfunction are related to advection and hence to mixing. This streamfunction is related in the vertical, via an analogous form of the depth integrated thermal wind equation. The tracer-contour inverse method combines aspects of the box, beta spiral and Bernoulli methods. The tracer-contour inverse method is validated against the output of a layered model and against in-situ observations from the eastern North Atlantic. The method accurately reproduces the observed mixing rates and reveals their vertical structure.

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6

Lawrence, David Hawkesford. "Ocean colour analysis using CZCS data." Thesis, University of Plymouth, 1987. http://hdl.handle.net/10026.1/1731.

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7

Melo, Jose Luis Branco Seabra de. "Nonlinear parametric wave model compared with field data." Monterey, Calif. : Naval Postgraduate School, 1985. http://catalog.hathitrust.org/api/volumes/oclc/57738811.html.

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8

Liu, Liyan Jones C. K. R. T. "Lagrangian data assimilation into layered ocean model." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,786.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.
Title from electronic title page (viewed Dec. 18, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics." Discipline: Mathematics; Department/School: Mathematics.

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9

Phillipson, Luke. "Ocean data assimilation in the Angola Basin." Thesis, Imperial College London, 2018. http://hdl.handle.net/10044/1/62645.

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The predictability of the ocean currents and the Congo River plume within the Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with data assimilation (4D-Var). Firstly, the impact of assimilating a novel remote-sensing data set, satellite-derived ocean currents (OSCAR) as compared to the more conventional satellite sea surface height (SSH) on ocean current predictability was assessed. In comparing 17 simulated and observed drifters throughout January-March 2013 using four different metrics, it was found that OSCAR assimilation only improves the Lagrangian predictability of ocean currents as much as altimetry assimilation. The impact of combining the aforementioned remote-sensing observations (OSCAR or SSH) with drifters was then investigated throughout the same period to assess whether this combination could improve upon assimilating the drifters alone on ocean current predictability. It was found that the addition of drifters significantly improves the Lagrangian predictability of the ocean currents in comparison to either altimetry or OSCAR as expected. More surprisingly, the assimilation of either SSH or OSCAR with the drifter velocities does not significantly improve the Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. Additionally, a new metric denoted the crossover time was formulated using the drifters, defined as the time it takes for a numerical model to equal the performance of persistence. In addition to ROMS, a global ocean model was also evaluated to demonstrate and quantify the metric fully. Finally, the impact of assimilating a recently available advanced version of a satellite salinity product (SMOS), on the Congo River plume was investigated. With some metrics specifically focusing on validating the Congo River plume, it was found that the assimilation of SMOS improved the representation of the plume within the model as well as the modelled salinity fields.

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10

Navarro, Moisés M. "Ocean wave data analysis using Hilbert transform techniques." Thesis, Monterey, California. Naval Postgraduate School, 1996. http://hdl.handle.net/10945/32022.

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A novel technique to determine the phase velocity of long-wavelength shoaling waves is investigated. Operationally, the technique consists of three steps. First, using the Hilbert transform of a time series, the phase of the analytic signal is determined. Second, the correlations of the phases of analytic signals between two points in space are calculated and an average time of travel of the wave fronts is obtained. Third, if directional spectra are available or can be determined from time series of large array of buoys, the angular information can be used to determine the true time of travel. The phase velocity is obtained by dividing the distance between buoys by the correlation time. Using the Hilbert transform approach, there is no explicit assumption of the relation between frequency and wavenumber of waves in the wave field, indicating that it may be applicable to arbitrary wave fields, both linear and nonlinear. Limitations of the approach are discussed.

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Книги з теми "Ocean data"

1

Ocean data viewer. Cambridge: UNEP World Conservation Monitoring Centre, 2011.

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2

Mark, Donelan, Wallops Flight Facility, and United States. Office of Naval Research, eds. SWADE data guide. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1996.

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3

Oberholzner, Werner. SWADE data guide. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1996.

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4

Office, WOCE International Project, and World Ocean Circulation Experiment. Data Information Unit, eds. WOCE Global Data. 2nd ed. Southampton, U.K: WOCE International Project Office, Data Information Unit, 2000.

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5

Briscoe, Melbourne G. Status report on ocean data telemetry. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1986.

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6

Computational ocean acoustics. 2nd ed. New York: Springer, 2011.

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7

University of Colorado, Boulder. Cooperative Institute for Research in Environmental Sciences., United States. National Oceanic and Atmospheric Administration., Environmental Research Laboratories (U.S.), National Center for Atmospheric Research (U.S.), and National Climatic Data Center (U.S.), eds. Comprehensive ocean-atmosphere data set: Release 1. Boulder, Colo: [Environmental Research Laboratories], 1985.

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8

Mendelssohn, Roy. Comprehensive Ocean Data Set Extraction: User's guide. [La Jolla, Calif.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southwest Fisheries Science Center, 1996.

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9

University of Colorado, Boulder. Cooperative Institute for Research in Environmental Sciences., United States. National Oceanic and Atmospheric Administration., Environmental Research Laboratories (U.S.), National Center for Atmospheric Research (U.S.), and National Climatic Data Center (U.S.), eds. Comprehensive ocean-atmosphere data set: Release 1. Boulder, Colo: [Environmental Research Laboratories], 1985.

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10

L, Linton R. H., Natural Environment Research Council (Great Britain), and International Cartographic Association, eds. Methods of display of ocean survey data. [Swindon, Wiltshire]: Natural Environment Research Council, 1985.

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Частини книг з теми "Ocean data"

1

Haines, Keith. "Ocean Data Assimilation." In Data Assimilation, 517–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-74703-1_20.

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2

Rapp, Donald. "Ocean Sediment Data." In Ice Ages and Interglacials, 119–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10466-5_5.

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3

Prior-Jones, Michael R. "Ocean Data Telemetry." In Encyclopedia of Remote Sensing, 429–33. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_117.

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4

Rapp, Donald. "Ocean sediment data." In Ice Ages and Interglacials, 171–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30029-5_5.

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5

Evensen, Geir. "An ocean prediction system." In Data Assimilation, 255–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03711-5_16.

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6

Corredor, Jorge E. "Coastal Ocean Observing Data Quality Assurance and Quality Control, Data Validation, Databases, and Data Presentation." In Coastal Ocean Observing, 125–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78352-9_7.

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7

Lorenc, Andrew C. "Atmospheric Data Assimilation and Quality Control." In Ocean Forecasting, 73–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-22648-3_5.

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8

Cummings, James A. "Ocean Data Quality Control." In Operational Oceanography in the 21st Century, 91–121. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0332-2_4.

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9

Makris, N. C., J. M. Berkson, W. A. Kuperman, and J. S. Perkins. "Ocean-Basin Scale Inversion of Reverberation Data." In Ocean Reverberation, 195–201. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2078-4_26.

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10

Heard, Garry J., D. J. Thomson, and G. H. Brooke. "Upslope Propagation Data Versus Two-Way PE." In Ocean Reverberation, 247–52. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2078-4_34.

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Тези доповідей конференцій з теми "Ocean data"

1

Weaver, R., and R. Barry. "Cryospheric data management system for special sensor microwave imager DMSP data." In OCEANS '85 - Ocean Engineering and the Environment. IEEE, 1985. http://dx.doi.org/10.1109/oceans.1985.1160269.

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2

Enabnit, D. "Shipboard data system III." In OCEANS '85 - Ocean Engineering and the Environment. IEEE, 1985. http://dx.doi.org/10.1109/oceans.1985.1160218.

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3

Shumchenia, Emily. "THE NORTHEAST OCEAN DATA PORTAL: DATA AND MAPS FOR NEW ENGLAND'S OCEANS." In 54th Annual GSA Northeastern Section Meeting - 2019. Geological Society of America, 2019. http://dx.doi.org/10.1130/abs/2019ne-328305.

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4

Seta, Takahiro. "A data compression method by dropping Most Significant Bits and its application to transmission of position data." In 2016 Techno-Ocean (Techno-Ocean). IEEE, 2016. http://dx.doi.org/10.1109/techno-ocean.2016.7890642.

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5

Sagawa, Genki. "Ice navigation performance analysis using satellite data." In 2016 Techno-Ocean (Techno-Ocean). IEEE, 2016. http://dx.doi.org/10.1109/techno-ocean.2016.7890629.

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6

Szabados, M., G. Withee, and K. Schultz. "NOAA's shipboard environmental data acquisition system." In OCEANS '85 - Ocean Engineering and the Environment. IEEE, 1985. http://dx.doi.org/10.1109/oceans.1985.1160248.

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7

Zhuge, Xu, Hao Wang, and Girts Strazdins. "Evaluating the data visualization for demanding marine operations." In 2016 Techno-Ocean (Techno-Ocean). IEEE, 2016. http://dx.doi.org/10.1109/techno-ocean.2016.7890700.

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8

Egles, D. "RANGER 1: A self-propelled data buoy." In OCEANS '85 - Ocean Engineering and the Environment. IEEE, 1985. http://dx.doi.org/10.1109/oceans.1985.1160295.

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9

McCandless, S. "The future bonanza in marine data from space." In OCEANS '85 - Ocean Engineering and the Environment. IEEE, 1985. http://dx.doi.org/10.1109/oceans.1985.1160243.

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Greguska, Frank R., Thomas Huang, Brian Wilson, Nga Quach, and Joe Jacob. "Analyzing big ocean science data with NEXUS." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258530.

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Звіти організацій з теми "Ocean data"

1

Bennett, Andrew F. Open Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada627701.

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2

Bennett, Andrew F. Open Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada629134.

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3

Bennett, Andrew F. Open Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada629176.

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4

Mariano, Arthur J., and Toshio M. Chin. Coastal and Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612624.

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5

Mariano, Arthur J., and Toshio M. Chin. Coastal And Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533825.

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6

Moura, Jose M. Data Assimilation in Ocean Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630869.

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7

Chapman, R. J., and Joseph A. Hauser. Ocean Data Telemetry Microsat Link (ODTML). Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533972.

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8

Phoebus, Patricia A., and James A. Cummings. Ocean Data Assimilation for Coupled Models. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada628445.

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9

Moura, Jose M. Efficient Data Assimilation in Ocean Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada628565.

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10

Phoebus, Patricia A., and James A. Cummings. Ocean Data Assimilation for Coupled Models. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630737.

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