Collaborative Research: U.S.-Turkey Cooperative Research: Stochastic Modeling of Turbulent Flows for the Prediction of Lagrangian Trajectories in the Ocean

合作研究：美国-土耳其合作研究：用于预测海洋拉格朗日轨迹的湍流随机建模

• 批准号: 0352637
• 负责人: Tamay Ozgokmen
• 金额: $ 1.5万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0352637&HistoricalAwards=false
• 关键词: Collaborative; Research; U.S.; Turkey; Cooperative; Collaborative; Research; U.S.; Turkey; Cooperative

0352637 OzgokmenDescription: This project supports a cooperative research between Dr. Tamay Ozgokmen, School of Marine and Atmospheric Science, University of Mimai, Miami, Florida, Dr. Leonid Piterbarg, Department of Mathematics, University of Southern California, Los Angeles, California and Dr. Mine Caglar, Department of Mathematics, Koc University, Istanbul, Turkey. The aim of this project is to bring together the expertise of interdisciplinary researchers to develop and implement mathematical methods for modeling turbulent flows in the ocean. The main goal of such modeling is to studythe Lagrangian motion predictability with application to rescue and search operations in the sea, dispersion of pollutants and fish larvae, design of observing systems and navigation routes. The PIs will draw upon existing theory of stochastic flows, numerical modeling and Lagrangian studies of the ocean for establishing a united approach. The project aims to contribute novel Lagrangian prediction algorithms and model identification procedures from time/space observations. Submesoscale eddies are evident in the high-resolution velocity field data observed along Florida coast. Current ocean models are able to resolve meso-scale structures in the ocean, however small eddies cannot be resolved and should be parameterized. Scope: The potential impact of the project includes the enhancement of Lagrangian prediction studies by stochastic modeling of such eddy structures, and the capability to represent sub-grid turbulence in the ocean models. The methods employed will be statistical estimation of stochastic flow model parameters from ocean data, incorporation of stochastic modeling into numerical modeling of mesoscale oceanic processes, and construction of prediction algorithms that perform well in the presence of coherent structures. The expected scientific benefits of this project include: (i) understanding of turbulent flows through the collaboration of disciplines, namely physical oceanography, applied mathematics, probability and statistics; (ii) contribution to the theory of statistics of stochastic processes by a complex problem, namely the study of spatial and temporal data for fitting the parameters of a vector field; (iii) establishment of better Lagrangian stochastic models for oceanographic applications. The broader impacts of this project are: (i) collaboration among two US academic institutions will be supported; (ii) algorithms for prediction will be developed and disseminated through scientific publications; (iii) the results of the project will be publicized in international conferences of applied mathematics and probability, and physical oceanography; (iv) scientists from US and Turkey will develop future international collaborations for both research and also education such as exchange or recruitment of graduate students ; (v) the project contributes to understanding the environment and addresses a very practical safety problem.

0352637 OzgokmenDescription: This project supports a cooperative research between Dr. Tamay Ozgokmen, School of Marine and Atmospheric Sc​​ience, University of Mimai, Miami, Florida, Dr. Leonid Piterbarg, Department of Mathematics, University of Southern California, Los Angeles, California and Dr. Mine Caglar, Department of Mathematics, Koc University, Istanbul, Turkey. 该项目的目的是汇集跨学科研究人员的专业知识，以开发和实施数学方法来建模海洋中的动荡流。这种建模的主要目的是研究拉格朗日运动可预测性，并应用在海上营救和搜索操作，污染物和鱼幼虫的分散，观察系统和导航途径的设计。 PI将借鉴现有的随机流，数值建模和对海洋的拉格朗日研究的理论，以建立联合方法。该项目旨在从时间/空间观察中贡献新颖的拉格朗日预测算法和模型识别程序。在佛罗里达海岸观察到的高分辨率速度田间数据中，尺度涡流是显而易见的。当前的海洋模型能够解决海洋中的中尺度结构，但是小涡流无法解析，应进行参数化。范围：该项目的潜在影响包括通过这种涡流结构的随机建模来增强拉格朗日预测研究，以及代表海洋模型中亚网格湍流的能力。所采用的方法将是从海洋数据中对随机流量模型参数的统计估计，将随机建模纳入中尺度海洋过程的数值建模以及在存在相干结构的情况下表现良好的预测算法的构建。 该项目的预期科学益处包括：（i）通过学科的合作，即物理海洋学，应用数学，概率和统计数据来理解湍流； （ii）通过复杂问题对随机过程统计理论的贡献，即对拟合向量场参数的空间和时间数据的研究； （iii）为海洋应用建立更好的拉格朗日随机模型。 该项目的更广泛影响是：（i）将支持美国两个学术机构之间的合作； （ii）将通过科学出版物制定和传播预测算法； （iii）该项目的结果将在应用数学和概率以及物理海洋学的国际会议上宣传； （iv）来自我们和土耳其的科学家将开展未来的研究以及教育的未来国际合作，例如交流或招募研究生； （v）该项目有助于理解环境并解决一个非常实际的安全问题。