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: 0352448
• 负责人: Leonid Piterbarg
• 金额: $ 1.5万
• 依托单位: University of Southern California
• 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=0352448&HistoricalAwards=false
• 关键词: Collaborative; Research; U.S.; Turkey; Cooperative

0352448 PiterbargDescription: 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.

0352448 Piterbarg 描述：该项目支持佛罗里达州迈阿密米迈大学海洋与大气科学学院 Tamay Ozgokmen 博士、加利福尼亚州洛杉矶南加州大学数学系 Leonid Piterbarg 博士和 Dr. Leonid Piterbarg 之间的合作研究. Mine Caglar，土耳其伊斯坦布尔科克大学数学系。 该项目的目的是汇集跨学科研究人员的专业知识，开发和实施用于模拟海洋湍流的数学方法。这种建模的主要目标是研究拉格朗日运动可预测性，并将其应用于海上救援和搜索行动、污染物和鱼类幼虫的扩散、观测系统和导航路线的设计。 PI 将利用现有的随机流理论、数值模拟和拉格朗日海洋研究来建立统一的方法。该项目旨在从时间/空间观测中贡献新颖的拉格朗日预测算法和模型识别程序。在佛罗里达海岸沿线观测到的高分辨率速度场数据中，亚中尺度涡流十分明显。当前的海洋模型能够解析海洋中的中尺度结构，但是小涡流无法解析，应该参数化。范围：该项目的潜在影响包括通过对此类涡流结构进行随机建模来增强拉格朗日预测研究，以及表示海洋模型中的子网格湍流的能力。所采用的方法将是根据海洋数据对随机流模型参数进行统计估计，将随机建模纳入中尺度海洋过程的数值建模中，以及构建在存在相干结构的情况下表现良好的预测算法。 该项目的预期科学效益包括：（i）通过物理海洋学、应用数学、概率和统计学等学科的合作了解湍流； (ii) 复杂问题对随机过程统计理论的贡献，即研究空间和时间数据以拟合向量场的参数； (iii) 建立更好的海洋学应用拉格朗日随机模型。 该项目更广泛的影响是：(i) 将支持两个美国学术机构之间的合作； (ii) 将开发预测算法并通过科学出版物传播； (iii) 该项目的成果将在应用数学和概率以及物理海洋学国际会议上公布； (iv) 来自美国和土耳其的科学家将在研究和教育方面开展未来的国际合作，例如交流或招收研究生； (v) 该项目有助于了解环境并解决非常实际的安全问题。