Collaborative Research: CMG: Ocean Circulation Climatology and Dynamics Using Bayesian Hierarchical Methods

合作研究：CMG：使用贝叶斯分层方法的海洋环流气候学和动力学

• 批准号: 0221939
• 负责人: Michael Lavine
• 金额: $ 28.95万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0221939&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Ocean; Circulation

This project is an application of modern Bayesian hierarchical methods of statistical analysis to the problem of ocean state estimation. The goal is to use this statistical technique to combine hydrographic data, dynamical constraints, and knowledge about the uncertainties in both observations and dynamical assumptions, to arrive at: (a) slowly varying maps of oceanic property fields on a selection of isopycnals in the North Atlantic, (b) an estimate of the probability distribution function for possible states of the North Atlantic circulation consistent with the available data. The project unites statisticians and ocean scientists and provides training in interdisciplinary research for a graduate student and two post-doctoral researchers.

该项目是现代贝叶斯分层统计分析方法在海洋状态估计问题上的应用。 目标是利用这种统计技术将水文数据、动力学约束以及有关观测和动力学假设中的不确定性的知识结合起来，以得出：（a）在北大西洋选择的等密度线上缓慢变化的海洋属性场图，（b）与现有数据一致的北大西洋环流可能状态的概率分布函数的估计。 该项目将统计学家和海洋科学家联合起来，为一名研究生和两名博士后研究人员提供跨学科研究培训。