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

本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中的应用。目标是利用这一统计技术将水文数据、动力约束以及观测和动力假设中的不确定性知识结合起来，以达到：(a)在北大西洋选定的等平线上缓慢变化的海洋性质场图，(b)估计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。该项目联合了统计学家和海洋科学家，并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。

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.