Collaborative Research: CMG: Multi-Scaled Dependent, Heavy Tailed Distributions in Geophysical Flow: Physical Mechanisms and Data Assimilation

合作研究：CMG：地球物理流中的多尺度相关重尾分布：物理机制和数据同化

• 批准号: 0327906
• 负责人: Richard McLaughlin
• 金额: $ 48万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0327906&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Multi; Scaled

This project involves two linked problems. Starting from the observation that the spatio-temporal distributions of tracers in the atmosphere and ocean are non-Gaussian, the first problem is to try and attach mechanisms to the different types of non-Gaussian behavior by looking at models of various physical processes and seeing what tracer distributions result. The second problem is to try and develop ways of performing data assimilation that take account of the non-Gaussian nature of data "errors" and of situations in which uncertainty combines multiplicatively rather than additively.One of the impacts of this work may be on the estimation and prediction of oceanic flows, a topic that is becoming increasingly important for a range of applications including coastal zone management and fisheries. Data from passive Lagrangian platforms, one of the methods used to observe the ocean, are frequently characterized by non-Gaussian statistics. Other potential areas of application include the forecasting of the distributions of chemical tracers in both the atmosphere and the ocean.

该项目涉及两个链接的问题。 从观察到大气和海洋中示踪剂的时空分布是非高斯的，第一个问题是尝试通过查看各种物理过程的模型并看到不同类型的非高斯行为的机制来将机制附加到不同类型的非高斯行为上示踪剂分布产生了什么。 第二个问题是尝试开发执行数据同化的方法，以考虑到数据“错误”的非高斯性质，而不确定性的情况下，不确定性将多重而不是附加性结合在一起。这项工作的影响之一可能是对这项工作的影响。海洋流量的估计和预测，这个主题对于包括沿海地区管理和渔业在内的一系列应用变得越来越重要。 被动拉格朗日平台的数据，一种用于观察海洋的方法之一，经常以非高斯统计数据为特征。 其他潜在的应用领域包括对大气和海洋中化学示踪剂的分布进行预测。