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

这个项目涉及两个相互关联的问题。从观察到大气和海洋中示踪剂的时空分布是非高斯的这一观察开始，第一个问题是试图通过查看各种物理过程的模型并观察示踪剂分布的结果来尝试将机制附加到不同类型的非高斯行为上。第二个问题是尝试和发展考虑到数据“误差”的非高斯性和不确定性以乘法而不是相加方式组合的情况下进行数据同化的方法。这项工作的影响之一可能是对海洋流动的估计和预测，这一主题对于包括海岸带管理和渔业在内的一系列应用正变得越来越重要。被动拉格朗日平台是用于海洋观测的方法之一，其数据往往具有非高斯统计特性。其他潜在的应用领域包括预测化学示踪剂在大气和海洋中的分布。

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.