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。 从大气和海洋中示踪剂的时空分布是非高斯的观察开始，第一个问题是通过观察各种物理过程的模型并观察示踪剂分布的结果，尝试将机制附加到不同类型的非高斯行为。 第二个问题是设法发展进行数据同化的方法，考虑到数据“误差”的非高斯性质和不确定性以倍增方式而不是相加方式结合的情况，这项工作的影响之一可能是对洋流的估计和预测，这是一个对包括沿海区管理和渔业在内的一系列应用越来越重要的主题。 被动拉格朗日平台的数据，用于观察海洋的方法之一，往往具有非高斯统计的特点。 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.