A Random Field Framework for Boundary Conditions in Numerical Models of Coastal and Ocean Circulation

本项目介绍了一种表示海洋模型边界条件的新方法。这个想法是，在没有能力详细跟踪小尺度物理过程的情况下，通过随机过程来表示这些小尺度过程对由模型解决的大尺度动力学的影响。该项目将尝试几种不同的边界条件的自回归表示，并研究流动的大规模结构如何对这些条件作出反应。模型结果将与佛罗里达海流附近用高频雷达观测到的数据进行比较。利用逆技术，基于马尔可夫随机场方法，估计边界条件随机模型中的参数值。这些反转的灵敏度也将被确定。在更大的区域，动态类似于北大西洋，沿东部边界的随机边界条件是否会导致内部喷流（β羽流）的问题将被研究。最初，数值环流模式将是一个理想几何形状的减重力、准地转模式。最有效的随机边界条件的方法将被转移到原始方程OGCM。

0136700/ChinThis project introduces a novel approach to representing boundary conditions in ocean models. The idea is, absent the ability to follow small-scale physical processes in detail, to represent the influence of these small-scale processes on the larger-scale dynamics resolved by the model by a stochastic process. The project will try several different auto-regressive representations of the boundary conditions and examine how the large-scale structure of the flow reacts to these. The model results will be compared with data from observations near the Florida Current made with HF radar. Using inverse techniques, based on a Markov random field approach, the values of parameters in the stochastic models of boundary conditions will be estimated. The sensitivity of these inversions will also be determined. In larger domains, dynamically similar to the North Atlantic, the question of whether stochastic boundary conditions along the eastern boundary can lead to internal jets (beta plumes) will be studied. Initially, the numerical circulation model will be a reduced-gravity, quasigeostrophic model in idealized geometries. The most effective approaches to stochastic boundary conditions will then be transferred to a primitive-equation OGCM.