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

0136700/ChinThis project introduces a novel approach to represent boundary conditions in ocean models. 这个想法是，缺乏详细跟踪小尺度物理过程的能力，通过随机过程来表示这些小尺度过程对模型所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。 模型结果将与高频雷达在佛罗里达海流附近的观测数据进行比较。 使用逆技术，基于马尔可夫随机场方法，将估计边界条件的随机模型中的参数值。 还将确定这些反演的敏感性。 在更大的领域，动力学类似于北大西洋，随机边界条件是否沿着东部边界可以导致内部射流（β羽流）的问题将进行研究。 最初，数值环流模型将是理想几何形状的减重力准地转模型。 最有效的方法随机边界条件，然后将转移到一个连续方程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.