Visualization for Ocean General Circulation Model via Multi-dimensional Transfer Function and Multivariate Analysis

With advances in supercomputing technology, ultra-high resolution large-scale simulation study has been the mainstream in the field of ocean science. The dataset from such simulation is not only large in size, but also contains a lot of useful information. However, as the geophysical fluid simulation, it is difficult to understand such information hiding in the dataset due to the complexity of ocean dynamics and the number of physical variables. In order to obtain scientific knowledge from large-scale simulation data, it is important to effectively extract and to efficiently express the characteristic feature [1, 2]. The aim of this study is how to solve the above problem. In this study, a multi-dimensional transfer function to emphasis the feature such as ocean currents and vortices from OFES (Ocean general circulation model for the Earth Simulator) [3, 4] dataset is proposed. Furthermore, a multivariate analysis to extract such features is developed. This paper describes the experimental results of the applying the multi-dimensional transfer function and multivariate analysis to the visualization of Oceanic Global Circulation Model data.