Guangjun Xu , Wenhong Xie , Xiayan Lin , Yu Liu , Renlong Hang , Wenjin Sun , Dazhao Liu , Changming Dong
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引用次数: 0
Abstract
Oceanic mesoscale eddies play an important role in transports of heat, freshwater, mass in the ocean, therefore understanding three-dimensional structure of oceanic eddies is of significance to climate study and oceanic applications. However, detection of three-dimensional (3D) structures is a big challenge though many algorithms of sea surface 2D eddy detection are developed. In this study, we present a novel approach by using 3D U-Net residual architecture (3D-U-Res-Net) to identify 3D structure of oceanic eddies. The sensitivity tests to input variables are conducted to optimalize the input setting. Trained by 3D eddy data provided by a kinetic eddy detection method, the AI-based method can identify different kinds of eddy vertical structures and moreover can dig out more eddy information in deeper layers. This study has significant implications for the further application of the AI-based algorithm in oceanic study.
期刊介绍：
The main objective of Ocean Modelling is to provide rapid communication between those interested in ocean modelling, whether through direct observation, or through analytical, numerical or laboratory models, and including interactions between physical and biogeochemical or biological phenomena. Because of the intimate links between ocean and atmosphere, involvement of scientists interested in influences of either medium on the other is welcome. The journal has a wide scope and includes ocean-atmosphere interaction in various forms as well as pure ocean results. In addition to primary peer-reviewed papers, the journal provides review papers, preliminary communications, and discussions.
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