Recent Developments in Artificial Intelligence in Oceanography  

作　　者：Changming Dong Guangjun Xu Guoqing Han Brandon JBethel Wenhong Xie Shuyi Zhou 

机构地区：[1]Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519000,China [2]School of Marine Sciences,Nanjing University of Information Science and Technology,Nanjing 210044,China [3]UNIVER-NUIST Joint AI Oceanography Academy,Nanjing University of Information Science and Technology,Nanjing 210044,China [4]School of Electronics and Information Engineering,Guangdong Ocean University,Zhanjiang 524088,China [5]Marine Science and Technology College,Zhejiang Ocean University,Zhoushan 316000,China [6]Department of Earth System Science,Ministry of Education Key Laboratory for Earth System Modeling,Institute for Global Change Studies,Tsinghua University,Beijing 100084,China

出　　处：《Ocean-Land-Atmosphere Research》2022年第1期1-26,共26页海洋-陆地-大气研究（英文）

基　　金：supported by Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2020SP007);Project on Exploring Interdisciplinary Frontier from Chinese Academy of Sciences:Artificial Intelligence Oceanography,2022,the Innovation Group Project of the Southern Marine Science and Engineering Guangdong(Zhuhai)under contract No.311020004;the National Key Research and Development Program of China(2017YFA0604100,2016YFC1402004,and 2017YFC1404200)for supporting this research.

摘　　要：With the availability of petabytes of oceanographic observations and numerical model simulations,artificial intelligence(AI)tools are being increasingly leveraged in a variety of applications.In this paper,these applications are reviewed from the perspectives of identifying,forecasting,and parameterizing ocean phenomena.Specifically,the usage of AI algorithms for the identification of mesoscale eddies,internal waves,oil spills,sea ice,and marine algae are discussed in this paper.Additionally,AI-based forecasting of surface waves,the El Niño Southern Oscillation,and storm surges is discussed.This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics.Moreover,physics-informed deep learning and neural networks are discussed within an oceanographic context,and further applications with ocean digital twins and physics-constrained AI algorithms are described.This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.

关 键 词：OCEAN forecasting STORM 

分 类 号：P73[天文地球—海洋科学] TP18[自动化与计算机技术—控制理论与控制工程]