文献类型：期刊文献

英文题名：Recent Developments in Artificial Intelligence in Oceanography

作者：Dong C.; Xu G.; Han G.; Bethel B.J.; Xie W.; Zhou S.

机构：[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

年份：2022

卷号：2022

外文期刊名：Ocean-Land-Atmosphere Research

收录：Scopus(收录号：2-s2.0-85149365102)

语种：英文

外文摘要：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 Ni?o 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. Copyright ? 2022 Changming Dong et al.