深度学习在印度洋偶极子预测中的应用研究综述  

Review of application of deep learning in Indian Ocean Dipole prediction

作　　者：郑梦轲 方巍[2,3,4] 张霄智 ZHENG Mengke;FANG Wei;ZHANG Xiaozhi(School of Electronic and Information Engineering,Anhui Jianzhu University,Hefei 230601,China;School of Computer Science,Nanjing University of Information Science&Technology,Nanjing 210044,China;Key Laboratory of Transportation Meteorology of China Meteorological Administration(Nanjing Joint Institute for Atmospheric Sciences),Nanjing 210041,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]安徽建筑大学电子与信息工程学院,安徽合肥230601 [2]南京信息工程大学计算机学院,江苏南京210044 [3]中国气象局交通气象重点开放实验室(南京气象科技创新研究院),江苏南京210041 [4]南京信息工程大学江苏省大气环境与装备技术协同创新中心,江苏南京210044

出　　处：《海洋学研究》2024年第3期51-63,共13页Journal of Marine Sciences

基　　金：国家自然科学基金面上项目(42475149);中国气象局流域强降水重点开放实验室开放研究基金(2023BHR-Y14);江苏省研究生科研与实践创新计划项目(KYCX24_1533,SJCX24_0476,SJCX24_0477)。

摘　　要：印度洋偶极子(Indian Ocean Dipole,IOD)是影响区域及全球气候变化的关键气候现象。准确预测IOD对于理解全球气候至关重要,但传统方法在捕捉其复杂性和非线性方面的局限限制了预测能力。该文首先概述了IOD的相关理论,并评估了传统预测方法的优缺点。然后,综合分析了深度学习在IOD预测领域的应用和发展,特别强调了深度学习模型在自动特征提取、非线性关系建模和大数据处理方面相较于传统方法的优势。与此同时,该文还讨论了深度学习模型在IOD预测中所面临的挑战,包括数据稀缺、过拟合以及模型可解释性等问题,并提出了未来研究的方向,旨在推动深度学习技术在气候预测领域的创新与进步。The Indian Ocean Dipole(IOD)is a pivotal climate phenomenon in the Indian Ocean region,exerting a significant impact on the climate change of the surrounding areas and the global climate system.Accurate prediction of IOD is essential for comprehending the dynamics of the global climate,yet traditional forecasting methods are limited in capturing its complexity and nonlinearity,constraining predictive capabilities.This paper begins by outlining the relevant theories of IOD and evaluates the strengths and weaknesses of traditional forecasting methods.It then provides a comprehensive analysis of the application and development of deep learning in the field of IOD prediction,highlights the advantages of deep learning models over traditional methods in terms of automatic feature extraction,nonlinear relationship modeling,and large data processing capabilities.Additionally,the paper discusses the challenges faced by deep learning models in IOD forecasting:including data scarcity,overfitting,and model interpretability issues,and proposes future research directions to promote innovation and progress in the application of deep learning technology in the field of climate prediction.

关 键 词：全球气候变化 神经网络 CNN LSTM ConvLSTM 气候预测 气象变化 数据处理 

分 类 号：P732[天文地球—海洋科学]