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深度学习在台风强度估计与预报中的应用研究综述

郑小妹 方巍

气象科学2025,Vol.45Issue(3):309-319,11.
气象科学2025,Vol.45Issue(3):309-319,11.DOI:10.12306/2024jms.0032

深度学习在台风强度估计与预报中的应用研究综述

Survey of application of deep learning in typhoon intensity estimation and prediction

郑小妹 1方巍2

作者信息

  • 1. 南京信息工程大学计算机学院,南京 210044
  • 2. 南京信息工程大学计算机学院,南京 210044||南京气象科技创新研究院中国气象局交通气象重点开放实验室,南京 210041||中国气象局流域强降水重点开放实验室/暴雨监测预警湖北省重点实验室中国气象局武汉暴雨研究所,武汉 430205||苏州大学江苏省计算机信息处理技术重点实验室,江苏苏州 215000
  • 折叠

摘要

Abstract

The estimation and prediction of typhoon intensity has always been a direction of concern for meteorologists.The traditional research methods for estimating and predicting typhoon intensity have shortcomings such as low accuracy in estimating and predicting typhoon intensity.The rise of deep learning has provided new ideas for the research on typhoon intensity.This paper reviews the research on the application of deep learning in the estimation and prediction of typhoon intensity.Firstly,the importance of typhoon intensity estimation and prediction is introduced,while the traditional methods of typhoon intensity estimation and prediction are reviewed,and the advantages and shortcomings of the deep learning methods and the traditional methods in the estimation and prediction of typhoon intensity are analyzed.Next,some of the deep learning-based typhoon intensity estimation and prediction methods are reviewed.Finally,the opportunities and existing challenges in the task of typhoon intensity estimation and prediction are summarized,and an outlook on the future development trend of deep learning in typhoon intensity estimation and prediction is given.

关键词

台风强度/强度估计/强度预报/深度学习/多模态

Key words

typhoon intensity/intensity estimation/intensity prediction/deep learning/multi-modal

分类

天文与地球科学

引用本文复制引用

郑小妹,方巍..深度学习在台风强度估计与预报中的应用研究综述[J].气象科学,2025,45(3):309-319,11.

基金项目

国家自然科学基金资助项目(42075007 ()

42475149) ()

中国气象局交通气象重点开放实验室开放研究基金资助项目(北极阁基金项目,BJG202306) (北极阁基金项目,BJG202306)

中国气象局流域强降水重点开放实验室开放研究基金资助项目(2023BHR-Y14) (2023BHR-Y14)

苏州大学江苏省计算机信息处理技术重点实验室开放研究基金资助项目(KJS2275) (KJS2275)

江苏省研究生科研与实践创新计划项目(NO.KYCX23_1388) (NO.KYCX23_1388)

气象科学

1009-0827

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