深度学习在极端灾害性天气预测中的应用及展望

杨浩; 寿涵锐; 刘泽润; 梁紫怡; 刘瑞; 徐立明; 杨明

doi:10.12263/newIT.2024.04.004

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深度学习在极端灾害性天气预测中的应用及展望

综述 | 更新时间：2025-05-15

- 深度学习在极端灾害性天气预测中的应用及展望
- Application and Prospects of Deep Learning in Extreme Disaster Weather Prediction
- 新一代信息技术 2024年7卷第4期页码：16-24
- 作者机构：
  
  1.浙江省气象信息网络中心，浙江杭州 310051
  2.浙江仙鹤特种纸有限公司，浙江衢州 324022
- 作者简介：
  
  [ "杨浩（1998—），男，硕士，助理工程师，研究方向为气象人工智能。E-mail： hao.y.0728@qq.com" ]
  [ "寿涵锐（1996—），男，硕士，工程师，研究方向为气象学。" ]
  [ "刘泽润（1998—），男，硕士，助理工程师，研究方向为人工智能。" ]
  [ "梁紫怡（1998—），女，硕士，助理工程师，研究方向为人工智能。" ]
  [ "刘瑞（1996—），女，硕士，工程师，研究方向为气象学。" ]
  [ "徐立明（1976—），男，助理工程师，研究方向为人工智能。" ]
  [ "杨明（1983—），男，硕士，正研究员，研究方向为气象人工智能。E-mail： yangmingstudy@126.com" ]
- 基金信息：
  
  浙江省自然科学基金联合基金探索项目(LZJMY24D050009);中国气象局创新发展专项(CXFZ2024J084);面向云服务的气象决策通用分众式定制式服务技术研究与应用
- DOI：10.12263/newIT.2024.04.004
  中图分类号： TP183;X43
- 录用日期：2025-03-12，
  
  纸质出版日期：2024-04-15
- 稿件说明：
移动端阅览
杨浩, 寿涵锐, 刘泽润, 等. 深度学习在极端灾害性天气预测中的应用及展望[J]. 新一代信息技术, 2024, 7(4): 16-24

YANG Hao, SHOU Han-rui, LIU Ze-run, et al. Application and Prospects of Deep Learning in Extreme Disaster Weather Prediction[J]. New Generation of Information Technology, 2024, 7(4): 16-24
杨浩, 寿涵锐, 刘泽润, 等. 深度学习在极端灾害性天气预测中的应用及展望[J]. 新一代信息技术, 2024, 7(4): 16-24 DOI： 10.12263/newIT.2024.04.004.

YANG Hao, SHOU Han-rui, LIU Ze-run, et al. Application and Prospects of Deep Learning in Extreme Disaster Weather Prediction[J]. New Generation of Information Technology, 2024, 7(4): 16-24 DOI： 10.12263/newIT.2024.04.004.

摘要

极端灾害性天气的发生不仅对人类生存造成威胁还会对社会经济发展造成破坏，如何及时、精准地预测灾害性天气对防范和应对其带来的影响具有重要意义。深度学习近年来在气象预测领域中发挥了关键作用，本文从极端灾害性天气预测角度出发，探究了深度学习在该领域中的应用与进展。本文分别介绍了雷暴和台风预测领域中深度学习算法的发展以及相关模型的应用，并进一步基于代表性的深度学习算法介绍了其网络模型架构和损失函数等细节。最后，本文针对深度学习的发展趋势与极端灾害性天气的特点给出了对未来的展望。

Abstract

The occurrence of extreme disaster weather poses a threat to human survival and can significantly impact social and economic development. Timely and accurate prediction of such weather is crucial for mitigating and responding to its effects. In recent years

deep learning has played a key role in the field of meteorological forecasting. This paper explores the applications and advancements of deep learning in the prediction of extreme disaster weather. It introduces the development of deep learning algorithms in the prediction of thunderstorms and typhoons

along with the application of relevant models. Additionally

the paper details the architecture of representative deep learning models

including their network structures and loss functions. Finally

this paper offers insights into future trends of deep learning in light of its development trajectory and the characteristics of extreme disaster weather.

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references

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