Home Journals Articles Updates About
CN
image
收藏切换
ENSO prediction based on Swin-Transformer and spatio-temporal fusion attention mechanism
收藏切换
PDF
Xiaozhi Zhang1, Wei Fang1, 2, 3, *, Haoxi Wang1
Haiyang Xuebao | 2024, 46(12) : 111 - 121
Less
收藏切换
Haiyang Xuebao | 2024, 46(12): 111-121
• Article •
ENSO prediction based on Swin-Transformer and spatio-temporal fusion attention mechanism
Full
Xiaozhi Zhang1, Wei Fang1, 2, 3, *, Haoxi Wang1
Affiliations
  • 1. School of Computer Science & School of Software , Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. China Meteorological Administration, China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, Wuhan 430205, China
  • 3. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Published: 2024-12-31 doi: 10.12284/hyxb2024127
Outline
收藏切换

The prediction of El Niño-Southern Oscillation is one of the hot issues in climate change research. This paper combines swin-transformer model with spatio-temporal fusion attention mechanism, and uses CMIP6 multi-model simulation historical data from 1850 to 2014, SODA assimilated data from 1871 to 1979 and GODAS assimilated data from 1980 to 2023 to construct El Niño-Southern Oscillation prediction model—ENSO-STformer. The model was fully trained on CMIP6 and SODA datasets and evaluated on GODAS data. The results show that the average skill of this model in predicting the Niño3.4 index at 11-month lead times exceeds those of CanCM4, CCSM3, and GFDLaer04 by 5.1%, 21.6%, and 12.4% respectively. Meanwhile, the Niño3.4 index related skills of the proposed model are significantly better than other deep learning models in the medium and long term. Effective ENSO forecasts can be made for up to 24 months, and the 2015−2016 El Niño event simulation shows strong ability to cope with spring forecast obstacles.

deep learning  /  ENSO predicting  /  spatio-temporal fusion attention mechanism  /  convolutional neural network  /  Niño3.4 index
Xiaozhi Zhang, Wei Fang, Haoxi Wang. ENSO prediction based on Swin-Transformer and spatio-temporal fusion attention mechanism[J]. Haiyang Xuebao, 2024 , 46 (12) : 111 -121 . DOI: 10.12284/hyxb2024127
Appendix
Less
Year 2024 volume 46 Issue 12
PDF
201
84
Cite this Article
BibTeX
Article Info
doi: 10.12284/hyxb2024127
  • Receive Date：2024-07-24
  • Online Date：2025-11-27
  • Published：2024-12-31
Article Data
Affiliations
History
  • Received：2024-07-24
  • Revised：2024-10-30
Funding
Affiliations
    1. School of Computer Science & School of Software , Nanjing University of Information Science and Technology, Nanjing 210044, China
    2. China Meteorological Administration, China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, Wuhan 430205, China
    3. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
References
Share
https://castjournals.cast.org.cn/joweb/hyxb/EN/10.12284/hyxb2024127
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House