中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 海洋研究所
  3. 中国科学院海洋研究所
  4. 海洋环流与波动重点实验室
Deep neural network based on adversarial training for short-term high-resolution precipitation nowcasting from radar echo images

文献类型：期刊论文

作者Yang, Ruikai2; Jiao, Shuangjian2; Yang, Nan1,3
刊名JOURNAL OF OCEANOLOGY AND LIMNOLOGY
出版日期2025-08-26
页码14
关键词precipitation nowcasting deep learning Simple Parameter-Free Attention Module (SimAM) Generative Adversarial Networks (GANs)
ISSN号2096-5508
DOI10.1007/s00343-025-4354-3
通讯作者Jiao, Shuangjian(hnbc_7@163.com) ; Yang, Nan(yangnan@qdio.ac.cn)
英文摘要Precipitation nowcasting is of great importance for disaster prevention and mitigation. However, precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors. Even slight changes in the initial precipitation field can have a significant impact on the future precipitation patterns, making the nowcasting of short-term high-resolution precipitation a major challenge. Traditional deep learning methods often have difficulty capturing the long-term spatial dependence of precipitation and are usually at a low resolution. To address these issues, based upon the Simpler yet Better Video Prediction (SimVP) framework, we proposed a deep generative neural network that incorporates the Simple Parameter-Free Attention Module (SimAM) and Generative Adversarial Networks (GANs) for short-term high-resolution precipitation event forecasting. Through an adversarial training strategy, critical precipitation features were extracted from complex radar echo images. During the adversarial learning process, the dynamic competition between the generator and the discriminator could continuously enhance the model in prediction accuracy and resolution for short-term precipitation. Experimental results demonstrate that the proposed method could effectively forecast short-term precipitation events on various scales and showed the best overall performance among existing methods.
资助项目National Natural Science Foundation of China[42306214] ; Postdoctoral Innovative Talents Support Program of Shandong Province[SDBX2022026] ; China Postdoctoral Science Foundation[2023M733533] ; Special Research Assistant Project of the Chinese Academy of Sciences
WOS研究方向Marine & Freshwater Biology ; Oceanography
语种英语
WOS记录号WOS:001556818900001
出版者SCIENCE PRESS
源URL[http://ir.qdio.ac.cn/handle/337002/203191]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Jiao, Shuangjian; Yang, Nan
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266000, Peoples R China
2.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
3.Chinese Acad Sci, Key Lab Ocean Observat & Forecasting, Qingdao 266000, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Ruikai,Jiao, Shuangjian,Yang, Nan. Deep neural network based on adversarial training for short-term high-resolution precipitation nowcasting from radar echo images[J]. JOURNAL OF OCEANOLOGY AND LIMNOLOGY,2025:14.
APA Yang, Ruikai,Jiao, Shuangjian,&Yang, Nan.(2025).Deep neural network based on adversarial training for short-term high-resolution precipitation nowcasting from radar echo images.JOURNAL OF OCEANOLOGY AND LIMNOLOGY,14.
MLA Yang, Ruikai,et al."Deep neural network based on adversarial training for short-term high-resolution precipitation nowcasting from radar echo images".JOURNAL OF OCEANOLOGY AND LIMNOLOGY (2025):14.

入库方式： OAI收割

来源：海洋研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。