Knowledge Commons of Institute of Automation,CAS
Preformer: Simple and Efficient Design for Precipitation Nowcasting With Transformers | |
Jin, Qizhao1,2; Zhang, Xinbang1,2; Xiao, Xinyu1,2; Wang, Ying1,2; Xiang, Shiming1,2; Pan, Chunhong1,2 | |
发表期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
ISSN | 1545-598X |
2024 | |
卷号 | 21页码:5 |
产权排序 | 1 |
摘要 | The primary objective of precipitation nowcasting is to predict precipitation patterns several hours in advance. Recent studies have emphasized the potential of deep learning methods for this task. To harness the correlations among various meteorological elements, existing frameworks project multiple meteorological elements into a latent space and then utilize convolutional-recurrent networks for future precipitation prediction. Although effective, the escalating model complexity may impede practical applications. This letter develops the Preformer, a streamlined Transformer framework for precipitation nowcasting that efficiently captures global spatiotemporal dependencies among multiple meteorological elements. The Preformer implements an encoder-translator-decoder architecture, where the encoder integrates spatial features of multiple elements, the translator models spatiotemporal dynamics, and the decoder combines spatiotemporal information to forecast future precipitation. Without introducing complex structures or strategies, the Preformer achieves state-of-the-art performance even with the least parameters. |
关键词 | Data mining Precipitation nowcastin Transformer |
学科领域 | 计算机应用 |
学科门类 | 工学::控制科学与工程 |
DOI | 10.1109/LGRS.2023.3325628 |
收录类别 | SCI |
所属项目编号 | 62076242 |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:001136775600033 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+科学 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/55525 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Wang, Ying |
作者单位 | 1.University of Chinese Academy of Sciences, School of Artificial Intelligence, Beijing 100049, Peoples Republic of China 2.Chinese Academy of Sciences, Institute of Automation, Beijing 100190, Peoples Republic of China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Jin, Qizhao,Zhang, Xinbang,Xiao, Xinyu,et al. Preformer: Simple and Efficient Design for Precipitation Nowcasting With Transformers[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2024,21:5. |
APA | Jin, Qizhao,Zhang, Xinbang,Xiao, Xinyu,Wang, Ying,Xiang, Shiming,&Pan, Chunhong.(2024).Preformer: Simple and Efficient Design for Precipitation Nowcasting With Transformers.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,21,5. |
MLA | Jin, Qizhao,et al."Preformer: Simple and Efficient Design for Precipitation Nowcasting With Transformers".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21(2024):5. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Preformer_Simple_and(6713KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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