CASIA OpenIR  > 多模态人工智能系统全国重点实验室
Preformer: Simple and Efficient Design for Precipitation Nowcasting With Transformers
Jin, Qizhao1; Zhang, Xinbang1; Xiao, Xinyu1; Wang, Ying2; Xiang, Shiming2; Pan, Chunhong2
Source PublicationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN1545-598X
2024
Volume21Pages:5
Corresponding AuthorWang, Ying(ywang@nlpr.ia.ac.cn)
AbstractThe 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.
KeywordPrecipitation Transformers Spatiotemporal phenomena Decoding Humidity Correlation Computer architecture Data mining precipitation nowcasting transformer
DOI10.1109/LGRS.2023.3325628
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:001136775600033
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55525
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorWang, Ying
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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