CASIA OpenIR  > 多模态人工智能系统全国重点实验室
SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion
Jin, Qizhao1,2; Zhang, Xinbang1,2; Xiao, Xinyu1,2; Wang, Ying2; Meng, Gaofeng2; Xiang, Shiming2; Pan, Chunhong2
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
2024
Volume17Pages:1299-1314
Corresponding AuthorXiao, Xinyu(xinyu.xiao@nlpr.ia.ac.cn)
AbstractPrecipitation plays a significant role in global water and energy cycles, largely affecting many aspects of human life, such as transportation and agriculture. Recently, meteorologists have tried to predict precipitation with deep learning methods by learning from much historical meteorological data. Under this paradigm, the task of precipitation nowcasting is formulated as a spatiotemporal sequence forecasting problem. However, current studies suffer from two inherent drawbacks of the definition of the problem. First, considering that the weather patterns vary in spatial and temporal dimensions, a spatiotemporally shared kernel is not optimal for capturing features across different regions and seasons. Second, these methods isolate the precipitation from other meteorological elements, such as temperature, humidity, and wind. The disability of cross-model learning prevents the possibility of the promotion of precipitation prediction. Therefore, this article proposes a spatiotemporal inference network (STIN) to produce precipitation prediction from multimodal meteorological data with spatiotemporal specific filters. Specifically, we first design a spatiotemporal-aware convolutional layer (STAConv), in which kernels are generated conditioned on the incoming spatiotemporally features vector. Replacing normal convolution with STAConv enables the extraction of spatiotemporal specific information from the meteorological data. Based on the STAConv, the spatiotemporal-aware convolutional neural network (STACNN) is further proposed, fusing the multimodal information, including temperature, humidity, and wind. Then, an encoder-decoder framework composed of RNN layers is built to extract representative temporal dynamics from multimodal information. To investigate the practicality of the proposed method, we employ STIN to predict the following precipitation intensity. Extensive experiments on three meteorological datasets demonstrate the effectiveness of our model on precipitation nowcasting.
KeywordData mining multimodal knowledge discovery precipitation nowcasting
DOI10.1109/JSTARS.2023.3321963
WOS KeywordPRODUCTS ; IMERG
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:001127459900015
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/54839
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorXiao, Xinyu
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 10004, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
First Author AffilicationChinese 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. SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:1299-1314.
APA Jin, Qizhao.,Zhang, Xinbang.,Xiao, Xinyu.,Wang, Ying.,Meng, Gaofeng.,...&Pan, Chunhong.(2024).SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,1299-1314.
MLA Jin, Qizhao,et al."SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):1299-1314.
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