SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion

doi:10.1109/JSTARS.2023.3321963

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Qizhao 1,2; Zhang, Xinbang 1,2; Xiao, Xinyu 1,2; Wang, Ying 2; Meng, Gaofeng 2; Xiang, Shiming 2; Pan, Chunhong 2
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2024
卷号	17 页码:1299-1314
关键词	Data mining multimodal knowledge discovery precipitation nowcasting
ISSN号	1939-1404
DOI	10.1109/JSTARS.2023.3321963
通讯作者	Xiao, Xinyu(xinyu.xiao@nlpr.ia.ac.cn)
英文摘要	Precipitation 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.
资助项目	National Natural Science Foundation of China
WOS关键词	PRODUCTS ; IMERG
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001127459900015
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54838]
专题	多模态人工智能系统全国重点实验室
通讯作者	Xiao, Xinyu
作者单位	1.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
推荐引用方式 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.