An Individual Motion-Driven Artificial Intelligence Method for Precipitation Forecasting Using Radar Image Sequencing

doi:10.1109/TGRS.2024.3439871

IOCAS-IR > 海洋环流与波动重点实验室

	An Individual Motion-Driven Artificial Intelligence Method for Precipitation Forecasting Using Radar Image Sequencing
	Yang, Nan1,2 ; Li, Xiaofeng 1,2
	2024
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	62 页码:16
通讯作者	Li, Xiaofeng([email protected])
摘要	Precipitation forecasting, encompassing high-resolution regional and accurate intensity forecasting, has long been a central focus in the artificial intelligence (AI) field. Generally, AI models forecast future radar precipitation sequences from historical radar precipitation sequences. However, current precipitation forecasting suffers from three issues: 1) mismatched precipitation patterns, for example, motion speed; 2) blurred precipitation field generation; and 3) inaccurate intensity forecasts. It is owing that AI models: 1) tend to simulate the average motion states and overlook individual motion (defined as the description of the motion speed, trajectory, and direction for a single precipitation sample), leading to either overestimation or underestimation of the motion speed for a specific precipitation field and 2) are inclined to filter out high-frequency components to reduce noise in the information transmission, resulting in low resolution and blurred precipitation fields. The modeling challenges impose constraints on achieving high-resolution regional and accurate intensity forecasting. Here, to the former, we propose an individual motion-driven AI (IMD-AI) method, incorporating motion matching, alignment, and refinement through constructed pattern groups. It effectively solves the mismatch in motion estimation for an individual precipitation field, ensuring global and intact regional precipitation forecasting. To the latter, we put forward a schedule sampling, patch embedding, and adversarial strategies (SPA) training mechanism, which eliminates sharp and local information loss issues during the filtering of high-frequency components. Extensive experimental results demonstrate that IMD-AI achieves accurate motion estimation of individual precipitation fields and generates high-resolution regional and accurate intensity forecasting.
关键词	Precipitation Forecasting Artificial intelligence Predictive models Training Accuracy Convolution Artificial intelligence (AI) generative adversarial networks (GANs) precipitation forecasting
DOI	10.1109/TGRS.2024.3439871
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[42306214]; Shandong Province Postdoctoral Innovative Talents Support Program[SDBX2022026]; China Postdoctoral Science Foundation[2023M733533]; Special Research Assistant Project of the Chinese Academy of Sciences
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:001297495600033
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词	NEURAL-NETWORK ; MODELS
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/198171
专题	海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Key Lab Ocean Observat & Forecasting, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
推荐引用方式 GB/T 7714	Yang, Nan,Li, Xiaofeng. An Individual Motion-Driven Artificial Intelligence Method for Precipitation Forecasting Using Radar Image Sequencing[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:16.
APA	Yang, Nan,&Li, Xiaofeng.(2024).An Individual Motion-Driven Artificial Intelligence Method for Precipitation Forecasting Using Radar Image Sequencing.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,16.
MLA	Yang, Nan,et al."An Individual Motion-Driven Artificial Intelligence Method for Precipitation Forecasting Using Radar Image Sequencing".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):16.