Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation

doi:10.1109/LGRS.2016.2597170

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation
	Kuang, Qiuming1 ; Yang, Xuebing1 ; Zhang, Wensheng1 ; Zhang, Guoping 2
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2016-08-01
卷号	13 期号:11 页码:1601-1605
文章类型	Article
摘要	Radar-based rainfall estimation is one of the most important inputs for various meteorological applications. Although exciting progresses have been made in this area, accurate real-time rainfall estimation is still a significant opening topic that requires practical modeling. The research study presented in this letter improves rainfall estimation accuracy by proposing a random forest and linear chain conditional random-field-based spatiotemporal model (RANLIST). To apply this model for rainfall estimation, the implementing approach is presented. The advantages are listed as follows: 1) RANLIST improves rainfall estimation accuracy by exploiting both underlying local spatial structure of multiple radar reflectivity factors and time-series information of rain processes. 2) The time-series information of rain processes can be utilized in virtue of the presented implementation method. Experiments have been carried out over the radar-covered area of Quanzhou, China, in June and July 2014. Results show that RANLIST is superior to previous works.
关键词	Radar Reflectivity Rain Processes Rainfall Estimation Spatiotemporal Model
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/LGRS.2016.2597170
关键词[WOS]	QUANTITATIVE PRECIPITATION ESTIMATION ; ALGORITHM ; NETWORK
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61432008 ; 61532006 ; 61472423 ; 61305018)
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:000386255600003
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13324
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 2.China Meteorol Adm, Publ Meteorol Serv Ctr, Beijing 100081, Peoples R China
第一作者单位	精密感知与控制研究中心
推荐引用方式 GB/T 7714	Kuang, Qiuming,Yang, Xuebing,Zhang, Wensheng,et al. Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2016,13(11):1601-1605.
APA	Kuang, Qiuming,Yang, Xuebing,Zhang, Wensheng,&Zhang, Guoping.(2016).Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,13(11),1601-1605.
MLA	Kuang, Qiuming,et al."Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 13.11(2016):1601-1605.

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