Radar and Rain Gauge Merging-Based Precipitation Estimation via Geographical-Temporal Attention Continuous Conditional Random Field
Tang, Yongqiang1,2; Yang, Xuebing1,2; Zhang, Wensheng1,2; Zhang, Guoping3
2018-09-01
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷号56期号:9页码:5558-5571
文章类型Article
摘要An accurate, high-resolution precipitation estimation based on rain gauge and radar observations is essential in various meteorological applications. Although numerous studies have demonstrated the effectiveness of merging two information sources rather than using separate sources, approaches that simultaneously consider the local radar reflectivity, the neighborhood rain gauge observations, and the temporal information are much less common. In this paper, we present a new framework for real-time quantitative precipitation estimation (QPE). By formulating the QPE as a continuous conditional random field (CCRF) learning problem, the spatiotemporal correlations of precipitation can be explored more thoroughly. Based on the CCRF, we further improve the accuracy of the precipitation estimation by introducing geographical and temporal attention. Specifically, we first present a data-driven weighting scheme to merge the first law of geography into the proposed framework, and hence, the neighborhood sample closer to the estimated grid can receive more attention. Second, the temporal attention penalizes the similarity between two adjacent timestamps via the discrepancy of two-view estimates, which can model the local temporal consistency and tolerate some drastic changes. A sufficient evaluation is conducted on 11 rainfall processes that occurred in 2015, and the results confirm the advantage of our proposal for real-time precipitation estimation.
关键词Continuous Conditional Random Field (Ccrf) Merging Method Precipitation Estimation Spatiotemporal Correlation
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2018.2819802
关键词[WOS]INTERPOLATION ; PREDICTION ; ALGORITHM ; MODEL ; RECOGNITION ; EVENT
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(U1636220 ; Beijing Natural Science Foundation(4182067) ; 61432008 ; 61602482 ; 61772524)
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:000443147600047
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21826
专题精密感知与控制研究中心_人工智能与机器学习
作者单位1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.China Meteorol Adm, Publ Meteorol Serv Ctr, Joint Lab Meteorol Data & Machine Learning, Beijing 100081, Peoples R China
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Tang, Yongqiang,Yang, Xuebing,Zhang, Wensheng,et al. Radar and Rain Gauge Merging-Based Precipitation Estimation via Geographical-Temporal Attention Continuous Conditional Random Field[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(9):5558-5571.
APA Tang, Yongqiang,Yang, Xuebing,Zhang, Wensheng,&Zhang, Guoping.(2018).Radar and Rain Gauge Merging-Based Precipitation Estimation via Geographical-Temporal Attention Continuous Conditional Random Field.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(9),5558-5571.
MLA Tang, Yongqiang,et al."Radar and Rain Gauge Merging-Based Precipitation Estimation via Geographical-Temporal Attention Continuous Conditional Random Field".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.9(2018):5558-5571.
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