CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
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
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
2018-09-01
Volume56Issue:9Pages:5558-5571
SubtypeArticle
Abstract

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.

KeywordContinuous Conditional Random Field (Ccrf) Merging Method Precipitation Estimation Spatiotemporal Correlation
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2018.2819802
WOS KeywordINTERPOLATION ; PREDICTION ; ALGORITHM ; MODEL ; RECOGNITION ; EVENT
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(U1636220 ; Beijing Natural Science Foundation(4182067) ; 61432008 ; 61602482 ; 61772524)
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000443147600047
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21826
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorZhang, Wensheng
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
2018--TGRS--Radar an(3564KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tang, Yongqiang]'s Articles
[Yang, Xuebing]'s Articles
[Zhang, Wensheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tang, Yongqiang]'s Articles
[Yang, Xuebing]'s Articles
[Zhang, Wensheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tang, Yongqiang]'s Articles
[Yang, Xuebing]'s Articles
[Zhang, Wensheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2018--TGRS--Radar and rain gauge merging-based precipitation estimation via geographical-temporal attention continuous conditional random field.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.