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An Effective High Resolution Rainfall Estimation Based on Spatiotemporal Modeling
Qiuming Kuang1; Xuebing Yang1; Wensheng Zhang1; Guoping Zhang2; Naixue Xiong3
2017
Conference NameFutureTech 2017
Conference DateMay 22-24,2017
Conference PlaceSeoul,Korea
AbstractHigh resolution rainfall estimation is one of the most significant input for numerous meteorological applications, such as agricultural irrigation, water power generation, and flood warning. However, rainfall estimation is a challenging task because it subjects to various sources of errors. In this paper, an effective high resolution rainfall estimation system is presented which employs a spatiotemporal model named RANLIST. The merits of this system are listed as follows: (1) RANLIST, which exploits both spatial structure of multiple radar reflectivity factors and time-series information of rain processes, is superior to other methods for rainfall estimation. (2) RANLIST is used for rainfall estimation with temporal resolution of six minutes, while this system can estimate rainfall every minute which will do more help for coping with emergencies such as flood. Experiments have been implemented over radar-covered areas of Quanzhou and Hangzhou of China in June and July, 2014. Results show that the presented rainfall estimation system can obtain good performance with spatial resolution of 1km×1km, temporal resolution of six minutes or one minutes.
KeywordRainfall Estimation System Spatiotemporal Model Radar Reflectivity High Resolution
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14628
Collection精密感知与控制研究中心_人工智能与机器学习
Affiliation1.Research Center of Precision Sensing and Control, Chinese Academy of Sciences,Beijing, 100190, China
2.Public Meteorological Service Center of CMA, Beijing 100081, China;
3.School of Computer Science, Colorado Technical University, 4435 North Chestnut Street, Colorado Spring, CO, 80907, USA
Recommended Citation
GB/T 7714
Qiuming Kuang,Xuebing Yang,Wensheng Zhang,et al. An Effective High Resolution Rainfall Estimation Based on Spatiotemporal Modeling[C],2017.
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