An Effective High Resolution Rainfall Estimation Based on Spatiotemporal Modeling
文献类型：会议论文
| 作者 | Qiuming Kuang1 ; Xuebing Yang1; Wensheng Zhang1; Guoping Zhang2; Naixue Xiong3
|
| 出版日期 | 2017 |
| 会议日期 | May 22-24,2017 |
| 会议地点 | Seoul,Korea |
| 关键词 | Rainfall Estimation System Spatiotemporal Model Radar Reflectivity High Resolution |
| 英文摘要 | High 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14628]  |
| 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.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 |
| 推荐引用方式 GB/T 7714 | Qiuming Kuang,Xuebing Yang,Wensheng Zhang,et al. An Effective High Resolution Rainfall Estimation Based on Spatiotemporal Modeling[C]. 见:. Seoul,Korea. May 22-24,2017. |
入库方式： OAI收割
来源：自动化研究所
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