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An Effective High Resolution Rainfall Estimation Based on Spatiotemporal Modeling

  • Qiuming Kuang
  • Xuebing Yang
  • Wensheng Zhang
  • Guoping Zhang
  • Naixue Xiong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

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 1 km × 1 km, temporal resolution of six minutes or one minutes.

Keywords

Rainfall estimation system Spatiotemporal model Radar reflectivity High resolution 

Notes

Acknowledgments

The authors thank for financial support from national natural science foundation of China (61432008, 61472423, 61532006, U1636220). Meanwhile, the authors would like to thank Public Meteorological Service Center of China Meteorological Administration (CMA) for offering meteorological data.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Qiuming Kuang
    • 1
    • 2
  • Xuebing Yang
    • 1
    • 2
  • Wensheng Zhang
    • 1
    • 2
  • Guoping Zhang
    • 3
    • 4
  • Naixue Xiong
    • 5
  1. 1.Research Center of Precision Sensing and ControlChinese Academy of SciencesBeijingChina
  2. 2.School of Computer and Control EngineeringUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Joint Laboratory of Meteorological Data and Machine LearningBeijingChina
  4. 4.Public Meteorological Service Center of CMABeijingChina
  5. 5.School of Computer ScienceColorado Technical UniversityColorado SpringUSA

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