Chinese Journal of Agrometeorology ›› 2017, Vol. 38 ›› Issue (07): 417-425.doi: 10.3969/j.issn.1000-6362.2017.07.003

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Quantitative Rainfall Estimation Using Weather Radar Based on Improved Kalman Filter Method

QU Xiao-kang, RUI Xiao-ping, YU Xue-tao, LEI Qiu-liang   

  1. 1.College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; 2. Transportation Institute, Shijiazhuang Tiedao University, Shijiazhuang 050043; 3.Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Non-point Source Pollution Control, Ministry of Agriculture, Beijing 100081
  • Received:2016-11-24 Online:2017-07-20 Published:2017-07-14

Abstract: To minimum the error of radar rainfall evaluation, an improved Kalman filter method was presented to calibrate the radar quantitative rainfall estimation (QRE). Firstly, the G/R (rain gauge rain rate/radar rain rate) calibration factor model was established. Secondly, the prediction and measurement system of G/R was set up based on the Kalman filter (KF). The calibration process of system parameters and adaptive estimation process of system error was introduced to adjust the parameters of KF dynamically. Thirdly, the G/R calibration ratio was used to correct radar quantitative rainfall estimation. The radar and rain gauge hourly rain data of two rain cases on 2015-08-19-20 and 2016-08-06-07 from Changchun were used to test the efficiency of the proposed method. The results showed that the QRE result with KF calibration was better than that without calibration. And the average relative errors of two rain cases were reduced from 0.6047 to 0.3557 and 0.2645, from 0.8052 to 0.3096 and 0.1715 by ordinary KF and improved KF respectively. Moreover, the improved KF was even better than the ordinary KF.

Key words: G/R ratio, Improved Kalman filter, Quantitative rainfall estimation, Adaptive estimation