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Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

Wen Zhang1,2, Wei Fang1,3,*, Xuelei Jia1,2, Victor S. Sheng4

1 Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer & Software, Nanjing University of Information Science & Technology, China
2 Nanjing Xinda Institute of Meteorological Science and Technology Co., Ltd., Nanjing, 210044, Jiangsu, China
3 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China
4 Department of Computer, Texas Tech University, Lubbock, TX 79409, USA

* Corresponding Author: Wei Fang. Email: email

Journal of Quantum Computing 2022, 4(1), 23-37.


In order to solve the rainfall estimation error caused by various noise factors such as clutter, super refraction, and raindrops during the detection process of Doppler weather radar. This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter. After data preprocessing, the radar data should be classified according to the precipitation intensity. And then, they are respectively substituted into the improved filter for calibration. The state noise variance and the measurement noise variance can be adaptively calculated and updated according to the input observation data during this process. Then the optimal parameter value of each type of precipitation intensity can be obtained. The state noise variance and the measurement noise variance could be assigned optimal values when filtering the remaining data. This rainfall estimation based on semi-adaptive Kalman filter calibration not only improves the accuracy of rainfall estimation, but also greatly reduces the amount of calculation. It avoids errors caused by repeated calculations, and improves the efficiency of the rainfall estimation at the same time.


Cite This Article

W. Zhang, W. Fang, X. Jia and V. S. Sheng, "Research on rainfall estimation based on improved kalman filter algorithm," Journal of Quantum Computing, vol. 4, no.1, pp. 23–37, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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