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Advances in Meteorology
Volume 2017 (2017), Article ID 1475029, 6 pages
Research Article

An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis

1Anhui Meteorology Institute, Anhui Key Lab of Atmospheric Science and Satellite Remote Sensing, Hefei, Anhui 230031, China
2Shouxian National Climate Observation Station, Huainan, Anhui 232200, China

Correspondence should be addressed to Yong Huang

Received 17 November 2016; Accepted 15 February 2017; Published 5 March 2017

Academic Editor: Zhe Li

Copyright © 2017 Yong Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Relationships between radar reflectivity factor and rainfall are different in various precipitation cloud systems. In this study, the cloud systems are firstly classified into five categories with radar and satellite data to improve radar quantitative precipitation estimation (QPE) algorithm. Secondly, the errors of multiradar QPE algorithms are assumed to be different in convective and stratiform clouds. The QPE data are then derived with methods of -, Kalman filter (KF), optimum interpolation (OI), Kalman filter plus optimum interpolation (KFOI), and average calibration (AC) based on error analysis on the Huaihe River Basin. In the case of flood on the early of July 2007, the KFOI is applied to obtain the QPE product. Applications show that the KFOI can improve precision of estimating precipitation for multiple precipitation types.