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

Yong Huang,Yun Yao,Ting Ni,Yan Feng,Huijuan Liu

doi:10.1155/2017/1475029

Yong Huang, Yun Yao + Show 3 more

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https://doi.org/10.1155/2017/1475029

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Abstract

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 Z-R, 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.

Highlights

With operational running of the China New Generation Weather Radar in mainland China, a lot of studies have been reported with focus on radar quantitative precipitation estimation (QPE) in China [1,2,3,4,5,6]
QPE algorithms in these studies include the relationship between the radar echo and the observed precipitation, precipitation estimation using merged radar and rain gauge data, integrated approach of multiradar precipitation estimation methods, and QPE for specific region
The Z-R relation is a precipitation estimation method based on the relationship between the radar reflectivity factor (Z) and rainfall intensity (R)

Summary

Introduction

With operational running of the China New Generation Weather Radar in mainland China, a lot of studies have been reported with focus on radar quantitative precipitation estimation (QPE) in China [1,2,3,4,5,6]. The Z-R relation is a precipitation estimation method based on the relationship between the radar reflectivity factor (Z) and rainfall intensity (R). The results of Guan’s study show that the accuracy of rainfall estimation can be improved based on the combination of different approach with Principal Component Analysis (PCA) method. The objective of this study is to improve the accuracy of previous integrated method based on error analysis in fixed boundary of regions in Huaihe River Basin (HRB) [18]. Since the rainfall is related to the precipitating cloud system and not related to the boundary of river basin or boundaries of administrative areas, the errors of QPEs are only calculated in fixed 15 areas in previous error analysis. Because the regions of error analysis are dynamically changing with the cloud system, the method in this study is named as Dynamic Errors Analysis (DEA)

Dynamic Errors Analysis Integrated Method

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