Correlation Analysis on Estimating Rainfall Using Radar-rain Gauge Calibration

Dong Gaohong; Liu Liping

Vol.23, NO.1, 2012

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2012 > 23(1): 30-39

Dong Gaohong, Liu Liping. Correlation analysis on estimating rainfall using radar-rain gauge calibration. J Appl Meteor Sci, 2012, 23(1): 30-39.

Citation:

Dong Gaohong, Liu Liping. Correlation analysis on estimating rainfall using radar-rain gauge calibration. J Appl Meteor Sci, 2012, 23(1): 30-39.

Dong Gaohong, Liu Liping. Correlation analysis on estimating rainfall using radar-rain gauge calibration. J Appl Meteor Sci, 2012, 23(1): 30-39.

Citation:

Dong Gaohong, Liu Liping. Correlation analysis on estimating rainfall using radar-rain gauge calibration. J Appl Meteor Sci, 2012, 23(1): 30-39.

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Correlation Analysis on Estimating Rainfall Using Radar-rain Gauge Calibration

Dong Gaohong^{1,2
,
,},
Liu Liping³

1.
College of Atmospheric Science, Lanzhou University, Lanzhou 730000
2.
Tianjin Meteorological Observatory, Tianjin 300074
3.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2011-05-16
Rev Recd Date: 2011-10-18
Publish Date: 2012-02-29

Abstract

Abstract

In order to give full play to the advantages of radar-gauge calibration algorithms, the correlation of radar data and rain gauge data is studied before and after quality control by analyzing the quality of radar data and rain gauge data. Based on 11 major precipitation processes during 2008—2009, impacts of 14 types of rain gauge densities on radar rainfall estimation are analyzed by using three radar-gauge calibration algorithms, which are variational calibration method, optimal interpolation method and Kalman filter method. The results show that the quality of rain gauge precipitation data of Tianjin is reliable, only 0.5% of the rain gauge precipitation data has larger error, and equipment failure or external factors is the main cause of its larger error. A reflectivity quality control (QC) procedure has been developed by Chinese Academy of Meteorological Sciences for identifying and removing non-precipitation echoes (such as ground clutter or anomalously propagated ground returns) from the radar base reflectivity fields, and quality control of radar data is implemented using the QC procedure. These non-meteorological echoes can be effectively removed, while retaining precipitation echoes, and thus the rainfall overestimation phenomenon of radar can be significantly improved. The correlation of radar reflectivity data and rain gauge data is analyzed before and after controlling their qualities by selecting different types of precipitation process, results show that quality control of the radar and rain gauge data is necessary to significantly increase the correlation between them and to improve the capacity of radar rainfall estimation. Using some radar-gauge calibration algorithms, impacts of different rain gauge densities on radar rainfall estimation are analyzed. The conclusion is that the capacity of radar rainfall estimation on rain gauge calibration can be improved significantly. The precision of radar rainfall estimation is continuously improved and then become stable with the density of rain gauge increased. The impacts of radar rainfall estimation and the calibration gauge density are related to the types of rainfall. To achieve equal calibration results, convective precipitation caused by cumulus needs the rain gauge density of about a gauge per 182 km², mixed cloud precipitation needs about a gauge per 211 km², and for stratiform precipitation a gauge per 405 km² is enough. The impacts of different radar-gauge calibration algorithms are different. It shows that Kalman filter method is suitable for the calibration of stratiform precipitation or for the low rain gauge density area, and variational method and optimal interpolation method are suitable for the calibration of convective precipitation or for the high rain gauge density area.
- quality control (QC);
- correlation analysis;
- radar-rain gauge calibration;
- rain gauge density

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References(29)

Figures and Tables

Fig. 1 Distributions of Tianjin weather stations and rain gauges (the interval between adjacent circles is 50 km)(a) with Tianjin urban rain gauges (b)

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图 2 2005年7月30日07:37天津雷达0.5°仰角PPI图 (a) 质量控制前，(b) 质量控制后

Fig. 2 0.5° elevation angle Tianjin radar PPI at 0737 UTC 30 July 2005

(a) before quality control, (b) after quality control

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Fig. 3 Scatterplot of radar rainfall estimation and rain gauge rainfall

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Fig. 4 Rain gauge site density distribution schematic for calibrated radar (the interval between adjacent circles is 50 km)

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图 5 2008年6月27日、2008年7月14日、2009年4月19日3次降水过程统计指标随校准雨量计密度的变化

(＞50 km表示全区只有1部雨量计参加校准)

Fig. 5 Performance measures for rain gauge calibration density on 27 June 2008, 14 July 2008 and 19 April 2009

( > 50 km denotes only one rain gauge calibration)

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Fig. 6 The effects of variational calibration method

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Table 1 Different density of rain gauge calibration program distribtution

站点距离/km	密度/(10^-3·km^-2)	个数
8	15.6	183
9	12.3	145
10	9.9	117
11	8.2	97
12	6.9	81
13	6.0	70
14	5.1	60
15	4.4	52
18	3.1	36
20	2.5	29
23	1.9	22
25	1.5	18
30	1.1	13
＞50	0.09	1

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