Identification of Non-precipitation Meteorological Echoes with Doppler Weather Radar

Li Feng; Liu Liping; Wang Hongyan; Jiang Yuan

Vol.23, NO.2, 2012

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Article Navigation > Journal of Applied Meteorological Science > 2012 > 23(2): 147-158

Li Feng, Liu Liping, Wang Hongyan, et al. Identification of non-precipitation meteorological echoes with Doppler weather radar. J Appl Meteor Sci, 2012, 23(2): 147-158.

Citation:

Li Feng, Liu Liping, Wang Hongyan, et al. Identification of non-precipitation meteorological echoes with Doppler weather radar. J Appl Meteor Sci, 2012, 23(2): 147-158.

Li Feng, Liu Liping, Wang Hongyan, et al. Identification of non-precipitation meteorological echoes with Doppler weather radar. J Appl Meteor Sci, 2012, 23(2): 147-158.

Citation:

Li Feng, Liu Liping, Wang Hongyan, et al. Identification of non-precipitation meteorological echoes with Doppler weather radar. J Appl Meteor Sci, 2012, 23(2): 147-158.

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Identification of Non-precipitation Meteorological Echoes with Doppler Weather Radar

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

Received Date: 2011-07-27
Rev Recd Date: 2012-02-02
Publish Date: 2012-04-30

Abstract

Abstract

When it is clear or there are just clouds without rain, wide spread non-precipitation meteorological echoes are often observed near the radars, which have notable effect on rainfall estimation and radar data assimilation. To discriminate these echoes efficiently, a Non-Precipitation Meteorological echo Detection Algorithm (NPMDA) based on fuzzy logical and SCIT is developed. Using surface and star data, a standard to identify non-precipitation meteorological echoes is established. With data observed by the SA radar in Beijing, after analyzing the statistical characteristics of non-precipitation meteorological echo, membership functions are obtained. Echoes are assembled to pieces using SCIT. If an echo piece meets one of some special conditions, the whole PPI would be recognized as precipitation echoes. If no piece meets any of the special conditions, the threshold is set as 0.5, for the echoes which can't be assembled to pieces. If the area of an echo piece is less than 2000 km², the echoes in the piece are also recognized with a threshold of 0.5. If the area of an echo piece reaches 2000 km², the attribute value of this echo piece would be calculated with an algorithm based on fuzzy logical. The threshold of echoes in the piece would be calculated with the attribute value of this echo piece. If the attribute value of one piece is greater than or equal to 0.5, the echoes in the piece would be recognized with the threshold of 0.5. If the attribute value of one piece is less than 0.5, the echoes in the piece would be recognized with a threshold obtained by subtracting the attribute value of the piece from 1. The echoes would be recognized as non-precipitation meteorological echoes if their attribute values are greater than the threshold or equal to it. After using dynamic thresholds, the thresholds of most precipitation echoes would be greater than 0.5. The method can efficiently avoid this situation that the precipitation echoes are recognized as non-precipitation echoes. It should also be noticed that the thresholds of some non-precipitation echoes would also be greater than 0.5, which would decrease the identifiable accuracy for non-precipitation echoes to some extent. The algorithm does well with most of non-precipitation meteorological echoes and precipitation echoes, but it does not handle some weak precipitation echoes well. Compared with the ICADA used by NCAR, the identifiable accuracy for non-precipitation echoes can be improved remarkably with NPMDA, and the erroneous recognition for precipitation is also decreased obviously.
- non-precipitation meteorological echo;
- fuzzy logical;
- echo identification;
- dynamic threshold

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References(20)

Figures and Tables

Fig. 1 Probability distribution of W_M, R_{A_R} and R_{P30_A}

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Fig. 2 Membership functions of W_M, R_{A_R} and R_{P30_A} for calculating the attributes of echo pieces

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Fig. 3 Probability distribution of M_DZ, S_DBZ, D_MDZ, T_VE and M_SW

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Fig. 4 Membership functions of M_DZ, S_DBZ, D_MDZ, T_VE and M_SW

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图 5 2010年10月4日23:00北京雷达PPI识别效果对比 (相邻距离圈间隔为50 km)

(a)0.4°仰角识别前反射率因子, (b)1.2°仰角识别前反射率因子, (c)0.4°仰角ICADA识别后剩余的反射率因子，(d)1.2°仰角ICADA识别后剩余的反射率因子, (e)0.4°仰角NPMDA识别后剩余的反射率因子, (f)1.2°仰角NPMDA识别后剩余的反射率因子

Fig. 5 PPI of Beijing radar at 2300 BT 4 Oct 2010(range rings at 50-km intervals)

(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.2° elevation before echo identification, (c) reflectivity remained at 0.4° elevation after echo identification with ICADA, (d) reflectivity remained at 1.2° elevation after echo identification with ICADA, (e) reflectivity remained at 0.4° elevation after echo identification with NPMDA, (f) reflectivity remained at 1.2° elevation after echo identification with NPMDA

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图 6 2008年8月31日21:00北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)

(a)0.4°仰角识别前反射率因子, (b)1.3°仰角识别前反射率因子, (c)0.4°仰角ICADA识别后剩余的反射率因子，(d)1.3°仰角ICADA识别后剩余的反射率因子, (e)0.4°仰角NPMDA识别后剩余的反射率因子, (f)1.3°仰角NPMDA识别后剩余的反射率因子

Fig. 6 PPI of Beijing radar at 2100 BT 31 Aug 2008(range rings at 50-km intervals)

(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.3° elevation before echo identification, (c) reflectivity remained at 0.4° elevation after echo identification with ICADA, (d) reflectivity remained at 1.3° elevation after echo identification with ICADA, (e) reflectivity remained at 0.4° elevation after echo identification with NPMDA, (f) reflectivity remained at 1.3° elevation after echo identification with NPMDA

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图 7 2008年9月24日06:24北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)

(a)0.4°仰角识别前反射率因子, (b)1.3°仰角识别前反射率因子, (c)0.4°仰角ICADA误判反射率因子，(d)1.3°仰角ICADA误判反射率因子, (e)0.4°仰角NPMDA误判反射率因子, (f)1.3°仰角NPMDA误判反射率因子

Fig. 7 PPI of Beijing radar at 0624 BT 24 Sep 2008(range rings at 50-km intervals)

(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.3° elevation before echo identification, (c) false detections of reflectivity at 0.4° elevation with ICADA, (d) false detections of reflectivity at 1.3° elevation with ICADA, (e) false detections of reflectivity at 0.4° elevation with NPMDA, (f) false detections of reflectivity at 1.3° elevation with NPMDA

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图 8 2010年3月29日17:06北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)

(a)0.4°仰角识别前反射率因子, (b)1.3°仰角识别前反射率因子, (c)0.4°仰角ICADA误判反射率因子, (d)1.3°仰角ICADA误判反射率因子, (e)0.4°仰角NPMDA误判反射率因子, (f)1.3°仰角NPMDA误判反射率因子

Fig. 8 PPI of Beijing radar at 1706 BT 29 Mar 2010(range rings at 50-km intervals)

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图 9 2010年10月24日15:48北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)

Fig. 9 PPI of Beijing radar at 1548 BT 24 Oct 2010(range rings at 50-km intervals)

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Table 1 Conditions of echoes identified as precipitation

条件	内容
1	回波片中回波顶高在5 km以上的面积比例≥20%
2	20 dBZ以上的回波面积比例≥35%
3	25 dBZ以上的回波面积比例≥15%
4	25 dBZ以上的回波面积≥4500 km²

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Table 2 Identifiable accuracy of each characteristic parameter

参量	T_VE	M_SW	M_DZ	S_DBZ	D_MDZ
正确率/%	65.3	73.1	89.5	69.4	72.6

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Table 3 Identifiable accuracy for non-precipitation echoes and false detection rate of precipitation echoes (unit:%)

方法	总样本识别正确率	非降水气象回波识别正确率	非降水气象回波识别误判率	降水回波识别正确率	降水回波识别误判率
ICADA	78.6	65.9	34.1	91.3	8.7
NPMDA	93.3	86.7	13.3	100.0	0.0

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