Key Technologies of Hydrometeor Classification and Mosaic Algorithm for X-band Polarimetric Radar

Wu Chong; Liu Liping; Yang Meilin; Ma Jianli; Li Juan

doi:10.11898/1001-7313.20210206

Vol.32, NO.2, 2021

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Article Navigation > Journal of Applied Meteorological Science > 2021 > 32(2): 200-216

Wu Chong, Liu Liping, Yang Meilin, et al. Key technologies of hydrometeor classification and mosaic algorithm for X-band polarimetric radar. J Appl Meteor Sci, 2021, 32(2): 200-216. DOI: 10.11898/1001-7313.20210206.

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Wu Chong, Liu Liping, Yang Meilin, et al. Key technologies of hydrometeor classification and mosaic algorithm for X-band polarimetric radar. J Appl Meteor Sci, 2021, 32(2): 200-216. DOI: 10.11898/1001-7313.20210206.

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Key Technologies of Hydrometeor Classification and Mosaic Algorithm for X-band Polarimetric Radar

DOI: 10.11898/1001-7313.20210206

Wu Chong^{1)
,
,},
Liu Liping¹⁾,
Yang Meilin²⁾,
Ma Jianli²⁾,
Li Juan³⁾

1).
Chinese Academy of Meteorological Sciences, Beijing 100081
2).
Institute of Urban Meteorology, CMA, Beijing 100089
3).
Nanjing University of Information Science & Technology, Nanjing 210044

Received Date: 2020-07-01
Rev Recd Date: 2020-10-19
Publish Date: 2021-03-31

Abstract

Abstract

The advantages of X-band polarimetric weather radar focus on its high spatio-temporal resolution and capability of multi-radar networking. However, the previously designed hydrometeor classification algorithm (HCA) for S-band weather radar is unsuitable for X-band weather radar due to the difference of backscattering characteristics and heavy precipitation attenuation. Therefore, the key technologies of hydrometeor classification algorithm and multi-radar mosaic algorithm for X-band polarimetric weather radar are proposed. First, it is found that the melting layer detection algorithm designed for S-band polarimetric weather radar is not suitable for X-band weather radar through analysis on the data collected by Beijing X-band radar network. A melting layer detection method based on quasi-vertical profile is proposed, which greatly improves the accuracy of obtaining the melting information. Second, a confidence threshold adjustment method is proposed to accurately estimate the data quality in the case of precipitation and clutter superposition. Third, an optimization method of membership functions based on data statistics is proposed to reconstruct the classification parameters suitable for Beijing X-band radar network. Finally, a multi-radar mosaic method based on rainfall attenuation is proposed, in which the reflectivity factors of networking radars are weighted and averaged by the data quality factor. Compared with the traditional method, it is found that the structural inhomogeneity of X-band radar mosaic result is effectively reduced. Those modifications effectively enhance the reliability of classification mosaic results of X-band weather radar network and provide technical support for the rapid deployment of X-band radar in China. Three typical precipitation cases in Beijing during the flood season in 2016 are used to compare the observational efficiency between X-band weather radar network and S-band operational radar. For the cases of convective rainfall, fine echo structures and reasonable hydrometeor distributions are found in X-band radar mosaic results. Especially for convective rainfall with short duration and small spatial scale, the advantage of X-band radar is more obvious, which alleviates the limited detection ability of S-band operational radar in urban areas. In addition, the hail falling area identified by X-band radar can be verified by manual observation in national weather stations. The performance of X-band weather radar network in large-scale stratiform precipitation, however, is not as good as S-band weather radar.
- X-band polarimetric radar;
- hydrometeor classification;
- radar mosaic

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References(34)

Figures and Tables

图 1 BJ-Xnet与BJSDX分布及波束最低覆盖高度对比

(黑色虚线框表示北京市主城区)

Fig. 1 Site distribution and the minimum height of radar coverage of BJ-Xnet and BJSDX

(the black dotted line indicates the scope of Beijing downtown)

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图 2 BJXSY雷达分别使用MLDA算法和QVP算法得到的融化层识别结果

(a)2016年7月20日07:32 MLDA算法识别的融化物空间分布(散点)，(黑色实线表示算法识别的融化层顶和底层随方位的变化，黑色虚线表示探空的融化层顶高度)
(b)2016年7月20日01:00—17:00 BJXSY雷达使用QVP算法得到的融化层识别结果

Fig. 2 Melting Layers identified by MLDA and QVP for BJXSY radar

(a)distribution of melting particles identified by the MLDA at 0732 BT 20 Jul 2016(the scattered)
(black solid lines represent the top and bottom of melting layers varying with azimuth, and black dotted line represents the height of melting layer obtained by upair sounding),(b)melting layers identified by QVP from 0100 BT to 1700 BT on 20 Jul 2016

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图 3 BJXSY雷达置信度和灵敏度廓线

(a)F_att, (b)灵敏度, (c)F_snr, (d)F_rhv

Fig. 3 The profiles of Gauss functions and the minimum reflectivity of BJXSY radar

(a)F_att, (b)minimum reflectivity, (c)F_snr, (d)F_rhv

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图 4 BJXSY雷达2016年汛期观测数据统计得到的与不同降水相态对应的Z_H-Z_DR, Z_H-ρ_hv, Z_H-K_DP^(l)频次图

(黑框和红框分别表示默认和改进后的隶属函数参数, 实线和虚线分布表示值为1和0的隶属函数区间分界线)

Fig. 4 The frequency diagrams of Z_H-Z_DR, Z_H-ρ_hv and Z_H-K_DP^(l) of different hydrometeors from BJXSY radar in flood season of 2016

(the black and red boxes represent the default and modified membership functions respectively, and the solid line and dotted lines represent the boundary of membership function with values of 1 and 0)

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Fig. 5 The CAPPI of mosaic test at 2.5 km height observed by BJ-Xnet at 0620 BT 31 Jul 2016

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Fig. 6 Mosaic results of Z_H by DW method, PW method and QW method for BJ-Xnet at 2.5 km height at 0620 BT 31 Jul 2016

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图 7 2016年7月27日BJSDX与BJ-Xnet 2.5 km高度CAPPI对比

(黑色虚线框表示北京市主城区, 黑色实线表示垂直剖面的选取位置)

Fig. 7 The comparison of CAPPI between BJSDX and BJ-Xnet at 2.5 km height on 27 Jul 2016

(the black dotted line indicates the scope of Beijing downtown, the black solid line indicates position of vertical sections)

DownLoad: Full-Size Img PowerPoint

图 8 与图 7相同, 但为BJSDX与BJ-Xnet的在相同时刻下的垂直结构对比

(剖面的选取位置在水平结构中以黑实线标出)

Fig. 8 The same as in Fig. 7, but for comparison of vertical structures between BJSDX and BJ-Xnet

(positions of vertical section are marked by black solid lines in the CAPPIs)

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Fig. 9 The same as in Fig. 7, but for comparison between BJSDX and BJ-Xnet on 30 Jul 2016

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Fig. 10 The same as Fig. 8, but for comparison between BJSDX and BJ-Xnet on 30 Jul 2016

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Fig. 11 The same as Fig. 7, but for comparison between BJSDX and BJ-Xnet on 20 Jul 2016

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Table 1 Parameters of membership functions of BJ-Xnet before and after modification

相态	隶属函数	单位	默认隶属函数参数				改进后隶属函数参数
相态	隶属函数	单位	x₁	x₂	x₃	x₄	x₁	x₂	x₃	x₄
干雪	P[Z_DR]	dB	-0.3	0	0.3	0.6	-0.3	-0.1	0.4	0.6
干雪	P[ρ_hv]		0.95	0.98	1	1.01	0.95	0.97	1	1.01
冰晶	P[ρ_hv]		0.95	0.98	1	1.01	0.95	0.97	1	1.01
冰晶	P[K_DP^(l)]	dB·km^-1	-5	0	10	15	-30	-25	10	20
湿雪	P[Z_H]	dBZ	25	30	40	50	20	23	37	45
湿雪	P[Z_DR]	dB	0.5	1	2	3	0	0.5	2	2.5
湿雪	P[ρ_hv]		0.88	0.92	0.95	0.985	0.86	0.88	0.96	0.985
霰	P[Z_DR]	dB	-0.3	0.0	f₁	f₁+0.3	-1.5	-0.8	0.0	0.5
雨夹雹	P[Z_DR]	dB	-0.3	0.0	f₁	f₁+0.5	-1.5	-1.0	f₁+0.3	f₁+0.8
雨夹雹	P[ρ_hv]	dB	0.85	0.90	1.00	1.01	0.80	0.90	1.00	1.01
雨夹雹	P[K_DP^(l)]	dB·km^-1	-10	-4	g₁	g₁+1	-10	-4	5	7
晴空回波	P[Z_DR]	dB	0	2	10	12	-5	-2	2	7
晴空回波	P[ρ_hv]		0.3	0.5	0.8	0.83	0.2	0.3	0.75	0.83
晴空回波	P[σ(Z_H)]	(°)	1	2	4	7	1	1.5	5	8
晴空回波	P[σ(Φ_DP)]	(°)	8	10	40	60	8	15	120	150

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