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遥感技术与应用  2019, Vol. 34 Issue (5): 1101-1110    DOI: 10.11873/j.issn.1004-0323.2019.5.1011
降水遥感观测专栏     
星载雷达在评估地基天气雷达非降水回波识别算法效果中的应用
张帅1,2(),王振会1(),赵兵科2,冷亮3
1. 南京信息工程大学气象灾害预报预警与评估协同创新中心中国气象局 气溶胶-云-降水重点实验室,江苏 南京 210044
2. 中国气象局上海台风研究所台风探测室,上海 200030
3. 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室,湖北 武汉 430205
Use of Space-borne Radar Observations in Evaluating Ground Radar Non-precipitation Echo Identification Algorithm
Shuai Zhang1,2(),Zhenhui Wang1(),Binke Zhao2,Liang Leng3
1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. Shanghai Typhoon Institute, CMA, Shanghai 200030, China
3. Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration,Wuhan 430205, China
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摘要:

非降水回波识别算法的效果直接影响着雷达数据后续应用(如定量降水估测)的结果,因此对其进行客观定量检验是很重要的。文中所用算法在SWAN系统雷达反射率因子质量控制算法基础上增加了晴空回波识别算法,使用回波延展度因子并增加高度限制来对晴空回波进行识别和去除。使用星载雷达反射率强度数据与非降水回波去除前后的地基雷达数据进行时空匹配,对非降水回波识别算法效果进行客观的直观和定量验证。参照星载雷达观测结果,文中算法针对与降水回波无混叠的超折射回波有很好的识别效果,效果优于存在降水与超折射混叠的情况,当降水回波中存在与超折射回波水平纹理相近的对流降水时,经算法处理后该部分回波会丢失部分信息。针对存在降水与超折射回波混叠的情况,将算法处理前后的地基雷达降水区域反射率因子分别与星载雷达数据进行比较,结果表明经算法处理后的数据更接近星载雷达观测值。评价结果可为算法适用性分析及改进提供依据。

关键词: 星载雷达地基天气雷达非降水回波识别算法评估    
Abstract:

The performance of non-precipitation echo identification algorithm directly affects the results of downstream applications (such as quantitative precipitation estimation). Therefore, it is important to evaluate the algorithm performance objectively and quantitatively. The algorithm used in this paper is based on the quality control algorithm used in SWAN system. The method of clear air echoes identification was added in the algorithm, the echo extension was used and the height limitation of the echoes was added to identify and remove the clear air echoes. The spatial and temporal matching between radar data from the Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite and the ground-based radar (GR) before and after the non-precipitation echo removal was carried out to objectively assess the performance the algorithm visually and quantitatively. According to observations of PR, the algorithm has a good identifying performance for the AP echoes which is not aliased to the precipitation echoes, and the result is better than that AP echoes aliased to the precipitation echoes. Part of the convective echo information lost after processing by the algorithm because of the factor TDBZ. For the situation of precipitation aliased to AP echoes, the evaluation was carried out by comparing the PR reflectivity factor values with those of GR before and after the identifying. The result indicated that the observations after identifying was closer to those of PR. The evaluation results can provide basis for the applicability analysis and improvement of the identifying algorithm.

Key words: Space-borne radar    Ground radar    Identification algorithm    Evaluate
收稿日期: 2018-07-10 出版日期: 2019-12-05
ZTFLH:  P412.2  
基金资助: 国家自然科学基金项目(41675028);公益性行业科研专项(GYHY201306078);科技部重点专项(2017YFE0107700);江苏高校优势学科建设工程资助项目(PAPD)
通讯作者: 王振会     E-mail: zhangs@typhoon.org.cn;eiap@nuist.edu.cn
作者简介: 张 帅(1984-),男,河南漯河人,博士研究生,主要从事雷达资料处理与分析方面的研究。E?mail:zhangs@typhoon.org.cn
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引用本文:

张帅,王振会,赵兵科,冷亮. 星载雷达在评估地基天气雷达非降水回波识别算法效果中的应用[J]. 遥感技术与应用, 2019, 34(5): 1101-1110.

Shuai Zhang,Zhenhui Wang,Binke Zhao,Liang Leng. Use of Space-borne Radar Observations in Evaluating Ground Radar Non-precipitation Echo Identification Algorithm. Remote Sensing Technology and Application, 2019, 34(5): 1101-1110.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.1011        http://www.rsta.ac.cn/CN/Y2019/V34/I5/1101

图1  几何匹配法示意图(实线是PR扫描波束,虚线是地基雷达波束,阴影部分是重合区域)
图2  武汉天气雷达2010年9月13日11:27匹配时次在0.5°仰角的PPI图。
图3  武汉天气雷达2006年8月8日06:08
图4  武汉天气雷达2007年9月23日13:56
图5  武汉天气雷达2010年7月21日18:50
图6  武汉天气雷达2004年6月12日20:49
轨道号非降水回波数正确识别数错误识别数PODFARCSI
730761 9661 9443198.9%1.6%97.4%
497409939837199.0%7.2%92.4%
561511 4901 435996.3%0.6%95.7%
722391 1151 1083599.4%3.1%96.4%
374831 8871 88410499.8%5.5%94.6%
表1  与降水回波无混叠的非降水回波识别结果统计
图7  非降水回波算法识别前后GR与PR反射率因子强度散点分布图(0.5度仰角)
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