| X波段双偏振雷达水凝物粒子相态识别应用研究 |
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| 引用本文： | 冯亮,肖辉,孙跃.X波段双偏振雷达水凝物粒子相态识别应用研究[J].气候与环境研究,2018,23(3):366-386. |
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| 作者姓名： | 冯亮 肖辉 孙跃 |
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| 作者单位： | 1.中国科学院大气物理研究所中国科学院云降水物理与强风暴重点实验室, 北京 1000292.中国科学院减灾中心, 北京 1000293.中国科学院大学, 北京 100049 |
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| 基金项目： | 国家自然科学基金项目41575037、41605019，国家重点基础研究计划（973计划）项目2014CB441403、2013CB430105 |
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| 摘 要： | 对云中水凝物粒子分类识别是双偏振雷达的主要应用之一。本文利用IAP-714XDP-A X波段双偏振雷达观测数据，在对其进行质量控制的基础上，利用滑动自适应订正算法对雷达反射率及差分反射率进行衰减订正，进而采用纹理参数SD（ZH）和SD（?DP）区分气象回波与非气象回波，最后建立基于X波段双偏振雷达偏振参量（ZH、ZDR、KDP、ρHV）、环境温度T和纹理参数（SD（ZH）、SD（?DP））的模糊逻辑水凝物粒子分类识别算法。本文通过对2016年8月7日一次低仰角的观测，检验了纹理参数SD（ZH）和SD（?DP）对气象回波和非气象回波的识别效果，结果表明:SD（ZH）与SD（?DP）两者结合可有效区分气象回波和非气象回波；用2015年8月7日北京一次较大范围的降雹个例，对建立的模糊逻辑水凝物粒子分类识别算法进行效果验证，识别降雹落点与地面观测降雹落点一致，表明各种水凝物粒子对应偏振参量取值范围合理；对2016年9月14日一次处于不同发展阶段的多单体对流云进行水凝物粒子分类识别，结果显示处于发展阶段对流云中存在过冷水柱，其形成的微物理过程是对流云中强烈的上升气流将暖层的水滴抬升到0℃层之上形成过冷云雨水，进而冻结形成雹胚并发展成为冰雹。
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| 关 键 词： | X波段双偏振雷达 数据质量控制 衰减订正 粒子识别 |
| 收稿时间： | 2017/8/31 0:00:00 |
A Study on Hydrometeor Classification and Application Based on X-band Dual-polarization Radar Measurements |
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| FENG Liang,XIAO Hui and SUN Yue.A Study on Hydrometeor Classification and Application Based on X-band Dual-polarization Radar Measurements[J].Climatic and Environmental Research,2018,23(3):366-386. |
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| Authors: | FENG Liang XIAO Hui and SUN Yue |
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| Institution: | 1.Key Laboratory of Cloud-Precipitation and Severe Storms of Chinese Academy of Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 1000292.Disaster Reduction Center of Chinese Academy of Sciences, Beijing 1000293.University of Chinese Academy of Sciences, Beijing 100049 |
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| Abstract: | Hydrometeor classification is one of the most important applications of the dual-polarization radar. In this paper, based on quality-controlled observations of the IAP-714XDP-A X-band dual-polarization radar, the radar reflectivity and differential reflectivity are corrected by the slide self-consistency correction method. Texture parameters SD(ZH) and SD(fDP) are then used to distinguish between meteorological echoes and non-meteorological echoes. Finally, a fuzzy logic hydrometeor classification method is established for the X-band dual-polarization radar based on radar parameters ZH, ZDR, KDP, rHV, temperature T, and texture parameters SD(ZH) and SD(fDP). The classification of the meteorological echoes and non-meteorological echoes using SD(ZH) and SD(fDP) is verified against low-elevation observations on 7 August 2016. The results show that the combination of SD(ZH) and SD(fDP) can effectively distinguish meteorological and non-meteorological (ground cluster) echoes. Based on the case study of a hail event that occurred in Beijing on 7 August 2015, the fuzzy logic hydrometeor classification method is validated. The classification results of the hail are in good agreement with the ground observation results, indicating that the input parameters of different hydrometeor classification in the method are reasonable. The method is also used to classify hydrometeors in convective clouds that were in the developing stage on 14 September 2016. It is found that the strong updraft could bring low-level raindrops up into the layer above the freezing level. These raindrops then became supercooled rain and froze to form hail embryos that can develop into hailstones. |
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| Keywords: | X-band dual-polarization radar Data quality control Attenuation correction Hydrometeor classification |
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