为了解雷暴单体内部水成物粒子分布结构及演变过程,综合X波段双线偏振天气雷达的参量及环境温度参数,结合小波去噪和自适应约束算法进行资料预处理后,基于模糊逻辑算法对北京一个结构演变相对完整的典型雷暴单体内水成物粒子分布随时间...为了解雷暴单体内部水成物粒子分布结构及演变过程,综合X波段双线偏振天气雷达的参量及环境温度参数,结合小波去噪和自适应约束算法进行资料预处理后,基于模糊逻辑算法对北京一个结构演变相对完整的典型雷暴单体内水成物粒子分布随时间演变特征进行系统的分析,得到如下结果:(1)按雷暴单体的宏观特征将其演变过程分为发展、成熟和消散阶段。三个阶段中单体平均高度分别为11、12、10 km;回波强度最大可达40~45 d BZ、大于50 d BZ和40~45 d BZ;霰粒子占各自阶段单体内所有粒子百分比分别为2%、12%和1%。(2)各阶段主要微物理过程及演变特征是:发展阶段,单体0°C层以下由暖云过程主导,毛毛雨占5%,雨滴占24%;少量液态粒子上升至0°C层以上与冰晶反应生成1%干霰,冷云过程较弱。成熟阶段,相较发展阶段0°C层以下毛毛雨减少约2个百分点,雨滴增多约2个百分点,粒子碰并加强,暖云过程增强;较多液态粒子上升至0°C层以上,约有4%的雨滴与5%的冰晶通过凇附作用生成7%的霰,冷云过程增强。消散阶段,下层液态粒子难以上升至0°C层以上形成初始冰晶,使暖云及冷云过程都减弱,0°C层以下毛毛雨相较成熟阶段平均增多约1个百分点,粒子碰并减弱;0°C层以上冰晶消耗减少2个百分点,霰生成减少5个百分点。(3)基于雷暴单体内各类水成物粒子分布、演变及其动力场背景特征建立了雷暴单体演变过程微物理模型。本文研究有助于加深对典型雷暴单体内部水成物粒子分布和微物理过程的认识,可以为雷暴天气的预警和预报提供必要的指导。展开更多
In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retriev...In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retrieve the coverage area of supercooled water and a fuzzy logic algorithm was used to classify the observed meteorological targets. The macrophysical characteristics of supercooled water could be accurately identified by combing the threshold method with the fuzzy logic algorithm. In order to acquire microphysical characteristics of supercooled water in a mixed phase, the unimodal spectral distribution of supercooled water was extracted from a bimodal or trimodal spectral distribution of a mixed phase cloud, which was then used to retrieve the effective radius and liquid water content of supercooled water by using an empirical formula. These retrieved macro- and micro-physical characteristics of supercooled water can be used to guide aircrafts during takeoff, flight, and landing to avoid dangerous areas.展开更多
A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm f...A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection（POD）,false alarm rate（FAR）,and clutter–signal ratio（CSR）.In addition,the issues in using filtered dual-polarization data are alleviated.展开更多
文摘为了解雷暴单体内部水成物粒子分布结构及演变过程,综合X波段双线偏振天气雷达的参量及环境温度参数,结合小波去噪和自适应约束算法进行资料预处理后,基于模糊逻辑算法对北京一个结构演变相对完整的典型雷暴单体内水成物粒子分布随时间演变特征进行系统的分析,得到如下结果:(1)按雷暴单体的宏观特征将其演变过程分为发展、成熟和消散阶段。三个阶段中单体平均高度分别为11、12、10 km;回波强度最大可达40~45 d BZ、大于50 d BZ和40~45 d BZ;霰粒子占各自阶段单体内所有粒子百分比分别为2%、12%和1%。(2)各阶段主要微物理过程及演变特征是:发展阶段,单体0°C层以下由暖云过程主导,毛毛雨占5%,雨滴占24%;少量液态粒子上升至0°C层以上与冰晶反应生成1%干霰,冷云过程较弱。成熟阶段,相较发展阶段0°C层以下毛毛雨减少约2个百分点,雨滴增多约2个百分点,粒子碰并加强,暖云过程增强;较多液态粒子上升至0°C层以上,约有4%的雨滴与5%的冰晶通过凇附作用生成7%的霰,冷云过程增强。消散阶段,下层液态粒子难以上升至0°C层以上形成初始冰晶,使暖云及冷云过程都减弱,0°C层以下毛毛雨相较成熟阶段平均增多约1个百分点,粒子碰并减弱;0°C层以上冰晶消耗减少2个百分点,霰生成减少5个百分点。(3)基于雷暴单体内各类水成物粒子分布、演变及其动力场背景特征建立了雷暴单体演变过程微物理模型。本文研究有助于加深对典型雷暴单体内部水成物粒子分布和微物理过程的认识,可以为雷暴天气的预警和预报提供必要的指导。
基金Supported by the Natural Science Foundation of Jiangsu Province(BK20170945)Open Fund of the Key Laboratory for Aerosol–Cloud–Precipitation of CMA–NUIST(KDW1703)+3 种基金National(Key)Basic Research and Development(973)Program of China(2014CB441405)National Natural Science Foundation of China(41275004,61372066,and 41571348)Startup Fund for Introduced Talents of the Nanjing University of Information Science&Technology(2016r028)Earth Science Virtual Simulation Experiment Teaching Course Construction Project(XNFZ2017C02)
文摘In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retrieve the coverage area of supercooled water and a fuzzy logic algorithm was used to classify the observed meteorological targets. The macrophysical characteristics of supercooled water could be accurately identified by combing the threshold method with the fuzzy logic algorithm. In order to acquire microphysical characteristics of supercooled water in a mixed phase, the unimodal spectral distribution of supercooled water was extracted from a bimodal or trimodal spectral distribution of a mixed phase cloud, which was then used to retrieve the effective radius and liquid water content of supercooled water by using an empirical formula. These retrieved macro- and micro-physical characteristics of supercooled water can be used to guide aircrafts during takeoff, flight, and landing to avoid dangerous areas.
基金supported by a grant(14AWMP-B079364-01) from Water Management Research Program funded by Ministry of Land,Infrastructure and Transport of Korean government
文摘A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection（POD）,false alarm rate（FAR）,and clutter–signal ratio（CSR）.In addition,the issues in using filtered dual-polarization data are alleviated.
