Using China New Generation Weather Radar （CINRAD） level-Ⅱ data, the original algorithms for removing isol-ated non-meteorological echoes and ground clutters in radar data, which have been applied to Weather Surveil...Using China New Generation Weather Radar （CINRAD） level-Ⅱ data, the original algorithms for removing isol-ated non-meteorological echoes and ground clutters in radar data, which have been applied to Weather Surveillance Radar-1988 Doppler （WSR-88D） in the USA and Severe Weather Automatic Nowcast （SWAN） system in China, are modified and improved. To remove isolated non-meteorological echoes, the new algorithm introduces a constraint parameter （Po） to distinguish whether a window of 5 x 5 points is isolated as external echoes. A statistical analysis of 150 radar scans （5 cases, with each case comprising 30 scans） under three different echo types （small-scale convec-tion, typhoon, and large-scale synoptic system） shows that the constraint parameter Po ≤ 0.167 is suitable for remov- ing isolated non-meteorological echoes while preserving the edge of meteorological echoes. A new parameter, NDZ, which promotes the ability of the algorithm to identify the ground clutters appearing at two adjacent elevation angles, is constructed based on the vertical continuity of reflectivity. These improved algorithms are tested for four cases （three cases of isolated non-meteorological echoes and one case of ground clutters）. Based on the statistics of 232 volume scans of radar data （on a temporal resolution of 1 h） measured at Nanchang station from 0000 UTC 5 to 1600 UTC 14 March 2015, it is found that the improved algorithms not only eliminate most （over 95% under clear-sky conditions） of the isolated non-meteorological echoes and ground clutters （including those appearing at two adjacent elevation angles）, but also well preserve the structure of meteorological echoes （storms）.展开更多
提出一种基于模糊逻辑的新一代天气雷达地物回波识别方法。通过统计典型个例的回波特性得到隶属度函数及权重,并根据反射率因子范围的不同设置相应的隶属度函数及权重。该方法针对降水强度量级的回波,即反射率因子不小于15 d Bz,对于非...提出一种基于模糊逻辑的新一代天气雷达地物回波识别方法。通过统计典型个例的回波特性得到隶属度函数及权重,并根据反射率因子范围的不同设置相应的隶属度函数及权重。该方法针对降水强度量级的回波,即反射率因子不小于15 d Bz,对于非降水强度回波则不进行处理,从而保留对短临预报具有指示作用、且强度较弱的特征回波,如晴空湍流回波以及阵风锋回波。根据雷达回波垂直方向连续性对剔除地物回波所产生的"空洞"进行填补,从而进一步减小地物回波对雷达数据质量造成的影响。最后通过两种方法对识别算法进行效果检验,结果表明该算法对地物回波有显著的识别效果。展开更多
In mountain areas, radar observations are often contaminated （1） by echoes from high-speed moving vehicles and （2） by point-wise ground clutter under either normal propagation （NP） or anomalous propagation （AP...In mountain areas, radar observations are often contaminated （1） by echoes from high-speed moving vehicles and （2） by point-wise ground clutter under either normal propagation （NP） or anomalous propagation （AP） conditions. Level Ⅱ data are collected from KMTX （Salt Lake City, Utah） radar to analyze these two types of contamination in the mountain area around the Great Salt Lake. Human experts provide the ＂ground truth＂ for possible contamination of either type on each individual pixel. Common features are then extracted for contaminated pixels of each type. For example, pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width. Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways. These contaminated pixels are only seen in areas of large terrain gradient （in the radial direction along the radar beam）. The same is true for the second type of contamination - pointwise ground clutters. Six quality control （QC） parameters are selected to quantify the extracted features. Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels. Based on the computed histograms, a fuzzy logical algorithm is developed for automated detection of contaminated pixels. The algorithm is tested with KMTX radar data under different （clear and rainy） weather conditions.展开更多
基金Supported by the Jiangxi Provincial Department of Science and Technology project(20171BBG70004)Open Project of the State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences(2016LASW-N11)National Natural Science Foundation of China(41575098)
文摘Using China New Generation Weather Radar （CINRAD） level-Ⅱ data, the original algorithms for removing isol-ated non-meteorological echoes and ground clutters in radar data, which have been applied to Weather Surveillance Radar-1988 Doppler （WSR-88D） in the USA and Severe Weather Automatic Nowcast （SWAN） system in China, are modified and improved. To remove isolated non-meteorological echoes, the new algorithm introduces a constraint parameter （Po） to distinguish whether a window of 5 x 5 points is isolated as external echoes. A statistical analysis of 150 radar scans （5 cases, with each case comprising 30 scans） under three different echo types （small-scale convec-tion, typhoon, and large-scale synoptic system） shows that the constraint parameter Po ≤ 0.167 is suitable for remov- ing isolated non-meteorological echoes while preserving the edge of meteorological echoes. A new parameter, NDZ, which promotes the ability of the algorithm to identify the ground clutters appearing at two adjacent elevation angles, is constructed based on the vertical continuity of reflectivity. These improved algorithms are tested for four cases （three cases of isolated non-meteorological echoes and one case of ground clutters）. Based on the statistics of 232 volume scans of radar data （on a temporal resolution of 1 h） measured at Nanchang station from 0000 UTC 5 to 1600 UTC 14 March 2015, it is found that the improved algorithms not only eliminate most （over 95% under clear-sky conditions） of the isolated non-meteorological echoes and ground clutters （including those appearing at two adjacent elevation angles）, but also well preserve the structure of meteorological echoes （storms）.
文摘提出一种基于模糊逻辑的新一代天气雷达地物回波识别方法。通过统计典型个例的回波特性得到隶属度函数及权重,并根据反射率因子范围的不同设置相应的隶属度函数及权重。该方法针对降水强度量级的回波,即反射率因子不小于15 d Bz,对于非降水强度回波则不进行处理,从而保留对短临预报具有指示作用、且强度较弱的特征回波,如晴空湍流回波以及阵风锋回波。根据雷达回波垂直方向连续性对剔除地物回波所产生的"空洞"进行填补,从而进一步减小地物回波对雷达数据质量造成的影响。最后通过两种方法对识别算法进行效果检验,结果表明该算法对地物回波有显著的识别效果。
基金the NOAA A8R2WRPproject and FAA (Federal Aviation Administration) con-tract IA#DTFA03-01-X-9007 to NSSL (National SevereStorms Laboratory)the ONR (Offce of NavalResearch)Grant N000140310822 to the University of Ok-lahoma.
文摘In mountain areas, radar observations are often contaminated （1） by echoes from high-speed moving vehicles and （2） by point-wise ground clutter under either normal propagation （NP） or anomalous propagation （AP） conditions. Level Ⅱ data are collected from KMTX （Salt Lake City, Utah） radar to analyze these two types of contamination in the mountain area around the Great Salt Lake. Human experts provide the ＂ground truth＂ for possible contamination of either type on each individual pixel. Common features are then extracted for contaminated pixels of each type. For example, pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width. Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways. These contaminated pixels are only seen in areas of large terrain gradient （in the radial direction along the radar beam）. The same is true for the second type of contamination - pointwise ground clutters. Six quality control （QC） parameters are selected to quantify the extracted features. Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels. Based on the computed histograms, a fuzzy logical algorithm is developed for automated detection of contaminated pixels. The algorithm is tested with KMTX radar data under different （clear and rainy） weather conditions.
