Gust Front Statistical Characteristics and Automatic Identification Algorithm for CINRAD |
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| Authors: | ZHENG Jiafeng ZHANG Jie ZHU Keyun LIU Liping and LIU Yanxia |
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| Institution: | Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044;State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225;College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225;Air Force Meteorological Center in Chengdu, Chengdu 610041;College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225;State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;Fuzhou Meteorological Bureau, Fuzhou 350008 |
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| Abstract: | Gust front is a kind of meso- and micro-scale weather phenomenon that often causes serious ground wind and wind shear. This paper presents an automatic gust front identification algorithm. Totally 879 radar volume-scan samples selected from 21 gust front weather processes that occurred in China between 2009 and 2012 are examined and analyzed. Gust front echo statistical features in reflectivity, velocity, and spectrum width fields are obtained. Based on these features, an algorithm is designed to recognize gust fronts and generate output products and quantitative indices. Then, 315 samples are used to verify the algorithm and 3 typical cases are analyzed. Major conclusions include: 1) for narrow band echoes intensity is between 5 and 30 dBZ, widths are between 2 and 10 km, maximum heights are less than 4 km (89.33% are lower than 3 km), and the lengths are between 50 and 200 km. The narrow-band echo is higher than its surrounding echo. 2) Gust fronts present a convergence line or a wind shear in the velocity field; the frontal wind speed gradually decreases when the distance increases radially outward. Spectral widths of gust fronts are large, with 87.09% exceeding 4 m s-1. 3) Using 315 gust front volume-scan samples to test the algorithm reveals that the algorithm is highly stable and has successfully recognized 277 samples. The algorithm also works for small-scale or weak gust fronts. 4) Radar data quality has certain impact on the algorithm. |
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| Keywords: | gust front statistical characteristics identification algorithm narrow-band echo |
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