| 雷达数据外推与特征识别的下击暴流预警方法 |
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| 引用本文： | 王兴,闵锦忠,张露萱,丁柳丹,蔡舒心.雷达数据外推与特征识别的下击暴流预警方法[J].气象科学,2019,39(3):377-385. |
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| 作者姓名： | 王兴 闵锦忠 张露萱 丁柳丹 蔡舒心 |
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| 作者单位： | 南京信息工程大学 大气科学与环境气象国家级实验教学示范中心, 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 南京 210044 |
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| 基金项目： | 国家自然科学基金资助项目（41430427;41805033）;江苏省高等学校大学生创新创业训练计划（201610300056Y）;2017年度地球科学虚拟仿真实验教学课程建设项目（XNFZ2017B10） |
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| 摘 要： | 现今利用普通气象资料对下击暴流进行识别预警的可行性很低,因此提出一种基于多普勒天气雷达资料的下击暴流识别追踪和预警方法。首先,采用光流法对反射率因子垂直剖面的光流场进行分析,得出风暴核心随时间演变的规律。然后,采用拉格朗日力学模型对风暴核心顶高的下降过程进行函数拟合。再采用直方图和巴氏系数统计分析法,对风暴核心中层径向速度场中的"正负速度对"图像进行匹配识别,综合一系列阈值的判定,最终实现下击暴流的智能预警。提出基于多层迭代的局部约束光流算法,有效改善了传统光流法对回波这类非刚体移动目标的不适用性。提出直方图和巴氏系数统计分析法,解决了因径向"正负速度对"图像的非对称结构而造成的图像匹配难题。个例检验结果表明,该方法可在风暴核心抬升和急速下沉的前期识别出潜在的下击暴流,并对风暴核心下沉速率和到达近地面的时间做出估测,从而实现下击暴流的智能识别、预警。
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| 关 键 词： | 下击暴流 天气雷达 光流法 外推预报 智能预警 |
| 收稿时间： | 2017/11/20 0:00:00 |
| 修稿时间： | 2018/6/4 0:00:00 |
Method of downburst warning based on radar data extrapolation and feature recognition |
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| WANG Xing,MIN Jinzhong,ZHANG Luxuan,DING Liudan and CAI Shuxin.Method of downburst warning based on radar data extrapolation and feature recognition[J].Scientia Meteorologica Sinica,2019,39(3):377-385. |
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| Authors: | WANG Xing MIN Jinzhong ZHANG Luxuan DING Liudan and CAI Shuxin |
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| Institution: | National Demonstration Center for Experimental Atmospheric Science and Environmental Meteorology Education, Nanjing University of Information Science & Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center of Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center of Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center of Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China and Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center of Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | At present, the feasibility of using ordinary weather data to detect the downburst is very low, so this paper presents a method to detect, track and forecast downburst based on data from Doppler Weather Radar. Firstly, the optical-flow field of the reflectivity factor''s vertical profile was analyzed by the optical-flow method, resulting in the regularity that the storm core evolves with time. Then, the function fitting was performed for the drop of the crest elevation of the storm-core within the Lagrangian mechanical model. Finally, the image matching and recognition of "positive-negative velocity pairs" in the middle-level radial velocity field of storm core was carried out using histogram and Bhattacharyya coefficient statistical analysis method. The intelligent early warning of the downburst is finally achieved based on a series of threshold judgments. The optical-flow algorithm based on multi-layer iteration was proposed to improve the inapplicability compared with the traditional one when it''s utilized in returning echo wave of the non-rigid moving target. The histogram and Bhattacharyya coefficient statistical analysis method solved the problem of difficult image matching caused by asymmetric structure in the "positive-negative velocity pairs" appeared in the radial direction. Example testing results show that the method can effectively identify potential downburst during the early process of storm-core''s uplifting and rapid sinking, and estimate its sinking rate and the time reaching to the near surface. At last, the intelligent detecting and warning for downburst are realized. |
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| Keywords: | downburst weather radar optical flow extrapolation forecast intelligent warning |
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