| 长沙机场阵列天气雷达地物识别算法 |
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| 引用本文： | 魏万益,马舒庆,杨玲,甄小琼,吕寺炜.长沙机场阵列天气雷达地物识别算法[J].应用气象学报,2020,31(3):339-349. |
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| 作者姓名： | 魏万益 马舒庆 杨玲 甄小琼 吕寺炜 |
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| 作者单位： | 1.成都信息工程大学电子工程学院, 成都 610225 |
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| 摘 要： | 地物杂波是影响雷达产品准确性的重要因素。该文提出了一种改进的基于模糊逻辑的阵列天气雷达地物识别算法。在Kessinger模糊逻辑基础上，加入回波强度时间变化量（time variability of reflectivity factor，TVR）参数，利用收集到的雷达数据统计出各输入参数的概率分布，确定隶属函数；分析TVR参数对地物识别算法的贡献，并在不同天气情况下进行识别算法有效性验证。试验结果表明:加入TVR参数，长沙机场阵列天气雷达地物识别准确率最大可提高4%，降水识别误判率最多可降低2%。该文提出的地物杂波识别算法，无降水时，地物识别准确率达96%；有降水时，地物识别准确率达92%；降水回波误判为地物杂波的误判率约为10%，能较好地区分降水回波和地物杂波。
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| 关 键 词： | 地物杂波 模糊逻辑 回波强度时间变化量(TVR) 阵列天气雷达 |
| 收稿时间： | 2019-11-01 |
Ground Clutter Detection Algorithm for Array Weather Radar at Changsha Airport |
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| Institution: | 1.College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 6102252.CMA Key Laboratory of Atmospheric Sounding, Chengdu 6102253.Meteorological Observation Center of CMA, Beijing 1000814.Institute of Atmospheric Physics, China Academy of Sciences, Beijing 1000295.Rayshon Technology Co., Ltd., Beijing 100089 |
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| Abstract: | In order to obtain more detailed small-scale weather system data, Meteorological Observation Center of China Meteorological Administration (CMA) designed and developed X-band array weather radar (AWR), cooperating with relevant manufacturers. In March of 2018, the first prototypeis deployed at Changsha Airport for field experiments. Combing advantages of networked radars and a distributed phased array technology, the AWR has a highly coordinated scanning mode and high spatial and temporal resolutions to acquire fine echo intensity and wind field data. Compared with conventional parabolic antenna weather radars, a phased array antenna has wider beams and stronger side lobes, so that more ground clutter will appear in radar echoes. If the ground clutter cannot be effectively detected and removed, the accuracy of radar products will be affected seriously.Data collected by the X-band AWR at Changsha Airport are used to study the ground clutter detection algorithm for the AWR. According to the research progress all over the world, characteristic parameters of reflectivity factors, radial velocity and velocity spectrum width are extracted. In addition, time variability of reflectivity factor (TVR), a new parameter, is added due to the high temporal and spatial resolution of the AWR. Based on analyzing statistical characteristics of each feature parameter, membership functions are determined. The contribution of TVR to the clutter detection algorithm and the performance of the algorithm on different weather conditions are analyzed. Results show that the accuracy of ground clutter detection for Changsha Airport AWR can be maximally increased by 4% by adding TVR, and the error rate of detecting the precipitation echo as clutter echo can be decreased by 2%. The accuracy of the proposed ground clutter detection algorithm reaches 96% in the detection of ground clutter when no precipitation processes happen. In the precipitation weather, the accuracy is 92%, and the error rate of detecting the precipitation echo as clutter echo is about 10%. The algorithm can basically detect and remove the ground clutter echoes from precipitation echoes. |
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