湖北农业科学 ›› 2021, Vol. 60 ›› Issue (22): 64-68.doi: 10.14088/j.cnki.issn0439-8114.2021.22.014

• 资源·环境 • 上一篇    下一篇

新一代雷达特征参数在人工防雹决策中的应用

何阳1, 李红斌1, 张靖萱2, 夏葳1, 闻家梁1, 濮文耀1, 张殿刚1   

  1. 1.大连市人工影响天气办公室,辽宁 大连 116001;
    2.沈阳市气象服务中心,沈阳 110168
  • 收稿日期:2021-02-04 出版日期:2021-11-25 发布日期:2021-12-10
  • 通讯作者: 李红斌(1963-),女,河南濮阳人,正研级高级工程师,主要从事人工影响天气业务与科研工作,(电话)13052722159(电子信箱)lhb7159@163.com。
  • 作者简介:何阳(1981-),男,辽宁大连人,工程师,主要从事人工影响天气业务和科研工作,(电话)0411-82822131(电子信箱)25108827@qq.com。
  • 基金资助:
    国家自然科学基金项目(41375038)

Application of new generation radar characteristic parameters in artificial prevention of hail decision

HE Yang1, LI Hong-bin1, ZHANG Jing-xuan2, XIA Wei1, WEN Jia-liang1, PU Wen-yao1, ZHANG Dian-gang1   

  1. 1. Dalian Weather Modification Office,Dalian 116001,Liaoning,China;
    2. Shenyang Meteorological Service Center,Shenyang 110168,China
  • Received:2021-02-04 Online:2021-11-25 Published:2021-12-10

摘要: 基于2016—2018年所观测到的109个冰雹云个例雷达基数据资料,进行PUP软件处理和分析,结合原大连市人工防雹决策指挥系统相关业务功能进行雷达特征参数辅助分析取值,得到了冰雹云发生发展雷达回波强度、云顶高度、强中心高度和垂直积分液态水含量雷达特征参数值。根据雹云理论及催化原理等,研究和建立了基于SA新一代多普勒雷达的雹云识别、类型判别,以及人工防雹决策判别指标及模型;并利用该指标对2019年28个冰雹云个例进行验证和分析,准确率达85.2%~86.8%,特别是对秋季1次强冰雹天气过程雷达防雹决策指挥系统进行雹云识别、防雹决策等雷达资料反演分析,以及新旧判别指标对比分析,效果明显,表明不同型号雷达其特征参数及人影决策判别指标存在一定差异,并通过对本次强雹云人工防雹技术分析及实际调研得到启示,总结探讨了强雹云有效人工防雹新思路和方法。

关键词: 雷达特征参数, 人工防雹, 决策指标模型及应用

Abstract: Based on the radar cardinality data of 109 hailcloud cases observed from 2016 to 2018, PUP software processing and analysis were carried out. Combined with relevant business functions of the original Dalian artificial hail prevention decision command system, assisted analysis and value of radar characteristic parameters were conducted. The radar characteristic parameters of hail cloud occurrence and development, cloud top height, strong center height and vertical integral liquid water content were obtained. According to hail cloud theory and catalytic principle, hail cloud recognition and type discrimination based on SA new generation Doppler radar, as well as artificial hail prevention decision discrimination index and model were studied and established. In addition, 28 hail cloud cases in 2019 were verified and analyzed by using this index, and the accuracy rate reached 85.2%~86.8%. In particular, the radar data inversion analysis, such as hail cloud identification and hail prevention decision making, was carried out on the radar hail prevention decision command system during a severe hail weather process in autumn, as well as the comparative analysis of the old and new discriminant indexes, showed obvious results. It showed that there were some differences in characteristic parameters and human figure decision discriminant indexes of different types of radar, and the new ideas and methods of effective artificial hail prevention of strong hail cloud were summarized and discussed through the analysis and practical investigation of the artificial hail prevention technology of strong hail cloud.

Key words: radar characteristic parameters, artificial hail prevention, decision discrimination index and application

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