基于激光测风雷达的机场低空风切变识别算法
作者:
作者单位:

1.西南技术物理研究所,四川 成都 610225;2.成都信息工程大学 大气科学学院 高原大气与环境四川省重点实验室,四川 成都 610225

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中图分类号:

TN958.98

基金项目:

国家重点研发计划 2018YFF01014100;四川省重大科技专项 2018TZDZX0006;成都信息工程大学人才引进项目 KYTZ201728国家重点研发计划(2018YFF01014100),四川省重大科技专项(2018TZDZX0006),成都信息工程大学人才引进项目(KYTZ201728)


Research on airport low-level wind shear identification algorithm based on laser wind radar
Author:
Affiliation:

1.Chengdu Southwest Institute of Technical Physics, Chengdu 610225, China;2.College of Atmospheric Science, Chengdu University of Information Technology, Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu 610225, China

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    摘要:

    介绍了低空风切变识别的研究情况,提出了一种基于激光测风雷达的机场低空风切变识别算法,针对性设计了重点监测区域和告警方式;并于2016年春季和2018年春夏季,在气候、地形复杂度不同的多个机场进行低空风切变监测试验,通过分别与国外某型激光测风雷达以及同时段航空器报告结果做对比,评估本文算法的切变识别能力。试验结果表明,本文算法可以有效监测到激光测风雷达探测范围内的低空风切变,命中率可达88%以上。

    Abstract:

    This paper introduces the research situation of low-level wind shear identification, and proposes a low-altitude wind shear recognition algorithm based on airport application for laser wind radar. The key monitoring areas and warning methods are designed in a targeted manner. Low-level wind shear data in this paper are collected form the LIDAR wind field detection tests conducted from January to April 2016 and December 2017 to June 2018 at several airports with different climate and terrain complexity. The shear identification capability of the algorithm was evaluated by comparing with the results of a foreign company"s LIDAR and aircraft voice report. The experimental results show that the algorithm can effectively detect low-level wind shear in the detection range of laser wind radar, and the hit rate can reach over 88%.

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范琪,郑佳锋,周鼎富,朱克云,张杰,童文皓,罗雄.基于激光测风雷达的机场低空风切变识别算法[J].红外与毫米波学报,2020,39(4):462~472]. FAN Qi, ZHENG Jia-Feng, ZHOU Ding-Fu, ZHU Ke-Yun, ZHANG Jie, TONG Wen-Hao, LUO Xiong. Research on airport low-level wind shear identification algorithm based on laser wind radar[J]. J. Infrared Millim. Waves,2020,39(4):462~472.]

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  • 收稿日期:2019-09-19
  • 最后修改日期:2020-04-03
  • 录用日期:2019-12-05
  • 在线发布日期: 2020-04-01
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