大气与环境光学学报2025，Vol.20Issue(3)：312-324,13.DOI:10.3969/j.issn.1673-6141.2025.03.006
不同云雾和降水条件下相干多普勒测风激光雷达探测性能分析
Performance analysis of coherent Doppler wind lidar detection under different cloud and precipitation conditions
摘要
Abstract
Exploring the detection performance of coherent Doppler wind lidar(CDWL)under different meteorological conditions is of great significance for the network observation of aviation meteorological equipment.Using the CDWL deployed at Guangzhou Baiyun International Airport,combined with aviation routine weather reports and meteorological data from the National Centers for Environmental Information of the United States,this study analyzed the effects of different cloud cover and daily cumulative precipitation on the effective detection range and signal-to-noise ratio of the CDWL,and further explored the dynamic impacts of visibility and precipitation weather phenomena on the detection performance of CDWL through typical observation cases.The experimental results show that under non precipitation conditions,the effective detection distance and signal-to-noise ratio of CDWL show a decreasing trend with the increase of cloud cover and the decrease of visibility,and the case analysis shows that,when the visibility is less than 3 km,the effective detection range of CDWL decreases significantly,with a detection range of only 2-4 km.While under precipitation conditions,the intensity and spatiotemporal distribution characteristics of precipitation will affect the detection performance of CDWL,and the case analysis shows that under weak precipitation intensity,the suppression of intermittent precipitation on CDWL detection performance is more significant than that under thunderstorm conditions,and even under heavy rainstorm conditions,CDWL can still maintain a detection range of 2 km.关键词
相干多普勒激光雷达/探测性能/探测距离/信噪比Key words
coherent Doppler lidar/detection performance/detection distance/signal-to-noise ratio分类
天文与地球科学引用本文复制引用
孙灿,王豫,刘晓英,张洪玮,戴光耀,张芯瑜,王琪超,周伟,吴松华..不同云雾和降水条件下相干多普勒测风激光雷达探测性能分析[J].大气与环境光学学报,2025,20(3):312-324,13.基金项目
中国博士后科学基金特别资助(2024T170867),山东省重点研发计划(2023CXGC010408) （2024T170867）
