Automatic Identification of Clear-Air Echoes Based on Millimeter-wave Cloud Radar Measurements

被引:0
|
作者
Ling Yang
Yun Wang
Zhongke Wang
Qian Yang
Xingang Fan
Fa Tao
Xiaoqiong Zhen
Zhipeng Yang
机构
[1] Chengdu University of Information Technology,Electronic Engineering College
[2] Chengdu University of Information Technology,Information Security Engineering College
[3] Western Kentucky University,Department of Geography and Geology
[4] Chinese Academy of Sciences,Institute of Atmospheric Physics
[5] Chengdu University of Information Technology,CMA Key Laboratory of Atmospheric Sounding
[6] Nanjing University of Information Science and Technology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters
[7] CMA,Meteorological Observation Centre
来源
Advances in Atmospheric Sciences | 2020年 / 37卷
关键词
millimeter-wave cloud radar; clear-air echoes; neural network; laser ceilometer; all-sky camera; feature extraction; feature selection; 毫米波云雷达; 晴空回波; 神经网络; 激光云高仪; 全天空成像仪; 特征提取; 特征选择;
D O I
暂无
中图分类号
学科分类号
摘要
Millimeter-wave cloud radar (MMCR) provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds. Therefore, MMCR has been widely applied in cloud observations. However, due to the influence of non-meteorological factors such as insects, the cloud observations are often contaminated by non-meteorological echoes in the clear air, known as clear-air echoes. It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background. The characteristics of clear-air echoes are studied here by combining data from four devices: an MMCR, a laser-ceilometer, an L-band radiosonde, and an all-sky camera. In addition, a new algorithm, which includes feature extraction, feature selection, and classification, is proposed to achieve the automatic identification of clear-air echoes. The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions. The recognition accuracy can reach up to 95.86% for the simple cases when cloud echoes and clear-air echoes are separate, and 88.38% for the complicated cases when low cloud echoes and clear-air echoes are mixed.
引用
收藏
页码:912 / 924
页数:12
相关论文
共 26 条
  • [21] Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar
    Zhao, Zihao
    Song, Yuying
    Cui, Fucheng
    Zhu, Jiang
    Song, Chunyi
    Xu, Zhiwei
    Ding, Kai
    IEEE ACCESS, 2020, 8 : 26012 - 26021
  • [22] Gesture-mmWAVE: Compact and Accurate Millimeter-Wave Radar-Based Dynamic Gesture Recognition for Embedded Devices
    Jin, Biao
    Ma, Xiao
    Hu, Bojun
    Zhang, Zhenkai
    Lian, Zhuxian
    Wang, Biao
    IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2024, 54 (03) : 337 - 347
  • [23] Millimeter-Wave Radar Monitoring for Elder's Fall Based on Multi-View Parameter Fusion Estimation and Recognition
    Feng, Xiang
    Shan, Zhengliang
    Zhao, Zhanfeng
    Xu, Zirui
    Zhang, Tianpeng
    Zhou, Zihe
    Deng, Bo
    Guan, Zirui
    REMOTE SENSING, 2023, 15 (08)
  • [24] Anti-Fixed-Interference Fall Detection Based on Doppler-Time-Range Maps Using Millimeter-Wave Radar
    Ma, Zhanchao
    Wu, Xiaochuan
    Zhang, Xin
    Yao, Di
    Deng, Weibo
    IEEE SENSORS JOURNAL, 2025, 25 (02) : 3358 - 3368
  • [25] Single-pixel 3D imaging based on fusion temporal data of single-photon detector and millimeter-wave radar
    Lai, Tingqin
    Liang, Xiaolin
    Zhu, Yi
    Wu, Xinyi
    Liao, Lianye
    Yuan, Xuelin
    Su, Ping
    Sun, Shihai
    CHINESE OPTICS LETTERS, 2024, 22 (02)
  • [26] A Fusion of Graph- and Grid-Based Hybrid Model of Object Detection and Semantic Segmentation for 4-D Millimeter-Wave Radar
    Wang, Hongyan
    Huang, Zifeng
    Ma, Jiakang
    Feng, Huimei
    IEEE SENSORS JOURNAL, 2024, 24 (24) : 42268 - 42280