Infrared and Laser Engineering, Volume. 49, Issue S2, 20200379(2020)

Lidar cloud detection based on improved simple multiscale method

Chen Siying*, Wang Jiaqi, Chen He, Zhang Yinchao, Guo Pan, Nian Xuan, Sun Zhuoran, and Chen Su
Author Affiliations
  • [in Chinese]
  • show less

    As one of the important means of active remote sensing of atmosphere, lidar is widely used in aerosol and cloud detection. Based on the simple multiscale algorithm for layer detection, a cloud detection algorithm was proposed that could improve the accuracy of the cloud-base height when the SNR was greater than 5 by adding the threshold of the number of scales. By simulating the echo signal of multi-peak inside the cloud, the algorithm obtained the threshold range of the optimization scale. At the same time, by adding feature segment merge, the disadvantage of the simple multiscale method, missing the single-layer with multipeak, could be improved. The effective 532 nm Mie lidar data was processed which had more than 75 min detection time and stable structure from May to July 2019 by using differential zero-crossing method, simple multiscale method and improved algorithm were proposed respectively, and based on the cloud-base height of differential zero-crossing method, the average error of the mean square root obtained by the improved algorithm decreased by 32.65%, the uncertainty average decreased by 33.80%. Then the effectiveness of the improved algorithm in improving the accuracy of the cloud-base height is proved.

    Tools

    Get Citation

    Copy Citation Text

    Chen Siying, Wang Jiaqi, Chen He, Zhang Yinchao, Guo Pan, Nian Xuan, Sun Zhuoran, Chen Su. Lidar cloud detection based on improved simple multiscale method[J]. Infrared and Laser Engineering, 2020, 49(S2): 20200379

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: 大气光学

    Received: Sep. 27, 2020

    Accepted: Oct. 21, 2020

    Published Online: Feb. 5, 2021

    The Author Email: Siying Chen (csy@bit.edu.cn)

    DOI:10.3788/irla20200379

    Topics