Two-wavelength Lidar inversion algorithm for determining planetary boundary layer height. (February 2018)

• Record Type: Journal Article
• Authors: Liu, Boming; Ma, Yingying; Gong, Wei; Jian, Yang; Ming, Zhang
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 206(2018)
• Page Start: 117
• Page End: 124
• Publication Date: 2018-02
• Subjects: Lidar -- Aerosol -- Color ratio -- Depolarization ratio -- Planetary boundary layer; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2017.11.008
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: Based on the particles clustering to determine the top of the boundary layer. The proposed algorithm can overcome the effects of complex aerosol layers. The proposed algorithm has the better reliability and stability. Abstract: This study proposes a two-wavelength Lidar inversion algorithm to determine the boundary layer height (BLH) based on the particles clustering. Color ratio and depolarization ratio are used to analyze the particle distribution, based on which the proposed algorithm can overcome the effects of complex aerosol layers to calculate the BLH. The algorithm is used to determine the top of the boundary layer under different mixing state. Experimental results demonstrate that the proposed algorithm can determine the top of the boundary layer even in a complex case. Moreover, it can better deal with the weak convection conditions. Finally, experimental data from June 2015 to December 2015 were used to verify the reliability of the proposed algorithm. The correlation between the results of the proposed algorithm and the manual method is R 2 = 0.89 with a RMSE of 131 m and mean bias of 49 m; the correlation between the results of the ideal profile fitting method and the manual method is R 2 = 0.64 with a RMSE of 270 m and a mean bias of 165 m; and the correlation between the results of the wavelet covariance transform method and manual method is R 2 = 0.76, with a RMSE of 196 m and mean bias of 23 m. These findings indicate that the proposed algorithm has better reliability and stability than traditional algorithms.