Articles | Volume 17, issue 19
https://doi.org/10.5194/acp-17-12133-2017
https://doi.org/10.5194/acp-17-12133-2017
Research article
 | 
12 Oct 2017
Research article |  | 12 Oct 2017

Impact of aerosol hygroscopic growth on retrieving aerosol extinction coefficient profiles from elastic-backscatter lidar signals

Gang Zhao, Chunsheng Zhao, Ye Kuang, Jiangchuan Tao, Wangshu Tan, Yuxuan Bian, Jing Li, and Chengcai Li

Abstract. Light detection and ranging (lidar) measurements have been widely used to profile the ambient aerosol extinction coefficient (σext). The particle extinction-to-backscatter ratio (lidar ratio, LR), which strongly depends on the aerosol dry particle number size distribution (PNSD) and aerosol hygroscopicity, is introduced to retrieve the σext profile from elastic-backscatter lidar signals. Conventionally, a constant column-integrated LR that is estimated from aerosol optical depth is used by the retrieving algorithms. In this paper, the influences of aerosol PNSD, aerosol hygroscopic growth and relative humidity (RH) profiles on the variation in LR are investigated based on the datasets from field measurements in the North China Plain (NCP). Results show that LR has an enhancement factor of 2.2 when RH reaches 92 %. Simulation results indicate that both the magnitude and vertical structures of the σext profiles by using the column-related LR method are significantly biased from the original σext profile. The relative bias, which is mainly influenced by RH and PNSD, can reach up to 40 % when RH at the top of the mixed layer is above 90 %. A new algorithm for retrieving σext profiles and a new scheme of LR enhancement factor by RH in the NCP are proposed in this study. The relative bias between the σext profile retrieved with this new algorithm and the ideal true value is reduced to below 13 %.

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Short summary
In this paper, influences of aerosol hygroscopic growth on the lidar ratio are studied. Results indicate that both the magnitude and vertical structures of the retrieved aerosol extinction coefficient (σext) profile from lidar signals are significantly biased. This study proposes a feasible method for reducing the bias of retrieving the σext profile and this method can be implemented in operational retrieval of the aerosol σext profile and for pollution monitoring.
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