Laser & Optoelectronics Progress, Volume. 55, Issue 2, 020101(2018)

Vertical Distribution Characteristics of Aerosols in Different Weathers Based on CALIPSO Satellite

Xiaolei Chen* and Lianji Jin
Author Affiliations
  • Key Laboratory of Meteorological Disaster, Minstry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China
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    Aim

    ing at the region of 33.5°N-34.5°N on the satellite transit route, we compare the aerosol vertical distribution characteristics during clean period, haze, dust, and pollution caused by fireworks based on CALIPSO satellite laser radar data. The results show that, in the sunny day, most of aerosols are clean continental aerosols at high altitude; in haze, most of aerosols are polluted continental aerosols whose backscatter and extinction are strong, and most of aerosol particles are spherical particles with small diameters; in dust, aerosols have a wide vertical distribution from the ground to the high altitude, and most of aerosol particles are non-spherical particles with big sizes; in pollution caused by fireworks, aerosol particles are the small size particles at low altitude, whose types are the polluted continental aerosol and the polluted dust aerosol. It is concluded that the vertical distributions of aerosol under different population types are different. We can use CALIPSO satellite laser radar data, together with meteorological element and HYSPLIT model to characterize the category of the atmospheric aerosol.

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    Xiaolei Chen, Lianji Jin. Vertical Distribution Characteristics of Aerosols in Different Weathers Based on CALIPSO Satellite[J]. Laser & Optoelectronics Progress, 2018, 55(2): 020101

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    Paper Information

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    Received: Jul. 27, 2017

    Accepted: --

    Published Online: Sep. 10, 2018

    The Author Email: Chen Xiaolei (1739136658@qq.com)

    DOI:10.3788/LOP55.020101

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