Spatiotemporal distribution of aerosols in East China Seas based on spaceborne lidar

Ren Wenhe; Song Xiaoquan; Wang Fanghan

doi:10.3788/IRLA201847.1230002

Volume 47 Issue 12

Jan. 2019

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Ren Wenhe, Song Xiaoquan, Wang Fanghan. Spatiotemporal distribution of aerosols in East China Seas based on spaceborne lidar[J]. Infrared and Laser Engineering, 2018, 47(12): 1230002-1230002(6). doi: 10.3788/IRLA201847.1230002

Citation:

Ren Wenhe, Song Xiaoquan, Wang Fanghan. Spatiotemporal distribution of aerosols in East China Seas based on spaceborne lidar[J]. Infrared and Laser Engineering, 2018, 47(12): 1230002-1230002(6). doi: 10.3788/IRLA201847.1230002

Spatiotemporal distribution of aerosols in East China Seas based on spaceborne lidar

doi: 10.3788/IRLA201847.1230002

1.
Department of Marine Technology,College of Information Science and Engineering,Ocean University of China,Qingdao 266100,China;
2.
Laboratory for Regional Oceanography and Numerical Modeling,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266100,China

Received Date: 2018-07-28
Rev Recd Date: 2018-08-28
Publish Date: 2018-12-25

Abstract

Based on the CALIPSO satellite observations from June 2006 to December 2016, the aerosol spatiotemporal distribution characteristics above the East China Seas (the Bohai Sea, the Yellow Sea and the East China Sea) were analyzed. The results indicate that:(1) Aerosols in the three seas are mainly Clean Marine, Dust and Polluted Dust, and the sum of the three percentages is nearly 90%. The predominant aerosol type is different in different seas; (2) Aerosols above each sea tend to exponentially decay as the height increases. The aerosol type changes significantly in the space below 4 km, but there exists only Dust, Polluted Dust and Smoke above 4 km; (3) Temporal distribution characteristics show that almost all aerosol types vary with months and seasons. In spring, the three seas are Dust aerosols accounted for the largest proportion. In summer, Clean Marine aerosols obviously have the greatest impact for the East China Sea. In autumn and winter, the Bohai Sea and the Yellow Sea are mainly composed of Dust aerosol, while the East China Sea is Clean Marine aerosol as main component.
- lidar,
- CALIPSO,
- aerosol types,
- the East China Seas

References

[1]	Li L P, Fukushima H, Frouin R, et al. Influence of submicron absorptive aerosol on sea-viewing wide field-of-view sensor(SeaWiFS)-derived marine reflectance during Aerosol Characterization Experiment (ACE)-Asia[J]. Journal of Business Industrial Marketing, 2013, 28(1-2):147-158.
[2]	He M X, He S, Hu L, et al. MERIS Performance in the East China Seas:Evaluation of atmospheric correction and optical inversion algorithms[C]//ENVISAT Symposium Proceedings, 2007:23-27.
[3]	Generoso S, Bey I, Labonne M, et al. Aerosol vertical distribution in dust outflow over the Atlantic:comparisons between GEOS-Chem and Cloud-aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)[J]. Journal of Geophysical Research:Atmospheres, 2008, 113(D24):10.1029/2008JD010154.
[4]	Winker D M, Vaughan M A, Omar A, et al. Overview of the CALIPSO mission and CALIOP data processing algorithms[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(11):2310-2323.
[5]	Sun Xianming, Wan Long, Wang Haihua. Sensitivity study on lidar detection of the depolarization ratio of water clouds[J]. Infrared and Laser Engeering, 2016, 45(9):0906001. (in Chinese)孙贤明, 万隆, 王海华. 激光雷达探测水云退偏振比的敏感性研究[J]. 红外与激光工程, 2016, 45(9):0906001.
[6]	Cesana G, Chepfer H, Winker D, et al. Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO[J]. Journal of Geophysical Research:Atmospheres, 2016, 121(10):5788-5808.
[7]	Zhao Jian, Sun Xuejin, Zhang Riwei, et al. Application of CALIPSO in global aerosol detection[J]. Meteorological Hydrological and Marine Instrument, 2014, 31(1):50-54. (in Chinese)赵剑, 孙学金, 张日伟, 等. CALIPSO星载激光雷达系统在全球气溶胶探测中的应用[J]. 气象水文海洋仪器, 2014, 31(1):50-54.
[8]	Liu D, Wang Z, Liu Z, et al. A height resolved global view of dust aerosols from the first year CALIPSO lidar measurements[J]. Journal of Geophysical Research:Atmospheres, 2008, 113(D16):10.1029/20075D009776.
[9]	Shen Xianxia, Liu Chaoshun, Shi Runhe, et al. Vertical distribution of aerosols during different air pollution periods around Shanghai[J]. Acta Scientiae Circumstantiae, 2014, 34(3):582-591. (in Chinese)沈仙霞, 刘朝顺, 施润和, 等. 上海不同污染等级下气溶胶光学特性垂直分布特征[J]. 环境科学学报, 2014, 34(3):582-591.
[10]	Liu Z, Vaughan M, Winker D, et al. The CALIPSO lidar cloud and aerosol discrimination:Version 2 algorithm and initial assessment of performance[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(7):1198-1213.
[11]	Liu Z, Vaughan M A, Winker D M, et al. The CALIPSO lidar cloud and aerosol discrimination:version 2 algorithm and initial assessment of performance[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(7):1198-1213.
[12]	Winker D M, Vaughan M A, Omar A H, et al. Overview of the CALIPSO mission and CALIOP data processing algorithms[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(11):2310-2323.
[13]	Liu Bingyi, Zhuang Quanfeng, Qin Shengguang, et al. Aerosol classification method based on high spectral resolution lidar[J]. Infrared and Laser Engeering, 2017, 46(4):0411001. (in Chinese)刘秉义, 庄全风, 秦胜光, 等. 基于高光谱分辨率激光雷达的气溶胶分类方法研究[J]. 红外与激光工程, 2017, 46(4):0411001.
[14]	Omar A H, Won J G, Winker D M, et al. Development of global aerosol models using cluster analysis of Aerosol Robotic Network(AERONET) measurements[J]. Journal of Geophysical Research:Atmospheres, 2005, 110(D10):2227-2252.
[15]	Ma Xiaojun, Qin Yan, Chen Yonghang, et al. Seasonal variation of vertical distribution of aerosol types around Shanghai during haze periods[J]. China Environment Science, 2015, 35(4):961-969. (in Chinese)马骁骏, 秦艳, 陈勇航, 等. 上海地区霾时气溶胶类型垂直分布的季节变化[J]. 中国环境科学, 2015, 35(4):961-969.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Spatiotemporal distribution of aerosols in East China Seas based on spaceborne lidar

1. Department of Marine Technology,College of Information Science and Engineering,Ocean University of China,Qingdao 266100,China;

2. Laboratory for Regional Oceanography and Numerical Modeling,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266100,China

Received Date: 2018-07-28

Rev Recd Date: 2018-08-28

Publish Date: 2018-12-25

Key words:

Abstract: Based on the CALIPSO satellite observations from June 2006 to December 2016, the aerosol spatiotemporal distribution characteristics above the East China Seas (the Bohai Sea, the Yellow Sea and the East China Sea) were analyzed. The results indicate that:(1) Aerosols in the three seas are mainly Clean Marine, Dust and Polluted Dust, and the sum of the three percentages is nearly 90%. The predominant aerosol type is different in different seas; (2) Aerosols above each sea tend to exponentially decay as the height increases. The aerosol type changes significantly in the space below 4 km, but there exists only Dust, Polluted Dust and Smoke above 4 km; (3) Temporal distribution characteristics show that almost all aerosol types vary with months and seasons. In spring, the three seas are Dust aerosols accounted for the largest proportion. In summer, Clean Marine aerosols obviously have the greatest impact for the East China Sea. In autumn and winter, the Bohai Sea and the Yellow Sea are mainly composed of Dust aerosol, while the East China Sea is Clean Marine aerosol as main component.

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Reference (15)

[1]	Li L P, Fukushima H, Frouin R, et al. Influence of submicron absorptive aerosol on sea-viewing wide field-of-view sensor(SeaWiFS)-derived marine reflectance during Aerosol Characterization Experiment (ACE)-Asia[J]. Journal of Business Industrial Marketing, 2013, 28(1-2):147-158.
[2]	He M X, He S, Hu L, et al. MERIS Performance in the East China Seas:Evaluation of atmospheric correction and optical inversion algorithms[C]//ENVISAT Symposium Proceedings, 2007:23-27.
[3]	Generoso S, Bey I, Labonne M, et al. Aerosol vertical distribution in dust outflow over the Atlantic:comparisons between GEOS-Chem and Cloud-aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)[J]. Journal of Geophysical Research:Atmospheres, 2008, 113(D24):10.1029/2008JD010154.
[4]	Winker D M, Vaughan M A, Omar A, et al. Overview of the CALIPSO mission and CALIOP data processing algorithms[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(11):2310-2323.
[5]	Sun Xianming, Wan Long, Wang Haihua. Sensitivity study on lidar detection of the depolarization ratio of water clouds[J]. Infrared and Laser Engeering, 2016, 45(9):0906001. (in Chinese)孙贤明, 万隆, 王海华. 激光雷达探测水云退偏振比的敏感性研究[J]. 红外与激光工程, 2016, 45(9):0906001.
[6]	Cesana G, Chepfer H, Winker D, et al. Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO[J]. Journal of Geophysical Research:Atmospheres, 2016, 121(10):5788-5808.
[7]	Zhao Jian, Sun Xuejin, Zhang Riwei, et al. Application of CALIPSO in global aerosol detection[J]. Meteorological Hydrological and Marine Instrument, 2014, 31(1):50-54. (in Chinese)赵剑, 孙学金, 张日伟, 等. CALIPSO星载激光雷达系统在全球气溶胶探测中的应用[J]. 气象水文海洋仪器, 2014, 31(1):50-54.
[8]	Liu D, Wang Z, Liu Z, et al. A height resolved global view of dust aerosols from the first year CALIPSO lidar measurements[J]. Journal of Geophysical Research:Atmospheres, 2008, 113(D16):10.1029/20075D009776.
[9]	Shen Xianxia, Liu Chaoshun, Shi Runhe, et al. Vertical distribution of aerosols during different air pollution periods around Shanghai[J]. Acta Scientiae Circumstantiae, 2014, 34(3):582-591. (in Chinese)沈仙霞, 刘朝顺, 施润和, 等. 上海不同污染等级下气溶胶光学特性垂直分布特征[J]. 环境科学学报, 2014, 34(3):582-591.
[10]	Liu Z, Vaughan M, Winker D, et al. The CALIPSO lidar cloud and aerosol discrimination:Version 2 algorithm and initial assessment of performance[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(7):1198-1213.
[11]	Liu Z, Vaughan M A, Winker D M, et al. The CALIPSO lidar cloud and aerosol discrimination:version 2 algorithm and initial assessment of performance[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(7):1198-1213.
[12]	Winker D M, Vaughan M A, Omar A H, et al. Overview of the CALIPSO mission and CALIOP data processing algorithms[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(11):2310-2323.
[13]	Liu Bingyi, Zhuang Quanfeng, Qin Shengguang, et al. Aerosol classification method based on high spectral resolution lidar[J]. Infrared and Laser Engeering, 2017, 46(4):0411001. (in Chinese)刘秉义, 庄全风, 秦胜光, 等. 基于高光谱分辨率激光雷达的气溶胶分类方法研究[J]. 红外与激光工程, 2017, 46(4):0411001.
[14]	Omar A H, Won J G, Winker D M, et al. Development of global aerosol models using cluster analysis of Aerosol Robotic Network(AERONET) measurements[J]. Journal of Geophysical Research:Atmospheres, 2005, 110(D10):2227-2252.
[15]	Ma Xiaojun, Qin Yan, Chen Yonghang, et al. Seasonal variation of vertical distribution of aerosol types around Shanghai during haze periods[J]. China Environment Science, 2015, 35(4):961-969. (in Chinese)马骁骏, 秦艳, 陈勇航, 等. 上海地区霾时气溶胶类型垂直分布的季节变化[J]. 中国环境科学, 2015, 35(4):961-969.

Spatiotemporal distribution of aerosols in East China Seas based on spaceborne lidar

doi: 10.3788/IRLA201847.1230002

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通讯作者: 陈斌, bchen63@163.com

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