12-year LIDAR Observations of Tropospheric Aerosol over Hefei (31.9°N, 117.2°E), China

Wu, Decheng; Zhou, Jun; Wang, Zhenzhu; Zhong, Zhiqing; Xie, Chenbo; Qi, Fudi; Fan, A.; Wang, Yingjian

doi:10.3807/josk.2011.15.1.090

Cited by 14 publications

(13 citation statements)

References 11 publications

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“…Hayashida et al [8] observed the volcanic disturbances in the stratospheric aerosol layer over Tsukuba, Japan. Some Chinese institutions such as the Anhui Institute of Optics and Fine Mechanics [9], Xi'an University of Technology [10], the Institute of Atmospheric Physics [11], and Lanzhou University [12] are also studying LIDAR systems to detect aerosols. Examples of studies are given by Yan et al [13], who observed the boundary layer structure and aerosol properties over Xi'an, Jinhuan et al [14], who analyzed the variation characteristics of the atmospheric aerosol optical depths and visibility in North China, and Huang et al [15], who detected the dust aerosol vertical structure.…”

Section: Introductionmentioning

confidence: 99%

Mie LIDAR Observations of Tropospheric Aerosol over Wuhan

Gong

Liu

et al. 2015

Atmosphere

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Abstract:Wuhan is a rapidly developing large city in central China. To analyze the aerosol characteristics over Wuhan, the optical properties of the nocturnal aerosol layers in the lower troposphere were observed using a ground-based LIDAR(Light Detection And Ranging) located in the Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) from Wuhan University, China (114°21′E, 30°32′N) in January 2013-January 2015. The vertical distribution and temporal variation of tropospheric aerosols over Wuhan were summarized. The atmospheric boundary layer height (ABLH) was mainly at an altitude of 1.5-2 km (~33.1% probability), with an annual average of 1.66 km. The ABLH was higher in spring-summer (~2 km) and lower in autumn-winter (~1.2 km). The aerosol optical depth (AOD) was higher in spring-autumn than in summer-winter. The highest AOD was about 0.79 in October and the lowest was about 0.08 in January. The annual average was about 0.3. To study the relationship between the AOD and the particulate matter ≤2.5 µm in the aerodynamic diameter (PM2.5) in the lower troposphere, a typical haze event from 9-14 October 2014 was investigated. The results showed a correlation coefficient of 0.5165 between these two variables.

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Section: Introductionmentioning

confidence: 99%

Mie LIDAR Observations of Tropospheric Aerosol over Wuhan

Gong

Liu

et al. 2015

Atmosphere

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“…The seasonal aerosol vertical distribution over China has been studied using ground-based lidar observations at several sites (He et al, 2008;Huang et al, 2008a;Wu et al, 2011;Cao et al, 2013). The three-dimensional structure of aerosols over China has been estimated using the frequency of aerosol occurrence derived from CALIOP observations (Guo et al, 2016a).…”

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confidence: 99%

“…The diverse natural and anthropogenic aerosol sources as well as the geographical and meteorological conditions and transport pathways make China a unique natural laboratory for examination of seasonal dust particles, biomass burning, anthropogenic pollution and aerosols of mixed types . For example, spring dust particles originating from the source regions in the northwest of China are transported to the middle and upper troposphere (Wu et al, 2011; and to the downstream regions (Logan et al, 2010;. Long-range-transported particles typically are externally mixed with other aerosol constituents along their transport pathway (Logan et al, 2013;, because of atmospheric processing Zhang et al, 2008).…”

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confidence: 99%

Aerosol vertical distribution and optical properties over China from long-term satellite and ground-based remote sensing

et al. 2017

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Abstract. The seasonal and spatial variations of vertical distribution and optical properties of aerosols over China are studied using long-term satellite observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and ground-based lidar observations and Aerosol Robotic Network (AERONET) data. The CALIOP products are validated using the ground-based lidar measurements at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL). The Taklamakan Desert and Tibetan Plateau regions exhibit the highest depolarization and color ratios because of the natural dust origin, whereas the North China Plain, Sichuan Basin and Yangtze River Delta show the lowest depolarization and color ratios because of aerosols from secondary formation of the anthropogenic origin. Certain regions, such as the North China Plain in spring and the Loess Plateau in winter, show intermediate depolarization and color ratios because of mixed dust and anthropogenic aerosols. In the Pearl River Delta region, the depolarization and color ratios are similar to but higher than those of the other polluted regions because of combined anthropogenic and marine aerosols. Long-range transport of dust in the middle and upper troposphere in spring is well captured by the CALIOP observations. The seasonal variations in the aerosol vertical distributions reveal efficient transport of aerosols from the atmospheric boundary layer to the free troposphere because of summertime convective mixing. The aerosol extinction lapse rates in autumn and winter are more positive than those in spring and summer, indicating trapped aerosols within the boundary layer because of stabler meteorological conditions. More than 80 % of the column aerosols are distributed within 1.5 km above the ground in winter, when the aerosol extinction lapse rate exhibits a maximum seasonal average in all study regions except for the Tibetan Plateau. The aerosol extinction lapse rates in the polluted regions are higher than those of the less polluted regions, indicating a stabilized atmosphere due to absorptive aerosols in the polluted regions. Our results reveal that the satellite and ground-based remote-sensing measurements provide the key information on the long-term seasonal and spatial variations in the aerosol vertical distribution and optical properties, regional aerosol types, long-range transport and atmospheric stability, which can be utilized to more precisely assess the direct and indirect aerosol effects on weather and climate.

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“…Many equipments have been utilized to detect the optical properties of clouds and aerosol particles, such as nephelometer and sun-photometer, but LIDAR is a popular probe that can be used to scan the spacial physical properties in three dimensions, thereby reflecting clouds and aerosol particles distribution completely [5,6]. However, the original LIDAR signal and related retrieval parameters are affected by background noise and multiple scattering seriously.…”

Section: Introductionmentioning

confidence: 99%

A method based on iterative morphological filtering and multiple scattering for detecting layer boundaries and extinction coefficients with LIDAR

Jiang

Xiong

et al. 2016

Opt Rev

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Layer boundaries detection with LIDAR is of great significance for the meteorological and environmental research. Apart from the background noise, multiple scattering can also seriously affect the detection results in LIDAR signal processing. To alleviate these issues, a novel approach was proposed based upon morphological filtering and multiple scattering correction with multiple iterations, which essentially acts as a weighted algorithm with multiple scattering factors in different filtering scales, and applies integral extinction coefficients as media to perform correction. Simulations on artificial signals and real LIDAR signals support this approach.

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12-year LIDAR Observations of Tropospheric Aerosol over Hefei (31.9°N, 117.2°E), China

Cited by 14 publications

References 11 publications

Mie LIDAR Observations of Tropospheric Aerosol over Wuhan

Mie LIDAR Observations of Tropospheric Aerosol over Wuhan

Aerosol vertical distribution and optical properties over China from long-term satellite and ground-based remote sensing

A method based on iterative morphological filtering and multiple scattering for detecting layer boundaries and extinction coefficients with LIDAR

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