&lt;title&gt;Atmospheric particulate matter variability in an industrial center from multi-wavelength lidar and Sun-sky radiometer measurements&lt;/title&gt;

Chaikovsky, Anatoly; Иванов, А. П.; Korol, Michail; Slesar, Alexander; Денисов, С. В.; Осипенко, Ф. П.; Hutko, I. S.; Dubovik, Оleg; Holben, B. N.; Goloub, P.

doi:10.1117/12.675466

Cited by 2 publications

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“…Obtaining this missing information from a nearby radiometer is evidently preferable to a simple assumption of these properties from climatologies. Therefore, the positive influence of the radiometer data on the lidar retrievals was emphasized in a number of previous studies (Chaikovsky et al, 2006c;Cuesta et al, 2008). However, all previous radiometer/lidar synergy approaches used AERONET retrievals in the form of a priori assumptions for improving lidar retrievals.…”

Section: Improvements Introduced By Joint Inversion Of Lidar and Aeronetmentioning

confidence: 99%

“…These results lead us to conclude that supplementing radiometer data by lidar observations helps to improve the retrieval of aerosol properties of minor mode in the aerosol mixture. Consequently, the retrieval of the vertical profile of the minor mode concentration also should be more accurate compared to the retrievals by the approaches of Chaikovsky et al (2006c) and Cuesta et al (2008) which assume lidar ratios from the AERONET retrievals.…”

Section: Improvements Introduced By Joint Inversion Of Lidar and Aeronetmentioning

confidence: 99%

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Enhancement of aerosol characterization using synergy of lidar and sun-photometer coincident observations: the GARRLiC algorithm

et al. 2013

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Abstract. This paper presents the GARRLiC algorithm (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data) that simultaneously inverts coincident lidar and radiometer observations and derives a united set of aerosol parameters. Such synergetic retrieval results in additional enhancements in derived aerosol properties because the back-scattering observations by lidar improve sensitivity to the columnar properties of aerosol, while radiometric observations provide sufficient constraints on aerosol amount and type that are generally missing in lidar signals. GARRLiC is based on the AERONET algorithm, improved to invert combined observations by radiometer and multi-wavelength elastic lidar observations. The algorithm is set to derive not only the vertical profile of total aerosol concentration but it also differentiates between the contributions of fine and coarse modes of aerosol. The detailed microphysical properties are assumed height independent and different for each mode and derived as a part of the retrieval. The GARRLiC inversion retrieves vertical distribution of both fine and coarse aerosol concentrations as well as the size distribution and complex refractive index for each mode. The potential and limitations of the method are demonstrated by the series of sensitivity tests. The effects of presence of lidar data and random noise on aerosol retrievals are studied. Limited sensitivity to the properties of the fine mode as well as dependence of retrieval accuracy on the aerosol optical thickness were found. The practical outcome of the approach is illustrated by applications of the algorithm to the real lidar and radiometer observations obtained over Minsk AERONET site.

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Section: Improvements Introduced By Joint Inversion Of Lidar and Aeronetmentioning

confidence: 99%

Section: Improvements Introduced By Joint Inversion Of Lidar and Aeronetmentioning

confidence: 99%

Enhancement of aerosol characterization using synergy of lidar and sun-photometer coincident observations: the GARRLiC algorithm

et al. 2013

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Enhancement of aerosol characterization using synergy of lidar and sun – photometer coincident observations: the GARRLiC algorithm

Lopatin¹,

Dubovik²,

Chaikovsky³

et al. 2013

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Currently most of experiments pursuing comprehensive characterization of atmosphere include coordinated observations by both lidar and radiometers in order to obtain important complimentary information about aerosol properties. The passive observations by radiometers from ground are mostly sensitive to the properties of aerosol in total atmospheric column and have very limited sensitivity to vertical structure of the atmosphere. Such observations are commonly used for measuring aerosol optical thickness and deriving the information about aerosol microphysics including aerosol particles shape, size distribution, and complex refractive index. In a contrast, lidar observations of atmospheric responses from different altitudes to laser pulses emitted from ground are designed to provide accurate profiling of the atmospheric properties. The interpretation of the lidar observation generally relies on some assumptions about aerosol type and loading. Here we present the GARRLiC algorithm (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data) that simultaneously inverts co-incident lidar and radiometer observations and derives a united set of aerosol parameters. Such synergetic retrieval is expected to result in additional enhancements in derived aerosol properties because the backscattering observations by lidar add some sensitivity to the columnar properties of aerosol, while radiometric observations provide sufficient constraints on aerosol type and loading that generally are missing in lidar signals.

GARRLiC is based on AERONET algorithm for inverting combined observations by radiometer and multi-wavelength elastic lidar observations. It is expected that spectral changes of backscattering signal obtained by multi-wavelength lidar at different altitudes provide some sensitivity to the vertical variability of aerosol particle sizes. In order to benefit from this sensitivity the algorithm is set to derive not only the vertical profile of total aerosol concentration but it also differentiates between the contributions of fine and coarse modes of aerosol. The detailed microphysical properties are assumed height independent and different for each mode and expected to be derived as a part of the retrieval. Thus, the GARRLiC inversion algorithm retrieves vertical distribution of both fine and coarse aerosol concentrations as well as the size distribution, complex refractive index and single scattering albedo for each mode.

The potential and limitations of the method are demonstrated by the series of sensitivity tests. The practical outcome of the approach is illustrated by applications of the algorithm to the real lidar and radiometer observations obtained over selected AERONET site

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<title>Atmospheric particulate matter variability in an industrial center from multi-wavelength lidar and Sun-sky radiometer measurements</title>

Cited by 2 publications

References 3 publications

Enhancement of aerosol characterization using synergy of lidar and sun-photometer coincident observations: the GARRLiC algorithm

Enhancement of aerosol characterization using synergy of lidar and sun-photometer coincident observations: the GARRLiC algorithm

Enhancement of aerosol characterization using synergy of lidar and sun – photometer coincident observations: the GARRLiC algorithm

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