&lt;title&gt;Lidar network CIS-LiNet for monitoring aerosol and ozone in CIS regions&lt;/title&gt;

Chaikovsky, Anatoli; Иванов, А. П.; Balin, Yu. S.; Elnikov, Andrey V.; Tulinov, G. F.; Plusnin, I.; Букин, О. А.; Chen, B.

doi:10.1117/12.675920

Cited by 26 publications

(18 citation statements)

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“…2001), MPL-Net (Welton et al, 2002), ALiNe (Antuña et al, 2006), Cis-LiNet (Chaikovsky et al, 2006b) and a recent global lidar network GALION (Bösenberg and Hoff, 2007) have been established during the last two decades. Aerosol data collected by these networks provide valuable aerosol information that is widely used for validating satellite observations (e.g.…”

Section: A Lopatin Et Al: Garrlic -Synergetic Inversion Of Lidar Anmentioning

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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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: A Lopatin Et Al: Garrlic -Synergetic Inversion Of Lidar Anmentioning

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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“…However, the complexity of the processes involved and the great spatiotemporal variability of the atmospheric particulate strongly complicate this task and requires continuous acquisition of aerosol data in the different regions of the world. The several aerosol‐dedicated instruments onboard satellite missions (e.g., MODIS [ Kaufman et al , 1997; Tanré et al , 1997], POLDER [ Deuzé et al , 2000, 2001], and CALIPSO [ Winker et al , 2003]), the worldwide network of sun photometers (AERONET [ Holben et al , 1998]), and the establishment of networks of aerosol lidar systems (e.g., Micro Pulse Lidar Network (MPLNET) [ Welton et al , 2001], European Aerosol Research Lidar Network (EARLINET) [ Bosenberg et al , 2003], and Commonwealth of Independent States Lidar Network (CISLINET) [ Chaikowsky et al , 2006]) represent the best evidence of the effort paid in this respect by the international scientific community.…”

Section: Introductionmentioning

confidence: 99%

Observed and simulated vertically resolved optical properties of continental aerosols over southeastern Italy: A closure study

et al. 2007

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[1] A 2.5-year data set of Raman lidar measurements performed in Lecce within the European Aerosol Research Lidar Network and a numerical model are used to characterize continental aerosols over southeast Italy. Continental conditions are selected by means of back trajectories, limiting the data set to cases of advection from the European continent. The model is used to constrain the relevant aerosol microphysical parameters needed to reproduce the altitude-resolved optical properties measured by lidar, namely, aerosol extinction ( ), backscatter ( ), and lidar ratio (LR = / ) at 351 nm. The observed variability of , , and LR is simulated by randomly varying, within appropriate ranges, all the microphysical parameters describing the aerosol composition and size distribution (assumed as bimodal). An optimal model configuration capable of reproducing within ±10% the mean optical properties retrieved by lidar, including their vertical pattern, is presented and discussed with sensitivity studies. Results indicate rather clean continental aerosols to characterize the site, with water-soluble fine-mode aerosols (dry modal radius of 0.05-0.10 m) accounting for 98-99% of the total number of particles. The (dry) imaginary refractive index of fine-mode particles is extended up to 0.02 to better fit the lidar data. This suggests some mixing of water-soluble substances with more absorbing material. Coarse-mode particles (modal radius 0.4-0.5 m) are shown to play a crucial role in determining the observed vertical profile of and LR.

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“…To evaluate the SCC performance in analyzing raw data measured by different lidar systems, D 'Amico et al (2015) considered the EARLI09 session taken on 25 May 2009 from 21:00-23:00 UT for which a comparison among the SCC optical products (aerosol backscatter and extinction coefficient profiles) and the corresponding manually retrieved ones is reported. A subset of five EARLI09 participating systems has been selected on the basis of instrumental differences and representativeness within EARLINET comprising the following lidar systems: the Multi-wavelength Raman Lidar -RALI of the Bucharest station (Nemuc et al, 2013), the MARTHA (Mattis et al, 2004) and the Polly XT (Althausen et al, 2013) systems of the Leipzig station, the MSTL-2 system of the Minsk station (Chaikovsky et al, 2006), and the MUSA of the Potenza station (Madonna et al, 2011). In Fig.…”

Section: Applications and Validationmentioning

confidence: 99%

EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data

et al. 2016

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Abstract. In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EAR-LINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations.During the development of ELPP, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented.The primary goal of ELPP is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product.Several tests have been performed to check the proper functioning of ELPP. The whole SCC has been tested with the same synthetic data sets, which were used for the EAR-LINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time preprocessing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.

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<title>Lidar network CIS-LiNet for monitoring aerosol and ozone in CIS regions</title>

Cited by 26 publications

References 0 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

Observed and simulated vertically resolved optical properties of continental aerosols over southeastern Italy: A closure study

EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data

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