Parameterization of aerosol number concentration distributions from aircraft measurements in the lower troposphere over Northern China

Di, Huige; Zhao, Jing; Zhao, Xuan; Zhang, Yuxing; Wang, Zhixiang; Wang, Xuewen; Wang, Yufeng; Zhao, Henan; Hua, Dengxin

doi:10.1016/j.jqsrt.2018.07.009

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…Several authors referred this shape to a triangular shape, such as in (Veselovskii et al, 2004), defined on equidistant or a logarithmic-equidistant grid for the effective radius of the particles. In agreement with Di et al (2018b) and Sun et al (2013) and with the standard AERONET inversion procedure Dubovik et al (2006) we find more viable working with log-normal distributions. In particular we reconstruct the particle size distribution as a superposition of a small number of log-normal distributions, on a logarithmic interval of particles radius.…”

supporting

confidence: 73%

Retrieving the atmospheric number size distribution from lidar data

Sorrentino

Sannino

Spinelli

et al. 2021

Preprint

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Abstract. We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modelled as a superposition of log–normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes, and perturbed by Gaussian noise, and on three real datasets obtained from AERONET. We show that the proposed algorithm provides satisfactory results even when the assumed number of modes is different from the true number of modes, and substantially excellent results when the right number of modes is selected. In general, an over-estimate of the number of modes provides better results than an under-estimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

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supporting

confidence: 73%

Retrieving the atmospheric number size distribution from lidar data

Sorrentino

Sannino

Spinelli

et al. 2021

Preprint

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“…Several authors referred to this shape as triangular, such as in (Veselovskii et al, 2004), defined on equidistant or a logarithmic-equidistant grid for the effective radius of the particles. In agreement with Di et al (2018b) and Sun et al (2013) and with the standard AERONET inversion procedure (Dubovik et al, 2006), we find it more viable to work with log-normal distributions. In particular we reconstruct the particle size distribution as a superposition of a small number of log-normal distributions on a logarithmic interval of particle radius.…”

Section: Introductionsupporting

confidence: 68%

A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

et al. 2022

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Abstract. We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modeled as a superposition of log-normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes and perturbed by Gaussian noise as well as on three datasets obtained from AERONET. We show that the proposed algorithm provides good results when the right number of modes is selected. In general, an overestimate of the number of modes provides better results than an underestimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

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“…The combination of α and β can provide more information and increase the detection range for aerosol size distributions. The extinction efficiency of 1064 nm cannot easily be obtained [18]. The peak radius of the 1064 nm extinction efficiency (~1.46 µm) is analogous to the peak radius (~1.38 µm) of the 532 nm backscattering coefficient; therefore, it was not considered for determining the aerosol distribution.…”

Section: Inversion Techniquementioning

confidence: 99%

“…Most of the cases were double or three-peak modes. Di et al [18] studied the aerosol number distribution on the vertical height and found that the measured aerosol number concentration distributions could be fitted by multi-lognormal distributions or single-lognormal distributions. The key point of the regularized inversion algorithm was to seek the weight coefficients of base functions.…”

Section: Selection Of Base Functionmentioning

confidence: 99%

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Aerosol Microphysical Particle Parameter Inversion and Error Analysis Based on Remote Sensing Data

Wang

Hua

et al. 2018

Remote Sensing

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The use of Raman and high-spectral lidars enables measurements of a stratospheric aerosol extinction profile independent of backscatter, and a multi-wavelength (MW) lidar can obtain additional information that can aid in retrieving the microphysical characteristics of the sampled aerosol. The inversion method for retrieving aerosol particle size distributions and microphysical particle parameters from MW lidar data was studied. An inversion algorithm for retrieving aerosol particle size distributions based on the regularization method was established. Based on the inversion of regularization, the inversion method was optimized by choosing the base function closest to the aerosol distribution. The logarithmic normal distribution function was selected over the triangle function as the base function for the inversion. The averaging procedure was carried out for three main types of aerosol. The 1% averaging result near the minimum of the discrepancy gave the best estimate of the particle parameters. The accuracy and stabilization of the optimized algorithm for microphysical parameters were tested by scores of aerosol size distributions. The systematic effects and random errors impacting the inversion were also considered, and the algorithm was tested by the data, showing 10% systematic error and 15% random error. At the same time, the reliability of the proposed algorithm was also verified by using the aerosol particle size distribution data of the aircraft. The inversion results showed that the algorithm was reliable in retrieving the aerosol particle size distributions at vertical heights using lidar data.In the last 20 years, research has been performed on solving the underlying inverse integral equation system for obtaining the microphysical parameters of aerosol particles from their optical properties. The regularization algorithm [6], the principal component analysis (PCA) technique [13], and the linear estimation algorithm [14] have been used for determining the aerosol bulk properties. The regularization algorithm is used most commonly for inverting multi-wavelength measurements [6], allowing the retrieval of particle size, concentration, and to some extent the main features of the particle size distribution.Using the regularized inversion algorithm, the particle size distribution and microphysical parameters of aerosols are obtained without assuming the initial complex refractive index and aerosol distribution. The inversion results are unstable, and there will be good results under certain spectral types; however, in some cases, the inversion error is very large. In Veselovskii's modified regularized inversion algorithm [15,16], the effective radius, number, surface area, and volume concentration for three distributions were retrieved with an average accuracy of 55%, 70%, 40%, and 50%, respectively. D. analyzed the effects of systematic and random errors on particle microphysical properties from multi-wavelength lidar measurements using an inversion with regularization. Three ASDs were used for this analys...

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Parameterization of aerosol number concentration distributions from aircraft measurements in the lower troposphere over Northern China

Cited by 6 publications

References 6 publications

Retrieving the atmospheric number size distribution from lidar data

Retrieving the atmospheric number size distribution from lidar data

A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

Aerosol Microphysical Particle Parameter Inversion and Error Analysis Based on Remote Sensing Data

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