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

Alberto Sorrentino,Valentino Tontodonato,Xuan Wang,Alessia Sannino,Michele Piana,Nicola Spinelli,Pasquale Castellano,Antonella Boselli

doi:10.5194/amt-15-149-2022

Abstract

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.

Highlights

Lidar is a remote sensing technique similar to radar which uses light in the form of short laser pulses to invest a target and obtain, through elastic and inelastic scattering processes, information on the target properties as a function of the distance from the lidar system.In the atmospheric application, lidar systems can be used to obtain spatially resolved information about the optical properties of the atmospheric aerosols (Giannakaki et al, 2010; Ritter et al, 2018; Lee and Wong, 2018; Stelitano et al, 2019; Chazette, 2020) over a distance of several kilometers and with high spatial and temporal resolutions
Information on the microphysical properties of the atmospheric aerosols is seldom obtained using the lidar signal alone. This information, which is essential for a complete aerosol characterization useful to understand their effect on climate, is instead frequently obtained through the synergistic use of in situ instruments; incidentally these measurements allow a validation of the lidar retrievals, but only for those values that are closest to the ground and for a particular aerosol typology (Saharan dust, biomass burning aerosol, etc.); alternatively, validation can be done using synthetic data (Alados-Arboledas et al, 2011; Di Girolamo et al, 2012; Veselovskii et al, 2013; Osterloh et al, 2013; Samaras et al, 2015; Whiteman et al, 2018)
This article is organized as follows: in the “Methods” section, we provide the mathematical formulation of the problem and a description of the Monte Carlo algorithm; we analyze the results obtained for synthetic data using five exemplar cases; in the final section we briefly summarize our conclusions

Summary

Introduction

Lidar (light detection and ranging) is a remote sensing technique similar to radar (radio detecting and ranging) which uses light in the form of short laser pulses to invest a target and obtain, through elastic and inelastic scattering processes, information on the target properties as a function of the distance from the lidar system.In the atmospheric application, lidar systems can be used to obtain spatially resolved information about the optical properties of the atmospheric aerosols (desert dust, volcanic ash, smog and many other types of substances) (Giannakaki et al, 2010; Ritter et al, 2018; Lee and Wong, 2018; Stelitano et al, 2019; Chazette, 2020) over a distance of several kilometers and with high spatial and temporal resolutions. Information on the microphysical properties of the atmospheric aerosols is seldom obtained using the lidar signal alone This information, which is essential for a complete aerosol characterization useful to understand their effect on climate, is instead frequently obtained through the synergistic use of in situ instruments; incidentally these measurements allow a validation of the lidar retrievals, but only for those values that are closest to the ground and for a particular aerosol typology (Saharan dust, biomass burning aerosol, etc.); alternatively, validation can be done using synthetic data (Alados-Arboledas et al, 2011; Di Girolamo et al, 2012; Veselovskii et al, 2013; Osterloh et al, 2013; Samaras et al, 2015; Whiteman et al, 2018).

Methods

Results

Discussion

Conclusion