Retrieving the atmospheric number size distribution from lidar data

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 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.

Lidar Optical and Microphysical Characterization of Tropospheric and Stratospheric Fire Smoke Layers Due to Canadian Wildfires Passing over Naples (Italy)

“…Also, AERONET columnar properties revealed a dominance of fine particles in the atmosphere with low absorption properties (absorption AOD < 0.0025 and SSA > 0.97) and addressed aerosol properties in good agreement with those evidenced by the lidar observations. Finally, we would like to anticipate some results on the refractive index estimated by using an improved version of our lidar data inversion method [31] that will be the subject of a future publication [66]. These data allow gaining further information on the different aerosol layers recognized by the lidar measurement since they provide slightly different values of the refractive index for the three aerosol layers located at the ranges R 1 , R 2 , and R 3 .…”

Lidar Optical and Microphysical Characterization of Tropospheric and Stratospheric Fire Smoke Layers Due to Canadian Wildfires Passing over Naples (Italy)

“…[48] for biomass burning aerosol. Finally, we would like to anticipate some results on the refractive index estimated by using an improved version of our lidar data inversion method [27] that will be the subject of a future publication [62]. These data are useful to further characterize the different aerosol layers recognized by the lidar measurement, since they provide slightly different values of the refractive index for the three aerosol layers located at the R1, R2 and R3 ranges.…”