Preprints
https://doi.org/10.5194/amt-2021-152
https://doi.org/10.5194/amt-2021-152

  04 Jun 2021

04 Jun 2021

Review status: this preprint is currently under review for the journal AMT.

Retrieving the atmospheric number size distribution from lidar data

Alberto Sorrentino1, Alessia Sannino2, Nicola Spinelli2, Michele Piana1, Antonella Boselli3, Valentino Tontodonato5, Pasquale Castellano5, and Xuan Wang4 Alberto Sorrentino et al.
  • 1Dipartimento di Matematica, Università di Genova
  • 2Dipartimento di Fisica, Università di Napoli Federico II
  • 3CNR–IMAA, Potenza
  • 4CNR–SPIN, Napoli
  • 5ALA Srl Advanced Lidar Applications, Napoli

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.

Alberto Sorrentino et al.

Status: open (until 30 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Alberto Sorrentino et al.

Alberto Sorrentino et al.

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Short summary
We present a novel approach that can be used to obtain microphysical properties of atmospheric aerosol, up to several kilometers in the atmosphere, from lidar measurements taken from the ground. Our approach provides accurate reconstructions under many different experimental conditions. Our results can contribute to expand the use of remote sensing techniques for air quality monitoring and atmospheric science in general.