Articles | Volume 15, issue 1
Atmos. Meas. Tech., 15, 149–164, 2022
https://doi.org/10.5194/amt-15-149-2022
Atmos. Meas. Tech., 15, 149–164, 2022
https://doi.org/10.5194/amt-15-149-2022

Research article 06 Jan 2022

Research article | 06 Jan 2022

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

Alberto Sorrentino et al.

Data sets

Aeronet NASA https://aeronet.gsfc.nasa.gov/

Download
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 the expansion of the use of remote sensing techniques for air quality monitoring and atmospheric science in general.