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

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

doi:https://doi.org/10.5194/amt-15-149-2022

Articles | Volume 15, issue 1

https://doi.org/10.5194/amt-15-149-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-15-149-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 1

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

Data sets

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

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.