Abstract

We herein present the spectral linear particle depolarization ratios (δp) from an Aerosol Robotics NETwork (AERONET) sun/sky radiometer with respect to the aerosol type. AERONET observation sites, which are representative of each aerosol type, were selected for our study. The observation data were filtered using the Ångström exponent (Å), fine-mode fraction (FMF) and single scattering albedo (ω) to ensure that the obtained values of δp were representative of each aerosol condition. We report the spectral δp values provided in the recently released AERONET version 3 inversion product for observation of the following aerosol types: dust, polluted dust, smoke, non-absorbing, moderately-absorbing and high-absorbing pollution. The AERONET-derived δp values were generally within the range of the δp values measured from lidar observations for each aerosol type. In addition, it was found that the spectral variation of δp differed according to the aerosol type. From the obtained results, we concluded that our findings provide potential insight into the identification and classification of aerosol types using remote sensing techniques.

Highlights

  • Atmospheric aerosols influence Earth’s energy budget by scattering and absorbing radiation and altering cloud processes [1]

  • We report on values of δp obtained from Aerosol Robotics NETwork (AERONET) observation sites selected as representative of an aerosol type based on previous literature

  • The AERONET-derived δp values were generally within the range of independent lidar observations for each aerosol type they are provided at different wavelengths

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Summary

Introduction

Atmospheric aerosols influence Earth’s energy budget by scattering and absorbing radiation (direct effects) and altering cloud processes (indirect effects) [1]. The impact of atmospheric aerosols on climate is quantified in terms of the aerosol radiative forcing. To accurately quantify the impact of aerosol radiative forcing on regional and global climate, atmospheric aerosols need to be properly classified [1,2,3]. Accurate classification of atmospheric aerosol types would markedly improve the accuracy of aerosol radiative forcing in numerical models and is of high importance to climate modelling [4,5]. Observations from integrated remote sensing techniques, including sun/sky radiometer, light detection and ranging (LIDAR) and satellite techniques have been used to classify aerosol types worldwide [5,7,8,9,10,11,12,13,14]

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