What question did this study set out to answer?

The goal is to develop a machine learning method for categorizing aerosols from remote sensing data.

April 16, 20260 citations

A machine learning approach for aerosol classification using sun photometer and lidar data

JBJoelle Buxmann MOMartin Osborne MPMike ProttsMet Office

Key Points

The goal is to develop a machine learning method for categorizing aerosols from remote sensing data.
Implemented supervised machine learning using ground-based lidar and sun photometer data.
Utilized Mahalanobis distance to classify various aerosol types.
Analyzed AERONET measurements for known aerosol types.
Validated aerosol types using particle linear depolarization ratio and lidar ratio.
Successfully classified multiple aerosol types including marine, urban, biomass burning, and dust.
Demonstrated the effectiveness of combining lidar and sun photometer data for aerosol identification.

Abstract

The Met Office operates a ground based operational network of nine polarisation Raman lidars (aerosol profiling instruments) and sun photometers (column integrated information) across the United Kingdom (UK). An aerosol classification scheme using supervised machine learning has been developed. The concept of Mahalanobis (~normalized) distance to identify the aerosol type from individual Aerosol Robotic Network (AERONET) measurements including Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scattering Albedo and Index of refraction is used for a subset of AERONET stations around the globe of known main aerosol types (training set). The aerosol types so far include marine, urban industrial, biomass burning and dust. The relation of particle linear depolarisation ratio (PLDR) and lidar ratio (LR) from the Raman lidar is used in synergy to validate the particle type.

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