Aerosol Characterization Using Machine Learning

Abstract

Atmospheric aerosols play a central role in the Earth's radiative budget. Together with various greenhouse gases, aerosols represent the most significant anthropogenic forcing responsible for climate change. However, uncertainties about the origin and composition of aerosol particles, their size distribution, concentration, spatial and temporal variability, make climate change prediction challenging. In order to quantify the influence of aerosols on the Earth's climate and to better validate climate models, information about their global abundance, properties and height distribution are needed. We use measurements of the Oxygen A band from the Orbiting Carbon Observatory-2, in conjunction with a machine learning approach, to retrieve aerosol parameters such as optical depth and layer height. The retrievals compare well with collocated lidar measurements.

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