AODisaggregation: toward global aerosol vertical profiles
Shahine Bouabid, Duncan Watson-Parris, Sofija Stefanovi\'c, Athanasios, Nenes, Dino Sejdinovic

TL;DR
This paper introduces a Bayesian nonparametric framework for disaggregating aerosol optical depth into detailed vertical extinction profiles using meteorological data, improving accuracy over existing methods.
Contribution
It develops a novel Gaussian process-based model that infers aerosol vertical profiles from AOD and meteorological predictors, validated with aerosol-climate simulation data.
Findings
Successfully reconstructs realistic extinction profiles
Outperforms baseline methods by an order of magnitude
Captures aerosol water uptake effects in the boundary layer
Abstract
Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of the anthropogenic climate change. This uncertainty arises in part from the difficulty in measuring the vertical distributions of aerosols, and only sporadic vertically resolved observations are available. We often have to settle for less informative vertically aggregated proxies such as aerosol optical depth (AOD). In this work, we develop a framework for the vertical disaggregation of AOD into extinction profiles, i.e. the measure of light extinction throughout an atmospheric column, using readily available vertically resolved meteorological predictors such as temperature, pressure or relative humidity. Using Bayesian nonparametric modelling, we devise a simple Gaussian process prior over aerosol vertical profiles and update it with AOD observations to infer a distribution over vertical extinction…
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Taxonomy
TopicsAtmospheric aerosols and clouds · Atmospheric and Environmental Gas Dynamics · Atmospheric chemistry and aerosols
MethodsGaussian Process
