Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models

Abstract

Accurate modeling of aerosol optical properties is critical to simulate aerosol radiative effects. However, uncertainties regarding the simulation of aerosol-intensive optical properties are still significant. Therefore, the use of observations to constrain aerosol optical properties in models has been indicated as an option. Also, explicit computations of optical properties are still too costly for operational models, which makes observation-based prescriptions a convenient solution. We developed an observation-based prescription of aerosol optical properties driven by machine-learning techniques that can be applied in models. The Iberian Peninsula (IP) was taken as the reference domain, and the aerosol products from the AERONET sites across the IP were the main dataset. First, clustering was applied to define the typical aerosol optical regimes affecting the IP atmosphere. Five typical regimes were identified. Two of them were dominated by coarse mode, which was associated with Saharan dust. One was found to be close to pure dust, while the other indicated a mixed scenario of dust and pollution. Two of the non-dust regimes, strongly and moderately absorbing, were found to be associated with smoke. The remaining non-dust regime, with no clear association, occurs mostly in the eastern portion of the IP. Afterward, using aerosol-type columnar mass density from MERRA-2, a model was trained as a predictor of the optical regimes using the Random Forest method. The model was tested under distinct aerosol scenarios. Predictions' accuracy ranged from 60 % to 75 %, depending on the regime, while presenting an average accuracy of 70 %.