Retrievals of Biomass Burning Aerosol and Liquid Cloud Properties from Polarimetric Observations Using Deep Learning Techniques

Biomass Burning (BB) aerosol are the largest source of absorbing aerosol on Earth. Coupled with marine stratocumulus clouds (MSC), their radiative effects are enhanced and can cause cloud property changes (first indirect effect) or cloud burn-off and warming up the atmospheric column (semi-direct effect). Nevertheless, the derivation of their quantity and optical properties in the presence of MSC clouds is confounded by the uncertainties in the retrieval of the underlying cloud properties. Therefore, a robust methodology is needed for the coupled retrievals of absorbing aerosol above clouds. Here, we present a new retrieval approach implemented for a Spectro radiometric multi-angle polarimetric airborne platform, the research scanning polarimeter (RSP), during the ORACLES campaign over the SEA. Our approach transforms the 1D measurements over multiple angles and wavelengths, into a 3D image-like input, which is then processed using various deep learning (DL) schemes to yield aerosol optical depth (AOD), aerosol effective radii and effective variance distributions, aerosol single scattering albedo (SSA), together with cloud optical depth (COD), cloud effective radii and variance. We received good test results using the different schemes (>95% accuracy), but with different accuracy, stability and generalization levels when performing validation on airborne data. We will discuss and compare the results between the different DL approaches, as well as their comparison to existing algorithms. We will also discuss paths forward for making this approach flexible and readily applicable over multiple platforms.