Absorbing aerosols optical depth from satellite ultra-violet aerosol index: a deep learning approach

Abstract:

A global database of aerosol absorption is important to reduce the aerosol radiative forcing estimate uncertainty. Currently the quantitative aerosol absorption properties, e.g. the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are mainly provided by the ground-based Aerosol RObotic NETwork (AERONET). However, the spatial distribution of AERONET sites is sparse and uneven so that geo-statistical interpolation/extrapolation is not feasible on a global scale. Quantitative retrievals of aerosol absorption are even more challenging for satellite remote sensing (e.g. multi-angular measurements), while the qualitative satellite ultra-violet aerosol index (UVAI) is much easier to obtain without a priori assumptions on aerosol optical properties in retrieval algorithms. The global UVAI record has been continuously contributed by various sensors for over four decades, which is beneficial to construct a long-term global aerosol absorption climatology. Hence, we build a numerical relationship between OMAERUV UVAI and AERONET AAOD to estimate the global AAOD based on a deep neural network algorithm. The training data set is constructed by independent measurements and/or model simulations with strict quality controls. The input features are selected by both filter and wrapper methods. The optimal model parameters are determined by 10-fold cross validations. The predicted AAOD and further derived SSA show better agreements with AERONET observations, compared with that provided by the OMAERUV and the MERRA-2.