A Three-Parameter Aerosol Optical Discrimination Framework Integrating Fine-Mode Fraction, Single Scattering Albedo, and Differential ？ngstr？m Exponent over Selected AERONET Sites in Africa
Accurate aerosol optical discrimination remains challenging due to overlap among aerosol types under mixed atmospheric conditions. This study proposed a novel dAE-enhanced aerosol discrimination model using Fine Mode Fraction (FMF), Single Scattering Albedo (SSA), and ？ngstr？m Exponent Difference (dAE) derived from AERONET observations across African sites. Daily Level 2.0 aerosol retrievals were analyzed using FMF-SSA, FMF-dAE, and SSA-dAE relationships to evaluate the contribution of dAE in improving aerosol separation. Results showed that incorporation of dAE significantly reduced overlap between aerosol clusters and enhanced discrimination between fine-mode absorbing aerosols, mixed aerosols, and coarse mineral dust particles. Positive dAE values were associated with biomass burning and polluted continental aerosols, while near-zero or negative dAE values indicated coarse dust dominance. The proposed framework improves aerosol characterization and provides valuable applications for climate modeling, satellite validation, and radiative forcing assessment across Africa.
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