A Hybrid Algorithm for Dust Aerosol Detection: Integrating Forward Radiative Transfer Simulations and Machine Learning

A hybrid algorithm based on radiative transfer simulations and machine learning for dust aerosol detection is developed for the Advanced Himawari Imager (AHI) carried by the geostationary satellite Himawari-8. The sensitivities of the AHI thermal infrared (TIR) channels for dust aerosols are analyzed through radiative transfer simulations. The sensitivity study demonstrates that the simulated clear-sky brightness temperatures (BTs) show an obvious improvement in identifying dust aerosols compared to BT difference techniques, especially optically thin dust. Therefore, the simulated clear-sky BTs and AHI TIR observed BTs, in addition to ground information, are used as inputs to add physical knowledge in the machine learning model. The performance of an artificial neural network constructed for dust aerosol detection is evaluated by comparing its results with those of active Cloud-Aerosol Lidar with Orthogonal Polarization measurements. The proposed algorithm effectively achieves dust aerosol detection during both daytime and nighttime, with a precision of over 86% and a recall of over 85% on an independent testing dataset. The proposed algorithm is applied to three typical dust events to further illustrate its applicability. Although some thin dust aerosols near the ground are misclassified due to weak signals, most dust aerosols are successfully detected, and the identification is generally not affected by other types of aerosols. The results of the regional classification demonstrate that our algorithm is superior in detecting tenuous dust aerosols compared to Dust RGB images using the AHI TIR channels and physical-based algorithm.