Classification and Attribution of Low-Visibility Events Using Deep Learning

Abstract: The accurate detection of low-visibility events such as fog, haze, and fog-haze is a persistent challenge for satellite remote sensing, hindered by poor spatial generalization in existing models and unreliable aerosol retrievals. This study introduces a unified deep learning framework that integrates geostationary satellite data, meteorological reanalysis, and fine particulate matter (PM₂.₅) observations to identify these events. The model is able to produce the intensity of different low-visibility events and can be linked to visibility reduction. By incorporating PM_2.5, the polluted fog-haze can be discriminated from clean fog. The method can be extended to sea fog. To isolate the impact of PM₂.₅ on fog-haze formation, sensitivity experiments were conducted. The findings reveal that the high frequency of winter fog-haze is primarily driven by elevated pollution; reducing winter PM₂.₅ concentrations to summer-like levels (a 60% reduction) causes the simulated fog-haze distribution to align with summer observations. This response is linked to the microphysical role of aerosols, where the primary effect of reducing PM₂.₅ is to cause a transition of fog-haze to fog, rather than to suppress the formation of low-visibility events entirely. We are able to investigate the mitigation of PM_2.5 in reducing the hazardous fog-haze. By reducing the overall concentration of PM_2.5 by 40% can reduce 75% of fog-haze area. For the first time, this work dynamically attributes the seasonal characteristics of fog-haze to pollution levels, providing a quantitative framework for evaluating the visibility co-benefits of air quality policies.

Keywords: Deep Learning; Satellite Remote Sensing; Low-Visibility Events