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Abstract Atmospheric visibility profoundly impacts daily life, and accurate prediction is crucial, particularly in conditions of low visibility characterized by high aerosol loading and humidity. This study employed the WRF‐Chem model to simulate a severe wintertime haze pollution episode that transpired from January 17 to 19, 2010, in Central‐East China (112–122° E, 34–42° N). The results reveal that excluding aerosol–meteorology interactions led to underestimated PM 2.5 concentrations and relative humidity in comparison with ground‐based measurement data, accompanied by a significant overestimation of visibility. Aerosols can engage with meteorological elements, particularly humidity, resulting in positive feedback. Upon considering these feedback interactions, the simulation results showed an increase of 5.17% and 1.99% in PM 2.5 concentration and relative humidity, respectively, compared with the original simulation. This adjustment narrowed the bias between simulated and measured data. The overestimation of simulated visibility was reduced by 16% and 25% for the entire study period and the severe haze pollution period, respectively. These findings underscore the vital role of incorporating aerosol–meteorology interactions in visibility simulations using the WRF‐Chem model. Notably, the inclusion of aerosol–meteorological feedback significantly enhances the accuracy of visibility predictions, particularly during heavily polluted periods.
Zhang et al. (Wed,) studied this question.