General Comments

This study proposes a unified convolutional neural network (CNN) framework that integrates geostationary meteorological satellite observations, meteorological reanalysis data, and PM_2.5 observations to identify all types of low-visibility events. The framework shows good potential for quantifying the intensity and spatial extent of low-visibility events. In addition, this quantitative framework reveals the nonlinear responses of fog-haze events and population exposure to PM_2.5 reduction, providing a useful reference for visibility improvement and air quality management. Overall, this study provides a valuable methodological contribution to the monitoring and assessment of low-visibility events. I suggest that the manuscript be accepted after the authors adequately address the specific comments below.

Specific Comments

1. The discussion in the second paragraph of the Introduction on the development of fog identification methods needs to be further clarified, especially regarding the advantages of machine learning methods in nonlinear feature extraction and multi-source data fusion. In addition, I suggest further focusing on the necessity of integrating satellite spectral data, meteorological reanalysis data, and ground-based PM_2.5 observations.
2. In Sect. 2.4, the authors are suggested to explain how PM_2.5 and visibility station data were interpolated or mapped onto the FY-4A grid, how missing values were handled, and whether the use of nearest-neighbor interpolation may introduce spatial uncertainty in regions with sparse monitoring stations.
3. In Sect. 4.1, I suggest that the authors supplement the model performance for the classification of different types of low-visibility events.
4. In the Results and Discussions section, several typical cases are selected for analysis. I suggest that the authors provide the basis for case selection and explain whether these cases are representative. In addition, if annual results are available, more complete seasonal statistics should be supplemented to enhance the robustness of the conclusions.
5. In Fig. 4b, the authors compare the sea fog intensity simulated by the Sat model with the visibility reduction observed at Dinghai, Putuo, and Shengsi stations. However, the Sat model result in the figure seems to be shown as only one unified simulated value. Since the three stations are located at different spatial positions and the model output is gridded sea fog intensity, the simulated intensity should theoretically be extracted separately from the corresponding or nearest grid cells of each station for comparison.
6. I suggest that the authors further discuss the reasons for the seasonal differences in the area of low-visibility events.
7. In Lines 346-357, I suggest that the authors supplement quantitative results on the response of fog-haze area to PM_2.5 reduction.
8. In Lines 371-378, the authors show that reducing winter PM_2.5 concentrations to 40% leads to simulated fog-haze areas that are highly consistent with the summer results. I suggest that the authors further clarify whether this sensitivity experiment controlled for or considered seasonal differences in meteorological conditions.
9. In Line 404, the main text states that the “mean fog-haze area declined by 42%”, whereas the value labeled in Fig. 6e seems to be 43%. I suggest that the authors check the consistency between the value in the main text and that in the figure.

Technical Corrections

1. Line 38: There is a formatting error with double parentheses in “transportation safety ((Ord Us Epa...”.
2. The capitalization style of words in the section headings should be made consistent throughout the manuscript.

see attached text.