P4, L 106, What is the accuracy of the PARASOL measurements? 

 Response: For the bands used in the study, we assumed intensity has a 2% relative noise and DoLP 0.007 absolute noise in the original version of the manuscript. Based on the questions of the reviewer, we did a quick test on noise settings and found a variable relative noise between 1-3% for intensity and a 0.012 absolute noise for DoLP gave a better performance in term of effectiveness. Therefore, in the revised version, we use the latter noise settings. 

P5, L132, “The cloud fraction (referred to as MODIS cloud fraction hereafter) is calculated as the fraction of confidently- and uncertain-cloudy-flagged 1-km-resolution MODIS pixels within a 6km°ø6km PARASOL grid.” 

Should different weight be applied to the confidently cloudy and uncertain cloudy mask in calculating the cloud fraction? Since later the authors reported MODIS cloud fraction values are larger, would this be part of the reason? 

 Response:  We tested using only confidently-cloudy flag as cloudy in collocating MODIS cloud fraction from MYD35 data, but no significant differences are observed. The reason is that there are usually not many MODIS (MYD35) uncertain pixels in a PARASOL grid, e.g. on 01 Jan 2008, 90% PARASOL grids have less than 5 MODIS uncertain flags, so we won't expected there are obvious differences between the two different definition of MODIS cloud fraction. 

P5, L149: one shape of ice crystal is used, does the shape matters? Would it possible to distinguish ice cloud or water cloud fraction maybe in future work? 

 Response: The hexagonal ice crystals with varying aspect ratios and surface distortions represent the scattering properties of ice crystals with variable complex shapes. This is demonstrated in the reference given (van Diedenhoven et al. 2020) and references therein. We modified the sentence to mention that the hexagonal ice crystals are used as proxies for ice crystals with variable complex shapes. Furthermore, based on the results in the article, the current settings are sufficient for the main focus of cloud masking for aerosol retrievals. Distinguishing ice / liquid cloud fraction is one of our on-going work. 

P6, L178, “We do not consider situations that are partly covered by both ice and liquid clouds.”. With the large pixel size of 6km x 6 km, there could be higher chances to observe partially covered cloud or cloud edge. I wonder whether the NN can be applied to every angle of the multi-angle observations, which may help detect partially covered cloud? There are some works which seems study such scenarios but with less efficiency (example: Gao et al, 2021: https://doi.org/10.3389/frsen.2021.757832), a more flexible NN cloud mask can be a remedy. 

 Response: There are indeed situations where different angles see different cloud fractions because of inhomogeneity. Therefore, we include in our training set also samples where different angles have different cloud fraction. This increases the capability of the cloud screening (see l 185 of the revised version). We also added a reference to Gao et al, 2021 in the revised version. 

P7, Table 1, what does “RemoTAP” mean for distribution? You may already explain other-where, but it would be useful to include some information in the caption. 

 Response: “RemoTAP” means the properties are randomly taken from RemoTAP global aerosol retrieval for the year 2008, as explained in line 159 of article. We added a short explanation in the caption of the table in the revised version. 

P8, L189, does smaller size of NN is needed when trained on parts of the total data? 

 Response: We cannot give a universal answer to this question. Actually, the best size of NN depends more on the task itself (e.g. task complexity).   

P8, L205, “The neural network architecture consists of three hidden layers with 80 neurons each layer for over ocean scenes and 40 neurons for over land scenes.” 

I would expect cloud detection is harder over land due to its complexity, but here smaller NN is used over land comparing with the ocean one. Can you explain more? 

 Response: The choice of NN is based on the performance on real measurements (with comparison to goodness-of-fit mask, i.e. experiment in section 4.2), which can reflect the generalization ability of the NN. Based on this performance we found smaller NN is better for retrievals over land than over ocean. An explanation can be that a larger NN usually has larger risk in overfitting data and thus struggling to generalize to new data. Therefore, sometimes it can be possible that a smaller NN behaves better for the more complicated over land case.  

P9, L233, “Therefore,we suggest that a threshold of 0.05 for the retrieved cloud fraction should be good for aerosol retrieval.” I wonder whether the threshold should relate to the measurement uncertainty. Or how the measurement uncertainty relates to the accuracy of the cloud fraction accuracy? 

 Response: See our response above to the general comment. 

P15, L309, “the AOD error should be smaller than 0.03 or 10%”, can you confirm the definition of the error? As retrieval-truth or 1-sigma uncertainty etc? 

 Response: AOD error is here defined as the absolute value of ‘true-retrieved’ . We add the explanation at I 335 of the revised version. 

Page 17, Figure 8, the title of the figure seems indicate goodness-of-fit has been applied for all three columns of the plots, but it seems not the case as indicated in the caption. Can you verify? 

 Response: The goodness-of-fit mask is always applied on top of the NN and MODIS in this section of assessing their effect on aerosol retrievals. We revised the titles in the section to avoid this confusion. Figure 8 is moved to supplement. 

Page 19, Figure 9, first row, the maximum percentage is around 65%, which is roughly the percentage within 1-sigma of a Gaussian distribution. Is this the correct explanation? 

Response: Indeed if the 1-sigma uncertainty would be the same as the GCOS requirement this would result in 67% of the pixels within the requirement. This only is the case for SSA in our retrievals (although please note the number of validation points for SSA is low).