Review of the manuscript titled  "Optimizing cloud optical parameterizations in RTTOV for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images" by Yongbo Zhou, Tianrui Cao, and Lijian Zhu.

This paper evaluates parametrizations of visible reflectance simulated from CMA-MESO and ERA5 using RTTOV with observations from instruments onboard the satellites FY-4B and Himawari-9.

Results show that the observed reflectance is higher than modelled reflectance, on average, and that the choice of an optimal configuration yields the lowest bias while only slightly increasing unbiased error (standard deviation of O-B). In clear-sky pixels, the atmospheric contribution to visible reflectance is near zero. Thus, the choice of parametrization had no effect. The bias is induced by the land surface representation in the NWP model. In cloudy sky, bias is more relevant than in clear sky cases, because the bias is responsible for a larger fraction of error, as shown in Figures 10, 11, and 12. In clear sky, bias was mostly lower than unbiased error, except of experiment CM-FY-DM in April (Figure 3).

The authors already nicely addressed my comments to an earlier version of the manuscript in review for another journal. For example, the authors extended the analysis by the standard deviation of O-B. The manuscript is now an extensive evaluation and in a very good condition. I recommend publication after minor comments are addressed.

Minor comments

L243-244: "the increased B for C213 would amplify the O-B differences, leading to the increased standard deviations". The standard deviation (SD) is not influenced by the bias (average O-B). Thus, an change in average B does not change the SD. The explanation for an increased SD must be larger O-B differences,  due to B being too high and too low (increase in |O-B|).

Figure 4: The caption seems to have an error, as the x-axis label is "reflectance" not "O-B departure".  

Equation 6 seems to be incorrect, not all terms are in the exponent, compared to Equation 2 of McFarquar et al. (2003).

L237, L275: Language: Instead of "was expected", you probably mean "was found"? Or was there an expectation before seeing the results?

L243: You state "opposite circumstance" but the same sign (O-B>0) as in the line above. Probably you mean O-B < 0?

L248: "at the low- or high-reflectance ends": Maybe you can quantify this, e.g. "at low reflectance (<0.1)" or similar.

L271: "The results in Figure 4 suggested that the Baran 2018 ice scheme should be used with caution ...".

I am confused, because Figure 4 shows results for ice_scheme=1 (SSEC) and not for "Baran" parametrization. Maybe you meant Figure 5?

Figure 7 caption (c) missing.

L367: Language, "B was consistently underestimated compared with O", I think this should be "B consistently underestimated O".

L393: "The smallest O-B bias". Good, but can you quantify it? For example "the optimal configuration reduced the bias by 0.04 on average, while standard deviation increased by less than 0.005.

Overall evaluations

The research is highly relevant as it focuses on optimizing cloud optical parameterizations in RTTOV for satellite visible reflectance data assimilation. With the increasing importance of satellite - based data in weather forecasting and climate studies, improving the accuracy of radiative transfer models like RTTOV is crucial. The use of data from recent geostationary meteorological satellites (FY - 4B and Himawari - 9) makes the study timely. The authors conducted four different experiments, covering two observing systems, two radiative solvers, and two atmospheric fields. A wide range of data was used, including forecasts from the CMA - MESO model, ERA5 reanalysis data, and observations from FY - 4B and Himawari - 9 satellites. The data was carefully processed, and multiple statistical methods were employed, such as analyzing O - B biases, standard deviations, and probability density functions. These analyses provided in - depth insights into the performance of different cloud optical parameterizations. The study identified the optimal cloud optical parameterization suite (“Deff” + “SSEC” + B02), which can significantly reduce the O - B biases. This finding has practical implications for improving the accuracy of radiative transfer simulations and data assimilation in weather prediction models.

Major issues:

1. The number concentration in Eq. (1) was set to 300 cm^-3 Is this generally applicable for all types of clouds? If not, what is the sensitivity of the simulated reflectivity to the other number concentration?
2. For cloud ice, is the effective diameter in Eq. (2) independent of cloud ice content and number concentration? What is the number concentration assumed for cloud ice?
3. In CMA-MESO, there are six types of hydrometeors. How is the scattering from rain, snow and graupel is treated in this study?
4. Since FY-4B AGRI has an aerosol AOD product, it is better to use AOD to quantify the contribution from aerosol? Eq. (8) and (9) seems to be too objective.
5. Captions for Figure 7b do not make sense. What is Fig 7c? Also, it is strange to show O-B bias from one month data.
6. The DOM solver simplified the scattering interactions between clouds and gaseous molecules into single - scattering processes in non - cloudy layers. This simplification may lead to inaccuracies in the simulated reflectance, especially in areas with complex cloud - gas interactions. Although the MFASIS solver has some improvements in this regard, the overall treatment of scattering in the study could be more refined.