The manuscript present total ozone results from the GEMS geostationary satellite instrument, and compares them with independent measurements from other satellite as well as from ground based remote sensing. Comparisons with ground-based data are generally good, however, it reveals a time-dependent drift, which may be instrumental and/or seasonal. Comparisons with OMPS and TROPOMI showed high correlation and low bias, however a latitude dependent error in the GEMS data.

The paper gives a good overview of the current state of the GEMS total ozone data, however the algorithm description needs significant expansion and additional analysis is recommended to provide a better insight in the reported drifts between GEMS and the validation data.

Major points

The algorithm that has been developed for GEMS has not been published in the open literature. The algorithm description in the current paper leaves many aspects unanswered. Although it is based on a well-known total ozone algorithm, specific aspects import for a GEO instrument versus a LEO instrument are not addressed. I recommend significantly expanding section 2.2 to include the following aspects:

• The algorithm uses a LUT based radiative transfer forward model. Provide an assessment of the error that this LUT based forward model makes wrt an online RTM (VLIDORT) and how this error propagates to total ozone.
• Provide in a supplemental section a full description of the LUT RTM, its dimensions and methods used for interpolating this LUT. Also, I think this LUT and tools to interpolate it should be made available.
• A unique aspect of GEMS is the hourly observations. However, geometries vary strongly over the GEMS field-of-view. What is the expected effect of the viewing geometries on the vertical sensitivity of the ozone observations. How does the averaging kernel vary of the FOV and over time of the day? This is important information to understand the GEMS observations and the difference with LEO observations.
• What is the impact of the choice of a-priori ozone profiles and the assumed a-priori errors? This especially important as you are fitting an ozone profile with 11 layers, using only 3 wavelengths. Hence the retrieval is heavily underdetermined and thus depending on a-prior information.

The abstract leaves out important findings of the validation. Specifically, the time dependent drift and the latitudinal dependent errors shall be mentioned in the abstract.

Minor points

In figure 4 comparisons are shown for GEMS, TROPOMI and OMPS. I propose to include in the figure (or in a supplemental figure) the results of the GEMS-Pandora comparison at the mean overpass time of TROPOMI/OMPS. In this way potential errors that very over the day are not folded into this comparison, and the comparison with TROPOMI and OMPS is much cleaner.

What is the status of the GEMS data set? Is produced by the operational processor and available for users?

For all datasets (GEMS, OMPS, TROPOMI, Pandora), the version used in the work should be clearly documented. When available the doi of the dataset should be used.

Figure 11 and 12 appear exactly the same to me. Is by mistake the wrong figure used in the manuscript?

To overcome issues with the calibration of the solar spectrum, I would suggest processing (part of) the GEMS data with a fixed solar spectrum. What is the impact on the seasonality if this approach is followed?

In the conclusions the authors mention that the ozone data is expected to improve by improving the GEMS characterization. What is the timeline for this. How is this coupled to public data release and/or version of the GEMS data?

Review of Baek et al.

This manuscript describes validation of the world’s first geostationary-satellite-based total ozone observations over Asia, achieved by GEMS, using Pandora spectrometers, and comparisons with TROPOMI/OMPS satellite observations. Basically its high performance was confirmed, with high correlation coefficients and small biases. I find the methodology and logics are almost sound to draw this main conclusion. However, more explanation and justification are needed to support details. First, though the authors state that retrieval of diurnal variation (line 15) and providing retrieval error information (line 17) are the features with GEMS, validation and comparisons are not given as a function of time of day and no discussion about the error of retrievals is made. Second, only overall "positive" mean bias is mentioned in the abstract but in detail the bias is rather strong negative (up to -6%) for the mid/high latitudes. This is mentioned in conclusion but should be mentioned in Abstract as well. Third, algorithm versions or product names from TROPOMI, OMPS, and Pandora are lacking and thus the results are not traceable. To consider publication, major revisions are required about the points above and the specific points listed below.

1. Line 16. Be clear in which aspect the GEMS retrieval is advanced. Maybe those listed in lines 60-61. Mention them in short here.
2. Lines 15 and 17. Results of the retrieval error information should be discussed in the main text. Biases should be analyzed and depicted as a function of time of day, as the diurnal observation capability is highlighted.
3. Lines 21 and 27. Small positive biases and "very well agreement" are mentioned but in reality negative biases for mid/latitudes are found against satellites and Pandora. This should be described with a good balance.
4. Line 115, 21 ozone profiles are mentioned but how this is applied is not very clear, particularly with the statement of "three to ten ozone profiles" in line 152.
5. Section 2.2.2 and Figure 1. Step 1, 2, and 3 should be mentioned in Figure 1 caption. Maybe red, green and blue parts are the steps, individually.
6. Line 216. What is the "situation"?
7. Line 223. TROPOMI
8. Section 3.2. Need to mention algorithm versions or product names for Pandora, TROPOMI, and OMPS. Acknowledgments to the PIs need to be included.
9. Table 1. Slash characters are required to separate month and day at several positions.
10. Line 251. Remove "However,"
11. Line 255. This decrease (likely the one shown in Figure 5 and 11) could be seasonal (as mentioned in conclusion) or long-term degrading trend (as implied here)?
12. Line 271. The statement that Pandora uses a fixed-temperature ozone absorption coefficient needs to be checked. In the recent algorithm version 1.8, the products "out2" for example considers the temperature dependence as climatology. For this perspective, mentioning algorithm version/product name is necessary for the traceability.
13. Line 297. Rewrite the sentence starting with "These bad pixels ..."
14. Line 332. Are the -0.14 +/- 2.00 % and +0.10+/-2.31% mean biases?
15. Line 344. Perhaps Nishinoshima?
16. Line 364. Are the distinct spatial and seasonal variability relevant to the features of the bias discussed here?
17. Figure 12. No difference is found with Figure 11.