This work focuses on integrating high-spatial-resolution sea surface height (SSH) measurements from the SWOT mission into high-level gridded SSH maps derived from a constellation of nadir altimeters. The integration method is based on the Multiscale Inversion of Ocean Surface Topography (MIOST) technique (Ubelmann et al., 2021, 2022). In addition to data integration, the paper explores hypothetical scenarios for future wide-swath mission development and assesses the trade-offs between nadir altimeters and SWOT-type wide-swath altimeters. The study concludes that while SWOT's contribution is moderate with six nadir altimeters, it becomes substantial if the number of nadir altimeters is reduced to three, suggesting potential future configurations. The study also highlights the promise of several alternative data-driven methods for gridding.

The research is well-designed and thoroughly executed, with clear and well-structured writing. Here I have a major comment which only involves adding some discussion in the abstract and a minor suggestion in using SWOT data. 

Major comment:

SWOT has demonstrated exceptional performance, with extremely low noise levels, capturing SSH signals beyond the geostrophic flow. However, this study focused on signals larger than 65 km, meaning the full potential of SWOT was not fully realized. This limitation is understandable, as neither the methodology nor the validation data is designed to capture signals below 65 km. As a result, the conclusion regarding SWOT's contribution is only partially realized, as the existing gridding framework and validation data impose constraints. It could be a missed opportunity if we focus solely on gridding geostrophic information, which represents only a small portion of SWOT's advancements. I recommend the authors include this discussion in the abstract and highlight that new innovations are needed to fully harness SWOT's potential beyond geostrophic surface flow, which include nonlinear eddy dynamics as well as linear and nonlinear internal waves. Those small-scale phenomena are non-retrievable in the nadir altimeter regardless of the number of conventional satellites. This is important factor to consider in discussing advantages and disadvantages of including SWOT in the future constellation. I do not believe that we have yet setup the data gridding or assimilation framework to address this question in an OSE environment. 

Minor suggestion: 

Since the validation relies on withheld nadir altimeter data and lacks reliable small-scale (<65 km) ground truth, I suggest applying a low-pass filter to SWOT data before integrating it into the MIOST for gridding. This would effectively remove high-amplitude internal tides, solitary waves, and small-scale residual mean sea surface variations from SWOT, thereby improving the accuracy of the MIOST gridding, especially in low latitudes where internal solitons are prevalent. In these regions, the inclusion of unfiltered SWOT data tends to degrade the mapping accuracy as shown in the manuscript. 

Review of “Integrating wide swath altimetry data into Level-4 multi-mission

Maps”, by Ballarotta et al.

This paper describes an application that combines SWOT data and nidar altimeter data. This is one of the first to combine these data and demonstrates very good results. This is very encouraging. The figures in this paper are excellent, and the analysis is thorough and clear. The authors separate analysis by scale, making it more meaningful to more readers. I would suggest that this is acceptable in the current form, but I think it would benefit from a bit more detail in the methods section. The authors refer readers to a different paper, where the method is described and assessed. I think it’d be better to include sufficient details in this paper that a reader can read this paper in isolation.

Detailed comments

L62: “only through OSSEs”. This is not quite true. An exception is Lui et al. (2024; Frontiers in Marine Science; doi: 10.3389/fmars.2024.1456205).

Figure 2: is excellent

Section 2.2 is too brief. A reader of this paper cannot understand the method (MIOST) from the description offered. Consider providing a more complete description, and quoting some details of the expected error. I took some time to look at the Ballarotta et al. (2023) paper, because I did not know about MIOST. I can see that the system performance is good – better than the well-established DUACS system. I think it’d be better to include enough information in this paper so a reader doesn’t have to go to another paper to find the relevant information.

L155: I like the separation of scales for assessment.

Eq (2) and (3): I wonder if sigma squared would be better, since you report variances. But more generally, I wish you’d use error standard deviation so the units are more easily understood (in m, instead of m^2, for example).

Table 2 defines the experiments (EXP1, EXP2 etc), but these names are not used in Figure 3. I prefer the names used in Figure 3. Maybe you needn’t give the experiments meaningless names (EXP1, EXP2 etc).

Figure 3, 4, 6: “X vs Y” is unclear. I presume this is the difference between two experiments (X – Y). Can you make this clear?

Figure 3: is beautiful

L283: I would regard an improvement of 10% very good – not modest. Recall that we have taken decades to learn how to fully exploit the nadir altimeter data. This demonstrates that we can immediately improve this by 10% at (one of ) the first serious attempt. I like that the authors are stating this conservatively. But perhaps they are under-stating the improvement.