The manuscript presents an analysis of gravity wave (GW) kinetic energy distributions, derived from new Aeolus satellite wind profiles, that shows great promise in pushing the needle forward in the construction of observational constraints of gravity waves and their impacts on upper troposphere/lower stratosphere circulation.   A methodology is presented for deriving the kinetic energy associated with small-scale GWs in regions of deep convection in the tropics over a period spanning June 2019 to August 2022.  Comparisons with ERA5 suggest that the reanalysis product underestimates GW-associated kinetic energy; conversely, GW-associated potential energy comparisons between ERA5 and temperature-profiles from an independent instrument (GNSS-RO) show much more consistency, suggesting that the use of kinetic energy highlights a distinct feature of the GW energy spectrum that is not typically assessed (and, incidentally, is not well represented in ERA5).  The authors further speculate that this underestimate may reflect lack of assimilated direct wind observations, in contrast to temperatures, which are assimilated. All in all, the manuscript does a good job of presenting a new dataset with all necessary caveats, while also making a generally convincing case that this new data will be valuable.  To this end, I recommend acceptance, pending that minor revisions be made to address the following concerns:

#1. Page 4:  There is no description of the GW drag parameterization employed in ERA5. In particular, does the model have an explicit parameterization for non-orographic GW drag due to parameterized convection?  If so, what is it and how has it been evaluated/performed in past assessments?  This will be important in terms of interpreting the dearth of kinetic energy in the model, relative to the Aeolus-derived energy. 

#2. Page 7: Presumably the definition of "background" based on "the arguments presented in Alexander et al. (2008b)" apply to past analysis of temperature, not wind, profiles, no?  More generally, it would be good for the reader to have a better sense of the sensitivity of the profiles depicted in Figure 1a to choice of grid box averaging domain, the temporal period over which profiles are averaged (currently set to 7 days, etc.), etc.   I  imagine the authors have already done this sensitivity analysis, so they could consider showing in an appendix figure.  

#3. Equation (1): This notation becomes slightly confusing/counterintuitive as the text moves on, since the meridional component often goes to zero due to the pointing vector retaining its approximate angle at ~100 degrees. In other words, V_HLOS would be more intuitively referred to as U_HLOS (or something similar) since, indeed, it primarily reflects the zonal component of the flow. Is there any particular reason why "v" is used instead of something more generic? I suggest changing.

#4. Figure 14, lines 354-355: The first sentence of this paragraph does not make sense to me.  In particular, the bit referring to "ERA5 shows a considerable reduction" is vague. Reduction relative to what? Please clarify.

#5. Figure 5: The temporal resolution labeled on the y-axes of these hovmoller plots is too high/unnecessary as it crowds the figures. Please show only every other two or three months. Same comment applies to Figure 7.

#6. Figure 16, Discussion concluding Section 3.2: The discussion here seems weak and understates the disagreement between the Aeolus and ERA5 Ek temporal patterns. The second-to-last paragraph highlights the common features between Aeolus and ERA5, but I think the plots look very different. In particular, the hotspots coincident with low OLR are totally missing in ERA5 (Fig. 5b).  The phrasing in the text, however, seems to suggest that the differences are only minor. Please rephrase.

#7. Section 4: Doesn't the ratio of Ek/Ep (shown for ERA-5 in Fig. 8a) suggest that these two quantities are extremely different and not meaningful to compare with each other?  I appreciate that the authors want to move beyond traditional (conservative) analysis and attempt to do a bit more, but Figure 8a suggests that the two quantities are in much more disagreement than the discrepancy predicted by llinear wave theory (i.e., factor of 4, not factor of 2).  My suggestion here is to introduce Figure 8a earlier as a way to more directly address the concerns with comparing potential and kinetic energy (within a self-consistent product like ERA-5).  

#8. Last paragraph on page 19 (lines 467-470): How do you know it's the failure to assimilate the winds directly that's causing the poor representation of GW-associated EK?  In principle, one might be able to capture these features using a convective non-orgraphic gravity wave drag parameterization within the ERA-5 model, no?  In other words, the assimilation is one way to correct the problem, but an alternative approach is to tackle the model bias directly. However, without having more knowledge about the underlying GW drag parameterization in the model it's hard for the reader to know how many degrees of freedom are afforded to the modeler.  Can the authors please comment on the role played here by model bias? And how this is/is not handled by the GW drag parameterization?

#9. Discussion: No mention is made of how these observations might be used to develop constraints on the momentum fluxes (which is what modelers seek most).  Is that something that the author has considered?  This is a challenging question, so I am not seeking any complete answers here; I am just wondering if the author can speculate in a sentence or two how to potentially bridge V_HLOS with the momentum fluxes.

The premise of this study is very promising and the results, if robust, are of high significance in comparing Aeolus, ERA5 and GNSS-RO gravity wave energy parameters in the tropical UTLS. 

Still, the more I go through the manuscript I find too many details in the methodology unjustified, or their interpretation too stretched. Almost half of the text belongs to data and methods section which should be streamlined a lot. The text overall lacks an organized and concise structure, and some method details or datasets (e.g. NCEP reanalysis or the OLR datasets) seem to appear out of the blue. 

I have several major comments about methodology that need to be clarified, because some of the results do not look very robust to me from the beginning, and this cascades then to the rest. 

Figures could be improved a lot, and the authors should make a big effort in the text to avoid repetitive sentences, unnecessarily long explanations / verbose in methods or results (a lot of examples in minor/technical comments). 

Also I feel that in many instances things are presented in a rather bombastic way, e.g. without really specifying where and how these valuable results have applications. 

In section 5-6, some of the conclusions might change if some small tweaks in methodology were applied -- the authors make many choices and assumptions in the method -- and many grand statements with what comes out of it. Unless one shows very convincing and robust results (which would require a fair amount of supplement material), in the plots provided in this manuscript I see some inconsistencies that make me remain a bit skeptical. 

To be clear, I'd very much like to see this study on such relevant topic published, and I hope the large amount of comments I assembled below are helpful for this. I recommend a major revision, and at least an additional round of reviews will be needed after that since the required changes are very substantial.

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# Major comments

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# 1: vertical grid and filtering choice 

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-l.124-126: this grid penalizes ERA5 and RO a lot more than Aeolus, and 'acceptable middle ground' does not really justify your choice in my opinion. Is there any other literature doing this kind of middle-ground approach with other datasets? 

-There are undesirable sources of uncertainty if you sub-sample or interpolate onto your 0.5km vertical grid: this might affect the resulting profile if a wave is not well aligned with your 0.5km vertical grid. Also ERA5, Aeolus and RO have each a very different vertical (original) grid alignment with your 0.5km grid. 

In my opinion one should err on the side of caution and interpolate to a finer grid that retains all dataset's vertical structures as much as possible, and then filter out the scales that the coarser dataset cannot see, I explain below: 

-In section 2.2 you specify that you apply vertical high-pass filter to the data. Why not use a finer vertical grid of e.g. 0.1km, and apply bandpass between e.g. 1km and 9km? This way the uncertainty with sub-sampling is gone, and you remove the shortest vertical scales that Aeolus cannot see to even the field among all datasets. To me, this would be the fairest way to make the comparison by taking the vertical scales resolved by all datasets. 

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# 2: NCEP reanalysis and smoothing (l.198-204)

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A lot of things appearing out of the blue here without proper justification. 

--> Is this the NCEP-DOE Reanalysis 2? It is not referenced either. How come this dataset is not mentioned in section 2.1? 

--> Just because it's easier to integrate does not justify using it. I just don't understand why ERA5 is not used with its own tropopause. 

--> Also, you don't show anywhere how similar are the results compared to ERA5. It certainly has poorer vertical resolution than ERA5, and this choice just adds an unnecessary layer of uncertainty. Not even some comparison material in a supplement?

"The profile is then smoothed using a 14-point moving average over the 49-point profile"

--> No justification given anywhere for this. Any other studies doing similar things that you could reference here?

--> You should explain what the purpose of this smoothing is. My impression is that it's not even necessary (see last part of my Major Comment #1 for a better option to compare what's resolved by all datasets).

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# 3: treatment of GNSS-RO data, details not properly justified

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l.206-211: details are very vague, e.g. which windowing is used for both in the end? Please state clearly what settings are applied to RO data and Aeolus. 

l.213-214: 

"Where the Brunt-Vaisala frequency squared (N2) is smoothed using binomial (Gaussian) smoothing of 10th order." 

--> This is not justified, where is this coming from? Any reference for this?

"Consequently, the data treatment across various instruments, whether wind or temperature remains consistent"

--> I strongly disagree!

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# 4: Most figures are low quality

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Fig.2: 

--> please use the degree sign  °  and not "DegN/E" (also present in Figs.4,5,6,7... all figures with lon or lat dimension...)

--> color scale is not the best for visibility, a color every 0.1 would improve guessing the exact value by eye. 

--> It appears a bit pixelated if one zooms in just a bit. 

Fig.3

--> unreasonably large to show only four lines

--> way too many labels on the x-axis

Fig.5

--> label sizes too small

--> too many labels on y-axis

--> odd alignment of a)b)c) with subpanel titles

--> b) panel title size mismatch with the others, looks like put there by hand unlike a) and c)

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# 5: pages 11-14

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-whole pages 11-12: this can be briefly summarized in the main manuscript and all the details moved to a supplement, including Fig.3  

-The noise correction makes a quite long list of assumptions, could the authors provide some results/comparison of Aeolus results without noise correction for reference? 

-Fig.4: the stark contrast of MAM 2019 and MAM 2020 does not give a reader a lot of confidence in your method. I am skeptical of how realistic the evolution of the left column is (the noise-corrected AEOLUS HLOS*). 

-l.351-352: "The geographical distribution and evolution of energy hotspots are largely similar between the two datasets"

--> I disagree, the evolution of their strength, even in relative terms, seems quite different: e.g. compare the last 3-4 rows. 

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# Minor / technical comments

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# Abstract

"revealing opportunities to refine reanalysis products and model parameterizations, as well as improving the energy ratio."

--> too vague, is there any specific recommendation here?

-l.2: cite ERA5 reference here

-l.28-30: but the Podglajen study is from before ERA5 was around, please rephrase sentence for consistency.

-l.45-46: "short-wavelength waves are primarily lower frequency gravity waves, as dictated by the dispersion relation"

--> To avoid confusion please specify that it's short vertical wavelength, and give a ballpark number of the range of vert. wavelengths you are referring to.

--> Also in the next sentence, specify what vertical wavelenghts can be captured by Aeolus.

-l.59-61: calling it "climatology" from 3 years sounds a bit stretched...

--> perhaps simply state this as an observational estimate for Jun.2019-Aug.2022

--> also this is an example of a repetitive sentence. E.g. 'and its link with deep convection' could be removed without any loss of information

-l.70: I would support sub-subsections for each separate dataset and methods.

-l.72: range bin settings and other specifications should have an earlier reference.

--> Also please update the Rennie and Isaksen 2020 reference to the 2024 ESA contract report (which includes all information from the 2020 TM). Check throughout the manuscript.

--> https://www.ecmwf.int/en/elibrary/81546-nwp-impact-aeolus-level-2b-winds-ecmwf

-l.87-88: please confirm whether you got the data on that native resolution?

-l.94: best candidate (by far in my opinion), especially when compared to other reanalysis products.

-l.95: "standard" --> you mean the  137 hybrid levels? Standard is usually associated with the 37 standard pressure levels, I recommend not using this term here to avoid confusion.

-l.99: GNSS-RO datasets --> please list which missions are included + their references, and I presume COSMIC-2 dominates the overall data amount? If so, mentioning a bit

-l.111-113: perhaps merge with l.97-98 at the beginning of the paragraph, otherwise to me feels a bit repetitive.

-l.103-104: defined by bending angle gradient, which increases near inversion layers / humidity gradients

--> I recommend to refer to Kursinski et al. 1997 here --> https://doi.org/10.1029/97JD01569

-l.115-118: feels very repetitive and could be streamlined

-l.130-131: overselling and too vague, remove or specify recommendations to enhance reanalyses and models from the results of your study.

-l.131-133: just say they are independent datasets, this sentence can be streamlined and toned down.

############### 2.2 Methods and limitations

-l.139-150: regarding the trickiness of background state removal, I miss a discussion about research that used GNSS-RO and Aeolus to study Kelvin waves, their vertical scales and (in the case of Randel et al., 2021) the behavior of the small-scale residual.

These references are very relevant to your study's methodology, the more so since they use the same datasets as you.

--> Randel and Wu (2005) --> https://doi.org/10.1029/2004JD005006 (using GPS-RO)

"Vertical wavelengths of ∼6–8 km are observed near and above the tropopause in December 2001 to January 2002 (Figures 6a and 6b), while shorter vertical wavelengths (∼4–5 km) are observed in May and August–September 2002 (Figures 6c and 6d). "

--> Randel et al. (2021) --> https://doi.org/10.1029/2020JD033969 (using COSMIC-2)

"strong residual variance occurs in the longitudinal shear zones of Kelvin waves" and this small-scale residual T variance is associated with GWs.

--> Zagar et al. (2021) --> https://doi.org/10.1029/2021GL094716 --> "Aeolus assimilation modifies the representation of vertically propagating Kelvin waves in the tropical UTLS" (Aeolus)

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-l.160-161: "Aeolus now provides the necessary tools to apply the same approach for GW Ek."

--> Sorry to be picky here, but what tools does Aeolus bring now that it didn't before. You use the same approach (your tool) to calculate GW Ek from Aeolus (data, not a tool). Such phrasing is just unnecessary verbose.

- Fig.1: please include the U(z) notation in the labels, and specify which datasets you take U from. 

-l.216-219: you should state all this when introducing Ek{hlos}. 

--> Fig.2 belongs in a supplement

--> And wouldn't it be fairer to compare EK from ERA5 U to EK_HLOS??

-l.228-231: belongs also in a supplement in my opinion

-l.245-249: a lot of verbose here, show the figure in a supplement and move the text there

-l.343-344: another example of verbose.