General Comments 

The manuscript describes a method to derive turbulence parameters from observations by ground-based GNSS stations. These observations are processed to obtain time series of the equivalent total zenith propagation delay. After a filtering process a time series of estimated variations in the equivalent zenith wet delay are used to characterise turbulence. I find the work interesting and unique, but have some thoughts concerning a critical assessment of the results and the structure and the clarity of presentation.

>>>> Many thanks for your positive comments on the manuscript, which help us improve it.

A first reflection is why the manuscript was submitted to ACP rather than AMT (Atmospheric Measurement Techniques). I find that the method, rather than the estimated turbulence parameters, is the motivation for publishing (and the title also starts with "Measurement Report:". I assume this is a question for the editor.

>>>> The article was intentionally submitted to ACP because it offers the opportunity to publish 'measurement reports.' In our opinion, a publication in AMT would require a longer review process. By publishing here, we have the chance to present preliminary results and outline the methodology in one paper, ensuring we don’t need to repeat these steps in future publications.

The time series of the Zenith Wet Delay (ZWD) from the GNSS data processing has a temporal resolution of 30 s. It is stated that no constraint is applied (line 122) but the estimates of the ZWD are averages of the air in the sampled volume.

>>>> By 'constraint,' we mean that the ZWD is not assumed to follow a specific noise structure (e.g., a random walk). Assuming such a structure could bias our results, which are based on identifying slopes in the power spectrum.

This volume depends on the elevation cutoff angle and which GNSS that were used. I miss information about this. If a frozen flow is assumed (as you do in the manuscript) and an elevation cutoff angle of say 5 or 10 degrees is used, quite a high wind speed is required in order to have independent air volumes with a sample period of 30 s.

>>>> The ZWD values are mapped as 'slant wet delays' from all GNSS satellites to the vertical. While the frozen approximation is clearly an approximation but well documented in Wheelon (2001), we are confident in its validity for two main reasons: (i) we observe the expected slope in real data using both visual and statistical methods, and (ii) further investigations using machine learning strategies—combining real data from radiometers and other sensors at PAYN—indicate that the atmospheric layers sensed by the ZWD are at an altitude of 1500–2000 m, where the geostrophic wind predominates (study to be published in a next step). This point is further discussed in the corresponding section on the Taylor Frozen hypothesis and in the introduction. Additionally, we have enhanced the description of how the ZWD is computed.

This issue should be discussed and how the results are affected by this fact. Depending on the conclusions you may want to reduce the temporal resolution. 

>>>>> This could be done, but not with the stations we selected (frequency is not available). Using a lower temporal resolution introduces further noise, which would require more advanced filtering strategies. We plan to address this in a future contribution on the topic. Once more, we derive the methodology with some examples.

A related issue is that there is a need to validate the GNSS wet delay time series obtained with the method you describe. One possibility is the use of microwave radiometry. The main advantage of the radiometer is that it samples a much smaller volume and it can measure in the same direction continuously. (Also in this case, however, the temporal resolution for independent samples is limited by the sampled air volume, which in turn is determined by the antenna beam width(s).) Probably none of the sites you study is equipped with a radiometer.

>>>> Our aim is not to validate the ZWD, as this has been extensively addressed in many other contributions. Instead, we focus on the short-term variations related to turbulence. As you correctly noted, the ZWD is a 'particular' parameter, representing an integrated quantity along the signal path and a time series, whereas satellite images, such as those from OLCI, derive spatial quantities (e.g., 'outer scale length'). In our view, it is unlikely that an instrument will be developed that allows direct comparison and that is the reason why we would carry on with machine learning (gradient boosting) strategies to combine several instruments for validation/insvestigations. This uniqueness makes the parameters we estimate both novel and innovative. The article highlights the need for further investigation to better understand these parameters, such as through large eddy simulations or the aforementioned gradient boosting. While we do not claim to hold the ultimate truth, our goal is to spark curiosity and foster further exploration. We have add a section "note" in the taylor frozen section to comment on that and further related topics.

However, there are several GNSS/VLBI sites that offer this possibility, see e.g. Teke et al. (2013) (Troposphere delays from space geodetic techniques, water vapor radiometers, and numerical weather models over a series of continuous VLBI campaigns, J. Geod.,87:981–1001, DOI 10.1007/s00190-013-0662-z).

>>>> thanks for point out these stations. We will keep that in mind and are currently making further work on PAYN.

You present results for one summer and one winter day for each GNSS station. I find this to be insufficient. If I may be a bit provokative, but a data point in a climate time series is often an average over 30 years.

>>>> I also work on climate time series and completely agree with your point. However, we are not claiming in the article to draw climatological conclusions. Instead, we demonstrate that the parameters we estimate appear to be related to the climate zone, potentially offering new insights. We are also not suggesting that the same parameters or identical patterns will emerge every day. The values naturally vary, as turbulence changes from minute to minute. However, a periodical patterns for some stations seem evident.

Although I have not visited all the studied sites, I have been in similar climates, and in my experience, weather conditions can vary significantly from one day to the next (with the possible exception of the tropical site).

I fully understand your concern about the need for more extended data collection. While we are not arguing against the value of multi-year data, given the considerable effort we put into comparing results across sites, we recognize the risk of overinterpretation. Collecting at least one month—or at minimum, one week—of data from each site across different seasons would indeed provide a more robust basis for analysis. Additionally, comparing parameters estimated for adjacent days could offer valuable insights.

>>>> We did conduct such investigations but chose not to include them in this article. The reason is that presenting multiple days could be confusing, as readers might focus on finding 'exactly the same' patterns (or not), leading to additional questions such as 'why only two days,' and so on. Instead, we restricted our analysis to one day to highlight the differences in patterns between seasons and stations. This approach also makes it easier to present the results (figure are not overloaded), as the goal of this work is to demonstrate the potential of the parameters (as a report) rather than conduct a deep investigation.

For a more comprehensive analysis, we would consider data from a single station over 30 years, incorporating techniques like moving averages and other methods. Understanding these parameters is a complex and challenging task that requires significant effort. However, in the revised version of the article, we have added some comments regarding adjacent days. We have add futher days in the repository.

A related matter is that the argument that the spring and the autumn may be slightly different for the tropical station (SEY2) is definitely valid for all the stations.

>>>> you are right but we are intented to think that the differences should be more extrem for a tropical station than for the other one.

Consequently I recommend to handle all the stations in the same way. Furthermore, to me it does not make sense to present the "extra" results for RIO2 and SEY2 in appendices. It will be easier for the reader to follow if all the results for each station are presented together. (See more details in the section with specific comments below)

>>>> we would like to keep these two particular cases in appendices so that the main body of the article focuses on two days. We have the feeling that it would break the dynamic of the article and make it more difficult to follow. We thank you for your comprehension.

Specific comments

In the abstract (and in line 30) you state that "microwave signals are almost unaffected by clouds but are delayed as they travel the troposphere". In many geodesy applications the induced effect on the delay by clouds are ignored because thy are comparable to the uncertainties. In your case however, you surpress the large variations in the estimated propagation delay by filtering and the question is then how the small scale variability in the delay caused by clouds affect your interpretations of the turbulence parameters. For example, cumulus clouds may cause delays of several millimetres. Solheim et al. (1999)( Propagation delays induced in GPS signals by dry air, water vapor, hydrometeors, and other particulates, J. Geophys. Res., 104, 9663–9670) state that "A cloud droplet concentration of 1 g/m^3 for a distance of 1 km has an integrated liquid value of 1 mm and would therefore induce a radio path delay of 1.45 mm." In Line 36 it is stated that the topic is on WV turbulence. Perhaps it will be more correct to refer to turbulence due to WV and liquid water clouds?

<>>>> Thank you for pointing out this reference; it is indeed very interesting. We have added it to the abstract/introduction. While we would like to retain the term 'WV,' we acknowledge that it may be more accurate to refer to 'WV and liquid water clouds.' This insight has inspired us to explore the parameters further, specifically comparing days with and without clouds or precipitation.

In Section 2.1 you mention briefly that the ZTD can be estimated from GNSS observation. I think this is the appropriate place to give the details on how this is done rather than in the paragraph starting on Line 120.

Line (L) 219: It is not clear to me why satellite orbit and clock errors can be ignored. Do you mean that the products from GFZ are free of error? This cannot be true. If you believe that these errors are small enough to be ignored, it needs to be motivated.

>>>>> We have changed it accordingly. Further, the GFZ satellite orbit and clock are estimated using a global GNSS network comprising approximately 140 stations. While these products are not error-free, the large number of stations ensures that high-frequency signals caused by turbulence have no significant impact on the satellite orbit and clock estimates. In other words, the orbit and clock products are very smooth, making them suitable for detecting high-frequency signals observed by individual stations.

Table 1: "Gravity waves" is not a type of climate. You may instead call it characterisation of GNSS station. Additionally, it is not really needed to present this in a table (with only one line as an entry). Each site can be described/characterised in the running text.

>>>> We have corrected it but would like to keep the table for clarity, i.e., as a summary

L 238-240: When checking the supplementary material I find that there is only one additional day for each site and season. From just reading the information available one cannot conclude that this material support your conclusions. As mentioned above you need more than one day of data to characterise the turbulence at a site. 

>>>> We have corrected the text accordingly. This is not the topic to "characterise" the turbulence but rather to show that there is evident differences in pattern and range of values. In the future, we will need to be more specific about which kind of turbulence can be monitored with GNSS, using, e.g., large eddy simulation or gradient boosting to identify the main contributions (and potentially dependencies with height). It is only a measurement report in our opinion, giving an idea about what can be done.

Another question related to the supplementary material is that when I randomly checked some of the data files I find that the ground pressure is constant over the entire day in all cases. Does that mean that when you subtract the hydrostatic delay from the ZTD it has a constant value and that any variations in the hydrostatic delay will alias with the wet delay variability? This needs to be explained / discussed.

>>>>> The pressure is from GTP2 model. The daily variation of the pressure is slow and usually within several hpa. The constant pressure can introduce an error with low frequency in ZWD, but should have no impact by detecting turbulent signal. We have added this information in the text describing the computation.

L 400-404: You have not presented any results for the PAYN station, so there is no need to discuss such results. If it is of relevance for this study I think it shall be included rather than referring to the "next contribution".

>>>> you are right. We have added a more general sentence.

L 420: Also here the "next contribution" is mentioned. It is sufficient say that confirmation is needed because the reader will not know where the next contribution is to be found.

>>>> we have corrected it accordingly. Technical Corrections 

A general comment: At many places you use the wording "in this contribution" (or something similar). In most cases these words should be deleted. It is obvious, e.g. L 8, 55, 178, 184, 215-216, 240, and 380.  >>>> We have deleted most of them.

L 3: 90%  -->  90 % (Also at many other places: insert a "space" between the value and the unit (SI recommendation). Also there shall be no dash between the value and the unit.) L 32: You probably mean hydrostatic delay (not dry), which is the term you use elsewhere?

>>>> in our opinion, this is the same L 48: Why is the slope not mentioned here?

>>> because the slope is not estimated in this contribution. Figure 1: Correct the labels on the y axis in the ZWD' graph (too close for a good readability). L 104: Better to write the mathematical expression in one line, or make it a numbered equation. L 106: Units shall not be in italics. L 109: 1 hour  -->  1 h L 121: 30-second rate  -->  30 s rate  L 126: 4h  -->  4 h UT L 170: Better to write the mathematical expression in one line, or make it a numbered equation. Figure 2: The time series graphs are too small, make them bigger or delete them (they are not really needed)?

>>>> we would like to keep them as they servie as illustration. We mention it in the caption now. L 220, 221, 222, 223, 226: shall read 30 s, 1 h, 24 h L 284: Do not start a sentence with a symbol. L 318 & 326.  RIA2  -->  RIO2 L 411: 375 unit? L 422: automn  -->  autumn

>>>> corrected when appropriate