Fast Numerical Wind Turbine Candidate Site Evaluation

Round 1

Reviewer 1 Report

Although the paper might provide a novel approach of evaluating turbine sites, the authors have failed to represent their work properly in compliance with the standard academic writing approach.

The methods section is vague and does not ensure the reproducibility of the results. The result and the introduction presentations are unacceptable. It looks more like lecture notes than a research paper. Based on this evaluation, authors are advised to carefully complying with the academic writing criteria. The methodology shall be precisely described (using words like ''some'' without further elaboration is not acceptable).   

Again, although the novelty of the paper might be significant, my comments are made to motivate the authors to polish the paper structure, readability, and representation. 

 

Author Response

Although the paper might provide a novel approach of evaluating turbine sites, the authors have failed to represent their work properly in compliance with the standard academic writing approach.

The methods section is vague and does not ensure the reproducibility of the results. The result and the introduction presentations are unacceptable. It looks more like lecture notes than a research paper. Based on this evaluation, authors are advised to carefully complying with the academic writing criteria. The methodology shall be precisely described (using words like ''some'' without further elaboration is not acceptable).   

Again, although the novelty of the paper might be significant, my comments are made to motivate the authors to polish the paper structure, readability, and representation. 

Thank you for the comment. We hope the extensive editing of the manuscript has aligned it with the academic writing standards. We have rewritten the introduction, clarifying the purpose, the content, and the contribution of the presented work. The description of the methods is reorganised, clarified, extended and made more precise in order to be more easily reproducible. We have improved the presentation of the results and added more discussion of the results, rectifying some omissions of the reviewed version. The conclusion section is heavily edited and improved as well. We provide a copy of the manuscript with all the numerous changes marked.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

thanks for a good paper, I enjoyed reading and I think it is an overall good read. I think ti has value in terms of extrapolating measurements into different areas, using a model that seems easy to train despite its simplicity.

I hope my comments will help you improve the paper, also from the point of view of a reader that does not have a specific exact knowledge in the field you work in.

I have some general remarks here:

• You say that your method is especially suitable for siting. But I did not really see how you used your model for siting, or how you use the information that you gather to make a siting decision.
• The description of the main figures of the paper, F8 to F10 is a bit lacking. I can't see that you have critically described the details in the figures and what the plots mean. The Q-Q plots show a deviation at some point, but what does it mean in terms of the assessment of your model? The sunflower plots look similar between model and experiments, but if there are any deviations, what do they mean for the current model?
• You choose your parameter as the average wind speed and the average of the cube wind speed (related to power density). But what about the wind distribution? I think that this is a more important factor when one has to decide the siting of a wind turbine, as different turbines will have different AEP depending on the wind speed distribution.
• At the end of the paper, you say that extrapolating measurements is preferably avoided, while I understood that this is an important part of your work. Moreover, it seems that a lot of the good performance of the model is due to the choice of the regressors; is that something of concern when having to generalize the model to other locations? In other words, how can a user of the model select meaningful regressors and get an idea on the accuracy of the prediction he will get?

More specific comments are attached in the pdf.

With best regards,

The reviewer

Comments for author File: Comments.pdf

Author Response

Dear Authors,

thanks for a good paper, I enjoyed reading and I think it is an overall good read. I think ti has value in terms of extrapolating measurements into different areas, using a model that seems easy to train despite its simplicity.

I hope my comments will help you improve the paper, also from the point of view of a reader that does not have a specific exact knowledge in the field you work in.

Thank you very much for the valuable comments!

I have some general remarks here:

• You say that your method is especially suitable for siting. But I did not really see how you used your model for siting, or how you use the information that you gather to make a siting decision.

• The method only addresses the stage in which the candidate site is already selected and instrumented. It substitutes a long-term measurement time series from a candidate location with a shorter-term one and with other historical signals available independently from the wind power project. It saves the time that would otherwise be spent waiting for the measurements. It doesn't address the spatial dimension: it works only for the selected instrumented site.

 

We hope the corrections addressing the specific comments added to the PDF, particularly the new Table 1, clarify it. The new paragraph at the end of the Discussion reiterates that the further steps in decision-making are the same if modelling is used than if measurements were collected. We have modified some parts of the article, such as by adding a bullet point on page 2 and modifying the penultimate paragraph of the Introduction, to make it clear that adjusting the location is assumed to not be an option.

 

• The description of the main figures of the paper, F8 to F10 is a bit lacking. I can't see that you have critically described the details in the figures and what the plots mean. The Q-Q plots show a deviation at some point, but what does it mean in terms of the assessment of your model? The sunflower plots look similar between model and experiments, but if there are any deviations, what do they mean for the current model?

• Thank you for pointing out the omission. We discuss the figures more into detail close to what was line 342 in the reviewed version:

"To this end, the measured and predicted wind speed distributions are compared in Q–Q plots and sunflower diagrams in Figs. 8, 9, and 10. We see that the distributions predicted by the linear regression with least squares models match the distributions of measured values better than the NWP model does. The match is quite good, particularly for speeds under 5 m/s. Beyond that point, the mismatch in Q–Q plots increases, while the frequency of the speeds decreases. Fig. 10 demonstrates that the predicted and the measured speed distributions match by hour of day. This is beneficial if one is interested in the dependence of power on time and is also a confirmation of the skill of the model."

 

• You choose your parameter as the average wind speed and the average of the cube wind speed (related to power density). But what about the wind distribution? I think that this is a more important factor when one has to decide the siting of a wind turbine, as different turbines will have different AEP depending on the wind speed distribution.

• The used statistics (v and v³) are derived from the distribution and completely describe it. We clarified it in the text close to what was line 280 in the reviewed version, now the paragraph in the lines 320 to 327.

 

Other parameters of the distribution could be computed but it is unclear to us which ones would be the most meaningful. Regarding the distributions themselves, they are shown in Figure 10, separated by the hour of the day.

 

• At the end of the paper, you say that extrapolating measurements is preferably avoided, while I understood that this is an important part of your work. Moreover, it seems that a lot of the good performance of the model is due to the choice of the regressors; is that something of concern when having to generalize the model to other locations? In other words, how can a user of the model select meaningful regressors and get an idea on the accuracy of the prediction he will get?

• We agree that the paragraph was (unintentionally) deceiving, we have corrected it and expanded it into two paragraphs (lines 463 to 478 in the revised manuscript). We have improved on the explanation on what to expect from similar models in different regions. We have added some discussion on the demonstrated transferability of the method in the short range which was missing (we show that we aren't simply lucky with the choice of the location but the method performs equally well at another site in the area using the same regressors).

More specific comments are attached in the pdf.

With best regards,

The reviewer

Response to the comments in the PDF:

• Line 19: Thank you for the remark. The previous version had no explanation of what makes small wind turbines special outside of the abstract. It is fixed now: we avoid the repetitive word "small" until the newly added passage on why the small wind turbines are the ones that are likely to benefit.

• Figure 1 is replaced with a similar photo that we do have the rights to use and the author is given credit.

• Line 31: Thank you for the suggestion, we have improved the unclear statement. Now it is explicitly stated that the distribution is more important than the time series. The studied averages are derivatives of the distribution and the distribution itself is shown in the plots.

• Line 34: Thank you for the remark. By "budget", we meant human labour, as the time of computation for the chosen linear model is negligible. We added a paragraph describing the necessary effort to the end of the discussion.

• Line 49: Yes, it is a general fact about NWP models. We have clarified that it includes WRF (the paragraph also got significantly changed for other reasons). Thank you for the questions!

• Line 61: It is both a challenging exercise scenario (what works in complex terrain, works everywhere) and particularly useful because fewer alternatives exist in complex terrain. We have clarified it.

• Line 74: We have clarified it, thanks for the remark.

• Line 79: We have clarified it.

• Line 88: Thank you for the remark. We have fixed it.

• Line 92: We find the idea of including a table excellent, thanks a lot for the suggestion!

• Line 95: We choose to test the method on 1 year of data in order to demonstrate it over all the seasons. 1 year is not a limitation of the method: if a model trained on December of one year can predict the wind in June of the same year, it can also be used for December (or June) of the year before.

• Line 102: It indeed was an unnecessary repetition, we have removed it. Thank you.

• Line 105: We have clarified it.

• Line 116: Thank you for the observation. You are right that we apply the model to two locations and not to a large area. The reason is that the model can only be trained for locations for which a month of measurement data is available. It is meant as a substitute of waiting longer and collecting a year or more of measurement data. Wind resource assessment models may be used at an earlier stage of planning to help select candidate locations in a region (such as a 10 km by 10 km square) in which to start the measurements. The presented work does not apply to wind resource assessment models and is compatible with them. We hope that the new table clarifies it and we believe the title of the paper honestly summarizes the scope of the work.

• Line 136: The confusion arises from the difference between the training data and the model input when using the model for prediction. Training data consists of the measurements of the model output quantity (wind at the candidate turbine location) and the model input signals covering the same time period, 1 month in our case. The trained model can then be used for prediction for time periods for which the input signals are available but the output signal is not. In order to predict the local wind for the past year(s), we use only the quantities that are available for the past years (regardless of the wind power project) as model inputs.

In order to clarify the distinction between training data and past input data, we rewrote the paragraph on system identification (lines from 53 on in the reviewed version, from 66 on in the revised version) and made it more precise. Thank you for pointing out the ambiguity.

• Line 150, 160: We have improved the descriptions, thank you for the suggestion.

• Line 177: We have clarified that the temperature difference is related to the vertical gradient.

• Line 182: Thank you for the question. We have expanded the explanation on the strengths and weaknesses of regressor selection algorithms compared to our method.

• Line 184: We decided to keep the section on linear regression even though it is short (now slightly lengthened). It is one of the two modelling techniques used, the other being Gaussian process modelling. We think both techniques should be presented in a balanced way with a section for each, but not much has to be said about linear regression. We added the information that 24 of 60 models are obtained with linear regression and 36 with GPs.

• Line 190: Gaussian process regression can be used to approximate an arbitrary nonlinear function. Contributing to the confusion is the fact that 12 of the 60 presented models are GP models that are linear, which is otherwise not typical. We clarified that GP models are typically nonlinear.

• Line 192: Yes, we prefer GP models because of the model uncertainty they provide, which is more or less their only advantage. We tried to clarify it. Thank you for the question.

• Line 232: We agree that the proposed organisation makes more sense, thank you for the suggestion.

• Line 238: We have clarified it.

• Line 244: We added a paragraph on why we use so many figures of merit and what they mean to us in lines 313-316 of the revised manuscript. Thank you for the suggestion.

• Line 278: Thank you for the opinion, you are right, we misunderstood the reference [7] and overestimated how hard it would be to use the power curve. The text is corrected in this aspect. We still choose not to predict the energy production for a power curve because, as non-specialists, we cannot add value to the article in this way.

• Line 279: Thank you for pointing out the ambiguities, we have fixed the text and better specified what average is meant. Regarding the effect of the variance on the expected power production, it is not clear to us how it could be used. The predictive variance is just a measure of how confident the model is in its prediction. Correctly and convincingly turning it into expected power, which has a very firm physical meaning, would be challenging.

• Line 314: Thank you for the observation. We clarify that the averages are time averages over the 12 11-month test periods. Regarding the distributions, the predicted and the measured ones are qualitatively compared to each other in Figures 8 to 10, particularly in the Q-Q plots. As soon as one fits a curve to the obtained distribution, one is left with a few best-fit parameters that can then be compared to each other, which is exactly equivalent to comparing statistics like v and v³. Now we emphasise close to the line 280 of the reviewed version (322, 323 in the revised version) that these statistics describe the wind distribution.

• Figures 8 and 9: Thank you for the suggestions, we have modified the figures in accordance with them. The blue labels are the data series names; we have decided to keep them but to rename them with English-derived names.

• Figure 10: The percentages shown in the centre of the sunflower diagram represented the percentage of the time with the wind speed lower than 0.3 m/s. However, as this piece of information is not relevant for the problem at hand, we have modified the diagrams and now present all wind in the histograms regardless of the speed. Thank you for the question.

• Line 342: We agree that the questions you pose are interesting. However, we do not know the answers to most of them and we dare not speculate. We added what we could.

• Line 351: We clarify that the wind speeds at the study sites are too low for most uses of wind power and that the sites are nevertheless good for the presented study.

• Line 354: Thanks for the suggestion. We have tried to reasonably follow the suggestion throughout the text.

• Line 378: Thank you for the observation. You are right: the theoretical support for the intuition that the (measured or predicted) wind speed in the vicinity is proportional to the wind speed at the site is unclear. The best we can say is that it is not surprising; the text is corrected accordingly.

• Line 383: We meant either, it is clarified now.

• Line 400: Thank you for the remark. We agree, we have not clearly distinguished between the result of the presented experiment and between what the theory says. We have improved the text.

Author Response File: Author Response.pdf

Reviewer 3 Report

It is believed that the article deals with a very interesting and important topic, however it is necessary to specify in introduction that the long-term wind data are necessary for various environmental issues such as noise, low frequencies noise and the shadow flikering, problems Inherent of the install sites of wind turbines. In this regard, It is suggested to the authors to insert some reference to these issues for example: "Assessment of the Noise Generated by Wind Turbines at Low Frequencies", "Shadow Analysis of Wind Turbines for Dual Use of Land For Combined Wind and Solar PhotoVoltaic Power Generation and "Assessment of wind power plants with limited wind resources in developing countries: Application to Ko Yai in southern Thailand "

 

 

 

Abstract

Line 2

series from the location à series in the same location 

 

Line 2

This data takes a long time to collectà These data take a long time to be collected.

 

line 4

for training an experimental model à (?) to adjust an experimental model? perhaps it is cryptic as in an abstract

 

line 5

most of this wait à what wait? perhaps most of the usual needed wait in planning a wind farm(??)

 

line 13

the main reason that the proposed à the main reason because the proposed

 

line 11-14

The turbines sited... to big onesà The overall meaning of the sentence is not clear. Low wind speeds and not accurate weather prediction make small turbines preferable to big ones? Probably the phrase is cryptic and as a whole the abstract could be profitably shortened  

 

1. Introduction

Line 1

as an important general equipment à as an important general purpose equipment

 

Line 8-12

However, due to the complex...chamber [7,8]à The quite long sentence should be reformulated as the overall meaning can be grasped but it is expressed in a confused way

 

Line 7-8

Gaussian process model à models based on a model? Can you reformulate?

 

line 23

turbine at a particular à turbine in a particular (also elsewhere why at?)

 

line 31

is the more important à is the most important

 

line 33

would be easy to à why would? when applied?

 

line 33-34

requiring additional research – the goal is for the application of the method to fit within the budget of a small wind à I suggest to simplify: requiring additional research. The goal is the application of the method to fit the budget of a small wind

 

line 47

parts of natureà parts of the natural environment(?)

 

line 50

the feasibility of this utilization of modellingà the feasibility of this modelling

 

line 64

is typically neededàis typically available(?) Wind prediction is easier for flat terrain etc. so why needed?

 

line 85

coupled measurements from several mastsà coupled with measurements from several masts (stations?)

 

line 114

representing the time pressureà time pressure?

 

line 124

In addition,....is smallà The sentence is not very clear. You are insisting on the fact that the method is suitable for   small wind turbines. But what is the rated power are you referring to? and are these turbines grid connected or stand lone systems? Probably these aspects go without saying for you but they deserve some attention.

 

2. Methods

 

Figure 3                                                                                           

à near the figure a caption with numbers. Do they indicate meters [m]?

 

line 157

30-minute averages are recorded. àwhy 30-minute averages and not 10-minutes averages as more usual?

 

Line 161

standard ground level meteorological stationsà ground level mean that measurement are taken at what distance from ground?

 

 

line 210

All the models are of the Finite impulse response (FIR) structureà All the models share the Finite impulse response (FIR) structure

 

line 227

model is the data à model are the data

 

Figure 5: Splitting the data in different waysà Splitting of the data in different ways

 

line 280

the average third power of the wind speed ¯v³ à do you mean the cubic average? it would be more significant than the third power

 

3. Results

 

Line 297

of a model.àof the model

 

line 312

value compares the average windà value is compared  with the average wind

 

figure 10

Sunflower diagrams are not very clear usually, the do not add much in fact

4. Discussion

 

 

line 344-345

The reason for choosing v¯ is that it is the simplest wind speed statistic that is related to wind powerà The cubic average is meaningful for evaluating wind power, did you calculated the linear average?

 

5. Conclusion

 

line 387

turbines with short time series of local measurementà turbines installation with short time series of local measurement

 

Line 393

At the main study site, linear à As the main site of the study is concerned, linear

 

line 398

when planning a small wind turbine à when planning the installation and operation of a small wind turbine

 

line 401-412

in conditions outside of the range of the training data points. à(??)

 

line 409-410

Verifying that the findings generalize to different climates and topographies would be beneficial à Verifying that the findings can be generalized to different climates and topographies is an objective (?)

 

 

Abbreviations

 

line 426

in this manuscript: à in the text:

 

References

The list of references is accurately written, just a misprint.

 

Ref. 33

sky conditions based on perceptron à perception

 

 

 

Final comment

 

The analysis prescinds from the wind turbine features completely. It is stated in the text but sometimes it involves some problems as, for instance, the special interest to small turbines which is repeated but not justified as far as my expertise in the field is concerned. The usefulness of a method to shorten the wind measurement extension is

clear and the discussion of the various steps is coherent with the scope of the research which is, for the above reasons, somewhat limited.

Author Response

It is believed that the article deals with a very interesting and important topic, however it is necessary to specify in introduction that the long-term wind data are necessary for various environmental issues such as noise, low frequencies noise and the shadow flikering, problems Inherent of the install sites of wind turbines. In this regard, It is suggested to the authors to insert some reference to these issues for example: "Assessment of the Noise Generated by Wind Turbines at Low Frequencies", "Shadow Analysis of Wind Turbines for Dual Use of Land For Combined Wind and Solar PhotoVoltaic Power Generation and "Assessment of wind power plants with limited wind resources in developing countries: Application to Ko Yai in southern Thailand "

 

 Thank you for reminding us to these aspects. We have added the considerations of noise and sustainable development related to the necessary wind speed.

 Thank you for all the valuable comments!

Abstract

Line 2

series from the location à series in the same location 

 We have made an edit to clarify the meaning.

Line 2

This data takes a long time to collectà These data take a long time to be collected.

 We have corrected it as advised, thank you.

line 4

for training an experimental model à (?) to adjust an experimental model? perhaps it is cryptic as in an abstract

 We are afraid we cannot replace the established term, training or learning as called in machine learning, also identifying in the field of system theory.

line 5

most of this wait à what wait? perhaps most of the usual needed wait in planning a wind farm(??)

 We have clarified it, thank you for the suggestion.

line 13

the main reason that the proposed à the main reason because the proposed

 We have made an edit to clarify the meaning.

line 11-14

The turbines sited... to big onesà The overall meaning of the sentence is not clear. Low wind speeds and not accurate weather prediction make small turbines preferable to big ones? Probably the phrase is cryptic and as a whole the abstract could be profitably shortened  

 We have made an edit to clarify the meaning, thank you for the remark.

1. Introduction

Line 1

as an important general equipment à as an important general purpose equipment

 We believe the comment does not refer to our manuscript.

Line 8-12

However, due to the complex...chamber [7,8]à The quite long sentence should be reformulated as the overall meaning can be grasped but it is expressed in a confused way

 We believe the comment does not refer to our manuscript.

Line 7-8

Gaussian process model à models based on a model? Can you reformulate?

 We have edited the Abstract in order not to refer to specific terms introduced later in the manuscript. Line 10 in the revised version no longer refers to Gaussian processes but uses more general modelling terms.

line 23

turbine at a particular à turbine in a particular (also elsewhere why at?)

 Sorry, we believe that it is better to use "at" than "in" in this context.

line 31

is the more important à is the most important

 Thanks for the suggestion, we rewrote the sentence.

line 33

would be easy to à why would? when applied?

 Thank you for the questions, the most direct answers are given in the new last paragraph of Discussion, lines 439 to 446. We would like to keep the mention of the applicability at the beginning of the text as well.

line 33-34

requiring additional research – the goal is for the application of the method to fit within the budget of a small wind à I suggest to simplify: requiring additional research. The goal is the application of the method to fit the budget of a small wind

 We changed it. Thanks.

line 47

parts of natureà parts of the natural environment(?)

 Thank you, we made the change.

line 50

the feasibility of this utilization of modellingà the feasibility of this modelling

 Thank you, we have simplified the sentence.

line 64

is typically neededàis typically available(?) Wind prediction is easier for flat terrain etc. so why needed?

 Thank you for pointing out the contradiction in writing, we have fixed it.

line 85

coupled measurements from several mastsà coupled with measurements from several masts (stations?)

 Thank you for the suggestion, we rewrote the paragraph.

line 114

representing the time pressureà time pressure?

 Thank you for the suggestion, we reorganized and split the sentence.

line 124

In addition,....is smallà The sentence is not very clear. You are insisting on the fact that the method is suitable for   small wind turbines. But what is the rated power are you referring to? and are these turbines grid connected or stand lone systems? Probably these aspects go without saying for you but they deserve some attention.

 Thank you for the observation. We believe that smaller wind turbines are more likely to benefit from our work than bigger ones. The reasons are that big ones are higher and less influenced by local effects, and perhaps too expensive to take the risk of approximating the wind with a model instead of just waiting for the measurements. It is also true that small turbines may be off-grid and thus more economical in weak winds that we are modelling.

We remedy the situation by defining small wind turbines in line 22 of the revised manuscript and adding a reference [1]. We reduce the use of the word "small" up to the paragraph where we explain why it is small turbines that are likely to benefit. We also use your suggestion of explicitly referring to off-grid turbines, adding the reference [29] in line 144 of the revised manuscript. We expand the paragraph in the lines 142-151 of the revised manuscript with additional explanation on why the method is best suited for small turbines.

2. Methods

 

Figure 3                                                                                           

à near the figure a caption with numbers. Do they indicate meters [m]?

 They do, we fixed it. Thank you!

line 157

30-minute averages are recorded. àwhy 30-minute averages and not 10-minutes averages as more usual?

 10-minute averages are unfortunately not available as the installed meteorological stations happen to be recording 30-minute averages every 30 minutes. We have clarified in the text, line 189 in the revised manuscript

Line 161

standard ground level meteorological stationsà ground level mean that measurement are taken at what distance from ground?

 Thank you for the question, we clarified it.

 

line 210

All the models are of the Finite impulse response (FIR) structureà All the models share the Finite impulse response (FIR) structure

 We changed it. Thanks.

line 227

model is the data à model are the data

 We changed it. Thanks.

Figure 5: Splitting the data in different waysà Splitting of the data in different ways

 Corrected, thank you.

line 280

the average third power of the wind speed ¯v³ à do you mean the cubic average? it would be more significant than the third power

 Yes, it can be called cube. We changed it. Thanks.

3. Results

 

Line 297

of a model.àof the model

 We have rephrased the sentence.

line 312

value compares the average windà value is compared  with the average wind

 Thank you for the remark. We have rewritten the sentence.

figure 10

Sunflower diagrams are not very clear usually, the do not add much in fact

The purpose of sunflower diagram is to show conveniently the wind speed distributions. We believe that observing the wind speed distributions at given times of day is useful so we decided to keep them. We have clarified it in the text in the line 390 of the revised version.

4. Discussion

 

 

line 344-345

The reason for choosing v¯ is that it is the simplest wind speed statistic that is related to wind powerà The cubic average is meaningful for evaluating wind power, did you calculated the linear average?

 We calculate both the average wind speed and the average cube of the wind speed and compare the results obtained from the model with the measurements in Table 4 of the revised version. We have expanded the paragraph as well (lines 385 to 400 in the revised version).

5. Conclusion

 

line 387

turbines with short time series of local measurementà turbines installation with short time series of local measurement

 We changed it. Thanks.

Line 393

At the main study site, linear à As the main site of the study is concerned, linear

 We have reformulated the sentence.

line 398

when planning a small wind turbine à when planning the installation and operation of a small wind turbine

 Thank you for the suggestion, we have reformulated the sentence.

line 401-412

in conditions outside of the range of the training data points. à(??)

 We have rewritten the paragraph, thank you for the remark.

line 409-410

Verifying that the findings generalize to different climates and topographies would be beneficial à Verifying that the findings can be generalized to different climates and topographies is an objective (?)

 Thank you, we have reformulated the sentence.

 

Abbreviations

 

line 426

in this manuscript: à in the text:

 We changed it. Thanks.

References

The list of references is accurately written, just a misprint.

 

Ref. 33

sky conditions based on perceptron à perception

 It is not a misprint, the title is correct as stated. Perceptron is a type of artificial neural network.

 

 

Final comment

 

The analysis prescinds from the wind turbine features completely. It is stated in the text but sometimes it involves some problems as, for instance, the special interest to small turbines which is repeated but not justified as far as my expertise in the field is concerned. The usefulness of a method to shorten the wind measurement extension is

clear and the discussion of the various steps is coherent with the scope of the research which is, for the above reasons, somewhat limited.

We believe that the suggested edits of the text make the motivation easier to understand and the method easier to use.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Based on the authors' reply, the paper has been significantly improved and within the academic writing style. 

I recommend accepting the paper in the present form.

Good Luck.