SUMMARY

This work compares wake simulations from three different genres of models: mesoscale wind farm parameterizations, RANS models, and low-cost engineering models. This comparison also involves measurements from a meteorological mast and SCADA. This type of wake model comparison study is extremely valuable, and it requires a large amount of work to conduct. The major limitation of this comparison is the small amount of easterly flow under neutral conditions with 9–12 m/s winds, leading to a small amount of WRF data for this inter-model comparison. While this is unfortunate, several other wake modeling papers have been published with similarly small datasets, so it would be unfair to hold this paper to a different standard. The authors also acknowledge this limitation and urge some caution when interpreting these results. The manuscript also does a month-long comparison of the Fitch wind farm parameterization to the EWP. The manuscript is extremely well written, which makes it easier to understand the methodology and the results. Some major conclusions are (1) the mesoscale WFPs (especially Fitch) and the RANS models agree well about average intra-farm wind speed reductions, whereas the engineering models have weaker average intra-farm waking (2) similarly, the mesoscale WFPs and the RANS models predict similar median wind speed and power reductions due to farm-to-farm waking, whereas the engineering models tend to show negligible farm-to-farm waking (ZON being the exception) (3) the Fitch WFP tends to do better than the EWP throughout the study. 

MINOR COMMENTS

* Throughout the manuscript, please update legends where necessary (e.g. Fig 3). WES guidelines state "A legend should clarify all symbols used and should appear in the figure itself, rather than verbal explanations in the captions (e.g. "dashed line" or "open green circles")." https://www.wind-energy-science.net/submission.html

* L11 and L449: possibly "reasonably" instead of "reasonable"

* L11-13: Please quantitatively state the bias of both Fitch and the EWP. The Fitch wind speed bias is only 0.12 m/s (or ~15%) smaller than the EWP bias, but based on the current writing, I assumed they had a much larger difference in my first readthrough

* L14-15: Please clarify that you are refering to peak wind speed deficits (because average deficits differ between PyWake and RANS)

* L36: "Pay off" is somewhat ambiguous

* L40-41: Fishereit et al. (2021a) detail a vast number of WRF WFP studies that simulate wakes inside of a farm (e.g. Shepherd et al. (2020) compares FIT and EWP). I am confused by the statement "only one study… evaluated intra-farm wakes with EWP". Perhaps a different definition from "intra-farm" is being used than as I understand it.

* L54-55: What is a "high-resolution" wake model?

* L70: While I understand why the power and trust curves are important, why is the rotor speed curve important?

* L104,L106: Missing parenthesis

* L117: Is there a particular reason to aim for 7% TI at hub height?

* L121: What is meant by "in full balance"? Time-varying WRF models show blockage effects, so steady-state is not required to study this effect

* L191: What is a Gaussian average?

* Sec 3.1: This doesn't necessarily have to be mentioned in the manuscript, but I believe a month-long WRF WFP comparison to met mast data is the longest WFP-to-mast comparison to date 

* Figure 8: I am having a tough time understanding the vertical red and yellow bars. Do these mark the 9 and 17 timesteps with data? 

* L237: In WRF WFP validation studies, we always struggle with two interrelated questions. (1) How accurately does WRF simulate the background flow? (2) How accurately does WRF model wake effects? Because the observational mast is very close to a turbine, it is technically not possible to truly calculate WRF's bias in background flow. However, it is possible to characterize bias during "strong waking" (as is done in L247) and "weak waking". Please quantify bias under these two conditions, defined as you see fit.

* L242: Please quantify "medium high wind speeds"

* Figure 10: The small map showing the location of the transect is an excellent feature

* L267: In contrast to what?

* Sec 3.2.2: "Average deficits" are often discussed, but they are not quantified anywhere. Please share these values throughout this subsection.

* Figure 11: It is difficult to distinguish between the models within each of the spikes. For example, I believe there should be orange spikes in panel b, but I do not see any. Consider shifting some of the models downward on the y-axis by 50D and note that this shift is being done to help improve legibility

* L304: Please roughly quantify "close to turbines"

* Figure 14: Thank you for showing both absolute and relative differences

* L329: Consider reminding the reader that these reductions are focused on only ~10 m/s winds for this inflow direction

* Figure 15: Please add an arrow that points to the left to remind readers that the wind goes in that direction

* L410: Please quantify the relative performance of FIT and the EWP

* L416: Please clarify "perform well relative to ?"

* L418-424: This paragraph reads very well

* L438: Thank you for urging some caution due to the small amount of data

* L449: It was nice to see that the three main aims from the intro were returned to. I will say, the 3rd aim (computational cost) feels like it is not discussed much throughout the paper. I would consider stating that this paper has two main aims. 

* L475: Thank you for citing the Python packages!

Overall comment: This work compares and validates the intra-farm and farm-to-farm wakes across different mesoscale and wake models. I think the topic is certainly interesting and relevant to the wind energy community. In particular, I enjoy reading the part where the authors applied a filtering method to compare WRF with the engineering model. However, more details about that approach are needed. Overall, I think the paper can be accepted for publication after addressing the following comments.

• Line 8: It is weird to abbreviate Wind Farm Parametrization to FIT. Maybe rewrite to as Fitch scheme. That would make more sense.

• Lines 199-200; Lines 221-222, can the authors provide more detailed (or an example) about the “averaged weighted according to the WRF-NWF wind speed at the WRF inflow grid point” using information from Table 6? I am not sure what it really means. In addition, I thought only the WRF and SCADA data are subjected to filtering, does the simulations from wake models also undergo some kind of filtering as well based on the quoted sentence.

• Wind direction filtered (Table 6; lines 214-215): Since Rodsand II mast is largely influenced by the wake, I am not sure how valid is f2 for wind direction filter. Is there any coincide periods between f1 and f2?

• Lines 237-242: I think it stresses the value of the added TKE in the WFP. Authors could consider adding that in their discussion.

Reference:     

Xia, G., Zhou, L., Minder, J.R. et al. Simulating impacts of real-world wind farms on land surface temperature using the WRF model: physical mechanisms. Clim Dyn 53, 1723–1739 (2019). https://doi.org/10.1007/s00382-019-04725-0

Tomaszewski, J. M. and Lundquist, J. K.: Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1, Geosci. Model Dev., 13, 2645–2662, https://doi.org/10.5194/gmd-13-2645-2020, 2020.

• Figure 9: There should be more spacing between the top and bottom panels to avoid confusion.

• Line 256: what about the results from RANS-ASL?

• Missing comma (eg., Line 299: In addition, the deficits are also narrower in south-north direction)

The paper uses SCADA data from an offshore wind farm as well as measurements from a co-located met mast to validate different wake models in the situation where the wind farm is affected by wakes from an upstream neighbour wind farm. Both the intra-farm and farm-to-farm wakes are modelled using three different types of models. (1) A mesoscale model that includes details of the regional flow with two different parameterizations of the wind farms. (2) A RANS model that describes the turbine interactions in greater detail that the mesoscale model, but in contrast relies on more idealised inflow conditions. (3) Three engineering wake models. The models are compared to data and to each other. The authors conclude that one of the mesoscale wind farm parameterizations (Fitch) and the RANS simulations agree reasonably well with the data, while the other mesoscale parameterization (EWP) and the three engineering models underestimate the farm-to-farm wake loss.

The topic is of great interest considering the plans for an extensive buildout of offshore wind capacity around the world. The paper is easy to follow, and the conclusions are sound given the available data. The analysis does only cover a limited period of one month. I assume that limited computational resources for the mesoscale simulations were the driving factor for this limitation, but the authors should make this clear, since the analysis would be much stronger if a longer period had been analysed. The criteria for selecting the specific month were high data availability for both met mast and SCADA data. Unfortunately, this specific month saw only a limited number of flow cases consistent with the farm-to-farm interactions that form the core of the study. In my view, the met mast data are not as interesting as the SCADA data, so it is possible that a better month could have been chosen based on high SCADA data availability combined with a higher frequency of useable flow cases. It may even have been better to not run the models on a specific calendar month but rather for specific days or weeks to maximize the number of useable flow cases. Nonetheless, the paper is still a solid and important contribution and I enjoyed reading it.

Minor comments:

Figure 1- consider making this figure larger for better readability

Line 41 – consider including a reference to https://zenodo.org/record/3637944/files/1.7_Poulsen%20Validation%20of%20wind%20farm%20parametrisation%20in%20WRF%20using%20wind%20farm%20data%20%28Thesis%29.pdf

Line 43: split “with a”

Line 75: I don’t understand the remark about the effect of wake rotation on the velocity deficit. Consider if it is necessary or if additional explanation can be added.

Line 117 – Please discuss why a hub height TI of 7% was chosen. Is this based on on-site measurements from the met mast. Does the TI depend a lot on wind direction for this site given the proximity to land in some directions?

Line 145 – why was the wake decay constant in the Jensen model chosen as k=0.1? This is a very large value given the typical recommended range of 0.03-0.05 for offshore wind farms. Please add a discussion in the text

Line 149 – the BAS wake expansion constant is given as k=0.0324555. This may be following the original paper, but it seems like more digits than can be justified. How many decimal places can be seen in the results?

Line 158 – please comment on the grids used in the PyWake engineering model calculations. Are they only used for visualization purposes, or are they an integral part of the calculations of wake effects?

Page 10 – please indicate the order of magnitude of computation time for all the simulations. This is relevant for a reader interested in performing similar calculations.

Line 191 – why is the width of the Gaussian wind direction average 5 degrees? Where does this number come from? A reference to Gaumond et al, Wind Energy would be good to add as well.

Figure 7 – The Rødsand II wind farm is abbreviated RS2 in this figure, while in table 5 it is referred to as RØ. It would be better to use a consistent abbreviation.

Figure 8 – I can only really make out the black, orange and blue lines (sonic, NWT_in and FIT_e).  Consider reducing the number of lines. I cannot know which of the other lines the curves I cannot see are hiding behind.

Figure 8 – The text states that the WRF simulations generally agree with the sonic measurements, with one exception being the storm on 28 October. I would say that there are a few examples where the deviation between WRF and measurements is even larger. I think in general the agreement is good, but you should maybe rephrase this so it doesn’t sound like the storm is the only instance of disagreement between simulations and measurements.

Line 239 – Referring to Figure 9 the text states that Fitch clearly performs best for simulating the wind speed deficit. But Figure 9 does not show the wind speed deficit, only the wind speed. In addition, only a few points in Figure 9 are affected by wakes from Nysted. To support the statement, it would be more relevant with a scatter plot of wind speeds showing only the directions where Rødsand II is in the wake from Nysted.

 Line 243 – The estimation of the wind farm wake effect for October 2013 by subtracting the NWF wind speed bias from the FIT or EWP wind speed bias neglects the global blockage effect on the met mast. The mast is quite close to the wind farm, so for wind directions where it is upstream of the wind farm it likely experiences a wind speed reduction which will be included in the estimated farm wake loss.

Line 246 – I think Figure 7 is a better reference than Figure 8.

Line 249 – “based on 127 10-minute values”. Table 6 says there are 19 periods with wind direction between 80 and 100 degrees and wind speeds between 9 and 12 m/s. Are the 127 m/s 10-minute values including wind speeds outside the 9-12 m/s range?

Figures 10-12 – These are hard to read, the features are quite small. Consider making them larger for example by re-arranging the subplots into a 3x2 array instead of the 2x3 used, and by making the full page width.

Figure 10 – Consider making the SCADA (f1) and SCADA (f2) dots different colours to better tell them apart.

Line 267 – I find it difficult to see from Figure 10 that SCADA (f2) agrees better with RANS-ABL than SCADA (f1). Consider making a separate plot showing only the RANS predictions at the turbine positions together with the SCADA data to show this or quantify the deviation.

Line 269 – “indicate” is there twice.

Line 270 – Lolland is not identified on the map.

Figure 14 – what data are used for the histograms? Do they include all wind directions and wind speeds?

Figure 15 – Include a description of the vertical dashed lines in the figure caption. Also, the bottom x-axis gives distances relative to some unknown point. Either specify in the caption what zero corresponds to or make the axis relative to the same origin as the top x-axis.

Line 363 – Are you trying to say that even far west of Rødsand II the NYRØ and RØ scenarios differ in wind speed, meaning the effect of Nysted on the flow can be still be seen at the western edge of the plot? Consider rephrasing the sentence.

Line 375 – Are you trying to say that wind resources are affected by wind farms more than 25 km away according to the RANS and WRF simulations? Consider rephrasing the sentence.

Line 378 – Instead of point measurements I suggest snapshots.

Line 436 – constraint -> constrained.

Line 453 – consider adding a reference to van der Laan, P., Peña, A., Volker, P., Hansen, K. S., Sørensen, N. N., Ott, S., & Hasager, C. B. (2017). Challenges in simulating coastal effects on an offshore wind farm: Paper. Journal of Physics: Conference Series, 854, [012046].