the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Mar 2026
An in-depth observational and modeling analysis to explore long-range offshore wakes under different stability regimes
Abstract. As wind energy areas continue to be built out worldwide, it is increasingly important to understand the implications of long-range wind farm wakes on wind energy generation. While gross capacity factors can be estimated, the impacts of upstream wind farm wakes on downstream energy production, especially under stable conditions, are largely unknown. A clear understanding of marine atmospheric boundary layer (MABL) stability in offshore regions is still evolving, as continuous high-resolution thermodynamic profiles in the MABL are uncommon. Given the relationship between stability and long-range (>50 km) offshore wakes, it is increasingly important to reliably estimate stability conditions in offshore regions. With the lack of consistent observations in and around offshore wind farms, it is necessary to rely on mesoscale models such as the Weather Research and Forecasting (WRF) modeling system to estimate stability and wake lengths. For this work we test WRF's ability to reproduce wake effects and potential losses using flight data over wind farms in North Sea to evaluate its performance. As thermal stability is critical to understanding wake length, different metrics are evaluated to determine the best way to parameterize atmospheric stability from the WRF model. Results show that the bulk Richardson number derived from WRF can be used as a reliable metric to classify stability and that wake lengths are well represented under stable conditions.
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Status: open (until 04 May 2026)
- CC1: 'Comment on wes-2026-55', Stefan Emeis, 19 Mar 2026 reply
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RC1: 'Comment on wes-2026-55', Anonymous Referee #1, 17 Apr 2026
reply
Revised Manuscript Assessment (wes-2026-55)
The manuscript entitled “An in-depth observational and modeling analysis to explore long-range offshore wakes under different stability regimes” presents a comparative study between aircraft-based measurement data—collected during the WIPAFF project—and WRF simulations for the corresponding period. The manuscript is well-structured and systematically addresses the research questions posed at the outset.
However, while the logical flow is sound, the manuscript would benefit significantly from a more robust discussion of the findings. Specifically, there is a lack of critical interpretation regarding the comparison of wake characteristics between the empirical measurements and the numerical simulations.
Recommendations:
- Model Reproducibility: While the WRF model setup is introduced in Section 3.1, a more comprehensive description of the configuration (e.g., specific parameterizations, domain nesting, and boundary conditions) is required to ensure that the results are reproducible by the broader research community.
- Data Methodology: The acquisition and post-processing of the airborne measurement data are currently described with limited detail. It remains unclear, for instance, what filtering criteria were applied to the raw data or how potential outliers were handled.
- Scientific Contribution: The inclusion of a dedicated Discussion section is highly recommended. This would allow the authors to contextualize their findings, highlight the most significant results, and explicitly state the contribution of this analysis to the fields of wind energy and mesoscale modeling.
Detailed Comments
Line 22: Please clarify the term "turbines facilities". It is recommended to use standard industry terminology, such as "wind energy converters" or "wind turbines."
Line 43: The current sentence structure, particularly the dependent clause in German, is difficult to follow. Please restructure this for improved readability.
Line 46: The authors should acknowledge the role of Lidar measurements in this context. Several studies have utilized such data; for example, Cañadillas et al. (2022) investigated offshore wind farm cluster wakes using long-range scanning wind Lidar and mesoscale modeling.
Line 69: Consider referencing Berge et al. (2024) regarding their comparison with X-Wakes measurement flights not WIPAFF.
Figure 1a: Please provide a justification for the selection of this specific vertical profile over others. There appear to be significant spatial and temporal discrepancies between the available profiles that should be addressed.
Line 219: The authors state that wakes from all 41 flights were compared; however, 16 of these are not wake measurements (line 94). Please clarify the methodology used to include these non-wake cases in the comparison.
Line 226: Siedersleben et al. (2018) already compared WIPAFF data to the WRF model for a single flight rather than the entire ensemble. So this sentence should be rephrased to accurately reflect the scope of that study.
Table 4: * Regarding the phase error: Were comparisons conducted only at full-hour increments, or did the error always result in full hours? Would analysis at smaller temporal increments yield more precise results?
- How were flights with mean altitudes significantly above hub height (e.g., 180 m + ) analyzed for wakes and subsequent time shifts? Please elaborate on this process.
Line 325: Please provide the relevant citations for the studies mentioned here.
Line 337: Have other vertical aircraft profiles been examined? The spatio-temporal bias noted may vary significantly depending on the time of day and the geographical position of the measurement.
Line 363: It is suggested to look into the X-Wakes dataset 8 https://doi.pangaea.de/10.1594/PANGAEA.955382), as it may contain extreme stability cases that could strengthen the analysis.
Figure 10: Please provide a potential physical explanation for the three outliers in subfigures (a) and (b), where the measured Capped Inversion Height (CIH) is more than triple the WRF-simulated CIH. Were there specific meteorological conditions during these flights that caused this divergence?
Line 390: Typo correction: "can be used."
Line 418: Grammar correction: "...profiles show that..." (or "...profile shows that...").
Figure 13: The text and labels in this figure are currently too small. Please increase the font size to ensure legibility.
Line 460: What is the hypothesized reason for the shift in the theta_v gradient observed around 2012/2013? Furthermore, do the subsequent years show an ongoing trend?
Line 489: The discussion comparing the measured and modeled (WRF) wakes requires significant expansion. While these results appear in Table 4, they are underrepresented in the text. Given that this is a core finding, more emphasis should be placed on interpreting the model's performance in both the near- and far-wake regions.
Line 503: Please justify the selection of 2012 for this analysis instead of the years during which the WIPAFF flights were conducted.
Citation: https://doi.org/10.5194/wes-2026-55-RC1 -
RC2: 'Comment on wes-2026-55', Anonymous Referee #2, 22 Apr 2026
reply
Review of manuscript wes-2026-55
This manuscript investigates offshore wind farms wakes under different atmospheric stability conditions. The authors compare in situ measurements with model results from WRF.
Overall, the manuscript is clear and well-written. However, there are a few things that could improve the work. Specifically, the figures should be made clearer and the font must be made readable.
Specific comments
Specific humidity, as shown in Figures 6 and 11, is quite different for observations and model results. Please explain/discuss this further.
The results in part II seems a bit apart from the rest of the manuscript. Consider if it would make more sense to put it into an appendix.
Point 3 in Conclusions seems to introduce results not presented elsewhere. Please move this to an appropriate place in the manuscript (including Fig. 17).
Technical corrections
Line 22: Is “un-waked” an established term?
Line 53: “Phase error” is used frequently in the manuscript. Perhaps it would be clearer to specify, at least the first time, that it refers to a time shift?
Line 68: I guess “wake loses” should be “wake losses”?
Figure 2: It is very difficult to find the blue star of FINO3 against the background of black dots.
Line 155-156: U and V are referred to as gradients. Should it be ΔU and ΔV?
Figure 3: Please check if you can choose another color for “hour offset 0” as it is very difficult to separate from “hour offset 1” in a printed document.
Figure 3: Please add what the grey lines represent (like you do in Fig. 6)
Figures 4 and 5: Font for coordinates is too small to read.
Line 272: Here you write “time synching problem”. Is that the same as the “phase error”?
Figure 7: It is difficult to distinguish the “Stability measurement location” from the black dots of the wind turbines.
Line 376: Please remove extra “that”.
Line 418: “hows” -> “shows”
Figure 13: Font is too small to be readable.
Citation: https://doi.org/10.5194/wes-2026-55-RC2
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This is a very interesting paper dealing with an important issue: the wakes of larger offshore wind farms. This note is not to be considered as critics but just serves in order to give some hints to additional studies addressing the same subject.
First of all, atmospheric stability had been neglected in offshore wind energy projects for a long time. See Emeis (2010) https://onlinelibrary.wiley.com/doi/abs/10.1002/we.367?trk=public_post_comment-text for a first study wihich analyses the principle impact of atmospheric stability on wind farms wakes by a simple analytical model.
Atmospheric stability information could be displayed by so-called stability wind roses. Fig. 6 in Emeis et al. (2016) https://iopscience.iop.org/article/10.1088/1742-6596/753/9/092014/pdf gives an example how to do this. The stunning result is that stability and wind direction are strongly correlated for offshore areas of the midlatitudes. In the North Sea stable conditions are linked to southwesterly winds.
There had been earlier studies on how to reproduce WIPAFF data by WRF simulations. Maybe a look at Siedersleben et al. https://www.schweizerbart.de/papers/metz/detail/prepub/89817/Evaluation_of_a_Wind_Farm_Parametrization_for_Meso?l=DE may be helpful.
Anyhow, this paper deserves publication finally.