the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jul 2025
The impact of low-level jets on the power generated by offshore wind turbines
Abstract. Low-level jets (LLJ) are local maxima in the vertical wind speed profile. They are frequently observed at heights of approximately 50 m to 500 m above sea level in offshore regions. The influence of low-level jets on the power production and loads of wind turbines has not been researched thoroughly. In this paper we investigate the influence of low-level jets on wind turbine performance in an offshore wind farm. We derive vertical wind profiles up to heights of 350 m from lidar plan position indicator scans with different elevation angles at the wind farm Nordergründe in the German Bight, located approximately 15 km from the coast. We detect LLJs with a frequency of occurrence between 2.4 % to 22.6 %, based on different definitions used in literature at the observed location. We analyse their influence on the power production of the turbines using operational wind farm data. We observe a negative influence on power production and increased power fluctuations in low-level jet situations compared to situations with equal wind-veer-corrected rotor equivalent wind speed (REWS) but without LLJs. Further, we conduct aeroelastic simulations for a set of wind profiles with varying veer, shear, turbulence intensity and shape of the LLJ core. Increasing veer and shear both have a negative impact on the simulated power production, while the shape of a low-level jet only slightly alters the energy conversion process at the wind turbine for the same REWS. Thus, we conclude the main driver for the efficiency-lowering effect during the presence of low-level jets to be the combination of positive and negative shear, causing a high absolute shear across the rotor area as well as increased absolute veer.
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EC1: 'Comment on wes-2025-118', Etienne Cheynet, 14 Jul 2025
Dear authors,
Thank you for the submission of the article. Please find below some feedback that may help strengthen the manuscript. Hopefully, you will find some of them useful
- The literature review appears to be incomplete or at least selective. The work of Gutierrez et al. [1], Murphy et al. [2], and others on low-level jets (LLJs) is noteworthy and may be directly relevant to the present study. The manuscript could better establish its novelty by more clearly identifying the knowledge gap relative to prior research. The current self-citation rate is approximately 20%, which further suggests that the literature review would benefit from broader coverage.
- The use of lidar PPI scanning for wind profiling is indeed an interesting component of the study. However, this technique has been applied in previous research in both wind engineering and meteorology. As such, it may not be considered fundamentally novel, contrary to what is suggested in the cover letter. See, for instance, references [3] and [4] for both recent and earlier applications of this scanning mode. Notably, Visich and Conan [4] also used PPI scanning to detect LLJs.
- The study reports large wind veer across the rotor, with Δθ ranging from 0 to 40 degrees. It may be worth clarifying whether values of ∣Δθ∣>20∘ are realistic under typical atmospheric conditions. For example, wind veer is rarely above 0.1°/m, even in stable conditions. For a turbine with a rotor diameter of 126 m, this would suggest a typical Δθ<15∘. If previous studies have reported larger directional shear in the atmospheric boundary layer, it would be helpful to cite them in support of the current findings.
- The reported frequency of LLJ occurrence under convective conditions appears higher than that found in previous studies, such as Wagner et al. [5]. An interpretation of these results in light of existing literature could help contextualise the findings.
References
[1] Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., & Castillo, L. (2017). Impacts of the low-level jet's negative wind shear on the wind turbine. Wind Energy Science, 2(2), 533–545.
[2] Murphy, P., Lundquist, J. K., & Fleming, P. (2019). How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine. Wind Energy Science Discussions, 2019, 1–46.
[3] Goit, J. P., Yamaguchi, A., & Ishihara, T. (2020). Measurement and prediction of wind fields at an offshore site by scanning Doppler LiDAR and WRF. Atmosphere, 11(5), 442.
[4] Visich, A., & Conan, B. (2025). Measurement and analysis of high altitude wind profiles over the sea in a coastal zone using a scanning Doppler LiDAR: Application to offshore wind energy. Ocean Engineering, 325, 120749.
[5] Wagner, D., Steinfeld, G., Witha, B., Wurps, H., & Reuder, J. (2019). Low level jets over the southern North Sea. Meteorologische Zeitschrift, 28(5), 389–415. https://doi.org/10.1127/metz/2019/0948Citation: https://doi.org/10.5194/wes-2025-118-EC1 -
RC1: 'Comment on wes-2025-118', Anonymous Referee #1, 31 Jul 2025
This manuscript presents a very comprehensive study of the impact of low-level jets (LLJs) on offshore wind turbine performance, effectively integrating lidar data, SCADA, and aeroelastic simulations with OpenFAST. The paper's key methodological strength is its use of a veer-corrected rotor equivalent wind speed (REWS) to compare LLJ and non-LLJ cases. This approach allows for a direct assessment of energy conversion efficiency by comparing scenarios with an equivalent energy flux, rather than relying on a simple hub-height wind speed.
The primary finding—that LLJ profiles tend to reduce power production and increase power fluctuations for the same REWS due to wind shear and veer—is an important contribution. This result, while in line with the established understanding that wind variability across the rotor reduces conversion efficiency, provides valuable new evidence from a real-world offshore environment. The work is well-executed and addresses a topic of significant relevance to the wind energy industry.
However, I have a few concerns related to the framing of the study's conclusions, which risks misinterpretation, and the justification for key methodological choices, particularly the LLJ detection algorithm.
Major Comments and Concerns
(a) My primary concern is that the wording used throughout the manuscript (including the title, abstract, and discussion) creates a strong impression that LLJs lead to a general decrease in turbine power (e.g., Title; Line 416; Line 441), not just a decrease in conversion efficiency. This framing undermines the well-known physical mechanisms of LLJs (e.g., inertial oscillation), which accelerate wind to super-geostrophic speeds, thereby increasing the total available wind energy and potentially increasing absolute power output compared to non-LLJ conditions. The depiction in Figure 2, while illustrative of profile shape, could also enhance this potentially false impression.
To rectify this, the authors should reframe the manuscript to focus explicitly on the impact of LLJs on the turbine's energy conversion efficiency. This is the true finding of the study. Consequently, the title should be revised to reflect this focus (e.g., "...on the power conversion efficiency of offshore wind turbines"). To test the hypothesis of overall power impact, the authors could perform a direct comparison of absolute power from SCADA data during LLJ episodes versus non-LLJ episodes on the same days (thus excluding non-LLJ extreme events, e.g. cyclones). Without this analysis, claims about overall power reduction are unsubstantiated.
(b) The study's conclusions are heavily dependent on the choice of the shear-based LLJ definition from Hallgren et al. (2023), which yields an occurrence frequency (22.6%) that is nearly an order of magnitude larger than other methods (e.g., Wagner, Kalverla), which are in closer agreement with one another (Table 5). The authors must provide a more robust justification for using this "odd one out" definition. The author should clarify how the shear is computed. Is it the maximum shear above and below the jet core? Given the sensitivity of shear computation to the variations and noise, which are known to be present in measurements, how are these factors treated?
Minor Comments and Suggestions
1. Line 172 (and Introduction): The concept of REWS is fundamental to this paper's methodology and novelty, yet it is not formally introduced until Section 2.4. REWS and its motivation should be introduced much earlier, in the Introduction (Section 1), to properly frame the study for the reader.
2. Line 170: To highlight its significance, consider changing the section title to better reflect the use of REWS, for example: "Performance Analysis Using an Equal Rotor Equivalent Wind Speed Framework."
3. Line 133: How is the wind direction estimated from the VAD algorithm? This assumption of a spatially homogeneous wind direction is critical, as the lidar scans cover a range of nearly 10 km. A brief discussion of the validity and potential uncertainty of this assumption is needed.
4. Lines 149-152: The vertical profile is derived from multiple low-angle scans. It is unclear where the resulting vertical profile is horizontally located relative to the turbine. An illustration depicting the scan geometry and the effective location of the final wind profile would be extremely helpful for clarity.
5. Figure 2: As mentioned in the major comments, this figure risks creating a false impression. This is a perfect opportunity to visually demonstrate the paper's core concept: including a non-LLJ (e.g., logarithmic) profile that has the exact same REWS. This would clarify that the study is about the shape of the profile, not the absolute magnitude of the wind.
6. Figure 13: This figure is insightful but could be improved. Is it possible to add a color code or different markers to the data points that relates back to the specific profile characteristics shown in Figure 5? This would help the reader understand the source of the scatter in the LLJ results.
7. Section 2.5: The simulations confirm the general trend of reduced performance. However, as seen in Figure 13, there appear to be a few intriguing cases where the turbine performance for LLJ profiles is better than for the logarithmic or even uniform profiles with the same REWS. A deeper analysis and discussion of these specific cases could reveal valuable insights into turbine energy conversion under complex inflow. It also raises the question: could this be a limitation or artifact of the Blade Element Momentum (BEM) theory used in OpenFAST under such extreme shear?
Citation: https://doi.org/10.5194/wes-2025-118-RC1 -
RC2: 'Comment on wes-2025-118', Anonymous Referee #2, 11 Aug 2025
General comments:
The article is a good and valuable piece of work, I do not have major issues to point out. A number of medium and minor ones are listed below. I would recommend that the authors perform a consistency check on the article as a whole, as it is in some places noticeable that different parts of it were written by different people (which is completely fine, but having consistent style, especially when it comes to figures, would benefit the overall impression; but that’s just a suggestion). The methods section could be expanded a bit more, keeping in mind that not every reader may be familiar with the specific technology used in the study. The authors might also find the following article (and its reference list) of some interest, as it presents a somewhat similar study but was not referenced in the manuscript: https://doi.org/10.1016/j.oceaneng.2025.120749
Questions and remarks:
- Lines 29-30: what is the difference between thermal-driven winds and thermal wind? Also, a brief explanation of reverse shear flow could be nice.
- Line 107: it might be worth it to mention if the turbines are fixed or floating, as it might affect the measurements.
- Line 108: it can be deduced from the context that Vaisala Windcube 400S is a lidar but mentioning it explicitly can improve clarity.
- A sketch showing the turbine, its transitional piece, the location of the lidar, the size of the rotor, the sea level and the direction of the lidar scans would be very illustrative.
- Figure 1: the map would benefit from a frame in subfigure (a) that corresponds to the zoom-in in subfigure (b).
- Table 1: please specify what is meant by “PPI opening angle” and “range gates”. Better still, elaborate a bit more on how the scans are performed, as it might be not obvious for the readers that do not have much experience with lidars.
- Table 1: in “elevation angles”, what does “-0.2” stand for?
- Line 124: please explain briefly what SCADA data contains.
- Lines 142-144: the first and the last sentence in these lines seem to be contradicting each other, unless I misunderstood something. Please consider revising the paragraph for clarity.
- Line 155: is there any justification for using a 30 m window?
- Lines 186-187: isn’t “we applied a filter neglecting…” sentence repeating what was already described in lines 179-181?
- Line 245: by “four dimensional wind fields”, do you mean a 3D wind field that varies with time?
- Line 250-251: are the subroutines named in accordance with the jobs they do? I.e., ElastoDyn is used for structural dynamics, SubDyn for sub-structural dynamics and so on. A clear indication could be valuable for a reader who wants to perform similar simulations.
- Line 263: can you elaborate why exactly there are fewer measurement points at higher altitudes? An illustration would be helpful.
- Line 266 and in general: is there any correction for LLJ duration in time? For example, if three 10-minute profiles in a row feature an LLJ, then two don’t, and then 30 more do, will the two outliers be counted or not? As I understand it, LLJs as a meteorological phenomenon have to be prolonged in time and space and are not just a feature of individual averaged profiles. Profile-based detection, as you mentioned earlier, can be a good tool but is prone to errors.
- Figure 6 (a): the coastlines are helpful but consider shading or hatching land vs sea for a fuller picture. Also, maybe a bit of zoom-out would aid comprehension, because based on the current picture the reasoning for the authors’ choice of land and sea sectors is not clear. Especially on the top-left it would seem that the land-sea sector border should be placed further clockwise. It is understandable, of course, that such a complex coastline makes the land-sea direction definition rather complicated.
- Figure 6 (b): consider marking the same land & sea sectors as in (a) on the wind rose plot.
- Line 293: please provide the formula used for obtaining the Obukhov’s length from the temperatures, unless it’s a very complicated one (then the reference is sufficient).
- Figure 10: adding an edge line of the same shade but darker to each of the two histograms might improve the graphs’ readability (just a suggestion, feel free to ignore)
- Figure 11: consider adding a cross-reference to the definition of Veq in the figure’s caption.
- Line 334: a box plot doesn’t fully reflect the distribution of a quantity, only some metrics of it
- Figure 13: I am not sure I understood: does every marker correspond to one simulation?
- Line 375: the statement of a good agreement would be stronger with some quantification of it
- Lines 409-410: the sentence starting with “Our results…” is a bit confusing. Isn’t LLJ a feature of the whole profile? The authors probably meant that the cores of LLJs tend to be located at higher altitudes.
- Line 428: it is not quite clear if “the authors” refers to Zhang et al. or the authors of the present paper.
- Figure A4: making the axes square and starting at 0 would be more visually appealing. Also, especially for (a), consider making the markers more transparent, like in Figure A5: this would help the reader to judge the density of the cloud.
- Eqs B5: in the second equation, shouldn’t the right-hand term include (vi/rho0) as a multiplier?
Small issues (spelling, punctuation, phrasing, etc.):
- Line 19: “… low-level jets (LLJ), are are typically…” – extra “are”
- Line 24: “Meteorological situations with LLJs” – unclear phrasing; simply LLJs?
- Lines 33-36: The sentence starting with “Onshore, …” is very long and difficult to understand, consider splitting it into several smaller ones for clarity. Also, detections range between 20% and what?
- Lines 172-173: both “we use” and “is used” appear in the same sentence.
- Lines 219-220: “while following… they are not fully compatible…” seems to be grammatically awkward.
- Line 225: missed comma between “shear” and “Obukhov length”
- Line 238: “an unstable atmospheric stability” sounds weird, maybe “unstable atmospheric conditions” could be a better fit?
- Line 245: hyphen missing in “four dimensional”
- Lines 260-262: the sentence starting with “After applying…” is long and hard to understand, consider breaking it into smaller ones. Also, are the wakes included or excluded? It says “including” but it’s not logical from the context.
- Line 265: “across” is not needed
- Line 290: “similar for both, land and sea…” – comma not needed
- Figure 7 (caption): aren’t the lines dashed and not dotted?
- Line 366: the reference was probably supposed to point towards Figure 15b and not Figure 15a
- Lines 386 and 387, also 420: commas are not needed before “that”
- Line 433: comma is not needed between “found” and “decreased”
- Lines 449-450: the part “on scanning lidar… aeroelastic simulations” is hard to read because of “and… and…”. Consider using comma instead.
- Line 451: “proving to be” -> “proving them to be”
- Line 457: comma is needed between “temperature” and “which”
- Line 459: convert the energy to what? Else, maybe a word like “harvest” or similar would be a better fit.
- Lines 461-463: the “while” clause is very long and hard to read, consider rephrasing the sentence for clarity.
- Line 468: comma is not needed between “although” and “a clear”.
- Line 512: “Figures” -> “Figure”
- Line 513: “All two” -> “Both”
- Figure A5: (just a style remark) choosing the same intervals on axis labels could be more visually appealing
- Line 519: comma missing after “devices”
- Line 521: comma missing after “rho(z)”
- Eqs B5: in the first equation, the index “i” might be missing before “corr”
Citation: https://doi.org/10.5194/wes-2025-118-RC2
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