the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jun 2026
On the effects of wind shear and veer on the power of a downstream turbine
Abstract. Understanding flow behavior within wind farms remains one of the central challenges in wind energy research, where wakes play a prominent role by coupling individual machines together. In fact, turbines are frequently operated under waked conditions, with wake effects on power production further modulated by atmospheric stability at the site. This paper investigates the influence of wind shear and veer on the power output of a waked wind turbine. The analysis is based on field measurements from two aligned wind turbines operating under inflow conditions that are often strongly sheared and veered. The results demonstrate that both shear and veer significantly affect wake characteristics and trajectory. Because isolating their individual contributions from field data alone is challenging, dedicated computational fluid dynamics (CFD) simulations were performed, confirming the experimental observations and enabling the effects of shear and veer to be disentangled.
The performance of several wake models was evaluated against experimental data, showing that prediction accuracy can be improved by explicitly accounting for shear and veer effects. This improved accuracy could be leveraged to support various applications. To explore the potential benefits in one exemplary use case, we consider shear- and veer-enhanced models in the context of wake-steering wind farm control. This application, however, requires real-time estimates of wind gradients, which are not available from standard onboard anemometry. To overcome this limitation, a wind sensing technique based on blade load measurements is employed to estimate shear and veer during operation. Furthermore, the strong correlation between these two quantities observed at the test site is exploited to simplify practical implementation. Wake-steering simulations indicate that incorporating shear and veer into the control strategy can lead to improved power capture.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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- CC1: 'Comment on wes-2026-93', J. Gordon Leishman, 13 Jul 2026 reply
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CC2: 'Comment on wes-2026-93', J. Gordon Leishman, 13 Jul 2026
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Because the Editor-in-Chief is listed as a co-author and corresponding author, the public record would benefit from explicit clarification that he was fully recused from editorial handling, reviewer selection, discussion moderation, and decision-making. The identification of a handling editor is helpful, but it does not by itself clarify whether the Editor-in-Chief had no role in the editorial process for a manuscript on which he is a co-author.
Disclaimer: this community comment is written by an individual and does not necessarily reflect the opinion of their employer.Citation: https://doi.org/10.5194/wes-2026-93-CC2 -
RC1: 'Comment on wes-2026-93', Anonymous Referee #1, 31 Jul 2026
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Review of ‘On the effects of wind shear and veer on the power of a downstream turbine’
The article investigates the effect of wind shear and veer on the power of a waked wind turbine using a combination of field and numerical data. In addition, it explores data-driven improvements to existing analytical wake models to account for the effect of wind veer and shear and tests their efficacy in a wake control application. The article addresses an important topic and is of broader interest for the wind energy community.
There are, however, several concerns which need to be addressed before the article can be considered for publication.
Major Comments
- I find the introduction a bit lacking in documenting the previous literature. The authors spend a lot of time discussing what the objectives of this study are; however, there is already a great body of literature on the effects of shear and veer of wind turbines, which are not properly introduced/discussed. For instance, for the stated research question 1, the authors state some general observations in prior literature; however, they do not discuss the chronological development of the literature and the current state-of-the-art. More importantly, how does the work done in this study build on top of existing literature? This should be properly documented. Again, for research question 2, a large body of analytical models developed for shear, veer, or wake control situations are not discussed.
- There are several key limitations of the field data (as pointed out in the manuscript) that make the discussion in section 2 a little less convincing: first, the information on wind shear and veer is not available across the whole rotor, and secondly, the two turbines do not have the same hub height, leading to a vertical offset. Perhaps the authors could supplement the missing information from some reanalysis data (e.g., model-level data from ERA5)? This would give some degree of confidence in the assumption that the shear/veer estimated in the bottom half of the rotor is still valid in the upper half.
- In lines 143-145, the authors mention that partial and full wake scenarios are considered. How is the partial wake scenario defined? If I understand correctly, the subsequent results in section 2 concern both (full and partial) scenarios. If so, this makes the analysis a bit more complex, as it is no longer the effect of shear and veer; it is the effect of shear, veer, vertical turbine offset, and (potential) partial wake situations that can all influence the power ratio. I think the manuscript will benefit if the authors could separate full wake scenarios from the partial wake ones. Perhaps, this will also help explain the unusually large wake displacement noted in line 235?
- In section 2.3.1, what is the reasoning behind defining a wind veer range of ±4° for isolating the effect of shear? Why not have a smaller range? Since there still might be some veer effect, the claim that wake deflection in fig 4 reduces compared to fig3 and comes solely from wind shear needs further discussion. How is it established that the deflection in fig 4 is only due to shear and not due to residual veer effect? Similarly, the choice of wind shear range in section 2.3.2 should be justified/explained.
- Is the wake displacement method defined in lines 342-347 a standard in the wind energy community, e.g., for wake steering studies? If so, please also give reference to other studies that use a similar method. If not, why did you not use a standard method used in previous literature (e.g., wake center of mass/energy, etc.)
- In section 3.4.1, does equation 10 come from the original publication (Annoni et al., 2018) or is it introduced in the current work? Similarly, in section 3.4.2, equation 11 seems to be modified from the original work of Abkar et al. (2018). I think the authors should explicitly state how the equations have been adapted/modified from the original work(s).
- In section 4.1, the authors perform a ‘tuning’ of the model parameters. I think it would have been more beneficial if the authors used the default parameters for the original model inputs rather than tuning them on their own dataset, especially when they conclude that all original model parameters only slightly differ from default values. This will help validate the model beyond the site-specific parameter fitting.
- The authors use Gaussian-based analytical wake models (Bastankhah & Porté-Agel 2014, Abkar et al. 2018). These models conserve momentum in the far wake of a turbine and cannot be fully trusted in the near-wake region. This comes from the fact that the wake only becomes Gaussian self-similar in the far wake, an assumption fundamental to the models. In the current study, the two-turbine setup has a very short spacing (2.7D). Unless the inflow is characterized by highly turbulent, convective conditions, it is highly likely that the waked turbine is in the near wake of the upstream one, especially if the inflow is dominated by low-turbulence, stable flows (which is the case in the current study). It is therefore critical to identify the inflow turbulence and stability conditions. If indeed the waked turbine is in the near wake of the upstream one, how can we trust the model comparison? Ideally, the analysis/model comparison should have been done with the waked turbine clearly located in the far wake region. Without a clarification of this point, the results from the study are a bit speculative.
- In fig 13, the model difference with respect to the baseline model is presented. Why do the authors choose this metric? Why not compare with the measured data? The differences between different augmented models in fig 13 are very marginal. Also, if I understand correctly, ΔMAD closer to 0 would mean a better model performance? If so, it seems to me that the AV model performs the best and AVaugm the worst (especially at high shear). Please clarify this.
- The study identified three questions in the introduction:
- What is the impact of wind shear and veer on the power produced by a waked wind turbine?
The authors used a combination of field and LES data to answer this question. However, a lot of emphasis was put on analyzing wake deflection rather than quantifying the effect of shear and veer on the power of the waked turbine. For instance, it would have been beneficial to analyze if the drop in power of the waked turbine relative to the freestream one can be related to the level of shear and veer.
So, to me, the first question is not fully addressed in the manuscript.
- Can wind farm model predictions be improved by explicitly accounting for the effects of wind shear and veer?
The authors tried to address this; however, as in point 8 earlier, the whole analysis can be questioned due to the relatively small turbine spacing. The authors must provide a convincing argument to apply far-wake analytical models for a turbine setup as in the current study.
- How can shear and veer be estimated in the field?
The solution to estimate the shear effect is not novel (as pointed out by the authors). Also, no reason is given as to why they do not estimate both shear and veer using the ‘rotor as a sensor’ method, especially given that originally the method can be used to estimate both effects.
Minor Comments
- Lines 45-50: ‘The influence of … ellipsoidal shapes.’ The authors start by mentioning the effect of atmospheric stability on wakes, however, what they describe is primarily the effect of Coriolis force, and not atmospheric stability. Please clarify this.
- Some of the results from the study are already presented/stated in the introduction section (e.g., lines 63-66). I think the introduction should not state/discuss results from the study. These should be presented/discussed together with supporting data in the results section.
- Line 383: An elevation map of the surrounding terrain will benefit the claim that terrain-induced effects may have an influence on the differences between field and numerical results.
Citation: https://doi.org/10.5194/wes-2026-93-RC1
Data sets
Figures for the paper: On the effects of wind shear and veer on the power of a downstream turbine C. R. Sucameli, M. Bertelè, R. Braunbehrens, F. Campagnolo, S. Tamaro, P. Hulsman, and C. L. Bottasso https://doi.org/10.5281/zenodo.19484475
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The manuscript addresses a relevant topic, but the central conclusions are not supported by the analysis presented. The study is described as a head-to-head comparison of UAV and LiDAR deployments, yet no side-by-side UAV and LiDAR measurements are used. Instead, the scenarios are constructed from one LiDAR dataset combined with literature-derived uncertainty values and assumed reductions in flow-model uncertainty.
These assumptions largely determine the outcome. Flow-model uncertainty is prescribed as 8\% for LiDAR_1, 5\% for LiDAR_2 and UAV_2, and zero for UAV_4. The resulting progression in P90, NPV, and LCOE therefore follows directly from the imposed scenario structure. In particular, assigning no flow-model uncertainty to UAV_4 produces an AEP standard deviation of only 0.02\%, which is not credible for a wind-resource assessment. The model excludes long-term correction, interannual variability, wake uncertainty, power-curve uncertainty, persistent calibration bias, and correlated spatial and temporal errors. Treating measurement errors as zero-mean perturbations of 10-minute data also allows much of the uncertainty to cancel over a full year.
The increase in P50 across the scenarios is also unexplained. Reducing zero-mean uncertainty should primarily narrow the distribution, not systematically raise its median. This suggests that the reported gains may be produced by the numerical formulation rather than by a physical difference between the measurement strategies.
The financial analysis introduces further problems. Equation 2 is described as an equity NPV calculation, but it discounts after-tax EBIT using WACC and subtracts total CAPEX. The debt-sizing method, debt-service schedule, and derivation of the reported debt shares are not shown. The use of P90 rather than expected production in the LCOE denominator also makes reduced uncertainty appear as a reduction in generation cost.
There are additional inconsistencies, including the use of 20 turbines rated at 6.2 MW while describing the project as 120 MW, and the extension of conclusions from a site classified as low-complexity terrain to moderate-to-high-complexity terrain.
These are not matters that can be resolved by limited revision. The uncertainty model, scenario construction, and financial analysis would need to be reformulated, and the claimed technology comparison would require independent comparative data or a substantially more cautious scope. Therefore, the paper should be declined for publication.