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
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CC2: 'Comment on wes-2026-93', J. Gordon Leishman, 13 Jul 2026
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
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 -
RC2: 'Comment on wes-2026-93', Anonymous Referee #2, 24 Aug 2026
Review of "On the effects of wind shear and veer on the power of a downstream turbine"
This study investigates the power of waked wind turbine as a function of wind shear and wind veer for a two turbine configuration at an onshore field site. The power produced by the downstream turbine is observed to produce minimum power (i.e., maximum wake effect) at an incident wind direction that is not fully aligned with the geometry of the array; instead, an offset of up to ~5 degrees is observed, which depends on the shear and veer. The mechanism for this offset is explained as a non-zero lateral forcing, due to they symmetry-breaking wind shear, which induces wake deflection similar to yaw misalignment. A simple model is used to account for this shear-induced wake deflection, which is compared to large eddy simulations. Furthermore, a lateral offset correction is added to the Abkar et al. (2018) skewed wake model which adds a lateral wake deflection dependent on the measured or inferred wind veer. This wake model extension is compared with the field measurements and leads to a marginal reduction in model error for waked turbine power prediction.
Overall, the manuscript introduces and/or applies several interesting and novel concepts, such as the lateral force induced by symmetry-breaking wind shear and a mechanism for shear-induced wake deflections. However, there are also significant gaps in the reported results. For example, the lateral rotor forcing model is compared against LES, and wake deflections are tuned to match with moderate agreement, but this wake deflection model is not compared against the field data. Instead, the wake model improvements are highly empirical and site specific, and it is also unclear whether they were evaluated on out-of-sample data from their calibration. Furthermore, the model augmentation does not seem to statistically improve estimates of the wake turbine power production. Overall, I believe the manuscript could be strengthened significantly by a narrowed focus to clarify these questions. Specific comments are listed below.
Major comments:
- Wake deflections, which are shown in the binned power data in Figs. 3-5, are explained through two mechanisms: first, advection due to wind veer combined with the offset turbine hub heights, and second, lateral forces from wind shear. In my opinion, neither of these mechanisms are definitively linked with the field observations. Specifically,
a. It is stated/hypothesized that the influence of wind veer is through wake advection, which causes an asymmetry due to the unique vertical offset of the turbine hub heights in the array studied. However, this mechanism is never definitively shown, for example, using LES or a simple skewed Gaussian wake model. Wind veer can also induce deflections in other ways, for example through turbulent mixing (e.g. van der Laan and Sørensen, WES (2017)), which is not discussed at all.
b. Smaller, but non-zero, wake deflections are noted due to isolated wind shear in the field data, which are shown in Fig. 4. However, a confounding variable is that the mean wind veer for each plot is likely changing. This is because the veer is binned from -4 to 4 degrees, but there are more instances of positive wind veer than negative wind veer, as shown in Fig. 2. Furthermore, I suspect that the mean wind veer becomes increasingly positive as the windowed shear range increases because of the correlation between shear and veer observed by the authors. One option might be to show binned data such that the ensemble mean wind veer for each bin/subplot is zero. - The spacing between the two wind turbines at the test site (2.7D) is likely too tight for far-wake models like the Gaussian wake model to be used reliably. For example, Carbajo Fuertes et al. (2018) report near-wake lengths between 3D-6D for a utility-scale onshore turbine where the turbulence intensity is below about 7%, and about 2.5D above 7%. Consider alternative models, perhaps a tuned double Gaussian (Schneider et al. 2020) or near-wake tuned super Gaussian (Cathelain et al. (2020)).
- The shear-induced lateral forcing is interesting, but contextualization with yaw misalignment seems to be straightforward and important. Rearranging the Jiménez et al. (2010) model (Eq. 8) to find an "equivalent yaw misalignment" for the observed C_S gives a yaw magnitude of yaw = arctan(C_S/C_T). Even for very strong shear shown in Fig. 8a (alpha = 0.6), this only gives a misalignment of 2-3 degrees, which may not even be within the uncertainty of the inflow direction or yaw controller.
- The LES-derived wake deflections (shown in Fig. 8b) seem to have a different structure than well-characterized deflections due to yaw misalignment. Specifically, the increase linearly or even super-linearly with distance, while deflections from yaw misalignment asymptote (c.f., Bastankhah and Porté-Agel (2016)). More numerical details on the LES are critical to understanding this result. For example, if Coriolis effects are included, then those could contribute (Heck and Howland (2025); Lanzilao and Meyers (2025)).
- It is unclear why there is an uncertainty envelope around the wake center in LES if multiple time windows are not used. Bootstrapping the LES data in the same way that the field data were bootstrapped does not make sense as only one time-averaged flow field is instantiated. Furthermore, the simple model of Jiménez et al. (2010) is well known to overestimate wake deflections, and deflections depend completely on the tuned parameter ξ.
- Throughout the model tuning section, it is unclear if there is any attempt at a train/test split of the data (Sec. 4.1). This drastically limits the applicability of these findings to other field sites. If simply eyeballing the uncertainty range from Fig. 12 onto Fig. 13 (unclear why an uncertainty envelope is not included in Fig. 13), it seems like all of the augmented model errors would overlap with "AV". To me, it seems then that either (1) there is too much spread in the field data to draw conclusions about any model improvements, or (2) the metric shown is insufficient to cut through the noise in field data.
- A stronger conclusion of the manuscript would be to integrate the shear-deflection rotor aerodynamics with the wake model for calibration-free evaluation of the physics. This would more definitively assess the validity and importance of the shear-deflection effect. Assuming an equivalence between C_S and C_T (as is done in Eq. 8) on the impact of lateral wake deflections, it seems straightforward to model the wake deflection as a function of shear/veer.
- To what extent can Fig. 7 be reconstructed only with the AV model? Do any of the tuned models (or the integrated shear-lateral forcing-wake deflection model mentioned in comment 7) a model reconstruct the trend in Fig. 7?
- The wake steering section is brief, simplistic, and lacks validation. If a shear-dependent lateral deflection is imposed in the wake model, it is straightforward that there will be a corresponding shift in the yaw set point with the incident wind direction. But the extent to which this shift of a couple degrees may actually affect wake steering in a field site or using LES is not presented. Given the length of the manuscript, I recommend that this section be removed entirely and left for a separate, dedicated in-depth study. Further, the concept of "wrong-way steering" is misleading. I would hypothesize that there are still power gains when using the yaw set points from the baseline models in the "wrong-way steering" wind directions, but that they are just suboptimal if computed using the augmented AV model to compute power. The yaw set point is not the "wrong way", it is simply suboptimal when evaluated using a different low-fidelity model.
Minor comments (in order as they appear):
- The introduction intersperses contributions of this paper with literature review, making it difficult for the reader to understand what is new and what has already been accomplished in other studies. For example, the third question proposed in the paper, "How can shear and veer be estimated in the field?" is seemingly already addressed by the other studies cited (Kim et al. (2023); Bertelè et al. (2024)), and any research gap which may be filled by this manuscript is not clear.
- The first paragraph of the introduction can be removed to avoid confusion from "farm-scale effects" such as gravity waves, wake superposition and merging, and ABL-wind farm coupling.
- L49: Other (earlier) studies also observed enhanced wake recovery due to wind veer, notably Abkar and Porté-Agel (2016) and Churchfield and Sirnivas (2018).
- L94: Many other engineering models also account for "effects of yaw misalignment", including wake deflection (Jiménez et al. (2010); Bastankhah and Porté-Agel (2016); Shapiro et al. (2018), King et al. (2021); Bay et al. (2023)) and wake curling (Bastankhah et al. (2022)).
- L99: The "curled-wake model" typically refers to the numerical model introduced by Martínez-Tossas et al. WES (2019, 2021). Additionally, other studies have proposed veer-aware extensions to the vortex sheet model of Bastankhah et al. JFM (2022), see Narasimhan et al. PRF (2022); Narasimhan et al. JRSE (2025).
- L131: To be clear, only two measurement heights are provided by the met mast? Previous work (c.f. Kim et al. (2023)) has highlighted the importance of measurements spanning the rotor extent, particularly for use of the rotor "as a sensor".
- L171 and surrounding discussion of Fig. 2: a relevant citation may be Kelly and van der Laan, WES (2023).
- Fig. 6, 7, and throughout the results: It seems like all of the field data suggest deflections in the "positive y" direction, but then the model calibration uses the flipped convention (for example, a_d is calibrated to -0.1, L506). Please clarify the sign convention.
- L348: Why is the modeled C_S not used in Fig. 8b? It seems like the agreement with LES is pretty good and there should be only minor downside to using model C_S rather than LES-computed C_S.
- L358: Please explain what is meant by "emergence of complex wake structures at high shear levels".
- L414: it is unclear to me where the wake displacement model is from. I cannot find it in "the original publication" (L400), which presumably refers to Annoni et al. (2018).
- L434: for an axisymmetric Gaussian wake, the veer rotation (Eq. 9) has zero effect. Are the wakes compared in Figs. 12-13 axisymmetric (sigma_y = sigma_z, when yaw aligned)? If so, then I think the manuscript would be much clearer if an axisymmetric Gaussian model was used as the baseline comparison, or the default Bastankhah and Porté-Agel (2016) Gaussian model in FLORIS, which includes yaw misalignment, if the wake steering discussion is retained.
- L574: I do not understand what is meant by "inflow-dependent model-plant mismatch".
Technical points (in order as they appear):
- L29: "horizontal direction veer" is rather atypical phrasing, and could be confused with "horizontal wind shear".
- L46-47: the "numerical modeling" studies of Bromm et al. (2017) and Churchfield and Sirnivas (2018) both use LES, but are segregated from the list of LES studies immediately preceding it.
- L221: "shear-law" -> "power-law".
- L260: the purpose of running CFD is not merely "direct visualization of the flow field", rather to analyze the flow field and resulting physics with spatiotemporal resolution unachievable in experiments.
- L290: define or rename "velocity triangle".
References:
Abkar, M. & Porté-Agel, F. Influence of the Coriolis force on the structure and evolution of wind turbine wakes. Phys. Rev. Fluids 1, 063701 (2016).
Abkar, M., Sørensen, J. N. & Porté-Agel, F. An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes. Energies 11, 1838 (2018).
Bastankhah, M. & Porté-Agel, F. Experimental and theoretical study of wind turbine wakes in yawed conditions. J. Fluid Mech. 806, 506–541 (2016).
Bastankhah, M., Shapiro, C. R., Shamsoddin, S., Gayme, D. F. & Meneveau, C. A vortex sheet based analytical model of the curled wake behind yawed wind turbines. J. Fluid Mech. 933, A2 (2022).
Bay, C. J. et al. Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model. Wind Energy Science 8, 401–419 (2023).
Bertelè, M. et al. The rotor as a sensor – observing shear and veer from the operational data of a large wind turbine. Wind Energy Science 9, 1419–1429 (2024).
Carbajo Fuertes, F., Markfort, C. D. & Porté-Agel, F. Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation. Remote Sensing 10, 668 (2018).
Cathelain, M., Blondel, F., Joulin, P. A. & Bozonnet, P. Calibration of a super-Gaussian wake model with a focus on near-wake characteristics. J. Phys.: Conf. Ser. 1618, 062008 (2020).
Churchfield, M. J. & Sirnivas, S. On the Effects of Wind Turbine Wake Skew Caused by Wind Veer. in 2018 Wind Energy Symposium (American Institute of Aeronautics and Astronautics, Kissimmee, Florida, 2018)
Heck, K. S. & Howland, M. F. Coriolis effects on wind turbine wakes across neutral atmospheric boundary layer regimes. Journal of Fluid Mechanics 1008, A7 (2025).
Jiménez, Á., Crespo, A. & Migoya, E. Application of a LES technique to characterize the wake deflection of a wind turbine in yaw. Wind Energy 13, 559–572 (2010).
Kelly, M. & van der Laan, M. P. From shear to veer: theory, statistics, and practical application. Wind Energy Science 8, 975–998 (2023).
Kim, K.-H., Bertelè, M. & Bottasso, C. L. Wind inflow observation from load harmonics via neural networks: A simulation and field study. Renewable Energy 204, 300–312 (2023).
King, J. et al. Control-oriented model for secondary effects of wake steering. Wind Energy Science 6, 701–714 (2021).
Lanzilao, L. & Meyers, J. Wind-farm wake recovery mechanisms in conventionally neutral boundary layers. J. Fluid Mech. 1015, (2025).
Narasimhan, G., Gayme, D. F. & Meneveau, C. Effects of wind veer on a yawed wind turbine wake in atmospheric boundary layer flow. Phys. Rev. Fluids 7, 114609 (2022).
Narasimhan, G., Gayme, D. F. & Meneveau, C. An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer. Journal of Renewable and Sustainable Energy 17, 033302 (2025).
Schreiber, J., Balbaa, A. & Bottasso, C. L. Brief communication: A double-Gaussian wake model. Wind Energy Science 5, 237–244 (2020).
Shapiro, C. R., Gayme, D. F. & Meneveau, C. Modelling yawed wind turbine wakes: a lifting line approach. J. Fluid Mech. 841, R1 (2018).
van der Laan, M. P. & Sørensen, N. N. Why the Coriolis force turns a wind farm wake clockwise in the Northern Hemisphere. Wind Energy Science 2, 285–294 (2017).Citation: https://doi.org/10.5194/wes-2026-93-RC2 - Wake deflections, which are shown in the binned power data in Figs. 3-5, are explained through two mechanisms: first, advection due to wind veer combined with the offset turbine hub heights, and second, lateral forces from wind shear. In my opinion, neither of these mechanisms are definitively linked with the field observations. Specifically,
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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.