the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using an Airborne Wind Energy System as a turbulence sensor
Abstract. Atmospheric turbulence characterisation in the upper atmospheric boundary layer (ABL) remains a challenge, as conventional measurement techniques, such as masts and lidars, have fundamental limitations at those altitudes. Airborne Wind Energy Systems (AWES), which operate tethered kites at altitudes beyond the surface-layer, show great promise as turbulence sensing platforms. This paper describes a methodology to characterise atmospheric turbulence from quantities that are available from standard onboard sensors. The methodology has been developed in UniSimAWE, Kitemill's in-house simulator framework, where the Mann model has been employed to generate atmospheric turbulence. The two operational phases of ground generation AWES, production and return phase, have been exploited as complementary sampling strategies. During the return phase, the kite follows a near-streamwise trajectory, enabling measurement of the turbulence intensity and integral time and length scales for the three velocity components. Moreover, Taylor's frozen turbulence hypothesis has been shown to hold for this sampling strategy, both through the integral length-to-time scale ratio, and the wavenumber-to-frequency power spectral density ratio. In the production phase, the kite flies in crosswind manoeuvres, following a helical path; the dominant crosswind component allows measurement of the integral time and length scales in the crosswind direction which are unavailable from conventional fixed-point sensors. In addition, the helical path enables the measurement of streamwise integral time scale. A novel time scale is introduced to characterise the interaction between the atmospheric turbulent structures and the AWES trajectory, with relevance for turbulence-adaptive control. The results demonstrate the potential of AWES to measure relevant atmospheric turbulence quantities throughout its operation, with advantages over conventional measurement techniques.
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Status: open (until 31 Oct 2026)
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CC1: 'Comment on wes-2026-150', Duc Nguyen, 30 Sep 2026
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CC2: 'Reply on CC1', Duc Nguyen, 30 Sep 2026
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Publisher’s note: the content of this comment was removed on 2 October 2026 since the comment was posted by mistake.
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-2026-150-CC2
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CC2: 'Reply on CC1', Duc Nguyen, 30 Sep 2026
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RC1: 'Comment on wes-2026-150', Anonymous Referee #1, 16 Oct 2026
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This study presents a novel method to characterise turbulence using an airborne wind energy system as a sensor. The methodology is demonstrated with data from an in-house simulator that combines a rigid-body kite model and a lumped-mass tether model with Kitemill’s own flight controller and path planner. The inflow is a uniform (shear-free) wind profile, with turbulence generated using the Mann model. Sensor measurements are assumed to be exact, so the wind measured at the kite represents a best-case reconstruction.
Overall, the paper demonstrates the methodology for obtaining relevant turbulence characteristics from measurements of the flow where the kite flies, under highly idealised inflow and velocity-reconstruction conditions. The idea is novel and relevant, and it could form a useful basis for follow-up studies that use the kite as an inflow sensor. The idealised sensor measurements provide a clean identification of which turbulence characteristics can be obtained from measurements along the kite trajectory alone. However, given the idealised sensor and inflow assumptions, the authors should be careful in drawing conclusions about applicability until the method is tested with experimental data, or at least with added sensor noise and uncertainty.
GENERAL COMMENTS
1.In the introduction (lines 32–40), you present previous studies that use the kite as an inflow sensor, noting that they do not focus on turbulence, whereas this study does. As I understand it, those sensor-fusion studies aim to estimate the wind speed, which can then be used to derive turbulence metrics. In other words, they provide the input that a realistic version of the present study would require. In addition, one of the cited studies (Cayon, 2025) includes a comparison of some turbulence metrics against lidar estimates. I would therefore suggest revising how these studies are positioned, presenting them as complementary to, or a prerequisite for, the present work rather than as not addressing turbulence.
2. The wind measurements are obtained from a v_a sensor and a v_k sensor. Does this yield the same wind speed as sampling the flow field directly? If so, could you explain why? If not, please state this explicitly. If they are different Figure 9 should include both.
3. For this method to be applied in practice, an important element is missing: a methodology for estimating the mean wind as a function of height from kite data. The authors acknowledge the shear simplification in the current simulated data in relation to Eq. 5, but no method for estimating the mean profile in practice is given. A general approach should be added, including a justification of the averaging timescales and height binning, and a discussion of how errors in the estimated mean would affect the turbulence characteristics. I consider this the main point requiring revision.
4. In line 207 and elsewhere, you state that the frozen-turbulence assumption is validated. Correct me if I am wrong, but is the Mann model not itself built on this assumption? If so, this would be a consistency check or verification rather than a validation.
5. The conclusions state that an AWES can serve as an effective atmospheric turbulence sensor. Is this conclusion generalisable? I think sensor noise and uncertainty should at least be accounted for before making such a statement. Showing that relevant turbulence characteristics can be obtained from measurements along the kite trajectory alone is already a valuable result, but broader generalisations should be made carefully until the method is validated with operational data.
SPECIFIC COMMENTS
1. L156: You mention a lidar dataset. Is this dataset publicly available, or will it be made available? It could be a valuable contribution to the AWES research community.
2. L200: The expression “w_i = 1/i” at the end of the sentence appears to be a leftover and should be removed.
3. L222: Could you specify the turbulence box sizes used? Do they change with the length scale?
4. Eq. 11: There is a negative logarithm in the integral. Please check that the equation is correct.
RECOMMENDATION
Overall, I recommend this paper for publication after revision. It presents a novel methodology that is directly relevant to the development of AWES technology. Before publication, however, one major point should be addressed: General comment 3 on the estimation of the mean wind with height. The remaining general and specific comments are minor and mainly concern clarifications and more carefully tempered wording.
Citation: https://doi.org/10.5194/wes-2026-150-RC1
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Publisher’s note: the content of this comment was removed on 2 October 2026 since the comment was posted by mistake.