| 11 Jun 2026
How accurate is the beam position of scanning lidars? An intercomparison
Abstract. Scanning wind lidar provide a flexible measurement solution to accurately quantify near-shore and offshore wind resource and site conditions to help reduce risk and costs for prospective offshore wind developments. Typically these devices are set up using a near-horizontal beam position (elevation angle) in order to obtain wind speed measurements at distances of 3-10 km at heights in the atmospheric boundary layer relevant to wind turbines. Consequently, precise knowledge of the beam position and any associated positioning errors of the lidar scan head is required to minimise the uncertainty in the measurement height and thus the resulting wind speed measurements. A number of Hard Target Testing (HTT) methods are typically used to determine and correct for beam position errors, and include the use of static hard targets (SHT; e.g. buildings), dynamic hard targets (DHT; i.e. drone) and the sea surface itself (Sea Surface Levelling; SSL). However, to-date, these methods have not been consistently compared. This paper therefore systematically compares the performance of the SHT, DHT and SSL methods in determining the static elevation angle error (elevation offset) for scanning lidar wind measurement campaigns. A comparison experiment is conducted using two independent pulsed scanning lidar devices (type Vaisala WindCube 400S) at a near-shore wind measurement test site in the UK, using an offshore meteorological mast as the static reference hard target across all methodologies. Differences in the mean elevation offset relative to the reference target range from -0.03◦ to -0.20◦ for SHT, -0.002◦ for DHT, and from -0.05◦ to +0.07◦ for SSL. The uncertainties found from the methods range from approximately ±0.03◦ for DHT to up to ±0.20◦ for SSL. Overall, the results show that in the absence of offshore static hard targets, the DHT method can reliably determine the elevation offset with lowest uncertainty. While practically easier to execute, the SSL method showed more varying results between the two devices tested and exhibited larger uncertainties due to a number of factors including the determination of the sea surface distance, sea state and device tilt. When performed thoroughly, the elevation offset can be obtained from all methods to within the specified angular positioning error (±0.1◦) of the scanning lidar model. While the SSL method exhibits larger uncertainties, it offers an alternative approach when resources (e.g. suitable SHT candidates) are limited and can be implemented regularly to quantify time-dependent angular drifts for both near-shore and offshore applications.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
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The manuscript addresses an interesting topic of clear relevance to Wind Energy Science. The discussion provides useful information on wind-speed errors and on the practical advantages and limitations of the different hard-target test methodologies. However, I find that the wind-energy implications of the required angular accuracy could be discussed more broadly.
At present, the discussion mainly considers the effect of angular errors on wind speed. This is relevant, but the acceptable angular error depends strongly on the intended application. For example, an error of 0.2° may be acceptable for estimating one-point turbulence statistics at a range of 1 km, but too large for coherence studies based on two lidars with nearly parallel beams, as highlighted during the COTUR experiment by Cheynet et al. (2021). I therefore recommend that the authors clarify which level of angular accuracy, calibration procedure, or hard-target test is appropriate for different applications, including mean wind-speed estimation, one-point turbulence statistics, and two-point turbulence statistics. This broader discussion would help strengthen the manuscript’s relevance to Wind Energy Science, rather than framing the work mainly as a technical contribution to Doppler wind lidar technology.
The literature review should also be completed and better contextualised with respect to previous and recent work on long-range scanning lidars for wind-energy applications. Vasiljević et al. (2016) is mentioned only briefly, although it contains relevant quantitative information on static pointing accuracy, hard-target mapping, backlash, and repeatability of long-range WindScanner systems. I recommend that the authors compare their results with those reported by Vasiljević et al. (2016). This would help readers assess whether the present results are consistent with earlier long-range scanning lidar systems and, if not, whether the differences may be due to differences in hardware or calibration procedure.
In addition, Mann et al. (2026), which includes some of the present manuscript’s co-authors, reports a related offshore lidar campaign focused on turbulence measurements in the marine boundary layer and novel hard target tests. This recent study could be used to provide more context on the wind-energy applications and practical motivation for accurate beam positioning.
References:
Cheynet, E., Flügge, M., Reuder, J., Jakobsen, J. B., Heggelund, Y., Svardal, B., ... & Godvik, M. (2021). The COTUR project: remote sensing of offshore turbulence for wind energy application. Atmospheric Measurement Techniques, 14(9), 6137–6157.
Mann, J., Patel, A., Sjöholm, M., Thorsen, G. R., Simon, E. I., Hung, L. Y., & Gottschall, J. (2026). An experimental campaign to measure turbulence in the marine boundary layer. Wind Energy Science Discussions, 2026, 1–27.
Vasiljević, N., Lea, G., Courtney, M., Cariou, J. P., Mann, J., & Mikkelsen, T. (2016). Long-range WindScanner system. Remote Sensing, 8(11), 896.