## 1 Introduction  
The arrangement of wind turbines into dense and efficient clusters is a central challenge in the design of wind farms. The detrimental effect of wake shading decreases energy yield and increases dynamic loads [1; 2]. These additional loads lead to increased fatigue on the turbine components, increasing the likelihood of failure and early maintenance [3; 4]. This additional and unexpected maintenance will deteriorate the economic and energy efficiency of the wind farm [5]. Wake models were developed early in the history of wind-turbine design to account for the loss of yield, wake-induced loads, and to aid in the design of wind turbines. Since single-point measurements are limited to cup anemometers mounted on the turbines themselves or on meteorological masts, reference and validation data can usually only be applied in the form of time averages [6; 7; 8]. Since wakes have highly dynamic behaviour, dynamic wake models have been developed for the sake of simulating how the wake meanders over time, to generate more realistic inflow conditions for load calculations in the design phase or for wind farm control. The dynamic wake meandering model (DWM) of [PERSON] et al. [9], which is unique in its method for simulating meanderingbehaviour, has been updated and adjusted to ease comparisons with measured data [10; 11]. However, in [PERSON] et al. [10] and [PERSON] et al. [11], a previously reported simulation of wake meandering from [PERSON] et al. [9] was used. [PERSON] et al. [12] introduced a stochastic approach that considers meandering as it is related to atmospheric stability. Validation of these dynamic wake models requires wind velocity data collected with high spatial and temporal resolution, either from suitably recorded measurements or from high-fidelity computational fluid dynamics (CFD) simulations. One of the most promising measuring devices for understanding wakes in free-field conditions is wind-speed Doppler light detection and ranging (LiDAR). LiDARs are configured as continuous-wave (CW) devices for short distances (<200 m) [13] or as pulsed systems for longer distances (<6000 m) [14]. Though LiDAR is commonly used for free-field measurements, [PERSON] et al. [15] took LiDAR measurements in a wind tunnel under controlled conditions in order to investigate wake behaviour using a synchronised dual-short-range LiDAR setup. [PERSON] et al. [15] captured two-dimensional (2D) flow situations of a model wind farm with three turbines in the range of 20 s per horizontal scan. [PERSON] et al. [16] investigated how selected yaw misalignments affected wake properties by varying the inflow turbulence and shear in a wind tunnel. In experimental and numerical tests, atmospheric conditions are not trivial to reconstruct, especially the recreation of fluctuations of length scales larger than the turbine. Thus, CFD and wind-tunnel tests cannot replace measurements of a full-scale system in the full field, but instead extend the range of research possibilities. Due to the measurement principle used, some limitations apply when using a single long-range LiDAR setup for full-scale wake measurements. In Section 2, we discuss the constraints due to the one-dimensionality of the measurements, which include the complications in volume averaging due to the laser pulse. We also discuss how all the scanning measurements must strike a compromise between spatial and temporal resolution. These restrictions apply to both CW and pulsed LiDAR systems. A significant advantage of short-range CW systems over long-range systems is their high measurement frequency, which allows temporal processes to be captured on a shorter time scale. In contrast, pulsed devices can take measurements at multiple quasi-instantaneous positions along the laser beam at long range. This spatial distribution allows the flow pattern of one or many wind turbines to be mapped within one scan.