## 1 Introduction
Depending on the scan-repetition time and the maximum measurement range, characteristic air parcels can be analysed in the form of flow structures at multiple times at different positions with successive scans. Thus, the advection speed of the parcels can be determined and conclusions about their behaviour can be drawn [17]. The reasonableness of the reconstruction must be considered for each application. Resource assessment places more emphasis on time-interval-based values, like 10-min average wind speed and the ambient turbulence intensity [18], than on more fine-grained high-resolution flow dynamics. The numerical and temporal resources required to reconstruct high-resolution flow dynamics are inconsistent with the resulting benefits. In contrast, high-resolution time series are required for the characterisation and validation of models of wake meandering; [PERSON] et al. [9], [PERSON] et al. [19], and [PERSON] et al. [20] used LiDAR-based wake wind-speed time series with moderate sampling frequencies, between 0.04 Hz and 0.06 Hz, in order to compare the wake deficit position with wake meander model predictions from the DWM [9]. However, when analysing wake measurements recorded with LiDAR systems, especially in connection with other measurements, high-resolution multidimensional wake measurement data is helpful for the sake of synchronisation and correlation with data about atmospheric or turbine conditions' information, like stability indicators, wind shear, wind veer, turbine thrust, and turbine loads, measured at specific moments in time [21]. As measurement campaigns tend to become more and more complex to allow detailed analysis of flow situations [21], the limitations of long-range LiDAR systems need to be understood in detail. Specifically, researchers need to know how planar measurement results can be used as time series in addition to the standard mode of statistical averages. To the same end, data-processing methods that can overcome the limitations of individual measurement systems with sufficient accuracy must be improved. The first objective of this paper is to produce synchronised temporal and spatial high-resolution up-sampled flow data. The secondary objective is to facilitate comparison of this flow data with LES reference data. This comparison requires that the dynamics of planar full-field wake measurements are compatible with data from wind-tunnel experiments or CFD simulations. Further, this full-scale measurement data can be used directly for the evaluation of temporally variable flow structures or dynamic wake models. To this end, Section 2 reports our parametric study of the measurement frequency of numerically simulated LiDAR data from an LES wake flow field, to understand how the measurement parameters influence the mapping quality. In Section 3, we present a method for temporal fluid-dynamics interpolation based on space-time conversion, which improves the temporal resolution of long-range planar LiDAR data to a sub-measurement-time scale. With this improvement in the temporal resolution, the time shift within a scan can be corrected, and we can mutually synchronise distinct measurements independent of their sampling time or temporal resolution. In Section 4, we show how the mapping error of horizontal and vertical nacelle-mounted wake measurements shifts from a spatial to a temporal nature as the measurement frequency changes. This analysis addresses how the representation of the mean value and standard deviation is affected by retrospectively increasing the temporal resolution of planar long-range LiDAR measurements. The effects of propagation and implied restrictions are discussed in Section 5 and conclusions are drawn in Section 6. While the paper focuses mostly on the presentation of horizontal measurements, appendices include detailed figures that show the corresponding results for vertical measurements. The effects of temporal up-sampling of LiDAR scans with different angular velocities are detailed in supplementary online video material.