The two main effects of a wind turbine wake on those within its region of influence are: first, the wind speed deficit results in a decrease in the power production and second, a more turbulent flow results in higher unsteady structural forces, potentially reducing the lifespan of the turbines. Accurate quantification and modeling of wind turbine wakes is of utmost importance during the planning phase of new wind farms in order to optimize the layout and to accurately estimate the future power production. Whether wind turbine wake models are analytical, numerical, empirical, or a mixture, they need to be validated with experimental data. Wind tunnel experiments present some advantages for validation purposes (e.g. repeatability, flow control, wind turbine control, wind farm layout, etc.) although incomplete similarity limits the validity of the comparison. Ideally, model validation would be complemented with measurements of full-scale wind turbine wakes. The measurement technique that likely provides the most complete characterization of the wake is pulsed wind LiDAR, due to the relatively high spatial and temporal resolutions that can be achieved with a maximum measurement range up to a few kilometers. We will present preliminarily analysis from a measurement campaign that uses two nacelle-mounted, pulsed scanning LiDARs. The first LIDAR is oriented upwind to characterize vertical profiles of wind speed, wind direction, and yaw angle, as well as longitudinal and transversal turbulence intensity. The second LiDAR is oriented downwind to measure the horizontal field of the longitudinal wind in the wake region, including the average and standard deviation of the wind as shown in the left part of the figure below. The far wake of a wind turbine is defined by the self-similarity of the velocity deficit profile, which has been shown to follow a Gaussian shape. Fitting a Gaussian function to the velocity deficit allows for the study of the near wake length and the evolution of the maximum velocity deficit as well as the wake width at different distances downstream of the wind turbine. The results demonstrate the advantage of the dual LiDAR setup and possible data treatment. The full-scale measurements can help us to understand how higher atmospheric turbulence conditions result in a shortened length of the near wake and increased growth rate of the far wake. Data from the full-scale experiment are presented in the right part of the figure, together with results from validated LES simulations and a wind tunnel experiment, and can be used to validate analytical wake models [1,2] derived from the laws of conservation of mass and momentum and assuming a Gaussian distribution for the wake velocity deficit. [1] M. Bastankhah and F. Porté-Agel. A New Analytical Model For Wind-Turbine Wakes, in Renewable Energy, vol. 70, p. 116-123, 2014. [2] M. Bastankhah and F. Porté-Agel. Experimental and theoretical study of wind turbine wakes in yawed conditions. Journal of Fluid Mechanics, 806, 506–541, 2016. Figure caption: the left part of the figure presents an example of the reconstruction of the wake of a 2.5 MW wind turbine for a period of 30 minutes in terms of the longitudinal velocity deficit (top) and the local longitudinal turbulence intensity (bottom) for low turbulence inflow conditions. The right part of the figure presents the relationship of the spanwise wake growth rate (top) and the length of the near wake (bottom) with the longitudinal turbulence intensity. The setup and technique used do not allow to calculate near wake lengths shorter than 2D. This is represented by the shaded region on the bottom right part of the figure.