Wind lidars are increasingly used for wind turbine power estimation, load analysis, and control purposes, but their ability to accurately capture turbulence is limited by the large probe volumes of lidars, which attenuate measured turbulence through spatial averaging. We investigate the effects of probe volume averaging on lidar turbulence characteristics as a function of the probe volume over integral length scale ratios using a wind tunnel study. This is facilitated by generating tailor-made turbulent flow conditions using an active grid and by adjusting the focus distance of a short-range continuous-wave (CW) lidar. A hot-wire anemometer is used as reference sensor. First, a spectrum model for the lidar averaging effect is implemented by applying a Lorentzian filter to Mann-model spectra derived from hot-wire measurements, accurately capturing velocity spectrum attenuation with increasing probe volume averaging. Second, the ability of the lidar to estimate turbulence statistics including the integral length scale and velocity variance is evaluated by comparing lidar to hot-wire ratios across all cases. Results show that conventional lidar-derived length scales are overestimated while velocity variances are underestimated with increasing probe volume averaging. Both the modelled and conventionally derived lidar velocity variance are attenuated from 10 % to 80 % with increasing probe volume averaging. In contrast, the spectrum-based lidar variance, derived directly from the averaged lidar Doppler spectrum compensates for probe volume averaging, yielding variance estimates that are significantly less affected by this ratio with an average relative error of +20 % compared to the hot-wire data. The correction for velocity gradients in the wind tunnel flow reduces the average relative error of spectrum-based lidar velocity variances to +10 %.