Wind is an important indicator of circulation related processes in the atmosphere. Accurate wind in- formation is used as an input for weather and circulation models. Wind data with a high temporal and spatial resolution are useful for research on the microphysics of the atmosphere, which is the area of application for the three-beam Transportable Atmospheric Radar (TARA) located at Cabauw Ex- perimental Site for Atmospheric Research (CESAR) in the Netherlands. Although some comparisons were performed using radiosondes, no thorough quality assessment of the wind estimation results has been done so far. During this research, the quality assessment is performed for the estimation of the wind speed and direction during the Analysis of the Composition of Clouds with Extended Polarization Techniques (ACCEPT) campaign, which took place in October and November 2014. This research shows that the wind estimation is working well during precipitation and a TARA-based criteria for the data selection to guarantee the quality of the wind retrievals is identified in the form of the coefficient of variation for the mean Doppler velocities. The quality assessment of the results of the TARA wind estimation is done by comparing the wind retrievals to the measurements of the meteorological tower at a height of 200 m above the surface at minute resolution. For the assessment to be performed, an effective way of removing clear air measurements is needed, which is done for the whole ACCEPT-campaign using a rough selection of time steps including rain. This approach showed that the wind estimation is working well during precipitation and shows that an improved data selection is needed. Several approaches towards improving the data selection for the wind estimation of TARA are per- formed. The most successful is the use of the coefficient of variation, which is defined as the standard deviation divided by the mean. This coefficient is calculated for the mean Doppler velocities, which are the input for the wind estimation algorithm. Comparing the results of the coefficient of variation method to the one based on rain selection shows that both return good results. This leads to the conclusion that the coefficient of variation is useful to improve the data selection.