The advent of the WSR-88D Doppler radar and its associated vortex detection algorithms have had a significant positive impact in severe weather forecasting. However, the algorithms contain many flaws that inhibit the large scale quantitative study of this impact. For this work, mesocyclone detections from the Mesocyclone Detection Algorithm were used to illustrate the difficulties and possible remedies in producing a synthetic mesocyclone climatology. Radar geometry forces a certain set of problems even on the “perfect” detection algorithm. These include larger bin sizes as range from the radar increases resulting in lower resolution velocity and reflectivity data and the detection of large amounts of ground clutter during certain atmospheric conditions. Proper dealiasing of the radial velocity data is also important in making accurate mesocyclone detections. Unfortunately, the current dealiasing algorithms leave a lot to be desired. Often incorrectly dealiased data from ground clutter returns produces false circulations which are picked up by the MDA as strong mesocyclone detections. While a human forecaster can make this distinction and account for it readily, a study involving synthetic climatologies of algorithm output must find other means of dealing with these errors. In order to improve the quality of the mesocyclone data sets acquired under the Mesocyclone Climatology Project, several filtering techniques have been developed to aid in the removal of false mesocyclone detections. Techniques shown in this work include the removal of detections at certain “trip” rings, the removal of detections very near (<5 km) of the radar site, and most importantly the removal of mesocyclones that cannot be associated with a storm as defined by the SCIT algorithm. This final technique uses that assumption that a mesocyclone (using most conventional definitions) is associated with some sort of convection. Using this assumption a program was written to look for storm cell detections within a certain radius of a mesocyclone detection during the same volume scan. Since the SCIT algorithm bases its detections of storms solely on reflectivity data, velocity dealiasing errors will not produce false detections. Also, it appears that the SCIT algorithm is smart enough not to identity storm cell identification in regions of heavy ground clutter return. If a SCIT cell cannot by found within a certain radius of a mesocyclone detection, it is removed from the data set. Data from multiple Southern Plains radars during 2000 and 2001 were processed using these algorithms. Different combinations of these filters using different search criteria were used to determining the best possible settings. Since there is no absolute, ground truth method for determine mesocyclone characteristics, the quality of the filtering was determined using statistical rather than comparison techniques. Still, the results reveal that the quality of a synthetic mesocyclone data set can be significantly improved using these rather simple filtering techniques. It is hoped to further refine these filters in the future to produce the highest quality synthetic mesocyclone data sets possible.