What is already known:
What this study adds:
The strong wind, shear, and turbulence associated with gust fronts can negatively impact aircraft operations at terminals, vegetation and other structures. Currently, the Machine Intelligence Gust-Front detection Algorithm (MIGFA) identifies gust front based on signatures from Doppler radar measurements. The upgrade of the Weather Surveillance Radar-1988 Doppler (WSR-88D) network to polarimetric capabilities was recently completed in 2013. Therefore it is timely to exploit the additional polarimetric measurements to improve gust front detection. The Neuro-Fuzzy Gust-front Detection Algorithm (NFGDA) was developed for this task. NFGDA preliminary results yielded a higher performance than MIGFA, motivating this study to investigate more gust front cases to confirm polarimetric signatures of gust fronts and verify the performance of NFGDA. In this study, eight gust front cases are identified and analyzed using the NFGDA. Findings included similarities between these and preliminary polarimetric gust front signatures. Additionally, the performance results yielded suggested refinements based on the statistical analysis of the algorithm. More specific guidelines can be placed in defining a gust front, as it is not a well-defined storm feature. Overall, there is room promising algorithm.