Thunderstorm gale (TG) monitoring in the severe convective weather is a complex and important task, in which the difficulty is how to distinguish the TG from nonthunderstorm gale (NTG). Based on the multisource data, including radar, satellite, lightning, temperature and dewpoint temperature, this paper proposes a fuzzy logic algorithm to tell them apart, which is proved to be an effective and efficient method. First, get the member functions of the multisource data according to their probability distribution which were extracted from longterm historical data. Second, acquire the weight ratio of each data by calculating the overlap areas of probability distributions. Finally, get the TG probability Q, and choose a threshold of Q to distinguish TG from NTG. In order to evaluate its performance, the algorithm is used to find TGs in the 50873 gale records of China in 2010. The results show that when Q is 0.55, the POD of TG is 0.76, and the FAR of TG is 0.18, and the CSI of TG is about 0.67. Two mixing weather processes, caused by cold air and typhoon, are chosen to evaluate its performance, showing 11 NTGs and 5 TGs are correctly identified. The algorithm would enhance the accuracy and effectiveness of the severe weather monitoring significantly.