Abstract: A thunderstorm gale recognition model is established using support vector machine based on data of radar and automatic weather stations from Beijing Weather Observatory. Firstly, 18 thunderstorms in Beijing during 2010-2014 are analyzed quantitatively in terms of the statistical method and 9 forecast factors are selected, i.e., the height of the echo top, the maximum albedo, the height of the maximum reflectivity, the total vertical liquid water content, the time rate change of total vertical liquid water content, the total vertical liquid water content density, the height of the maximum reflectivity factor, the storm moving speed and the width of the velocity spectrum. 451 non-high wind samples and 425 high wind samples are selected by matching the time and place of automatic weather stations with the value of the quantitative index of the PUP storm monomer recognition product in all the cases. Secondly, the probability distribution of prediction factors in the wind and non-wind samples are calculated, and relationships with corresponding forecast factors are obtained, and then sample data are normalized by the obtained membership function. Finally, the kernel function and model parameters are established, and the thunderstorm gale recognition model is established using support vector machine. Two typical cases in Beijing are analyzed and tested, one caused by a line thunderstorm which happened on 7 July 2017, and the other caused by an isolated single-cell storm which happened on 19 May 2012. Results show that the identified wind range is consistent with reality, and the hit rate, the false alarm rate and the critical success index are 92.0%, 22.1%, 73.0% and 99.1%, 40.5%, 59.2%, respectively. It will help to improve the accuracy of thunderstorm gale warning and forecasting. However, sometimes multiple thunderstorm cells can be misjudged as one according to these forecast factors. In this case, it is necessary for forecasters to conduct manual intervention in combination with the overall radar base reflectivity and weather conditions, to reduce the misjudgment rate of gale. In the future, long time series radar data should be used to carry out "large sample census" research and an automatic thunderstorm identification system based on weather radar can be established.