中国气象学会气象青年科技交流会暨2022年青年科学家论坛

The trend detection of the sudden change of typhoon intensity has always been a difficult issue in typhoon forecast. Artificial intelligence (AI) can implicitly extract the complex features in the images through learning a large number of samples, and it has been widely applied in the meteorological field nowadays. In this study, based on the deep residual network (ResNet) model and the long short-term memory (LSTM) model, an automatic and objective method of identifying the trend of typhoon rapid intensification (RI) is presented through marking and learning the key information on the satellite images of the typhoons on the Northwest Pacific and South China Sea from 2005 to 2018. This method introduces the typhoon lifecycle indication and can effectively forecast and identify the trend of typhoon RI. By applying the detecting method in analyzing the operational typhoon satellite cloud images in 2019, we find that the method can well capture the sudden change tendency of typhoon intensity, and the threat score of independent sample estimation in 2019 reached 0.24. In addition, four typhoon cases with RI processes from 2019 to 2021 are tested, and the results show that the AI-based identification method of typhoon RI is superior to the traditional subjective intensity prediction method, and it has important application values.

artificial intelligence; satellite cloud images; typhoon rapid intensification; ResNet network; LSTM model