Abstract:Typhoon objective strength determination is an important supporting technique to improve the modernization level of typhoon forecasting operation. Deep learning can implicitly extract the deep complex features in the images through the learning of a large number of samples, and it has been increasingly applied to the meteorological field nowadays. In this paper, a ResNet deep learning model is used to study the satellite cloud images as samples by pre-training and transfer-learning. After studying the 2005-2018 typhoon images of the Northwest Pacific and South China Sea, we consturct an automatic and objective typhoon intensity estimation technique. By using the deep learning technique to analyze the typhoon satellite images in 2019, we find that this technique can be used to estimate the objective intensity of typhoon in different intensity and different developing stages, and the mean absolute error (MAE) and root mean square error (RMSE) of independent samples in 2019 are 4.3 m·s-1 and 5.5 m·s-1 respectively. The accuracy is better than that of the traditional objective intensity estimation method, so it has certain application values.