Enhancing Tropical Cyclone Intensity Estimation from Satellite Imagery through Deep Learning Techniques

Abstract

This study first utilizes four well-performing pre-trained convolutional neural networks (CNNs) to gauge the intensity of tropical cyclones (TCs) using geostationary satellite infrared (IR) imagery. The models are trained and tested on TC cases spanning from 2004 to 2022 over the western North Pacific Ocean. To enhance the models performance, various techniques are employed, including fine-tuning the original CNN models, introducing rotation augmentation to the initial dataset, temporal enhancement via sequential imagery, integrating auxiliary physical information, and adjusting hyperparameters. An optimized CNN model, i.e., visual geometry group network (VGGNet), for TC intensity estimation is ultimately obtained. When applied to the test data, the model achieves a relatively low mean absolute error (MAE) of 4.05 m s⁻¹. To improve the interpretability of the model, the SmoothGrad combined with the Integrated Gradients approach is employed. The analyses reveal that the VGGNet model places significant emphasis on the distinct inner core region of a TC when estimating its intensity. Additionally, it partly takes into account the configuration of cloud systems as input features for the model, aligning well with meteorological principles. The several improvements made to this model’s performance offer valuable insights for enhancing TC intensity forecasts through deep learning.