Estimating tropical cyclone intensity based on deep learning and satellite imagery

The intensity of tropical cyclones (TCs) is highly associated with their structure. Geostationary satellite cloud products provide rich information about the TCs’ structure like storm morphology, and can be used for estimating TC intensity. This study utilizes the Swin-Unet architecture as a backbone model for an objective deep learning (DL)-based TC intensity estimation method over the Western North Pacific. This model incorporates several key components, including the self-attention mechanism, shift-window mechanism, and Unet structure. The most important point in this study is that the model introduces a rotation index and a dispersion index as part of the loss function to characterize storm morphology. These two indexes can be computed based on the comprehensive feature extraction from time-series geostationary satellites imagery. The input of this model includes five cloud products from the Fengyun series geostationary satellites: sectional image (SEC), cloud top temperature (CTT), temperature of the brightness black-body (TBB), precipitation estimation (PRE), and humidity profile derived from cloud analysis (HPF). Results show that the model obtains an exceptionally low mean absolute error (MAE) of 3.71 m/s and root mean square error (RMSE) of 5.05 m/s. Furthermore, the ablation study (component-impact analysis) was conducted to quantify the contribution of the rotation index and dispersion index which enhance the model’s estimation performance to some extent. Finally, through an analysis of feature importance across the five cloud products, HPF, CTT, and TBB received higher importance scores, indicating the model concentrates on the thermodynamic and dynamic features that are strongly associated with TC convective activities. This study is expected to provide hydrometeorological departments with technical support for real-time TC intensity estimation in coastal regions and contribute to disaster warning systems.