Estimating the tropical cyclone wind structure using physics-incorporated networks

Abstract

Satellite-based remote sensing technology plays a significant role in identifying tropical cyclones (TCs), and most of the current research focuses on intensity estimation. However, analyzing the wind structure of TCs, which is directly related to the danger they bring, remains a challenge. By adding prior knowledge of TCs into the model, we propose a physics-incorporated network based on multi-task learning to estimate wind radii and intensity, whose layers can automatically extract rotation-invariant features related to the TC core from multichannel satellite imageries. In addition, we build a more comprehensive dataset, including global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors, to tackle the structure task. We compare our model with existing methods, and it shows that our model gets better results in estimating 50-knot and 64-knot wind radii and achieves a 4.87-knot root-mean-squared error (RMSE) of intensity. By predicting probability density functions, our model quantifies the uncertainty of the result. The experimental results show that the incorporation of rotation equivariance into the layers can enhance TC structure estimation. By considering the feature importance of multi-source predictors, we find that our model pays attention to key predictors related to the TC structure. Specifically, the tangential wind speed at 500 km from the TC center and the radius of the 5-knot wind both greatly reduce the error of the estimated parameters. Finally, two case studies show that the proposed model performs well most of the time during TCs’ rapid intensification. However, when TCs’ system is not well organized, estimating the wind structure is challenging.