Real-Time Statistical Weather Estimation and Prediction for Tropical Cyclone Intensity in an Interpretable Manner via Causal Inference

Currently, integrating mathematical-physical (MP) knowledge into the deep learning (DL) model in an interpretable manner for tropical cyclone (TC) intensity estimation and prediction remains a challenge. In this article, we propose the statistical weather prediction for tropical cyclone intensity (SWP-TCI) model, focusing on real-time estimation and prediction of the TC intensity over the Numerical weather prediction (NWP). SWP-TCI incorporates multiple physical factors and utilizes the causal statistical fusion ensemble module (CSFEM) to integrate this physical knowledge into the model. By leveraging constraints from various physical factors, SWP-TCI can identify more distinct TC features from the satellite cloud images. CSFEM is developed using causal inference (CI), establishing the causal relationship between the TC and physical factors, thus supporting the learning of SWP-TCI and enhancing the model’s interpretability. In experiments, our results are obtained without using the historical sequence of TC intensities. The average MAE of 24-h predictions across from 2015 to 2021 is 5.9 m/s, standing in a close comparison with the official object forecast methods employed worldwide.