Hybrid AI-Physics Models for Improving Tropical Cyclone Rapid Intensification Forecasts

One of the major challenges in forecasting is the rapid intensification (RI) of tropical cyclones, wherein cyclone intensity can increase rapidly under dynamic and changing atmospheric and oceanic conditions. This paper explores how a hybrid artificial intelligence (AI)-physics model can be used to enhance tropical cyclone RI forecasting. The proposed method integrates the nonlinear pattern-recognition capability of AI with physically meaningful environmental predictors, such as sea-surface temperature, ocean heat content, vertical wind shear, relative humidity, central pressure, and previous intensity change. Historical tropical cyclone observations are analysed and organized in Microsoft Excel, and model performance is evaluated using accuracy, precision, recall, F1-score, mean absolute error (MAE), and root mean square error (RMSE). The comparative analysis involves a physics-informed baseline, a standalone AI model, and the proposed hybrid model. The findings show that the hybrid model gives better RI classification and reduces intensity forecasting error as compared to the alternative methods. The results reveal that incorporating a combination of physical information and AI can enhance the precision and physical applicability of cyclone forecasts. This paper concludes that hybrid AI-physics modelling offers a valuable framework for developing tropical cyclone RI prediction and can aid improved early warning, disaster preparedness, and operational forecasting.