Prediction of tropical cyclone categories in the North-Western Pacific using a long short-term memory network

Introduction Short-range forecasting of tropical cyclone intensity remains challenging because storm evolution is governed by complex, nonlinear dynamics. Accurate 24-h wind speed prediction is particularly important for operational decision-making but is difficult to achieve using traditional approaches alone. Sequence-based deep learning models offer a data-driven way to learn temporal dependencies in intensity evolution from historical observations. This study proposes a transparent and computationally efficient sequence-to-one deep learning framework as a proof of concept for short-term cyclone intensity forecasting. Methods This study presents a sequence-based deep learning framework for 24-h wind speed forecasting, formulated as a sequence-to-one prediction task using historical cyclone observations. Multivariate best-track data from the International Best Track Archive for Climate Stewardship (IBTrACS) are organized into fixed-length temporal sequences and modeled using a Long Short-Term Memory (LSTM) network to capture temporal dependencies in intensity evolution. Results Model performance is evaluated using normalized error metrics, demonstrating low relative prediction error and stable learning of short-term intensity trends. Predicted wind speeds are further mapped to standard cyclone intensity categories to enable qualitative assessment of categorical consistency. Case-based analyses and interpretability experiments suggest that recent intensity history and physically meaningful track-related variables play a dominant role in the model’s forecasts, consistent with established understanding of tropical cyclone behavior. Discussion The proposed framework emphasizes transparency and computational efficiency and is intended as a proof-of-concept demonstration of sequence-based modeling for cyclone intensity forecasting. While the current implementation does not incorporate environmental predictors or provide direct quantitative comparison with baseline forecasting methods, it establishes a foundation for future extensions involving additional predictors, broader basin coverage, and systematic benchmarking against operational approaches.