Interval-Based Tropical Cyclone Intensity Forecasting with Spatiotemporal Transformers

Accurate tropical cyclone (TC) intensity forecasting remains challenging due to the strong nonlinearity of intensity evolution and the rapid structural changes associated with storm development. In this work, we propose TC-QFormer, an interval-based probabilistic framework for 24 h TC intensity forecasting that combines transformer-based spatiotemporal modeling with scalar conditioning. Specifically, we adapt the PredFormer video prediction model for multi-horizon scalar regression and introduce a lightweight Scalar–Image Fusion Block to incorporate historical intensity information into the visual representations. A two-stage training strategy is adopted, in which the model is first pretrained for deterministic median prediction and subsequently fine-tuned to directly predict multiple conditional quantiles using the pinball loss. Experiments are conducted on the TCIR dataset using geostationary infrared and water vapor satellite imagery together with aligned historical intensity records. The proposed method is evaluated against representative recurrent and non-recurrent baselines, including ConvLSTM, PredRNN, and SimVP. Results indicate that the proposed framework achieves improved deterministic accuracy and produces well-calibrated 80% prediction intervals, particularly at longer forecast lead times and during rapidly evolving intensity regimes. These findings suggest that combining transformer-based spatiotemporal modeling with scalar–image conditioning provides an effective and interpretable approach for probabilistic TC intensity forecasting.