CycloDiff：Multi-Task Conditional Diffusion Framework for Spatiotemporal Tropical Cyclone Genesis Forecasting

Accurately predicting when and where tropical cloud clusters will develop into tropical cyclones (TCs), and what form they will evolve into, is essential for improving typhoon forecasting and enabling earlier identification of tropical cyclones genesis (TCG) and longer warning lead times. Existing TCG studies, particularly deep learning-based methods, mainly rely on classification paradigms that distinguish TC precursor clusters from non-precursors, yet struggle to model the spatiotemporal evolution of cloud systems. To address these limitations, we propose CycloDiff, a multi-task conditional diffusion framework to generate future spatiotemporal evolution of cloud systems along with the corresponding genesis time and location. To generate future cloud-image sequences that better conform to the evolution of the ocean thermal system, we incorporate subsurface temperature profiles, which provide vertical ocean thermal information that cannot be captured by sea surface temperature alone. Experiments on the Digital Typhoon dataset show that CycloDiff generates more realistic and temporally coherent cloud evolution than existing spatiotemporal prediction methods. These results demonstrate the potential of generative spatiotemporal forecasting for representing TC development and predicting its genesis time and location.