Physics-guided score-based diffusion for 3D reconstruction of tropical cyclones from sparse observations

Tropical cyclones (TCs) three-dimensional (3D) structures are crucial for understanding their intensification processes and assessing associated risks. However, observational data remain sparse, especially for complete dynamic and thermodynamic variables. Dropsondes are considered one of the best available in situ observations providing high-quality vertical profiles, but they are extremely sparse in spatial distribution. This study constructs a physics-guided generative AI framework capable of reconstructing complete 3D TC fields, including wind, temperature, and humidity, from sparse dropsonde observations. Our method utilizes a score-based diffusion model, pretrained on global climate simulation data and fine-tuned on high-resolution operational analysis fields, to learn priors of TC structures. By integrating this generative prior with a score-based posterior sampling algorithm and imposing physical constraints of divergence, vorticity, and thermodynamic consistency, we obtain physically consistent TC 3D reconstructions. Results from systematic Observing System Simulation Experiments (OSSEs) and extensive real-world operational cases indicate that our method can reconstruct the complete TC 3D dynamic and thermodynamic structures, providing a data-driven pathway for reconstructing high-dimensional atmospheric states from sparse in situ data.