High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations

ArXi:2604.02850v1 Announce Type: cross The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data. Our approach employs a conditional denoising diffusion probabilistic model (DDPM) trained on sea surface height and temperature observations with up to 99.9% sparsity, without reliance on a background dynamical model.