High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations
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 percent sparsity, without reliance on a background dynamical model. By incorporating continuous depth embeddings, the model learns a unified vertical representation of the ocean states and generalizes to previously unsee...
| Authors | Niloofar Asefi, Tianning Wu, Ruoying He et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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