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

TL;DR

This paper introduces a depth-aware generative diffusion model that reconstructs high-resolution 3D ocean states from sparse surface data, improving climate monitoring capabilities.

Contribution

It presents a novel depth-embedded diffusion framework that accurately reconstructs subsurface ocean dynamics without relying on background models.

Findings

Accurately reconstructs subsurface temperature, salinity, and velocity fields.

Demonstrates recovery of large-scale circulation and multiscale variability.

Operates effectively with up to 99.9% data sparsity.

Abstract

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 unseen depths. Applied to the Gulf of Mexico, the framework accurately reconstructs subsurface temperature, salinity, and velocity fields across multiple depths. Evaluations using statistical metrics, spectral analysis, and heat transport diagnostics demonstrate recovery of both large-scale circulation and multiscale variability. These results establish generative diffusion models as a scalable approach for probabilistic ocean reconstruction in data-limited regimes, with implications for climate monitoring and forecasting.