Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence
This work addresses inverse problems in geophysical simulations like ocean and climate modeling, offering practical guidance for deployment, though it is incremental in applying existing diffusion methods to a specific domain.
The paper tackled super-resolution and inference of quasi-geostrophic turbulence from coarse, sparse observations using four generative diffusion models, finding that conditional approaches reconstructed fine-scale features with correct statistics, while guided methods like DPS were computationally cheaper but smoothed details.
Typically, numerical simulations of the ocean, weather, and climate are coarse, and observations are sparse and gappy. In this work, we apply four generative diffusion modeling approaches to super-resolution and inference of forced two-dimensional quasi-geostrophic turbulence on the beta-plane from coarse, sparse, and gappy observations. Two guided approaches minimally adapt a pre-trained unconditional model: SDEdit modifies the initial condition, and Diffusion Posterior Sampling (DPS) modifies the reverse diffusion process score. The other two conditional approaches, a vanilla variant and classifier-free guidance, require training with paired high-resolution and observation data. We consider eight test cases spanning: two regimes, eddy and anisotropic-jet turbulence; two Reynolds numbers, 10^3 and 10^4; and two observation types, 4x coarse-resolution fields and coarse, sparse and gappy observations. Our comprehensive skill metrics include norms of the reconstructed vorticity fields, turbulence statistical quantities, and quantification of the super-resolved probabilistic ensembles and their errors. We also study the sensitivity to tuning parameters such as guidance strength. Results show that SDEdit generates unphysical fields, while DPS generates reasonable reconstructions at low computational cost but with smoothed fine-scale features. Both conditional approaches require re-training, but they reconstruct missing fine-scale features, are cycle-consistent with observations, and possess the correct statistics such as energy spectra. Further, their mean model errors are highly correlated with and predictable from their ensemble standard deviations. Results highlight the trade-offs between ease of implementation, fidelity (sharpness), and cycle-consistency of the diffusion models, and offer practical guidance for deployment in geophysical inverse problems.