Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models

TL;DR

This paper demonstrates that latent diffusion models can accurately reconstruct three-dimensional turbulent flows in the atmospheric boundary layer from extremely limited measurements, enabling realistic flow simulations.

Contribution

It introduces the use of latent diffusion models for 3D flow reconstruction in the atmospheric boundary layer from minimal data, a novel approach in fluid mechanics.

Findings

Diffusion models generate physically plausible turbulent structures.

Reconstructed flows serve as initial conditions for further simulations.

Models perform well with less than 1% of volume observed.

Abstract

Due to costs and practical constraints, field campaigns in the atmospheric boundary layer typically only measure a fraction of the atmospheric volume of interest. Machine learning techniques have previously successfully reconstructed unobserved regions of flow in canonical fluid mechanics problems and two-dimensional geophysical flows, but these techniques have not yet been demonstrated in the three-dimensional atmospheric boundary layer. Here, we conduct a numerical analogue of a field campaign with spatially limited measurements using large-eddy simulation. We pose flow reconstruction as an inpainting problem, and reconstruct realistic samples of turbulent, three-dimensional flow with the use of a latent diffusion model. The diffusion model generates physically plausible turbulent structures on larger spatial scales, even when input observations cover less than 1% of the volume. Through a combination of qualitative visualization and quantitative assessment, we demonstrate that the diffusion model generates meaningfully diverse samples when conditioned on just one observation. These samples successfully serve as initial conditions for a large-eddy simulation code. We find that diffusion models show promise and potential for other applications for other turbulent flow reconstruction problems.