Multimodal Atmospheric Super-Resolution With Deep Generative Models

TL;DR

This paper introduces a novel approach using score-based diffusion models for multimodal atmospheric super-resolution, effectively integrating sparse observational data to reconstruct high-resolution atmospheric states with uncertainty quantification.

Contribution

It applies score-based diffusion modeling to high-dimensional atmospheric data, enabling real-time super-resolution from multimodal sparse measurements with uncertainty estimates.

Findings

Accurate high-dimensional atmospheric state recovery from sparse data.

Effective integration of multiple data modalities during reconstruction.

Demonstrated super-resolution on ERA5 and IGRA datasets.

Abstract

Score-based diffusion modeling is a generative machine learning algorithm that can be used to sample from complex distributions. They achieve this by learning a score function, i.e., the gradient of the log-probability density of the data, and reversing a noising process using the same. Once trained, score-based diffusion models not only generate new samples but also enable zero-shot conditioning of the generated samples on observed data. This promises a novel paradigm for data and model fusion, wherein the implicitly learned distributions of pretrained score-based diffusion models can be updated given the availability of online data in a Bayesian formulation. In this article, we apply such a concept to the super-resolution of a high-dimensional dynamical system, given the real-time availability of low-resolution and experimentally observed sparse sensor measurements from multimodal data. Additional analysis on how score-based sampling can be used for uncertainty estimates is also provided. Our experiments are performed for a super-resolution task that generates the ERA5 atmospheric dataset given sparse observations from a coarse-grained representation of the same and/or from unstructured experimental observations of the IGRA radiosonde dataset. We demonstrate accurate recovery of the high dimensional state given multiple sources of low-fidelity measurements. We also discover that the generative model can balance the influence of multiple dataset modalities during spatiotemporal reconstructions.