Multimodal Atmospheric Super-Resolution With Deep Generative Models

• URL: http://arxiv.org/abs/2506.22780v1
• Date: Sat, 28 Jun 2025 06:47:09 GMT
• Title: Multimodal Atmospheric Super-Resolution With Deep Generative Models
• Authors: Dibyajyoti Chakraborty, Haiwen Guan, Jason Stock, Troy Arcomano, Guido Cervone, Romit Maulik,
• Abstract summary: Score-based diffusion modeling is a generative machine learning algorithm that can be used to sample from complex distributions.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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