Physics-Informed Super-Resolution of Atmospheric Data

2026-07-21

AI Summary

Atmospheric observation data are often limited in spatial resolution, and existing super-resolution methods frequently neglect the physical laws governing Earth system processes, leading to reconstructions with insufficient credibility for climate applications. This work proposes a physics-informed super-resolution (PISR) approach that, for the first time, embeds the hydrostatic primitive equations directly into the super-resolution objective function, thereby enforcing multiscale physical constraints to ensure thermodynamic and dynamical consistency across multiple atmospheric variables. Additionally, a Normalized Physical Consistency (NPC) metric is introduced to quantitatively assess the reliability of reconstructed outputs. Experiments on ERA5, CERRA, and COSMO datasets demonstrate that PISR significantly improves reconstruction accuracy, enhances physical consistency, and better captures extreme events such as heatwaves and strong winds.

Abstract

In the context of global warming, extreme events have become more frequent and intense, making their trustworthy detection and forecasting more important than ever. Yet, atmospheric observations lack sufficient spatial resolution, motivating atmospheric data downscaling as a way to reconstruct high-resolution data from coarse observations. This task is now being formulated as a super-resolution (SR) problem with machine learning methods featuring high efficiency. Nevertheless, it remains unclear whether the super-resolved atmospheric data still satisfies fundamental physics governing the Earth system, raising concerns about their trustworthiness in climate-related applications. In this work, we address this challenge by constraining SR models to respect hydrostatic primitive equations that represent multivariate atmospheric physics. First, we propose a Physics-Informed Super-Resolution (PISR) method involving multi-scale physics-informed objectives based on primitive equations. PISR favors the SR outputs to respect these equations and therefore naturally encodes inter-variable relationships. In addition, we propose a metric called Normalized Physical Consistency (NPC) derived from said primitive equations to measure the physical consistency of super-resolved data. Experiments on ERA5, CERRA, and COSMO demonstrate that PISR enhances the reconstruction fidelity by improving physical consistency, SR accuracy, and downstream detection of extreme events, as demonstrated by case studies in heatwaves and extreme winds.

Problem

• super-resolution
• atmospheric data
• physical consistency
• climate modeling
• extreme events

Innovation

• Physics-Informed Super-Resolution
• Primitive Equations
• Physical Consistency
• Atmospheric Downscaling
• Extreme Event Detection