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This paper introduces a Physics-Informed Super-Resolution (PISR) method that constrains atmospheric data downscaling to adhere to hydrostatic primitive equations, thereby ensuring that the super-resolved outputs respect fundamental atmospheric physics. By employing multi-scale physics-informed objectives, PISR enhances the fidelity of reconstructed data from coarse observations while maintaining inter-variable relationships. Experimental results show that PISR significantly improves physical consistency and super-resolution accuracy, leading to better detection of extreme weather events such as heatwaves and strong winds.
Super-resolved atmospheric data can now be trusted to respect fundamental physics, enhancing extreme event detection accuracy.
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.
