发表机构
EPFL; University of Cambridge（洛桑联邦理工学院; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对全球变暖下大气数据超分辨率问题，提出基于原始方程的PISR方法及NPC度量，通过约束SR模型遵循物理方程，实验表明该方法能提升重建保真度，增强极端事件检测能力。
AI 中文摘要
在全球变暖背景下，极端事件愈发频繁强烈，可靠检测与预测至关重要。大气观测的空间分辨率不足，促使通过降尺度从粗观测重建高分辨率数据，此任务现被视为超分辨率（SR）问题并用机器学习方法解决。但超分辨后的大气数据是否符合地球系统基本物理存疑。本文通过约束SR模型遵循代表多元大气物理的静水压原始方程应对挑战。提出基于原始方程的多尺度物理信息目标的物理信息超分辨率（PISR）方法，还提出归一化物理一致性（NPC）度量。在ERA5、CERRA和COSMO上的实验表明，PISR通过改善物理一致性、SR精度及极端事件下游检测增强了重建保真度。
英文摘要
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.
