CASCADE：基于气候同化与降尺度演化的跨尺度输运超分辨率
CASCADE: Cross-scale Advective Super-resolution with Climate Assimilation and Downscaling Evolution
AI总结：
CASCADE通过显式输运过程实现跨尺度超分辨率，提供物理一致且质量守恒的重建，适用于大气和海洋中的输运主导极端事件。
摘要：
地物理场的超分辨率呈现了超越自然图像增强的独特挑战：细尺度结构必须遵守物理动力学，保持质量与能量，并在时间上保持一致性。这些约束在极端事件中尤为重要，因为罕见、局部、高强度特征驱动影响，而时间不一致的“幻觉”细节可能导致灾害误判。我们引入CASCADE（基于气候同化与降尺度演化的跨尺度输运超分辨率），一种将时空超分辨率重新框架为显式输运过程的框架。而不是每像素生成高频内容，CASCADE通过迭代将粗信息沿学习的、流条件化的速度场通过半拉格朗日变形进行输运来重建细结构。该架构将运动分解为解析（大尺度）和子网格（未解析）成分，模仿数值天气预测中的闭合问题，并通过类似同化的创新步骤强制低分辨率一致性。在SEVIR雷达数据上对4倍超分辨率严重对流风暴进行评估时，CASCADE在连续指标（PSNR、SSIM、MAE）和基于阈值的技能分数（CSI、HSS、POD）上均优于强基线模型，同时通过可可视化速度和修正场提供可解释的诊断。通过将输运编码为基本操作符而非隐式学习，CASCADE产生时间一致、物理一致且质量守恒的重建，适用于大气和海洋应用中的输运主导极端事件。
英文摘要：
Super-resolution of geophysical fields presents unique challenges beyond natural image enhancement: fine-scale structures must respect physical dynamics, conserve mass and energy, and evolve coherently in time. These constraints are especially critical for extreme events, where rare, localized, high-intensity features drive impacts and where temporally inconsistent "hallucinated" detail can misrepresent hazards. We introduce CASCADE (Cross-scale Advective Super-resolution with Climate Assimilation and Downscaling Evolution), a framework that reframes spatiotemporal super-resolution as an explicit transport process across scales. Rather than hallucinating high-frequency content per pixel, CASCADE reconstructs fine structure by iteratively advecting coarse information along learned, flow-conditioned velocity fields through semi-Lagrangian warping. The architecture decomposes motion into resolved (large-scale) and subgrid (unresolved) components, mirroring the closure problem in numerical weather prediction, and enforces low-resolution consistency through an assimilation-style innovation step. Evaluated on SEVIR radar data for 4x super-resolution of severe convective storms, CASCADE outperforms strong baselines across both continuous metrics (PSNR, SSIM, MAE) and threshold-based skill scores (CSI, HSS, POD) while providing interpretable diagnostics through visualizable velocity and correction fields. By encoding advection as the fundamental operator rather than learning it implicitly, CASCADE produces temporally coherent, physically consistent, and mass-conserving reconstructions well suited to advection-dominated extremes in atmospheric and oceanic applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
