SolarFlowRefiner：面向地表太阳辐射降尺度的精化感知流匹配
SolarFlowRefiner: Refinement-Aware Flow Matching for Surface Solar Radiation Downscaling
AI总结：
针对粗重分析数据降尺度地表太阳辐射时存在的过度平滑和阶段不匹配问题，提出精化感知流匹配框架，联合优化生成与精化，在ERA5–SolarCube基准上取得一致改进。
AI中文摘要：
高分辨率的地表太阳辐射（SSR）对于太阳预报和电网运行至关重要。然而，物理上一致的重分析产品过于粗糙，无法解析局部云驱动的变率。本文研究了一个多源降尺度任务，即从粗分辨率的ERA5辐射变量和共配准的卫星通道重建高分辨率的SolarCube SSR场。该任务具有挑战性，因为单个ERA5网格单元可能同时包含阳光照射和云阴影区域。因此，缺失的高分辨率修正可能在空间上尖锐且本质上具有模糊性。单阶段预测器常常过度平滑这些结构。事后精化也引入了阶段间不匹配：生成器被独立优化，尽管其输出决定了精化器的初始状态。我们提出了SolarFlowRefiner，一种用于SSR降尺度的精化感知流匹配框架。条件FlowMatch生成器首先预测对上采样ERA5基线的归一化修正。然后，精化器在当前FlowMatch输出与目标残差之间的预测条件状态上进行训练。这使精化器暴露于生成器产生的结构化误差。精化目标也通过FlowMatch采样器反向传播，使得生成和修正能够针对最终重建进行联合优化。在按天分块的ERA5–SolarCube基准上的实验表明，相对于独立生成和事后精化，该方法取得了一致的改进。更广泛地，SolarFlowRefiner为将生成式预测器与迭代修正器耦合提供了一种通用策略。
英文摘要：
High-resolution surface solar radiation (SSR) is important for solar forecasting and grid operation. However, physically consistent reanalysis products are too coarse to resolve localized cloud-driven variability. In this paper, we study a multisource downscaling task that reconstructs high-resolution SolarCube SSR fields from coarse ERA5 radiative variables and co-registered satellite channels. The task is challenging because a single ERA5 grid cell may contain both sunlit and cloud-shadowed regions. As a result, the missing high-resolution correction can be spatially sharp and inherently ambiguous. One-stage predictors often oversmooth these structures. Post-hoc refinement also introduces a stage-wise mismatch: the generator is optimized independently, even though its output determines the refiner's initial state. We introduce SolarFlowRefiner, a refinement-aware flow-matching framework for SSR downscaling. A conditional FlowMatch generator first predicts a normalized correction to an upsampled ERA5 baseline. The refiner is then trained on prediction-conditioned states between the current FlowMatch output and the target residual. This exposes the refiner to the structured errors produced by the generator. The refinement objective is also backpropagated through the FlowMatch sampler, allowing generation and correction to be jointly optimized for the final reconstruction. Experiments on a day-blocked ERA5--SolarCube benchmark show consistent improvements over standalone generation and post-hoc refinement. More broadly, SolarFlowRefiner provides a general strategy for coupling generative predictors with iterative correctors.
