用站点数据引导伪监督，实现欧洲颗粒物高精度降尺度
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- 用站点观测构建空间高斯融合伪标签，解决稀疏地面数据与高分辨率图像不匹配问题
- 在欧洲范围内实现40倍超分辨（约1公里），显著还原细粒度空间结构
- 无需时序建模，适合无连续观测数据的区域空气质量精细化建模
将粗粒度大气场超分辨为局部PM₂.₅变化，面临像素代表区域平均值而真实观测为离散、非对齐样本的挑战。为此，本文提出一种基于站点引导的欧洲区域PM₂.₅降尺度框架。以粗分辨率CAMS大气成分数据及异质侧信息（人类活动、土地利用、高程、卫星气溶胶观测和风场）为基础，联合超分辨（×40，≈1 km）并校正CAMS栅格数据，无需依赖时间序列建模。为应对多尺度Transformer网络因稀疏现场数据难以密集监督的问题，引入一种时间无关的传播策略，利用插值后OpenAQ观测的空间高斯融合生成伪标签。在欧洲范围内的定性和站点级评估表明，该模型能有效恢复细粒度空间结构，并显著缓解局部CAMS偏差。
原文摘要 · Abstract (English)
Super-resolving coarse atmospheric fields to local PM$_{2.5}$ variations is uniquely challenged by a mismatch in spatial support: while pixels represent regional averages, ground-truth observations are discrete, unaligned samples of a continuous spatial signal. To bridge this gap, we present a station-guided framework for high-resolution PM$_{2.5}$ downscaling over Europe. Taking coarse CAMS atmospheric composition fields alongside heterogeneous side information (i.e., human activity, land cover, elevation, satellite aerosol observations, and wind fields) our framework jointly super-resolves ($\times 40$, $\approx$ 1 km) and bias-corrects CAMS rasters, without relying on temporal sequence modelling. To address the challenge of densely supervising our multi-scale transformer network with sparse in-situ data, we introduce a time-agnostic propagation strategy that utilises spatial Gaussian blending of interpolated OpenAQ observations. Extensive qualitative and station-level evaluations across Europe demonstrate that our model recovers fine-grained spatial structures and effectively mitigates localised CAMS biases.
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