发表机构
Shiv Nadar University; Western Kentucky University; Ashoka University（希夫·纳达尔大学; 西肯塔基大学; 阿育王大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出条件扩散模拟器，结合分数数据同化引导，利用稀疏观测实现高分辨率温度降尺度，在观测密度达1%时显著优于基线，并给出最优引导强度操作方案。
AI 中文摘要
局部热浪危害取决于精细尺度的气温，但地面站点稀疏，而ERA5等再分析产品无法分辨塑造真实热暴露的地形和地表对比。我们提出了一种条件扩散模拟器，用于高分辨率2米温度降尺度，其条件包括静态地理信息、训练气候学、精确时刻的ERA5温度以及太阳和 temporal 特征，并在推理时通过基于分数的数据同化（SDA）进行引导：一个可微的高斯观测似然将扩散分数引向稀疏的实测温度观测，而无需任何重新训练。在AORC上受控的32例合成网格协议中，当观测密度达到1%时，引导改善了隐藏单元的重建，优于ERA5和强观测近邻基线（RMSE 0.318 K对比0.431 K，赢得全部32例），而更稀疏的机制仍偏向直接插值。我们进一步绘制了三种观测密度下完整引导强度景观，显示最优强度随密度系统性变化，且过度引导导致急剧、可预测的退化——这提供了一个具体的操作方案，而非单一的未调优设置。所得场旨在作为下游热浪危害产品（如阈值超限和累积热负担）的温度层。当前证据是受控的合成网格验证；站点网络和留出年份评估是走向部署的下一步。
英文摘要
Local heatwave hazard depends on fine-scale air temperature, but ground stations are sparse and reanalysis products such as ERA5 cannot resolve the terrain and land-surface contrasts that shape real heat exposure. We present a conditional diffusion emulator for high-resolution 2-m temperature downscaling, conditioned on static geography, a training climatology, exact-time ERA5 temperature, and solar and temporal features, guided at inference by score-based data assimilation (SDA): a differentiable Gaussian observation likelihood steers the diffusion score toward sparse revealed temperature observations without any retraining. On a controlled 32-case synthetic-grid protocol over AORC, guidance improves hidden-cell reconstruction over both ERA5 and a strong observation-proximal nearest-neighbor baseline once observation density reaches 1\% (RMSE 0.318 vs.\ 0.431~K, winning all 32 cases), while sparser regimes still favor direct interpolation. We further map the full guidance-strength landscape across three observation densities, showing that the optimal strength shifts systematically with density and that over-guiding causes sharp, predictable degradation -- giving a concrete operating recipe rather than a single untuned setting. The resulting fields are intended as a temperature layer for downstream heatwave-hazard products such as threshold exceedance and cumulative heat-burden. The present evidence is a controlled synthetic-grid validation; station-network and held-out-year evaluations are the next steps toward deployment.
