Steering Diffusion Priors with Sparse Observations for High-Resolution Temperature Downscaling

2026-09-08

AI Summary

该研究通过条件扩散模拟器结合稀疏观测数据来提高高分辨率气温降尺度精度，用于改善热浪灾害预测。

Abstract

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.

Problem

• temperature downscaling
• sparse observations
• heatwave hazard
• high-resolution

Innovation

• Conditional Diffusion Emulator
• Score-Based Data Assimilation (SDA)
• High-Resolution Temperature Downscaling
• Sparse Observations
• Gaussian Observation Likelihood