用扩散模型预测100米分辨率气温，助力城市热岛研究
Urban Air Temperature Prediction using Conditional Diffusion Models
- 首次将扩散模型用于高分辨率气温预测
- 在100米地面间距下优于已有方法
- 适合城市规划与气候研究者使用
城市化带来诸多环境挑战，尤其是城市热岛效应。地表以上2米的空气温度（T_a）是衡量该效应的关键指标。土地利用与覆盖（LULC）如何影响T_a是重要科学问题，需社区尺度的高分辨率（HR）T_a数据。然而，气象站分布稀疏（间隔超10公里），数值模型则计算成本过高。本文提出新方法，基于卫星可获取的地表温度（LST）及其他LULC特征，利用扩散模型生成100米地面间距的高分辨率T_a地图。该方法首次将扩散模型应用于此类任务，生成结果准确且视觉真实，性能超越现有方法。研究还构建了时空覆盖更广、空间分辨率更高的基准数据集，为未来气象与计算机视觉交叉研究铺路。此外，模型可用于模拟不同城市设计对气温的影响，支持城市规划决策。
原文摘要 · Abstract (English)
Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. The increase in temperature has a significant impact on the well-being of urban residents. Air temperature ($T_a$) at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects $T_a$ is a critical research question which requires high-resolution (HR) $T_a$ data at neighborhood scale. However, weather stations providing $T_a$ measurements are sparsely distributed e.g. more than 10km apart; and numerical models are impractically slow and computationally expensive. In this work, we propose a novel method to predict HR $T_a$ at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from satellite imagery. Our method leverages diffusion models for the first time to generate accurate and visually realistic HR $T_a$ maps, which outperforms prior methods. We pave the way for meteorological research using computer vision techniques by providing a dataset of an extended spatial and temporal coverage, and a high spatial resolution as a benchmark for future research. Furthermore, we show that our model can be applied to urban planning by simulating the impact of different urban designs on $T_a$.
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