用图神经网络实现高分辨率局部温度预报，精准捕捉城市热岛效应。
A Graph Neural Network Approach for Localized and High-Resolution Temperature Forecasting
- 构建图神经网络模型，基于空间关系学习实现多时距预报。
- 在加拿大西南部实现1-48小时预报，平均误差仅1.93℃，48小时误差2.93℃。
- 适用于数据匮乏地区，推动气候公平预警系统建设。
热浪正日益加剧，是全球最致命的天气灾害之一，对边缘化群体和全球南方地区影响尤为严重。这些地区因医疗资源不足、城市热岛效应及缺乏适应性基础设施，风险被进一步放大。然而现有数值天气预报模型常无法捕捉微尺度极端天气，使最脆弱人群难以获得及时预警。本文提出一种基于图神经网络的局部高分辨率温度预报框架，通过空间建模与高效计算，在多个预报时间尺度上生成结果，最长可达48小时。以加拿大西南部为例，使用24小时输入窗口，该模型在1-48小时预报中均方误差为1.93℃，48小时误差为2.93℃。尽管当前在数据丰富区域验证，但本工作为迁移学习在数据有限的全球南方地区实现本地化、公平的预报提供了基础。
原文摘要 · Abstract (English)
Heatwaves are intensifying worldwide and are among the deadliest weather disasters. The burden falls disproportionately on marginalized populations and the Global South, where under-resourced health systems, exposure to urban heat islands, and the lack of adaptive infrastructure amplify risks. Yet current numerical weather prediction models often fail to capture micro-scale extremes, leaving the most vulnerable excluded from timely early warnings. We present a Graph Neural Network framework for localized, high-resolution temperature forecasting. By leveraging spatial learning and efficient computation, our approach generates forecasts at multiple horizons, up to 48 hours. For Southwestern Ontario, Canada, the model captures temperature patterns with a mean MAE of 1.93$^{\circ}$C across 1-48h forecasts and MAE@48h of 2.93$^{\circ}$C, evaluated using 24h input windows on the largest region. While demonstrated here in a data-rich context, this work lays the foundation for transfer learning approaches that could enable localized, equitable forecasts in data-limited regions of the Global South.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。