用图神经网络改进极端降雨预报，提升精度与可靠性。
Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
- 用图神经网络建模降雨的空间依赖关系
- 显著提升极端降雨事件的预报准确率
- 适合气象预报与防灾减灾研究者使用
气候变化正加剧极端降水事件的发生，威胁基础设施、农业和公共安全。集合预报系统虽能提供概率性预测，但在捕捉极端天气方面仍存在偏差且表现不佳。现有后处理技术多聚焦于温度等变量，较少关注具有复杂空间依赖和尾部行为特征的降水。本文提出一种新型框架，利用图神经网络对集合预报进行后处理，特别针对分布尾部极端值进行建模，有效捕捉空间相关性，显著提升极端降雨事件的预报准确性，从而增强预测可靠性，降低极端降水及洪涝风险。
原文摘要 · Abstract (English)
Climate change is increasing the occurrence of extreme precipitation events, threatening infrastructure, agriculture, and public safety. Ensemble prediction systems provide probabilistic forecasts but exhibit biases and difficulties in capturing extreme weather. While post-processing techniques aim to enhance forecast accuracy, they rarely focus on precipitation, which exhibits complex spatial dependencies and tail behavior. Our novel framework leverages graph neural networks to post-process ensemble forecasts, specifically modeling the extremes of the underlying distribution. This allows to capture spatial dependencies and improves forecast accuracy for extreme events, thus leading to more reliable forecasts and mitigating risks of extreme precipitation and flooding.
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