用虚拟节点让无观测区风速预测误差降低三成以上。
A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions
- 在图神经网络中引入虚拟节点，结合扩散与对比学习提升预测能力。
- 在荷兰无观测区，风速、阵风和风向的平均绝对误差降低30%~46%。
- 适合风电调度、农业规划等数据稀疏区域的精准短期气象预报。
精准天气短临预报仍是大气科学的核心挑战，对气候韧性、能源安全和防灾减灾具有重要意义。由于无法在所有区域部署观测站，部分区域缺乏密集观测网络，导致这些无观测区的短期风力预测不可靠。本文提出一种深度图自监督框架，无需新增传感器即可将预报能力延伸至无观测区域。该方法在基于扩散与对比学习的图神经网络中引入“虚拟节点”，使模型能够学习无直接测量点处的风况（包括风速、风向和阵风）。利用荷兰高时间分辨率气象站数据验证，该方法在无观测区的风速、阵风和风向预测上，相比插值与回归方法，平均绝对误差（MAE）降低30%至46%。该技术为可再生能源整合、农业规划及数据稀疏区域的早期预警系统开辟了新路径。
原文摘要 · Abstract (English)
Accurate weather nowcasting remains one of the central challenges in atmospheric science, with critical implications for climate resilience, energy security, and disaster preparedness. Since it is not feasible to deploy observation stations everywhere, some regions lack dense observational networks, resulting in unreliable short-term wind predictions across those unobserved areas. Here we present a deep graph self-supervised framework that extends nowcasting capability into such unobserved regions without requiring new sensors. Our approach introduces "virtual nodes" into a diffusion and contrastive-based graph neural network, enabling the model to learn wind condition (i.e., speed, direction and gusts) in places with no direct measurements. Using high-temporal resolution weather station data across the Netherlands, we demonstrate that this approach reduces nowcast mean absolute error (MAE) of wind speed, gusts, and direction in unobserved regions by more than 30% - 46% compared with interpolation and regression methods. By enabling localized nowcasts where no measurements exist, this method opens new pathways for renewable energy integration, agricultural planning, and early-warning systems in data-sparse regions.
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