A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions

2026-04-11

AI Summary

This study addresses the challenge of nowcasting wind fields—including wind speed, direction, and gusts—in regions without observational stations. The authors propose a novel graph neural network framework that integrates diffusion mechanisms with contrastive self-supervised learning. A key innovation is the incorporation of virtual node modeling, which enables effective generalization to unobserved areas without requiring additional physical sensors. Evaluated on high-temporal-resolution meteorological station data from the Netherlands, the method achieves a 30%–46% reduction in mean absolute error for wind speed, gust, and direction predictions in unobserved regions compared to conventional interpolation and regression approaches, substantially enhancing nowcasting capability in data-sparse areas.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• wind nowcasting
• unobserved regions
• data-sparse areas
• short-term wind prediction
• atmospheric science

Innovation

• virtual nodes
• diffusion-contrastive learning
• graph neural network
• wind nowcasting
• unobserved regions