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

TL;DR

This paper introduces a graph neural network with virtual nodes that improves wind nowcasting accuracy in unobserved regions by over 30%, aiding climate resilience and energy planning.

Contribution

It proposes a novel diffusion-contrastive graph neural network with virtual nodes to extend wind nowcasting into data-sparse regions without additional sensors.

Findings

Reduces wind speed and gusts MAE by 30-46% in unobserved areas.

Uses high-resolution weather data from the Netherlands for validation.

Enables localized nowcasts in regions lacking direct measurements.

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.