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

arXiv 2604.10328 ↗First posted 11 Apr 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 58 references that are in the map. First posted on arXiv 11 Apr 2026.

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19 of 58 references.

References · 19

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  3. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  4. 2019Diffusion Improves Graph Learningcited by 414
  5. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 311
  6. 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71
  7. 2021Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Datacited by 55
  8. 2024Continuous Ensemble Weather Forecasting with Diffusion modelscited by 30
  9. 2021Broad-UNet: Multi-scale feature learning for nowcasting taskscited by 25
  10. 2022AA-TransUNet: Attention Augmented TransUNet For Nowcasting Taskscited by 22
  11. 2021Deep Graph Convolutional Networks for Wind Speed Predictioncited by 19
  12. 2020Wind speed prediction using multidimensional convolutional neural networkscited by 17
7 more
  1. 2021Symbolic regression for scientific discovery: an application to wind speed forecastingcited by 16
  2. 2020Deep coastal sea elements forecasting using U-Net based modelscited by 14
  3. 2021Multistream Graph Attention Networks for Wind Speed Forecastingcited by 12
  4. 2021Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Surveycited by 10
  5. 2024GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcastingcited by 9
  6. 2024Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcastingcited by 7
  7. 2021Prototyping Low-Cost Automatic Weather Stations for Natural Disaster Monitoringcited by 2

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