Graph-based Neural Space Weather Forecasting

• URL: http://arxiv.org/abs/2509.19605v2
• Date: Thu, 16 Oct 2025 18:49:57 GMT
• Title: Graph-based Neural Space Weather Forecasting
• Authors: Daniel Holmberg, Ivan Zaitsev, Markku Alho, Ioanna Bouri, Fanni Franssila, Haewon Jeong, Minna Palmroth, Teemu Roos,
• Abstract summary: We introduce a graph-based neural emulator trained on Vlasiator data to autoregressively predict near-Earth space conditions.<n>We show how to achieve both fast deterministic forecasts and, by using a generative model, produce ensembles to capture forecast uncertainty.
• Score: 3.7612198046810814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate space weather forecasting is crucial for protecting our increasingly digital infrastructure. Hybrid-Vlasov models, like Vlasiator, offer physical realism beyond that of current operational systems, but are too computationally expensive for real-time use. We introduce a graph-based neural emulator trained on Vlasiator data to autoregressively predict near-Earth space conditions driven by an upstream solar wind. We show how to achieve both fast deterministic forecasts and, by using a generative model, produce ensembles to capture forecast uncertainty. This work demonstrates that machine learning offers a way to add uncertainty quantification capability to existing space weather prediction systems, and make hybrid-Vlasov simulation tractable for operational use.

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