Extended lead‐time geomagnetic storm forecasting with solar wind ensembles and machine learning

Billcliff, M. ORCID: https://orcid.org/0009-0007-2960-9388, Smith, A. W. ORCID: https://orcid.org/0000-0001-7321-4331, Owens, M. ORCID: https://orcid.org/0000-0003-2061-2453, Woo, W. L., Barnard, L. ORCID: https://orcid.org/0000-0001-9876-4612, Edward‐Inatimi, N. ORCID: https://orcid.org/0009-0001-6211-5781 and Rae, I. J. ORCID: https://orcid.org/0000-0002-2637-4786 (2026) Extended lead‐time geomagnetic storm forecasting with solar wind ensembles and machine learning. Space Weather, 24 (3). e2025SW004823. ISSN 1542-7390 doi: 10.1029/2025SW004823

Abstract/Summary

Geomagnetic storms are large disruptions of the magnetosphere, which can impact satellites, communications systems, and power grids, causing significant technological and economic impacts. Current forecasting models utilize L1 satellite data, constraining lead time to a few hours, often insufficient for effective mitigation. We investigate how to extend the lead times of these forecasts with solar data. Associated spatial and propagation uncertainties of solar data are captured with a solar‐wind ensemble, of the computationally efficient one‐dimensional HUXt numerical model. The solar‐wind ensemble once propagated to Earth is processed through logistic regressions, weighting ensemble members by comparison with historical observed velocities, effectively filtering out high error ensemble members. Performance was evaluated across different storm intensities and lead times, demonstrating the models predictive capabilities in a variety of circumstances. Although not including transient phenomena such as Coronal Mass Ejections, our approach demonstrates strong predictive capability, achieving a Brier Skill Score relative to climatology (BSSclim) of 0.595 and a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.751 at 6‐hr lead time for storms defined as Hp30MAX ≥ 5 within a 24‐hr forecast window. Overall, these results highlight the strong potential of the coupled numerical model and machine learning framework to extend the forecast lead time for geomagnetic storms.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/128858
Identification Number/DOI	10.1029/2025SW004823
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Wiley
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