Quantifying uncertainties in solar wind forecasting due to incomplete solar magnetic field information

Heinemann, S. G. ORCID: https://orcid.org/0000-0002-2655-2108, Pomoell, J. ORCID: https://orcid.org/0000-0003-1175-7124, Caplan, R. M. ORCID: https://orcid.org/0000-0002-2633-4290, Owens, M. J. ORCID: https://orcid.org/0000-0003-2061-2453, Jones, S. ORCID: https://orcid.org/0000-0001-9498-460X, Upton, L. ORCID: https://orcid.org/0000-0003-0621-4803, Jha, B. K. ORCID: https://orcid.org/0000-0003-3191-4625 and Arge, C. N. ORCID: https://orcid.org/0000-0001-9326-3448 (2025) Quantifying uncertainties in solar wind forecasting due to incomplete solar magnetic field information. The Astrophysical Journal, 986 (2). 166. ISSN 0004-637X doi: 10.3847/1538-4357/adcf9e

Abstract/Summary

Solar wind forecasting plays a crucial role in space weather prediction, yet significant uncertainties persist duet to incomplete magnetic field observations of the Sun. Isolating the solar wind forecasting errors due to these effects is difficult. This study investigates the uncertainties in solar wind models arising from these limitations. We simulate magnetic field maps with known uncertainties, including far-side and polar field variations, as well as resolution and sensitivity limitations. These maps serve as input for three solar wind models: the Wang–Sheeley–Arge, the Heliospheric Upwind eXtrapolation, and the European Heliospheric FORecasting Information Asset. We analyze the discrepancies in solar wind forecasts, particularly the solar wind speed at Earth’s location, by comparing the results of these models to a created ground truth magnetic field map, which is derived from a synthetic solar rotation evolution using the Advective Flux Transport model. The results reveal significant variations within each model with a root mean square error ranging from 59 to 121 km s −1 . Further comparison with the thermodynamic Magnetohydrodynamic Algorithm outside a Sphere model indicates that uncertainties in the different models can lead to even larger variations in solar wind forecasts compared to those within a single model. However, predicting a range of solar wind velocities based on a cloud of points around Earth can help mitigate uncertainties by up to 20%–77%.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123318
Identification Number/DOI	10.3847/1538-4357/adcf9e
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Astronomical Society
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