Speaker
Description
Accurate forecasting of solar wind is essential for space weather predictions, but uncertainties persist due to incomplete solar magnetic field observations of the Sun. Disentangling the impact of these limitations on solar wind predictions remains challenging. This research explores the sources of uncertainty in solar wind models caused by the lack of comprehensive full-Sun magnetic field data. We simulate magnetic field maps that account for uncertainties such as far-side and polar field variations, as well as resolution and sensitivity constraints. These maps are used as inputs for three distinct solar wind models: the Wang-Sheeley-Arge (WSA), the Heliospheric Upwind eXtrapolation (HUXt), and the European Heliospheric FORecasting Information Asset (EUHFORIA). We assess the differences in solar wind forecasts, particularly at Earth’s location, by comparing the model outputs to a synthetic ”ground truth” magnetic field map derived from the Advective Flux Transport (AFT) model. The findings show considerable variation in solar wind speeds within each model, with root mean square errors (RMSE) ranging from 59 − 121 km s−1. Additional comparison with the thermodynamic Magnetohydrodynamic Algorithm outside a Sphere (MAS) model as well as inter-model comparison suggests even larger discrepancies in solar wind predictions than those observed within individual models. Nonetheless, using a range of solar wind velocities, represented by a cloud of points around Earth, can ignificantly reduce forecast uncertainties by up to 20 − 77%.
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