Improving ambient solar wind model predictions with machine learning

Rachel Louise Bailey, Martin Reiß, Charles N. Arge, Christian Möstl, Mathew J. Owens, Carl J. Henney, U. Amerstorfer, Tanja Amerstorfer, Andreas Jeffrey Weiss, Jürgen Hinterreiter

Research output: Contribution to journalArticle

Abstract

The study of ambient solar wind, a continuous pressure-driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth's magnetic field. Accurately forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. The results are compared to observations and existing models in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth.
Original languageEnglish
JournalThe Astrophysical Journal / Supplement series
Publication statusSubmitted - Aug 2020

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