Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions

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

Publikation: Beitrag in einer FachzeitschriftArtikel

Abstract

Studying the 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. In addition, this approach offers a new perspective to discuss the role of different input data to ambient solar wind modeling, and what this tells us about the underlying physical processes. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth
Originalspracheenglisch
Aufsatznummere2020SW002673
FachzeitschriftSpace Weather
Jahrgang19
Ausgabenummer5
DOIs
PublikationsstatusVeröffentlicht - Mai 2021

ASJC Scopus subject areas

  • !!Atmospheric Science

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