TY - JOUR
T1 - Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions
AU - Bailey, Rachel Louise
AU - Reiß, Martin
AU - Arge, Charles N.
AU - Möstl, Christian
AU - Henney, Carl J.
AU - Owens, Mathew J.
AU - Amerstorfer, U. V.
AU - Amerstorfer, Tanja
AU - Weiss, Andreas Jeffrey
AU - Hinterreiter, Jürgen
PY - 2021/5
Y1 - 2021/5
N2 - 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
AB - 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
UR - http://www.scopus.com/inward/record.url?scp=85107183327&partnerID=8YFLogxK
U2 - 10.1029/2020SW002673
DO - 10.1029/2020SW002673
M3 - Article
SN - 1542-7390
VL - 19
JO - Space Weather
JF - Space Weather
IS - 5
M1 - e2020SW002673
ER -