EGU21-15803, updated on 04 Mar 2021
https://doi.org/10.5194/egusphere-egu21-15803
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using Gradient Boosting Regressors to forecast the ambient solar wind from coronal magnetic models

Rachel Bailey1, Martin A. Reiss2, Christian Möstl2, C. Nick Arge3, Carl Henney4, Matt Owens5, Ute Amerstorfer2, Tanja Amerstorfer2, Andreas Weiss2, and Jürgen Hinterreiter2
Rachel Bailey et al.
  • 1Conrad Observatory, Zentralanstalt für Meteorologie und Geodynamik, Wien, Austria (rachel.bailey@zamg.ac.at)
  • 2Space Research Institute, Austrian Academy of Sciences, Graz, Austria
  • 3Heliophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 4Air Force Research Laboratory, Space Vehicles Directorate, Kirtland AFB, NM, USA
  • 5Space and Atmospheric Electricity Group, Department of Meteorology, University of Reading, Reading, UK

In this study we present a method for forecasting the ambient solar wind at L1 from coronal magnetic models. Ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and accurately modelling and forecasting the ambient solar wind flow is therefore imperative to space weather awareness. We describe a novel machine learning approach in which solutions from models of the solar corona based on 12 different ADAPT magnetic maps are used to output the solar wind conditions some days later at the Earth. A feature analysis is carried out to determine which input variables are most important. The results of the forecasting model are compared to observations and existing models for one whole solar cycle in a comprehensive validation analysis. We find that the new model outperforms existing models and 27-day persistence in almost all metrics. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth, and is specifically well-suited for ensemble modelling or for application with other coronal models.

How to cite: Bailey, R., Reiss, M. A., Möstl, C., Arge, C. N., Henney, C., Owens, M., Amerstorfer, U., Amerstorfer, T., Weiss, A., and Hinterreiter, J.: Using Gradient Boosting Regressors to forecast the ambient solar wind from coronal magnetic models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15803, https://doi.org/10.5194/egusphere-egu21-15803, 2021.

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