EGU2020-5202, updated on 10 Jan 2024
https://doi.org/10.5194/egusphere-egu2020-5202
EGU General Assembly 2020
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Helio4Cast - a real time test environment to enhance space weather prediction at Earth

Christian Möstl, Rachel L. Bailey, Ute V. Amerstorfer, Tanja Amerstorfer, Andreas J. Weiss, Martin A. Reiss, Jürgen Hinterreiter, and Maike Bauer
Christian Möstl et al.
  • Space Research Institute, Austrian Academy of Sciences, Graz, Austria (christian.moestl@oeaw.ac.at)

We introduce Helio4cast, an open source python package to provide real time solar wind predictions at the Sun-Earth L1 point, and to directly couple them to forecasts of the aurora oval, geomagnetically induced currents and further geomagnetic indices. We present its current status, using a combination of our PREDSTORM solar wind forecast and the real time modeling of the aurora with the OVATION model. The solar wind prediction is driven by data from either STEREO-A, a recurrence model, an empirical background solar wind model or a future L5 mission. For coronal mass ejections (CMEs), we plan to use our semi-empirical 3DCORE model to produce in situ magnetic flux rope signatures constrained by real-time solar observations, or a machine learning approach based on many previous observations of in situ CMEs. We are particularly interested in how the errors in the solar wind prediction propagate to ground-based observations. Challenges and future plans of the real-time implementation are discussed.

How to cite: Möstl, C., Bailey, R. L., Amerstorfer, U. V., Amerstorfer, T., Weiss, A. J., Reiss, M. A., Hinterreiter, J., and Bauer, M.: Helio4Cast - a real time test environment to enhance space weather prediction at Earth, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5202, https://doi.org/10.5194/egusphere-egu2020-5202, 2020.

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