EGU2020-21455, updated on 10 Jan 2024
https://doi.org/10.5194/egusphere-egu2020-21455
EGU General Assembly 2020
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

An Adaptive Prediction System for Specifying Solar Wind Conditions Near the Sun

Martin Reiss1,2, Peter MacNeice2, Karin Muglach2,3, Nick Arge2, Christian Möstl1, Pete Riley4, Jürgen Hinterreiter1, Rachel Bailey1, Andreas Weiss1, Mathew Owens5, Tanja Amerstorfer1, and Ute Amerstorfer1
Martin Reiss et al.
  • 1Space Research Institute, Austrian Academy of Sciences, Graz, Austria
  • 2Heliophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, USA
  • 3Catholic University of America, Washington, USA
  • 4Predictive Science Inc., San Diego, USA
  • 5Space and Atmospheric Electricity Group, Department of Meteorology, University of Reading, Reading, UK

The ambient solar wind flows and fields influence the complex propagation dynamics of coronal mass ejections in the interplanetary medium and play an essential role in shaping Earth's space weather environment. A critical scientific goal in the space weather research and prediction community is to develop, implement and optimize numerical models for specifying the large-scale properties of solar wind conditions at the inner boundary of the heliospheric model domain. Here we present an adaptive prediction system that fuses information from in situ measurements of the solar wind into numerical models to better match the global solar wind model solutions near the Sun with prevailing physical conditions in the vicinity of Earth. In this way, we attempt to advance the predictive capabilities of well-established solar wind models such as the Wang-Sheeley-Arge model. We perform a statistical analysis of the resulting solar wind predictions for the years 2006 to 2015. The proposed prediction scheme improves all the coronal/heliospheric model combinations investigated by approximately 15-20 percent in terms of various comprehensive prediction validation measures. We discuss why this is the case, and conclude that our findings have important implications for future practice in applied space weather research and prediction.

How to cite: Reiss, M., MacNeice, P., Muglach, K., Arge, N., Möstl, C., Riley, P., Hinterreiter, J., Bailey, R., Weiss, A., Owens, M., Amerstorfer, T., and Amerstorfer, U.: An Adaptive Prediction System for Specifying Solar Wind Conditions Near the Sun, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21455, https://doi.org/10.5194/egusphere-egu2020-21455, 2020.

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