Modeling the Sun – Earth propagation of solar disturbances for the H2020 SafeSpace project

0542779 - ÚFA 2022 DE eng A - Abstract
Lavraud, B. - Pinto, R. - Kieokaew, R. - Samara, E. - Poedts, S. - Génot, V. - Rouillard, A. - Brunet, A. - Bourdarie, S. - Grison, Benjamin - Souček, Jan - Daglis, Y.
Modeling the Sun – Earth propagation of solar disturbances for the H2020 SafeSpace project.
EGU General Assembly 2021 (vEGU21: Gather Online). Göttingen: European Geosciences Union, 2021.
[EGU General Assembly Conference 2021. 19.04.2021-30.04.2021, online]
EU Projects: European Commission(XE) 870437
Institutional support: RVO:68378289
Keywords : magnetosphere * heliospheric propagation models * solar wind
OECD category: Fluids and plasma physics (including surface physics)
https://meetingorganizer.copernicus.org/EGU21/EGU21-10796.html

We present the solar wind forecast pipeline that is being implemented as part of the H2020
SafeSpace project. The Goal of this project is to use several tools in a modular fashion to address
the physics of Sun – interplanetary space – Earth’s magnetosphere. This presentation focuses on
the part of the pipeline that is dedicated to the forecasting – from solar measurements – of the
solar wind properties at the Lagrangian L1 point. The modeling pipeline puts together different
mature research models: determination of the background coronal magnetic field, computation of
solar wind acceleration profiles (1 to 90 solar radii), propagation across the heliosphere (for
regular solar wind, CIRs and CMEs), and comparison to spacecraft measurements. Different
magnetogram sources (WSO, SOLIS, GONG, ADAPT) can be combined, as well as coronal field
reconstruction methods (PFSS, NLFFF), wind (MULTI-VP) and heliospheric propagation models
(CDPP 1D MHD, EUHFORIA). We aim at providing a web-based service that continuously supplies a
full set of bulk physical parameters of the solar wind at 1 AU several days in advance, at a time
cadence compatible with space weather applications. This work has received funding from the
European Union’s Horizon 2020 research and innovation programme under grant agreement No
870437.

Permanent Link: http://hdl.handle.net/11104/0320163