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Modelling the propagation of solar disturbances to Earth for the EU H2020 SafeSpace project
  • +10
  • Rungployphan Kieokaew,
  • Rui Ferreira Pinto,
  • Benoit Lavraud,
  • Antoine Brunet,
  • Guillerme Bernoux,
  • Evangelia SAMARA,
  • Stefaan Poedts,
  • Vincent Génot,
  • Alexis Rouillard,
  • Sebastien Bourdarie,
  • Benjamin Grison,
  • Jan Souček,
  • Ioannis Daglis
Rungployphan Kieokaew
Institut de recherche en astrophysique et planétologie

Corresponding Author:rkieokaew@irap.omp.eu

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Rui Ferreira Pinto
LDE3, DAp/AIM, CEA Saclay
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Benoit Lavraud
Laboratoire d’Astrophysique de Bordeaux, Univ. Bordeaux, CNRS,
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Antoine Brunet
Office National d’Etudes et de Recherches Aérospatiales
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Guillerme Bernoux
Office National d’Etudes et de Recherches Aérospatiales
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Evangelia SAMARA
Royal Observatory of Belgium
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Stefaan Poedts
KU Leuven,Uniwersytet Marii Curie-Skłodowskiej
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Vincent Génot
Institut de recherche en astrophysique et planétologie
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Alexis Rouillard
Institut de recherche en astrophysique et planétologie
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Sebastien Bourdarie
Office National d’Etudes et de Recherches Aérospatiales
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Benjamin Grison
Institute of Atmospheric Physics, Czech Academy of Sciences
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Jan Souček
Institute of Atmospheric Physics, Czech Academy of Sciences
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Ioannis Daglis
Hellenic Space Center,National and Kapodistrian University of Athens
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Abstract

The EU H2020 SafeSpace project aims to develop a prototype pipeline that connects several tools in a modular fashion to address the physics of the Sun – Interplanetary space – Earth’s magnetosphere with the ultimate goal to forecast radiation belts dynamics. We present a part of the pipeline called Helio1D that is dedicated to forecasting the solar wind properties at the Lagrangian L1 point. Helio1D models solar wind propagation using input data obtained from the MULTI-VP model, which itself models solar wind emergence near Sun based on magnetograms and coronal field reconstruction. In particular, we aim to forecast the properties of the regular solar wind, as well as Corotating Interaction Regions and their high-speed streams which are most geo-effective (for radiation belts in particular). We take an ensemble forecasting approach to provide optimum forecast up to 2 – 4 days of lead time. We also couple the Helio1D pipeline with neural network models that predict geomagnetic indices such as the Kp index for magnetospheric space weather forecasting. In this presentation, we will present the Helio1D pipeline status, as well as its benchmarking and calibration in order to provide optimum forecasting in real-time. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870437.