Models and data analysis tools for the Solar Orbiter mission
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Context. The Solar Orbiter spacecraft will be equipped with a wide range of remote-sensing (RS) and in situ (IS) instruments to record novel and unprecedented measurements of the solar atmosphere and the inner heliosphere. To take full advantage of these new datasets, tools and techniques must be developed to ease multi-instrument and multi-spacecraft studies. In particular the currently inaccessible low solar corona below two solar radii can only be observed remotely. Furthermore techniques must be used to retrieve coronal plasma properties in time and in three dimensional (3D) space. Solar Orbiter will run complex observation campaigns that provide interesting opportunities to maximise the likelihood of linking IS data to their source region near the Sun. Several RS instruments can be directed to specific targets situated on the solar disk just days before data acquisition. To compare IS and RS, data we must improve our understanding of how heliospheric probes magnetically connect to the solar disk. Aims. The aim of the present paper is to briefly review how the current modelling of the Sun and its atmosphere can support Solar Orbiter science. We describe the results of a community-led effort by European Space Agency’s Modelling and Data Analysis Working Group (MADAWG) to develop different models, tools, and techniques deemed necessary to test different theories for the physical processes that may occur in the solar plasma. The focus here is on the large scales and little is described with regards to kinetic processes. To exploit future IS and RS data fully, many techniques have been adapted to model the evolving 3D solar magneto-plasma from the solar interior to the solar wind. A particular focus in the paper is placed on techniques that can estimate how Solar Orbiter will connect magnetically through the complex coronal magnetic fields to various photospheric and coronal features in support of spacecraft operations and future scientific studies. Methods. Recent missions such as STEREO, provided great opportunities for RS, IS, and multi-spacecraft studies. We summarise the achievements and highlight the challenges faced during these investigations, many of which motivated the Solar Orbiter mission. We present the new tools and techniques developed by the MADAWG to support the science operations and the analysis of the data from the many instruments on Solar Orbiter. Results. This article reviews current modelling and tool developments that ease the comparison of model results with RS and IS data made available by current and upcoming missions. It also describes the modelling strategy to support the science operations and subsequent exploitation of Solar Orbiter data in order to maximise the scientific output of the mission. Conclusions. The on-going community effort presented in this paper has provided new models and tools necessary to support mission operations as well as the science exploitation of the Solar Orbiter data. The tools and techniques will no doubt evolve significantly as we refine our procedure and methodology during the first year of operations of this highly promising mission.
Rouillard , A P , Pinto , R F , Vourlidas , A , De Groof , A , Thompson , W T , Bemporad , A , Dolei , S , Indurain , M , Buchlin , E , Sasso , C , Spadaro , D , Dalmasse , K , Hirzberger , J , Zouganelis , I , Strugarek , A , Brun , A S , Alexandre , M , Berghmans , D , Raouafi , N E , Wiegelmann , T , Pagano , P , Arge , C N , Nieves-Chinchilla , T , Lavarra , M , Poirier , N , Amari , T , Aran , A , Andretta , V , Antonucci , E , Anastasiadis , A , Auchère , F , Bellot Rubio , L , Nicula , B , Bonnin , X , Bouchemit , M , Budnik , E , Caminade , S , Cecconi , B , Carlyle , J , Cernuda , I , Davila , J M , Etesi , L , Espinosa Lara , F , Fedorov , A , Fineschi , S , Fludra , A , Génot , V , Georgoulis , M K , Gilbert , H R , Giunta , A , Gomez-Herrero , R , Guest , S , Haberreiter , M , Hassler , D , Henney , C J , Howard , R A , Horbury , T S , Janvier , M , Jones , S I , Kozarev , K , Kraaikamp , E , Kouloumvakos , A , Krucker , S , Lagg , A , Linker , J , Lavraud , B , Louarn , P , Maksimovic , M , Maloney , S , Mann , G , Masson , A , Müller , D , Önel , H , Osuna , P , Orozco Suarez , D , Owen , C J , Papaioannou , A , Pérez-Suárez , D , Rodriguez-Pacheco , J , Parenti , S , Pariat , E , Peter , H , Plunkett , S , Pomoell , J , Raines , J M , Riethmüller , T L , Rich , N , Rodriguez , L , Romoli , M , Sanchez , L , Solanki , S K , St Cyr , O C , Straus , T , Susino , R , Teriaca , L , del Toro Iniesta , J C , Ventura , R , Verbeeck , C , Vilmer , N , Warmuth , A , Walsh , A P , Watson , C , Williams , D , Wu , Y & Zhukov , A N 2020 , ' Models and data analysis tools for the Solar Orbiter mission ' , Astronomy & Astrophysics , vol. 642 , A2 . https://doi.org/10.1051/0004-6361/201935305
Astronomy & Astrophysics
Copyright © A. P. Rouillard et al. 2020. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DescriptionFunding: European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (grant agreement No. 647214).
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