Millas, D;
Innocenti, ME;
Laperre, B;
Raeder, J;
Poedts, S;
Lapenta, G;
(2020)
Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting.
Frontiers in Astronomy and Space Sciences
, 7
, Article 571286. 10.3389/fspas.2020.571286.
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Abstract
Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of Representer analysis (RA) and Domain of Influence (DOI) analysis to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind—magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps toward the ambitious goal of identifying the most relevant heliospheric parameters for modeling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.
| Type: | Article |
|---|---|
| Title: | Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fspas.2020.571286 |
| Publisher version: | https://doi.org/10.3389/fspas.2020.571286 |
| Language: | English |
| Additional information: | Copyright © 2020 Millas, Innocenti, Laperre, Raeder, Poedts and Lapenta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | solar wind, coronal mass ejections (CMEs), magnetohydrodynamics (MHD), numerical simulations, statistical tools, domain of influence, observations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10119814 |
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