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      Measuring Information Coupling between the Solar Wind and the Magnetosphere–Ionosphere System

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          Abstract

          The interaction between the solar wind and the Earth’s magnetosphere–ionosphere system is very complex, being essentially the result of the interplay between an external driver, the solar wind, and internal processes to the magnetosphere–ionosphere system. In this framework, modelling the Earth’s magnetosphere–ionosphere response to the changes of the solar wind conditions requires a correct identification of the causality relations between the different parameters/quantities used to monitor this coupling. Nowadays, in the framework of complex dynamical systems, both linear statistical tools and Granger causality models drastically fail to detect causal relationships between time series. Conversely, information theory-based concepts can provide powerful model-free statistical quantities capable of disentangling the complex nature of the causal relationships. In this work, we discuss how to deal with the problem of measuring causal information in the solar wind–magnetosphere–ionosphere system. We show that a time delay of about 30–60 min is found between solar wind and magnetospheric and ionospheric overall dynamics as monitored by geomagnetic indices, with a great information transfer observed between the z component of the interplanetary magnetic field and geomagnetic indices, while a lower transfer is found when other solar wind parameters are considered. This suggests that the best candidate for modelling the geomagnetic response to solar wind changes is the interplanetary magnetic field component B z . A discussion of the relevance of our results in the framework of Space Weather is also provided.

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          Most cited references43

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          A Mathematical Theory of Communication

          C. Shannon (1948)
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            Investigating Causal Relations by Econometric Models and Cross-spectral Methods

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              Measuring information transfer

              An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.
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                Author and article information

                Journal
                Entropy (Basel)
                Entropy (Basel)
                entropy
                Entropy
                MDPI
                1099-4300
                28 February 2020
                March 2020
                : 22
                : 3
                : 276
                Affiliations
                [1 ]Department of Physics, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133 Roma, Italy; mirko.stumpo@ 123456inaf.it
                [2 ]INAF-Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere 100, 00133 Roma, Italy; giuseppe.consolini@ 123456inaf.it (G.C.); virgilio.quattrociocchi@ 123456inaf.it (V.Q.)
                [3 ]Dipartimento Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, 67100 L’Aquila, Italy
                Author notes
                Author information
                https://orcid.org/0000-0002-6303-5329
                https://orcid.org/0000-0002-3403-647X
                https://orcid.org/0000-0001-6096-0220
                https://orcid.org/0000-0001-7288-1087
                Article
                entropy-22-00276
                10.3390/e22030276
                7516727
                34ef1429-edb0-447f-8a67-a9543b63b554
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 February 2020
                : 27 February 2020
                Categories
                Article

                information theory,time series analysis,solar wind-magnetosphere–ionosphere system,space weather

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