Common solar wind drivers behind magnetic storm–magnetospheric substorm dependency
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Georgios Balasis | Jakob Runge | Constantinos Papadimitriou | Reik V Donner | J. Runge | R. Donner | I. Daglis | G. Balasis | C. Papadimitriou | Ioannis A Daglis
[1] Manuel Grande,et al. Current understanding of magnetic storms: storm-substorm relationships , 1998 .
[2] Theodore A. Fritz,et al. Role of substorm-associated impulsive electric fields in the ring current development during storms , 2005 .
[3] Y. Kamide. Is Substorm Occurrence a Necessary Condition for a Magnetic Storm , 1992 .
[4] B. Mauk,et al. The role of small‐scale ion injections in the buildup of Earth's ring current pressure: Van Allen Probes observations of the 17 March 2013 storm , 2014 .
[5] T. Pulkkinen,et al. Solar wind—magnetosphere coupling: A review of recent results , 2007 .
[6] T. Mukai,et al. Effects of the fast plasma sheet flow on the geosynchronous magnetic configuration : Geotail and GOES coordinated study , 2006 .
[7] Georgios Balasis,et al. From pre-storm activity to magnetic storms: A transition described in terms of fractal dynamics , 2006 .
[8] L. Lanzerotti,et al. Storm time dynamics of ring current protons: Implications for the long‐term energy budget in the inner magnetosphere , 2015 .
[9] B Hellwig,et al. On Studentising and Blocklength Selection for the Bootstrap on Time Series , 2005, Biometrical journal. Biometrische Zeitschrift.
[10] D. Vassiliadis,et al. Intense space storms: Critical issues and open disputes , 2003 .
[11] T. Moore,et al. Modeling of inner plasma sheet and ring current during substorms , 1999 .
[12] Jürgen Kurths,et al. Escaping the curse of dimensionality in estimating multivariate transfer entropy. , 2012, Physical review letters.
[13] Jürgen Kurths,et al. Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System , 2013, Entropy.
[14] Edward J. Smith,et al. The nonlinear response of AE to the IMF BS driver: A spectral break at 5 hours , 1990 .
[15] P. Newell,et al. SuperMAG‐based partial ring current indices , 2012 .
[16] H. W. Kroehl,et al. What is a geomagnetic storm , 1994 .
[17] Schreiber,et al. Measuring information transfer , 2000, Physical review letters.
[18] British Antarctic Survey,et al. Scaling of solar wind ϵ and the AU, AL and AE indices as seen by WIND , 2002, physics/0208021.
[19] N. Tsyganenko. Data-based modelling of the Earth's dynamic magnetosphere: a review , 2013 .
[20] R. Dahlhaus. Graphical interaction models for multivariate time series1 , 2000 .
[21] C. Russell,et al. Multipoint analysis of a bursty bulk flow event on April 11, 1985 , 1996 .
[22] Dimitris Vassiliadis,et al. Systems theory for geospace plasma dynamics , 2006 .
[23] J. Borovsky,et al. Multistep Dst development and ring current composition changes during the 4–6 June 1991 magnetic storm , 2002 .
[24] Daniel N. Baker,et al. The organized nonlinear dynamics of the magnetosphere , 1996 .
[25] S. Frenzel,et al. Partial mutual information for coupling analysis of multivariate time series. , 2007, Physical review letters.
[26] Jürgen Kurths,et al. Identifying causal gateways and mediators in complex spatio-temporal systems , 2015, Nature Communications.
[27] I. Daglis,et al. The Role of Substorms in Storm‐Time Particle Acceleration , 2013 .
[28] D. Baker,et al. IMF control of geomagnetic activity , 1988 .
[29] M. Friel,et al. Substorm behavior of the auroral electrojet indices , 2004 .
[30] Dino Sejdinovic,et al. Detecting and quantifying causal associations in large nonlinear time series datasets , 2017, Science Advances.
[31] Daniel N. Baker,et al. Magnetospheric Impulse Response for Many Levels of Geomagnetic Activity , 1985 .
[32] Jürgen Kurths,et al. Quantifying Causal Coupling Strength: A Lag-specific Measure For Multivariate Time Series Related To Transfer Entropy , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[33] S. Chapman,et al. The magnetic field of a model radiation belt, numerically computed , 1961 .
[34] Enrico Camporeale,et al. Information theoretical approach to discovering solar wind drivers of the outer radiation belt , 2016 .
[35] Georgios Balasis,et al. Investigating dynamical complexity in the magnetosphere using various entropy measures , 2009 .
[36] S. Wing,et al. Transfer entropy and cumulant-based cost as measures of nonlinear causal relationships in space plasmas: applications to Dst , 2018, Annales Geophysicae.
[37] M. Eichler. Graphical modelling of multivariate time series , 2006, math/0610654.
[38] Jens Timmer,et al. Block-bootstrapping for noisy data , 2013, Journal of Neuroscience Methods.
[39] D. Baker,et al. The nonlinearity of models of the vB South‐AL coupling , 1996 .
[40] A. Kraskov,et al. Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[41] A. Sharma,et al. The Storm‐Substorm Relationship: Current Understanding and Outlook , 2013 .
[42] Jürgen Kurths,et al. Optimal model-free prediction from multivariate time series. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[43] Jakob Runge,et al. Quantifying information transfer and mediation along causal pathways in complex systems. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[44] K. Shiokawa,et al. “Fine structure” of the storm-substorm relationship: Ion injections during DST decrease , 2000 .
[45] Jakob Runge,et al. Quantifying the Strength and Delay of Climatic Interactions: The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models , 2014 .
[46] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[47] G. Consolini,et al. Multifractal structure of auroral electrojet index data. , 1996, Physical review letters.
[48] Robert L. McPherron,et al. Decay of the Dst field of geomagnetic disturbance after substorm onset and its implication to storm-substorm relation , 1996 .
[49] Massimo Materassi,et al. An information theory approach to the storm-substorm relationship , 2011 .
[50] J Runge,et al. Causal network reconstruction from time series: From theoretical assumptions to practical estimation. , 2018, Chaos.