Recurrence‐Based Quantification of Dynamical Complexity in the Earth's Magnetosphere at Geospace Storm Timescales

Donner, Reik V.; Balasis, Georgios; Stolbova, Veronika; Γεωργίου, Μαρίνα; Wiedermann, Marc; Kurths, Jürgen

doi:10.1029/2018ja025318

Cited by 25 publications

(36 citation statements)

References 118 publications

(250 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…

S a m p E n

is more stable than the frequently used approximate entropy

A p E n

with respect to variations of maximum allowed distance

r

and series length

N

(Richman & Moorman, ). But it still requires a selection of the most appropriate parameters

N

r

m

, and

τ

for a given application (Donner et al, ; Yentes et al, ).…”

Section: Correlations Between Sample Entropy Of Geomagnetic Indices Amentioning

confidence: 99%

“…The selection of such parameters was performed here (see full details in Appendix ) based on recommendations from previous works using

S a m p E n

(Balasis et al, ; Cao, ; Donner et al, , ; Yentes et al, ). These works have demonstrated that

S a m p E n

could really discriminate between storm and non‐storm periods as accurately as other refined metrics, such as recurrence quantification analysis (Donner et al, , ) when appropriate parameters are used. These parameters must represent a reasonable compromise between different constraints.…”

Section: Correlations Between Sample Entropy Of Geomagnetic Indices Amentioning

confidence: 99%

“…SampEn is unbiased by self-counting and noticeably more stable than the frequently used approximate entropy (ApEn) with respect to variations of r and series length N (Richman & Moorman, 2000). But it still requires a selection of the most appropriate parameters N, r, m, and for a given application (Donner et al, 2019;Yentes et al, 2013). Donner et al (2018) recently studied the relative temporal variations of SampEn(Dst) over 2001, using the traditionally considered embedding dimension m = 2 together with N = 256, = 1 (in hours) and r = 0.65STD as in an earlier work (Balasis et al, 2013).…”

Section: Appendix a : Parameter Selection For Sample Entropy Analysismentioning

confidence: 99%

“…Here, we expand upon previous pioneering entropy studies (Balasis et al, , ; Donner et al, ; Johnson et al, ) that mainly focused on the instantaneous 1‐hr

D s t

index, by additionally considering other indices of geomagnetic activity (such as

A E

a p

) that are often more appropriate than

D s t

for describing strong increases of fluxes of

\sim 1

‐ to

3

‐MeV satellite‐killer electrons in the radiation belts (Borovsky & Shprits, ; Mourenas et al, ), by studying their entropy variations as a function of the peak or time‐integrated strengths of geomagnetic events and by examining the correlations between their entropies and the entropy of energetic particle fluxes measured by satellites at various locations. This will allow us to explore the possible roles of physical system organization and stochasticity in the development of large and long‐duration events at different geomagnetic latitudes that affect different locations in the radiation belts.…”

Section: Introductionmentioning

confidence: 99%

“…Over the past 30 years, many studies based on various complementary metrics developed to analyze nonlinear dynamics and complex systems have advanced our knowledge of the inner magnetosphere's response to solar wind driving, which is generally complex and nonlinear (Borovsky, 2014;Donner et al, 2019;Johnson et al, 2018;Klimas et al, 1996;Sitnov et al, 2001;Tsurutani et al, 1990;Vassiliadis et al, 1995). Observations have revealed the existence of turbulence and intermittency in the plasma sheet (Angelopoulos et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dynamical Properties of Peak and Time‐Integrated Geomagnetic Events Inferred From Sample Entropy

2020

View full text Add to dashboard Cite

We provide a comprehensive statistical analysis of the sample entropy of peak and time‐integrated geomagnetic events in 2001–2017, considering different measures of event strength, different geomagnetic indices, and a simplified solar wind‐magnetosphere coupling function P*. Our investigations reveal the existence of significant correlations between the entropies of Dst, ap, and P*, and between such entropies and event strengths, as well as good correlations between peak levels of solar wind‐magnetosphere coupling and ring current ( Dst) and ap entropies, suggesting a potential predictability of significant Dst and ap events on the basis of appropriate functions of P*. Sensibly weaker correlations are found with AE entropy. We further show the presence of several significant entropy correlations between geomagnetic indices, solar wind‐magnetosphere coupling, and trapped or precipitated energetic electron and ion fluxes measured by geostationary and low Earth orbit satellites in the outer radiation belt during the same periods. Entropy correlations between Dst and trapped or precipitated 30‐ to 80‐keV ion fluxes at low L and between AE and trapped 40‐keV electron fluxes at geostationary orbit correspond well with ring current properties and substorm‐induced injections, respectively. Entropy correlations between ap and precipitation rates of energetic ion and electron fluxes demonstrate the sensibility of ap index entropy to both low‐energy (5–30 keV) electron injections and ring current. The stronger entropy correlation between solar wind‐magnetosphere coupling and ap than AE likely stems from the more stochastic behavior of electron injections and fast losses near geostationary orbit.

show abstract

“…

S a m p E n

is more stable than the frequently used approximate entropy

A p E n

with respect to variations of maximum allowed distance

r

and series length

N

(Richman & Moorman, ). But it still requires a selection of the most appropriate parameters

N

r

m

, and

τ

for a given application (Donner et al, ; Yentes et al, ).…”

Section: Correlations Between Sample Entropy Of Geomagnetic Indices Amentioning

confidence: 99%

“…The selection of such parameters was performed here (see full details in Appendix ) based on recommendations from previous works using

S a m p E n

(Balasis et al, ; Cao, ; Donner et al, , ; Yentes et al, ). These works have demonstrated that

S a m p E n

Section: Correlations Between Sample Entropy Of Geomagnetic Indices Amentioning

confidence: 99%

Section: Appendix a : Parameter Selection For Sample Entropy Analysismentioning

confidence: 99%

“…Here, we expand upon previous pioneering entropy studies (Balasis et al, , ; Donner et al, ; Johnson et al, ) that mainly focused on the instantaneous 1‐hr

D s t

index, by additionally considering other indices of geomagnetic activity (such as

A E

a p

) that are often more appropriate than

D s t

for describing strong increases of fluxes of

\sim 1

‐ to

3

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamical Properties of Peak and Time‐Integrated Geomagnetic Events Inferred From Sample Entropy

2020

View full text Add to dashboard Cite

show abstract

Complex Systems Methods Characterizing Nonlinear Processes in the Near-Earth Electromagnetic Environment: Recent Advances and Open Challenges

et al. 2023

View full text Add to dashboard Cite

Learning from successful applications of methods originating in statistical mechanics, complex systems science, or information theory in one scientific field (e.g., atmospheric physics or climatology) can provide important insights or conceptual ideas for other areas (e.g., space sciences) or even stimulate new research questions and approaches. For instance, quantification and attribution of dynamical complexity in output time series of nonlinear dynamical systems is a key challenge across scientific disciplines. Especially in the field of space physics, an early and accurate detection of characteristic dissimilarity between normal and abnormal states (e.g., pre-storm activity vs. magnetic storms) has the potential to vastly improve space weather diagnosis and, consequently, the mitigation of space weather hazards.This review provides a systematic overview on existing nonlinear dynamical systems-based methodologies along with key results of their previous applications in a space physics context, which particularly illustrates how complementary modern complex systems approaches have recently shaped our understanding of nonlinear magnetospheric variability. The rising number of corresponding studies demonstrates that the multiplicity of nonlinear time series analysis methods developed during the last decades offers great potentials for uncovering relevant yet complex processes interlinking different geospace subsystems, variables and spatiotemporal scales.

show abstract

Ultra-high resolution multivariate record and multiscale causal analysis of Pridoli (late Silurian): Implications for global stratigraphy, turnover events, and climate-biota interactions

et al. 2020

View full text Add to dashboard Cite

Recurrence‐Based Quantification of Dynamical Complexity in the Earth's Magnetosphere at Geospace Storm Timescales

Cited by 25 publications

References 118 publications

Dynamical Properties of Peak and Time‐Integrated Geomagnetic Events Inferred From Sample Entropy

Dynamical Properties of Peak and Time‐Integrated Geomagnetic Events Inferred From Sample Entropy

Complex Systems Methods Characterizing Nonlinear Processes in the Near-Earth Electromagnetic Environment: Recent Advances and Open Challenges

Ultra-high resolution multivariate record and multiscale causal analysis of Pridoli (late Silurian): Implications for global stratigraphy, turnover events, and climate-biota interactions

Contact Info

Product

Resources

About