Dynamical complexity in Swarm electron density time series using Block entropy

Balasis, Georgios; Papadimitriou, Constantinos; Boutsi, Adamantia Zoe; Daglis, Ioannis A.; Giannakis, Omiros; Anastasiadis, A.; Michelis, Paola De; Consolini, Giuseppe

doi:10.1209/0295-5075/131/69001

Cited by 6 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We believe that the presented results contribute to a better understanding of solar wind-magnetosphere–ionosphere interactions as well as to modeling and predictions of space weather events. This study adds further compelling evidence to previous studies (e.g., [ 21 , 58 , 59 , 60 , 61 , 62 , 63 ]), highlighting the great potential of information-theoretic approaches to contribute in the development of Space Weather diagnostics and tackle contemporary research problems in Space Physics.…”

Section: Discussionsupporting

confidence: 83%

Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Manshour

Balasis

Consolini

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

An information-theoretic approach for detecting causality and information transfer is used to identify interactions of solar activity and interplanetary medium conditions with the Earth’s magnetosphere–ionosphere systems. A causal information transfer from the solar wind parameters to geomagnetic indices is detected. The vertical component of the interplanetary magnetic field (Bz) influences the auroral electrojet (AE) index with an information transfer delay of 10 min and the geomagnetic disturbances at mid-latitudes measured by the symmetric field in the H component (SYM-H) index with a delay of about 30 min. Using a properly conditioned causality measure, no causal link between AE and SYM-H, or between magnetospheric substorms and magnetic storms can be detected. The observed causal relations can be described as linear time-delayed information transfer.

show abstract

Section: Discussionsupporting

confidence: 83%

Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Manshour

Balasis

Consolini

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several authors have examined the same storm event using Swarm time series and applying information theory approaches (e.g. [22,23,25,26]). Table 1 shows the three strongest geospace magnetic storms of 2015, based on minimum Dst index values.…”

Section: Data Descriptionmentioning

confidence: 99%

“…In this context, information theory has been shown to be quite useful for studies of this coupled system [12][13][14][15][16][17][18][19][20][21]. In particular, a few recent studies exploit Swarm data using information theory techniques to study the complex dynamics of the near-Earth electromagnetic environment [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Dynamical Complexity in Swarm-Derived Geomagnetic Activity Indices Using Information Theory

et al. 2023

Self Cite

View full text Add to dashboard Cite

In 2023, the ESA’s Swarm constellation mission celebrates 10 years in orbit, offering one of the best ever surveys of the topside ionosphere. Among its achievements, it has been recently demonstrated that Swarm data can be used to derive space-based geomagnetic activity indices, similar to the standard ground-based geomagnetic indices monitoring magnetic storm and magnetospheric substorm activity. Recently, many novel concepts originating in time series analysis based on information theory have been developed, partly motivated by specific research questions linked to various domains of geosciences, including space physics. Here, we apply information theory approaches (i.e., Hurst exponent and a variety of entropy measures) to analyze the Swarm-derived magnetic indices from 2015, a year that included three out of the four most intense magnetic storm events of the previous solar cycle, including the strongest storm of solar cycle 24. We show the applicability of information theory to study the dynamical complexity of the upper atmosphere, through highlighting the temporal transition from the quiet-time to the storm-time magnetosphere, which may prove significant for space weather studies. Our results suggest that the spaceborne indices have the capacity to capture the same dynamics and behaviors, with regards to their informational content, as traditionally used ground-based ones.

show abstract

“…The selected time interval of study is centered around the strongest magnetic storm of solar cycle #24, i.e., the storm which occurred on 17 March 2015 with a minimum Dst index of −223 nT[60][61][62][63]. Information about the network's architecture (number of filters on each CONV layer, kernel size (f ), stride (s)).…”

mentioning

confidence: 99%

Convolutional Neural Networks for Automated ULF Wave Classification in Swarm Time Series

et al. 2022

Self Cite

View full text Add to dashboard Cite

Ultra-low frequency (ULF) magnetospheric plasma waves play a key role in the dynamics of the Earth’s magnetosphere and, therefore, their importance in Space Weather phenomena is indisputable. Magnetic field measurements from recent multi-satellite missions (e.g., Cluster, THEMIS, Van Allen Probes and Swarm) are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites, one of the most successful missions for the study of the near-Earth electromagnetic environment, have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence (AI), we are now able to use more robust approaches devoted to automated ULF wave event identification and classification. The goal of this effort is to use a popular machine learning method, widely used in Earth Observation domain for classification of satellite images, to solve a Space Physics classification problem, namely to identify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network (ConvNet) that takes as input the wavelet spectrum of the Earth’s magnetic field variations per track, as measured by Swarm, and whose building blocks consist of two alternating convolution and pooling layers, and one fully connected layer, aiming to classify ULF wave events within four different possible signal categories: (1) Pc3 wave events (i.e., frequency range 20–100 MHz), (2) background noise, (3) false positives, and (4) plasma instabilities. Our preliminary experiments show promising results, yielding successful identification of more than 97% accuracy. The same methodology can be easily applied to magnetometer data from other satellite missions and ground-based arrays.

show abstract

Dynamical complexity in Swarm electron density time series using Block entropy

Cited by 6 publications

References 37 publications

Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Investigation of Dynamical Complexity in Swarm-Derived Geomagnetic Activity Indices Using Information Theory

Convolutional Neural Networks for Automated ULF Wave Classification in Swarm Time Series

Contact Info

Product

Resources

About