Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy
Abstract In this study, we examine the dynamical complexity transitions during HILDCAA events. HILDCAA preceded by an Interplanetary Coronal Mass Ejection (ICME) storm recovery phase, HILDCAA preceded by a Corotating Interaction Region (CIR) storm recovery phase, and non‐storm driven HILDCAA and geo...
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Wiley
2023-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003475 |
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| author | I. A. Oludehinwa A. Velichko B. O. Ogunsua O. I. Olusola O. O. Odeyemi |
| author_facet | I. A. Oludehinwa A. Velichko B. O. Ogunsua O. I. Olusola O. O. Odeyemi |
| author_sort | I. A. Oludehinwa |
| collection | DOAJ |
| description | Abstract In this study, we examine the dynamical complexity transitions during HILDCAA events. HILDCAA preceded by an Interplanetary Coronal Mass Ejection (ICME) storm recovery phase, HILDCAA preceded by a Corotating Interaction Region (CIR) storm recovery phase, and non‐storm driven HILDCAA and geomagnetically quiet periods were investigated using the Auroral Electrojet index time series. Neural Network Entropy (NNetEn) was used to capture the dynamical complexity transitions during these sporadic events. The NNetEn was able to decipher the distinct dynamical features associated with the emergence of HILDCAA and the geomagnetically quiet periods. Our analysis revealed a high value of NNetEn during HILDCAA signifying that the complexity levels of the coupled solar wind‐magnetosphere‐ionosphere system for HILDCAA, driven by different interplanetary structures were high with no significance difference. Thus, indicating that during HILDCAA, the dynamical behavior of the underlying physical processes due to the energy deposition driven either by ICME, CIR or non‐storm HILDCAA remain the same. However, a deciphering feature of dynamical complexity between the geomagnetically quiet period and HILDCAA events was evident. It was noticed that as the HILDCAA emerges, the NNetEn depicts an increment in entropy value signifying that the complexity levels of the coupled solar wind‐magnetosphere‐ionosphere system increases, and as the dynamics transcend to its recovery state, a reduction in entropy was observed implying a decline in complexity levels. Low values of NNetEn revealing lower complexity levels are found to be associated with geomagnetically quiet periods. |
| format | Article |
| id | doaj-art-3a67c29c74374ff8bfa6954d152267c0 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-3a67c29c74374ff8bfa6954d152267c02025-01-14T16:31:22ZengWileySpace Weather1542-73902023-09-01219n/an/a10.1029/2023SW003475Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network EntropyI. A. Oludehinwa0A. Velichko1B. O. Ogunsua2O. I. Olusola3O. O. Odeyemi4Department of Physics Caleb University Lagos NigeriaInstitute of Physics and Technology Petrozavodsk State University Petrozavodsk RussiaKey Laboratory for Middle Atmospheric and Global Environment Observation (LAGEO) Institute of Atmospheric Physics (IAP) Chinese Academy of Science Beijing ChinaDepartment of Physics University of Lagos Lagos NigeriaDepartment of Physics University of Lagos Lagos NigeriaAbstract In this study, we examine the dynamical complexity transitions during HILDCAA events. HILDCAA preceded by an Interplanetary Coronal Mass Ejection (ICME) storm recovery phase, HILDCAA preceded by a Corotating Interaction Region (CIR) storm recovery phase, and non‐storm driven HILDCAA and geomagnetically quiet periods were investigated using the Auroral Electrojet index time series. Neural Network Entropy (NNetEn) was used to capture the dynamical complexity transitions during these sporadic events. The NNetEn was able to decipher the distinct dynamical features associated with the emergence of HILDCAA and the geomagnetically quiet periods. Our analysis revealed a high value of NNetEn during HILDCAA signifying that the complexity levels of the coupled solar wind‐magnetosphere‐ionosphere system for HILDCAA, driven by different interplanetary structures were high with no significance difference. Thus, indicating that during HILDCAA, the dynamical behavior of the underlying physical processes due to the energy deposition driven either by ICME, CIR or non‐storm HILDCAA remain the same. However, a deciphering feature of dynamical complexity between the geomagnetically quiet period and HILDCAA events was evident. It was noticed that as the HILDCAA emerges, the NNetEn depicts an increment in entropy value signifying that the complexity levels of the coupled solar wind‐magnetosphere‐ionosphere system increases, and as the dynamics transcend to its recovery state, a reduction in entropy was observed implying a decline in complexity levels. Low values of NNetEn revealing lower complexity levels are found to be associated with geomagnetically quiet periods.https://doi.org/10.1029/2023SW003475HILDCAAAuroral electrojetsolar wind‐magnetosphere‐ionosphere systemdynamical complexityNeural Network Entropy (NNetEn)chaotic behavior |
| spellingShingle | I. A. Oludehinwa A. Velichko B. O. Ogunsua O. I. Olusola O. O. Odeyemi Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy Space Weather HILDCAA Auroral electrojet solar wind‐magnetosphere‐ionosphere system dynamical complexity Neural Network Entropy (NNetEn) chaotic behavior |
| title | Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy |
| title_full | Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy |
| title_fullStr | Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy |
| title_full_unstemmed | Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy |
| title_short | Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy |
| title_sort | dynamical complexity transitions during high intensity long duration continuous auroral activities hildcaa events feature analysis based on neural network entropy |
| topic | HILDCAA Auroral electrojet solar wind‐magnetosphere‐ionosphere system dynamical complexity Neural Network Entropy (NNetEn) chaotic behavior |
| url | https://doi.org/10.1029/2023SW003475 |
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