Modeling the Dynamic Global Distribution of the Ring Current Oxygen Ions Using Artificial Neural Network Technique Article Swipe
Qiushuo Wang
,
Chao Yue
,
Jinxing Li
,
Jacob Bortnik
,
Donglai Ma
,
Chae‐Woo Jun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2023sw003779
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2023sw003779
The ring current is an important component of the Earth's near‐space environment, as its variations are the direct driver of geomagnetic storms that can disrupt power grids, satellite communications, and navigation systems, thereby impacting a wide range of technological and human activities. Oxygen ions (O + ) are one of the major components of the ring current and play a significant role in both the enhancement and depletion of the ring current during geomagnetic storms. Although a standard statistical study can provide average global distributions of ring current ions, it can't offer insight into the short‐term dynamic variations of the global distribution. Therefore, we employed the Artificial Neural Network technique to construct a global ring current O + ion model based on the Van Allen Probes observations. Through optimization of the combination of input geomagnetic indices and their respective time history lengths, the model can well reproduce the spatiotemporal variation of the oxygen ion flux distributions and demonstrates remarkable accuracy and minimal errors. Additionally, the model effectively reconstructs the temporal variation of ring current O + ions for non‐training set data. Furthermore, the model provides a comprehensive and dynamic representation of global ring current O + ion distribution. It accurately captures the dynamics of O + ions during a geomagnetic storm with the oxygen ion fluxes enhancement and decay, and reveals distinct characteristics for different energy levels, such as injection from the plasma sheet, outflow from the ionosphere, and magnetic local time asymmetry.
Related Topics
Concepts
Ring current
Geomagnetic storm
Earth's magnetic field
Current (fluid)
Ion
Outflow
Van Allen Probes
Computer science
Computational physics
Physics
Magnetosphere
Meteorology
Plasma
Van Allen radiation belt
Magnetic field
Thermodynamics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2023sw003779
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2023SW003779
- OA Status
- diamond
- Cited By
- 3
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399756021
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399756021Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1029/2023sw003779Digital Object Identifier
- Title
-
Modeling the Dynamic Global Distribution of the Ring Current Oxygen Ions Using Artificial Neural Network TechniqueWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-01Full publication date if available
- Authors
-
Qiushuo Wang, Chao Yue, Jinxing Li, Jacob Bortnik, Donglai Ma, Chae‐Woo JunList of authors in order
- Landing page
-
https://doi.org/10.1029/2023sw003779Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2023SW003779Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2023SW003779Direct OA link when available
- Concepts
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Ring current, Geomagnetic storm, Earth's magnetic field, Current (fluid), Ion, Outflow, Van Allen Probes, Computer science, Computational physics, Physics, Magnetosphere, Meteorology, Plasma, Van Allen radiation belt, Magnetic field, Thermodynamics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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