Results 101 to 110 of about 16,469 (214)

A theoretical model of the wave particle interaction of plasma in space [PDF]

A theoretical model, based on the kinetic theory for the perturbation of plasma in the magnetosphere, is proposed to study the observed disturbances which are caused by both natural and artificial sources that generate wave-like perturbations propagating
Hung, R. J., Wu, S. T.
core   +1 more source

Ground-Based GNSS and Satellite Observations of Auroral Ionospheric Irregularities during Geomagnetic Disturbances in August 2018. [PDF]

Sensors (Basel), 2021
Zakharenkova I, Cherniak I, Krankowski A.   +2 more
europepmc   +1 more source

Joint Analysis With Swarm and Ground Stations: Ionospheric Current System and Geomagnetically Induced Currents

Journal of Geophysical Research: Space Physics, Volume 131, Issue 2, February 2026.
Abstract Sudden changes in the ground magnetic field, driven by geomagnetic activity, can ultimately generate geomagnetically induced currents (GICs), which can have a significant impact on artificial technology systems. High rates of change in the horizontal geomagnetic field (dH/dt) can be used as a substitute for the strength of GICs.
C. M. Zhang, M. W. Dunlop, J. Y. Yang, X. Tan, O. Marghitu, A. Blagau, C. Xiong, X. C. Dong, D. Wei, V. Constantinescu, G. Kervalishvili   +10 more
wiley   +1 more source

The causes of recurrent geomagnetic storms [PDF]

The causes of recurrent geomagnetic activity were studied by analyzing interplanetary magnetic field and plasma data from earth-orbiting spacecraft in the interval from November 1973 to February 1974.
Burlaga, L. F., Lepping, R. P.
core   +1 more source

Forecasting Local Ionospheric Parameters Using Transformers

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract We present a novel method for forecasting key ionospheric parameters using transformer‐based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2‐layer peak plasma frequency (foF2), the F2‐layer peak density height (hmF2), and total electron content for a given geographic location.
D. J. Alford‐Lago, C. W. Curtis, A. T. Ihler, D. P. Drob, K. A. Zawdie   +4 more
wiley   +1 more source

Segmentation and Tracking of Eruptive Solar Phenomena With Convolutional Neural Networks

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Solar eruptive events are complex phenomena, which most often include coronal mass ejections (CME), CME‐driven compressive and shock waves, flares, and filament eruptions. CMEs are large eruptions of magnetized plasma from the Sun's outer atmosphere or corona, that propagate outward into the interplanetary space.
Oleg Stepanyuk, Kamen Kozarev
wiley   +1 more source

SPACE-TIME ANALYSIS OF DISTURBANCES IN THE PC4-5 PERIOD RANGE DURING MAGNETIC STORMS BY CORRELATION-SKELETON METHOD

Вестник Мининского университета, 2017
Special method of correlation-skeleton processing of long period MHD disturbed components of the geomagnetic field is developed. To analyze space-time distribution for field components recorded along the meridian 210 at different stages of geomagnetic ...
Yu. V. Manakova, K. A. Pekhteleva, N. A. Barkhatov, S. E. Revunov   +3 more
doaj   +2 more sources

ED‐Autoformer: A New Model for Precise Global TEC Forecast

Space Weather
Total electron content (TEC) is a key parameter for characterizing ionospheric morphology and significantly impacts the Global Navigation Satellite System. The ionosphere responds dramatically to solar and geomagnetic activity, leading to substantial TEC
Jiawei Zhou, Hongtao Cai, Xu Yan, Hong‐wen Xu, Kun Hu, Chao Xiong   +5 more
doaj   +1 more source

A Novel Deep Learning Approach for TEC Map Completion Using Image Equation‐Guided Loss

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract This study introduces the Image Equation Mechanism‐Completion Generative Adversarial Network (IEM‐CGAN), a groundbreaking deep learning framework that revolutionizes the reconstruction of large‐scale global ionospheric total electron content (TEC) maps.
Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren, Xuhui Shen   +6 more
wiley   +1 more source

Forecasting Total Electron Content During Geomagnetic Storms Using Convolutional Long Short‐Term Memory (ConvLSTM): Performance and Limitations

Space Weather, Volume 24, Issue 2, February 2026.
Abstract This study investigates the effects of quiet time ionospheric conditions and the number of storm events used for training on the prediction of ionospheric total electron content (TEC) during geomagnetic storms using a deep learning method.
Se‐Heon Jeong, Hyosub Kil, Woo Kyoung Lee, Jeong‐Heon Kim, Young‐Sil Kwak   +4 more
wiley   +1 more source