Results 101 to 110 of about 13,690 (192)

Reconstructing Magnetotail Reconnection Events Using Data Mining is Feasible and Repeatable

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Recently, Stephens et al. (2023), https://doi.org/10.1029/2022ja031066 utilized a data mining (DM) algorithm, applied to 26 years of magnetospheric magnetometer observations coupled with a flexible formulation of the magnetospheric magnetic field, to reconstruct the global configuration of the magnetotail when the Magnetospheric MultiScale ...
G. K. Stephens, R. S. Weigel, M. I. Sitnov, N. A. Tsyganenko   +3 more
wiley   +1 more source

Global Response of Vertical Total Electron Content to Mother’s Day G5 Geomagnetic Storm of May 2024: Insights from IGS and GIM Observations

Atmosphere
The G5 geomagnetic storm of May 2024 provided a significant opportunity to investigate global ionospheric disturbances using vertical total electron content (VTEC) data derived from 422 GNSS-IGS stations and GIM.
Sanjoy Kumar Pal, Soumen Sarkar, Kousik Nanda, Aritra Sanyal, Bhuvnesh Brawar, Abhirup Datta, Stelios M. Potirakis, Ajeet K. Maurya, Arnab Bhattacharya, Pradipta Panchadhyayee, Saibal Ray, Sudipta Sasmal   +11 more
doaj   +1 more source

Six‐Component Electromagnetic Wave Measurements of Sprite‐Associated Lightning

Radio Science, Volume 61, Issue 2, February 2026.
Abstract Low frequency electromagnetic waves emitted by sprite‐producing lightning are normally measured using vertical electric fields or horizontal magnetic fields. Here we report for the first time the simultaneous measurement of electromagnetic waves from sprite‐producing lightning in all six electromagnetic field components Ex,Ey,Ez,Hx,Hy,andHz ...
Martin Füllekrug, Michael Kosch, Gavin Dingley, Xue Bai, Liliana Macotela   +4 more
wiley   +1 more source

Forecast of Ionospheric TEC Maps Using ConvGRU Deep Learning Over China

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In this article, we propose a convolutional gated recurrent unit (ConvGRU) deep learning method to forecast ionospheric total electron content (TEC) over China based on the regional ionospheric maps (RIMs) from 2015 to 2018.
Jun Tang, Zhengyu Zhong, Mingfei Ding, Dengpan Yang, Heng Liu   +4 more
doaj   +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

High‐Precision Prediction of Auroral Substorm Expansion Phase Evolution Using a Spatio‐Temporal Attention Deep Learning Model

Space Weather, Volume 24, Issue 2, February 2026.
Abstract Auroral substorms are a key manifestation of solar‐terrestrial interactions and the release of energy within the Earth's magnetosphere. The expansion phase, which is characterized by rapid changes in auroral morphology and intensity, poses significant space weather risks to satellite operations and communication systems.
Qiuju Yang, Xiaojian Cao, Yong Wang, Zanyang Xing   +3 more
wiley   +1 more source

Solar cycle phase and occurrence of intense geomagnetic storms

Earth, Planets and Space
Solar cycles have an asymmetrical shape with a fast rise and a slow decline, which varies from cycle to cycle. This makes it difficult to study the solar cycle phase dependence of the occurrence of intense solar-terrestrial events such as intense ...
S. Watari
doaj   +1 more source

AI‐Based Improvement of IRI‐2020 Electron Density Profiles With COSMIC Radio Occultation Data

Space Weather, Volume 24, Issue 2, February 2026.
Abstract In this study, we propose an AI‐based method to improve the electron density profiles generated by the International Reference Ionosphere (IRI)‐2020 model using the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) data.
Eun‐Young Ji, Yong‐Jae Moon, Young‐Sil Kwak, Kangwoo Yi, Jeong‐Heon Kim, Woong Jeon   +5 more
wiley   +1 more source

High‐Latitude Electrodynamics and Thermospheric Density Variations During the 2024 Gannon Storm

Space Weather, Volume 24, Issue 2, February 2026.
Abstract This study investigates high‐latitude electrodynamics and thermospheric density variations during the 2024 Gannon's storm. First, the auroral precipitation module of Auroral Spectra and High‐Latitude Electric field variabilitY, ASHLEY‐A, is improved to more accurately capture the location of auroral precipitation observed during this ...
Qingyu Zhu, Sarah Vines, Marc Hairston, Yun‐Ju Chen, Phillip Anderson, Cheng Sheng, Yue Deng   +6 more
wiley   +1 more source

Data‐Driven Satellite Drag Modeling Without Dynamic Knowledge of the Atmosphere

Space Weather, Volume 24, Issue 2, February 2026.
Abstract Increasing congestion in low Earth orbit (LEO) from new satellite deployments and a growing debris population has made accurate orbital trajectory prediction essential for anticipating and avoiding potential collisions on a regular basis. Atmospheric drag is usually the dominant source of propagation error across much of LEO, especially during
William E. Parker, Richard Linares
wiley   +1 more source