Successful Observation of Space Environment Variations Induced by a Super Geomagnetic Storm

The near-Earth space environment is shaped by solar activity and includes regions such as the ionosphere (*1) and plasmasphere (*2). These regions play a crucial role in satellite positioning and communications using GNSS signal (*3). During geomagnetic storms (*4), disturbances in the Earth’s magnetic field lead to fluctuations in ionospheric electron density, phenomena known as ionospheric storms (*5), which can cause communication disruptions and navigation errors. The plasmasphere, located above the ionosphere, shrinks during geomagnetic storms. Although its recovery is believed to be influenced by ionospheric conditions, the detailed relationship between the two regions has not been fully understood.

The super geomagnetic storm of May 10, 2024, the most intense in approximately 21 years (*6), generated low-latitude auroras visible across the globe. JAXA’s Arase satellite and global GNSS receiver networks detected rapid changes in the plasmasphere’s structure and widespread ionospheric disturbances, respectively.

The research team combined ionospheric total electron content (TEC) data (*7) obtained from GNSS receiver networks with electron density data collected by JAXA’s Arase satellite, to analyze how ionospheric disturbances influenced plasmaspheric recovery.

The analysis revealed that the plasmasphere required more than four days to recover after the storm, an exceptionally long duration compared to past events. A statistical study of 77 geomagnetic storms between March 2017 and December 2024 confirmed that the May 2024 event exhibited an unusually prolonged recovery period.

Moreover, TEC observations indicated that ionospheric electron density decreased by 50–90% across the Northern Hemisphere during the storm. These findings demonstrate that the delayed recovery of the plasmasphere was caused by suppressed plasma supply from the ionosphere.

These findings will contribute to the advancement of space weather forecasting models and are expected to improve the accuracy of satellite communication and navigation predictions in the future.