What does this research mean for the field?

Severe geomagnetic storms significantly enhance low-latitude plasma irregularities and GNSS scintillation variability, driven by storm-time electrodynamic processes such as prompt penetration electric fields and post-sunset plasma instabilities. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To investigate storm-time ionospheric scintillation variability and multi-scale wave-like structures during severe geomagnetic storms under Solar Cycle 25 conditions.

September 14, 2026Geomagnetism and Aeronomy

PRN-Wise Investigation of Storm-Time GNSS S4 Scintillations and Wave-Like Ionospheric Structures over Agra during Solar Cycle 25

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Authors

DSDhananjali Singh VJVijay Janyani RYRajendra Prasad Yadav

Discussion

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Overview

Observational study reveals storm-induced GNSS signal scintillations and wave-like ionospheric structures, indicating strong coupling between geomagnetic disturbances and low-latitude plasma.

Key Points

To investigate storm-time ionospheric scintillation variability and multi-scale wave-like structures during severe geomagnetic storms under Solar Cycle 25 conditions.
Analyzed satellite-specific (PRN-wise) GNSS-derived S4 index data collected during 2024–2025 over Agra, India.
Evaluated geomagnetic activity using Dst and Kp indices across two major disturbance events: the May 2024 superstorm and the November 2025 G5 storm.
Applied Continuous Wavelet Transform (CWT) analysis to identify the dominant periodicities of wave-like ionospheric structures.
Scintillation peaked predominantly during post-sunset and nighttime hours (1700–2300 UT), with strong scintillation (S4 ≥ 0.6) occurring during severe geomagnetic activity.
The May 2024 superstorm produced stronger scintillation with peak S4 values approaching 1.2, whereas the November 2025 storm exhibited a broader distribution of weak-to-moderate scintillation.
Continuous Wavelet Transform identified dominant periodicities of 20 to 180 minutes, revealing multi-scale traveling ionospheric disturbances driven by prompt penetration electric fields and plasma instabilities.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3440926e14a848b21b7 https://doi.org/10.1134/s0016793226600189

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September 14, 2026Geomagnetism and Aeronomy

PRN-Wise Investigation of Storm-Time GNSS S4 Scintillations and Wave-Like Ionospheric Structures over Agra during Solar Cycle 25

View Full Paper

Ask AI

Bookmark

Authors

DSDhananjali Singh VJVijay Janyani RYRajendra Prasad Yadav

Discussion

Member takes

Overview

Observational study reveals storm-induced GNSS signal scintillations and wave-like ionospheric structures, indicating strong coupling between geomagnetic disturbances and low-latitude plasma.

Key Points

To investigate storm-time ionospheric scintillation variability and multi-scale wave-like structures during severe geomagnetic storms under Solar Cycle 25 conditions.
Analyzed satellite-specific (PRN-wise) GNSS-derived S4 index data collected during 2024–2025 over Agra, India.
Evaluated geomagnetic activity using Dst and Kp indices across two major disturbance events: the May 2024 superstorm and the November 2025 G5 storm.
Applied Continuous Wavelet Transform (CWT) analysis to identify the dominant periodicities of wave-like ionospheric structures.
Scintillation peaked predominantly during post-sunset and nighttime hours (1700–2300 UT), with strong scintillation (S4 ≥ 0.6) occurring during severe geomagnetic activity.
The May 2024 superstorm produced stronger scintillation with peak S4 values approaching 1.2, whereas the November 2025 storm exhibited a broader distribution of weak-to-moderate scintillation.
Continuous Wavelet Transform identified dominant periodicities of 20 to 180 minutes, revealing multi-scale traveling ionospheric disturbances driven by prompt penetration electric fields and plasma instabilities.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3440926e14a848b21b7 https://doi.org/10.1134/s0016793226600189

View Full Paper

Ask AI

Bookmark