t-STEP: An interpretable model for Total Electron Content predictions and irregularities estimations
arXiv:2606.29644v1 Announce Type: new Abstract: Earth system infrastructures relying on satellite-based technologies, such as Global Positioning System (GPS) communications, are affected by ionospheric Total Electron Content (TEC) gradients. Modeling these gradients under physical constraints remains challenging due to their dynamic and transient nature. While existing machine learning (ML) models can predict hourly TEC variations, it remains unclear whether their temporal resolution is sufficient to preserve small-scale TEC irregularities within predicted signals. To address this gap, we intr
Advances in AI research, particularly in interpretable models, are now being applied to complex geophysical phenomena like ionospheric dynamics, enabled by increased computational power and data availability.
Improved prediction of Total Electron Content and its irregularities is crucial for the reliability and precision of satellite-based navigation and communication systems, which underpin significant global infrastructure.
The ability to accurately model and predict small-scale TEC irregularities with interpretable AI marks a potential step forward in mitigating the impact of ionospheric disturbances on critical satellite-dependent technologies.
- · Satellite communication providers
- · GPS/GNSS manufacturers
- · Space weather researchers
- · Defense and intelligence agencies
- · Operators of older, less robust satellite systems
- · Industries heavily reliant on uncorrected GPS signals
More resilient and accurate satellite navigation and communication systems become feasible.
Enhanced precision in various commercial and military applications that depend on GPS, such as autonomous vehicles and precision agriculture.
Reduced economic losses and improved safety across sectors vulnerable to space weather events, potentially influencing international regulations on space infrastructure resilience.
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