The 2024 Nobel Prize in Physics is awarded to American John Hopfield and British-Canadian scientist Geoffrey Hinton for their pioneering work in machine learning and artificial neural networks. This transformative technology is currently making waves in various fields, including atmospheric research. One example is a remarkable study on predicting the behavior of Earth’s ionosphere, showing how neural networks can revolutionize scientific exploration.
Significant progress has been made in understanding the behavior of Earth’s ionosphere through a new study on the accuracy of predicting total electron content in the equatorial region. Researchers Dr. Olga Maltseva and Dr. Artem Kharakashyan of Russia’s Southern Federal University explored how forecast accuracy varies at different locations near the equator. Their findings, based on advanced learning methods, are detailed in the peer-reviewed journal Geodesy and Geodynamics.
The layer of the atmosphere called the ionosphere, filled with free electrons and ions, is crucial to global navigation systems and communications networks because it affects how signals travel through space. Total electron content is a measure of all charged particles in the ionospheric column, and accurate prediction has been a well-known challenge. While past research has often relied on limited data and older methods, this study uses a state-of-the-art learning model that can “see” in both time directions, resulting in significantly improved forecasts for both short- and long-term intervals.
Dr. Maltseva and her team examined data from 14 sites near the equator, using a global map created by the Jet Propulsion Laboratory to analyze changes in total electron content. These maps provide a detailed view of ionospheric changes around the world. The models are trained using data on solar activity, which refers to changes in the sun’s energy output, geomagnetic influence (the effect caused by the Earth’s magnetic field and other atmospheric factors). These innovative methods outperform earlier methods and provide more accurate forecasts while eliminating differences caused by geographic differences. Older methods tend to produce location-specific results, making them less reliable globally.
Dr. Maltseva emphasized the importance of this, “Our results demonstrate that the bidirectional approach not only improves prediction accuracy but also counteracts geographical variations in error margins, providing a powerful solution for global ionospheric monitoring.”
The comprehensive analysis includes stations such as Niue, Chicamaca and Darwin, which provide valuable insights into how the total electron content fluctuates under different atmospheric conditions. Notably, the models performed exceptionally well at maintaining accuracy during a severe geomagnetic storm (temporary disturbances in Earth’s magnetic field caused by solar activity) in December 2015, showing resilience even under extreme space weather conditions.
These breakthroughs highlight the potential of advanced technologies in ionospheric research. By accounting for differences in prediction reliability across locations, these models open the door to improving services such as global navigation and disaster response. Future uses may include instant integration with satellite data to further enhance predictions and help mitigate risks from natural or man-made disturbances.
Journal reference
Kharakashyan, A., & Maltseva, O. (2024). “Longitudinal dependence of the prediction accuracy of the total electron content of the ionosphere in the equatorial region.” Geodesy and Geodynamics, 15(2024), 528-541. DOI: https://doi.org/10.1016/j.geog.2024.02.001
About the author
Dr. Olga Maltseva is a principal researcher at the Institute of Physics of the Southern Federal University in Rostov-on-Don, Russia. During her long career, she published many journal articles and several monographs on the modeling of radio wave propagation in different frequency bands in the ionosphere and magnetosphere. Her current interests include validating empirical ionospheric models, assimilating total electron content (TEC) into these models, and studying the impact of magnetic storms on global TEC distributions.
