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AI-Enhanced Model Could Improve Space Weather Forecasting
Date: November 11, 2024
Published by: Los Alamos National Laboratory
"Killer electrons" that travel at nearly light speed inside Earth's Van Allen belts—the zone that surrounds the planet and traps energetic charged particles—pose a major threat to equipment in space by causing malfunctions in electronics.
Now, in a collaboration between Los Alamos National Laboratory and the University of North Carolina at Chapel Hill, researchers are using machine learning, an application of artificial intelligence, to improve upon a predictive model for measuring electrons inside the Earth's outer radiation belt.
"This study proves the feasibility of using the Laboratory's particle data to predict the dynamics of killer electrons," said Yue Chen, a Los Alamos physicist and lead author on the new research. "Meanwhile, it showcases the significance of long-term space observations in the AI age."
The research was published recently in the journal Space Weather and is an important step forward in improving space weather forecasting capabilities and protecting satellites.
New Forecasting Capability: PreMevE-MEO
The team's new forecasting capability, called Predictive MeV Electron—Medium Earth Orbit, or PreMevE-MEO, can provide more accurate and efficient hourly forecasts. PreMevE-MEO's model inputs include electrons observed by 12 medium-Earth-orbit GPS satellites, and one Los Alamos geosynchronous-Earth-orbit satellite. The team developed an innovative machine-learning algorithm, combining convolutional neural networks with transformers, to improve the model.
As a result, the researchers have shown that it is possible to make high-fidelity predictions driven by observations from longstanding space infrastructure in medium Earth orbit, the distance above the Earth at which many navigation and meteorological satellites operate. The model has the potential to become a valuable space weather operational warning tool.
The Uniqueness of Los Alamos’s Data
The new model also uses Los Alamos's unique GPS data, which combines X-ray dosimeter particle data that was first made available to the public in 2017 and is archived by the National Oceanic and Atmospheric Administration's National Centers for Environmental Information.
One unique aspect of that data is that, unlike traditional research missions run by NASA, it is a long-term constellation with more than 100 satellite-years of data available. It is one of the few space environment resources that truly falls into the category of big data, in which modern AI approaches can be applied.
Supporting National Space Weather Strategy
This work supports the recent Implementation Plan for the National Space Weather Strategy and Action Plan, which tasked agencies to identify and release historical data from satellites; U.S. government-funded, ground-based observatories and networks; measurements throughout the electric power grid; and magnetometer data streams that would be beneficial for improving the development, validation, and testing of models used for characterizing and forecasting space weather events.
Conclusion
The integration of AI in space weather forecasting showcases the potential for technological advancements to enhance predictive capabilities, ultimately aiming to safeguard critical infrastructure and improve operational readiness regarding space weather threats.
For More Information
Yinan Feng et al, PreMevE-MEO: Predicting Ultra-Relativistic Electrons Using Observations From GPS Satellites, Space Weather (2024). DOI: 10.1029/2024SW003975
For further reading and details, visit the official articles at Phys.org and explore more about space weather monitoring technology.
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