2024-11-08 ロスアラモス国立研究所(LANL)
＜関連情報＞
- https://discover.lanl.gov/news/1107-ai-space-weather-forecasting/
- https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024SW003975
PreMevE-MEO： GPS衛星からの観測を用いた超反対電子の予測 PreMevE-MEO: Predicting Ultra-Relativistic Electrons Using Observations From GPS Satellites
Yinan Feng, Yue Chen, Youzuo Lin
Space Weather Published: 28 September 2024
DOI:https://doi.org/10.1029/2024SW003975
Abstract
Ultra-relativistic electrons with energies greater than or equal to two megaelectron-volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium-Earth-orbits (MEO), is called PREdictive MEV Electron (PreMevE)-MEO model that nowcasts ultra-relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi-Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long-term in situ electron data from NASA’s Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE-MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE-MEO model demonstrates the feasibility of making high-fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.
Key Points
- Updated PREdictive MEV Electron (PreMevE) model is mainly driven by electron observations from GPS satellites in medium-Earth-orbits (MEOs)
- An innovative machine-learning algorithm combining convolutional neural networks with transformers is developed, optimized, and tested
- New PreMevE-MEO model makes hourly nowcasts of ≥2 MeV electrons inside Earth’s outer radiation belt with high fidelity
Plain Language Summary
Electrons traveling nearly light speed inside Earth’s outer Van Allen belt have high penetrating ability, and thus pose a major radiation threat to man-made satellites by causing malfunctions of space-borne electronics. Therefore, predicting those ultra-relativistic electrons is significant to all space sectors. Here we update our latest development of a predictive model for megaelectron-volt (MeV) electrons inside the Earth’s outer radiation belt, using satellite observations mainly from medium-Earth-orbits (MEO). This new model, called PREdictive MEV Electron (PreMevE)-MEO, focuses on nowcasting ultra-relativistic electron flux distributions across the outer radiation belt, with no need for local measurements of the whole population of trapped MeV electrons except at the geosynchronous orbit (GEO). Model inputs include electrons observed in MEO by up to 12 GPS satellites as well as in GEO by one Los Alamos satellite. We developed an innovative machine-learning algorithm, trained and evaluated a list of models using electron data from NASA’s Van Allen Probes mission, and successfully demonstrated the high performance of PreMevE-MEO model. This new model provides hourly nowcasts of incoming nearly light-speed electrons with high statistical fidelity, and thus can make an invaluable tool to space communities.
