PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites
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 pro...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW003975 |
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| _version_ | 1841536350283628544 |
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| author | Yinan Feng Yue Chen Youzuo Lin |
| author_facet | Yinan Feng Yue Chen Youzuo Lin |
| author_sort | Yinan Feng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a9dfa0b3e52c43bdab92198c68940ea3 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-a9dfa0b3e52c43bdab92198c68940ea32025-01-14T16:31:08ZengWileySpace Weather1542-73902024-10-012210n/an/a10.1029/2024SW003975PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS SatellitesYinan Feng0Yue Chen1Youzuo Lin2Los Alamos National Laboratory Los Alamos NM USALos Alamos National Laboratory Los Alamos NM USALos Alamos National Laboratory Los Alamos NM USAAbstract 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.https://doi.org/10.1029/2024SW003975radiation belt electronsMeV electron eventsspace weather predictionmachine‐learning modelGPS particle dataMEO electron observations |
| spellingShingle | Yinan Feng Yue Chen Youzuo Lin PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites Space Weather radiation belt electrons MeV electron events space weather prediction machine‐learning model GPS particle data MEO electron observations |
| title | PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites |
| title_full | PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites |
| title_fullStr | PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites |
| title_full_unstemmed | PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites |
| title_short | PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites |
| title_sort | premeve meo predicting ultra relativistic electrons using observations from gps satellites |
| topic | radiation belt electrons MeV electron events space weather prediction machine‐learning model GPS particle data MEO electron observations |
| url | https://doi.org/10.1029/2024SW003975 |
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