Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms

We present the recent progress in upgrading a predictive model for megaelectron‐volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L‐shells, by adding upstream solar wind speeds to the model�...

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Veröffentlicht in:Space Weather 2020-02, Vol.18 (2), p.n/a
Hauptverfasser: Pires de Lima, Rafael, Chen, Yue, Lin, Youzuo
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Chen, Yue
Lin, Youzuo
description We present the recent progress in upgrading a predictive model for megaelectron‐volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L‐shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42‐month MeV electron observations from Van Allen Probes. Out‐of‐sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1‐day (2‐day) forecasts of 1‐MeV electron flux distributions with performance efficiency values ~0.87 (~0.82) averaged over the L‐shell range of 2.8–6.6, significantly outperforming the previous version of PreMevE particularly at L‐shells > ~4.5. Interestingly, the linear regression model is often the most successful when compared to other models, which suggests the relationship between dynamics of trapped 1‐MeV electrons and precipitating electrons is dominated by linear components. Results also show that PreMevE 2.0 can reasonably well predict the onsets of MeV electron events in 2‐day forecasts. PreMevE 2.0 is designed to be driven by observations from longstanding space infrastructure to make high‐fidelity forecasts for MeV electrons, thus can be an invaluable space weather forecasting tool for the future. Key Points Several linear and artificial neural network models are tested for forecasting MeV electron events inside Earth's radiation belt New PreMevE 2.0 model makes 1‐ and 2‐day forecasts of 1‐MeV electron events with high fidelity Relationship between trapped 1‐MeV electrons and precipitating electrons appears to be dominated by linear components
doi_str_mv 10.1029/2019SW002399
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This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L‐shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42‐month MeV electron observations from Van Allen Probes. Out‐of‐sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1‐day (2‐day) forecasts of 1‐MeV electron flux distributions with performance efficiency values ~0.87 (~0.82) averaged over the L‐shell range of 2.8–6.6, significantly outperforming the previous version of PreMevE particularly at L‐shells &gt; ~4.5. Interestingly, the linear regression model is often the most successful when compared to other models, which suggests the relationship between dynamics of trapped 1‐MeV electrons and precipitating electrons is dominated by linear components. Results also show that PreMevE 2.0 can reasonably well predict the onsets of MeV electron events in 2‐day forecasts. PreMevE 2.0 is designed to be driven by observations from longstanding space infrastructure to make high‐fidelity forecasts for MeV electrons, thus can be an invaluable space weather forecasting tool for the future. Key Points Several linear and artificial neural network models are tested for forecasting MeV electron events inside Earth's radiation belt New PreMevE 2.0 model makes 1‐ and 2‐day forecasts of 1‐MeV electron events with high fidelity Relationship between trapped 1‐MeV electrons and precipitating electrons appears to be dominated by linear components</description><identifier>ISSN: 1542-7390</identifier><identifier>ISSN: 1539-4964</identifier><identifier>EISSN: 1542-7390</identifier><identifier>DOI: 10.1029/2019SW002399</identifier><language>eng</language><publisher>Washington: John Wiley &amp; Sons, Inc</publisher><subject>Algorithms ; Artificial neural networks ; ASTRONOMY AND ASTROPHYSICS ; Electron density ; Electron flux ; Electron precipitation ; forecast ; Geosynchronous satellites ; Low earth orbits ; Machine learning ; magaelectron‐volt ; Neural networks ; Outer radiation belt ; Parameters ; Prediction models ; Radiation ; Radiation belts ; Regression models ; Satellites ; Solar wind ; Solar wind velocity ; Space weather ; supervised learning ; Weather forecasting ; Wind speed</subject><ispartof>Space Weather, 2020-02, Vol.18 (2), p.n/a</ispartof><rights>2020. 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This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L‐shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42‐month MeV electron observations from Van Allen Probes. 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subjects Algorithms
Artificial neural networks
ASTRONOMY AND ASTROPHYSICS
Electron density
Electron flux
Electron precipitation
forecast
Geosynchronous satellites
Low earth orbits
Machine learning
magaelectron‐volt
Neural networks
Outer radiation belt
Parameters
Prediction models
Radiation
Radiation belts
Regression models
Satellites
Solar wind
Solar wind velocity
Space weather
supervised learning
Weather forecasting
Wind speed
title Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms
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