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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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 |
format | Article |
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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 & 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. The Authors.</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5033-9e55bee0684d314654ab3d19378d43626c46cc0aa33c07b26cb6026c8b83d6bd3</citedby><cites>FETCH-LOGICAL-c5033-9e55bee0684d314654ab3d19378d43626c46cc0aa33c07b26cb6026c8b83d6bd3</cites><orcidid>0000-0002-5079-0579 ; 0000-0002-6747-1291 ; 0000-0001-7337-6760 ; 0000000267471291 ; 0000000250790579 ; 0000000173376760</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2019SW002399$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2019SW002399$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,11541,27901,27902,45550,45551,46027,46451</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/1600854$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Pires de Lima, Rafael</creatorcontrib><creatorcontrib>Chen, Yue</creatorcontrib><creatorcontrib>Lin, Youzuo</creatorcontrib><creatorcontrib>Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)</creatorcontrib><title>Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms</title><title>Space Weather</title><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</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>ASTRONOMY AND ASTROPHYSICS</subject><subject>Electron density</subject><subject>Electron flux</subject><subject>Electron precipitation</subject><subject>forecast</subject><subject>Geosynchronous satellites</subject><subject>Low earth orbits</subject><subject>Machine learning</subject><subject>magaelectron‐volt</subject><subject>Neural networks</subject><subject>Outer radiation belt</subject><subject>Parameters</subject><subject>Prediction models</subject><subject>Radiation</subject><subject>Radiation belts</subject><subject>Regression models</subject><subject>Satellites</subject><subject>Solar wind</subject><subject>Solar wind velocity</subject><subject>Space weather</subject><subject>supervised learning</subject><subject>Weather forecasting</subject><subject>Wind speed</subject><issn>1542-7390</issn><issn>1539-4964</issn><issn>1542-7390</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>DOA</sourceid><recordid>eNp9kc9uEzEQxlcIJErhxgNYcOBCiv_vmltbbSFSoiIC9Gh57dnE0Xad2k5Rb5w58Yw8CQ5boZ64ePx5fvrGM1NVLwk-IZiqdxQTtbrCmDKlHlVHRHA6q5nCjx_cn1bPUtoWhgvKj6qfFyGCNSn7cY2WsDYwgM0xjL9__PoWhozae53QfEzeAWpNzJs3CV3uM0T02Thvsg8jOoMhv0efIizhtkX0BKMzk8ChklrtdxBv_UEtjd34EdACTBwPNU-HdYg-b67T8-pJb4YEL-7jcfX1ov1y_nG2uPwwPz9dzKzAjM0UCNEBYNlwxwiXgpuOOaJY3TjOJJWWS2uxMYxZXHdFdxKXs-ka5mTn2HE1n3xdMFu9i_7axDsdjNd_H0Jc69KitwNoYWhT94QB9B1XuDa4l8XMqh4TcEYUr1eTVygT1Mn6DHZjwziWoWkiMW4EL9DrCdrFcLOHlPU27ONYetSUSaYooYIV6u1E2RhSitD_-xrB-rBe_XC9BacT_t0PcPdfVq-uWoqVZOwPJESmLw</recordid><startdate>202002</startdate><enddate>202002</enddate><creator>Pires de Lima, Rafael</creator><creator>Chen, Yue</creator><creator>Lin, Youzuo</creator><general>John Wiley & Sons, Inc</general><general>American Geophysical Union</general><general>Wiley</general><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>8FD</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><scope>OTOTI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-5079-0579</orcidid><orcidid>https://orcid.org/0000-0002-6747-1291</orcidid><orcidid>https://orcid.org/0000-0001-7337-6760</orcidid><orcidid>https://orcid.org/0000000267471291</orcidid><orcidid>https://orcid.org/0000000250790579</orcidid><orcidid>https://orcid.org/0000000173376760</orcidid></search><sort><creationdate>202002</creationdate><title>Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms</title><author>Pires de Lima, Rafael ; Chen, Yue ; Lin, Youzuo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5033-9e55bee0684d314654ab3d19378d43626c46cc0aa33c07b26cb6026c8b83d6bd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>ASTRONOMY AND ASTROPHYSICS</topic><topic>Electron density</topic><topic>Electron flux</topic><topic>Electron precipitation</topic><topic>forecast</topic><topic>Geosynchronous satellites</topic><topic>Low earth orbits</topic><topic>Machine learning</topic><topic>magaelectron‐volt</topic><topic>Neural networks</topic><topic>Outer radiation belt</topic><topic>Parameters</topic><topic>Prediction models</topic><topic>Radiation</topic><topic>Radiation belts</topic><topic>Regression models</topic><topic>Satellites</topic><topic>Solar wind</topic><topic>Solar wind velocity</topic><topic>Space weather</topic><topic>supervised learning</topic><topic>Weather forecasting</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pires de Lima, Rafael</creatorcontrib><creatorcontrib>Chen, Yue</creatorcontrib><creatorcontrib>Lin, Youzuo</creatorcontrib><creatorcontrib>Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>OSTI.GOV</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Space Weather</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pires de Lima, Rafael</au><au>Chen, Yue</au><au>Lin, Youzuo</au><aucorp>Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms</atitle><jtitle>Space Weather</jtitle><date>2020-02</date><risdate>2020</risdate><volume>18</volume><issue>2</issue><epage>n/a</epage><issn>1542-7390</issn><issn>1539-4964</issn><eissn>1542-7390</eissn><abstract>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</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2019SW002399</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-5079-0579</orcidid><orcidid>https://orcid.org/0000-0002-6747-1291</orcidid><orcidid>https://orcid.org/0000-0001-7337-6760</orcidid><orcidid>https://orcid.org/0000000267471291</orcidid><orcidid>https://orcid.org/0000000250790579</orcidid><orcidid>https://orcid.org/0000000173376760</orcidid><oa>free_for_read</oa></addata></record> |
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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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