Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms
During the recovery phase of geomagnetic storms, the flux of relativistic (>2 MeV) electrons at geosynchronous orbits is enhanced. This enhancement reaches a level that can cause devastating damage to instruments on satellites. To predict these temporal variations, we have developed n...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2002-07-01
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| Series: | Annales Geophysicae |
| Online Access: | https://www.ann-geophys.net/20/947/2002/angeo-20-947-2002.pdf |
| _version_ | 1818304751592275968 |
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| author | M. Fukata M. Fukata S. Taguchi T. Okuzawa T. Obara |
| author_facet | M. Fukata M. Fukata S. Taguchi T. Okuzawa T. Obara |
| author_sort | M. Fukata |
| collection | DOAJ |
| description | During the recovery phase
of geomagnetic storms, the flux of relativistic (>2 MeV) electrons at
geosynchronous orbits is enhanced. This enhancement reaches a level that can
cause devastating damage to instruments on satellites. To predict these
temporal variations, we have developed neural network models that predict the
flux for the period 1–12 h ahead. The electron-flux data obtained during
storms, from the Space Environment Monitor on board a Geostationary
Meteorological Satellite, were used to construct the model. Various
combinations of the input parameters <i>AL, <font face="Symbol"><b>S</b></font>AL,
Dst </i>and <i><font face="Symbol"><b>S</b></font>Dst</i> were tested (where <i><font face="Symbol"><b>S</b></font></i>
denotes the summation from the time of the minimum <i>Dst</i>). It was found
that the model, including <i><font face="Symbol"><b>S</b></font>AL</i> as one
of the input parameters, can provide some measure of relativistic electron-flux
prediction at geosynchronous orbit during the recovery phase. We suggest from
this result that the relativistic electron-flux enhancement during the recovery
phase is associated with recurring substorms after <i>Dst</i> minimum and their
accumulation effect.<br><br><b>Key words. </b>Magnetospheric physics
(energetic particles, trapped; magnetospheric configuration and dynamics;
storms and substorms) |
| first_indexed | 2024-12-13T06:15:40Z |
| format | Article |
| id | doaj.art-45b855fcd4dc497aa0bba379bd180889 |
| institution | Directory Open Access Journal |
| issn | 0992-7689 1432-0576 |
| language | English |
| last_indexed | 2024-12-13T06:15:40Z |
| publishDate | 2002-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Annales Geophysicae |
| spelling | doaj.art-45b855fcd4dc497aa0bba379bd1808892022-12-21T23:56:58ZengCopernicus PublicationsAnnales Geophysicae0992-76891432-05762002-07-012094795110.5194/angeo-20-947-2002Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substormsM. Fukata0M. Fukata1S. Taguchi2T. Okuzawa3T. Obara4Correspondence to: S. Taguchi (taguchi@ice.uec.ac.jp)Dept. of Information and Communication Engineering, University of Electro-Communications, Chofu, 182-8585, JapanDept. of Information and Communication Engineering, University of Electro-Communications, Chofu, 182-8585, JapanDept. of Information and Communication Engineering, University of Electro-Communications, Chofu, 182-8585, JapanCommunications Research Laboratory, Koganei, 184-8795, JapanDuring the recovery phase of geomagnetic storms, the flux of relativistic (>2 MeV) electrons at geosynchronous orbits is enhanced. This enhancement reaches a level that can cause devastating damage to instruments on satellites. To predict these temporal variations, we have developed neural network models that predict the flux for the period 1–12 h ahead. The electron-flux data obtained during storms, from the Space Environment Monitor on board a Geostationary Meteorological Satellite, were used to construct the model. Various combinations of the input parameters <i>AL, <font face="Symbol"><b>S</b></font>AL, Dst </i>and <i><font face="Symbol"><b>S</b></font>Dst</i> were tested (where <i><font face="Symbol"><b>S</b></font></i> denotes the summation from the time of the minimum <i>Dst</i>). It was found that the model, including <i><font face="Symbol"><b>S</b></font>AL</i> as one of the input parameters, can provide some measure of relativistic electron-flux prediction at geosynchronous orbit during the recovery phase. We suggest from this result that the relativistic electron-flux enhancement during the recovery phase is associated with recurring substorms after <i>Dst</i> minimum and their accumulation effect.<br><br><b>Key words. </b>Magnetospheric physics (energetic particles, trapped; magnetospheric configuration and dynamics; storms and substorms)https://www.ann-geophys.net/20/947/2002/angeo-20-947-2002.pdf |
| spellingShingle | M. Fukata M. Fukata S. Taguchi T. Okuzawa T. Obara Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms Annales Geophysicae |
| title | Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms |
| title_full | Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms |
| title_fullStr | Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms |
| title_full_unstemmed | Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms |
| title_short | Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms |
| title_sort | neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase effects of recurring substorms |
| url | https://www.ann-geophys.net/20/947/2002/angeo-20-947-2002.pdf |
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