Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel)
Abstract The Earth's outer radiation belt response to geospace disturbances is extremely variable spanning from a few hours to several months. In addition, the numerous physical mechanisms, which control this response depend on the electron energy, the timescale, and the various types of geospa...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2021SW002936 |
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| author | C. Katsavrias S. Aminalragia‐Giamini C. Papadimitriou I. A. Daglis I. Sandberg P. Jiggens |
| author_facet | C. Katsavrias S. Aminalragia‐Giamini C. Papadimitriou I. A. Daglis I. Sandberg P. Jiggens |
| author_sort | C. Katsavrias |
| collection | DOAJ |
| description | Abstract The Earth's outer radiation belt response to geospace disturbances is extremely variable spanning from a few hours to several months. In addition, the numerous physical mechanisms, which control this response depend on the electron energy, the timescale, and the various types of geospace disturbances. As a consequence, various models that currently exist are either specialized, orbit‐specific data‐driven models, or sophisticated physics‐based ones. In this paper, we present a new approach for radiation belt modeling using Machine Learning methods driven solely by Solar wind speed and pressure, Solar flux at 10.7 cm, and the angle controlling the Russell‐McPherron effect (θRM). We show that the model can successfully reproduce and predict the electron fluxes of the outer radiation belt in a broad energy (0.033–4.062 MeV) and L‐shell (2.5–5.9) range, and moreover, it can capture the long‐term modulation of the semi‐annual variation. We also show that the model can generalize well and provide successful predictions, even outside of the spatio‐temporal range it has been trained with, using >0.8 MeV electron flux measurements from GOES‐15/EPEAD at a geostationary orbit. |
| format | Article |
| id | doaj-art-69f7f86f7a034aeb845a59ef74a4bc2c |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-69f7f86f7a034aeb845a59ef74a4bc2c2025-01-14T16:35:20ZengWileySpace Weather1542-73902022-01-01201n/an/a10.1029/2021SW002936Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel)C. Katsavrias0S. Aminalragia‐Giamini1C. Papadimitriou2I. A. Daglis3I. Sandberg4P. Jiggens5Department of Physics National and Kapodistrian University of Athens Athens GreeceDepartment of Physics National and Kapodistrian University of Athens Athens GreeceDepartment of Physics National and Kapodistrian University of Athens Athens GreeceDepartment of Physics National and Kapodistrian University of Athens Athens GreeceSpace Applications and Research Consultancy (SPARC) Athens GreeceESA/ESTEC Noordwijk The NetherlandsAbstract The Earth's outer radiation belt response to geospace disturbances is extremely variable spanning from a few hours to several months. In addition, the numerous physical mechanisms, which control this response depend on the electron energy, the timescale, and the various types of geospace disturbances. As a consequence, various models that currently exist are either specialized, orbit‐specific data‐driven models, or sophisticated physics‐based ones. In this paper, we present a new approach for radiation belt modeling using Machine Learning methods driven solely by Solar wind speed and pressure, Solar flux at 10.7 cm, and the angle controlling the Russell‐McPherron effect (θRM). We show that the model can successfully reproduce and predict the electron fluxes of the outer radiation belt in a broad energy (0.033–4.062 MeV) and L‐shell (2.5–5.9) range, and moreover, it can capture the long‐term modulation of the semi‐annual variation. We also show that the model can generalize well and provide successful predictions, even outside of the spatio‐temporal range it has been trained with, using >0.8 MeV electron flux measurements from GOES‐15/EPEAD at a geostationary orbit.https://doi.org/10.1029/2021SW002936outer radiation beltmachine learning modelforecastingrelativistic electronss |
| spellingShingle | C. Katsavrias S. Aminalragia‐Giamini C. Papadimitriou I. A. Daglis I. Sandberg P. Jiggens Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel) Space Weather outer radiation belt machine learning model forecasting relativistic electronss |
| title | Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel) |
| title_full | Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel) |
| title_fullStr | Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel) |
| title_full_unstemmed | Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel) |
| title_short | Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel) |
| title_sort | radiation belt model including semi annual variation and solar driving sentinel |
| topic | outer radiation belt machine learning model forecasting relativistic electronss |
| url | https://doi.org/10.1029/2021SW002936 |
| work_keys_str_mv | AT ckatsavrias radiationbeltmodelincludingsemiannualvariationandsolardrivingsentinel AT saminalragiagiamini radiationbeltmodelincludingsemiannualvariationandsolardrivingsentinel AT cpapadimitriou radiationbeltmodelincludingsemiannualvariationandsolardrivingsentinel AT iadaglis radiationbeltmodelincludingsemiannualvariationandsolardrivingsentinel AT isandberg radiationbeltmodelincludingsemiannualvariationandsolardrivingsentinel AT pjiggens radiationbeltmodelincludingsemiannualvariationandsolardrivingsentinel |