An operational approach to forecast the Earth’s radiation belts dynamics
The Ca index is a time-integrated geomagnetic index that correlates well with the dynamics of high-energy electron fluxes in the outer radiation belts. Therefore, Ca can be used as an indicator for the state of filling of the radiation belts for those electrons. Ca also has the advantage of being a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2021-01-01
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| Series: | Journal of Space Weather and Space Climate |
| Subjects: | |
| Online Access: | https://www.swsc-journal.org/articles/swsc/full_html/2021/01/swsc210026/swsc210026.html |
| _version_ | 1818971768528830464 |
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| author | Bernoux Guillerme Brunet Antoine Buchlin Éric Janvier Miho Sicard Angélica |
| author_facet | Bernoux Guillerme Brunet Antoine Buchlin Éric Janvier Miho Sicard Angélica |
| author_sort | Bernoux Guillerme |
| collection | DOAJ |
| description | The Ca index is a time-integrated geomagnetic index that correlates well with the dynamics of high-energy electron fluxes in the outer radiation belts. Therefore, Ca can be used as an indicator for the state of filling of the radiation belts for those electrons. Ca also has the advantage of being a ground-based measurement with extensive historical records. In this work, we propose a data-driven model to forecast Ca up to 24 h in advance from near-Earth solar wind parameters. Our model relies mainly on a recurrent neural network architecture called Long Short Term Memory that has shown good performances in forecasting other geomagnetic indices in previous papers. Most implementation choices in this study were arbitrated from the point of view of a space system operator, including the data selection and split, the definition of a binary classification threshold, and the evaluation methodology. We evaluate our model (against a linear baseline) using both classical and novel (in the space weather field) measures. In particular, we use the Temporal Distortion Mix (TDM) to assess the propensity of two time series to exhibit time lags. We also evaluate the ability of our model to detect storm onsets during quiet periods. It is shown that our model has high overall accuracy, with evaluation measures deteriorating in a smooth and slow trend over time. However, using the TDM and binary classification forecast evaluation metrics, we show that the forecasts lose some of their usefulness in an operational context even for time horizons shorter than 6 h. This behaviour was not observable when evaluating the model only with metrics such as the root-mean-square error or the Pearson linear correlation. Considering the physics of the problem, this result is not surprising and suggests that the use of more spatially remote data (such as solar imaging) could improve space weather forecasts. |
| first_indexed | 2024-12-20T14:57:37Z |
| format | Article |
| id | doaj.art-f74d34696baf4354b8a4398a84ee3910 |
| institution | Directory Open Access Journal |
| issn | 2115-7251 |
| language | English |
| last_indexed | 2024-12-20T14:57:37Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Journal of Space Weather and Space Climate |
| spelling | doaj.art-f74d34696baf4354b8a4398a84ee39102022-12-21T19:36:45ZengEDP SciencesJournal of Space Weather and Space Climate2115-72512021-01-01116010.1051/swsc/2021045swsc210026An operational approach to forecast the Earth’s radiation belts dynamicsBernoux Guillerme0https://orcid.org/0000-0002-6823-4407Brunet Antoine1https://orcid.org/0000-0003-2666-3227Buchlin Éric2https://orcid.org/0000-0003-4290-1897Janvier Miho3https://orcid.org/0000-0002-6203-5239Sicard Angélica4https://orcid.org/0000-0001-7810-1432ONERA/DPHY, Université de ToulouseONERA/DPHY, Université de ToulouseUniversité Paris-Saclay, CNRS, Institut d’Astrophysique SpatialeUniversité Paris-Saclay, CNRS, Institut d’Astrophysique SpatialeONERA/DPHY, Université de ToulouseThe Ca index is a time-integrated geomagnetic index that correlates well with the dynamics of high-energy electron fluxes in the outer radiation belts. Therefore, Ca can be used as an indicator for the state of filling of the radiation belts for those electrons. Ca also has the advantage of being a ground-based measurement with extensive historical records. In this work, we propose a data-driven model to forecast Ca up to 24 h in advance from near-Earth solar wind parameters. Our model relies mainly on a recurrent neural network architecture called Long Short Term Memory that has shown good performances in forecasting other geomagnetic indices in previous papers. Most implementation choices in this study were arbitrated from the point of view of a space system operator, including the data selection and split, the definition of a binary classification threshold, and the evaluation methodology. We evaluate our model (against a linear baseline) using both classical and novel (in the space weather field) measures. In particular, we use the Temporal Distortion Mix (TDM) to assess the propensity of two time series to exhibit time lags. We also evaluate the ability of our model to detect storm onsets during quiet periods. It is shown that our model has high overall accuracy, with evaluation measures deteriorating in a smooth and slow trend over time. However, using the TDM and binary classification forecast evaluation metrics, we show that the forecasts lose some of their usefulness in an operational context even for time horizons shorter than 6 h. This behaviour was not observable when evaluating the model only with metrics such as the root-mean-square error or the Pearson linear correlation. Considering the physics of the problem, this result is not surprising and suggests that the use of more spatially remote data (such as solar imaging) could improve space weather forecasts.https://www.swsc-journal.org/articles/swsc/full_html/2021/01/swsc210026/swsc210026.htmlspace weatherforecastingradiation beltsmachine learningsolar wind |
| spellingShingle | Bernoux Guillerme Brunet Antoine Buchlin Éric Janvier Miho Sicard Angélica An operational approach to forecast the Earth’s radiation belts dynamics Journal of Space Weather and Space Climate space weather forecasting radiation belts machine learning solar wind |
| title | An operational approach to forecast the Earth’s radiation belts dynamics |
| title_full | An operational approach to forecast the Earth’s radiation belts dynamics |
| title_fullStr | An operational approach to forecast the Earth’s radiation belts dynamics |
| title_full_unstemmed | An operational approach to forecast the Earth’s radiation belts dynamics |
| title_short | An operational approach to forecast the Earth’s radiation belts dynamics |
| title_sort | operational approach to forecast the earth s radiation belts dynamics |
| topic | space weather forecasting radiation belts machine learning solar wind |
| url | https://www.swsc-journal.org/articles/swsc/full_html/2021/01/swsc210026/swsc210026.html |
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