Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning
Abstract Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based c...
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Wiley
2023-11-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003474 |
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| author | D. Conde F. L. Castillo C. Escobar C. García J. E. García V. Sanz B. Zaldívar J. J. Curto S. Marsal J. M. Torta |
| author_facet | D. Conde F. L. Castillo C. Escobar C. García J. E. García V. Sanz B. Zaldívar J. J. Curto S. Marsal J. M. Torta |
| author_sort | D. Conde |
| collection | DOAJ |
| description | Abstract Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non‐linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine‐learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM‐H index characterizing geomagnetic storms multiple‐hour ahead, using public interplanetary magnetic field (IMF) data from the Sun‐Earth L1 Lagrange point and SYM‐H data. We implement a type of machine‐learning model called long short‐term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep‐learning model in the context of forecasting the SYM‐H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper‐parameters of the LSTM network and robustness tests. |
| format | Article |
| id | doaj-art-31712a477cae4c1ea871dfff6921e5e2 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-31712a477cae4c1ea871dfff6921e5e22025-01-14T16:26:48ZengWileySpace Weather1542-73902023-11-012111n/an/a10.1029/2023SW003474Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep LearningD. Conde0F. L. Castillo1C. Escobar2C. García3J. E. García4V. Sanz5B. Zaldívar6J. J. Curto7S. Marsal8J. M. Torta9Instituto de Física Corpuscular (IFIC) Centro Mixto CSIC ‐ Universitat de València Valencia SpainLaboratoire d’Annecy de Physique des Particules (LAPP) CNRS/IN2P3 Université Grenoble Alpes Université Savoie Mont Blanc Annecy FranceInstituto de Física Corpuscular (IFIC) Centro Mixto CSIC ‐ Universitat de València Valencia SpainInstituto de Física Corpuscular (IFIC) Centro Mixto CSIC ‐ Universitat de València Valencia SpainInstituto de Física Corpuscular (IFIC) Centro Mixto CSIC ‐ Universitat de València Valencia SpainInstituto de Física Corpuscular (IFIC) Centro Mixto CSIC ‐ Universitat de València Valencia SpainInstituto de Física Corpuscular (IFIC) Centro Mixto CSIC ‐ Universitat de València Valencia SpainObservatori de l’Ebre (OE) University Ramon Llull ‐ CSIC Roquetes SpainObservatori de l’Ebre (OE) University Ramon Llull ‐ CSIC Roquetes SpainObservatori de l’Ebre (OE) University Ramon Llull ‐ CSIC Roquetes SpainAbstract Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non‐linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine‐learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM‐H index characterizing geomagnetic storms multiple‐hour ahead, using public interplanetary magnetic field (IMF) data from the Sun‐Earth L1 Lagrange point and SYM‐H data. We implement a type of machine‐learning model called long short‐term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep‐learning model in the context of forecasting the SYM‐H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper‐parameters of the LSTM network and robustness tests.https://doi.org/10.1029/2023SW003474geomagnetic stormsdeep learningforecastingSYM‐Huncertaintieshyper‐parameter optimization |
| spellingShingle | D. Conde F. L. Castillo C. Escobar C. García J. E. García V. Sanz B. Zaldívar J. J. Curto S. Marsal J. M. Torta Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning Space Weather geomagnetic storms deep learning forecasting SYM‐H uncertainties hyper‐parameter optimization |
| title | Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
| title_full | Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
| title_fullStr | Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
| title_full_unstemmed | Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
| title_short | Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
| title_sort | forecasting geomagnetic storm disturbances and their uncertainties using deep learning |
| topic | geomagnetic storms deep learning forecasting SYM‐H uncertainties hyper‐parameter optimization |
| url | https://doi.org/10.1029/2023SW003474 |
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