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Forecasting Geomagnetic Storm Disturbances and Their Uncertainties using Deep Learning
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  • Carlos Escobar Ibáñez,
  • Daniel Eduardo Conde Villatoro,
  • Florencia Luciana Castillo,
  • Carmen García García,
  • Jose Enrique García Navarro,
  • Verónica Sanz González,
  • Bryan Zaldívar Montero,
  • Juan José Curto,
  • Santiago Marsal,
  • Joan Miquel Torta
Carlos Escobar Ibáñez
Instituto de Física Corpuscular
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Daniel Eduardo Conde Villatoro
Instituto de Física Corpuscular

Corresponding Author:[email protected]

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Florencia Luciana Castillo
Laboratoire d'Annecy de Physique des Particules
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Carmen García García
Instituto de Física Corpuscular
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Jose Enrique García Navarro
Instituto de Física Corpuscular
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Verónica Sanz González
Instituto de Física Corpuscular
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Bryan Zaldívar Montero
Instituto de Física Corpuscular
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Juan José Curto
Observatori de l'Ebre-CSIC
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Santiago Marsal
Ebre Observatory
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Joan Miquel Torta
Observatori de l'Ebre
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Abstract

Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude commercial flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents flowing on long ground-based conductors, such as power networks or pipelines, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against geomagnetically induced currents is to forecast them. This is a challenging task, though, 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 as the SYM-H. 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 characterising geomagnetic storms one hour in advance, using public interplanetary magnetic field data from the Sun--Earth L1 Lagrange point and SYM-H. We implement a type of machine-learning model called long short-term memory networks. Our scope is to estimate -for the first time to our knowledge- the prediction uncertainties coming from a deep-learning model in the context of space weather. The resulting uncertainties turn out to be sizeable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimisation of important hyper-parameters of the long short-term memory network and robustness tests.
07 Mar 2023Submitted to ESS Open Archive
09 Mar 2023Published in ESS Open Archive