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.

The equivalent source method of Spherical Elementary Current Systems (SECS) has contributed valuable results for spatial magnetic interpolation purposes where no observations are available, as well as for modeling equivalent currents both in the ionosphere and in the subsurface, thus providing a separation between external and internal sources. It has been successfully applied to numerous Space Weather (SW) events, whereas some advantages have been reported over other techniques such as Fourier or Spherical (Cap) Harmonic Analysis. Although different modalities of SECS exist (either 1-D, 2-D or 3-D) depending on the number of space dimensions involved, the method provides a sequence of instantaneous pictures of the source current. We present an extension of SECS consisting in the introduction of a temporal dependence in the formulation based on a cubic B-splines expansion. The technique thus adds one dimension, becoming 4-D in general (e.g., 3D + t), and its application is envisaged for, though not restricted to, the analysis of past events including heterogeneous geomagnetic datasets, such as those containing gaps, different sampling rates or diverse data sources. A synthetic model based on the Space Weather Modeling Framework (SWMF) is used to show the efficacy of the extended scheme. We apply this method to characterize the current systems of past and significant SW events producing geomagnetically induced currents (GIC), which we exemplify with an outstanding geomagnetic sudden commencement (SC) occurred on March 24, 1991.