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The 400 worst‐case severe environments for surface charging detected at Los Alamos National Laboratory satellites during the years of 1990–2005 as binned by the definitions of four criteria developed by Matéo‐Vélez et al. (2018, https://doi.org/10.1002/2017sw001689) and the solar wind and Interplanetary Magnetic Field (IMF) parameters and geomagnetic activity indices are analyzed. The conducted analysis shows that only Auroral Electrojet/Auroral Lower index determines the highest risk for severe environments for surface charging to happen. The presence of a substorm with the southward turning pattern in IMF Bz indicates that the environment can be severe for surface charging to occur but this environment will not depend on whether a substorm was moderate or intense. No clear dependence on IMF Bz is found for risk to a severe environment to occur. Appearances of severe environments for surface charging do not necessarily require high values of Kp (Planetarische Kennziffer) and no storm is needed for such an event to be detected. Among solar wind parameters, solar wind velocity Vsw is directly related to the highest risk of severe environments, dependent on the Vsw value; and number density Nsw is of no importance. Two criteria for severe environment events based on the enhancements of low energy particle fluxes exhibit clearer dependencies on the solar wind and IMF parameters and geomagnetic activity indices with more distinct patterns in their time history.

Separating the magnetotail into northern and southern lobes, the plasma sheet extends from the edge of the dominance of Earth's magnetic dipole field to several dozen Earth radii (R E ; R E is the Earth's radius, 6,371 km) downtail, and it's azimuthal extent ranges several hours of magnetic local time (MLT;Hill, 1974). The near-Earth (6-12R E , 06-18MLT) plasma sheet, which contains the transition region from stretched to dipolar magnetic field, is an important driver of inner magnetosphere dynamics (e.g.,

The Disturbance storm time (Dst) is a geomagnetic index related to the perturbation of the geomagnetic field at low latitudes (Burton et al., 1975;Rostoker, 1972). Currently, Dst is defined by using geomagnetic field measurements from four equatorial ground magnetometers: Hermanus, Honolulu, San Juan and Kakioka (Sugiura & Kamei, 1991). Dst has been widely used for monitoring geomagnetic storms, which pose one of the most severe space weather risks to our space-borne and ground-based electronic instruments, such as global navigation satellite systems and radio transmission systems (Z. Li et al., 2021;Wan et al., 2021).A short-term prediction of Dst is produced operationally at the NOAA Space Weather Prediction Center by means of a physics-based model (the Space Weather Modeling Framework developed at the University of Michigan, (Tóth et al., 2005)). A longer lead-time operational Dst forecast is provided by Space Environment Technologies, using the Anemomilos model (Tobiska et al., 2013). Dst is also used as an essential input for forecasting thermosphere mass density and ionospheric parameters, and to parameterize several empirical models. The following is a non-exhaustive list of models that use Dst as one of their inputs: O'Brien and Moldwin ( 2003) empirically estimate the location of the plasmapause; Agapitov et al. (2015) derive a statistical model for the lower band chorus distribution; S. Li et al. (2016) estimate the ionospheric global electron content storm-time response; Boardsen et al. (2000) derive an empirical model of the high-latitude magnetopause; and, Zhao et al. (2018) derive a model of radiation belt electron pitch-angle distributions.A large amount of literature has been devoted to Dst prediction, notably using empirical and machine learning techniques (Camporeale, 2019). Lundstedt et al. (2002) first attempted to implement a multi-layer perception neural network to forecast Dst 1 hour ahead using interplanetary magnetic field (IMF) data. Several researchers presented models to extend the Dst forecast up to 6 hr in advance (Bala & Reiff, 2012;Lazzús et al., 2017;Saiz et al., 2008). A Gaussian Process model was introduced by Chandorkar et al. (2017) and combined with a long short-term memory (LSTM) architecture in Gruet et al. (2018) to provide probabilistic predictions up to 6 hours in advance. An ensemble learning algorithm was applied by Xu et al. (2020) for the same purpose. Laperre et al. (2020) evaluated the performance of a LSTM model based on a Dynamical Time Warping metric as the cost function.