Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely the times when such predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times. To satisfy different needs and operational constraints, three models were developed: (1) a model that inputs nowcast Kp and solar wind parameters and predicts Kp 1 hour ahead; (2) a model with the same input as model 1 and predicts Kp 4 hour ahead; and (3) a model that inputs only solar wind parameters and predicts Kp 1 hour ahead (the exact prediction lead time depends on the solar wind speed and the location of the solar wind monitor). Extensive evaluations of these models and other major operational Kp forecast models show that while the new models can predict Kps more accurately for all activities, the most dramatic improvements occur for moderate and active times. Information dynamics analysis of Kp suggests that geospace is more dominated by internal dynamics near solar minimum than near solar maximum, when it is more directly driven by external inputs, namely solar wind and interplanetary magnetic field (IMF).

During periods of northward IMF, plasma sheet ions often have two components: hot (magnetospheric origin) and cold (magnetosheath origin). The temperatures of the cold‐component ions are ∼30–40% higher in the dawn sector compared to the dusk sector, implying the dawnside magnetosheath ion heating of ∼30–40%. As a result, the magnetosheath ions are less distinguishable from the hot‐component ions, which have lower temperatures on the dawnside, leading to a higher occurrence of the ions having (apparent) one‐component distribution. As the duration of the hourly averaged IMF being northward (Δt) increases from 1 to 10 hours, the occurrence of two‐component ions increases from 65% to 83% in the dusk flank, but in the dawn flank it remains relatively stable at around 45%. In contrast, the occurrence of ions best characterized by kappa (κ) distribution increases from 25% to 35% in the dawn flank whereas in the dusk flank it remains relatively insensitive to Δt (10%). The occurrence of a one‐component Maxwellian distribution appears to be most pronounced in the region of the plasma sheet close to the midnight meridian, and these ions appear to be characteristic of the nominal plasma sheet (hot component) ions. The densification of the plasma sheet, as Δt increases, mainly results from the influx of the magnetosheath ions. However, the cooling of the plasma sheet ions can be attributed not only to the influx of the cold magnetosheath ions but also to the cooling of the nominal plasma sheet ions. The dawn‐dusk asymmetries observed in the cold magnetosheath ion profiles should provide constraints that can help determine the roles of various proposed magnetosheath ion entry mechanisms.

The solar wind‐magnetosphere system is nonlinear. The solar wind drivers of geosynchronous electrons with energy range of 1.8–3.5 MeV are investigated using mutual information, conditional mutual information (CMI), and transfer entropy (TE). These information theoretical tools can establish linear and nonlinear relationships as well as information transfer. The information transfer from solar wind velocity (V_sw) to geosynchronous MeV electron flux (J_e) peaks with a lag time of 2 days. As previously reported, J_e is anticorrelated with solar wind density (n_sw) with a lag of 1 day. However, this lag time and anticorrelation can be attributed at least partly to the J_e(t + 2 days) correlation with V_sw(t) and n_sw(t + 1 day) anticorrelation with V_sw(t). Analyses of solar wind driving of the magnetosphere need to consider the large lag times, up to 3 days, in the (V_sw, n_sw) anticorrelation. Using CMI to remove the effects of V_sw, the response of J_e to n_sw is 30% smaller and has a lag time < 24 h, suggesting that the MeV electron loss mechanism due to n_sw or solar wind dynamic pressure has to start operating in < 24 h. n_sw transfers about 36% as much information as V_sw (the primary driver) to J_e. Nonstationarity in the system dynamics is investigated using windowed TE. When the data are ordered according to transfer entropy value, it is possible to understand details of the triangle distribution that has been identified between J_e(t + 2 days) versus V_sw(t).

Dawn–dusk asymmetries are ubiquitous features of the coupled solar-wind–magnetosphere–ionosphere system. During the last decades, increasing availability of satellite and ground-based measurements has made it possible to study these phenomena in more detail. Numerous publications have documented the existence of persistent asymmetries in processes, properties and topology of plasma structures in various regions of geospace. In this paper, we present a review of our present knowledge of some of the most pronounced dawn–dusk asymmetries. We focus on four key aspects: (1) the role of external influences such as the solar wind and its interaction with the Earth's magnetosphere; (2) properties of the magnetosphere itself; (3) the role of the ionosphere and (4) feedback and coupling between regions. We have also identified potential inconsistencies and gaps in our understanding of dawn–dusk asymmetries in the Earth's magnetosphere and ionosphere.