Glancing blows of three interplanetary shocks caused an unexpectedly large magnetic storm on November 4-6, 2023, which was popular for citizen scientists because of the surprising appearance of the crimson-red auroras world-wide in middle latitudes. Based on the analysis of the in-situ interplanetary magnetic field data at DSCOVR and STEREO-A, we show that the multi-step main phase of the magnetic storm is explained by the shock pileup, i.e. slow interplanetary shock was caught up from behind by the fast one, and the multi-step prolonged recovery phase by the remnant structure associated with the shock pileup.

Using a machine learning technique called echo state network (ESN), we have developed an emulator to model the physics-based global magnetohydrodynamic (MHD) simulation results of REPPU (REProduce Plasma Universe) code. The inputs are the solar wind time series with date and time, and the outputs are the time series of the ionospheric auroral current system in the form of two-dimensional (2D) patterns of field-aligned current, potential, and conductivity. We mediated a principal component analysis for a dimensionality reduction of the 2D map time series. In this study, we report the latest upgraded Surrogate Model for REPPU Auroral Ionosphere version 2 (SMRAI2) with significantly improved resolutions in time and space (5 min in time, ~1 degrees in latitude, and 4.5 degrees in longitude), where the dipole tilt angle is also newly added as one of the input parameters to reproduce the seasonal dependence. The fundamental dependencies of the steady-state potential and field-aligned current patterns on the interplanetary magnetic field (IMF) directions are consistent with those obtained from empirical models. Further, we show that the ESN-based emulator can output the AE index so that we can evaluate the performance of the dynamically changing results, comparing with the observed AE index. Since the ESN-based emulator runs a million times faster than the REPPU simulation, it is promising that we can utilize the emulator for the real-time space weather forecast of the auroral current system as well as to obtain large-number ensembles to achieve future data assimilation-based forecast.

We report a citizen science-motivated study on the cause of an unusually bright red aurora as witnessed from Hokkaido, Japan during a magnetic storm on December 1, 2023. Such an intense red aurora event has occurred in the Halloween 2003 super storm, but the Dst index peak of this December 2023 storm was only -107 nT. In spite of the moderate storm amplitude, the extremely high solar wind density of >50 /cc caused the aurora oval extension to 53 MLAT (L=2.8). We discuss that the drift loss of the ring current particles across the small-size magnetopause is important, and Hokkaido was at the right position to see the direct effect of the large particle injection of the storm-time substorm.

Super magnetic storm occurred on October 10, 2024, and a super substorm occurred at the beginning of the storm just after the shock arrival at 1520 UT. As a result, red aurora were photographed at multiple points over wide region of Japan from 1700 UT. A new Bayesian analysis enables us to estimate the time variation of the most probable height and latitude of the red aurora based on the citizen science dataset in combination of POES/MetOp satellite datasets of electron precipitation boundary. We found that the top height of red aurora extended to ~850 km and the red aurora shifted toward low latitude according to the storm development. The ultra-high altitude of the red aurora can be evidence of rapid atmospheric heating and the atmospheric expansion.

Physics-based simulations are important for elucidating the fundamental mechanisms behind the time-varying complex ionospheric conditions, such as field-aligned currents (FACs) and plasma convection patterns, against unprecedented solar wind variations incidents in the Earth’s magnetosphere. However, to perform a huge parameter survey for understanding the nonlinear solar wind density dependence of the FAC and convection patterns, for example, a large-scale cluster computer is not fast enough to run state-of-the-art global magnetohydrodynamic (MHD) simulations. Here we report the impressive performance of a machine-learning based surrogate model for the ionospheric outputs of a global MHD simulation, using the reservoir computing technique called echo state network (ESN). The trained ESN-based emulator is exceptionally fast to perform the parameter survey, suggesting a missing solar wind density dependence of the ionospheric polar cap potential. We discuss future directions including the promising application for the space weather forecast.

Relativistic electron precipitation (REP) refers to the release of high energy electrons initially trapped in the outer radiation belt that precipitate into the Earth’s upper atmosphere. REP plays an important role in the magnetosphere as it contributes to the fast depletion of radiation belt electron flux. This study presents a statistical analysis of the REP observations made by the Calorimetric Electron Telescope (CALET) experiment on board the International Space Station (2015- present). Specifically, a catalog based on count rates from the two top scintillators constituting the top charge detector, sensitive to electrons with energies $>$1.5 MeV and $>$3.4 MeV, respectively. Data from the CALET experiment reveals a previously unreported semi-annual variation in the occurrence of REP events. The REP periodicities are similar to those observed for trapped electrons in the outer belt, and correlates with the occurrence of solar wind high-speed streams and the solar cycle variations.

A specialized ground-based system has been developed for simultaneous observations of pulsating aurora (PsA) and related magnetospheric phenomena with the Arase satellite. The instrument suite is composed of 1) six 100-Hz sampling high-speed all-sky imagers (ASIs), 2) two 10-Hz sampling monochromatic ASIs observing 427.8 and 844.6 nm auroral emissions, 3) Watec Monochromatic Imagers, 4) a 20-Hz sampling magnetometer and 5) a 5-wavelength photometer. The 100-Hz ASIs were deployed in four stations in Scandinavia and two stations in Alaska, which have been used for capturing the main pulsations and quasi 3 Hz internal modulations of PsA at the same time. The 10-Hz sampling monochromatic ASIs have been operative in Tromsø, Norway with the 20-Hz magnetometer and the 5-wavelength photometer. Combination of these multiple instruments with the European Incoherent SCATter (EISCAT) radar enables us to reveal the energetics/electrodynamics behind PsA and further to detect the low-altitude ionization due to energetic electron precipitation during PsA. In particular, we intend to derive the characteristic energy of precipitating electrons during PsA by comparing the 427.8 and 844.6 nm emissions from the two monochromatic ASIs. Since the launch of the Arase satellite, the data from these instruments have been examined in comparison with the wave and particle data from the satellite in the magnetosphere. In the future, the system will be utilized not only for studies of PsA but also for other categories of aurora in close collaboration with the planned EISCAT_3D project.

Galactic cosmic rays (GCRs) are one of the possible mediators of the solar influence on climate. However, the impacts of GCR on clouds and climate systems are not fully understood. In this paper, we show that the high-altitude clouds associated with deep convective activities are responding to the decadal-scale cycles of GCRs and that the susceptible areas are seasonally variable. Most notable responses were found in August over tropical land areas, suggesting that the susceptivity of clouds to GCRs depends on the depth of convective activities and the abundance of aerosol precursor materials. Furthermore, following the activation of high-altitude cloud formation, an increase in sea surface temperature (SST) gradient was observed over the Pacific. Although the response of SST to solar activity has mostly been discussed as mediated by solar radiations, we propose that another mechanism is possible: through the impact of GCRs on clouds and the resultant changes in atmospheric circulations.

The properties of the auroral electrojets are studied by modeling the relationships between the solar-wind parameters and the AU and AL indices with a trained echo state network (ESN), a kind of recurrent neural network. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The synthetic data show that the AU and AL indices are significantly affected by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF). A contributions from IMF By is are also suggested. In addition, the synthetic data indicate nonlinear effects from the solar-wind density, which is strong when the solar-wind speed is high and when IMF Bz is near zero.