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Abstract Solar wind directional discontinuities, such as rotational discontinuities (RDs), significantly influence energy and transport processes in the Earth's magnetosphere. A recent observational study identified a long‐lasting double cusp precipitation event associated with RD in solar wind on 10 April 2015. To understand the magnetosphere‐ionosphere response to the solar wind RD, a global hybrid simulation of the magnetosphere was conducted, with solar wind conditions based on the observation event. The simulation results show significant variations in the magnetopause and cusp regions caused by the passing RD. After the RD propagates to the magnetopause, ion precipitation intensifies, and a double cusp structure at varying latitudes and longitudes forms near noon in the northern hemisphere, which is consistent with the satellite observations by Wing et al. (2023,https://doi.org/10.1029/2023gl103194). Regarding dayside magnetopause reconnection, the simulation reveals that the high‐latitude reconnection process persists during the RD passing, regardless of whether the interplanetary magnetic field (IMF) with a highB_y/B_zratio has a positive or negativeB_zcomponent, and low‐latitude reconnection occurs after the RD reaches the magnetopause at noon when the IMF turns southward. By examining the ion sources along the magnetic field lines, a connection is found between the single‐ or double‐cusp ion precipitation and the solar wind ions entering from both high‐latitude and low‐latitude reconnection sites. This result suggests that the double‐cusp structure can be triggered by magnetic reconnection occurring at both low latitudes and high latitudes in the opposite hemispheres, associated with a largeB_y/B_zratio of the IMF around the RD. 

Key Points Enhancement of field‐aligned warm ions observed in the plasma sheet was energy‐dispersive with increasing energy from 20 eV to >100 eV The probe at larger r observed the energy‐dispersive enhancements 20 min earlier than did the probe at smaller r The enhancements were likely caused by enhanced convection and the dispersion was likely due to acceleration by field‐aligned potential 

Hiss waves play an important role in removing energetic electrons from Earth’s radiation belts by precipitating them into the upper atmosphere. Compared to plasmaspheric hiss that has been studied extensively, the evolution and effects of plume hiss are less understood due to the challenge of obtaining their global observations at high cadence. In this study, we use a neural network approach to model the global evolution of both the total electron density and the hiss wave amplitudes in the plasmasphere and plume. After describing the model development, we apply the model to a storm event that occurred on 14 May 2019 and find that the hiss wave amplitude first increased at dawn and then shifted towards dusk, where it was further excited within a narrow region of high density, namely, a plasmaspheric plume. During the recovery phase of the storm, the plume rotated and wrapped around Earth, while the hiss wave amplitude decayed quickly over the nightside. Moreover, we simulated the overall energetic electron evolution during this storm event, and the simulated flux decay rate agrees well with the observations. By separating the modeled plasmaspheric and plume hiss waves, we quantified the effect of plume hiss on energetic electron dynamics. Our simulation demonstrates that, under relatively quiet geomagnetic conditions, the region with plume hiss can vary from L = 4 to 6 and can account for up to an 80% decrease in electron fluxes at hundreds of keV at L > 4 over 3 days. This study highlights the importance of including the dynamic hiss distribution in future simulations of radiation belt electron dynamics. 

Abstract Specification and forecast of ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionospheric research. Neural networks (NNs) emerge as a powerful modeling tool forNeprediction. However, heavy manual adjustments are time consuming to determine the optimal NN structures. In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to mitigate this problem. NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre‐defined hyperparameter search space. A total of 16‐year data from Millstone Hill incoherent scatter radar (ISR) are used for the NN models. One single‐layer NN (SLNN) model and one deep NN (DNN) model are both trained with NAS, namely SLNN‐NAS and DNN‐NAS, forNeprediction and compared with their manually tuned counterparts (SLNN and DNN) based on previous studies. Our results show that SLNN‐NAS and DNN‐NAS outperformed SLNN and DNN, respectively. These NN predictions ofNedaily variation patterns reveal a 27‐day mid‐latitude topsideNevariation, which cannot be reasonably represented by traditional empirical models developed using monthly averages. DNN‐NAS yields the best prediction accuracy measured by quantitative metrics and rankings of daily pattern prediction, especially with an improvement in mean absolute error more than 10% compared to the SLNN model. The limited improvement of NAS is likely due to the network complexity and the limitation of fully connected NN without the time histories of input parameters. 

This white paper is on the HMCS Firefly mission concept study. Firefly focuses on the global structure and dynamics of the Sun's interior, the generation of solar magnetic fields, the deciphering of the solar cycle, the conditions leading to the explosive activity, and the structure and dynamics of the corona as it drives the heliosphere.