Evaluating Proton Intensities for the SMILE Mission by Simon Mischel, Elena A. Kronberg, C. P. Escoubet

“…This data was then aligned with the Solar wind‐Magnetosphere‐Ionosphere Link Explorer (SMILE) mission's trajectory, to increase model accuracy in the relevant regions. …”
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Longitudinally spaced observations of a magnetic-cloud-like structure embedded in a co-rotating interaction region by M. L. Maunder, C. Foullon, R. Forsyth, D. Barnes, J. A. Davies

“…<p>Interaction mechanisms in the solar wind affect the evolution of magnetic structures, thereby mediating the properties acquired during their formation processes at the Sun as they propagate outward. …”
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The Polar Cap (PC) Index: Invalid Index Series and a Different Approach by Peter Stauning

“…Both versions include solar wind sector (SWS) effects in the calculation of the reference levels from which magnetic disturbances are measured. …”
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Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images by A. Hu, C. Shneider, A. Tiwari, E. Camporeale

“…Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. …”
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Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features by Armando Collado‐Villaverde, Pablo Muñoz, Consuelo Cid

“…Abstract In this work, we present an Artificial Neural Network for operational forecasting of the SYM‐H geomagnetic index up to 2 hr ahead using the Interplanetary Magnetic Field, the solar wind plasma features and previous SYM‐H values. …”
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On the Sources and Sizes of Uncertainty in Predicting the Arrival Time of Interplanetary Coronal Mass Ejections Using Global MHD Models by Pete Riley, Michal Ben‐Nun

“…Additionally, we build an ensemble of 12 ambient solar wind solutions using realizations from the ADAPT model. …”
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New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines by Daniel Iong, Yang Chen, Gabor Toth, Shasha Zou, Tuija Pulkkinen, Jiaen Ren, Enrico Camporeale, Tamas Gombosi

“…Abstract In this work, we develop gradient boosting machines (GBMs) for forecasting the SYM‐H index multiple hours ahead using different combinations of solar wind and interplanetary magnetic field (IMF) parameters, derived parameters, and past SYM‐H values. …”
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Mach Number Scaling of Foreshock Magnetic Fluctuations at Quasi-parallel Bow Shocks and Their Role in Magnetospheric Driving Throughout the Solar System by Brandon L. Burkholder, Li-Jen Chen, Katariina Nykyri, Norberto Romanelli, Menelaos Sarantos, Dave Sibeck, Jaye Verniero, Gina A. DiBraccio, Daniel Gershman, Martin Lindberg, Erin Kincade

“…The amplitude of magnetic fluctuations depends on the strength of the shock, quantified by the Alfvén Mach number ( M _A ), which is the ratio of solar wind velocity to the local Alfvén velocity. With increasing heliocentric distance, the solar wind M _A generally increases, such that Mercury typically experiences a lower M _A ∼ 5 compared to Earth ( M _A ∼ 8), and Mars a slightly higher M _A ∼ 9. …”
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Towards advanced forecasting of solar energetic particle events with the PARASOL model by Afanasiev Alexandr, Wijsen Nicolas, Vainio Rami

“…PARASOL requires input of solar wind and shock magnetohydrodynamic (MHD) parameters. …”
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Predicting Geostationary (GOES) 4.1–30 keV Electron Flux Over All MLT Using LEEMYR Regression Models by L. E. Simms, N. Y. Ganushkina, M. van deKamp, M. W. Liemohn

“…Abstract Regression models (LEEMYR: Low Energy Electron MLT geosYnchronous orbit Regression) predict hourly 4.1–30 keV electron flux at geostationary orbit (GOES‐16) using solar wind, IMF, and geomagnetic index parameters. Multiplicative interaction and polynomial terms describe synergistic and nonlinear effects. …”
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Modeling the Magnetic Connection from Earth to Solar Corona during the May 11 Geomagnetic Superstorm by Alessandro Ippolito, Tommaso Alberti, Fabio Giannattasio

“…A Monte Carlo simulation was applied to model the random walk of interplanetary magnetic field lines due to low-frequency turbulence in the solar wind. This model uses local diffusion coefficients dependent on magnetic fluctuations and the correlation length of solar wind turbulence. …”
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Response of the Magnetospheric Convection Electric Field (MCEF) to Geomagnetic Storms During the Solar Cycle 24 Maximum Phase by Bazié Nongobsom, Kaboré Salfo, Guibula Karim, Ouattara Frédéric

“…We examined the variations in solar wind parameters (VSW (solar wind speed), PSW (plasma pressure), and interplanetary magnetic field (IMF) Bz) and geomagnetic indices (Sym-H (symmetric horizontal) and auroral electrojet (AE)) during these storms. …”
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Forecasting Occurrence and Intensity of Geomagnetic Activity With Pattern‐Matching Approaches by C. Haines, M. J. Owens, L. Barnard, M. Lockwood, A. Ruffenach, K. Boykin, R. McGranaghan

“…Abstract Variability in near‐Earth solar wind conditions gives rise to space weather, which can have adverse effects on space‐ and ground‐based technologies. …”
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Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress) by Ryan M. McGranaghan, Jack Ziegler, Téo Bloch, Spencer Hatch, Enrico Camporeale, Kristina Lynch, Mathew Owens, Jesper Gjerloev, Binzheng Zhang, Susan Skone

“…We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by ML approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. …”
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PreMevE Update: Forecasting Ultra‐Relativistic Electrons Inside Earth's Outer Radiation Belt by Saurabh Sinha, Yue Chen, Youzuo Lin, Rafael Pires de Lima

“…Model inputs include precipitating electrons observed in low‐Earth‐orbits by NOAA satellites, upstream solar wind speeds and densities from solar wind monitors, as well as ultra‐relativistic electrons measured by one Los Alamos GEO satellite. …”
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Modeling of Ultraviolet Aurora Intensity Associated With Interplanetary and Geomagnetic Parameters Based on Neural Networks by Ze‐Jun Hu, Bing Han, Yisheng Zhang, Huifang Lian, Ping Wang, Guojun Li, Bin Li, Xiang‐Cai Chen, Jian‐Jun Liu

“…Abstract The spatial distribution of aurora intensity is an important manifestation of solar wind‐magnetosphere‐ionosphere energy coupling process, and it oscillates with the change of space environment parameters and geomagnetic index. …”
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Comparative Analysis of TPA‐LSTM and Transformer Models for Forecasting GEO Radiation Belt Electron Fluxes by Mengli Tan, Xu Si, Shangchun Teng, Xinming Wu, Xin Tao

“…Unlike most previous models, which only output electron fluxes, our models output the same parameters as the inputs, including magnetic local time, solar wind speed, solar wind dynamic pressure, AE, Kp, Dst, the north‐south component of the interplanetary magnetic field, and electron flux data from GOES‐15. …”
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NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux by D. A. Landis, A. A. Saikin, I. Zhelavskaya, A. Y. Drozdov, N. Aseev, Y. Y. Shprits, M. F. Pfitzer, A. G. Smirnov

“…Magnetic local time, Dst, Kp, solar wind dynamic pressure, AE, and solar wind velocity were found to perform as predicative indicators of GOES‐15 flux and therefore were used as the exogenous inputs. …”
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Parameter Distributions for the Drag‐Based Modeling of CME Propagation by Gianluca Napoletano, Raffaello Foldes, Enrico Camporeale, Giancarlo deGasperis, Luca Giovannelli, Evangelos Paouris, Ermanno Pietropaolo, Jannis Teunissen, Ajay Kumar Tiwari, Dario Del Moro

“…On the other hand, possible refinements to the current method are also identified, such as the dependence of the drag parameter distribution on the CME being accelerated or decelerated by the solar wind, which deserve further investigation.…”
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Research on improving deep space communication using hybrid RF-FSO system by Hongzhan LIU, Ting JIANG, Yuan HAO

“…In order to improve the quality of deep space communication,a mixed RF-FSO system and hybrid LPPM-BPSK-SIM scheme were introduced in deep space.The bit error rate of the hybrid RF-FSO system and the FSO system were compared and analyzed under the impact of solar wind fluctuation.The simulation results indicate that the deep space communication system achieves better bit error rate performance by using hybrid RF-FSO system,and the system performance can be further enhanced by adopting hybrid LPPM-BPSK-SIM.…”
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