MEMPSEP‐III. A Machine Learning‐Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approa... by Kimberly Moreland, Maher A. Dayeh, Hazel M. Bain, Subhamoy Chatterjee, Andrés Muñoz‐Jaramillo, Samuel T. Hart

“…For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer spacecraft. …”
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On the Regional Variability of dB/dt and Its Significance to GIC by A. P. Dimmock, L. Rosenqvist, D. T. Welling, A. Viljanen, I. Honkonen, R. J. Boynton, E. Yordanova

“…The dependency between regional variability, solar wind conditions, and geomagnetic indices are also investigated. …”
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How geomagnetic storms affect the loss of Starlink satellites in February 2022? by Nizam Ahmad, La Ode Muhammad Musafar Kilowasid, Hanif Fakhrurroja, Neflia, Abdul Rachman, Asnawi Husin, Haries Fathoni

“…We attempted to analyze the cause of orbital decay by sampling all Starlink satellites registered in the SpaceTrack database and then tracing some space weather parameters and species density variations in the thermospheric layer. We employed the solar wind and IMF Bz to see their impact on geomagnetic activity. …”
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Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms by Rafael Pires de Lima, Yue Chen, Youzuo Lin

“…This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L‐shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. …”
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Fluctuations Driven by Multicomponent Pickup Ion Distributions in the Outer Heliosheath by Ameneh Mousavi, Vadim Roytershteyn, Federico Fraternale, Nikolai Pogorelov

“…Instead, they primarily contribute to reducing the thermal spread anisotropy of the PUIs originating from the neutral solar wind. The unstable AC waves exhibit lower growth rates but higher saturation levels than the mirror waves. …”
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Thermospheric Neutral Density Data Assimilation System Based on the Whole Atmosphere Model During the November 2003 Storm by Ching‐Chung Cheng, Timothy Fuller‐Rowell, Eric K. Sutton, Tzu‐Wei Fang, Jann‐Yenq Liu, Daniel R. Weimer

“…The first was allowing the Kp geomagnetic index to exceed 9 and the second was changing the relationship between Kp and the solar wind parameters used to drive the model. With these changes, results show that IDEA effectively captures the thermospheric neutral density at the CHAMP satellite altitude and follows the time‐dependence through the November 2003 storm period. …”
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Variability of Ionosphere Over Indian Longitudes to a Variety of Space Weather Events During December 2006 by Alok Kuman Ranjan, M. V. Sunil Krishna, C. Amory‐Mazaudier, R. Fleury, S. Sripathi, Geeta Vichare, W. Younas

“…On the next day, a stream of fast solar wind hits the magnetosphere, causing a HILDCAA (High Intensity Long Duration Continuous Auroral Activity) preceded by moderate geomagnetic storm. …”
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A Series of Advances in Analytic Interplanetary CME Modeling by C. Kay, T. Nieves‐Chinchilla, S. J. Hofmeister, E. Palmerio, V. E. Ledvina

“…The drag reappears stronger if the CME reaches the stream interaction region or upstream solar wind, leading to a stronger shock with more compression until the CME sufficiently decelerates. …”
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Modeling TEC Irregularities in the Arctic Ionosphere Using Empirical Orthogonal Function Method by Yaqi Jin, Wojciech J. Miloch, Daria Kotova, Knut Stanley Jacobsen, Đorđe Stevanović, Lasse B. N. Clausen, Nicholas Ssessanga, Federico DaDalt

“…To build an empirical model, we fit the EOF coefficients using helio‐geophysical indices from four different categories (solar activity; geomagnetic indices; IMF; the solar wind coupling function). The final EOF model is dependent on seven selected indices (F10.7P, Kp, Dst, Bt, By, Bz, and EKL). …”
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Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning by Donglai Ma, Xiangning Chu, Jacob Bortnik, Seth G. Claudepierre, W. Kent Tobiska, Alfredo Cruz, S. Dave Bouwer, Joseph F. Fennell, J. Bernard Blake

“…The Outer Radiation belt Electron Neural net model for Medium energy electrons uses only solar wind conditions and geomagnetic indices as input. …”
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An Empirical Model of the Equatorial Electron Pitch Angle Distributions in Earth's Outer Radiation Belt by Artem Smirnov, Yuri Y. Shprits, Hayley Allison, Nikita Aseev, Alexander Drozdov, Peter Kollmann, Dedong Wang, Anthony Saikin

“…We introduce a two‐step modeling procedure that for the first time ensures a continuous dependence on L, magnetic local time and activity, parametrized by the solar wind dynamic pressure. We propose two methods to reconstruct equatorial electron flux using the model. …”
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Improving the Electron Radiation Belt Nowcast and Forecast Using the SafeSpace Data Assimilation Modeling Pipeline by A. Brunet, N. Dahmen, C. Katsavrias, O. Santolík, G. Bernoux, V. Pierrard, E. Botek, F. Darrouzet, A. Nasi, S. Aminalragia‐Giamini, C. Papadimitriou, S. Bourdarie, I. A. Daglis

“…In this paper, we present the inner magnetosphere section of the SafeSpace pipeline that relies on solar wind driven and hourly updated models that describe the trapped electron environment (VLF waves, cold plasma and seed population densities), as well as the physical processes to which the trapped electrons are subjected to, such as radial diffusion and wave particle interactions. …”
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Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Com... by L. E. Simms, N. Yu. Ganushkina, M. Van derKamp, M. Balikhin, M. W. Liemohn

“…Abstract We screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. …”
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Improving Neutral Density Predictions Using Exospheric Temperatures Calculated on a Geodesic, Polyhedral Grid by D. R. Weimer, P. M. Mehta, W. K. Tobiska, E. Doornbos, M. G. Mlynczak, D. P. Drob, J. T. Emmert

“…Accuracy is improved with the addition of the total Poynting flux flowing into the polar regions, from an empirical model that uses the solar wind velocity and interplanetary magnetic field. …”
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Long-duration energy storage in transmission-constrained variable renewable energy systems by Andrew K. Chu, Ejeong Baik, Sally M. Benson

“…Our modeling shows that when LDES is affordable, it can reliably provide steady power, filling a role that is difficult for solar, wind, and other storage technologies to replace. …”
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Protection and Security Method for Multiple Energy Power Plant-Based Microgrids Using Dual Filtering Algorithm by Danni Liu, Shengda Wang, Weijia Su, Xiaojuan Zhang, Shichun Hui

“…The multiple energy power plant-based microgrids (MEPPBM) gradually incorporates multiple energy sources such as solar, wind, and battery energy storage, ensuring reliable security & protection has become a paramount challenge. …”
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Unveiling the Space Weather During the Starlink Satellites Destruction Event on 4 February 2022 by Tong Dang, Xiaolei Li, Bingxian Luo, Ruoxi Li, Binzheng Zhang, Kevin Pham, Dexin Ren, Xuetao Chen, Jiuhou Lei, Yuming Wang

“…Model simulations driven by solar wind show that the first geomagnetic storm induced around 20% atmospheric density perturbations at 210 km altitude on 3rd February. …”
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Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model by Yun Yan, Zi‐Kang Xie, Chao Yue, Jiu‐Tong Zhao, Fan Yang, Lun Xie, Qiu‐Gang Zong, Xu‐Zhi Zhou, Shan Wang

“…In this study, we employed a multi‐output convolutional neural network to predict the storm‐time ring current plasma pressures of these particles. Taking solar wind parameters, interplanetary magnetic field (IMF) data, and geomagnetic indices with a time history of 3 days as input parameters, the model shows good performances for electron plasma pressure, H+ plasma pressure, He+ plasma pressure, and O+ plasma pressure in both quiet‐time and storm‐time periods, with high correlation coefficients and small root mean square errors between the measured and the predicted values. …”
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Ionospheric TEC Prediction Based on Ensemble Learning Models by Yang Zhou, Jing Liu, Shuhan Li, Qiaoling Li

“…The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30‐day and 90‐day TEC averages. …”
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Evaluation of CME Arrival Prediction Using Ensemble Modeling Based on Heliospheric Imaging Observations by Tanja Amerstorfer, Jürgen Hinterreiter, Martin A. Reiss, Christian Möstl, Jackie A. Davies, Rachel L. Bailey, Andreas J. Weiss, Mateja Dumbović, Maike Bauer, Ute V. Amerstorfer, Richard A. Harrison

“…The ELlipse Evolution model based on HI observations (ELEvoHI) assumes that the CME frontal shape within the ecliptic plane is an ellipse and allows the CME to adjust to the ambient solar wind speed; that is, it is drag based. ELEvoHI is used to perform ensemble simulations by varying the CME frontal shape within given boundary conditions that are consistent with the observations made by HI. …”
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