The monthly mean sunspot number has been larger in June-July 2023 than the double peak of solar cycle 24 (146 in February 2014 and 139 in November 2011) and brings us back to the sunspot level of solar cycle 23. However, the number of rocket launches, satellites in orbit and private space companies has increased dramatically in the past 20 years. Additionally, there is a growing interest for space exploration beyond Earth’s orbit, to the Moon and beyond, which comes with higher risk of being affected by space weather. Here, we discuss some of these trends and the role of the journal to improve awareness of space weather impacts.

Yulu Peng1, Shasha Zou1, Zihan Wang2, Xiantong Wang1, Aaron Ridley1, Grace Kwon1 and Mary Smirnova11Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA2Department of Physics, University of Texas at Arlington, Arlington, TX, USACorresponding author: Yulu Peng (yulupeng@umich.edu)

Accurately predicting the horizontal component of ground magnetic field perturbation (\text{d}$B_{\text{H}}$), a key quantity for calculating the geomagnetically induced currents (GICs), is crucial for assessing the space weather impact of geomagnetic disturbances. The current operational first-principles Michigan Geospace model can predict \text{d}$B_{\text{H}}$ with positive Heidke Skill Scores, but requires significant computational resources to achieve real-time speeds. Existing data-driven methods tend to underpredict \text{d}$B_{\text{H}}$ and lack uncertainty quantification, which is either overlooked or treated as secondary. In this work, we introduce GeoDGP, a novel and efficient data-driven model based on the deep Gaussian process (DGP). GeoDGP provides global probabilistic forecasts of \text{d}$B_{\text{H}}$ with a lead time of at least 1 hour, and at 1-minute time cadence and with arbitrary spatial resolution. The model takes solar wind measurements, the Dst index, and the prediction location in solar magnetic coordinate system as inputs, and is trained on 28 years of data from SuperMAG global magnetometer stations. Additionally, GeoDGP is also trained to predict the north (\text{d}$B_{\text{N}}$) and east (\text{d}$B_{\text{E}}$) components of perturbations. We evaluate GeoDGP’s performance at over 200 stations worldwide during 24 geomagnetic storms, including the Gannon extreme storm of May 2024. Comparisons with the first-principles Michigan Geospace model and the data-driven DAGGER model revealed that GeoDGP significantly outperforms both across multiple performance metrics.

Forecasting the arrival time of Earth-directed coronal mass ejections (CMEs) via physics-based simulations is an essential but challenging task in space weather research due to the complexity of the underlying physics and limited remote and in-situ observations of these events. Data assimilation (DA) techniques can assist in constraining free model parameters and reduce the uncertainty in subsequent model predictions. In this study, we show that CME simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white-light (WL) observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. The L1 observations are used to constrain the model of the solar wind background into which the CME is launched. Average speed of CME shock front over propagation angles are extracted from both synthetic WL images from the Alfv\’en Wave Solar atmosphere Model (AWSoM) and the WL observations. We observe a strong rank correlation between the average WL speed and CME arrival time, with the Spearman’s rank correlation coefficients larger than 0.90 for three events occurring during different phases of the solar cycle. This enables us to develop a Bayesian framework to filter ensemble simulations using WL observations, which is found to reduce the mean absolute error of CME arrival time prediction from about 13.4 hours to 5.1 hours. The results show the potential of assimilating readily available L1 and WL observations within hours of the CME eruption to construct optimal ensembles of Sun-to-Earth CME simulations.

Dr. Jennifer Gannon passed away suddenly on 2024 May 2 in Greenbelt, MD. Dr. Gannon had served as an editor for the Space Weather journal since April 2019, and she was the longest-serving editor on the current board, having started under the previous editor-in-chief, Dr. Delores Knipp. Abstract content goes here

Modeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework (SWMF) is a state-of-the-art framework that offers full Sun-to-Earth simulations by computing the background solar wind, CME propagation and magnetospheric impact. However, reliable long-term predictions of CME events require uncertainty quantification (UQ) and data assimilation (DA). We take the first steps by performing global sensitivity analysis (GSA) and UQ for background solar wind simulations produced by the Alfvén Wave Solar atmosphere Model (AWSoM) for two Carrington rotations: CR2152 (solar maximum) and CR2208 (solar minimum). We conduct GSA by computing Sobol indices that quantify contributions from model parameter uncertainty to the variance of solar wind speed and density at 1 au, both crucial quantities for CME propagation and strength. Sobol indices also allow us to rank and retain only the most important parameters, which aids in the construction of smaller ensembles for the reduced-dimension parameter space. We present an efficient procedure for computing the Sobol indices using polynomial chaos expansion (PCE) surrogates and space-filling designs. The PCEs further enable inexpensive forward UQ. Overall, we identify three important model parameters: the multiplicative factor applied to the magnetogram, Poynting flux per magnetic field strength constant used at the inner boundary, and the coefficient of the perpendicular correlation length in the turbulent cascade model in AWSoM.

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. Using Shapley Additive Explanation (SHAP) values to quantify the contributions from each input to predictions of the SYM-H index from GBMs, we show that our predictions are consistent with physical understanding while also providing insight into the complex relationship between the solar wind and Earth’s ring current. In particular, we found that feature contributions vary depending on the storm phase. We also perform a direct comparison between GBMs and neural networks presented in prior publications for forecasting the SYM-H index by training, validating, and testing them on the same data. We find that the GBMs have a comparable root mean squared error as the best published black-box neural network schemes.

We use the Space Weather Modeling Framework (SWMF) Geospace configuration to simulate a total of 122 storms from the period 2010-2019. With the focus on the storm main phase, each storm period was run for 54 hours starting from 6 hours prior to the start of the Dst depression. The simulation output of ground magnetic variations were compared with ground magnetometer station data provided by SuperMAG to statistically assess the Geospace model regional magnetic perturbation prediction performance. Our results show that the regional predictions at mid-latitudes are quite accurate, but the high-latitude regional disturbances are still difficult to predict.

Despite significant developments in global modeling, the determination of ionospheric conductance in the auroral region remains a challenge in the space science community. With advances in adiabatic kinetic theory and numerical couplings between global magnetohydrodynamic models and ring current models, the dynamic prediction of individual sources of auroral conductance have improved significantly. However, the individual impact of these sources on the total conductance and ionospheric electrodynamics remains understudied. In this study, we have investigated individual contributions from four types of auroral precipitation - electron & ion diffuse, monoenergetic & Alfven wave-driven - on ionospheric electrodynamics using a novel modeling setup. The setup encompasses recent developments within the University of Michigan’s Space Weather Modeling Framework (SWMF), specifically through the use of the MAGNetosphere - Ionosphere - Thermosphere auroral precipitation model and dynamic two-way coupling with the Global Ionosphere-Thermosphere Model. This modeling setup replaces the empirical idealizations traditionally used to estimate conductance in SWMF, with a physics-based approach capable of resolving 3-D high-resolution mesoscale features in the ionosphere-thermosphere system. Using this setup, we have simulated an idealized case of southward Bz 5nT & the April 5-7 “Galaxy15” Event. Contributions from each source of precipitation are compared against the OVATION Prime Model, while auroral patterns and hemispheric power during Galaxy15 are compared against observations from DMSP SSUSI and the AE-based FTA model. Additionally, comparison of field aligned currents (FACs) and potential patterns are also conducted against AMPERE, SuperDARN & AMIE estimations. Progressively applying conductance sources, we find that diffuse contributions from ions and electrons provide ~75% of the total energy flux and Hall conductance in the auroral region. Despite this, we find that Region 2 FACs increase by ~11% & cross-polar potential reduces by ~8.5% with the addition of monoenergetic and broadband sources, compared to <1% change in potential for diffuse additions to the conductance. Results also indicate a dominant impact of ring current on the strength and morphology of the precipitation pattern.

The next decadal survey process for space and solar physics will start soon with white papers due during the second half of 2022 and the committees and panels working over all of 2023. Space weather science and operations will play an essential role in this survey. Therefore, the community is invited to prepare white papers and get involved in advancing space weather research and capabilities in the upcoming decades. A summary of the recommendations related to space weather from the last two decadal surveys is also provided.

Global navigation satellite systems (GNSS) or satellite navigation is an important technological advancement; however, it is greatly impacted by the effects of space weather, such as ionosphere scintillation. Ionosphere scintillation is one of the causes of errors in the GNSS signals and also has the potential to cause a loss of access to GNSS. Ionosphere scintillation often impacts the polar region; however, the cause is not always known. One potential source of scintillation is polar cap patches. In Ren et al., [2018], a polar cap patch database was created based on the incoherent scatter radar measurements at Resolute Bay (RISR). Using data provided by the CHAIN Network of ionosphere scintillation detected near Resolute Bay in 2016, it can be determined how polar cap patches impact ionosphere scintillation. A statistical analysis as well as event analysis have been performed. Scintillation data from GNSS satellites with an elevation angle over 40 degrees were collected from each patch in the database and were compared to daily average. It was found that statistically there is no obvious phase scintillation or amplitude scintillation increase associated with patch in the polar cap. For the event analysis, three different patch events with and without enhanced scintillation were chosen for in-depth analysis and cross-comparison. Other datasets, including AMPERE FAC and RISR, are used to understand the plasma characteristics and geomagnetic activity conditions during these events.

This work conducts a statistical study of the subauroral polarization stream (SAPS) feature in the North American sector using Millstone Hill incoherent scatter radar measurements from 1979 to 2019, which provides a comprehensive SAPS climatology using a significantly larger database of radar observations than was used in seminal earlier works. Key features of SAPS and associated Ne/Ti/Te are investigated using a superposed epoch analysis method. The characteristics of these parameters are investigated with respect to magnetic local time, season, geomagnetic activity, solar activity, and interplanetary magnetic field orientation, respectively. The main results are as follows: (1) Conditions for SAPS are more favorable for dusk than near midnight, for winter compared to summer, for active geomagnetic periods compared to quiet time, for solar minimum compared to solar maximum, and for IMF conditions with negative By and negative Bz. (2) SAPS is usually associated with a midlatitude trough of 15–20\% depletion in the background density. The SAPS-related trough is more pronounced in the postmidnight sector and near the equinoxes. (3) Subauroral ion and electron temperatures exhibit a 3–8\% (50–120 K) enhancement in SAPS regions, which tend to have higher percentage enhancement during geomagnetically active periods and at midnight. Ion temperature enhancements are more favored during low solar activity periods, while the electron temperature enhancement remains almost constant as a function of the solar cycle. (4) The electron thermal content, Te \times Ne, in the SAPS associated region is strongly dependent on 1/Ne, with Te exhibiting a negative correlation with respect to $Ne$.