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.

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 $Ne$.

The accurate determination of auroral precipitation in global models has remained a daunting and rather inexplicable obstacle. Understanding the calculation and balance of multiple sources that constitute the aurora, and their eventual conversion into ionospheric electrical conductance, is critical for improved prediction of space weather events. In this study, we present a semi-physical global modeling approach that characterizes contributions by four types of precipitation - monoenergetic, broadband, electron and ion diffuse - to ionospheric electrodynamics. The model uses a combination of adiabatic kinetic theory and loss parameters derived from historical energy flux patterns to estimate auroral precipitation from magnetohydrodynamic (MHD) quantities. It then converts them into ionospheric conductance that is used to compute the ionospheric feedback to the magnetosphere. The model has been employed to simulate the April 5 - 7, 2010 “Galaxy15” space weather event. Comparison of auroral fluxes show good agreement with observational datasets like NOAA-DMSP and OVATION Prime. The study shows a dominant contribution by electron diffuse precipitation, accounting for ~74% of the auroral energy flux. However, contributions by monoenergetic and broadband sources dominate during times of active upstream conditions, providing for up to 61% of the total hemispheric power. The study also indicates a dominant role played by broadband precipitation in ionospheric electrodynamics which accounts for ~31% of the Pedersen conductance.

We present latest results from the Conductance Model for Extreme Events (CMEE) and the Magnetosphere-Ionosphere-Thermosphere (MAGNIT) Conductance Model. Both models have been integrated into the Space Weather Modeling Framework (SWMF) to couple dynamically with the BATS-R-US MHD model, the Rice Convection Model (RCM) of the ring current & the Ridley Ionosphere Model (RIM) to simulate the April 2010 “Galaxy15” Event. The model is used with three grid configurations: the low-resolution configuration currently employed by NOAA’s Space Weather Prediction Center and two additional configurations that decrease the minimum grid resolution from ¼ RE to ⅛ and 1/16 RE. In addition, the simulation is driven with and without the dynamic coupling with RCM to study the impact of the ring current’s pressure correction in the inner magnetospheric domain. Using this model setup for a Maxwellian distribution, aforementioned precipitation sources are progressively applied and compared against the DMSP SSUSI observations. Finally, data-model comparisons against AMPERE Field-Aligned Currents, geomagnetic indices & magnetometer measurements are shown, with additional comparison against the existing conductance model in RIM. Results show remarkable progress in auroral precipitation modeling & MI coupling layouts in global models.

Modeling the terrestrial impacts of the sun’s solar wind is critical to understanding geomagnetic storms. We use a database of 144 storms from 2010-2019 and showed how these storms affect magnetometers on the ground. We also extracted profiles of the magnetic field along the magnetotail. Skill scores are assigned to the individual stations on the ground based on how well they can forecast magnetic indeces like SYM-H and AL. We us our Space Weather Modeling Framework’s geospace configuration. Our model includes coupling of 3D MHD solver (BATSRUS), the Rice Convection Model, and the Ridley Ionospheric Model. We have found that all stations have a positive Heidke Skill Score which is encouraging in terms of space weather forecasting.

Space weather monitoring and predictions largely rely on ground magnetic measurements and geomagnetic indices such as the Disturbance Storm Time index (Dst or SYM-H), Auroral Electrojet Index (AL) or the Polar Cap Index (PCI) all constructed using the individual station data. The global MHD simulations such as the Space Weather Modeling Framework (SWMF) can give predictions of these indices, driven by solar wind observations obtained at L1 giving roughly one hour lead time. The accuracy of these predictions especially during geomagnetic storms is a key metric for the model performance, and critical to operational space weather forecasts. In this presentation, we perform the largest statistical study of global simulation results using a database of 140 storms with minimum Dst below -50 nT during the years from 2010 to 2020. We compare SWMF results with indices derived from the SuperMAG network, which with its denser station network provides a more accurate representation of the true level of activity in the ring current and in the auroral electrojets. We show that the SWMF generally gives good results for the SYM-H index, whereas the AL index is typically underestimated by the model with the model predicting lower than observed ionospheric activity. We also examine the Cross Polar Cap Potential (CPCP) and compare it with a model derived using the PCI (Ridley et al., 2004) as well as with results obtained from the SuperDARN network. We show that the Ridley et al. CPCP model is much closer to the SWMF values. The results are used to discuss factors governing energy dissipation in magnetosphere - ionosphere system as well as possibilities to improve on the operational space weather forecasts.