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.

We perform a geomagnetic event simulation using a newly developed magnetohydrodynamic with adaptively embedded particle-in-cell (MHD-AEPIC) model. We have developed effective criteria to identify reconnection sites in the magnetotail and cover them with the PIC model. The MHD-AEPIC simulation results are compared with Hall MHD and ideal MHD simulations to study the impacts of kinetic reconnection at multiple physical scales. At the global scale, the three models produce very similar SYM-H and SuperMag Electrojet (SME) indexes, which indicates that the global magnetic field configurations from the three models are very close to each other. We also compare the ionospheric solver results and all three models generate similar polar cap potentials and field aligned currents. At the mesoscale we compare the simulations with in situ Geotail observations in the tail. All three models produce reasonable agreement with the Geotail observations. At the kinetic scales, the MHD-AEPIC simulation can produce a crescent shape distribution of the electron velocity space at the electron diffusion region which agrees very well with MMS observations near a tail reconnection site. These electron scale kinetic features are not available in either the Hall MHD or ideal MHD models. Overall, the MHD-AEPIC model compares well with observations at all scales, it works robustly, and the computational cost is acceptable due to the adaptive adjustment of the PIC domain. It remains to be determined whether kinetic physics can play a more significant role in other types of events, including but not limited to substorms.

The largest moon in the solar system, Ganymede, is the only moon known to possess a strong intrinsic magnetic field and a corresponding magnetosphere. Using the latest version of Space Weather Modeling Framework (SWMF), we study the upstream plasma interactions and dynamics in this sub-Alfvenic system. Results from the Hall MHD and the coupled MHD with embedded Particle-in-Cell (MHD-EPIC) models are compared. We find that under steady upstream conditions, magnetopause reconnection occurs in a non-steady manner. Flux ropes of Ganymede’s radius in length form on the magnetopause at a rate about 2/minute and create spatiotemporal variations in plasma and field properties. Upon reaching proper grid resolutions, the MHD-EPIC model can resolve both electron and ion kinetics at the magnetopause and show localized non-gyrotropic behavior inside the diffusion region. The estimated global reconnection rate from the models is about 80 kV with 60% efficiency, and there is weak evidence of about 1 minute periodicity in the temporal variations due to the dynamic reconnection process.

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.

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.

5 For the first time, we simulate the detailed spectral line emission from a solar active 6 region (AR) with the Alfvén Wave Solar Model (AWSoM). We select an AR appearing 7 near disk center on 2018 July 13 and use an NSO/HMI synoptic magnetogram to specify 8 the magnetic field at the model’s inner boundary. To resolve smaller-scale magnetic 9 features, we apply adaptive mesh refinement to resolve the AR with a horizontal spatial 10 resolution of 0.35 • (4.5 Mm), four times higher than the background corona. We then 11 apply the SPECTRUM code informed with CHIANTI spectral emissivities to calculate 12 spectral lines forming at temperatures ranging from 0.5 to 3 MK. Comparisons are 13 made between the simulated line intensities and those observed by the Hinode/EIS 14 instrument where we find close agreement (about 20% relative error for both loop top 15 and footpoints at a temperature of about 1.5 MK) across a wide range of loop sizes and 16 temperatures. We also simulate and compare Doppler velocities and find that simulated 17 flow patterns are of comparable magnitude to what is observed. Our results demonstrate 18 the broad applicability of the low-frequency Alfvén wave balanced turbulence theory 19 for explaining the heating of coronal loops. 20

Flux Transfer Events (FTEs) are transient magnetic flux ropes typically found at the Earth’s magnetopause on the dayside. While it is known that FTEs are generated by magnetic reconnection, it remains unclear how the details of magnetic reconnection controls their properties. A recent study showed that the helicity sign of FTEs positively correlates with the east-west (By) component of the Interplanetary Magnetic Field (IMF). With data from the Cluster and Magnetospheric Multiscale missions, we performed a statistical study of 166 quasi force-free FTEs. We focus on their helicity sign and possible association with upstream solar wind conditions and local magnetic reconnection properties. Using both in situ data and magnetic shear modeling, we find that FTEs whose helicity sign corresponds to the IMF By are associated with moderate magnetic shears while those that does not correspond to the IMF By are associated with higher magnetic shears. While uncertainty in IMF propagation to the magnetopause may lead to randomness in the determination of the flux rope core field and helicity, we rather propose that for small IMF By, which corresponds to high shear and low guide field, the Hall pattern of magnetic reconnection determines the FTE core field and helicity sign. In that context we explain how the temporal sequence of multiple X-line formation and the reconnection rate are important in determining the flux rope helicity sign. This work highlights a fundamental connection between kinetic processes at work in magnetic reconnection and the macroscale structure of FTEs.

We present a data-driven and self-consistent SEP model, SOFIE, to simulate the acceleration and transport processes of energetic particles using the Space Weather Modeling Framework (SWMF). In this model, the background solar wind plasma in the solar corona and interplanetary space are modeled by the Alfven Wave Solar-atmosphere Model(-Realtime) (AWSoM(-R)) driven by the near-real-time hourly updated GONG (bihourly ADAPT-GONG) magnetograms. In the background solar wind, the CMEs are launched employing the Eruptive Event Generator using Gibson-Low configuration (EEGGL), by inserting a flux rope estimated from the free magnetic energy in the active region. The acceleration and transport processes are then modeled self-consistently by the multiple magnetic field line tracker (M-FLAMPA) and the Adaptive Mesh Particle Simulator (AMPS). We will demonstrate the capability of SOFIE to demystify the acceleration processes by the CME-driven shock in the low corona and the modulation of energetic particles by the solar wind structures. Besides, using selected historical SEP events, e.g. 2013 Apr 11 event, we will illustrate the progresses toward a faster-than-real-time prediction of SEPs.

Magnetospheric sawtooth oscillations are observed during strong and steady solar wind driving conditions. The simulation results of our global MHD model with embedded kinetic physics show that when the total magnetic flux carried by constant solar wind exceeds a threshold, sawtooth-like magnetospheric oscillations are generated. Different from previous works, this result is obtained without involving time-varying ionospheric outflow in the model. The oscillation period and amplitude agree well with observations. The simulated oscillations cover a wide range of local times, although the distribution of magnitude as a function of longitude is different from observations. Our comparative simulations using ideal or Hall MHD models do not produce global time-varying features, which suggests that kinetic reconnection physics in the magnetotail is a major contributing factor to sawtooth oscillations.

The MHD with embedded PIC (MHD-EPIC) model makes it feasible to incorporate kinetic physics into a global simulation. Still, this requires a large enough box-shaped PIC domain to accommodate the movement and changes of the magnetic reconnection regions over time. This wastes computational resources on simulating regions with the expensive PIC model where MHD would be sufficient to describe the physics. We have developed a new MHD with Adaptively Embedded PIC (MHD-AEPIC) algorithm that couples the BATS-R-US MHD model with the new FLexible Exascale Kinetic Simulator (FLEKS) PIC code. In the new coupled model the PIC domains can move with the magnetic reconnection regions and adapt to them with an arbitrary shape. In this work, we will first introduce the algorithms for selecting the reconnection regions in the MHD model that need to be resolved with the kinetic PIC model. Then we will compare simulations obtained with MHD-EPIC using fixed PIC regions versus MHD-AEPIC employing adaptive PIC regions to verify that the new model generates reliable results. Finally, we will apply the MHD-AEPIC model to a global magnetic storm simulation and demonstrate the improved efficiency.