We present new analysis methods of 3D MHD output data from the Space Weather Modeling Framework during a simulated storm event. Earth’s magnetosphere is identified in the simulation domain and divided based on magnetic topology and the bounding magnetopause definition. Volume energy contents and surface energy fluxes are analyzed for each subregion to track the energy transport in the system as the driving solar wind conditions change. Two energy pathways are revealed, one external and one internal. The external pathway between the magnetosheath and magnetosphere has magnetic energy flux entering the lobes and escaping through the closed field region and is consistent with previous work and theory. The internal pathway, which has never been studied in this manner, reveals magnetically dominated energy recirculating between open and closed field lines. The energy enters the lobes across the dayside magnetospheric cusps and escapes the lobes through the nightside plasmasheet boundary layer. This internal circulation directly controls the energy content in the lobes and the partitioning of the total energy between lobes and closed field line regions. Qualitative analysis of four-field junction neighborhoods indicate the internal circulation pathway is controlled via the reconnection X-line(s), and by extension, the IMF orientation. These results allow us to make clear and quantifiable arguments about the energy dynamics of Earth’s magnetosphere, and the role of the lobes as an expandable reservoir that cannot retain energy for long periods of time but can grow and shrink in energy content due to mismatch between incoming and outgoing energy flux.

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.

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.