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.

The question of how many satellites it would take to accurately map the spatial distribution of ionospheric outflow is addressed in this study. Given an outflow spatial map, this image is then reconstructed from a limited number virtual satellite pass extractions from the original values. An assessment is conducted of the goodness of fit as a function of number of satellites in the reconstruction, placement of the satellite trajectories relative to the polar cap and auroral oval, season and universal time (i.e., dipole tilt relative to the Sun), geomagnetic activity level, and interpolation technique. It is found that the accuracy of the reconstructions increases sharply from one to a few satellites, but then improves only marginally with additional spacecraft beyond ~4. Increased dwell time of the satellite trajectories in the auroral zone improves the reconstruction, therefore a high-but-not-exactly-polar orbit is most effective for this task. Local time coverage is also an important factor, shifting the auroral zone to different locations relative to the virtual satellite orbit paths. The expansion and contraction of the polar cap and auroral zone with geomagnetic activity influences the coverage of the key outflow regions, with different optimal orbit configurations for each level of activity. Finally, it is found that reconstructing each magnetic latitude band individually produces a better fit to the original image than 2-D image reconstruction method (e.g., triangulation). A high-latitude, high-altitude constellation mission concept is presented that achieves acceptably accurate outflow reconstructions.

Ionospheric conductance plays a crucial and active role in magnetosphere-ionosphere-thermosphere coupling processes. Despite its importance, direct global observations of conductance are unavailable. This limitation inspires the development of empirical models that are widely used to specify global distributions of conductance indirectly. In this work, a new model (COMPASS) describing the statistical relationships between conductance and Field-Aligned Currents (FACs) is presented. The conductance was determined by the electron densities measured by Poker Flat Incoherent Scattering Radar (PFISR), and the FACs were determined by the magnetic perturbations measured by SWARM. Between 2014 and 2020, there were $\sim$3900 conjunction events between PFISR and SWARM, providing a large dataset for investigating the relationship between conductance and FACs. It is found that both Hall and Pedersen conductances vary as a power of $|j_{\parallel}|$, and the power index $a$ is between 0 and 0.5. This power index $a$ depends on the Magnetic Local Time (MLT) and the direction of FACs: (1) The largest power index is obtained on the dawn side, and the minimum is at noon, suggesting the strongest/weakest correlation in the dawn/noon sector; (2) the power indices are positive for both upward and downward FACs and are larger for upward FACs than downward FACs, except in the dusk sector. The underlying physical mechanisms of the observed variations of the model parameters are also discussed. This work sheds light on the complicated relationship between FACs and conductance and provides a convenient way to specify global distributions of the auroral zone conductance.