One of the prominent effects of space weather is the variation of electric currents in the magnetosphere and ionosphere, which give rise to rapid geomagnetic field variations on the surface of the Earth. These Geomagnetic Disturbances (GMDs) can be highly localized and of large amplitude, causing disruptions in ground conducting systems. Because the source of localized GMDs is unresolved, we are prompted to model these effects, identify the physical drivers through examination of the model we use, and improve our prediction of these effects. We run a high-resolution simulation using the Space Weather Modeling Framework (SWMF) of the September 7, 2017 event. This configuration of the model combines three physical models: Block Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US), an ideal magnetohydrodynamic model of the magnetosphere; the Ridley Ionosphere Model (RIM), a shell ionosphere calculated by solving 2-D Ohm’s Law; and the Rice Convection Model (RCM), a kinetic drift model of the inner magnetosphere. The configuration mirrors that which is used in operations, however, the higher grid resolution used is capable of reproducing mesoscale structure in the tail and ionosphere. We use Regional Station Difference (RSD) and Regional Tail Difference (RTD) to quantify the success of the model against observation. RSD is a metric calculated using dB/dt or geoelectric field to pinpoint when a single magnetometer station records a significantly different value than others within a given radius. RTD is a metric calculated using relevant variables along the field lines connecting the magnetometer stations to the magnetosphere to determine what magnetospheric conditions drive localized signatures. Of spikes that occur during the event, we examine the differences between those that we can reproduce and those that we cannot. We investigate the improvements in our model when we switch from an empirical model of the ionosphere to a physics-based conductance model, MAGNetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Conductance Model. For small-scale effects we cannot reproduce, we explore the deficiencies in our comparison to determine what physical processes must be addressed in the model.