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.

We apply idealized scatter-plot distributions to the sliding threshold of observation for numeric evaluation (STONE) curve, a new model assessment metric, to examine the relationship between the STONE curve and the underlying point-spread distribution. The STONE curve is based on the relative operating characteristic (ROC) curve but is developed to work with a continuous-valued set of observations, sweeping both the observed and modeled event identification threshold simultaneously. This is particularly useful for model predictions of time series data, as is the case for much of terrestrial weather and space weather. The identical sweep of both the model and observational thresholds results in changes to both the modeled and observed event states as the quadrant boundaries shift. The changes in a data-model pair’s event status result in nonmonotonic features to appear in the STONE curve when compared to a ROC curve for the same observational and model data sets. Such features reveal characteristics in the underlying distributions of the data and model values. Many idealized datasets were created with known distributions, connecting certain scatter-plot features to distinct STONE curve signatures. A comprehensive suite of feature-signature combinations is presented, including their relationship to several other metrics. It is shown that nonmonotonic features appear if a local spread is more than 0.2 of the full domain, or if a local bias is more than half of the local spread. The example of real-time plasma sheet electron modeling is used to show the usefulness of this technique, especially in combination with other metrics.

We investigate the drivers of 40-150 keV hourly electron flux at geostationary orbit (GOES 13) using ARMAX (autoregressive moving average transfer function) models which remove the confounding effect of diurnal cyclicity and allow assessment of each parameter independently of others. By taking logs of flux and predictor variables, we create nonlinear models. While many factors show high correlation with flux (substorms, ULF waves, solar wind velocity (V), pressure (P), number density (N) and electric field (Ey), IMF Bz, Kp, and SymH), the ARMAX model identifies substorms as the dominant influence at 40-75 keV and over 20-12 MLT, with little difference seen between disturbed and quiet periods. Also over 40-75 keV, Ey has a modest effect: positive over 20-12 MLT but negative over 13-19 MLT. Pressure shows some negative influence at 150 keV. Hourly ULF waves, Kp, and SymH show little influence when other variables are included. Using path analysis, we calculate the total sum of influence, both directly and indirectly through the driving of intermediate parameters. Pressure shows a summed direct and indirect influence nearly half that of the direct substorm effect, peaking at 40 keV. N, V, and Bz, as indirect drivers, are equally influential. Neither simple correlation nor neural networks can effectively identify drivers. Instead, consideration of actual physical influences, removing cycles that artificially inflate correlations, and controlling the effects of other parameters using multiple regression (specifically, ARMAX) gives a clearer picture of which parameters are most influential in this system.

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.

We report results of our analysis of a solar wind reconnecting current sheet (RCS) and its solar wind magnetic hole observed on 20 November 2018. In the solar wind, the normal vector to the current sheet plane makes an angle of 32° with the Sun-Earth line. A combination of tilted current sheet plane and foreshock effects cause an asymmetric interaction with the bow shock, such that the structure arrives at the quasi-perpendicular side of the bow shock before the quasi-parallel side. The solar wind flow slowdown and deflection during the bow shock crossing significantly disrupt the reconnection exhausts within the RCS. Unlike localized magnetosheath jets, the solar wind RCS has a global impact on the bow shock and the magnetopause. Plasma flow deflection in the magnetosheath also increases with the passage of the RCS. The magnetic field strength inside the magnetic hole decreases by ~69 percent in the solar wind, with a similar depression rate observed inside the magnetosheath due to this structure. The ion density and temperature both increase within the current sheet to form a roughly pressure balanced structure. Field rotation and change in the dynamic pressure during this event modify the reconnection zones at the magnetopause and cause an inward motion of this boundary.