Ionospheric outflow supplies nearly all of the heavy ions observed within the magnetosphere, as well as a significant fraction of the proton density. While much is known about upflow and outflow energization processes, the full global pattern of outflow and its evolution is only known statistically or through numerical modeling. Because of the dominant role of heavy ions in several key physical processes, this unknown nature of the full outflow pattern leads to significant uncertainty in understanding geospace dynamics, especially surrounding storm intervals. That is, global models risk not accurately reproducing the main features of intense space storms because the amount of ionospheric outflow is poorly specified and thus magnetospheric composition and mass loading could be ill-defined. This study defines a potential mission to observe ionospheric outflow from several platforms, allowing for a reasonable and sufficient reconstruction of the full outflow pattern on an orbital cadence. An observing system simulation experiment is conducted, revealing that four well-placed satellites are sufficient for reasonably accurate outflow reconstructions. The science scope of this mission could include the following: reveal the global structure of ionospheric outflow; relate outflow patterns to geomagnetic activity level; and determine the spatial and temporal nature of outflow composition. The science objectives could be focused to be achieved with minimal instrumentation (only a low-energy ion spectrometer to obtain outflow reconstructions) or with a larger scientific scope by including contextual instrumentation. Note that the upcoming Geospace Dynamics Constellation mission will observe upwelling but not ionospheric outflow.

During its ongoing mission, the Cluster II constellation has provided the first small-scale multipoint measurements of the space environment, and dramatically advanced scientific understanding in numerous regimes. One such region is the Earth’s inner magnetospheric ring current, which could now be computed using the curl of the magnetic field over a spacecraft tetrahedron instead of via plasma moments. While this produced the first 3D current estimates, it also produced different results from prior ring current studies with differing magnitudes and correlations with storm indices/local times. In this analysis, we revisit Cluster ring current data via curlometry, and conduct additional quantitative sensitivity simulations using actual spacecraft position data. During the orbits that observed ring current structure, tetrahedron shape and linearity assumptions can create large errors up to 100% of physical current magnitude in curlometer output that contradict accepted estimated quality parameters. These false currents are directly related to the structure of the current environment, and cannot be distinguished from the actual currents without additional limiting assumptions. The trustworthiness of curlometer output in the ring current is therefore dependent on the linearity of the magnetic structure relative to the tetrahedron orientation, which requires additional characterization. The Cluster curlometer output in the ring current is then explored in light of these new uncertainties, with the computed current magnitude and direction both potentially impacted by the production of false currents.

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations; the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve – plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data-model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.

We have developed a new procedure for combining lists of substorm onset times from multiple sources. We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from 1-31 January, 2005. We show that this procedure is capable of rejecting false positive identifications and filling data gaps that appear in individual lists. The resulting combined onset lists produce a waiting time distribution that is comparable to previously published results, and superposed epoch analyses of the solar wind driving conditions and magnetospheric response during the resulting onset times are also comparable to previous results. Comparison of the substorm onset list from the MHD model to that obtained from observational data reveals that the MHD model reproduces many of the characteristic features of the observed substorms, in terms of solar wind driving, magnetospheric response, and waiting time distribution. Heidke skill scores show that the MHD model has statistically significant skill in predicting substorm onset times.

41 thousand years ago, the Laschamps geomagnetic excursion caused Earth’s geomagnetic field to drastically diminish to ~4% of modern values and modified the geomagnetic dipole axis. While the impact of this geomagnetic event on environmental factors and human lifestyle has been contemplated to be linked with modifications in the geospace environment, no concerted investigation has been conducted to study this until recently. We present an initial investigation of the global space environment and related plasma environments during the several phases of the Lachamps event using an advanced multi-model approach. We use recent paleomagnetic field models of this event to study the paleomagnetosphere with help of the global magnetohydrodynamic model BATS-R-US. Here we go beyond a simple dipole approximation but consider a realistic geomagnetic field configuration. The field is used within BATS-R-US to generate the magnetosphere during discrete epochs spanning the peak of the event. Since solar conditions have remained fairly constant over the last ~100k years, modern estimates of the solar wind were used to drive the model. Finally, plasma pressure and currents generated by BATS-R-US at their inner boundary are used to compute auroral fluxes using a stand-alone version of the MAGNIT model, an adiabatic kinetic model of the aurora. Our results show that changes in the geomagnetic field, both in strength and the dipole tilt angle, have profound effects on the space environment and the ensuing auroral pattern. Magnetopause distances during the deepest phase of the excursion match previous predictions by studies like Cooper et al. (2021), while high-resolution mapping of magnetic fields allow close examination of magnetospheric structure for non-dipolar configurations. Temporal progression of the event also exhibits rapid locomotion of the auroral region over ~250 years along with the movement of the geomagnetic poles. Our estimates suggest that the aurora extended further down, with the center of the oval located at near-equatorial latitudes during the peak of the event. While the study does not find evidence of any link between geomagnetic variability and habitability conditions, geographic locations of the auroral oval coincide with early human activity in the Iberian peninsula and South China Sea.

Magnetohydrodynamic (MHD) models have been used for nearly four decades to study the dynamics of magnetospheric substorms. However, until recently no demonstration has been made that MHD models can consistently reproduce substorm onset times in a statistical sense. To test whether MHD can reproduce observed substorm onset times, we developed a procedure for identifying substorm onsets that can be applied both to observational data and to MHD output. Our substorm identification procedure aims to improve upon existing methods of substorm identification by using multiple types of observations to corroborate each identified substorm. Using this procedure, we identified over 100 substorms from the period 1-31 January 2005. Using this list of substorm onset times, we show that the MHD model has weak, but statistically significant skill in predicting substorm onset times. We explore paths to improving the ability of the MHD model to predict substorm dynamics by testing different configurations of the MHD model.

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.