We have identified for the first time an energy-time dispersion of precipitating electron flux in a pulsating aurora patch, ranging from 6.7 keV to 580 keV, through simultaneous in-situ observations of sub-relativistic electrons of microburst precipitations and lower-energy electrons using the LAMP sounding rocket launched from the Poker Flat Research Range in Alaska. Our observations reveal that precipitating electrons with energies of 180-320 keV were observed first, followed by 250-580 keV electrons 0-30 ms later, and finally, after 500-1000 ms, 6.7-14.6 keV electrons were observed. The identified energy-time dispersion is consistent with the theoretical estimation that the relativistic electron microbursts are a high-energy tail of pulsating aurora electrons, which are caused by chorus waves propagating along the field line.

A number of interdependent conditions and processes contribute to ionospheric-origin energetic ion outflows. Due to these interdependences and the associated observational challenges, energetic ion outflows remain a poorly understood facet of atmosphere-ionosphere-magnetosphere coupling. Here we demonstrate the relationship between east-west magnetic field fluctuations ($\Delta B_{\textrm{EW}}$) and energetic outflows in the magnetosphere-ionosphere transition region. We use dayside cusp-region FAST satellite observations made at apogee ($\sim$4200-km altitude) near fall equinox and solstices in both hemispheres to derive statistical relationships between ion upflow and ($\Delta B_{\textrm{EW}}$) spectral power as a function of spacecraft-frame frequency bands between 0 and 4 Hz. Identification of ionospheric-origin energetic ion upflows is automated, and the spectral power $P_{EW}$ in each frequency band is obtained via integration of $\Delta B_{\textrm{EW}}$ power spectral density. Derived relationships are of the form $J_{\parallel,i} = J_{0,i} P_{EW}^\gamma$ for upward ion flux $J_{\parallel,i}$ at 130-km altitude. The highest correlation coefficients are obtained for spacecraft-frame frequencies $\sim$0.1–0.5 Hz. Summer solstice and fall equinox observations yield power law indices $\gamma \simeq$ 0.9–1.3 and correlation coefficients $r \geq 0.92$, while winter solstice observations yield $\gamma \simeq$ 0.4–0.8 with $r \gtrsim 0.8$. Mass spectrometer observations reveal that the oxygen/hydrogen ion composition ratio near summer solstice is much greater than the corresponding ratio near winter. These results thus reinforce the importance of ion composition in any outflow model. If observed $\Delta B_{\textrm{EW}}$ variations are purely spatial and not temporal, we show that spacecraft-frame frequencies $\sim$0.1–0.5 Hz correspond to perpendicular spatial scales of several to tens of kilometers.

The magnetosphere, ionosphere and thermosphere (MIT) act as a coherently integrated system (geospace), driven in part by solar influences and characterized by variability and complexity. Among the most important and yet uncertain aspects of the geospace system is energy and momentum coupling between regions, which is, in part, accomplished by the transfer of charged particles from the magnetosphere to the ionosphere in a process known as particle precipitation, and in the opposite direction by ion outflow. Both processes are inherently multiscale and manifest the variabilities and complexities of the geospace system. Despite the importance of the transfer of particles, existing models are increasingly ill-equipped to provide the specification necessary for the growing demand for geospace now- and forecasts. Due to recent trends in the availability of data, we now face an exciting opportunity to progress particle transfer in geospace through the intersection of traditional approaches and state-of-the-art data-driven sciences. We reveal novel particle transfer models utilizing machine learning (ML), present results from the models, and provide an evaluation of their capabilities including comparisons with observations and the current ’state-of-the-art’ models (e.g., OVATION Prime for particle precipitation and the Gamera-Ionosphere Polar Wind Model for ion outflow). We detail the data wrangling required to utilize the available geospace observations to make progress on the long-standing challenge of particle transfer and place specific emphasis on the discovery possible when ML models are appropriate and robustly interrogated in the context of physical understanding. Our presentation helps illustrate the trends in the application of data science in space science.

We present an analysis of in-situ thermal ion measurements from a cusp auroral sounding rocket. Using a forward modeling procedure, we find most-probable thermal ion temperature and parallel (field-aligned) bulk flow velocity along the trajectory. Spatially and temporally intermittent fine-scale structure in upflowing/downflowing features in the dayside cusp ionosphere are presented. We show that the observed ion temperatures are consistent with Joule heating expectations if spatially and temporally intermittent drivers and responses in the dynamic cusp environment are considered. Additionally, a forward modeling procedure for the ion data interpretation is improved and a sensitivity analysis is presented.