As societal dependence on transionospheric radio signals grows, space weather impact on these signals becomes increasingly important yet our understanding of the effects remains inadequate. This challenge is particularly acute at high latitudes where the effects of space weather are most direct and no reliable predictive capability exists. We take advantage of a large volume of data from Global Navigation Satellite Systems (GNSS) signals, increasingly sophisticated tools for data‐driven discovery, and a machine learning algorithm known as the support vector machine (SVM) to develop a novel predictive model for high‐latitude ionospheric phase scintillation. This work, to our knowledge, represents the first time an SVM model has been created to predict high‐latitude phase scintillation. We use the true skill score to evaluate the SVM model and to establish a benchmark for high‐latitude ionospheric phase scintillation prediction. The SVM model significantly outperforms persistence (i.e., current and future scintillation are identical), doubling the predictive skill according to the true skill score for a 1‐hr lead time. For a 3‐hr lead time, persistence is comparable to a random chance prediction, suggesting that the memory of the ionosphere in terms of high‐latitude plasma irregularities is on the order of, or shorter than, a few hours. The SVM model predictive skill only slightly decreases between the 1‐ and 3‐hr predictive tasks, pointing to the potential of this method. Our findings can serve as a foundation on which to evaluate future predictive models, a critical development toward the resolution of space weather impact on transionospheric radio signals.

We provide the first ever characterization of the primary modes of ionospheric Hall and Pedersen conductance variability as empirical orthogonal functions (EOFs). These are derived from six satellite years of Defense Meteorological Satellite Program (DMSP) particle data acquired during the rise of solar cycles 22 and 24. The 60 million DMSP spectra were each processed through the Global Airlglow Model. Ours is the first large‐scale analysis of ionospheric conductances completely free of assumption of the incident electron energy spectra. We show that the mean patterns and first four EOFs capture ∼50.1 and 52.9% of the total Pedersen and Hall conductance variabilities, respectively. The mean patterns and first EOFs are consistent with typical diffuse auroral oval structures and quiet time strengthening/weakening of the mean pattern. The second and third EOFs show major disturbance features of magnetosphere‐ionosphere (MI) interactions: geomagnetically induced auroral zone expansion in EOF2 and the auroral substorm current wedge in EOF3. The fourth EOFs suggest diminished conductance associated with ionospheric substorm recovery mode. We identify the most important modes of ionospheric conductance variability. Our results will allow improved modeling of the background error covariance needed for ionospheric assimilative procedures and improved understanding of MI coupling processes.

We explore the characteristics, controlling parameters, and relationships of multiscale field‐aligned currents (FACs) using a rigorous, comprehensive, and cross‐platform analysis. Our unique approach combines FAC data from the Swarm satellites and the Advanced Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) to create a database of small‐scale (∼10–150 km, 250 km) FACs. We examine these data for the repeatable behavior of FACs across scales (i.e., the characteristics), the dependence on the interplanetary magnetic field orientation, and the degree to which each scale “departs” from nominal large‐scale specification. We retrieve new information by utilizing magnetic latitude and local time dependence, correlation analyses, and quantification of the departure of smaller from larger scales. We find that (1) FACs characteristics and dependence on controlling parameters do not map between scales in a straight forward manner, (2) relationships between FAC scales exhibit local time dependence, and (3) the dayside high‐latitude region is characterized by remarkably distinct FAC behavior when analyzed at different scales, and the locations of distinction correspond to “anomalous” ionosphere‐thermosphere behavior. Comparing with nominal large‐scale FACs, we find that differences are characterized by a horseshoe shape, maximizing across dayside local times, and that difference magnitudes increase when smaller‐scale observed FACs are considered. We suggest that both new physics and increased resolution of models are required to address the multiscale complexities. We include a summary table of our findings to provide a quick reference for differences between multiscale FACs.

We report a new modeling capability that self‐consistently couples physics‐based magnetospheric electron precipitation with its impact on the ionosphere. Specifically, the ring current model RAM‐SCBE is two‐way coupled to an ionospheric electron transport model GLOW (GLobal airglOW), representing a significant improvement over previous models, in which the ionosphere is either treated as a 2‐D spherical boundary of the magnetosphere or is driven by empirical precipitation models that are incapable of capturing small‐scale, transient variations. The new model enables us to study the impact of substorm‐associated, spectrum‐resolved electron precipitation on the 3‐D ionosphere. We found that after each substorm injection, a high‐energy tail of precipitation is produced in the dawn sector outside the plasmapause, by energetic electrons (10 < E < 100 keV) scattered by whistler‐mode chorus waves. Consequently, an ionospheric sublayer characterized by enhanced Pedersen conductivity arises at unusually low altitude (∼85 km), with its latitudinal width of ∼5–10° in the auroral zone. The sublayer structure appears intermittently, in correlation with recurrent substorm injections. It propagates eastward from the nightside to the dayside in the same drift direction of source electrons injected from the plasma sheet, resulting in global impact within the ionosphere. This study demonstrates the model's capability of revealing complex cross‐scale interactions in the geospace environment.

We have developed a new optimal interpolation (OI) technique to estimate complete high‐latitude ionospheric conductance distributions from Defense Meteorological Satellite Program particle data. The technique combines particle precipitation‐based calculations of ionospheric conductances and their errors with a background model and its error covariance (modeled with empirical orthogonal functions) to infer complete distributions of the high‐latitude ionospheric conductances. We demonstrate this technique for the 26 November through 2 December 2011 period and analyze a moderate geomagnetic storm event on 30 November 2011. Quantitatively and qualitatively, this new technique provides better ionospheric conductance specification than past statistical models, especially during heightened geomagnetic activity. We provide initial evidence that auroral images from the Defense Meteorological Satellite Program Special Sensor Ultraviolet Spectrographic Imager instrument can be used to further improve the OI conductance maps. Our OI conductance patterns allow assimilative mapping of ionospheric electrodynamics reconstructions driven separately by radar and satellite magnetometer observations to be in closer agreement than when other, commonly used, conductance models are applied. This work (1) supports better use of the diverse observations available for high‐latitude ionospheric electrodynamics specification and (2) supports the Cousins et al. (2015b) assertion that more accurate models of the ionospheric conductance are needed to robustly assimilate ground‐ and space‐based observations of ionospheric electrodynamics. We find that the OI conductance distributions better capture the dynamics and locations of discrete electron precipitation that modulate the coupling of the magnetosphere‐ionosphere‐thermosphere system.