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.

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.Plain Language Summary Society is increasingly dependent on radio signals, particularly those from the Global Navigation Satellite Systems (GNSS), and the technologies (e.g., navigation and financial transactions) that they enable. The integrity and reliability of these signals is threatened by their travel from the GNSS satellites to the ground, which includes passage through a charged region between 100 and 1,000 km known as the ionosphere. Disturbances to the ionosphere from solar energy, or space weather, cause variations in GNSS signals that adversely affect the dependent systems and technologies. Currently, the effect of the ionosphere on these signals cannot be reliably predicted, and the challenge is particularly important at latitudes above 45 ∘ where space weather impacts are most direct. We have compiled a large volume of data from the regions important to space weather (i.e., from the Sun to the Earth) to develop a novel machine learning model capable of skillfully predicting disruptions to GNSS signals at high latitudes.To our knowledge, this model is the first of its kind. We find that the new model is capable of more accurate predictions than current methods and position this model as a benchmark on which future predictive models can be measured.

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.

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, <1° latitudinal width), mesoscale (∼150–250 km, 1–2° latitudinal width), and large‐scale (>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.