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Electron density irregularities in the ionosphere modify the phase and amplitude of trans-ionospheric radio signals. We aim to characterize the spectral and morphological features of E- and F-region ionospheric irregularities likely to produce these fluctuations or “scintillations”. To characterize them, we use a three-dimensional radio wave propagation model—“Satellite-beacon Ionospheric scintillation Global Model of upper Atmosphere” (SIGMA), along with the scintillation measurements observed by a cluster of six Global Positioning System (GPS) receivers called Scintillation Auroral GPS Array (SAGA) at Poker Flat, AK. An inverse method is used to derive the parameters that describe the irregularities by estimating the best fit of model outputs to GPS observations. We analyze in detail one E-region and two F-region events during geomagnetically active times and determine the E- and F-region irregularity characteristics using two different spectral models as input to SIGMA. Our results from the spectral analysis show that the E-region irregularities are more elongated along the magnetic field lines with rod-shaped structures, while the F-region irregularities have wing-like structures with irregularities extending both along and across the magnetic field lines. We also found that the spectral index of the E-region event is less than the spectral index of the F-region events. Additionally, the spectral slope on the ground at higher frequencies is less than the spectral slope at irregularity height. This study describes distinctive morphological and spectral features of irregularities at E- and F-regions for a handful of cases performed using a full 3D propagation model coupled with GPS observations and inversion. 

Abstract We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020,https://doi.org/10.1029/2020JA027908), the FAC‐derived auroral conductance model of Robinson et al. (2020,https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993,https://doi.org/10.1029/92gl02109). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (Φ_PC) from ML‐AIM, the Weimer (2005,https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005,https://doi.org/10.1029/2004ja010884) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times. 

Abstract This paper surveys six years of Global Positioning System (GPS) L1 and L2C ionospheric scintillation in the auroral zone and, with a collocated incoherent scatter radar, hypothesizes the ionospheric irregularity layer. The Scintillation Auroral GPS Array of six scintillation receivers is sited at Poker Flat Research Range, Alaska, as is the Poker Flat incoherent scatter radar (PFISR). Scintillation intervals are identified across at least four receivers of the array using S4 and sigma phi (σ_ϕ) indices at 100 s cadence. Classification as “amplitude,” “phase,” or “both‐phase‐and‐amplitude” scintillation is performed by analyzing common time intervals of elevated S4 andσ_ϕ. Scattering of Global Navigation Satellite System (GNSS) waves by refractive or diffractive effects is hypothesized to occur in the E or F layer, or a transition layer in between, based on the PFISR peak density altitude at the time of the scintillation event. We analyze the statistics of the irregularity layer from 2014 to 2019, spanning solar maximum to solar minimum. We find fewer scintillation events per day with the waning solar cycle, nearly all of them phase scintillations. We also find that the percentage of events hypothesized to be caused by irregularities in the E layer increases with the declining solar cycle. The local time dependence of phase scintillations is primarily at night and in the E layer. Phase scintillation events occurring during daytime occur at solar maximum and are nearly all in the F layer. The majority of the events containing amplitude scintillations are daytime F layer at solar maximum (2014).