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Abstract Electron density irregularities in the ionosphere can give rise to scintillations, affecting radio wave phase and amplitude. While scintillations in the cusp and polar cap regions are commonly associated with mesoscale density inhomogeneities and/or shearing, the auroral regions exhibit a strong correlation between scintillation and density structures generated by electron precipitation (arcs). We aim to examine the impact of electron precipitation on the formation of scintillation‐producing density structures using a high‐resolution physics‐based plasma model, the “Geospace Environment Model of Ion‐Neutral Interactions,” coupled with a radio propagation model, the “Satellite‐beacon Ionospheric‐scintillation Global Model of the upper Atmosphere.” Specifically, we explore the effects of varying spatial and temporal characteristics of the precipitation, including electron total energy flux and their characteristic energies, obtained from the all‐sky‐imagers and Poker Flat Incoherent Scatter Radar observations, on auroral scintillation. To capture small‐scale structures, we incorporate a power‐law turbulence spectrum that induces short wavelength features sensitive to scintillation. Finally, we compare our simulated scintillation results with satellite‐observed scintillations, along with spectral comparisons. 

Small-scale ionospheric plasma structures can cause scintillation in radio signals passing through the ionosphere. The relationship between the scintillated signal and how plasma structuring develops is complex. We model the development of small-scale plasma structuring in and around an idealized polar cap patch observed by the Resolute Bay Incoherent Scatter Radars (RISR) with the Geospace Environment Model for Ion-Neutral Interactions (GEMINI). Then, we simulate a signal passing through the resulting small-scale structuring with the Satellite-beacon Ionospheric-scintillation Global Model of the upper Atmosphere (SIGMA) to predict the scintillation characteristics that will be observed by a ground receiver at different stages of instability development. Finally, we compare the predicted signal characteristics with actual observations of scintillation from ground receivers in the vicinity of Resolute Bay. We interpret the results in terms of the nature of the small-scale plasma structuring in the ionosphere and how it impacts signals of different frequencies and attempt to infer information about the ionospheric plasma irregularity spectrum. 

Abstract Joule heating deposits a significant amount of energy into the high‐latitude ionosphere and is an important factor in many magnetosphere‐ionosphere‐thermosphere coupling processes. We consider the relationship between localized temperature enhancements in polar cap measured with the Resolute Bay Incoherent Scatter Radar‐North (RISR‐N) and the orientation of the interplanetary magnetic field (IMF). Based on analysis of 10 years of data, RISR‐N most commonly observes ion heating in the noon sector under northwards IMF. We interpret heating events in that sector as being primarily driven by sunwards plasma convection associated with lobe reconnection. We attempt to model two of the observed temperature enhancements with a data‐driven first principles model of ionospheric plasma transport and dynamics, but fail to fully reproduce the ion temperature enhancements. However, evaluating the ion energy equation using the locally measured ion velocities reproduces the observed ion temperature enhancements. This result indicates that current techniques for estimating global plasma convection pattern are not adequately capturing mesoscale flows in the polar cap, and this can result in underestimation of the energy deposition into the ionosphere and thermosphere. 

The Reproducible Software Environment (Resen) is an open-source software tool enabling computationally reproducible scientific results in the geospace science community. Resen was developed as part of a larger project called the Integrated Geoscience Observatory (InGeO), which aims to help geospace researchers bring together diverse datasets from disparate instruments and data repositories, with software tools contributed by instrument providers and community members. The main goals of InGeO are to remove barriers in accessing, processing, and visualizing geospatially resolved data from multiple sources using methodologies and tools that are reproducible. The architecture of Resen combines two mainstream open source software tools, Docker and JupyterHub, to produce a software environment that not only facilitates computationally reproducible research results, but also facilitates effective collaboration among researchers. In this technical paper, we discuss some challenges for performing reproducible science and a potential solution via Resen, which is demonstrated using a case study of a geospace event. Finally we discuss how the usage of mainstream, open-source technologies seems to provide a sustainable path towards enabling reproducible science compared to proprietary and closed-source software.