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Abstract Joule heating is a major energy sink in the solar wind‐magnetosphere‐ionosphere system and modeling it is key to understanding the impact of space weather on the neutral atmosphere. Ion drifts and neutral wind velocities are key parameters when modeling Joule heating, however there is limited validation of the modeled ion and neutral velocities at mid‐latitudes. We use the Blackstone Super Dual Auroral Radar Network radar and the Michigan North American Thermosphere Ionosphere Observing Network Fabry‐Perot interferometer to obtain the local nightside ion and neutral velocities at ∼40° geographic latitude during the nighttime of 16 July 2014. Despite being a geomagnetically quiet period, we observe significant sub‐auroral ion flows in excess of 200 ms⁻¹. We calculate an enhancement to the local Joule heating rate due to these ion flows and find that the neutrals impart a significant increase or decrease to the total Joule heating rate of >75% depending on their direction. We compare our observations to outputs from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM). At such a low geomagnetic activity however, TIEGCM was not able to model significant sub‐auroral ion flows and any resulting Joule heating enhancements equivalent to our observations. We found that the neutral winds were the primary contributor to the Joule heating rates modeled by TIEGCM rather than the ions as suggested by our observations. 

Abstract High‐latitude ionospheric convection is a useful diagnostic of solar wind‐magnetosphere interactions and nightside activity in the magnetotail. For decades, the high‐latitude convection pattern has been mapped using the Super Dual Auroral Radar Network (SuperDARN), a distribution of ground‐based radars which are capable of measuring line‐of‐sight (l‐o‐s) ionospheric flows. From the l‐o‐s measurements an estimate of the global convection can be obtained. As the SuperDARN coverage is not truly global, it is necessary to constrain the maps when the map fitting is performed. The lower latitude boundary of the convection, known as the Heppner‐Maynard boundary (HMB), provides one such constraint. In the standard SuperDARN fitting, the HMB location is determined directly from the data, but data gaps can make this challenging. In this study we evaluate if the HMB placement can be improved using data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), in particular for active time periods when the HMB moves to latitudes below . We find that the boundary as defined by SuperDARN and AMPERE are not always co‐located. SuperDARN performs better when the AMPERE currents are very weak (e.g., during non‐active times) and AMPERE can provide a boundary when there is no SuperDARN scatter. Using three geomagnetic storm events, we show that there is agreement between the SuperDARN and AMPERE boundaries but the SuperDARN‐derived convection boundary mostly lies equatorward of the AMPERE‐derived boundary. We find that disagreements primarily arise due to geometrical factors and a time lag in expansions and contractions of the patterns. 

Abstract The Super Dual Auroral Radar Network (SuperDARN) is a collection of radars built to study ionospheric convection. We use a 7‐year archive of SuperDARN convection maps, processed in 3 different ways, to build a statistical understanding of dusk‐dawn asymmetries in the convection patterns. We find that the data set processing alone can introduce a bias which manifests itself in dusk‐dawn asymmetries. We find that the solar wind clock angle affects the balance in the strength of the convection cells. We further find that the location of the positive potential foci is most likely observed at latitudes of 78° for long periods (>300 min) of southward interplanetary magnetic field (IMF), as opposed to 74° for short periods (<20 min) of steady IMF. For long steady dawnward IMF the median is also at 78°. For long steady periods of duskward IMF, the positive potential foci tends to be at lower latitudes than the negative potential and vice versa during dawnward IMF. For long periods of steady Northward IMF, the positive and negative cells can swap sides in the convection pattern. We find that they move from ∼0–9 MLT to 15 MLT or ∼15–23 MLT to 10 MLT, which reduces asymmetry in the average convection cell locations for Northward IMF. We also investigate the width of the region in which the convection returns to the dayside, the return flow width. Asymmetries in this are not obvious, until we select by solar wind conditions, when the return flow region is widest for the negative convection cell during Southward IMF. 

Abstract The Super Dual Auroral Radar Network (SuperDARN) was built to study ionospheric convection and has in recent years been expanded geographically. Alongside software developments, this has resulted in many different versions of the convection maps data set being available. Using data from 2012 to 2018, we produce five different versions of the widely used convection maps, using limited backscatter ranges, background models and the exclusion/inclusion of data from specific radar groups such as the StormDARN radars. This enables us to simulate how much information was missing from older SuperDARN research. We study changes in the Heppner‐Maynard boundary (HMB), the cross polar cap potential (CPCP), the number of backscatter echoes (n) and theχ²/nstatistic which is a measure of the global agreement between the measured and fitted velocities. We find that the CPCP is reduced when the PolarDARN radars are introduced, but then increases again when the StormDARN radars are added. When the background model is changed from the RG96 model, to the most recent TS18 model, the CPCP tends to decrease for lower values, but tends to increase for higher values. When comparing to geomagnetic indices, we find that there is on average a linear relationship between the HMB and the geomagnetic indices, as well asn, which breaks when the HMB is located at latitudes below ∼50° due to the low observational density. Whilstnis important in constraining the maps (maps withn > 400 data points are unlikely to differ), it is insufficient as the sole measure of quality.