Statistical Patterns of Ionospheric Convection Derived From Mid‐latitude, High‐Latitude, and Polar SuperDARN HF Radar Observations

Abstract

Over the last decade, the Super Dual Auroral Radar Network (SuperDARN) has undergone a dramatic expansion in the Northern Hemisphere with the addition of more than a dozen radars offering improved coverage at mid‐latitudes (50°–60° magnetic latitude) and in the polar cap (80°–90° magnetic latitude). In this study, we derive a statistical model of ionospheric convection (TS18) using line‐of‐sight velocity measurements from the complete network of mid‐latitude, high‐latitude, and polar radars for the years 2010–2016. These climatological patterns are organized by solar wind, interplanetary magnetic field (IMF), and dipole tilt angle conditions. We find that for weak solar wind driving conditions the TS18 model patterns are largely similar to the average patterns obtained using high‐latitude radar data only. For stronger solar wind driving the inclusion of mid‐latitude radar data at the equatorward extent of the ionospheric convection can increase the measured cross‐polar cap potential (ΦPC) by as much as 40%. We also derive an alternative model organized by the Kp index to better characterize the statistical convection under a range of magnetic activity conditions. These Kp patterns exhibit similar IMF By dependencies as the TS18 model results and demonstrate a linear increase in ΦPC with increasing Kp for a given IMF orientation. Overall, the mid‐latitude radars provide a better specification of the flows within the nightside Harang reversal region for moderate to strong solar wind driving or geomagnetic activity, while the polar radars improve the quality of velocity measurements in the deep polar cap under all conditions.

SuperDARN Observations During Geomagnetic Storms, Geomagnetically Active Times, and Enhanced Solar Wind Driving

“…Another factor than can affect the nature of the convection patterns is the spatial distribution of the observations that are used to derive them. When very few SuperDARN measurements are present in a map, the map parameters will tend to reflect the climatological map used in the RST map‐fitting procedure more closely (in this case the model from Thomas & Shepherd, ). As such, it is important to test how robust the distributions such as the ones shown in Figure f are to changes in the number of observations.…”

“…The solar wind data are time lagged to better represent the local conditions using the solar wind propagation time from Khan and Cowley (). The Heppner‐Maynard boundary (HMB; Heppner & Maynard, ), which is equivalent to where the zero potential contours are set in the map fitting, is chosen to match the lowest possible latitude for which a minimum of three line‐of‐sight vectors with velocities greater than 100 m/s lie along its boundary (Imber et al, ; Thomas & Shepherd, ). In our implementation of the fitting routine, we also changed the 50° latitude hard limit on the HMB in RST 4.2 to 40°, to better represent the latitudinal extent of the radar data (see https://github.com/SuperDARN/rst/pull/216).…”

Spatially Resolved Neutral Wind Response Times During High Geomagnetic Activity Above Svalbard

“…The line-of-sight velocity of ionospheric irregularities in the field of view of each radar can be inferred from the Doppler shifts of backscattered signals. More recently, however, newer models have fully utilized the global extent of SuperDARN and more complete historical data from all available radars, including newer radars which are located at midlatitudes (Thomas & Shepherd, 2018). More commonly, line-of-sight data are combined from all radars in the same hemisphere.…”

“…When multiple radars overlook the same region and receive backscatter, a true horizontal vector of the convective plasma may be obtained. For this study, we used data from all available Super-DARN radars and the most recent electrostatic potential fitting model from Thomas and Shepherd (2018) to obtain plasma velocity measurements at an altitude of approximately 250 km, using the technique described by Ruohoniemi and Baker (1998). An empirical model then contributes additional flow vectors that constrains a spherical harmonic fit in regions of poor data coverage.…”

“…This is understood to be due to the increased ionization brought about from particle precipitation, increasing ion-neutral collision frequencies, and thus coupling. Both of these studies exploited results from a Scanning Doppler Imager (SDI), a type of FPI which can measure more than one single point neutral wind measurement (Conde & Smith, 1997) Shepherd, 2014Shepherd, , as of 2018. Both of these studies exploited results from a Scanning Doppler Imager (SDI), a type of FPI which can measure more than one single point neutral wind measurement (Conde & Smith, 1997) Shepherd, 2014Shepherd, , as of 2018.…”