Global view of the E region irregularity and convection velocities in the high‐latitude Southern Hemisphere

Abstract

Occurrence of the E region plasma irregularities is investigated using two Super Dual Auroral Radar Network (SuperDARN) South Pole (SPS) and Zhongshan (ZHO) radars that sample the same magnetic latitude deep within the high‐latitude plasma convection pattern but from two opposite directions. It is shown that the SPS and ZHO velocity distributions and their variations with the magnetic local time are different, with each distribution being asymmetric; i.e., a particular velocity polarity is predominant. This asymmetry in the E region velocity distribution is associated with the bump‐on‐tail of the distribution near the nominal ion acoustic speed Cs that is most likely due to the Farley‐Buneman instability (FBI) echoes or an inflection point of the distribution below nominal Cs that is most likely due to the gradient drift instability echoes. In contrast, the distribution of the convection velocity component was found to be symmetric, i.e., with no bump‐on‐tail or an inflection point, but with a bias (i.e., uniform shift) toward a particular polarity. It is demonstrated that the asymmetry in the convection pattern between the eastward and westward zonal components is unexpectedly strong, with the westward zonal component being predominant, especially at lower latitudes, while also exhibiting a strong interplanetary magnetic field By dependence. The observations are consistent with the notion that the asymmetry in the E region velocity distribution is highly sensitive to the bias in the convection component caused by the zonal convection component asymmetry and that the bump‐on‐tail or inflection point features may also depend on the irregularity height and the presence of strong density gradients modifying the FBI threshold value.

Super Dual Auroral Radar Network Expansion and its Influence on the Derived Ionospheric Convection Pattern

“…By only including data from a minimum range of 800 km and a maximum far range of 2000 km, we try to eliminate all possible E-Region scatter and all backscatter with higher uncertainties in range and azimuth (i.e. projected location) (Chisham et al, 2008;Forsythe & Makarevich, 2017;Thomas & Shepherd, 2018). On a statistical level, we expect this method to remove most of the data with higher uncertainty, but this method will also remove some good quality data as a substantial amount of scatter comes from ranges greater than 2000 km.…”

“…Data from different radars are combined, which allows for the exclusion of data from particular radars or the specification of a range limit for the scatter. For example, slow moving E-region scatter can and should be removed by setting the minimum range gate limit to 800 km (Forsythe & Makarevich, 2017;Thomas & Shepherd, 2018). It has become apparent that far range -3-manuscript submitted to JGR: Space Physics data beyond 2000 km may also be problematic owing to geolocation uncertainties in the range finding algorithm (Chisham et al, 2008).…”

Occurrence of F region echoes for the polar cap SuperDARN radars

“…Some plots showed limited in time and minor spatial departures, both to closer and farther ranges, but these were limited occasions. One exception is the SPS radar, for which the nighttime observations show persistent occurrence of echoes at near range gates corresponding to detection of E region echoes, as described by Makarevich et al (2015) and Forsythe and Makarevich (2017). These echo bands often extend to large range gates (~ 20), and it is difficult to determine the location of the gates with pure E and pure F region echo detection.…”

Super Dual Auroral Radar Network Expansion and Its Influence on the Derived Ionospheric Convection Pattern

“…Data from different radars are combined, which allows for the exclusion of data from particular radars or the specification of a range limit for the scatter. For example, slow moving E‐region scatter can and should be removed by setting the minimum range gate limit to 800 km (Forsythe & Makarevich, 2017; Thomas & Shepherd, 2018). It has become apparent that far range data beyond 2,000 km may also be problematic owing to geolocation uncertainties in the range finding algorithm (Chisham et al., 2008).…”