Space Physics and Aeronomy, Ionosphere Dynamics and Applications. Группа авторов

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Figure 4.5 Polar cap patch median profiles compared with sector median and all‐sector median profiles. From left to right, plasma density, electron temperature, ion temperature, and ion flux are shown. The ion flux profiles are based on measurements from the RISR‐C vertical beam

      (modified based on Ren et al., 2018; Reproduced with permission of John Wiley and Sons).

In addition, Ren et al. (2018) found that the median electron temperature at the center of the patch is suppressed by as much as ~380 K compared with the sector median temperature in the noon sector. The patch median ion temperature is very close to the background ion temperature in all sectors except ~100 K higher in the noon sector, and the ion vertical flows and fluxes within patches are typically downward. Plasma temperature is often used to infer whether local particle precipitation is present in the patch. It can also shed light on the patch generation mechanisms, since higher electron temperature would be expected if local precipitation is responsible for the patch formation. Together with the evidence that the bottom F‐region patch density is smaller than that of the surrounding region in the noon sector, these observations suggest that the major plasma source for patches is the dayside solar EUV produced plasma transported into the polar cap region, which is consistent with the statistical UT dependence found in both modeling (Sojka et al., 1994) and observation (David et al., 2016). However, we also need to realize that the RISR‐C is deep in the center of the polar cap, where it takes time for the patch to drift from the dayside cusp region to reach, and the electron temperature can change along the trajectory. Therefore, a careful numerical modeling study is required to quantify the cooling rate inside the patch and evaluate whether the electron temperature is a good and sufficient indicator of the origin of the patch plasma.

      Although statistically the polar cap patches have lower electron temperature than that of the surrounding regions, “hot” patches, which have higher electron temperature than the surrounding regions, have also been identified using DMSP observations at ~850 km (Zhang et al., 2017). It is found that this type of patch is associated with local particle precipitation and convection flow shear, and might be produced when the traditional patches convect into the particle precipitation region.

4.4 DYNAMIC EVOLUTION OF POLAR CAP PATCHES

      Polar cap patches are density enhancements in the F region and topside ionosphere, and thus they should convect in the polar region at the ExB convection velocity. Thomas et al. (2015) carefully compared the patch drifting speed obtained from 630 nm airglow images and that measured by the SuperDARN radars. They confirmed that the horizontal motion of the optical patches is consistent with the background plasma convection, and thus patches can be used as tracers of polar cap convection. In their case, the convection flow speed within the patch exceeded 500 m/s. Because the ionospheric convection is controlled by the solar wind and IMF condition, as well as magnetospheric dynamics (in particularly on the nightside), the transport of patch across the polar cap can thus be of considerable complexity, including substantial rotation as observed in Oksavik et al. (2010).

      Optical images of patches provide clues of 2‐D horizontal structure of the patch. Hosokawa et al. (2010) provide a good example of using the optical signature of patches as a tracer for mesoscale plasma convection flow. They combined observations from a redline imager located at Resolute Bay with plasma convection flow estimates derived from SuperDARN line‐of‐sight plasma velocity measurements. In their case study, they showed the bifurcation of a patch as it transited the polar cap. The bifurcation occurred as the patch moved into a region where the plasma convection field diverged. However, the number of imagers is limited and they can provide images only under certain conditions, such as in the dark and under clear sky. Furthermore, the imagers are biased to patches that are at a sufficiently low enough altitude to allow for the chemistry to generate the observed emissions. Perry and St. Maurice (2018) provide evidence confirming the notion that higher altitude patches generate dimmer emissions than lower altitude patches. Thus, it is conceivable that high‐altitude patches and their dim optical signatures may go undetected by some imagers.

      The fast‐growing number of GNSS receivers in the high‐latitude regions provides another unprecedented opportunity to reveal the horizontal morphology of polar cap density structures and to visually track their dynamic motion on a regular basis. Taking advantage of the fact the patches can be used as convection flow tracer, studies have been trying to use it to infer the underlying coupling processes between the magnetosphere and ionosphere (Nishimura et al., 2014; Zhang et al., 2013a; Zou et al., 2015; Zhang et al., 2016a). For example, using the 2‐D GPS TEC maps with superposition of the SuperDARN convection patterns (Zhang et al., 2013a) reported direct observations of the full evolution of patches during a geomagnetic storm, including entering the polar cap from the dayside cusp and turning into a blob on the nightside after exiting the polar cap. Therefore, through observing the life cycle of a patch in the ionosphere, the timescale for the global magnetospheric convection can also be inferred. A follow‐up study by (Zhang et al., 2015) using global TEC maps revealed that a complete magnetospheric convection cycle, that is, Dungey cycle, takes about 3 hours.

While the number of ground‐based GNSS receivers continues to increase and TEC maps of better spatial resolution can be obtained, their imaging capability is limited by the inhomogeneous distribution of the continents. This limitation can be improved by using tomographic reconstruction and data assimilation techniques (e.g., Aa et al., 2016, 2018; Bust & Datta‐Barua, 2014; Bust & Mitchell, 2008; Gardner et al., 2014, 2018; Mannucci et al., 1998; Yin et al., 2008; Yin et al., 2017), which are able to take in heterogeneous ionospheric data sets and combine with a physics‐based or empirical models to best describe the ionosphere density and TEC distributions. For instance, by combing measurements from ground‐based GNSS receivers and LEO satellites, such as COSMIC (Constellation Observing System for Meteorology, Ionospheric, and Climate) and GRACE (Gravity Recovery And Climate Experiment), Yue et al. (2016) obtained the global ionospheric electron density and TEC with the spatial/temporal resolution of 5° in latitude, 10° in longitude, ~30 km around the F₂ peak, and 0.5 h in time, during the 17 March 2013 storm. Figure 4.6 shows the comparison between the original ground‐based GNSS TEC map and the reconstructed map combining both ground and space‐based measurements. SED and TOI signatures are clear in both plots, but the reconstructed map is able to provide a much more complete picture. This type of method has a huge potential for filling in the large СКАЧАТЬ