Data‐Driven Basis Functions for SuperDARN Ionospheric Plasma Flow Characterization and Prediction

Abstract

The interaction of the solar wind with the magnetosphere drives plasma circulation (convection) on a variety of spatial and temporal scales throughout near-Earth space (Abel et al., 2006(Abel et al., , 2007(Abel et al., , 2009. Within the strongly coupled magnetosphere-ionosphere system, communication of stresses via magnetic field lines leads to a mapping of plasma convection features between the collisional and collisionless environments. In this way, the ionosphere both modulates, and provides a projection surface for dynamics occurring throughout the magnetosphere-ionosphere system (Merkin et al., 2016). By observing the ionospheric plasma, we can infer the underlying physics of this large-scale natural plasma laboratory.

A Model of High Latitude Ionospheric Convection Derived From SuperDARN EOF Model Data

“…In addition, since the radars only measure signals along the line‐of‐sight direction, the accuracy of the velocity vectors will vary with location and time, causing variations in the degree to which the EOF analysis of the data set is able to represent the plasma motion in any given region. At low latitudes, data gaps have been filled by a sinusoidal fitting procedure (Shore et al., 2021). When the data coverage is very low, the fitted north and east directions can be unrepresentative of the true values, resulting in high‐error vectors at lower latitudes in the Shore EOF model values (e.g., Figure 7c), that do not resemble the surrounding flow.…”

“…We build our model using 144 months of data for the interval 1997 to 2008 inclusive. A previous study used data from February 2001 to demonstrate the derivation of Empirical Orthogonal Function (EOF) model patterns from SuperDARN data (Shore et al., 2021), as this month has particularly good data coverage. We also use February 2001 (Figure 1) to illustrate our method.…”

“…Further details about the selection of good quality data from the F‐region are available in the data section of Shore et al. (2021).…”

“…Indeed, the solar wind electric field magnitude ($$\vert {v}_{x}\vert \sqrt{{B}_{y}^{2}+{B}_{z}^{2}}$$), the IMF clock angle, and the dipole tilt of the Earth, drive the TS18 model (Thomas & Shepherd, 2018), which is also based on SuperDARN data. Second, we note that a study into the data‐driven basis functions of the SuperDARN velocity field that we use here (Shore et al., 2021) finds that the IMF B z component is the dominant driver for the background mean field and, as expected, B z also drives a series of non‐leading modes that describe the variability of the two‐cell motion (e.g., Cowley & Lockwood, 1992) and that of substorms (e.g., Akasofu, 1964). However, it is IMF B y that dominates the variability of the convection velocity (Shore et al., 2021).…”

“…Second, we note that a study into the data‐driven basis functions of the SuperDARN velocity field that we use here (Shore et al., 2021) finds that the IMF B z component is the dominant driver for the background mean field and, as expected, B z also drives a series of non‐leading modes that describe the variability of the two‐cell motion (e.g., Cowley & Lockwood, 1992) and that of substorms (e.g., Akasofu, 1964). However, it is IMF B y that dominates the variability of the convection velocity (Shore et al., 2021). This observation, and the strong azimuthal asymmetries imposed by IMF B y (e.g., Friis‐Christensen & Wilhjelm, 1975; Friis‐Christensen et al., 1985; Tenfjord et al., 2015), are motivations for the inclusion of IMF B y as a driver in our model.…”