Deriving Global Convection Maps From SuperDARN Measurements

A new statistical modeling technique for determining the global ionospheric convection is described. The Principal Component Regression (PCR) based technique is based on SuperDARN observations and is an advanced version of the principal component regression technique that Waters et al. [2015] used for the SuperMAG data. While SuperMAG ground magnetic field perturbations are vector measurements, SuperDARN provides line-of-sight (LOS) measurements of the ionospheric convection flow. Each LOS flow has a known azimuth (or direction) which must be converted into the actual vector flow. However, the component perpendicular to the azimuth direction is unknown. Our method uses historical data from the SuperDARN data base and Principal Component Regression to determine a fill-in model convection distribution for any given Universal Time (UT). The fill-in data process is driven by a list of state descriptors (magnetic indices and the solar zenith angle). The final solution is then derived from a spherical cap harmonic fit to the SuperDARN measurements and the fill-in model. When compared with the standard SuperDARN fill-in model we find that our fill-in model provides improved solutions, and the final solutions are in better agreement with the SuperDARN measurements. Our solutions are far less dynamic than the standard SuperDARN solutions which we interpret as being due to a lack of Magnetosphere-Ionosphere inertia and communication delays in the standard SuperDARN technique while it is inherently included in our approach. Rather, we argue that the magnetosphere-ionosphere system has inertia that prevents the global convection from changing abruptly in response to an IMF change.

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