[1] A discrete set of climatological patterns of high-latitude ionospheric convection are derived using line-of-sight plasma drift data from the Super Dual Auroral Radar Network (SuperDARN). The patterns are derived independently for the Northern Hemisphere and Southern Hemisphere and for varying solar wind, interplanetary magnetic field (IMF), and dipole tilt angle conditions. By interpolating between discrete patterns, a dynamical model of convection is obtained, which can uniquely specify the high-latitude electrostatic potential distribution for a wide range of solar wind, IMF, and dipole tilt parameter values. Accounting for solar wind velocity dependencies in convection leads to better resolving the large-scale convection pattern, as compared to previous statistical models based on SuperDARN data. It is shown that the mesoscale features of the climatological model compare favorably to the features seen in instantaneous patterns of convection observed with SuperDARN. Comparison of the model to other statistical or empirical models derived from ground-and space-based measurements shows good agreement with most models.Citation: Cousins, E. D. P., and S. G. Shepherd (2010), A dynamical model of high-latitude convection derived from SuperDARN plasma drift measurements,
[1] Empirical orthogonal function (EOF) analysis, a variant of principle component analysis, is applied to 20 months of plasma drift data from the Super Dual Auroral Radar Network radars in the high-latitude region of the Northern Hemisphere. Dominant modes of ionospheric electric field variability are identified and the spatial and temporal coherence of this variability is quantified. The first three modes of variability, which, together with the mean, account for 50% of the observed squared electric field (E 2 ), are characterized by global spatial scales and long time scales ( 1 h). The first and second modes of variability represent the strengthening/weakening of the global convection pattern and the shaping of the convection pattern into asymmetrical round-and crescent-shaped cells. These two modes are correlated with the B z and B y components of the interplanetary magnetic field. The third mode represents the expansion/contraction of the convection pattern and is weakly correlated with the solar wind velocity. For EOFs beyond EOF 3, the power contained in the modes falls off rapidly, the characteristic spatial and temporal scales decrease, and weak correlations with external driving parameters are observed. These higher-order EOFs likely capture more random behavior of the electric field variability. The notable exception to this trend is EOF 11, which captures midlatitude variations on the duskside and is enhanced during subauroral polarization stream events. The EOF technique described in this paper is applied in a companion paper to characterize the covariance of ionospheric electric fields for use in an assimilative mapping procedure.
[1] An assimilative mapping procedure is developed to optimally combine information from Super Dual Auroral Radar Network (SuperDARN) plasma drift observations and a background statistical convection model to derive global distributions of electrostatic potential. This procedure takes into account statistical properties of the background model errors, obtained through the empirical orthogonal function analysis technique described in a companion paper. The assimilative mapping procedure is evaluated quantitatively using cross-validation and is found to reduce median prediction errors by up to 43% as compared to the existing linear regression-based SuperDARN mapping procedure. Furthermore, the mapped results from the assimilative procedure show a greater dynamic range in convection strength than do those of the regression-based procedure (i.e., the cross-polar cap potential is smaller for weak driving conditions and larger for strong driving conditions). The application of the assimilative procedure is demonstrated for a case study containing a geomagnetic storm. It is shown that, qualitatively, the results of the assimilative procedure appear more smooth and consistent across both data-dense and data-sparse regions than do those of the regression-based procedure.
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