DOI: 10.5285/2b9f0e9f-34ec-4467-9e02-abc771070cd9
title: Dominant spatial and temporal patterns of horizontal ionospheric plasma velocity variation covering the northern polar region, from 1997.0 to 2009.0 - VERSION 2.0

description: We present a concurrent series of 144 monthly reanalyses of Super Dual Auroral Radar Network (SuperDARN) plasma velocity measurements, using the method of data-interpolating Empirical Orthogonal Functions (EOFs). For each monthly reanalysis, the 5-minute median values of the northern polar region's radar-measured line-of-sight Doppler plasma velocities are binned in an equal-area grid defined in quasi-dipole latitude and quasi-dipole magnetic local time (MLT). The grid cells each have an area of approximately 110,000km2, and the grid extends to 30 degrees colatitude. Within this spatial grid, the sparse binned data are infilled to provide a measurement at every spatial and temporal location via two different EOF analysis models: one tailored to instances of low data coverage, the other tailored to higher data coverage. These two models each comprise 144 monthly sets of orthogonal modes of variability (spatial and temporal patterns), along with the timestamps of each epoch, and the spatial coordinate information of all bin locations. A companion dataset determines which of the two models should be chosen in each location for each month, in order to ensure the best accuracy of the infill solution. We also provide the temporal mean of the data in each spatial bin, which is removed prior to the EOF analysis. Collectively, the reanalysis delivers the SuperDARN data in terms of cardinal north and east vector components (in the quasi-dipole coordinate frame), without its usual extreme sparseness, for studies of ionospheric electrodynamics for the period 1997.0 to 2009.0. Funding was provided by NERC Standard grant NE/N01099X/1, titled 'Thermospheric Heating Modes and Effects on Satellites' (THeMES) and the NERC grant NE/V002732/1, titled 'Space Weather Instrumentation, Measurement, Modelling, and Risk: Thermosphere' (SWIMMR-T).
description: The data were gathered using the northern hemisphere radars of the SuperDARN global array, and the fitted Doppler velocities were processed from the original autocorrelation functions using version 4.5 of the radar software toolkit (RSTv4.5) and within that toolkit, fitting routine 'FitACF v2.5'. Following the data binning into an equal-area grid and 5-min medians as described in section 2 above, the data gaps are infilled as follows. We initially infill the data gaps in the sparse binned data with zeros, and then we apply the method of data-interpolating Empirical Orthogonal Functions (EOFs). This allows global (i.e., the full extent of the binned data set) spatial and temporal basis vector patterns to be obtained. These basis vectors collectively describe the full variability of the dataset. The form (i.e., morphology/shape) of the basis vectors is controlled by the cross-correlations within the dataset. Since the ionospheric plasma velocity is strongly correlated in space and time, the spatial and temporal behaviour of the basis vectors with the largest eigenvalues (i.e., those which describe most of the variability in the dataset) are defined by the underlying physics of the ionospheric plasma. In contrast, since the missing data are relatively uncorrelated in space and time, the missing data contribute to lower-eigenvalue basis vectors. This provides us with a method to infill the missing values with the largest-eigenvalue basis vector, which is a better guess for the underlying plasma velocity field than the initial infill of zeros. Moreover, we have done this without any a priori specification of source geometry. The two models described in section 2 differ in how they compute the infilled values, as follows. In one of the two models, we apply a set of weights to the data before we compute the covariance matrix (from which the eigenvectors of the reanalysis are determined). These weights act to decrease the relative contribution of poorly sampled epochs to the solution for the variance of the data. In the second of the two models, we likewise apply weights, but this time the weights act to increase the relative contribution of poorly sampled spatial regions to the solution for the variance of the data. By combining both models together (dependent on which one reconstructs the existing data with best accuracy for a given location), we can optimise the full reanalysis for both high and low data coverage conditions. The EOF-solution-and-infill process is repeated iteratively, until the amplitude of the infill converges with that of the data measurements, where both overlap. This infill only converges when it reinforces patterns present in the original data, thus providing a self-consistent description of the plasma velocity at the original temporal resolution of the SuperDARN data set. This gives complete spatial and temporal coverage without resorting to climatological averages, spatially smoothed models, or a priori relationships determined from solar wind drivers. Following this retrieval of the un-measured variability of the data, we fit a sinusoid model to translate the basis vectors from their line-of-sight (i.e., radar look direction) basis to a basis of cardinal north and east plasma velocity vector components. This method is described in full in this paper: Shore, R. M., Freeman, M. P., & Chisham, G. (2021). Data-driven basis functions for SuperDARN ionospheric plasma flow characterization and prediction. Journal of Geophysical Research: Space Physics, 126, e2021JA029272. https://doi.org/10.1029/2021JA029272.
description: The SuperDARN data were processed to remove ground scatter, and to eliminate measurements with too low power (lower than 3dB), or which had a poor-quality flag (identified in RSTv4.0). When binning the data, range gates below 11 and above 150 (where those values correspond to multiple of 45 km range distance from the radar array location) were not used, since these gave inaccurate locational estimates.