DOI: 10.5285/f4245a21-dee9-46cf-85b2-114798cb7ebc
title: Dominant spatial and temporal patterns of horizontal ionospheric plasma velocity variation covering the northern polar region, for the month of February 2001

description: We present a reanalysis of SuperDARN plasma velocity measurements, using the method of data-interpolating Empirical Orthogonal Functions (EOFs). The northern polar region's radar-measured line of sight Doppler velocities are binned in an equal-area grid (areas of approximately 110,000km2) in quasi-dipole latitude and quasi-dipole magnetic local time (MLT). Within this spatial grid, which extends to 30 degrees colatitude, the plasma velocity is given in terms of cardinal north and east vector components (in the quasi-dipole coordinate frame), with the median of every SuperDARN measurement in the spatial bin taken every 5 minutes. These sparse binned data are infilled to provide a measurement at every spatial and temporal location via EOF analysis, ultimately comprising a reanalysis spanning the month of February 2001. This resource provides a convenient method of using SuperDARN data without its usual extreme sparseness, for studies of ionospheric electrodynamics. The reanalysis is provided in 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. We also provide the temporal mean of the data in each spatial bin, which is removed prior to the EOF analysis. Funding was provided by NERC standard grants NE/N01099X/1 (THeMES) and NE/V002732/1 (SWIMMR-T).
description: Following the data binning into an equal-area grid and 5-min medians as described in the abstract, 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 the majority 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 contributes 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 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 a paper ('Data-Driven Basis Functions for SuperDARN Ionospheric Plasma Flow Characterisation and Prediction'), presently under review in JGR Space Physics (2021).
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 4.0 of the radar software toolkit (RSTv4.0) and within that toolkit, fitting routine 'fitacfv2.5'.
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.
description: Data set 'PlasmaVelocityModes_Shore-ds01.nc': A netcdf-formatted file containing the location information for the two-dimensional spatial bins used in this analysis. Coordinates are in the Quasi-Dipole reference frame. Contains 4 variables: bin_centroids_colatitude, bin_centroids_longitude, bin_limits_colatitude and bin_limits_longitude. The centroids are the locations of the centre of each bin, and the limits of the bins give the region over which the EOF prediction at the centroid is assumed to apply. There are 559 bins in the northern polar region, which is the area of focus for this analysis. They are ordered approximately by latitude, then longitude, but this does not always apply near the 0/360 degree longitude boundary. The temporal dependence of the Quasi Dipole coordinates - required to translate them to other coordinate systems - is supplied by the values in data set ds02, described below. Data set 'PlasmaVelocityModes_Shore-ds02.nc': A netcdf-formatted file containing the temporal dependence for each analysis used in this study. The analysis is one calendar month in length. This netcdf file contains a variable named 'bin_times_YYYYMM', where YYYY is the year of the analysis, and MM is the month of the analysis. This variable is a set of 5-minute average epochs (the rows are sequentially ordered in time), whose 6 columns are in the format [year, month, day, hour, minute, second]. The 5-minute averages each encompass 5 separate 1-min boxcar-centered epochs, the first of which has a centroid at 0 minutes from midnight. Hence the 5-minute averaged epochs start on the 2nd minute of the hour. Data set 'PlasmaVelocityModes_Shore-ds03.nc': A netcdf-formatted file containing the temporal means of the binned data -- these were removed from the binned data set prior to the EOF analysis, and should be added back on for an accurate prediction of the plasma velocity. The file contains two variables, each named 'bin_means_YYYYMM_[component]', where YYYY is the year of the analysis, MM is the month of the analysis, and [component] indicates the cardinal direction component that the values pertain to. The two components are in Quasi-Dipole coordinates, and are either north or east. The units of the values in this file are ms-1. Each variable is a vector of 559 rows and 1 column, and the order of the rows is that same as that of the variables in data set ds01. Data set 'PlasmaVelocityModes_Shore-ds04.nc': A netcdf-formatted file containing the spatial eigenvectors of the EOF analysis. The file contains 10 sets of two variables, each named 'eig_s_YYYYMM_modeXX_[component]', where YYYY is the year of the analysis, MM is the month of the analysis, XX is the number of the mode that the eigenvector relates to (where the modes are ranked according to decreasing eigenvalue, with mode 1 corresponding to the largest eigenvalue), and [component] is the same information as given for data set ds03, described above. Each variable has 559 rows and 1 column. The row order is the same as that of the variables in data set ds01. The units of these data are non-physical, but they are normalised such that product of a pair of spatial and temporal eigenvectors (the latter from ds05) has units of ms-1. Data set 'PlasmaVelocityModes_Shore-ds05.nc': A netcdf-formatted file containing the temporal eigenvectors of the EOF analysis. The file contains 10 variables, each named 'eig_t_YYYYMM_modeXX', where YYYY is the year of the analysis, MM is the month of the analysis, and XX is the number of the mode that the eigenvector relates to (where the modes are ranked according to decreasing eigenvalue, with mode 1 corresponding to the largest eigenvalue). Each variable has 1 column, and the same number (and order) of rows as the corresponding temporal variable in data set ds02. The units of these data are ms-1. Data set 'PlasmaVelocityModes_Shore-ds06.nc': A netcdf-formatted file containing the full, reconstructed EOF model infill. This is created from the product of the spatial and temporal eigenvector components of each mode (i.e. datasets ds04 and ds05, described above), and subsequently summed over all modes. The temporal means of the binned data (i.e. dataset ds03, described above) are also added back onto each epoch of this reconstructed model. The file contains two variables, each named 'reconstruction_YYYYMM_[component]' where YYYY is the year of the analysis, MM is the month of the analysis, and [component] is the same information as given for data set ds03, described above. Each variable has 8064 (temporal) rows and 559 (spatial) columns. The row order is the same as that in the corresponding temporal variable in data set ds02, and the column order is the same as that of the variables in data set ds01. The units of these data are ms-1.