Two‐Dimensional Maps of In Situ Ionospheric Plasma Flow Data Near Auroral Arcs Using Auroral Imagery

Abstract

Some existing auroral data products are insufficient for ionospheric simulation input on subkilometer spatial scales and high (second) time resolution near the boundaries of arc structures. Ideally, two-dimensional data maps of the relevant parameters over these small scales would provide models with constraining inputs. Available in situ data have the time and spatial resolution for small-scale features but only provide a 1-D cut through the structure. Ground-based data can provide 2-D maps but have lower resolution in time and space than is required to accurately interpret the small-scale structure near an arc. This paper provides a method to construct two-dimensional maps of auroral parameters from the combination of one-dimensional in situ data cuts with two-dimensional ground-based (and time dependent) camera imagery. Arc boundaries for each image are defined, and the available 1-D ionospheric flow data are replicated into many 1-D cuts at different points along the arc, yielding an irregularly sampled 2-D flow map. These mapped data are fitted to a regular grid via a divergence minimization routine to generate a regularly sampled flow field that is enforced as divergence free. Comparison of the generated 2-D data maps to available information from camera inversions and other data products is shown, as are assumptions made through the replication process and alternative strategies. Reconstructed flow maps are shown to maintain the small-scale features near arc boundaries while increasing the dimensionality to 2-D and to follow the time evolution of the arc structure by comparisons to imagery. The average electric field magnitudes per unit area of the reconstructed and divergence-minimized flow fields are also calculated and compared between different data sources.

Horizontal electric fields from flow of auroral O<sup>+</sup>(<sup>2</sup>P) ions at sub-second temporal resolution

“…All data are dark-and flat-field corrected, background subtracted and intensity calibrated using star fluxes. Measured intensities of tens of stars per image are compared with spectral irradiances in absolute units from tabulated values (Gubanov et al, 1992;Cox, 2000;Grubbs et al, 2016).…”

Examining the Auroral Ionosphere in Three Dimensions Using Reconstructed 2D Maps of Auroral Data to Drive the 3D GEMINI Model

“…Equation 1 expectations for current continuity are explored, for examples, where the auroral structures are stable and relatively invariant in the longitudinal (along-arc) direction and in time. We begin to account for small variations in the along-arc direction using the arc boundary information from auroral imagery (Clayton et al, 2019), but further information is needed for more complex arc structures. Accounting for the non-idealized, non-sheetlike features of the arc allows progress in our understanding of the accurate determination of the 3D auroral environment (Hwang et al, 2006).…”

A New Framework to Incorporate High‐Latitude Input for Mesoscale Electrodynamics

“…The PFISR operation mode was isinglassv3.0 which took place between 0626 and 1700 UT (2226 to 0900 local time) on 2 March 2017 to support the ISINGLASS sounding rocket experiment (Clayton et al, 2019). This experiment was designed to provide high cadence F region measurements under the expected trajectory of the ISINGLASS sounding rocket.…”

“…Line-of-sight measurements from PFISR multibeam experiments are routinely used to construct two-dimensional plasma flow and electric fields (Clayton et al, 2019;Heinselman & Nicolls, 2008;Nicolls et al, 2014). Line-of-sight measurements from PFISR multibeam experiments are routinely used to construct two-dimensional plasma flow and electric fields (Clayton et al, 2019;Heinselman & Nicolls, 2008;Nicolls et al, 2014).…”

“…Line-of-sight measurements from PFISR multibeam experiments are routinely used to construct two-dimensional plasma flow and electric fields (Clayton et al, 2019;Heinselman & Nicolls, 2008;Nicolls et al, 2014). These sensors consisted of multiple wide angle and limited field of view cameras and Fabry-Perot interferometers (Clayton et al, 2019). These measurements can be used to drive, regional (Grubbs et al, 2017), or validate global, numerical models (Liuzzo et al, 2014;Ozturk et al, 2018).…”