SM004-0005
Two decades of SuperDARN convection mapping: effects of an ever evolving network

Monday, 7 December 2020
Poster
Maria-Theresia Walach, Lancaster University, Department of Physics, Lancaster, United Kingdom, Adrian Grocott, Lancaster University, Physics Department, Lancaster, United Kingdom, Frances Allana Staples, University College London, London, United Kingdom and Evan G Thomas, Dartmouth College, Hanover, NH, United States
Abstract:
The Super Dual Auroral Radar Network (SuperDARN) was built to study ionospheric convection and has in recent years been expanded equatorward to observe ionospheric flows over a larger latitude range than ever before. Using Doppler-shifted pulse sequences, line-of-sight measurements of plasma convection are obtained, which can be combined to create global convection maps. Software developments, as well as an expansion of the network have resulted in many different versions of the convection maps (or Map Potential) dataset being available. Using data from the years 2012 to 2018, we perform a statistical analysis on convection maps for the entire dataset and assess systematically how the dataset has changed over the years. We consider how the addition of more data and changes to the convection mapping procedures can affect scientific studies in the context of this large database.

We produce a number of different versions of the convection maps with the standard map potential technique, using limited backscatter ranges, background models and the exclusion/inclusion of data from specific radar groups such as the mid-latitude radars. This enables us to explore the differences that the addition of these radars makes to the dataset, as well as simulate how much information was missing from the previous decades of SuperDARN research. To show the importance of growing the radar network to include measurements at mid-latitudes we study the changes in a variety of parameters, such as the equatorward boundary of the ionospheric electric field, the cross polar cap potential, the number of backscatter echoes and the χ2-statistic which is a measure of the quality of fit of the background model. We show that there is a clear difference between the datasets, especially when comparing the measured parameters to geomagnetic indices, such as AE.