Determining the Validity of Multiple MSTIDs Detected by the SuperDARN Multiple Signal Classification (MUSIC) Algorithm

SuperDARN is a global network of High Frequency (HF) radars used to study ionospheric dynamics, including Medium Scale Traveling Ionospheric Disturbances (MSTIDs). MSTIDs are quasi-periodic variations in ionospheric electron densities that can be related to atmospheric gravity waves in the neutral atmosphere. The Multiple Signal Classification (MUSIC) algorithm has been implemented to use SuperDARN data and determine the presence of multiple, simultaneous MSTIDs observed within a SuperDARN field of view and provide estimates of MSTID parameters, including period, horizontal wavelength, and horizontal velocity. However, previous studies primarily focused only on the strongest MSTIDs detected by the MUSIC algorithm because it is challenging to separate weak signals from noise and artifacts. In this presentation, we evaluate the performance of the SuperDARN MUSIC algorithm in the presence of noise and provide recommendations for separating actual MSTIDs from noise or artifacts.