|Title||Porting the MUSIC Algorithm to the SuperDARN pyDARN Library for the Study of Traveling Ionospheric Disturbances|
|Publication Type||Conference Proceedings|
|Year of Conference||2022|
|Authors||Tholley, F, Frissell, NA, Liles, W|
|Conference Name||HamSCI Workshop 2022|
|Conference Location||Huntsville, AL|
Medium Scale Traveling Ionospheric Disturbances (MSTIDs) are quasi-periodic variations of the F-region ionosphere with periods of 15 to 60 minutes and horizontal wavelengths of a few hundred kilometers that are often associated with atmospheric gravity waves (AGWs). Understanding differences in characteristics such as wavelength, period, and propagation direction between MSTIDs populations in the northern and southern hemisphere can lead to a better understanding of MSTID sources and upper atmospheric dynamics. Previous studies have used SuperDARN radars to observe MSTIDs and determine these characteristics using an implementation of the multiple signal classification (MUSIC) algorithm. In this presentation, we port the MUSIC implementation written in Python 2 for use with the deprecated SuperDARN Data and Visualization Toolkit python (DaViTpy) to Python 3 for use with the current pyDARN library. This implementation will be used to study the differences between MSTID populations observed by SuperDARN radars in both the Northern and Southern hemispheres.