@proceedings {624, title = {Detecting Large Scale Traveling Ionospheric Disturbances using Feature Recognition and Amateur Radio Data}, year = {2022}, month = {03/2022}, publisher = {HamSCI}, address = {Huntsville, AL}, abstract = {

A Large-Scale Transient Ionospheric Disturbance (LSTID) is a traveling perturbation in ionosphere electron density with a horizontal wavelength of approximately 1000 km and a period between 30 to 180 minutes. These can be detected by SuperDARN HF radar and GNSS Total Electron Content measurements. Recently it has been discovered that these can also be detected in amateur (ham) radio signal reports, which are now being generated in vast numbers by operators world-wide. A machine-learning technique was developed to find patterns in these data that indicate the presence of LSTIDs using an object detection technique.

}, author = {William D. Engelke and Nathaniel A. Frissell and Travis Atkison and Philip J. Erickson and Francis Tholley} } @proceedings {636, title = {Porting the MUSIC Algorithm to the SuperDARN pyDARN Library for the Study of Traveling Ionospheric Disturbances}, year = {2022}, month = {03/2022}, publisher = {HamSCI}, address = {Huntsville, AL}, abstract = {

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.

}, author = {Francis Tholley and Nathaniel A. Frissell and William Liles} } @conference {538, title = {Antarctic SuperDARN Observations of Medium Scale Traveling Ionospheric Disturbances}, booktitle = {NSF CEDAR (Coupling, Energetics, and Dynamics of Atmospheric Regions)}, year = {2021}, month = {06/2021}, publisher = {CEDAR}, organization = {CEDAR}, address = {Virtual}, abstract = {

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. MSTIDs are typically associated with atmospheric gravity waves (AGWs). Statistical studies of MSTIDs using Super Dual Auroral Radar Network (SuperDARN) radars in the Northern Hemisphere have shown strong correlation with Polar Vortex activity, while a study of MSTIDs using the Antarctic Falkland Islands SuperDARN radar showed populations of MSTIDs with signatures suggestive of both solar wind-magnetosphere coupling sources and lower neutral atmospheric winds sources. The sources of the MSTIDs are still not well understood, and there are limited studies of MSTIDs using SuperDARN radars in the Southern Hemisphere. We present initial results of MSTID observations of using Antarctic SuperDARN radars, including the radar at McMurdo Station.

}, author = {Francis Tholley and Nathaniel A. Frissell and Joseph B. H. Baker and J. Michael Ruohoniemi and William Bristow} }