@proceedings {953,
	title = {Integration \& Validation of a Standardized Library \& File Format for PyDARNMUSIC \& DARNtids: Migrating From Legacy Pickle Files to HDF5 \& Implementing a Comprehensive Testing Suite},
	year = {2025},
	month = {03/2025},
	publisher = {HamSCI},
	address = {Newark, NJ},
	abstract = {<p>The PyDARN Multiple Signal Classification (PyDARN-MUSIC) algorithm is a general, widely used sign processing algorithm for determining the parameters of multiple wavefronts arriving at an antenna array from measurements made on the signals received at the array elements. It has been used in previous Super Dual Auroral Radar Network (SuperDARN) studies to determine the size, strength, speed, and direction of Medium Scale Traveling Ionospheric Disturbances (MSTIDs). DARNtids (Dual Auroral Radar Network Traveling Ionospheric Disturbances) is a python-based open-source software toolkit that first detects level of MSTID activity in SuperDARN data and then runs the MSTID MUSIC analysis software on event periods with significant MSTIDs. DARNtids can run the SuperDARN MUSIC algorithm automatically on thousands\&nbsp; individual analysis periods. Both PyDARN-MUSIC and DARNtids use pickle files for storing Python objects\&nbsp; byte streams. Pickle files are fragile and cannot be easily manipulated and used on modern systems. They are specific to Python, produce massive serialized values, and are slower than alternative options. As result, both repositories have experienced software engineering challenges related to portability, interoperability, testability, and maintainability, thereby forcing runs to take significantly longer than necessary, data to be stored inefficiently, and the debugging process to be difficult. We present the methodology taken to seamlessly replace dependencies on pickle files in PyDARN-MUSIC and DARNtids integrating the Hierarchical Data Format Version 5 (HDF5) file format and library. We further discuss validation techniques via the newly implemented PyDARN-MUSIC test suite to ensure numerical data accuracy between existing pickle file results and new HDF5 result.</p>
},
	author = {Nicholas Guerra and James Fox and Thomas Pisano and Michael Molzen and Nathaniel Frissell and Paul Jackowitz and Joseph B. H. Baker and J. Michael Ruohoniemi}
}
@proceedings {972,
	title = {Investigating January 2016 Traveling Ionospheric Disturbance Causes with SuperDARN},
	year = {2025},
	month = {03/2025},
	publisher = {HamSCI},
	address = {Newark, NJ},
	abstract = {<p>Medium-Scale Traveling Ionospheric Disturbances (MSTIDs) are quasi-periodic variations of F-region ionospheric densities that are most often associated with atmospheric gravity waves (GWs) generated in the lower neutral atmosphere. The ionospheric variations caused by MSTIDs can focus and defocus HF radio signals, propagating through the ionosphere, thereby causing changes in the range at which these radio signals are refracted back to Earth. Using this principle, high frequency radars such as those in the Super Dual Auroral Radar Network (SuperDARN) can be used to observe MSTIDs. The SuperDARN MSTID Multiple Signal Classification (MUSIC) Algorithm can be used to determine the period, wavelength, speed, and direction of propagation of the observed MSTIDs. We present SuperDARN observations over the North American continent for the period of 11-16 January 2016 as shown in Vadas et. Al. 2024. \&nbsp;This period is known to be important in terms of the polar vortex generating GWs that seed MSTID activity through multi-step vertical coupling processes. A period of low MSTID activity is observed from 11-13 January followed by a period of elevated MSTID activity from 14-16 January, which is observed across multiple radar fields of view. For this period of interest, an Investigation of MUSIC{\textquoteright}s radar-by-radar reports of observed MSTID parameters including direction of propagation, speed, and period and their comparison to polar vortex activity level observation is also presented.\&nbsp;</p>
},
	author = {Michael Molzen and Nathaniel Frissell and Sharon Vadas and Lynn Harvey}
}
