pyDARN: A Python software for visualizing SuperDARN radar data

Xueling Shi; Xueling Shi; Marina Schmidt; Carley J. Martin; Daniel D. Billett; Emma Bland; Francis H. Tholley; Nathaniel A. Frissell; Krishna Khanal; Shane Coyle; Shibaji Chakraborty; Marci Detwiller; Bharat Kunduri; Kathryn McWilliams

doi:10.3389/fspas.2022.1022690

Frontiers in Astronomy and Space Sciences (Dec 2022)

pyDARN: A Python software for visualizing SuperDARN radar data

Xueling Shi,
Xueling Shi,
Marina Schmidt,
Carley J. Martin,
Daniel D. Billett,
Emma Bland,
Francis H. Tholley,
Nathaniel A. Frissell,
Krishna Khanal,
Shane Coyle,
Shibaji Chakraborty,
Marci Detwiller,
Bharat Kunduri,
Kathryn McWilliams

Affiliations

Xueling Shi: Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, United States
Xueling Shi: High Altitude Observatory, National Center for Atmospheric Research, Boulder, CO, United States
Marina Schmidt: Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada
Carley J. Martin: Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada
Daniel D. Billett: Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada
Emma Bland: Department of Arctic Geophysics, The University Centre in Svalbard, Longyearbyen, Norway
Francis H. Tholley: Department of Computing Sciences, The University of Scranton, Scranton, PA, United States
Nathaniel A. Frissell: Department of Physics and Engineering, The University of Scranton, Scranton, PA, United States
Krishna Khanal: Center for Space Plasma and Aeronomic Research, University of Alabama in Huntsville, Huntsville, AL, United States
Shane Coyle: Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, United States
Shibaji Chakraborty: Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, United States
Marci Detwiller: Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada
Bharat Kunduri: Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, United States
Kathryn McWilliams: Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada

DOI: https://doi.org/10.3389/fspas.2022.1022690
Journal volume & issue: Vol. 9

Abstract

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The Super Dual Auroral Radar Network (SuperDARN) is an international network of high frequency coherent scatter radars that are used for monitoring the electrodynamics of the Earth’s upper atmosphere at middle, high, and polar latitudes in both hemispheres. pyDARN is an open-source Python-based library developed specifically for visualizing SuperDARN radar data products. It provides various plotting functions of different types of SuperDARN data, including time series plot, range-time parameter plot, fields of view, full scan, and global convection map plots. In this paper, we review the different types of SuperDARN data products, pyDARN’s development history and goals, the current implementation of pyDARN, and various plotting and analysis functionalities. We also discuss applications of pyDARN, how it can be combined with other existing Python software for scientific analysis, challenges for pyDARN development and future plans. Examples showing how to read, visualize, and interpret different SuperDARN data products using pyDARN are provided as a Jupyter notebook.

Published in Frontiers in Astronomy and Space Sciences

ISSN: 2296-987X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Science: Astronomy; Science: Physics: Geophysics. Cosmic physics
Website: http://journal.frontiersin.org/journal/astronomy-and-space-sciences

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