TY - JOUR
T1 - pyDARN
T2 - A Python software for visualizing SuperDARN radar data
AU - Shi, Xueling
AU - Schmidt, Marina
AU - Martin, Carley J.
AU - Billett, Daniel D.
AU - Bland, Emma
AU - Tholley, Francis H.
AU - Frissell, Nathaniel A.
AU - Khanal, Krishna
AU - Coyle, Shane
AU - Chakraborty, Shibaji
AU - Detwiller, Marci
AU - Kunduri, Bharat
AU - McWilliams, Kathryn
N1 - Publisher Copyright:
Copyright © 2022 Shi, Schmidt, Martin, Billett, Bland, Tholley, Frissell, Khanal, Coyle, Chakraborty, Detwiller, Kunduri and McWilliams.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
KW - Super Dual Auroral Radar Network
KW - ionosphere
KW - python
KW - radar
KW - space weather
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U2 - 10.3389/fspas.2022.1022690
DO - 10.3389/fspas.2022.1022690
M3 - Article
AN - SCOPUS:85144050730
SN - 2296-987X
VL - 9
JO - Frontiers in Astronomy and Space Sciences
JF - Frontiers in Astronomy and Space Sciences
M1 - 1022690
ER -