pyDARN: A Python software for visualizing SuperDARN radar data

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dc.contributor.authorShi, Xuelingen
dc.contributor.authorSchmidt, Marinaen
dc.contributor.authorMartin, Carley J.en
dc.contributor.authorBillett, Daniel D.en
dc.contributor.authorBland, Emmaen
dc.contributor.authorTholley, Francis H.en
dc.contributor.authorFrissell, Nathaniel A.en
dc.contributor.authorKhanal, Krishnaen
dc.contributor.authorCoyle, Shaneen
dc.contributor.authorChakraborty, Shibajien
dc.contributor.authorDetwiller, Marcien
dc.contributor.authorKunduri, Bharaten
dc.contributor.authorMcWilliams, Kathrynen
dc.date.accessioned2023-04-17T19:16:04Zen
dc.date.available2023-04-17T19:16:04Zen
dc.date.issued2022-12en
dc.description.abstractThe 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.en
dc.description.notesMS, CM, and MD are supported by funding from the Canada Foundation for Innovation, the Canadian Space Agency's Geospace Observatory (GO) Canada program, and Innovation Saskatchewan. DB is funded via the European Space Agency Living Planet Fellowship under project "HLPF-SSA". XS is supported by National Science Foundation (NSF) grants AGS-1935110 and AGS-2025570 and National Aeronautics and Space Administration (NASA) grant 80NSSC21K1677. BK is supported by NSF under grants AGS-1822056 and AGS-1839509. FT is supported by NASA grant 80NSSC21K0002, and NF is supported by NASA grant 80NSSC21K0002 and NSF grant AGS-2045755. SC is supported by NSF grants OPP-1744828 and AGS-2027168.en
dc.description.sponsorshipCanada Foundation for Innovation; Canadian Space Agency's Geospace Observatory (GO) Canada program; Innovation Saskatchewan; European Space Agency Living Planet Fellowship under project "HLPF-SSA"; National Science Foundation (NSF); National Aeronautics and Space Administration (NASA) [AGS-1935110, AGS-2025570, AGS-1839509, AGS-2045755, AGS-2027168, 80NSSC21K1677, 80NSSC21K0002]; [AGS-1822056]; [OPP-1744828]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fspas.2022.1022690en
dc.identifier.other1022690en
dc.identifier.urihttp://hdl.handle.net/10919/114529en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectpythonen
dc.subjectSuper Dual Auroral Radar Networken
dc.subjectradaren
dc.subjectionosphereen
dc.subjectspace weatheren
dc.titlepyDARN: A Python software for visualizing SuperDARN radar dataen
dc.title.serialFrontiers in Astronomy and Space Sciencesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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