pyDARN: A Python software for visualizing SuperDARN radar data

Shi, Xueling; Schmidt, Marina; Martin, Carley; Billett, Daniel; Bland, Emma; Tholley, Francis H.; Frissell, N. A.; Khanal, Krishna; Coyle, Shane; Chakraborty, Shibaji; Detwiller, Marci; Kunduri, B.; McWilliams, K. A.

doi:10.3389/fspas.2022.1022690

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…SuperDARN data can be found at https://www.frdr-dfdr.ca/repo/collection/superdarn. SuperDARN data has been processed using the Radar Software Toolkit developed by the SuperDARN Data Analysis Working Group (Burrell et al, 2022) and visualized by the pyDARN package developed by the SuperDARN Data Visualization Working Group (Martin et al, 2023;Shi et al, 2022). The MMS spacecraft data are accessed via the MMS Science Data Center (https://lasp.colorado.edu/mms/sdc/public/about/browse-wrapper/).…”

Section: Data Availability Statementmentioning

confidence: 99%

Characterization of Magnetic Flux Contents for Two Flux Transfer Events From Both In Situ and Ionospheric Observations

Wang,

Zou,

et al. 2024

JGR Space Physics

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Flux transfer events (FTEs) are a type of magnetospheric phenomena that exhibit distinctive observational signatures from the in situ spacecraft measurements. They are generally believed to possess a magnetic field configuration of a magnetic flux rope and formed through magnetic reconnection at the dayside magnetopause, sometimes accompanied with enhanced plasma convection in the ionosphere. We examine two FTE intervals under the condition of southward interplanetary magnetic field (IMF) with a dawn‐dusk component. We apply the Grad‐Shafranov (GS) reconstruction method to the in situ measurements by the Magnetospheric Multiscale (MMS) spacecraft to derive the magnetic flux contents associated with the FTE flux ropes. In particular, given a cylindrical magnetic flux rope configuration derived from the GS reconstruction, the magnetic flux content can be characterized by both the toroidal (axial) and poloidal fluxes. We then estimate the amount of magnetic flux (i.e., the reconnection flux) encompassed by the area “opened” in the ionosphere, based on the ground‐based Super Dual Auroral Radar Network (SuperDARN) observations. We find that for event 1, the FTE flux rope is oriented in the approximate dawn‐dusk direction, and the amount of its total poloidal magnetic flux falls within the range of the corresponding reconnection flux. For event 2, the FTE flux rope is oriented in the north‐south direction. Both the FTE flux and the reconnection flux have greater uncertainty. We provide a detailed description about a formation scenario of sequential magnetic reconnection between adjacent field lines based on the FTE flux rope configurations from our results.

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Section: Data Availability Statementmentioning

confidence: 99%

Characterization of Magnetic Flux Contents for Two Flux Transfer Events From Both In Situ and Ionospheric Observations

Wang,

Zou,

et al. 2024

JGR Space Physics

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“…RR is supported by NSERC CGS‐M and the University of Saskatchewan. pyDARN (Shi et al., 2022) was used for producing Figures 3 and 6, as well as the individual radar field‐of‐view outlines in Figures 7 and 8. The authors acknowledge the use of SuperDARN data.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Application of Wide‐Beam Transmission for Advanced Operations of SuperDARN Borealis Radars in Monostatic and Multistatic Modes

Rohel,

Ponomarenko,

McWilliams

2024

Radio Science

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The Super Dual Auroral Radar Network (SuperDARN) consists of more than 30 monostatic high‐frequency (HF, 8–20 MHz) radars to study dynamic processes in the ionosphere. SuperDARN provides maps of global‐scale ionospheric plasma drift circulation from the mid‐latitudes to the poles. The conventional SuperDARN radars consecutively scan through 16 beam directions with a lower limit of 1 minute to sample the entire field of view. In this work, we use the advanced capabilities of the recently developed Borealis digital SuperDARN radar system. Combining a wide transmission beam with multiple narrow reception beams allows us to sample all conventional beam directions simultaneously and to speed up scanning of the entire field‐of‐view by up to 16 times without noticeable deterioration of the data quality. The wide‐beam emission also enabled the implementation of multistatic operations, where ionospheric scatter signals from one radar are received by other radars with overlapping viewing areas. These novel operations required the development of a new model to determine the geographic location of the source of the multistatic radar echoes. Our preliminary studies showed that, in comparison with the conventional monostatic operations, the multistatic operations provide a significant increase in geographic coverage, in some cases nearly doubling it. The multistatic data also provide additional velocity vector components, increasing the likelihood of reconstructing full plasma drift velocity vectors. The developed operational modes can be readily implemented at other fully digital SuperDARN radars.

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Learn Land Features Using Python Language

Akeel Hussein Alaasam,

Ali Talib Al-Khazaali,

Hussein Aleiwi

et al. 2024

BIO Web Conf.

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Python has emerged as an essential programming language for research due to continuous technological advancements that emphasize its role in streamlining scientific workflows. This article elucidates Python's burgeoning impact on researchers across disciplines. Tracing Python's origins and applications within the earth sciences contextualizes its versatility. While acquiring proficiency in Python exceeds this article's scope, discussions detail its utilities for earth science data analysis, visualization, management, and rapid computations. With Python expertise, researchers can engineer customized software with domain-specific tools to advance all earth science spheres. Ultimately, this article underscores Python's position as a vital programming language for contemporary academic research through its flexibility and specialization for scientific use cases.

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pyDARN: A Python software for visualizing SuperDARN radar data

Cited by 4 publications

References 49 publications

Characterization of Magnetic Flux Contents for Two Flux Transfer Events From Both In Situ and Ionospheric Observations

Characterization of Magnetic Flux Contents for Two Flux Transfer Events From Both In Situ and Ionospheric Observations

Application of Wide‐Beam Transmission for Advanced Operations of SuperDARN Borealis Radars in Monostatic and Multistatic Modes

Learn Land Features Using Python Language

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