The open-source library of software for SuperDARN data visualization developed by the SuperDARN community and known as pyDARN has been reviewed in a paper published in Frontiers in Astronomy and Space Science by Xueling Shi and coauthors from the Data Visualization Working Group (DVWG):

Shi X, Schmidt M, Martin CJ, Billett DD, Bland E, Tholley FH, Frissell NA, Khanal K, Coyle S, Chakraborty S, Detwiller M, Kunduri B and McWilliams K (2022) pyDARN: A Python software for visualizing SuperDARN radar data. Front. Astron. Space Sci. 9:1022690. doi: 10.3389/fspas.2022.1022690

The paper is part of the Research Topic 'Snakes on a Spaceship - An Overview of Python in Space Physics.' It can be accessed at https://www.frontiersin.org/articles/10.3389/fspas.2022.1022690/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Astronomy_and_Space_Sciences&id=1022690(external link)

The last major release of pyDARN was v3.0 on April 20, 2022 as announced by Carley Martin (University of Saskatchewan) on behalf of the Data Visualization Working Group (DVWG). See 'Read More' for how to install and more information.

Release of pyDARNio v1.2.0 announced

By: miker  on: Wed., Nov. 30, 2022 05:12 PM EST  (40 Reads)
On behalf of the Data Visualization Working Group (DVWG), Carley Martin of the University of Saskatchewan has announced the release of the new version of pyDARNio. This is the Python SuperDARN io package used with pyDARN and other libraries. A description of the new features of pyDARNio v1.2.0 can be viewed by clicking below on 'Read More'.

The new version pyDARNio can be installed via
pip3 install pydarnio​
or an existing installation can be updated via
pip3 install --upgrade pydarnio

This new version will be the default version automatically installed on a new pyDARN installation.
A news article describing the last major release of pyDARN (Version 3.0) can be viewed at http://vt.superdarn.org/tiki-read_article.php?articleId=436(external link)
The DVWG is expecting a new release for pyDARN before the holidays with lots of new and improved features.
Dr. Atsuki Shinbori and colleagues at the Institute for Space-Earth Environmental Research (ISEE) at Nagoya University have published a paper in Earth, Planets, and Space (EPS) that uses data from the SuperDARN Hokkaido pair of radars to elucidate the physics of TIDs observed in TEC data following the 2022 Tonga volcanic eruption. It is shown that ionospheric effects reached the Japanese sector faster than atmospheric effects due to conjugacy. Here are links to press releases provided by the first author and coauthors including SuperDARN PI Dr. Nozomu Nishitani::
https://www.eurekalert.org/news-releases/958792(external link)
https://www.isee.nagoya-u.ac.jp/en/news/research-results/2022/20220714.html(external link)
The potential significance of this finding for advance warning of disturbance in the coupled atmosphere-ocean system was picked up by the United Nations Office for Disaster Risk Reduction (UNDRR) and reported here: https://www.preventionweb.net/news/shockwave-caused-tonga-underwater-eruption-may-help-scientists-predict-future-tsunami(external link)
Figure credit and citation: Shinbori, A., Otsuka, Y., Sori, T. et al. Electromagnetic conjugacy of ionospheric disturbances after the 2022 Hunga Tonga-Hunga Ha’apai volcanic eruption as seen in GNSS-TEC and SuperDARN Hokkaido pair of radars observations. Earth Planets Space 74, 106 (2022). https://doi.org/10.1186/s40623-022-01665-8(external link)
The PI of the NSSC SuperDARN group, Dr. Jiaojiao Zhang, has announced that the deadline for submission of items has been extended for the 2022 edition of the SuperDARN Workshop. The workshop will be hosted by the National Space Science Center (NSSC), Chinese Academy of Sciences (CAS). The format will be virtual. Dr. Zhang adds that the pre-recorded presentations will be available May 23-June 3 through the Pre-recorded Conference Entrance. Note that May 15, 2022 is the deadline for submission of your pre-recorded presentation as well as registration and abstract submission.

The conference web site is: https://superdarn2022.swl.ac.cn(external link)

For a synopsis of the SuperDARN Workshop, see 'Read More'

Major release of pyDARN software package announced by Data Visualization Working Group

By: miker  on: Thu., Apr. 21, 2022 04:49 PM EDT  (1556 Reads)
On behalf of the SuperDARN Data Visualization Working Group (DVWG) Carley Martin has announced the release of pyDARN v3.0.

In this release, we include the optional cartopy dependency for underlaid maps and convection maps in magnetic coordinates. Along with a number of other bug fixes and improvements.

To install:
pip3 install --upgrade pydarn​

Or you can download the zip on Zenodo: https://zenodo.org/record/6473574(external link)

Any information on using pyDARN can be found in our documentation here: https://pydarn.readthedocs.io/en/main/(external link)

SD Data Analysis Working Group Issues a minor release of the Radar Sofware Toolkit, RST 4.7

By: miker  on: Thu., Apr. 21, 2022 04:39 PM EDT  (1057 Reads)
On behalf of the SuperDARN Data Analysis Working Group (DAWG), Co-chairs Emma Bland and Kevin Sterne have announced a minor release of the Radar Software Toolkit, RST 4.7. It is available for download from Zenodo: https://doi.org/10.5281/zenodo.6473603(external link)

Key updates in RST 4.7 include:
New routine to list the transmission frequency bands present in a fitacf file (find_freqband)
New functionality and documentation for the data simulator (make_sim)
Updated C and IDL code for reading the new hardware file format (Note: the old hardware file format is incompatible with RST 4.7. The new hardware files are included in this release)
Bug fixes that affected compilation on some systems
Bug fixes in various analysis and plotting routines

To cite the software in publications:
SuperDARN Data Analysis Working Group, Burrell, A.G., Thomas, E.G., Schmidt, M.T., Bland, E.C., Coco, I., Ponomarenko, P.V., Sterne, K.T., & Walach, M.-T. (2022). SuperDARN Radar Software Toolkit (RST) (Version 4.7). https://doi.org/10.5281/zenodo.6473603(external link)
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