The 2023 SuperDARN Workshop will be held, in-person, in South Africa at a resort in the Ukhahlamba-Drakensberg Park (a UNESCO World Heritage site), May 29 - June 2, hosted by the South African National Space Agency (SANSA). The organizers, Michael Kosch and Judy Stephenson, have announced that the Workshop web page is now live and accepting registrations and abstracts:
https://superdarn.ukzn.ac.za
Although virtual attendance is not possible, presentations may be submitted electronically.

Early bird registration: April 14, 2023
Abstracts deadline: May 1, 2023
Late bird registration: May 1, 2023
Upload video presentations: May 19, 2023

For a synosis of the SuperDARN Workshops, see 'Read More'

On behalf of the Data Visulaization Working Group, co-Chair Carley Martin has announced the release of a new version of pyDARN software, namely, pyDARN v3.1

The new version pyDARNio (with correction of patch release v1.2.1 dated 7-FEB-2023) can be installed via
pip3 install pydarnio​
or an existing installation can be updated via
pip3 install --upgrade pydarnio

A list of the new features via the 'Read More' link
In Carley's words: 'The DVWG is always looking for more help testing and developing, I'm more than happy to walk through how to test or review code from students/staff new to SuperDARN and python. If anyone is interested in helping out with development of pyDARN and/or pyDARNio, let me or one of the DVWG chairs know!

Thanks to all involved in the development of our new release,
Special thanks to Emma Bland, we wish you all the best in Australia!'

On behalf of the SuperDARN Data Analysis Working Group, the co-Chair Emma Bland has announced that a new major release of the SuperDARN Radar Software Toolkit has been published on Zenodo: https://zenodo.org/record/7467337 . Click on 'Read More' to see a list of improvements.
Installation instructions and RST tutorials are available in the documentation: https://radar-software-toolkit-rst.readthedocs.io/en/latest/
To cite the software in publications:
SuperDARN Data Analysis Working Group, Thomas, E.G., Reimer, A.S., Bland, E.C., Burrell, A.G., Grocott, A., Ponomarenko, P.V., Schmidt, M.T., Shepherd, S.G., Sterne, K.T., & Walach, M.-T. (2022). SuperDARN Radar Software Toolkit (RST) 5.0 (v5.0). Zenodo. https://doi.org/10.5281/zenodo.7467337
In Emma's words: 'We encourage users to contact us if they have questions or find problems with the software. New contributions to the software are also most welcome. You can contact us by opening an "issue" on Github (https://github.com/SuperDARN/rst/issues), or alternatively by email.

The RST 5.0 release is a major milestone for the Data Analysis Working Group, and I'd like to thank everyone who has contributed. This is also time for me to announce that I have stepped down as the co-chair of the DAWG as I will be starting a new job in January. We welcome Adrian Grocott (Lancaster University) as the new scientific chair of the DAWG, who will co-chair the group together with Kevin Sterne (Virginia Tech).'

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

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.

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
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
https://www.isee.nagoya-u.ac.jp/en/news/research-results/2022/20220714.html
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
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