UPDATE: The offices for the VT SuperDARN group members have been relocated to a building on campus. To this point (14-AUG-2020) our service operations have not been significantly affected as the server system has remained in our old Lab. At some point over the next month the server will be moved and there will be an interruption. The services affected include the web site with its tools and access to data servers. If you have a time-critical task, contact us by email. Thank you for bearing with us.
During the 2020 SuperDARN workshop held online the SuperDARN Executive council welcomed a new group from the National Space Science Center (NSSC), Chinese Academy of Sciences (CAS) to the collaboration and voted the PI of the Jiamusi radar, Dr. Jiaojiao Zhang, to council membership.

The Jiamusi radar, also known as AgileDARN, features new kinds of digital control electronics and has been providing data to the SuperDARN data flow since mid-2019. The radar increases SuperDARN coverage at mid-latitudes in the east Asian Sector. First results were reported by Zhang et al. (2020), see 'Read More' for bio details.

Congratulations to Dr. Zhang and her team at the NSSC!

2020 SuperDARN Workshop moves to online format, scheduled for June 1-5

By: miker  on: Wed., May 20, 2020 11:01 AM EDT  (745 Reads)
The Chair of the SuperDARN Executive Council, Prof. Kathryn McWilliams has announced that the 2020 SuperDARN Workshop, originally planned for South Africa, has been moved to an online format, hosted by the University of Saskatchewan. There will be virtual Working Group meetings (live) and asynchronous conference presentations (view at your leisure) on Vimeo. The workshop will still take place over the first week of June. The workshop in South Africa is now planned for 2021. For more information and to register, go to the workshop website:
https://superdarn.ca/workshop2020(external link)

The deadline for registration and video transfer is 28th May 2020!

Email inquiries can be sent to:

Xueling and coauthors and Bharat and coauthors have been recognized with certificates from the Journal of Geophysical Research – Space Physics for papers that were among the top 10% for downloads that were published between January 2018 and December 2019. The paper citations are:

Kunduri, B. S. R., Baker, J. B. H., Ruohoniemi, J. M., Sazykin, S., Oksavik, K., Maimaiti, M., et al. (2018). Recent developments in our knowledge of inner magnetosphere‐ionosphere convection. Journal of Geophysical Research: Space Physics, 123, 7276– 7282. https://doi.org/10.1029/2018JA025914(external link)

Shi, X., Baker, J. B. H., Ruohoniemi, J. M., Hartinger, M. D., Murphy, K. R., Rodriguez, J. V., et al. ( 2018). Long‐lasting poloidal ULF waves observed by multiple satellites and high‐latitude SuperDARN radars. Journal of Geophysical Research: Space Physics, 123, 8422– 8438. https://doi.org/10.1029/2018JA026003(external link)

Congratulations to Bharat, Xueling, and coauthors!

Grid2, map2 files available back to July 2006

By: ksterne  on: Fri., Apr. 17, 2020 11:08 AM EDT  (823 Reads)
As a general update, it's been a few years since the major revisions to our grid- and map-level processing software were complete. These revisions date back to RST 4.0 (as of this writing, RST is at version 4.3.1) with the new files being named grid2 and map2 so that previous files could be maintained for data reproducibility. A number of other data issues were being sorted out and processed, including the expansion of the VT data storage systems, that delayed a mass processing of the new files. However with a break in some activities, grid2 and map2 files have now been generated back to the start of the modern dmap file format on July 1, 2006.

As always, for a listing of what files we have for certain dates please visit our Data Inventory(external link) page.

Release of pyDARN Version 1.0 announced

By: miker  on: Fri., Mar. 27, 2020 10:29 AM EDT  (972 Reads)
Marina Schmidt of the University of Saskatchewan SuperDARN group has announced the official release of pyDARN. In her words
'pyDARN is a python library used for data visualization of SuperDARN data.
Currently, it can:
- read and write SuperDARN DMap files
- read, write and convert Borealis hdf5 files
- range-time parameter plots
- summary plots
Thank you for all the support and help from the community.'

For directions on how to install and use pyDARN, clock 'Read More'
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