Published January 16, 2026 | Version 3.0

SuperDARN data in netCDF format (1996-Jan)

  • 1. JHU/APL

Description

1996-Jan SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.

The RST is available here: https://github.com/SuperDARN/rst

The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.

Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)

Recommended form of acknowledgement for the use of SuperDARN data:

'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

Files

19960101.nc.zip

Files (883.7 MB)

Name Size
md5:095d35be0f5902d4e95df23eff54f1c9
29.9 MB Preview Download
md5:88552e83160acc37cdf4ccdf15ae3f32
30.3 MB Preview Download
md5:46ffb71967b676a041f8e9ef26d513e2
28.9 MB Preview Download
md5:8ba1e9429bed65bc96e6aa653ee67908
30.6 MB Preview Download
md5:97789791ca767909efeac8f0e62b608d
31.9 MB Preview Download
md5:27f376d70e20c19494103f3cf4e68dd7
32.1 MB Preview Download
md5:9fd29251204d21da4a015b01a7215b52
32.1 MB Preview Download
md5:fcf190c25f860549f5dc447c333e1cd0
31.4 MB Preview Download
md5:85eb455935d4f170331ba8a3292efb46
32.1 MB Preview Download
md5:417e4883653ffd757d5ddaf78523f9ba
31.4 MB Preview Download
md5:8a710b78e562206172e1c7cfd18fbac3
34.1 MB Preview Download
md5:8f746103ffa4fd6b569dd378447e02d4
35.1 MB Preview Download
md5:1dcf46f096296989d61dea187eefdeb3
30.4 MB Preview Download
md5:7b81c2238e5055941f914913be257ada
30.7 MB Preview Download
md5:387d82760463f26e5dd7c9dd1bb22556
24.8 MB Preview Download
md5:b9519efed4b8e51b501b7a92c7e2f06f
18.9 MB Preview Download
md5:80217cb237669bcad46d8232a80b1c4b
24.5 MB Preview Download
md5:088b68823e7aaa38b873c0142911d5b0
22.3 MB Preview Download
md5:0fa52ea4ae85d0c223f23b85e3afd825
25.0 MB Preview Download
md5:27ca1f5a7b7f409069758013fc7cc5cf
25.0 MB Preview Download
md5:f67612ff2ff64c2a861d90600108498f
28.1 MB Preview Download
md5:53cae9fe4908330e6ee5eca9a190d9f3
29.0 MB Preview Download
md5:dd5c9075f560ba65207468c9e89b690a
27.4 MB Preview Download
md5:99282e7d4cae349f1832f5111c98e38e
31.2 MB Preview Download
md5:d63c432715b1896add5cb66d4910c6b2
30.3 MB Preview Download
md5:bc95906316f619b594efcb7e49bef30c
28.3 MB Preview Download
md5:e6aff1c4c8722a184e01abe43d65076a
30.1 MB Preview Download
md5:435c681c15b2f3d74ce6413ce410683b
21.0 MB Preview Download
md5:b3b3f49d8c4da72b82cfdb7d3a604f2d
26.6 MB Preview Download
md5:81d83279238add80285865dd7425e648
25.5 MB Preview Download
md5:b4214af41fed2af2be3dd299f931397f
24.6 MB Preview Download

Additional details

Related works

Is derived from
Dataset: 10.20383/102.0468 (DOI)