Select the radar, time period, beam, required file format and required data fields to convert FITACF
file data into selected file formats. The file formats available are CSV,
tab-delimited TXT and JSON. The file as default will provide time, date, beam number and channel for each data point, and will
supply the list of range gates with fitted data as standard if a vector field is chosen.
The table on the FITACF format page of RST shows the description for all data fields.
The FITACF 3.0 files converted here use the default options of the RST version 5.0 make_fit command, with the option -fitacf-version 3.0.
This conversion tool is limited to converting 24 hours of data at a time. Read more below.
There are no corresponding FITACF data files for the chosen inputs to convert, please try another date or radar.
Once you have selected a radar, you can use the Check Inventory tool above to choose a date that has FITACF data available.
Radar selection is required!
Start Time and Date required!
End Time and Date required!
Start Time and Date needs to be in past!
End Time and Date needs to be in past!
End Time and Date needs to be after Start Time and Date!
Beam selection is required!
At least one data field is required to be checked!
Time period is limited to 24 hours!
Your file is being produced, this may take several minutes (est. 30 seconds for 24 hours of data from 1 beam).
Please do not refresh the page.
Click the link below to start your download...
The files downloaded on this page will have a file name with the start and end date and time, the radar abbreviation (e.g. sas for Saskatoon) and the beam number, along with a unique file ID for book-keeping purposes. For example, I request a txt file of Saskatoon data from 2021-04-05 between 1pm and 2 pm for beam 8. The generated file name will be: 20210405T130000-20210405T140000_sas-bm8_{10 digit unique identifier}.txt
Inside the files, we continue to use the parameter names from the FITACF file which can be found here.
FITACF files contain only post-processed data range gates which successfully implemented the fitting algorithm to determine physical properties of the backscatter. The FITACF files used in this tool used the FITACF 3.0 algorithm.
If more than one channel was used during the time period of interest, data from channel a(0) will appear first in the file followed in alphabetical order: b(1), c(2), d(3). This means that data may not be in chronological order depending on the CPID!
The text files downloaded on this site consist of a one line header containing the requested parameters. Each line from here will contain the data from one beam at a given time, date and channel. If you have requested vector parameters (i.e. for each range gate within the beam) a field called 'slist' will appear after the date and time to show which range gate numbers had fitted values. The other vector fields will also have the same number of elements, and the index of these elements will correspond to the range gate at the equivalent index in 'slist'. The different parameters are delimited via tabs and the vector parameters are bounded by square brackets.
If you are a python user, it is recommended that you download a JSON file as the inbuilt python library, json, will convert the file into a dictionary for you. The files contain, in python notation, a list of dictionaries with keys requested from the above choices including keys for the date and time of each data point.
import ast
import json
with open('path/to/file.json') as f:
data = json.load(f)
# Output in dictionary form:
print(data)
# Example print the beam number of the first data point
print(data[0]['bmnum'])
# Vectors will require reading literally to convert from
# strings to lists
slist_list = ast.literal_eval(data[0]['slist'])
This data conversion tool is intended for small timescale case studies that do not require large amounts of SuperDARN data. Therefore, the conversion tool is limited to converting 24 hours of data at a time. If you require access to a large portion, or all of the SuperDARN dataset, please contact SuperDARN Canada, for access to our data mirror.
Using python, both pyDARN and pyDARNio can read in FITACF files directly and pyDARN can produce plots quickly and easily:
import pydarn
file = "path/to/file"
SDarn_read = pydarn.SuperDARNRead(file)
Any publications using SuperDARN Data must include the following text in their acknowledgements:
"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."
During your study, if using data from individual radars only, please contact the Principal Investigator (PI) of that radar about potential co-authorship. A list of radars, institutions, and their PI's information can be found here.
For SuperDARN Canada managed radars (Saskatoon, Rankin Inlet, Inuvik, Clyde River, and Prince George), contact Dr. Glenn Hussey of the University of Saskatchewan.
SuperDARN is in the process of placing DOI's on their data set. In the meantime, please use any local available services, such as zenodo or FRDR, to DOI your data set.
SuperDARN is a made up of 36 radars and 20 institutions, to cite SuperDARN generally, the following reference can be used:
Greenwald, R.A., Baker, K.B., Dudeney, J.R. et al. Space Sci Rev (1995) 71: 761. doi:10.1007/BF00751350
For the general achievements of the SuperDARN Network, the following papers and references within can be used:
Chisham, G., Lester, M., Milan, S.E. et al. A decade of the Super Dual Auroral Radar Network (SuperDARN): scientific achievements, new techniques and future directions. Surv Geophys 28, 33–109 (2007) doi:10.1007/s10712-007-9017-8
Nishitani, N., Ruohoniemi, J.M., Lester, M. et al. Review of the accomplishments of mid-latitude Super Dual Auroral Radar Network (SuperDARN) HF radars. Prog Earth Planet Sci 6, 27 (2019) doi:10.1186/s40645-019-0270-5
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