RBN-WSPRNet Histograms Explanation
RBN-WSPRNet Daily Histograms
The RBN-WSPRNet Daily Histograms are used to monitor global high frequency (HF) ham radio communications in relation to space weather activity. The following data are shown:
- Panel (a) shows geomagnetic activity indices derived from ground-based magnetometer data, including the SYM-H index (black line) and Kp Index (colored stems).
- Panel (b) shows X-ray flux measurements made by the GOES satellites for monitoring solar flares.
- Panels (c) - (h) show density maps and histograms of ham radio spots/QSOs from the Reverse Beacon Network and WSPRNet. The data are located at the midpoint of the transmitter and receiver. Map bin sizes are 1˚ lat by 1˚ lon, and histogram bin sizes are 10 min by 250 km. When a user‐reported location is not available, a lookup to a public database such as http://qrz.com or http://hamcall.net is made. If location is not provided and a database lookup is not available, the spot is discarded.
HF Ham Radio Data
To monitor HF radio communications, we use data from two separate automated amateur radio monitoring networks, the Reverse Beacon Network (RBN) and the Weak Signal Propagation Reporting Network (WSPRNet). These networks operate continuously and are built, operated, and maintained on a volunteer basis by members of the amateur radio community. For a detailed analysis of a solar flare and geomagnetic storm event using this type of data, please see Frissell et al. (2019). RBN and WSPRNet data have been aggregated and provided by Bill Engelke, AB4EJ, University of Alabama DXDisplay. Visualizations by Nathaniel Frissell, W2NAF, NJIT CSTR.
The Planetary K (Kp) index is a quasi‐logarithmic scale from 0 to 9 that quantifies the level of geomagnetic disturbance (Menvielle & Berthelier, 1991). This index is calculated using observations from 13 midlatitude (±44°–60° magnetic latitude) ground magnetometers located in North America, Europe, and Australia. Hence, Kp is most indicative of geomagnetic conditions in these regions. At each station, fluctuations in the strength of the horizontal component of the magnetic field are observed over a 3‐hr interval. The resulting value is subsequently associated with an individual K value based on the geomagnetic latitude of the measurement station, such that a station near the equator requires less geomagnetic fluctuation than a station near the poles in order to record the same K value. Finally, the weighted mean of measurements from all Kp observatories allows calculation of a global Kp value. Kp was obtained from the NASA Goddard Space Flight Center OMNIWeb (King & Papitashvili, 2006).
The SYM‐H index is a measure of disturbances from background in the low‐latitude horizontal component of the magnetic field and is considered a high‐time resolution (1 min) version of the hourly disturbance storm time Dst index (Iyemori, 1990; Sckopke, 1966; Wanliss & Showalter, 2006). Observations from 6 out of 11 possible ground magnetometer stations evenly distributed in longitude and in the range of ±10°–50° magnetic latitude contribute to the SYM‐H index. SYM‐H monitors the intensity of the magnetospheric ring current. A negative SYM‐H value indicates an intensification of the ring current and is associated with geomagnetic storm activity. SYM‐H was obtained from the Kyoto World Data Center for Geomagnetism.
GOES X-Ray Flux
In addition to the two previously described indices, data from the Geostationary Operational Environmental Satellite (GOES) system is also used. This consists of observations from the GOES‐13 (GOES‐EAST, θ = −75°E) and GOES‐15 (GOES‐WEST, θ = −135°E) platforms. Each satellite carries an XRS providing 0.1–0.8 nm X‐ray irradiance observations (Chamberlin et al., 2009). A solar flare can cause sudden and unexpected fluctuations in solar X‐ray irradiance. GOES XRS measurements were retrieved from the NOAA National Center for Environmental Information (NCEI).
We acknowledge the use of the Free Open Source Software projects used in this analysis: Ubuntu Linux, python (van Rossum, 1995), matplotlib (Hunter, 2007), NumPy (Oliphant, 2007), SciPy (Jones et al., 2001), pandas (McKinney, 2010), xarray (Hoyer & Hamman, 2017), iPython (Pérez & Granger, 2007), and others (e.g., Millman & Aivazis, 2011).
Chamberlin, P. C., Woods, T. N., Eparvier, F. G., & Jones, A. R. ( 2009). Next generation X‐ray sensor (XRS) for the NOAA GOES‐R satellite series, Solar Physics and Space Weather Instrumentation III. San Diego, CA: Society of Photo‐Optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.826807
Frissell, N. A., Vega, J. S., Markowitz, E., Gerrard, A. J., Engelke, W. D., Erickson, P. J., et al. ( 2019). High‐frequency communications response to solar activity in September 2017 as observed by amateur radio networks. Space Weather, 17, 118– 132. https://doi.org/10.1029/2018SW002008
Hoyer, S., & Hamman, J. ( 2017). xarray: N‐D labeled arrays and datasets in Python. Journal of Open Research Software, 5( 1), 10. https://doi.org/10.5334/jors.148
Hunter, J. D. ( 2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9( 3), 90– 95. https://doi.org/10.1109/MCSE.2007.55
Iyemori, T. ( 1990). Storm‐time magnetospheric currents inferred from mid‐latitude geomagnetic field variations. Journal of Geomagnetism and Geoelectricity, 42( 11), 1249– 1265. https://doi.org/10.5636/jgg.42.1249
Jones, E., Oliphant, T., & Peterson, P. ( 2001). SciPy: Open source scientific tools for Python. Retrieved from http://www.scipy.org/
King, J., & Papitashvili, N. ( 2006). One minute and five minute solar wind data sets at the Earth's bow shock nose. Greenbelt, MD: NASA Goddard Space Flight Center/Space Physics Data Facility. http://omniweb.gsfc.nasa.gov/html/HROdocum.html
McKinney, W. ( 2010). Data structures for statistical computing in Python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 51– 56). Austin, TX: SciPy.org. https://conference.scipy.org/proceedings/scipy2010/pdfs/mckinney.pdf
Menvielle, M., & Berthelier, A. ( 1991). The K‐derived planetary indices: Description and availability. Reviews of Geophysics, 29( 3), 415– 432. https://doi.org/10.1029/91RG00994
Millman, K. J., & Aivazis, M. ( 2011). Python for scientists and engineers. Computing in Science & Engineering, 13( 2), 9– 12. https://doi.org/10.1109/MCSE.2011.36
Oliphant, T. E. ( 2007). Python for scientific computing. Computing in Science & Engineering, 9( 3), 10– 20. https://doi.org/10.1109/MCSE.2007.58
Pérez, F., & Granger, B. E. ( 2007). IPython: A system for interactive scientific computing. Computing in Science & Engineering, 9( 3), 21– 29. https://doi.org/10.1109/MCSE.2007.53
Rossum, G. ( 1995). Python tutorial (Technical Report CS‐R9526). Amsterdam: Centrum voor Wiskunde en Informatica, (CWI). https://ir.cwi.nl/pub/5007/05007D.pdf
Sckopke, N. ( 1966). A general relation between the energy of trapped particles and the disturbance field near the Earth. Journal of Geophysical Research, 71( 13), 3125– 3130. https://doi.org/10.1029/JZ071i013p03125
Wanliss, J. A., & Showalter, K. M. ( 2006). High‐resolution global storm index: Dst versus SYM‐H. Journal of Geophysical Research, 111, A02202. https://doi.org/10.1029/2005JA011034