1. Refining AWDANet performance: a decade of whistlers
Whistlers are VLF (3-30 kHz) impulses generated by lightning, traveling along magnetic field lines, observable on the ground and/or in space.
Through propagation in the plasma content of the magnetosphere, they acquire a frequency-time signal with a characteristic shape
The time delay depends on the plasma density along the propagation path (it is also frequency dependent, causing their typical curved shape, see fig 1.)
Therefore, whistler measurements can tell us about the plasma density in the plasmasphere 1
Refining AWDANet performance: a decade of whistlers
Electron Densities from Whistlers
Dávid KORONCZAY1,2, János LICHTENBERGER2,1, Lilla JUHÁSZ2, Péter STEINBACH2,3, Csaba FERENCZ2,
Mark CLILVERD4, Craig RODGER5, Fabien DARROUZET6, Dmitry SANNIKOV7, Nina CHERNEVA7
(1) Geodetic and Geophysical Institute, HAS, Sopron, Hungary (2) Department of Geophysics and Space Sciences, Eötvös University, Budapest, Hungary (3) MTA-ELTE Research Group for Geology, Geophysics and Space Sciences, HAS, Budapest, Hungary (4) British Antarctic Survey (NERC), Cambridge, UK (5) Department of Physics, University of Otago, Dunedin, New Zealand (6) Royal Belgian Institute for Space Aeronomy, Brussels, Belgium (7) Institute of Cosmophysical Research and Radio Wave Propagation, FEB RAS, Paratunka, Russia
Abstract The Automatic Whistler Detector and Analyzer Network (AWDANet) detects whistlers on the ground that have travelled through the plasmasphere. In this study, we have analyzed seceral years of deetections, compaaring the arrival times with lightning stroke times from the WWLLN database, to determine the source regions of whistlers for each of the studied 15 Awdanet stations.
Whistlers have been regarded as cheap and effective tools for plasmasphere diagnostics since the early years of whistler research, but have never been routinely used, since the extraction of equatorial densities was very labour intensive. Recently (between 2002 and 2014) an Automatic Whistler Detector and Analyzer Network (AWDANet) was developed which is capable of automatically detecting and analysing whistlers [1].
Dunedin,
Karymshina,
Forks,
Fig. 1 Spectrogram of a typical whistler group
Grahamstown,
Sutherland,
Marion,
Acknowledgements
The research leading to these results has received funding from the European Community’s Seventh Framework Programme ([FP7/2007–2013]) under grant agreement number
Fig. 2 Distribution of existing (red and green) and planned (blue) Automatic Whistler Detector and Analyzer Network (AWDANet) stations in Europe (left panel) and around the world (right panel).

2. Refining AWDANet performance: a decade of whistlers
Abstract The Automatic Whistler Detector and Analyzer Network (AWDANet) detects whistlers on the ground that have travelled through the plasmasphere. In this study, we have analyzed seceral years of deetections, compaaring the arrival times with lightning stroke times from the WWLLN database, to determine the source regions of whistlers for each of the studied 15 Awdanet stations. 2
Refining AWDANet performance: a decade of whistlers
Green circle represents the location of the station, dashed line is the corresponding magnetic field line projected onto the ground, empty circle is the magnetic conjugate point of the station (calculated at 100km altitude).
Color scales represent the number of whistler detections that have been matched to a lightning stroke. Some stations have low detection rates due to either technical reasons, such as local noise levels, or can be actual low rates occurring at the given station. Also, some stations have been operational for only a small fraction of the given time period.
Tihany,
Tvarminne
Humain,
Eskdalemuir,
Nagycenk,
Gyergyóújfalu,
A small number of the matches between whistler and lightning timings are purely coincidental and not causal. These „false” matches tend to occur at regions with high lightning activity, i.e. Central America, Central Africa and Indonesia. Where the whistler statistics are poor, these „false” matches are not blanked out and remain visible on the maps above, but are not related to the actual source regions.

3. Refining AWDANet performance: a decade of whistlers3
Refining AWDANet performance: a decade of whistlers
Top panels show the source regions of whistlers at three Atlantic stations, determined as explained above. The bottom panels were obtained by dividing each pixel by the WWLLN climatology map (the absolute number of lightning strokes withinin the area of the given pixel, within in the same year or years as the whistler measurements).
Thus, the bottom panels represent the relative likelihood that lightning stroke within the given pixel area generates an observable whistler at the respective station (color scales on the bottom panels are base 10 logarithmic).
Example: WWLLN climatology map. (Average lightning stroke distribution)
Rothera, 2010
Halley,
Sanae, 2007
Rothera, Normalized,
Halley, Normalized,
Sanae, Normalized,

4. Comparison of lightning activity to whistler activity for some selected stations and years (top panels, showing daily sums) and months (bottom panels, showing hourly sums).
Whistler counrs (green bars) are compared to lightning counts in the corresponding source region, as determined above (colored curvese). Lightning strokes were counted in a circular region around the conjugate point, with a radius of 500 km (red), 1000 km (magenta) and 2000 km (blue).
Conclusion
We compared electron densities obtained from an inversion algorithm to in-situ density measurements by the Van Allen Probes (EMFISIS instrument suite).
This can validate the inversion method (which relies on assumptions on the wave propagation, the density profile along the field line and Earth's magnetic field model).
In method 1,our first results show good correlation between inversion's results of the ground-based observation and space-based observation of the same whistler events. Our future plan is to identify the type and extents of errors, and improve the inversion process.
In method 2, we carried out inversion of a sequence of Alpha signals. The obtained densities are consistent with in-situ measurements. The main source of error is the error in precisely determining the signal impulse arrival times. With a precise on-board clock, this method can be an independent method to determine electron densities in the plasmasphere.
Rothera, 2011
Tihany, 2008
Marion, 2011
Karymshina, 2013
Rothera, April 2011
Tihany, March 2008
Marion, June 2011
Karymshina, June 2013
References
[1] J. Lichtenberger, C. Ferencz, D. Hamar, P. Steinbach, C. J. Rodger, M. A. Clilverd, and A. B. Collier. Automatic Whistler Detector and Analyzer system: Implementation of the analyzer algorithm. Journal of Geophysical Research, 115:A12214, Dec doi: /2010JA
[2] A. B. Collier, J. Lichtenberger, M. A. Clilverd, C. J. Rodger, P. Steinbach: Source region for whistlers detected at Rothera, Antarctica. Journal of Geophysical Research, 2011.
[3] A. B. Collier, S. Bremner, J. Lichtenberger, J. R. Downs, C. J. Rodger, P. Steinbach, G. McDowell: Global lightning distribution and whistlers observed at Dunedin,
New Zealand. Ann. Geophys. 2010