ANGEOAnnales GeophysicaeANGEOAnn. Geophys.1432-0576Copernicus PublicationsGöttingen, Germany10.5194/angeo-35-1143-2017Plasma line observations from the EISCAT Svalbard Radar during the International Polar YearIvchenkoNickolaynickolay@kth.sehttps://orcid.org/0000-0003-2422-5426SchlatterNicola M.https://orcid.org/0000-0001-6802-1842DahlgrenHannahttps://orcid.org/0000-0001-5596-346XOgawaYasunobuSatoYukahttps://orcid.org/0000-0002-3776-1175HäggströmIngemarSchool of Electrical Engineering, Royal Institute of Technology KTH, Stockholm, SwedenSchool of Physics and Astronomy, University of Southampton, Southampton, UKNational Institute of Polar Research, Tokyo, JapanEISCAT Scientific Association, Kiruna, SwedenNickolay Ivchenko (nickolay@kth.se)24October20173551143114916March201718September201719September2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://angeo.copernicus.org/articles/35/1143/2017/angeo-35-1143-2017.htmlThe full text article is available as a PDF file from https://angeo.copernicus.org/articles/35/1143/2017/angeo-35-1143-2017.pdf
Photo-electrons and secondary electrons from particle precipitation enhance
the incoherent scatter plasma line to levels sufficient for detection. When
detectable the plasma line gives accurate measure of the electron density and
can potentially be used to constrain incoherent scatter estimates of electron
temperature. We investigate the statistical occurrence of plasma line
enhancements with data from the high-latitude EISCAT Svalbard Radar obtained
during the International Polar Year (IPY, 2007–2008). A computationally fast
method was implemented to recover the range-frequency dependence of the
plasma line. Plasma line backscatter strength strongly depends on time of
day, season, altitude, and geomagnetic activity, and the backscatter is
detectable in 22.6 % of the total measurements during the IPY. As
expected, maximum detection is achieved when photo-electrons due to the Sun's
EUV radiation are present. During summer daytime hours the occurrence of
detectable plasma lines at altitudes below the F-region peak is up to
90 %. During wintertime the occurrence is a few percent. Electron
density profiles recovered from the plasma line show great detail of density
variations in height and time. For example, effects of inertial gravity waves
on the electron density are observed.
Electromagnetics (instrumentation and techniques)Introduction
Incoherent scatter radars are ground-based instruments for measuring the
properties of the ionospheric plasma. The radars observe the Doppler-shifted
ionospheric backscatter which contains the signature of ion acoustic waves
and Langmuir waves . The narrow part of the spectrum,
typically several kilohertz, around the transmitter frequency which contains the
signature of ion acoustic waves is referred to as the ion line, whose
spectral shape contains information about ionospheric plasma parameters such
as electron density and electron and ion temperatures. However, the estimates
of plasma parameters from the ion line are often ambiguous, as they are
dependent on a correct assumption of the ionospheric ion composition. The
backscatter from Langmuir waves is Doppler-shifted by a few megahertz, roughly the
plasma frequency, and is referred to as the plasma line. A recent review
gives further details of theoretical and observational aspects of plasma line
in incoherent scatter radar context .
Although the plasma line is too weak to be detected when the plasma is
Maxwellian, suprathermal electron fluxes can excite the Langmuir waves and
consequently the plasma line to levels sufficient for detection
e.g.. The Doppler shifts of the
plasma lines from the transmitter frequency are nearly equal to the plasma
frequency, so that plasma line measurements provide an accurate way to
determine the electron density in the plasma volume directly from the
frequency shift. used the plasma line measurements from the
Sondrestrom incoherent scatter radar to determine plasma density profiles at
very high temporal resolution in order to study the variability of the energy and
flux of the electron precipitation. A coded long-pulse plasma line technique
for incoherent scatter radars has been developed to obtain measurements of
the plasma line at very high range and time resolution, and its strength was
demonstrated in a study of small density fluctuations in the ionosphere
caused by gravity waves .
It has also been shown that the plasma lines can be used to determine the
electron temperature in the plasma, independent of the ion line estimates
. used the found
asymmetry of up- and downshifted plasma lines to accurately determine the
electron temperature profile above Arecibo at high resolution. The same
asymmetry can also provide information on ionospheric currents, since the
Doppler shift of the electrons depend on the electron drift
e.g..
In this paper we present an algorithm for extracting plasma line frequency
profiles from recorded incoherent scatter radar observations. We apply the
method to data from the European Incoherent Scatter Scientific Association
(EISCAT) Svalbard Radar (ESR) during the International Polar Year (IPY,
2007–2008) and investigate the statistical occurrence of plasma line
enhancements.
Method
An alternating code experiment was run almost
continuously at ESR during the IPY. The experiment uses an alternating code
with 30 bits, each 30 µs long, and a code cycle of 64 pulses with a
code cycle length of 240 ms and an inter-pulse period of 3.75 ms. In
addition to the ion line data, the experiment contains two receiver bands
offset from the transmitter band, covering ± (3.1 to 4.8) MHz at
1.1 kHz resolution, to observe up- and downshifted plasma lines. These
plasma line channels are sampled at 0.6 µs and data are stored as
lag profiles which are integrated over 6 s. The measurements cover a range
of 127 to 278 km, with a range resolution of 4.5 km.
During the IPY about 3.5 million data files, each corresponding to a 6 s
interval, were recorded for the two plasma line channels. In order to process
this large data set the algorithm is required to have a short runtime for
each processed spectrum such that the entire dataset can be processed within
a few days.
Alternating codes are used to achieve high range resolution of the
measurements while using a high transmission duty cycle for high radar
efficiency. The alternating code experiments are designed for targets which
can be assumed to be stationary over the transmission of a code cycle. For
stationary backscatter targets the range–Doppler ambiguity function
is a sharp peak well defined by the code. For time variable
targets, however, the spread of the ambiguity function increases and signal
power is effectively lost to points in the range–Doppler space other than the
target. Langmuir waves in the ionosphere, and especially in the auroral
ionosphere, are such targets with high temporal variability. Therefore, the
radar plasma line when observed with alternating code experiments often
exhibits unwanted signal contributions referred to as clutter.
In order to effectively recover the shape of the plasma line, an algorithm
needs to be designed to remove this clutter. Since the time variability of
the plasma line backscatter is an unknown property the effective
range–Doppler ambiguity function is not known. Therefore deconvolution or
more general methods assuming a fixed global ambiguity function are not
suitable to recover the plasma line range-frequency dependence from the
measurements.
The method outlined here is based on thresholding of the plasma line spectrum
to detect points in range frequency for which the plasma line is enhanced.
The effect of clutter is reduced by removing the median power for each range
gate and frequency bin as outlined below. For each range gate a weighted mean
frequency is calculated over selected frequency bins. In the following the
steps of the method are outlined and described by the help of a data example
recorded on 17 June 2007 at 07:00:06 UT, and shown in Fig. :
Example of reduction
of plasma line data from 17 June 2007 at 07:00:06 UT.
Computation of the plasma line spectra
The auto-correlation function measured with the radar for each range gate is
stored for both the up- and downshifted plasma line channels. We use the
first 80 complex-valued lags of the autocorrelation function (at
0.6 µs sampling), with a Hamming window, to produce plasma line
spectra. The resulting frequency resolution of the spectra is 10 kHz.
In step A time integration can be applied by integrating the auto-correlation
function over consecutive measurements. For the IPY data the resulting time
resolution is multiples of 6 s.
Median filtering of the spectra
To reduce noise a median filter is applied to the up- and downshifted spectra
with a frequency width of nine points corresponding to 10 kHz width. The
resulting up-shifted plasma line spectrum is shown in Fig.
(B).
Removal of noise and clutter
Background variations in the noise level are removed in two steps:
For each frequency bin the median is calculated along the range gates
and subtracted from each of the contributing points. The resulting up-shifted
plasma line spectrum is shown in Fig. (C1).
For each range gate the median of the spectrum is subtracted.
The resulting up-shifted plasma line spectrum is shown in
Fig. (C2).
Averaging up- and downshifted plasma line spectra
In the IPY experiment the plasma line is recorded for up- and downshifted
frequencies. Since any frequency shifts of the plasma lines due to their
different wave numbers and strong currents would be less than or up to the
spectral resolution of the IPY measurements, an average plasma line spectrum
can be computed from the up- and downshifted spectra to increase the
signal-to-noise ratio (SNR). In Fig. (D) the resulting
average spectrum is shown.
Thresholding of the spectrum
Points with power above the background level trace out an altitude profile of
the plasma frequency. A suitable threshold must be selected to recover large
parts of the profile, while rejecting the outliers due to noise. The approach
taken here is to use a low detection threshold in combination with subsequent
elimination of falsely selected noise points. Thresholding is done in terms
of the median absolute deviation, MAD. The MAD is more robust to outliers,
which the features to be extracted represent, than the standard deviation. It
is defined as
MAD=medianr,fSr,f-medianr,fSr,f.
Here Sr,f is the spectral density for a given frequency f and range
r and the two medians are calculated over all points. To introduce a
threshold that is robust against noise, but low enough to not reject too many
points, a factor of 4.2 has been found to be useful, i.e. the threshold is
threshold=4.2⋅MAD.
For normally distributed data this threshold corresponds to 3σ. In the
data example discussed here threshold = 1.0 mK Hz-1.
Figure (E) shows the binary mask, which is defined by points of Sr,f
which exceed the median of Sr,f by the threshold. As one can see the
binary mask still includes points due to noise, which are removed in the
following steps.
Further reduction of falsely selected points
To reduce the number of falsely selected points the previously calculated mask is reduced by the following steps:
Removal of small areas
The mask is reduced by areas with less than 15 contributing points.
Convolution with kernel
The mask is then convoluted by a structuring kernel, with the size of one range gate and 20 frequency bins, to fill the gap between nearby areas.
Removal of small areas
The mask is reduced by areas with less than 140 contributing points.
Final mask
To the final mask points which exceed the median by 1.8 ⋅ threshold
are added. This step guarantees that strong features, which might otherwise
be removed in step F1 and F3, are included in the final mask. This
step has proven to be useful especially for
features where the plasma frequency has a sharp local maximum or minimum, such as the often occurring local minimum in
plasma frequency just below 150 km range.
The effect of steps F1–F4 on the mask is shown in the bottom four panels of
Fig. .
Computation of the plasma line frequency
For each range gate the plasma frequency is computed by using the weighted
average of the plasma line spectrum over frequency bins selected by the final
mask. For this step the spectrum computed in step D, i.e. the average
spectrum of the up- and downshifted plasma line channel, is used. In
Fig. (D) the estimated plasma line profile is shown by green
points.
When the plasma line backscatter is strong and the plasma line frequency is
outside the sampled frequency bandwidth aliasing might occur. That means that
plasma line backscatter at, for example, 3 MHz is observed at 4.6 MHz. The
implemented algorithm attempts to correct for aliasing by analysing the
range-frequency dependence of the plasma line (not outlined here).
Computation of the plasma density
In the last step the plasma density is computed from the plasma line
frequency. The frequency of Langmuir waves depends on the electron density
and the electron temperature. The linear dispersion relation for Langmuir
waves in the direction of the magnetic field is ω2=ωp2+k2vth2, where vth is the electron thermal velocity and
ωp the plasma frequency. The electron thermal velocity is calculated
from the electron temperature found by standard ion line analysis.
Results
Occurrence of detected plasma line backscatter with 30 s
integration: (a) May to July 2007; (b) August to
October 2007; (c) November 2007 to February 2008. Magnetic local
time at ESR is UT + 3.1 h.
The developed algorithm was run on the IPY dataset, from 21 May 2007 to
6 February 2008, using a 30 s integration of plasma line data. Plasma line
backscatter is detectable in 22.6 % of the range gates. However, the detectability
has strong dependence on season, time of day, and range.
Figure shows the percentage of detected plasma line
backscatter as a function of altitude and time of day for three time periods.
During May–July, panel (a), the occurrence is highest reaching up to
90 % between altitudes of 150 and 200 km pre-noon. The F-region peak, at
altitudes near 220 km and above, has lower occurrence, reaching a maximum of
80 %. During August–October, panel (b), the occurrence decreases and
highest occurrence is achieved for the F-region peak around 09:00 UT with
70 %. During November–February, panel (c), when there is no or little
photo-ionization the occurrence drops significantly and the plasma line is
detected more sporadically. Detections during this period occur mainly
between 02:00 and 07:00 and between 12:00 and 16:00 UT with an occurrence rate of around
10 %. Around local magnetic noon, 09:30 UT, a population of plasma line
detection around 240 km altitude is observed. As ESR is located at high
geomagnetic latitude, and the ionosphere is in the dark during the winter
season, neither conjugate photoelectrons nor local production can account
for the enhancement of the plasma line, which has to be related to particle
precipitation.
Electron density on 17 June 2007. Panels (a) and
(b) show electron density recovered from 30 s integrated plasma
line data. Panel (c) shows electron density resulting from ion line
analysis with 60 s integration, performed with the GUISDAP tool
.
Plasma density profiles computed from plasma line analysis neglecting
electron pressure are shown in Fig. for 17 June 2007. In
panel (a) data for the whole day are shown. From about 06:00 to 12:00 UT the
data coverage is good due to the presence of photoelectrons in the
ionosphere. Between 00:00 and 06:00 UT as well as between 12:00 and
24:00 UT the coverage is sporadic. During these intervals particle
precipitation occurs and the electron temperature above 200 km altitude
varies between 2000 and 3000 K. The sporadic plasma line detection during
these intervals might be due to either photo-electrons or secondary electrons
from particle precipitation; however, we do not attempt to investigate the
mechanisms here. In Fig. b a shorter time period of the
same data is shown. The electron density recovered from the plasma line shows
great detail in time, but also altitude. Structures propagating towards the
radar, i.e. decreasing in altitude, are clearly visible. These structures in
electron density are the effect of travelling ionospheric disturbances
. Figure c shows electron density
computed from the ion line for the same period as in panel (b). The electron
densities agree roughly with the ones in panel (b); however, it is evident
that panel (b) shows greater detail at the cost of incomplete data coverage.
A detailed comparison of electron density recovered from the plasma line and
the result from standard ion line analysis is shown in
Fig. . Panel (a) shows the electron density at 195 km
altitude on 17 June 2007, derived from plasma line measurements. In panel (b)
the electron density profile is shown for 09:09:54 UT. The electron density
profile is covered almost completely by the plasma line measurements (black
points with error bars), corrected for the electron temperature obtained from
the ion line analysis. The steps in electron density are due to the limited
accuracy of and low resolution in electron temperature. Also shown is the
result from the ion line analysis (pink asterisks). The discrepancy of the
electron densities at 150–200 km altitude is likely due to the known
problem of ion line fitting at these altitudes, where there is a transition
of ion composition from molecular to atomic oxygen .
(a) Plasma density recovered from plasma line data using
30 s integration (black points). (b) Electron density profile from
plasma line data (black) and from 60 s integrated ion line data (pink
asterisks).
Discussion
The outlined method describes a non-iterative algorithm for recovering the
range-frequency dependence of the plasma line backscatter. Since the
algorithm is non-iterative a relatively short runtime is achieved and large datasets can be readily
processed even on modern personal computers in short time. The algorithm
processes 1 day of plasma line data acquired with the IPY experiment in less
than 15 min and consequently 1 year of data in about 3.5 days.
For independent validation, the algorithm was run on a specific day
(21 July 2017, 2 h between 04:00 and 06:00 UT, 1200 dumps of 6 s
integration in total). Over 87 % of profiles (308 dumps) detectable by a
human were detected and correctly processed by the algorithm. The remaining
13 % were partially detected or resulted in non-contiguous mask regions.
The example used to demonstrate the method outlines the capabilities of the
algorithm, but also some weaknesses. The strongest features of the
range-frequency dependence of the plasma line are successfully recovered.
However, some weaker features, easily recognized by the human eye, are not
detected. These non-recovered features are close to the noise level and can
only be recognized due to their clustering. A cluster of points could, for
example, be identified in the mask resulting from step F1, but since the detection of clustering would result in significantly increased computation time, this was not implemented.
Conclusions
Plasma lines are only detectable when enhanced over thermal levels by
photoelectrons produced by solar EUV irradiation, by secondary superthermal
electrons caused by electron precipitation, or by other processes. The data
produced by alternating code experiments often exhibit clutter due to the
variability of the targets. We have presented a detailed method to declutter
such alternating code data in order to produce a clean measurement of the plasma line
frequency shift. The method has been used for ESR data from the IPY year
2007–2008. Plasma line backscatter was detected in 22.6 % of the data,
with higher detectability in the summer season (peaking at 90 % for
daytime F region).
The advantage of plasma line measurements compared with ion line data is
demonstrated with an event where ionospheric disturbances related to
atmospheric gravity waves are resolved, thanks to the higher time and range
resolution. The drawback of the plasma line technique to measure electron
density in the ionosphere is the incomplete data coverage; however, whenever
plasma line data are available the method is superior, and therefore plasma
line analysis should be conducted in addition to ion line analysis when
possible. A comparison of large statistical sets of plasma density
observations see is a natural application of
this study, but this is outside the scope of this report. We have not focused here
on strongly enhanced plasma lines, related to strong Langmuir turbulence
e.g., which are a topic of separate study.
Data are available through EISCAT
(https://www.eiscat.se/scientist/data/).
The authors declare that they have no conflict of
interest.
Acknowledgements
EISCAT is an international association supported by research organizations in
China (CRIRP), Finland (SA), Japan (NIPR and STEL), Norway (NFR), Sweden
(VR), and the United Kingdom (NERC). The continuous ESR run during the IPY
was supported by Norway (NFR) and the USA (NSF). Hanna Dahlgren is supported by
the Swedish Research Council under grant 350-2012-6591. The topical editor, Steve Milan, thanks Asti Bhatt and
two anonymous referees for help in evaluating this paper.
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