ANGEOAnnales GeophysicaeANGEOAnn. Geophys.1432-0576Copernicus PublicationsGöttingen, Germany10.5194/angeo-36-1657-2018Validation of Clyde River SuperDARN radar velocity measurements with the
RISR-C incoherent scatter radarValidation of Clyde River SuperDARN radar velocity measurementsKoustovAlexandersasha.koustov@usask.cahttps://orcid.org/0000-0002-3604-4991GilliesRobertBankolePeterDepartment of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan, CanadaDepartment of Physics and Astronomy, University of Calgary, Calgary, Alberta, CanadaAlexander Koustov (sasha.koustov@usask.ca)14December2018366165716666September201814September20185December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://angeo.copernicus.org/articles/36/1657/2018/angeo-36-1657-2018.htmlThe full text article is available as a PDF file from https://angeo.copernicus.org/articles/36/1657/2018/angeo-36-1657-2018.pdf
The study considers simultaneous plasma velocity measurements in
the eastward direction carried out by the Clyde River (CLY) Super Dual
Auroral Radar Network (SuperDARN) high-frequency (HF) radar and Resolute Bay
(RB) incoherent scatter radar – Canada (RISR-C). The HF velocities are found
to be in reasonable agreement with RISR velocities up to magnitudes of
700–800 m s-1 while, for faster flows, the HF velocity magnitudes are
noticeably smaller. The eastward plasma flow component inferred from
SuperDARN convection maps (constructed for the area of joint measurements
with consideration of velocity data from all the radars of the network) shows
the effect of smaller HF velocities more notably. We show that the
differences in eastward velocities between the two instruments can be
significant and prolonged for observations of strongly sheared plasma flows.
Introduction
The Super Dual Auroral Radar Network (SuperDARN) high-frequency (HF) radars
have been installed to continuously monitor the E×B
plasma drift in the Earth's ionosphere (Greenwald et al., 1995). To achieve
this goal, the radars detect coherent ionospheric echoes from the F region
and measure their Doppler velocity. It is assumed that the decameter
ionospheric irregularities, responsible for SuperDARN echoes, move with the
velocity close to the E×B plasma drift. A number of
comparisons of SuperDARN velocity measurements with concurrently operating
incoherent scatter radars (ISRs) that measure the E×B
plasma drift has been performed in the past (Ruohoniemi et al., 1987; Davies
et al., 1999, 2000; Milan et al., 1999; Xu et al., 2001; Gillies et al.,
2009, 2010, 2018; Bahcivan et al., 2013; Koustov et al., 2016). These
comparisons, overall, supported the above major assumption of the SuperDARN
measurements. However, occasional significant differences between HF radar
line-of-sight (LOS) velocities and E×B drift component
along the beam have been noticed. Initially, these were thought to originate
from differences in the echo collecting areas and in the signal integration
time (Davies et al., 1999) but the body of the data published so far
questions this notion. It is now accepted that the HF velocities of the F
region echoes are generally smaller (Gillies et al., 2018). One factor found
to lead to this result is an assumption made during SuperDARN velocity
measurements, which sets the index of refraction for the ionosphere of unity.
However, this explanation cannot account for large differences of more than
20 %–30 %. Koustov et al. (2016) stressed the original finding by Xu
et al. (2001) that the HF velocity magnitudes are substantially smaller (up
to a factor of 2) than the E×B drift component for
high-speed flows exceeding 1000 m s-1. Furthermore, the HF velocity
magnitudes are often larger than the E×B flow component
along the radar beam (e.g., Ruohoniemi et al., 1987; Koustov et al., 2016;
Gillies et al., 2018). Such observations have been interpreted in terms of
lateral deviation of HF radar beams (Koustov et al., 2016; Gillies et al.,
2018). Other SuperDARN–ISR velocity inconsistencies have been associated
with the occurrence of E region echoes at traditionally expected F region
ranges for SuperDARN (Bahcivan et al., 2013; Gillies et al., 2018).
Despite obvious progress in measurement interpretation, HF-based
E×B measurements require further investigation if one wants
to continue improving the quality of the convection mapping with HF radars.
In addition, although all SuperDARN radars work on the same principle and
often even have identical hardware, validation work for every unit is
necessary to be confident in the reliability and consistency of measurements
across the network.
In this study, we undertake a validation study for the Clyde River (CLY)
SuperDARN radar. In a broader context, this effort complements the previous
validation work for the Rankin Inlet (RKN) and Inuvik (INV) SuperDARN radars
by Koustov et al. (2009), Mori et al. (2012), Bahcivan et al. (2013),
Koustov et al. (2016), and Gillies et al. (2018). Since the CLY radar
currently provides a significant contribution to the global-scale convection
mapping with SuperDARN such work is of particular importance. We take
advantage of the availability of E×B plasma drift
measurements made by the recently installed Resolute Bay (RB) incoherent
scatter radar – Canada (RISR-C) (e.g., Gillies et al., 2016). In the present
work, we compare CLY- and ISR-based velocities in a different way than the
previous studies.
Traditionally, gate-by-gate comparison of data from two radar systems that
make measurements in roughly the same directions is performed (e.g., Gillies
et al., 2018). Such an approach cannot be implemented for the CLY–RISR-C
geometry because none of these radar's beams are close enough in terms of
their direction (see map on Fig. 1 in Gillies et al., 2018). For this
reason, we consider RISR-C two-dimensional vectors in a certain area (which
are inferred by merging data from multiple individual beams using the
approach by Heinselman and Nicolls, 2008) and compare them with CLY data
averaged over three beams and four gates. Thus, we assess the data in a statistical
sense, in terms of the average and median velocities over a large spatial
domain.
A validation using highly averaged data is appropriate since the SuperDARN
global-scale maps of plasma flow obtained with the Potential Fit technique
(Ruohoniemi and Baker, 1998; Shepherd and Ruohoniemi, 2000) are built using
median-filtered line-of-sight (LOS) velocities (the so-called gridded velocities). These are
inferred from up to 27 LOS velocity values in bins consisting of data in
neighboring range gates (one smaller number gate and one larger number gate) and
radar beams (one smaller number beam and one larger number beam) and for
three consecutive radar scans. This implies that the input to the Potential
Fit procedure is a highly smoothed HF velocity covering 3–6 min of raw data
and a significant space domain. In this view, there is a sense in
considering 2-D RISR-C data and comparing them with HF velocity medians, or
vectors from the convection maps, over large spatial areas of overlap.
Although our aim is to validate the CLY velocity measurements, there is
additional value from the CLY–RISR comparison. The RISR method of velocity
vector estimations also has some limitations (Heinselman and Nicolls, 2008)
that need testing. A couple of the limitations we will consider are a lack
of velocity measurements along magnetic field lines and the expectation of
spatially quasi-uniformity of flows, which is not always satisfied. Thus,
our work can be considered as a mutual validation of both radars'
performance. Compatibility of the vector estimates by RISR and SuperDARN is
expected, but the degree of this agreement is not yet known.
Geometry of RISR-C and Clyde River radar observations
Figure 1 shows the fields of view (FoVs) of the CLY and RKN SuperDARN radars
starting from range gate 5 and the location of the RB incoherent scatter
radar RISR-C, which we will simply refer to as the RISR radar hereafter.
This radar makes measurements in multiple beams; it uses 11 beams in the
so-called “world-day” mode and 51 beams in the so-called “imaging” mode.
Measured line-of-sight velocities in all the beams and at all ranges are
used to infer 2-D vectors of the E×B plasma flow according
to the procedure outlined by Heinselman and Nicolls (2008). The resultant
vectors are reported with 0.25∘ bin size of magnetic/geographic
latitude. The points to which the measurements are assigned are shown in
Fig. 1 for the height of 300 km. The actual centerline for the points of
data merging depends on data availability in specific beams (Gillies et al.,
2018).
Field of view of the SuperDARN radar at CLY. The black straight
lines are the orientation of specific beams (4–6) that
were considered in this study. Shaded areas represent areas of HF radar data averaging. RB is the
location of the RISR-C ISR. The radar reports the E×B
vector with a bin size of 0.25∘ of geographic latitude for points
shown as black circles stretching roughly along the magnetic meridian
crossing the RB zenith. The blue-colored circles are those locations whose
data were used for comparison with the CLY measurements. The solid red arcs
are the magnetic latitudes of 75, 80, and 85∘.
Figure 1 also shows the orientation of the CLY beams 4, 5, 6 (along their
centers), and the area from which data were considered, the shaded rectangle
region flanked by beams 4 and 6 between range gates 18 and 22. The monitored
ionospheric region is centered at geographic latitude of ∼72.5∘. An important feature of this area is that within these range
gates the CLY beams 4–6 are almost parallel to the lines of equal geographic
latitude at the chosen radar range gates, as shown in Fig. 1. This means
that one can directly compare CLY LOS velocities with the eastward component
(in geographic coordinates) of a RISR E×B velocity vector.
We note that the area of CLY observations was also covered by measurements
from the RKN and INV radars (and occasionally by the Saskatoon and Kodiak
SuperDARN radars), so that SuperDARN convection maps were usually well
constrained.
Methodology of the LOS velocity comparison
We consider here an extensive data set comprising of about 1000 h of
RISR measurements made over the entire year of 2016. On the days when the
radar was operational it typically worked for the whole 24 h, albeit
switching, once in a while, its mode of operation, except the world-day mode
which usually covered an entire day. The range resolution of measurements in
both modes is ∼50 km. The data are available for winter and
both equinoxes, with no measurements made in the summer. We consider 5 min
RISR data because they have much smaller errors than the 1 min data that are
also available.
Our approach to the CLY–RISR velocity comparison is as follows. We first
select a 5 min period of RISR velocity measurements at geographic latitudes
of ∼71.625–73.125∘ (see blue circles in Fig. 1) and compute the median velocity value for RISR. We then compute the
median value of the CLY velocity over matching 5 min interval in three beams
and four gates, as mentioned above. The matched data pair is then entered into
a common data set.
Figure 2 shows the total number of 5 min intervals of joint RISR–CLY radar
measurements, times when RISR and CLY both made measurements in the blue and
shaded regions shown in Fig. 1, as a function of UT. This histogram
distribution does not include individual events when CLY data were obviously
contaminated by ground scatter profoundly affecting the velocity comparison
(Gillies et al., 2018). The ground scatter was identified with the
conventional selection criteria (e.g., Sect. 4.1 in Ponomarenko et al.,
2007). The number of intervals was much larger from noon to dusk (local
solar noon is at about 19:00 UT).This is because of the preferential
occurrence of CLY echoes at ranges of interest during the daytime
(Ghezelbash et al., 2014).
Number of CLY–RISR 5 min intervals of joint observations for all
data considered. Total number of available intervals is shown in the top-left
corner. For the area of observations, local time (scale at the top) roughly
coincides with the magnetic local time.
Results for CLY LOS velocity and RISR comparison
Figure 3a shows the CLY LOS velocity versus the RISR eastward
E×Bcomponent for the entire data set, produced as described
above. The total number of points is close to 4000, which is a significant
number. Overall, both positive and negative velocities are well represented.
Although some spread is present, a significant amount of points are located
close to the line of equality. To assess the plot, we binned the data
according to the RISR measurements by using 100 m s-1bins of
the latter. Binned in this way CLY velocity medians are shown by black–white
dots. The vertical black–white bars crossing each dot are the binned CLY
velocity value ±1 standard deviation. We also binned the data of Fig. 3 according to
bins of the CLY velocity (pink asterisks, shown by thin
symbols in order to not contaminate the plot).
(a) Scatterplot of the CLY LOS velocity versus
E×B eastward velocity component as inferred by RISR.
Total number of points n is shown in the top-left corner. The black–white
dots are medians of the CLY velocity in 100 m s-1 bins of RISR
velocity. The black vertical lines are the standard deviations of the CLY
velocity in each bin. The pink dots are medians of the RISR velocity in
100 m s-1 bins of CLY measurements. (b) The same as
(a) but the eastward flow component inferred from SuperDARN flow
maps was considered.
The black–white dots are reasonably close to the line of the perfect
agreement.The pink asterisks are actually very close to the line of equality.
Good alignment with the line of equality and good correspondence between the
location of the black dots and pink asterisks
indicate that the velocities are
almost linearly related, especially in the range from -500 to
+500 m s-1. One clear departure of the back dots from the line of
equality are the RISR velocities with magnitudes greater than ∼750 m s-1.
An alternative way of assessing the data trends in Fig. 3a is to make a
linear fit to the cloud of points. Parameters of the linear fit are presented
in Table 1 for four ranges of the RISR velocity, ±500, ±750, ±1000, and ±1500 m s-1. The slope is 0.73 for the smallest
velocities of ±500 m s-1, which includes about 71 % of all
the data points. A linear fit to almost all the data has a decreased slope of
0.64.
Parameters of the linear fit line VelocityCLY=a⋅ VelocityRISR+b, the number of
points involved in the fitting, and the squared correlation coefficient for
various ranges of the RISR velocity. Left three columns are the LOS velocity
comparison while right three columns are for the 2-D velocity comparison.
LOS comparison 2-D comparison m s-1ab (m s-1)PointsR2ab (m s-1)PointsR2±5000.73-9.9028150.460.59-16.3222020.45±7500.71-10.2935580.580.56-11.3128510.57±10000.68-8.2738230.60.54-9.9131060.62±15000.64-5.3139320.60.502-7.0832270.61Methodology of vector comparison between SuperDARN and RISR
The approach to the velocity vector comparison between the RISR and
SuperDARN data is as follows. We restrict consideration to the same area of
joint CLY–RISR observations as in the LOS comparison, shown in Fig. 1. Here
the SuperDARN convection vectors are available at geomagnetic latitudes of
80.5–81.5∘ and ∼7∘ of magnetic
longitude. In this area, the convection maps and vectors are mostly based on
RKN, INV, and CLY radar measurements with only occasional contributions from
other SuperDARN radars. We selected the three grid nodes at 81.5∘
magnetic latitude (MLAT) that were closest to the area of the CLY LOS velocity
assessment and the two closest grid nodes at 80.5∘ magnetic
latitude, marked by red crosses in Fig. 1. For each vector location, the
geographic east component of the flow was computed and the median value (out
of potentially five values, although for some periods it was as low as one
measurement) was calculated to represent the eastward plasma flow component
of a 5 min SuperDARN map. This is not a traditional temporal resolution for
the SuperDARN mapping (which is usually 2 min); such data processing has
been done to avoid the need of additional averaging of 2 min SuperDARN maps.
Unfortunately, the start times of RISR measurement intervals were often
irregularly spaced while SuperDARN maps were synchronized to exactly
correspond to 5 min boundaries (i.e., 0–5, 10–15 min, etc.). For the
comparison, only HF and ISR data that were less than 2 min apart were
considered. For this reason, even when both radar systems were operational,
the actual number of joint points per hour was below the expected number of
12.
For RISR, the eastward E×B plasma velocity component was
usually available at all points shown by open circles in Fig. 1. For the
comparison with SuperDARN vectors, only measurements at geographic latitudes
between 71.625 and 73.125∘ (given with a bin size of
0.25∘, blue-colored circles in Fig. 1) were considered, and the
median value of the eastward component was computed. The selection criteria
produced a slightly shorter (but still statistically significant) data set
than was obtained for the LOS velocity comparison. We stress that although
the data for the comparison were along one specific direction, geographic
east, two-dimensional vectors were used in determination of the velocity
component for both systems with the SuperDARN vectors calculated using
measurements from all radars including CLY, RKN, and INV, as well as the
statistical model by Ruohoniemi and Greenwald (1996).
Results for vector comparison between SuperDARN and RISR
Figure 3b plots the eastward component of the plasma flow measured by RISR and
SuperDARN. The spread of the data looks similar to that of Fig. 3a (the LOS
comparison). We assessed Fig. 3b using the same methods as performed on Fig. 3a (see Sect. 4). Overall agreement of the data clearly holds.
Several results from Fig. 3b are consistent with the data of Fig. 3a. First,
the SuperDARN map-based velocities are somewhat smaller than those of RISR.
This is recognizable through an obvious deviation of the distribution maxima
from the line of equality, especially at RISR positive velocities of
> 500 m s-1. Secondly, the tendency for the
SuperDARN velocity being smaller is greater for larger RISR magnitudes. This
feature is seen for both positive and negative RISR velocities. Finally,
consistent with previous reports (Koustov et al., 2016; Gillies et al.,
2018), there is a number of points for which the radars show oppositely
directed flows. This was more frequent for small RISR velocities. Although
Fig. 3 shows good consistency of the data provided by the two radar systems,
the differences can be as large as a factor of 2 in individual measurements.
The agreement between the convection vectors given by RISR and SuperDARN is
expected. We see that the consistency deteriorates once 2-D data are
involved, but mostly at intermediate velocity magnitudes of 300–600 m s-1. The inconsistencies are characterized by slower
SuperDARN velocities. Interestingly, the differences for large velocity
magnitudes in Fig. 3b are comparable to those in Fig. 3a.
To assess the data trends in Fig. 3b in an alternative way, linear fits to the
scatter of points in Fig. 3b were made for four ranges of the RISR velocity
of ±500, ±750, ±1000, and ±1500 m s-1, similar to
those for the LOS velocity comparison. The slope of the fitted line, the
y intercept, the number of points involved in each fitting, and the squared
correlation coefficient are presented in Table 1. The slopes are close to 0.6
for the set of smallest velocities (±500 m s-1), which includes
about 68 % of all the available data. The slope decreases to 0.5 if
almost all the data are considered. We think that the deterioration of the
agreement at intermediate and large velocity magnitudes is due to the broader
area over which the SuperDARN data are averaged for the 2-D comparison. In
this case, there is more chance for SuperDARN to include
ground-scatter-contaminated measurements, giving effectively slower grid velocities to the
fitting procedure.
On possible reasons for velocity disagreements
One reason frequently given for the systematic underestimation of the
SuperDARN velocity measurements is the assumption that the index of
refraction is unity (Gillies et al., 2009; Ponomarenko et al., 2009). We
attempted to evaluate the importance of this effect in our data set. A plot
similar to Fig. 3a was produced, but with the CLY velocity being corrected
by considering the electron density (at the F region peak) measured by RISR.
The plot looked very much similar to Fig. 3a. We assessed the plot by
applying the linear fit line to the HF velocity medians in 100 m s-1 bins of RISR velocity, considering the range of almost linear
dependence, between -1000 and +1000 m s-1of RISR velocities.
The slope of the best fit line improved to ∼0.75 (from
∼0.65). This improvement is consistent with the previous
studies though it does not entirely account for the differences between the
radar measurements.
We also investigated the diurnal variation of the velocity ratio
R= VelHF/ VelRISR as done previously by
Gillies et al. (2018) to explore possible influences of the refractive index
on velocity using typical local time variations in the electron density as a
proxy for refractive index. For the winter and equinoctial ionosphere over
Resolute Bay, the largest densities are systematically observed near local
solar noon and during the afternoon hours (18:00–22:00 UT) (e.g.,
Ghezelbash et al., 2014; Themens et al., 2017). It is therefore expected that
the velocity ratio R would be smallest during these times, as reported by
Gillies et al. (2018) for the RKN radar. The nighttime results by Gillies et
al. (2018) are more confusing. First, strangely, the ratios here were often
above 1 at latitudes southward of RB and systematically below 1 (but not as
far below unity as they were near noon) at latitudes poleward of RB. Gillies
et al. (2018) indicated that the vertical plasma flow velocities in RISR
measurements were, very likely, incorrectly estimated for nighttime
observations. Since the observation area in our comparison is close to RB, we
expect that this effect will also affect the RISR–CLY comparison.
Figure 4 plots the hourly median ratio R as a function of time for our
CLY–RISR data set. One can see that R varies significantly. It is lower
during daytime (noon is at about 19:00 UT) than during dawn/prenoon (12:00–18:00 UT), but its values are smallest during
nighttime (midnight is at about 07:00 UT). Interestingly, the average ratio over all UTs is 0.83, which is closer
to 1 than the slopes of the lines in Fig. 3 (Table 1). This is probably
because the infrequent high-velocity data are averaged out by dominating
data at low velocities in certain RISR bins of Fig. 4.
We think that the low nighttime R values are caused by overestimation of
true plasma drift in a plane perpendicular to the magnetic field by RISR in
the midnight sector. We note that this is not quite consistent with Gillies
et al. (2018), who interpreted their nighttime data in terms of effectively
decreased RISR LOS velocities. Our data suggest effectively increased RISR
velocity magnitudes. One of the factors affecting the derivation of the
averaged flow pattern at nighttime, for both radar systems, is that the
flows in this sector are often very irregular, even in the polar cap
(Bristow et al., 2016). Under these conditions, the solution is subject to
large uncertainty.
Gillies et al. (2018) believe that large nighttime ratios of RKN to RISR
velocity could be due to errors in HF measurements because the RKN beams can
experience significant lateral deviations so that actual measurements are
performed at smaller flow angles with a larger LOS velocity component. This
explanation cannot be applied to our observations. This is because the CLY
radar observes azimuthally, along the average plasma flow most of the time
(except of short periods at near noon and near midnight when the flows are
predominantly meridional) so that lateral deviations of the CLY beams would
lead to, depending of the orientation of the plasma flow with respect to the
CLY beam, either smaller or larger LOS velocities.
Line plot of the hourly median velocity ratio R versus UT for the
CLY radar. The data set is the same as for Fig. 3a. For the area of
observations, local time (scale at the top) roughly coincides with the
magnetic local time.
We think that the HF–RISR velocity inconsistency can also originate, at
least partially, from the nature of HF signal formation. The effect has been
discussed in general terms by Uspensky et al. (1989) as applied to E region
coherent scatter and by Koustov et al. (2016) for F region coherent
backscatter. The flows in the nighttime ionosphere are very likely to be
more patchy/grainy with occasional occurrence of regions with enhanced flow
magnitude (low electron density) and decreased flow magnitude (high electron
density). We argue that in the case of a patchy ionosphere there is a good
chance that the ratio R would be smaller than in the case of a uniform
ionosphere and homogeneous flow. Flow enhancements and decreases affect both
RISR and HF measurements but in a profoundly different manner. The RISR
radar would average the velocity in patches with enhanced and depleted
electron density together, and it would report what can be classified as the
background flow velocity. In the presence of electron density patches
with enhanced and decreased E×B plasma flows, HF radars
would preferentially detect stronger signals from those areas where the
electron density is enhanced, and the electric field (flow magnitude) is
decreased, so that they would show somewhat smaller velocity than the
background value measured by an incoherent scatter radar.
It is conceivable to have the opposite situation with HF velocities above the
background flow if regions with enhanced density have a stronger local
electric field, as discussed in Uspensky et al. (1989). In this respect,
Koustov et al. (2016) and Gillies et al. (2018) noticed that HF velocities
could be larger than the E×B plasma drift component
measured by ISRs. Such points are occasionally seen in previously published
data (Ruohoniemi et al., 1987; Davies et al., 1999). Our data in Fig. 3 also
show such points but, in general, the data agree fairly well. Although the
work of Koustov et al. (2016) and Gillies et al. (2018) related the larger HF
velocity effect to lateral deviations of the HF radar beams from the expected
directions, it could partially be due to the aforementioned effect of
ionospheric microstructuring.
Potentially, low R values can be related to the occurrence of misidentified
ionospheric scatter because some ionospheric echoes with low velocities can
actually be ground or mixed ionospheric and ground scatter. Gillies et al. (2018) showed that removal of points that could potentially be affected by
ground scatter improves the RKN–RISR velocity agreement significantly. Our
analysis showed that ground scatter is rare during winter and equinox nighttime
for the CLY radar, which is consistent with low nighttime F region densities
(Ghezelbash et al., 2014; Themens et al., 2017). We also have to remind the
reader that presumably obvious events with CLY ground-scatter contamination
have been removed from our consideration in Fig. 3.
Eastward component of the E×B drift as measured
by RISR (diamonds, 5 min resolution data) and matched velocity medians of
CLY observations (blue circles, 5 min medians of original 1 min
measurements in beams and gates overlapping the region of RISR observations)
for the event of 4 March 2016. For the area of observations, local time
(scale at the top) roughly coincides with the magnetic local time.
Investigating our database, we identified one special situation when the
RISR–SuperDARN velocity disagreements were particularly strong. Figure 5
gives an example of CLY–RISR observations on 4 March 2016 where RISR and CLY
velocities differ consistently by several hundred m s-1 over a period
of almost 2 h.
(a) A CLY LOS velocity map at 19:55 UT on 4 March 2016 and
(b) a 5 min convection map calculated from all SuperDARN radar
measurements for the same period of time. The TS2018 statistical model
(Thomas and Shepherd, 2018) of order 8 for the solar wind electric field of 2.1 mV m-1 was used. Contours of
the electric potential are 6 kV apart.
Figure 6a illustrates the typical spatial velocity distribution within the
radar FoV, for one velocity scan during the above event. A sharp change in
the LOS velocity polarity in the poleward and equatorward portions of the
FoV is noticeable. The polarity transition occurs in the central beams 5–7.
Figure 6b gives a global-scale map of plasma flow inferred from all
SuperDARN radar measurements. The flow pattern in Fig. 6b was calculated by
applying the new SuperDARN statistical model by Thomas and Shepherd (2018)
which became available just recently. The map has a number of vectors
originating from the RKN and INV radar measurements as well as those from
CLY measurements. The presence of highly curved flows is evident near noon.
Under these conditions, both SuperDARN and RISR can have difficulties in the
construction of a 2-D vector field.
We comment that the flows seen in Fig. 6 are sunward, roughly along the
magnetic meridian near noon, signifying the occurrence of a reverse
convection cell. This is expected since the IMF Bz was steady at about
+4 nT starting from 18:30 UT all the way until ∼ 22:00 UT for
this event.
Evaluating the extent to which the SuperDARN and RISR vectors are affected by
the shear in the flow is difficult. We can see that the centers (foci) of
the convection cells, according to RISR and SuperDARN, do not coincide in
latitude for many maps in this event. In addition, the agreement between the
RISR and SuperDARN map data improves dramatically when only the lower
latitude SuperDARN map data are considered.
We investigated this further by determining the location of the convection
reversal boundary (CRB) for the reverse convection cell (like that shown in
Fig. 6b by the dashed contour). This is done by considering the standard 2 min SuperDARN maps,
the CLY LOS velocity maps, and by looking at the reversal in
the latitudinal profile of the RISR velocity (these are given for 5 min
intervals). The CRB location based on the SuperDARN maps was determined by
finding the middle latitude between the two neighboring points on a
standard plasma flow map with opposite directions of the flow, toward the
Sun and away from the Sun. The CRB location based on the CLY LOS velocity
maps was determined by plotting the LOS velocity versus beam number and
finding the azimuth and the range of the point at which the LOS velocity is
zero. The CRB location from the RISR measurements was found by plotting the
azimuthal component of the RISR plasma flow versus latitude and finding the
latitude with zero velocity. All the CRB locations were given in terms of
the geomagnetic latitude. The accuracy of the CRB determination in all cases
is on the order of half of a degree of geomagnetic latitude.
Magnetic latitude of the flow reversal location within the dayside
reverse convection cell as inferred from SuperDARN convection maps (crosses),
CLY LOS velocity maps (diamonds), and RISR measurements for the event of 4
March 2016. For the area of observations, local time (scale at the top)
roughly coincides with the magnetic local time.
The resulting data are presented in Fig. 7. The CRB inferred from SuperDARN
maps is located almost 2∘ higher in MLAT than that determined from
both CLY velocity maps and RISR data at the beginning of the event, and the
differences are minimal toward the end of the event. The fact that the CRB
location from CLY velocity maps is closer to that inferred from RISR data
hints that perhaps the SuperDARN fitting procedure is the major factor for
strong differences between the SuperDARN maps and RISR measurements in this
specific event. This is not to say that RISR measurements are exact; they
are very likely also subject to errors under these strongly sheared and
curved flows. One reason could be that the solution for the 2-D velocity
vector field from the original LOS RISR data (Heinselman and Nicolls, 2008)
smooths out the true sharp changes of the flow. Having a wider FoV for the
RISR radar is expected to improve the quality of the flow pattern derivation
under such conditions.
Summary and conclusions
In this study, we attempted to validate the CLY SuperDARN radar velocity
measurements by comparing them with the data collected by the Resolute Bay
incoherent scatter radar.
Because no line-of-sight velocity comparison is possible for the geometry of
joint observations, we adopted here a different approach. Namely, we
considered the eastward component of the E×B flow vector,
as inferred from RISR measurements in multiple beams, and compared it to CLY
velocities from a number of eastward-oriented beams and with the eastward
component of the plasma flow inferred from 2-D SuperDARN maps. The analysis
undertaken allows us to draw several conclusions.
The CLY radar velocities measured in beams 4–6 are statistically comparable
to the E×B component of the plasma drift along these beams
(eastward/azimuthal plasma flows) as measured by the RISR incoherent scatter
radar. This implies that the velocity data provided by the CLY radar to the
SuperDARN database are reliable and suitable for convection mapping
involving all SuperDARN radars. The comparisons performed are an addition to
the previous validation work for the RKN and INV SuperDARN radars.
The slope of the best linear fit line to the CLY velocity variation versus
E×B component (as measured by RISR) applied to the binned
values is on the order of 0.65 if all the available data (removing data with
obvious ground-scatter contamination) in the range ±1000 m s-1are considered.
Correction of HF velocities on the index-of-refraction effect improves the slope to ∼0.75. The slope of
the linear fit line for the corrected data is still below 1, implying that
additional factors affect the relationship. Additionally, diurnal variations
of the ratio of HF velocity to the RISR velocity show their strongest
decrease below 1 during nighttime but not daytime. This implies that the
deterioration of the RISR–SuperDARN velocity agreement at nighttime is caused
not by the index-of-refraction effect but by other factors.
The effect of HF velocity underestimation for the CLY radar becomes
progressively stronger for plasma drifts faster than about ∼750 m s-1.
One factor that may contribute to slower HF velocities, in addition to the
refractive index, is the nature of HF signal collection. HF radars receive
stronger signals from ionospheric regions with enhanced electron density
where the electric field and/or E×B plasma drift can be decreased compared to the background plasma.
In the case of highly sheared plasma flows, such as near dayside reverse
convection cells occurring under strongly dominant IMF Bz>0, the
differences between RISR and SuperDARN velocity vectors can be large.
The reasonable agreement between the velocities of the two systems
quantified as the slopes of the linear fit lines at the level of 0.6–0.8 for
both the LOS and 2-D comparisons implies that the RISR technique of the
E×B derivation from multiple individual radar beams is
usually a reliable method most of the time. The comparison suggests that the
RISR vectors are less reliable in the midnight sector where the flows are
often very irregular, and strong vertical motions occur.
SuperDARN data can be obtained from
https://superdarn.ca (last access: 11 December 2018). RISR-C data are
available at http://data.phys.ucalgary.ca (last access:
11 December 2018).
RG and PB worked on raw data processing and their preliminary analysis. PB
prepared some diagrams. AK did most of the comparison work and wrote the
initial manuscript. All authors participated in the writing, and all
commented on the paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
Continuous funding of SuperDARN radars by National Scientific Agencies of
Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United
Kingdom, and the United States of America is appreciated. The current
research would be impossible without ongoing support from the Canadian
Foundation for Innovation, Canadian Space Agency's Geospace Observatory (GO
Canada) continuation initiative to the University of Saskatchewan radar
group, and NSERC Discovery grant to Alexander Koustov. The University of
Calgary RISR-C radar is funded by the Canada Foundation for Innovation and is
a partnership with the US National Science Foundation and SRI International.
We thank C. Graf for the initial analysis of RISR density data and R. Fiori
for the help in software development. Discussions of various aspects of the
paper with Pavlo Ponomarenko and his help in software development are
appreciated. The authors are indebted to two anonymous reviewers who not only
identified weaknesses in the original manuscript but also made numerous
constructive suggestions and, in addition, tremendously improved the writing
style. Edited by: Steve Milan
Reviewed by: two anonymous referees
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