ANGEOAnnales GeophysicaeANGEOAnn. Geophys.1432-0576Copernicus PublicationsGöttingen, Germany10.5194/angeo-35-777-2017Tracking patchy pulsating aurora through all-sky imagesGronoEricemgrono@ucalgary.cahttps://orcid.org/0000-0001-9574-6267DonovanEricMurphyKyle R.University of Calgary, Calgary, Alberta, CanadaNASA Goddard Space Flight Center, Greenbelt, Maryland, USAEric Grono (emgrono@ucalgary.ca)3July201735477778420December201615March20178May2017This 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/35/777/2017/angeo-35-777-2017.htmlThe full text article is available as a PDF file from https://angeo.copernicus.org/articles/35/777/2017/angeo-35-777-2017.pdf
Pulsating aurora is frequently observed in the evening and morning
sector auroral oval. While the precipitating electrons span a wide range of
energies, there is increasing evidence that the shape of pulsating auroral
patches is controlled by structures in near-equatorial cold plasma; these
patches appear to move with convection, for example. Given the tremendous
and rapidly increasing amount of auroral image data from which the
velocity of these patches can be inferred, it is timely to develop and
implement techniques for the automatic identification of pulsating auroral patch
events in these data and for the automatic determination of the velocity of
individual patches from that data. As a first step towards this, we have
implemented an automatic technique for determining patch velocities from
sequences of images from the Time History of Events and Macroscale Interactions during
Substorms (THEMIS) all-sky imager (ASI) and applied it to many
pulsating aurora events. Here we demonstrate the use of this technique and
present the initial results, including a comparison between ewograms
(east–west keograms) and time series of patch position as determined by the
algorithm. We discuss the implications of this technique for remote sensing
convection in the inner magnetosphere.
Diffuse aurora is the product of the pitch-angle scattering of magnetically
bounce-trapped electrons and protons through wave–particle interactions
. While the most common and widely known aurora is the
auroral arc, the diffuse aurora is perhaps more ubiquitous, and its most
common type is pulsating aurora. Pulsating aurora is characterized by
quasi-periodic variations in intensity and precipitating electrons with
energies on the order of a few keV to several tens of keV. This type of aurora is most commonly seen in the
morning sector auroral oval and persists for 1.5 h on average
but has been observed to last upwards of 15 h
. Pulsating aurora often, but not exclusively, has an
irregular patchy structure, an example of which can be seen in
Fig. . The lifetime and size of patches is known to vary
substantially, ranging from a few seconds to tens of minutes for the former
and 10–200 km across for the latter .
Information about the nature of certain magnetospheric processes is reflected
in the structure and evolution of the aurora they produce. For this reason,
the auroral oval is often thought of as a screen onto which these processes
project their dynamics, offering the opportunity to remotely sense
activity in the magnetosphere through auroral observations, including
magnetospheric convection.
Magnetospheric convection is the dominant mechanism of bulk plasma transport
and circulation within the magnetosphere; it is driven by the coupling
between the solar wind and Earth's magnetosphere . This bulk motion, also referred to simply as
convection, is at its most basic level the result of E×B particle drift in the magnetosphere and
ionosphere. It is often assumed that magnetospheric convection maps to the
ionosphere due to magnetic field lines being treated as equipotentials
under many circumstances. Potential drops parallel to the magnetic field will
violate this assumption and introduce uncertainty into the mapping.
There are multiple ways of observing convection, including ground-based coherent and
incoherent scatter radars as well as spacecraft. Coherent scatter radars such
as the Super Dual Auroral Radar Network (SuperDARN)
measure the backscatter off of irregularities in ionospheric electron density,
the motion of which is directly related to convection in the ionosphere. Meanwhile,
incoherent scatter radars such as the Resolute Bay Incoherent Scatter Radar
(RISR) observe plasma motion through the detection of
Thomson radiation. In situ measurements of ionospheric convection have been
obtained from low-altitude satellites like the Defense Meteorological
Satellite Program (DMSP) and the Fast Auroral Snapshot
Explorer (FAST) . Higher-altitude spacecraft, including
Cluster and the Time History of Events and Macroscale
Interactions during Substorms (THEMIS) satellites ,
have measured magnetospheric convection. However, there are complications
with all of these techniques. SuperDARN benefits from extensive coverage, but
as with all global observing systems, this comes at the expense of spatial
and temporal resolution. Incoherent scatter radars have the opposite issue;
they offer precise measurements but with a limited field of view (FoV).
Satellites provide measurements of convection with high spatio-temporal
resolution along their trajectories, but ultimately they are limited by their
inability to separate variations in space and time. Convection is a
challenging phenomenon to observe and additional techniques would be
valuable.
There is increasing evidence that pulsating auroral patches are controlled by
structures in the near-equatorial cold plasma . Since the
motion of cold plasma is almost entirely determined by E×B drifting, these patches appear to move with ionospheric convection
and could be used to create two-dimensional maps of convection
. To create such maps, it is necessary to track large
numbers of pulsating auroral patches. Past studies have tracked aurora by
hand e.g., but there have been few attempts to apply an
automatic algorithm. developed a method
using optical flow analysis that extracts object motion from sequences of
images by assuming that variations in brightness are solely related to object
motion. For pulsating aurora, this is a significant limitation due to their
prominent pulsations and constantly evolving shape .
This study details a new method of routinely and quantitatively tracking
auroral forms, focusing particularly on pulsating auroral patches. The
technique provides information about persistent structures, including their
geographic location and lifespan, located in sequences of all-sky images.
Dataset
The images of pulsating aurora processed for this paper were captured by the
THEMIS all-sky imager (ASI) array , the
ground-based component of the NASA mission designed
to answer key questions about the aurora and substorms. Currently, the
network consists of 21 ASIs stationed across northern North America, each
capturing panchromatic, or “white light,” images of the aurora on a
256 × 256-pixel CCD with a 3 s cadence. These instruments
have been operating for over 10 years and have amassed tens of millions of
images; on the order of 10 % contain pulsating aurora,
which creates the ideal dataset for this study.
The rough estimate that 10 % of THEMIS images contain pulsating aurora
can be calculated from the results of , which state that up to
60 % of the images from the Gillam ASI contain pulsating aurora. Reducing this
estimate by half to account for morning hours constituting half of the
total operating hours of the camera and by half again to reflect cloud
cover that is present approximately half of the time brings the estimate to
15 %. In addition, more poleward stations see pulsating aurora less frequently, thus
decreasing the average to the order of 10 %.
The patches featured in this paper were imaged by the THEMIS ASIs located at
Fort Smith, Sanikiluaq, and Whitehorse, Canada. Raw THEMIS images were used
as inputs to the algorithm with data numbers cropped to 200 above the minimum
value in the sequence being tracked and a maximum of 9000; these thresholds
were determined through trial and error. The THEMIS ASI images and necessary
programs for replicating this study are provided via
.
A comparison of the output of the wavelet filter (a) to the
original ASI image (b) in which blob locations are identified with
green circles. The axes are in x–y pixel coordinates and the large
purple circle identifies the patch that is the subject of
Fig. a.
Tracking algorithm
The tracking algorithm applied in this study is based on the work of
. Sequences of images are pre-processed through the
application of a wavelet filter which convolves the images with a Gaussian
surface. This operation transforms structures within the bandpass into
Gaussian-like “blobs” and removes noise with a size outside of the
bandpass, thus allowing auroral forms within the FoV of an ASI to be more
easily identified by the tracking algorithm. The
algorithm locates the peak intensity of each blob in x–y pixel
coordinates by identifying the regional maxima which are within the upper 30th
percentile of brightness across the entire image. This location is then
refined to more accurately reflect the geometric centre of the structure by
calculating the offset between the brightest pixel and the
brightness-weighted centroid of neighbouring pixels.
Blobs in successive images are connected to form tracks under the assumption
that they are non-interacting Brownian particles. To reduce the number of
computations, a maximum distance is specified that is the farthest distance a
blob is allowed to travel between frames. When the algorithm is attempting to
identify where a blob moved, each candidate blob in the next image has a
probability associated with it; this probability is the likelihood that a
non-interacting Brownian particle would diffuse from the original location to
the new location in the next image. By selecting the location with the
greatest probability, blobs in successive images are classified as the same
structure. If a blob is not found in the next image – if it became too dim,
for example – the algorithm keeps it in memory for a number of frames before
declaring it to have permanently disappeared. This is an important feature
for tracking pulsating aurora since without it the algorithm would tend to
reidentify a patch as a new structure each time it leaves a dim state.
A number of algorithm parameters can be tuned, including the
feature size that the filter enhances, the number of frames for which a blob can
disappear or pixels it can travel before being identified as a different
structure, and the minimum blob brightness and size. For the purposes
of this study, the wavelet filter was set to enhance structures 11 pixels
in diameter, remove noise on the order of 3 pixels, and perform median
smoothing in a neighbourhood 5 pixels wide. The maximum distance blobs
were permitted to travel between images was 10 pixels, in effect setting an
upper limit to the patch velocities the algorithm could detect. For purely
north–south motion relative to the FoV of the ASI, the limit was approximately
1700 ms-1, while the limit for east–west motion was approximately
3700 ms-1. Blobs were allowed to disappear for no more than five
consecutive images; at the imaging cadence of the ASIs, this corresponds to
18 s, which is longer than the typical pulsation period of pulsating aurora.
Figure illustrates the effect of the filter as well as how
blobs are identified. The minimum peak brightness of blobs was set to four;
this number reflects the brightness of the byte-scaled image, not the raw
ASI data. The minimum blob size is defined by the area under the blob and
referred to as “mass”, the minimum of which was set to 20 000 and is a
reflection of the overall brightness of a feature. This parameter provides a
threshold that helps to remove dim features in the camera which were not
removed by the initial wavelet filtering. A final parameter informs the
algorithm of the approximate diameter of the features of interest; this size
was set to 17 pixels and provides an estimate of the typical size of
pulsating auroral features observed by the cameras.
Smoothed longitudinal patch velocities (top) calculated from the
locations over-plotted onto the bottom figures as white markers. The grey
bars represent an estimated error in the speed, which was calculated by
finding the standard error of the 30 values that went into the smoothed data
point. Ewograms (bottom)
comparing the actual patch motion to the result of the tracking algorithm,
which is represented as white markers.
Results
Included in Fig. are ewograms (east–west keograms) that
demonstrate a difference between well-tracked (Fig. a) and poorly
(Fig. b) tracked pulsating auroral patches. Each column in these
figures was created by taking an ASI image and isolating the horizontal band
of pixels between a patch's lifetime maximum and minimum latitude and
flattening it down into a single row of pixels. The flattening was
accomplished by manually choosing either the maximum or median value of each
column depending on the brightness of the patch relative to its surroundings.
When a patch was near another structure at least as bright as itself, the
median value would often produce a superior ewogram by preventing the other
structure from appearing in the ewogram instead of the desired patch.
Chronologically arranging the columns for each image produces the final
result. The white markers over-plotted onto the images indicate the longitude
at which the tracking algorithm determined the patches to be located. It is
important to note that the tracking algorithm can fail to identify a patch in
an image, particularly when it becomes very dim, but reacquire it later;
these instances are identified by gaps in the over-plotted markers.
The velocities shown in the top plots were calculated from the tracked
auroral patches. To find the distance the patches travelled between any two
frames, they were assumed to be at an altitude of 110 km and travelling at
constant latitudes equal to the midpoint between the patches' true latitudes
at subsequent time points. The time between frames was generally the 3 s resolution of the ASI, but it could be as high as 18 s if the patch was
not identified by the algorithm for a maximum of five frames. Smoothing was
necessary due to the presence of substantial jitter in the calculated patch
locations. The smoothing technique utilized was a 30-data-point-wide
moving boxcar average for which the uncertainties were estimated as the standard
error of the speeds used to calculate the mean.
Moving beyond examples of isolated events, Figs. and
depict examples of multiple distinct pulsating auroral
patches being tracked simultaneously within the FoV of an ASI. The former
includes ewograms of three patches as well as a time series comparing the
longitudinal component of their velocities. The latter figure considers
velocity time series of three separate periods that include six or seven
patches each; instead of ewograms, it includes ASI images containing markers
denoting the locations of the majority of the patches. In both of the top two
rows, a single patch is not marked in the ASI image since the lifetimes of the
patches do not entirely overlap. Error bars are not shown in either of these
two figures to avoid cluttering the images.
Time series (top image) of
longitudinal velocity for three distinct, simultaneous patches that occurred
between approximately 12:30 and 12:50 UT on 15 January 2011 at Fort Smith.
Ewograms of the three patches (bottom three images).
Three time series (right)
of longitudinal velocity for distinct, simultaneously tracked patches. Locations of the tracked patches (left) within the FoV of the ASI at an instant in
time. In the top two rows, one event is missing from the ASI image due to the
patches not being entirely simultaneous.
Discussion
The results produced by this algorithm suggest that it can be well suited to
tracking pulsating aurora. Given the correct parameters, it is capable of
automatically tracking all patches within a sequence of images despite the
pulsations characteristic of this type of aurora. In addition, patch speeds
are low enough and the time resolution of THEMIS is high enough that the
algorithm is generally able to accurately follow blobs between images.
While the algorithm can successfully track patches for extended periods if
they maintain their shape or evolve slowly with time, structures that change
dramatically or rapidly are problematic. Such patches can be recognized from
their ewograms, the creation of which currently requires the patch to be tracked
by hand with accelerations detected by the algorithm but not seen in the
images. Figure b is an example of a patch with a shape that evolved
substantially over the period it was tracked; notice that the white markers
plotted on the ewogram suggest patch motion that is not seen in the ewogram
itself. From the full ASI images for the lifetime of this patch, it was
seen that the shape of the patch often evolved rapidly, affecting the
location of the blobs produced by the wavelet filter. Thus, while the
mismatch between the ewogram and the white markers in Fig. b
suggests that the tracking algorithm did a poor job, the reality is that the
variations in speed arose from the evolving shape. These types of patches are
poorly suited for observing convection.
Conversely, the patch shown in Fig. a demonstrates that the
algorithm is capable of accurately extracting the velocity of a patch from
ASI images. Relative to the patch in Fig. b, that in
Fig. a evolves quite slowly. A challenge to be overcome before
pulsating aurora can be reliably used to observe convection is to separate
slowly from rapidly evolving patches.
Moreover, it is possible for structure shapes to change so rapidly that
they are quickly identified as a new blob; in fact, this is the most frequent
outcome. As seen in Fig. , the most common length of time for a patch
to be tracked was less than 20 frames. However, if the tracks of
these short-lived patches are smooth enough, the data for these events have
value in statistical studies as velocities can still be extracted from a
nominally single patch which the algorithm may separate over time into two
patches. In such a case, more than a single velocity would be calculated for
a patch, but since it continually moves with the background convection speed,
these velocities could still be used to observe ionospheric convection.
Histogram of patch lifetimes measured by the number of frames they
appeared in for the 13:00 UT hour of THEMIS Whitehorse ASI on
28 December 2005. The bins are 20 frames wide.
Regardless of how constant the shape of a patch remains, its pulsations
introduce jitter into the output of the algorithm. Even minor variations in
shape and brightness produce inconsistent results from the wavelet filter,
creating fluctuations in the location of the blob peaks. In well-tracked
patches, smoothing allows for velocities to be extracted which are comparable
to what would be calculated by hand. Complicating matters further is that the
elevation of a patch within the FoV of an ASI influences the quality of the
track produced. This is due to the proportional relationship between the
spatial resolution of the ASIs and the elevation within the images they produce;
pixels at lower elevations capture aurora farther away than what is
observed at zenith. Consequently, patches at lower elevations can exhibit
unusual behaviour in one of two ways. A patch can either experience an
extreme version of the jitter issue or have the opposite problem and not be
observed to move whatsoever; both result from the large distances between
pixel centres. Another issue with the output of the tracking algorithm at low
elevations is that the wavelet filter always creates blobs along the edge of
the FoV. These blobs have a consistent shape and can become the longest-tracked structures output by the algorithm. This issue arises because the
night sky is brighter than the black pixels outside the FoV, and the filter
enhances the edge of the FoV like it does to any of the auroral structures.
This effect can be seen in Fig. . Fortunately, issues related
to elevation can be resolved by restricting the patches studied to elevations
greater than a threshold of approximately 50∘.
It must be mentioned that when multiple patches pass closely to one another or
overlap, it is possible for the algorithm to switch from tracking one patch
to another. Fortunately, this issue does not affect statistical studies or
situations in which the velocity field is of interest. While
these instances can affect a case study of a particular event, adjustments to
certain algorithm parameters may remedy the issue.
Patches coincident in time and nearby in space will not move with identical
velocities, but it is often reasonable to assume that their motion will be
similar. On average, neighbouring plasma blobs will convect at approximately
equal velocities, but smaller-scale variations in plasma populations as well
as electric and magnetic fields produce velocity variations.
Figures and demonstrate that the
algorithm calculates patch velocities consistent with these expectations.
On larger timescales, the velocities exhibit similar behaviour even
though they may differ substantially at any particular moment.
Future work
The accurate tracking of the patches featured in Figs. a,
, and promotes optimism that this
algorithm will be successful if its complications can be mitigated or
altogether avoided. Methods of improving the overall quality of the patch
tracks that may warrant further exploration include aggressively limiting the
results to higher elevations in the FoV of the ASIs, ignoring patches that
were identified in too few frames for the data to be reasonably smoothed, and
adjusting the input parameters of the bandpass filter to produce blobs which
may be less sensitive to subtle changes in patch shape and brightness.
Possibilities that have not yet been investigated include alternate filtering
techniques and excluding patches with shapes that change too quickly based on
the properties of their velocity time series. Instruments with improved
spatial and temporal resolution would improve the tracks produced by this
technique, chiefly by increasing the usefulness of tracks at lower elevations
in the ASI FoV and decreasing the distance a patch is allowed to move between
successive images. However, certain key areas would not benefit from this,
including the jitter introduced by evolving patch structures and dim
patches that can be temporarily lost by the algorithm.
Early results of patch filtration suggest that pulsating aurora can be
separated into at least two distinct categories: one characterized by well-defined structures that follow convection and another more dynamic type
with a
motion that is difficult to ascertain and may be unrelated to structuring in cold
equatorial plasma. This work is an initial step toward the ultimate goal of
the automatic identification and tracking of the first type of pulsating aurora
such that accurate maps of convection can be produced. Increasing the number
of techniques available to observe and create maps of convection is
beneficial to the community because it is a critical aspect of geospace
dynamics which is difficult to observe. The coverage of the THEMIS ASI array
is extensive. Under ideal meteorological and geomagnetic conditions, such
as those described in , it would be possible to produce a map of
convection covering the majority of Canada and Alaska. Pulsating auroral
patches arise from wave–particle interactions with cold plasma located in the
region of the magnetosphere where magnetic field lines transition from
tail-like to dipolar. When the next-generation auroral imaging network
Transition Region Explorer (TREx) is deployed over the next few years,
the combined data will offer an unprecedented view of this part of the
magnetosphere.
The dataset and necessary IDL programs for replicating this analysis are available from .
Further questions can be directed to the corresponding author.
EG designed and analysed the work and wrote the paper. ED is his supervisor and assisted with analysis. KRM provided the initial set of programmes for the work.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was supported by grants from the Natural Science and
Engineering Research Council (NSERC) of Canada, the Canadian Space Agency
(CSA), and Danish Technical University (DTU). Thanks to Emma Spanswick for
assistance with data from NASA's Time History of Events and Macroscale Interactions
during Substorms (THEMIS) all-sky imager (ASI). Thanks to Stephen Mende
for the provision of THEMIS ASI data.
The topical editor, Yoshizumi Miyoshi, thanks two anonymous referees for help in evaluating this paper.
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