AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-1461-2016A fast SWIR imager for observations of transient features in OH airglowHannawaldPatrickpatrick.hannawald@physik.uni-augsburg.deSchmidtCarstenhttps://orcid.org/0000-0002-9580-724XWüstSabinehttps://orcid.org/0000-0002-0359-4946BittnerMichaelInstitute of Physics – University of Augsburg, Augsburg, GermanyGerman Remote Sensing Data Center – German Aerospace Center, Oberpfaffenhofen, GermanyPatrick Hannawald (patrick.hannawald@physik.uni-augsburg.de)4April201694146114727December201514January201611March201616March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/1461/2016/amt-9-1461-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/1461/2016/amt-9-1461-2016.pdf
Since December 2013 the new imaging system FAIM (Fast Airglow IMager) for the
study of smaller-scale features (both in space and time) is in routine
operation at the NDMC (Network for the Detection of Mesospheric Change)
station at DLR (German Aerospace Center) in Oberpfaffenhofen (48.1∘ N, 11.3∘ E).
Covering the brightest OH vibrational bands between 1 and 1.7 µm, this imaging system can
acquire two frames per second. The field of view is approximately 55 km
times 60 km at the mesopause heights. A mean spatial resolution of 200 m at
a zenith angle of 45∘ and up to 120 m for zenith conditions are
achieved. The observations show a large variety of atmospheric waves.
This paper introduces the instrument and compares the FAIM data with
spectrally resolved GRIPS (GRound-based Infrared P-branch Spectrometer) data.
In addition, a case study of a breaking gravity wave event, which we assume
to be associated with Kelvin–Helmholtz instabilities, is discussed.
Introduction
The OH airglow layer is located at a height of about 87 km with a half-width
of approximately 4 km (e.g. ). It results from different
chemical reactions leading to the emission of many vibrational–rotational
lines in the visual and infrared optical range (for further details see e.g.
; and ). Observing the
infrared emissions of the vibrational–rotational excited OH molecules offers
a unique possibility for studying atmospheric dynamics. Atmospheric gravity
waves are especially prominent features in the measurements.
Due to density decreasing exponentially with altitude, the wave amplitude of
upward propagating waves increases as long as the wave is not dissipating
energy. Therefore, small wave amplitudes in the tropopause can reach high
amplitudes in the mesosphere and lower thermosphere region (MLT). It is
widely accepted that propagating atmospheric gravity waves are important for
the understanding of atmospheric dynamics and the energy budget of the
atmosphere, as they provide the majority of the momentum forcing that drives
the circulation in the MLT (). A general overview of
gravity waves and their importance for middle atmospheric dynamics can be
found, besides others, in .
In order to acquire knowledge about gravity waves in this region, only a
limited number of instruments can be used. Radars and especially lidars, for
example, measure changes in temperature and wind with a comparatively high
vertical resolution (see e.g. ; ;
).
Besides these active instruments, passive spectrometers and imagers are
frequently used to observe airglow emissions, which originate in the MLT region
and are modulated by gravity waves. The Network for the Detection of
Mesospheric Change (NDMC), for example, currently lists 57 observing sites,
some of which are equipped with more than just one of these instruments.
Spectrometers used for airglow observations are either space-borne (e.g.
ENVISAT-SCIAMACHY; ; and references
therein) or ground-based (e.g. ; ;
). Airglow spectra are usually used for determining the
atmospheric temperature at the height of the emission layer.
The spectral range of imaging instruments covers a wide range of airglow
emissions – mostly at bandpasses between 500 and 1700 nm, depending on the
sensor material and applied filters. Many of these systems provide a large
field of view (FOV) using fisheye lenses (see e.g. ;
; and ). Others use a smaller aperture to focus on a distinct part of the night sky (e.g.
and ). The temporal resolution typically
varies from one image every few seconds to one every few minutes (see e.g.
or ). Another type of instrument related
to both spectrometers and imagers are the MTM (Mesospheric Temperature
Mapper; ) and the AMTM (Advanced Mesospheric Temperature
Mapper; ), both of which use very narrowband filters
to isolate individual emission lines, allowing the determination of airglow
temperature during later processing.
The imager presented here provides a high temporal and spatial resolution in
order to focus on small-scale and transient phenomena. In Sect. , the instrument and its set-up are described, and
observations are compared to the infrared spectrometer system GRIPS 13,
measuring in parallel direction to the imager at the same site. The data
analysis is explained in Sect. , while the results of a
case study are presented and discussed in Sect. .
Instrumentation and operation
In contrast to the design of most existing airglow imagers, our primary goal is to
acquire images of the airglow layer at the fastest rate possible. Therefore,
the system is designed to observe some of the brightest hydroxyl emission
bands in the short wave infrared between approximately 1400 and 1650 nm,
utilising a sensitive detector as well as optics with high transmission.
The 320 pixel by 256 pixel sensor array of the FAIM 1-Instrument (Fast
Airglow IMager 1), based on InGaAs technology, has a spectral responsivity in
the range from 950 to 1650 nm (model Xeva, manufactured by Xenics
nv). It is equipped with a three-stage thermoelectric cooler and usually
cooled to 235 K to reduce the dark current. Similar detectors are used by
. The standard optics consist of an F# 1.4
Schneider-Kreuznach SWIRON lens with a focal length of 23 mm. In front of
the lens is a mechanical shutter for protecting the sensor from aging
processes due to direct sunlight during the daytime (see sketch in Fig. for the instrument set-up).
Sketch of the instrument and measurement set-up.
See text for further details.
In this set-up, standard exposure times of only 500 ms are used. The images
are stored continuously, with a delay between two consecutive images due to
readout and processing of about 10 ms. This high temporal resolution enables
the study of transient features in the airglow, providing the possibility to
observe phenomena with frequencies significantly higher than the
Brunt–Väisälä frequency, e.g. infrasound (see e.g. )
or turbulence.
The airglow signal is converted into 12-bit greyscale images with values
ranging from 0 to 4095, hereafter denoted as counts or arbitrary units
(a.u.). A series of 100 darkened images with the same exposure time is
acquired in order to determine the noise with the current settings. The
standard deviation of the mean value of each image results in 25 counts.
Before analysis, all images are flat-fielded by being measured in front of
a homogeneous large-area black-body source. This procedure eliminates the
so-called fixed pattern noise as well as the vignetting of the lens.
The set-up of FAIM 1 is shown in Fig. . The instrument is
operated at a zenith angle of 45∘. The lens provides a field of view
(FOV) of 20∘× 24∘ with a barrel distortion of less than
1 %, which is neglected. Due to the fact that the instrument is not looking
into zenith direction, the resulting area observed is trapezium-shaped, with a
size of about 55 km × 60 km at the altitude of the OH emission peak,
at 87 ± 4 km (height according to Baker and Stair, 1988). The
instrument is located at Oberpfaffenhofen, Germany (48.09∘ N,
11.28∘ E) with an azimuth angle of 214 ± 1∘
(direction SSW). The observed area is located above parts of southern Germany
and the Austrian Alpine region (see Fig. c).
Measurement set-up with a zenith angle of
45∘ viewed from the side and from an aerial perspective. The axes show
the definition of the coordinate system used to reference the pixels to
geographical coordinates. The instrument is located at the centre of the
coordinate system, z=87 km is the altitude of the OH airglow layer,
γ is the zenith angle of the instrument and θ is the variable
in the range of γ±FOVvertical, FOV being the
aperture angle of the instrument of 10∘ (vertical) and 12∘
(horizontal).
Mean spatial resolution of the
acquired images as a function of zenith angle (black). The grey dashed line
shows the respective area of the trapezium-shaped FOV with the axis on the
right side of the graph. The standard angle of 45∘ is marked by thin
dashed lines for both curves, showing a mean spatial resolution of 200 m and
a corresponding area of 3400 km2.
(a) shows the flat-field corrected
image, which is distorted due to the geometry of the measurement set-up, with a
zenith angle of 45∘. In (b) this distortion is corrected by Eq. ().
Additionally, the image is mirrored onto the middle axis to
change the ground-based observer's view to a satellite perspective. (c) shows
the position of the FOV within the Alpine region (source: www.opentopomap.org,
October 2015).
The observed trapezium-shaped area of the airglow layer is the projection of
the rectangular-shaped sensor due to the observation geometry. As a result of
this, the images show a distorted view of the airglow layer and have to be
corrected to get an equidistant scale, which is necessary for the following
analyses. Therefore, a transformation is applied to the images to remap the
pixels to the original shape. This transformation, based on some
trigonometrical considerations, is mainly dependent on the zenith angle and
the FOV. The left-hand side of Eq. () gives the geographical
(Cartesian) coordinates depending on the zenith angle θ∈(45∘±19.5∘2) and the azimuth angle ϕ∈(±24.1∘2) for each pixel:
x(y(z,θ),z,ϕ)y(z,θ)z=y2+z2⋅tan(ϕ)z⋅tan(θ)87 km,
with Cartesian coordinates x,y and z; y is parallel to the line of
sight and x is perpendicular to it (see Fig. ). The
airglow layer is assumed to be at constant altitude z=87 km. The origin is
given by the location of the instrument.
After the geographical coordinates for each pixel have been determined, the
area covered by the entire image is calculated. It amounts to approximately
3400 km2 or 60 km × 55 km (height and width of the trapezium).
For the 320×256 pixel array used, this refers to an approximate
resolution of 200 m × 200 m (±10%) pixel-1. The relation
between the zenith angle and the mean spatial resolution is shown in Fig. as a thick solid line. The grey dashed line
shows the size of the observed area. The standard zenith angle of 45∘
is marked by thin dashed lines. In zenith direction, the mean spatial
resolution is 120 m with the current optics.
Keogram for one row and one column of images for
the night of 3 to 4 October 2014. Between 17:30 and 00:40 UTC stars
can be seen clearly, but after 00:40 UTC dense cloud cover appears. Several
wave events can easily be identified at 18:30, 20:00–21:00, 22:00 and 23:30–00:30 UTC. The time interval between 21:05 and 22:30 UTC is further
investigated in Sects. and .
The uncertainty depends on several contributions. First of all the airglow
layer height of 87 km is a statistical mean value and may vary
significantly. According to the variation is ±4 km.
Furthermore, the accuracy of the measurement set-up is limited to ±0.5∘ concerning the zenith angle, and ±0.3∘ and ±0.2∘ concerning the aperture of the lens. This results in an overall
uncertainty of ±400 km2 or about 12 % for the covered area and an
uncertainty of ±24 m pixel-1 respectively. Since the uncertainty is
dominated by the variability of the airglow layer height, these numbers are
taken as a measure of precision, although the observational set-up only limits
the accuracy of the measurements. If just considering the variability of the
airglow layer the uncertainty for the visible area is 300 km2 and
consequently ±18 m pixel-1.
However, for geographically mapping the pixels, each pixel is assigned a
preliminary coordinate based on Eq. (). A new equidistant grid
is then constructed with a scale equal to the mean spatial resolution of
200 m and the preliminary coordinates are transformed to the new grid. This
new grid is 320 by 306 pixels in the described set-up. This
results in some empty rows in the corrected image near the horizon where the
available values are further apart, and more values available for one new
grid point near the zenith where the original values are closer together. In
the former case, the missing values are interpolated by taking the mean value
of up to eight non-empty nearest neighbours. In the latter case, the mean of
all available original values within the new grid point is calculated.
Additionally to this mapping, the image is mirrored onto the y axis to change
the view from a ground-based perspective to a satellite perspective. Figure a shows the raw (flat-field corrected)
image. After the transformation and remapping we obtain the geographically
corrected image (b). According to this, a wave field can easily be referred
to a map (compare Fig. c).
In 2014 the instrument FAIM 1 was operated for 350 nights with the described
set-up. Measurements are taken for solar zenith angles larger than
96∘. For each night, one row and one column of about 1000 images is
taken and plotted versus time. These so-called keograms can easily be used to
obtain information about cloudiness, incident moonlight or high atmospheric
wave activity.
As a typical example, Fig. shows keograms for the night
from 3 to 4 October 2014. Between 17:30 and 00:40 UTC there is a fairly clear sky since stars are visible in the keograms, which is
confirmed by the respective video sequences of this night. From 00:40 UTC
onwards there are high-density clouds, which completely inhibit airglow
observations.
Since FAIM 1 covers a rather broad spectral range from 950 to 1650 nm,
several intercomparisons with co-located GRIPS systems have been performed.
These instruments usually acquire airglow spectra between 1.5
and 1.6 µm, but they can be adjusted to record any other part of
the airglow spectrum between approximately 0.9 and
1.65 µm. Usually, OH(3-1)-P-branch spectra acquired with these
spectrometers are used to derive rotational temperatures with a temporal
resolution of 5 s (GRIPS 13) or 15 s (GRIPS 16) (see
for further details). For the investigation of the FAIM performance both
spectrometers GRIPS 13 and GRIPS 16 were operated parallel to FAIM 1. The
FOV of GRIPS 13 (15∘× 15∘) is comparable in terms of
size to the FOV of FAIM 1, whereas the FOV of GRIPS 16 is significantly
smaller (2∘× 2∘). In order to match the FOV of
GRIPS 13, the two-dimensional greyscale images of FAIM 1 are reduced to
their mean value over the slightly smaller FOV of the spectrometer. On the
other hand, airglow spectra are integrated to yield one intensity value for
each spectrum. Both time series are then averaged to get the same temporal
resolution of 1 min. Since neither instrument was absolutely calibrated
at this time, both time series have been normalised independently to their
individual maximum intensities.
Intensity time series for the night of 3 to
4 October 2014. The black line shows the mean value over all pixels of
FAIM 1 within the FOV of GRIPS 13 and the grey line shows the intensity
measured with GRIPS 13 integrated between 1500 and 1600 nm. Both data
sets are averaged to 1 min mean values to avoid effects of different
sampling rates and normalised to their individual maximums. Top: the entire
time interval from the start of the measurement until 00:00 UTC when clouds
emerged. A correlation of only 0.15 is determined. Bottom: same as above,
but the first 25 min of twilight are avoided. The correlation now
increases to 0.87.
Measurements in the parallel direction of FAIM 1 with
spectral range from 950 to 1650 nm, GRIPS 13 adjusted for measuring
1.27 µm O2(0-0)-transition and GRIPS 16 for the
OH(3-1)-Q-branch-transition at 1.51 µm for the first 3 h of the
night. The case study shows an exponential decay of O2 and an increase of
OH intensity. The FAIM time series (averaged over the FOV of GRIPS 13) shows
a mixture of both behaviours.
Figure shows the intensity time series, again for the
night of October 3 to 4 2014 until midnight, when clouds started to
appear. The upper panel refers to the night from the beginning of the
measurement, whereas the lower panel does not consider the first 25 min
of twilight data. The time series appear to be anti-correlated during this
time. Therefore, the correlation coefficient increases from 0.15 to 0.87 when
avoiding twilight. Investigations of other nights in the same manner (not
shown) reveal even higher correlation coefficients up to 0.99. The
discrepancy at dusk conditions is due to the emission of O2(0-0) at
1.27 µm, which decreases exponentially after sunset (see e.g.
).
Series of consecutive images within the
chosen time interval from 21:05 to 22:30 UTC. The black dashed line
indicates the transverse section through the images used to analyse the
smaller-scale wave (I). The grey dotted line shows a transverse section
approximately in direction of the wave vector for the investigation of a
faint larger wave structure (II) almost perpendicular to the first one. The
whole observed area is about 55 km (central width of the FOV) × 60 km
(height of the FOV).
(a, b) show the temporal evolution of the
transverse sections of wave (I) and wave (II). The lines indicate the
position of the wavefronts. The abscissa corresponds to the position within
the transverse sections shown in Fig. (axis origin
corresponds to the upper part of the transverse sections). Panels (c, d) show the Fourier transforms of each line of (a, b) respectively. The
white areas are not significant on a 95 %-level.
Since the O2(0-0) emission originates from different (variable) heights
compared to OH and exhibits a rather long half-value time of approximately
1 h, the behaviour after sunset is further investigated in order to
estimate its impact on the observations. Hence, parallel measurements with
FAIM 1, GRIPS 13 and GRIPS 16 have been taken, with GRIPS 13 adjusted
to observe the 1.27 µm emission and GRIPS 16 limited to the
integrated OH(3-1)-Q-branch intensities (around 1.51 µm) to also
avoid the weaker O2(0-1) emission at 1.58 µm.
Figure shows the different evolution of these three intensities
normalised to their individual maximums. The start of each time series is
marked with dashed lines. The O2(0-0) intensity at 1.27 µm
(black) shows the expected exponential decay after sunset, which is
investigated in substantial detail by and almost no other
small-scale variation. The OH(3-1) intensity (blue) also shows the expected
behaviour with rising intensities from 18:00 to 19:00 UTC, caused by
increasing ozone concentrations, involved in the formation of excited OH.
However, short period variations clearly dominate after 18:45 UTC. The
evolution of the intensities recorded by FAIM 1 includes both oxygen and
hydroxyl emissions. However the influence of OH – including a wide range of
emissions between 0.9 and 1.65 µm – appears to be
rather strong. After a sharp and short decrease in intensity directly after
sunset, short periodic variations similar to the pure OH emissions recorded
by GRIPS 16 dominate the temporal evolution after 18:20 UTC. Obviously, the
influence of the O2 emission on the observation is of shorter duration
than its half-value time of about 1 h. This result has been validated by
using a longpass filter with 1260 nm cut-on wavelength in the FAIM set-up
(not shown). However, it does not improve the observations significantly;
on the contrary it also excludes large portions of the OH emissions.
Therefore, it is not used in the regular observational set-up any more.
Data analysis
Data from the keograms of the night from 3 to 4 October 2014 shown in
Fig. are chosen for a case study. The time period of
roughly one and a half hours between 21:05 and 22:30 UTC is investigated.
The entire data set used for the analysis consists of 10 000 images in this
time interval. To illustrate the wave structures, a series of images with a
time difference of about 4 min between each of them is shown in Fig. . The series reveals an emerging wave structure (wave
I) appearing and disappearing within about 20 min (21:40 to 22:00 UTC), clearly recognisable in the images (7) to (11). The black dashed line
marks the approximate direction of the wave vector. This line is used as a
transverse section through the images for further analysis.
A second wave (wave II) superimposes on wave (I) with a wave vector almost
exactly perpendicular to wave (I). It is not as easily recognisable in the
images as the first wave, but Fig. shows it without a doubt.
The grey dotted line marks the direction of the wave vector of this wave
(II). The superposition can best be seen in the images (7) to (11). Images
(9) to (11) show the presence of even smaller-scale structures of less than
2 km. Similarly images (1) to (6) show a variety of different waves not
further analysed here.
An intensity gradient is superimposed on each image, causing the upper part
(low zenith angle) of the images to be darker than the lower (high zenith
angle). This is due to the van Rhijn effect: the larger the zenith angle of
the set-up, the longer the line of sight through the airglow layer. This
results in systematically higher recorded intensity. It is corrected with the
formula derived by based on geometrical considerations,
used here in the representation given by :
I(θ)=11-RR+h2⋅sin2θ,
with zenith angle θ∈(45∘±10∘), earth radius R
(6371 km) and an airglow layer height h of 87 km. The correction factor
for each row of the original rectangular image is the following:
Irow, new(θ)=Irow, old⋅I(θ)I(γ),
where Irow, new is the corrected (new) intensity of the
original signal (Irow, old) recorded by one row of
pixels and γ is the zenith angle of the instrument's optical axis of
45 ∘ (see Fig. ).
Further preprocessing includes removing the stars in the images to avoid a
potential influence on later spectral analyses. They are identified by
applying a gaussian blur on the image and subtracting this modified image
from the original one. The gaussian blur is very sensitive for small
structures with a strong intensity gradient – as is the case with stars. All
pixels above a predefined threshold will be treated as star pixels and
further investigated to identify the radius of each star. For each star, all
pixels lying within this radius around the pixel with the highest intensity
are identified as star pixels and removed from the original image. Finally,
the missing values for these pixels are interpolated over nearest neighbours
of non-star pixels. The method works very well for bright stars. Some stars
with an intensity only slightly above the background (airglow) intensity,
resulting in an intensity below the threshold level, remain in the images,
but do not have a large impact on the result of subsequent spectral analyses.
After these corrections the above-mentioned wave events are analysed by
extracting data along the transverse sections shown in Fig. , obtaining 10 000 such sections for each wave. This is
actually similar to taking a keogram for each propagation direction with a
high temporal resolution.
Figure a and b depict the contour plots, with
ordinates indicating the time in UTC and abscissas indicating the position
along the transverse sections in kilometres (starting at the top part of the
sections, compare Fig. ). In order to determine the
wavelengths from Fig. a and b, some guiding lines are drawn
along the apparent wavefronts. The distance between two of these lines in
direction of the abscissa yields the wavelength and the ordinate distance
yields the period of the wave structures. Uncertainties of ±2 km for
the wavelengths and of ±100 s for the period are derived due to
ambiguities of the positions of the guiding lines.
The wave parameters are also derived in Fourier space.
Therefore, after removing linear trends and multiplication with a Hann
window, the fast Fourier transformation (FFT) is applied for each time step
shown in Fig. a and b.
The significance is derived by comparing 100 data sets with random numbers
with the same mean and standard deviation as the original one, for each line.
The insignificant values are indicated by white areas in the spectrograms
Fig. c and d. It should be mentioned that, especially for
larger wavelengths, the uncertainty retrieved from the FFT will be higher,
because of the discrete sampling bins and the calculation of the wavelength
from the wavenumbers.
Results and discussion
During the night from 3 to 4 October 2014, two prominent wave events could be
identified (see Fig. ). For the smaller wave (I) a
horizontal wavelength of about 6.8 km is determined on the basis of the
wavenumber in the Fourier domain during the time interval from 21:36 to
21:54 UTC (see Fig. c). Its respective uncertainty amounts to ±0.4 km.
Afterwards, from 21:54 to 21:56 UTC, the wavelength appears to shift to
about 9.0 km (uncertainty range: 8.4 to 9.9 km) before it fades. If
considering the transverse sections of wave (I) instead (Fig. a), the wavelength can be determined to 7.0 ± 2.0 km (with support
of the guiding lines); the change in wavelengths, however, appears to be
insignificant.
The main horizontal wavelength of wave (II), determined in the frequency
domain, is 24.0 km (uncertainty range: 20 km to 36 km, see Fig. d). Since the resolution for small wavenumbers is rather
imprecise in Fourier space, its wavelength can be determined more precisely
in position space (Fig. b), yielding in 20.2 ± 2.0 km. Apparently, the uncertainty of the wavelength is smaller for wave
(I) when analysed in the frequency domain, but it is the opposite for wave
(II). The values are summarised in Table .
Summary of the determined wave parameters identified in Fig.
. Wave structure (I) is separated into (I.1), lasting until
21:54 UTC and (I.2), emerging at 21:54 UTC. λ is the horizontal
wavelength once determined in position space λ1 (see
Fig. a and b), once in frequency domain
λ2 (Fig. c and d). Δt is the lifetime determined in the
frequency domain (Fig. c and d), T is the wave period (determined from Fig. a
and b) and v the horizontal phase velocity referring to
λ1 and λ2.
Upper panel: 1 min running averages of the
time series of GRIPS 13 measuring in parallel direction with FAIM 1. A
smooth spline is drawn as a thick line to guide the eye and show a long-period
wave structure which dissolves at the end of the shown interval. Bottom
panel: Fourier transform of the temperature time series. Periods of 2500,
1000, 510 and 390 s are visible in the range of higher periods. The dashed
line gives the 0.95 significance. See text for further details.
While wave (I) can be observed for about 20 min, wave (II) is apparent in
the Fourier spectrum (Fig. d) for about 35 min from 21:40 to 22:15 UTC. The Fourier amplitude maximises at 21:51 UTC, just when
wave (I) is starting to diminish.
The period of wave (I) amounts to 1400 ± 100 s determined by the
ordinate distance of the guiding lines in Fig. a. Wave (II) has a
period of 585 ± 100 s (see Fig. b).
Thus, the phase velocity can be calculated according to the following equation:
vphase, horizontal=λT±ΔλT+λT2⋅ΔT,
with the period T±ΔT and the horizontal wavelength λ±Δλ. Δλ is either the uncertainty of 2.0 km in
position space (referring to λ1) or half the size of the specific
uncertainty range (referring to λ2). As discussed before, the
analysis in frequency domain is more suitable for wave (I), whereas for wave (II)
the analysis in position space exhibits lower uncertainty. Considering
this, wave (I) propagates with a phase velocity of 4.9 ± 0.6 m s-1 and wave (II) with
34.5 ± 9.3 m s-1.
We speculate that the small wavelength, short lifetime and perpendicular
direction of propagation may indicate a ripple structure as defined by
and . They are distinguished from
larger-scale structures termed as bands. Ripples are strongly related to
Kelvin–Helmholtz instabilities (KHIs) and convective instabilities
(). Images (9) to (11) of Fig. show
further small-scale features, which we assume to be KHI billows.
show structures based on model calculations of the OH
airglow response to KHIs looking very similar to these. and
present similar phenomena in their measurements. The latter
provide a detailed analysis combining the measurements of different
instruments.
In order to investigate potential influences of these small-scale waves on
mesopause temperatures, the upper panel of Fig. shows the
variation of rotational temperatures observed with GRIPS 13 at the same
time, shown as 1 min running mean values and an original resolution of 5 s.
During this time span the instrument measured in parallel direction with
FAIM 1. The mean temperature in the observed time range is 205.7 K. The
thick smooth line represents a spline of the time series revealing the
underlying long-period structure (filtering periods lower than about 1000 s)
and is included to guide the eye. Obviously, two wave crests can be seen in
the temperature, dissolving after 22:10 UTC.
The bottom panel of Fig. shows the Fourier transform of the
time series, and the dashed line gives the 0.95 confidence level calculated
for each frequency, based on 10 000 random number time series with same mean
and standard deviation as the original time series. Significant oscillations
are identified at periods of 2500, 1000, 510 and 390 s. The periods
found in the FAIM images and the periods of the GRIPS temperatures are
difficult to match. A reason for that is observational selection, meaning
that, on the one hand, the small wave structure (I) in the images has several
wave crests and troughs within the FOV of GRIPS, which cannot resolve it
properly because its spatial resolution is similar to the size of the whole
FOV of FAIM. On the other hand, the period of 510 s found in the temperature
data of GRIPS lies within the uncertainty range of wave (II) observed in the
FAIM images (585 ± 100 s). This wave structure has a wavelength of
about 20 km and should be resolved by the GRIPS instrument. Other periods
may correspond to larger-scale waves which are not visible in the FAIM images
itself. However, the disappearing temperature oscillation can tentatively be
interpreted as a breakdown of a larger-scale wave into smaller structures,
which are clearly visible in the FAIM images and probably decay into KHIs.
Summary and conclusions
We developed the new airglow imager FAIM 1 based on an InGaAs detector,
sensitive to the bright OH emissions between 900 and 1650 nm. Thus, two
frames per second can be acquired at a spatial resolution of 200 m with
current optics. Important features of the instrument, in particular a noise
level of only 25 counts for a sensor temperature of 235 K, are determined,
while signal levels are typically around 850.
The processing chain, e.g. geographical correction of the images for the
standard set-up with 45∘ zenith angle are presented. The data of
FAIM 1 are compared to GRIPS airglow spectrometric observations
). In comparison with two such GRIPS instruments, one
recording the 1.27 µm O2(0-0) emission and one recording the
bright OH(3-1)-and OH(4-2) emissions, it was shown that O2 dominates the FAIM 1 data only during a rather short period of time after
sunset. It is worth noting that this time period is shorter than the chemical
lifetime of 1 h of the excited O2 state. During clear sky conditions
the broadband FAIM 1 data show a high correlation of up to 0.99 with the
spectrally resolved GRIPS data.
A case study was performed in order to demonstrate the capability of the
instrument to observe smaller-scale gravity wave structures in the OH airglow
layer at about 87 km altitude. During the night from 3 to 4 October 2014
two prominent wave structures were identified and analysed. A smaller
structure with about 7 km horizontal wavelength is probably part of a
dissipation process of a larger one with about 20 km horizontal wavelength.
The small wave has a nearly perpendicular direction of propagation compared
to the larger one and a short lifetime of 20 min. It is therefore
tentatively interpreted as a so-called ripple structure.
Where the superposition of both waves takes place, one can see even smaller
structures in the order of about 2 km, which we assume to be
Kelvin–Helmholtz instability billows (compare ;
).
In the FAIM 1 data of 2014 there are more examples for billow and ripple
phenomena which are not yet analysed in such great detail. It is an open
question whether these phenomena are actually common or the instrument's
set-up and site offer a unique possibility to study them. Further
investigation and statistics from more nights and at other sites may help
to answer this question.
Acknowledgements
This work is funded by the Bavarian State Ministry of the Environment and
Consumer Protection by grant number TUS01UFS-67093. The investigated data are
archived at WDC-RSAT (World Data Center for Remote Sensing of the
Atmosphere). The observations are part of NDMC
(http://wdc.dlr.de/ndmc).
Edited by: W. Ward
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