AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-12-457-2019Seasonal and intra-diurnal variability of small-scale gravity waves in OH airglow at two Alpine stationsGravity waves in OH airglow in the Alpine regionHannawaldPatrickpatrick.hannawald@physik.uni-augsburg.deSchmidtCarstenhttps://orcid.org/0000-0002-9580-724XSedlakRenéWüstSabinehttps://orcid.org/0000-0002-0359-4946BittnerMichaelUniversity of Augsburg, Institute of Physics, Augsburg, GermanyGerman Aerospace Center, German Remote Sensing Data Center, Oberpfaffenhofen, GermanyPatrick Hannawald (patrick.hannawald@physik.uni-augsburg.de)25January201912145746920September20184October201812December201818December2018This 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://amt.copernicus.org/articles/12/457/2019/amt-12-457-2019.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/12/457/2019/amt-12-457-2019.pdf
Between December 2013 and August 2017 the instrument FAIM (Fast Airglow
IMager) observed the OH airglow emission at two Alpine stations. A year of
measurements was performed at Oberpfaffenhofen, Germany (48.09∘ N,
11.28∘ E) and 2 years at Sonnblick, Austria (47.05∘ N,
12.96∘ E). Both stations are part of the network for the detection
of mesospheric change (NDMC). The temporal resolution is two frames per
second and the field-of-view is 55 km × 60 km and
75 km × 90 km at the OH layer altitude of 87 km with a spatial
resolution of 200 and 280 m per pixel, respectively. This resulted in two
dense data sets allowing precise derivation of horizontal gravity wave
parameters. The analysis is based on a two-dimensional fast Fourier transform
with fully automatic peak extraction. By combining the information of
consecutive images, time-dependent parameters such as the horizontal phase
speed are extracted. The instrument is mainly sensitive to high-frequency
small- and medium-scale gravity waves. A clear seasonal dependency concerning
the meridional propagation direction is found for these waves in summer in
the direction to the summer pole. The zonal direction of propagation is
eastwards in summer and westwards in winter. Investigations of the data set
revealed an intra-diurnal variability, which may be related to tides. The
observed horizontal phase speed and the number of wave events per
observation hour are higher in summer than in winter.
Introduction
Hydroxyl (OH) airglow, originally investigated by
, can be used as a tracer for atmospheric dynamics in the
middle atmosphere, especially for the investigation of gravity waves
( and many more). The OH airglow
layer is located at about 87 km altitude and has a half-width (full width at
half maximum) of roughly 8 km . Newer studies, for example from
or , show that the altitude change can
be up to a few kilometres, also the shape of the distribution with height may
vary. Many OH bands contribute to the overall intensity in the visible and
short-wave infrared range (see e.g. ). However, the
intensity in the short-wave infrared is much higher than in the visible
range. Therefore, exposure times of instruments observing the OH airglow can
be much lower when addressing the short-wave infrared emissions (mainly
OH(3-1) and OH(4-2)). Thus, the temporal resolution of the FAIM data is
comparatively high, up to two frames per second
.
Changes in pressure and temperature lead to intensity fluctuations in the OH
layer. These perturbations can be measured and are most often caused by
atmospheric gravity waves or other atmospheric wave types. Gravity wave
parameters such as horizontal wavelength and observed phase speed can be
derived from images of the OH airglow layer (see
and others).
It is well known that gravity waves influence circulation on a global
scale (see for an overview). The residual meridional
circulation in the mesosphere is driven by breaking gravity waves
. In this context small-scale and short-period
gravity waves are of particular interest .
Gravity waves are often generated in the troposphere, when large horizontal
flows of air masses encounter obstacles and get displaced vertically.
Mountain ridges or coastlines can serve as such an obstacle. In Europe, the
Alps significantly influence large scale flows in the troposphere. Dependent
on the vertical wind structure, some of these waves can travel upward up to
the mesosphere. In order to investigate the characteristics of gravity waves,
a short-wave infrared imager has been deployed at two Alpine stations
(belonging to the network for the detection of mesospheric change;
https://ndmc.dlr.de, last access: 10 January 2019)
that observed the OH airglow with high spatio-temporal resolution for over
3 years. This gives a rather large and dense data set of OH airglow images
which is investigated for the first time in this study. The focus of the
investigation is on small-scale gravity waves with horizontal wavelengths
smaller than 50 km.
In Sect. , the instrument, the data basis, and data
preprocessing is briefly described. Section explains the
analysis in detail, including the derivation of the unambiguous propagation
direction, phase speed, and period of the waves. The results are presented in
Sect. and discussed in Sect. .
Instrumentation and data
The instrument FAIM 1 (Fast Airglow IMager) is based on a cooled
InGaAs-photodiode array (256 pixel × 320 pixel) with a spectral
sensitivity from about 0.9 to 1.65 µm. Due to the spectral
intensity distribution of the OH airglow emission (e.g.
) the emissions of mainly OH(3-1) and OH(4-2)
contribute to the observed intensity (see e.g. for
further information). Therefore, the possible acquisition time for the images
is comparatively low allowing for a temporal resolution of two frames per
second. Equipped with a narrow angle lens with a field-of-view (FOV) of 19.5
to 24.1∘, the spatial resolution is comparatively high. The observed
area at 87 km is large enough to investigate both small-scale gravity waves
(up to about 50 km horizontal wavelength) as well as instability structures
(down to horizontal scales of 200 m). Further details concerning the
instrument, its FOV, spatial resolution, and comparison to spectrometer data
are given in .
From 17 December 2013 to 26 January 2015, FAIM 1 was located at
Oberpfaffenhofen (OPN), Germany (48.09∘ N, 11.28∘ E) with a
zenith angle of 45∘. The resulting average resolution at the height
of the OH airglow layer is about 200 m pixel-1 and the covered area is
55 km × 60 km. The average resolution is calculated by
AN with the area of the trapezium A and the number of
pixels N. The resolution of the x axis rx (along the top and base side
of the trapezium, respectively) ranges from 142 to 199 m pixel-1. For
the y axis along the zenith angle, the resolution ry ranges from 174 to
348 m pixel-1 calculated by ry=h⋅(arctan(ϕ2)-arctan(ϕ1)), with the airglow layer height h of 87 km and ϕ2
and ϕ1 the elevation angles of two subsequent pixels. The effective
pixel size rx⋅ry therefore ranges from 157 to
263 m pixel-1. Also, see Fig. 3 in for the
change of average resolution when tilting the instrument along the zenith
angle.
From 3 August 2015 to 26 July 2017, the instrument was located at Sonnblick
Observatory (SBO), Austria (47.05∘ N, 12.95∘ E). The zenith
angle of 55∘ for the second configuration is higher and corresponds to
a larger observed area and a smaller average spatial resolution
(70 km × 95 km and 280 m pixel-1, respectively). The
x axis resolution ranges from 164 to 269 m pixel-1 and the y axis
resolution ranges from 234 to 636 m pixel-1, therefore the effective
pixel sizes are 195 to 413 m pixel-1.
The position and size of the FOV within the Alps and at their foothills can
be read from Fig. , where the left FOV corresponds to the site
OPN and the right FOV to SBO. The OPN data are acquired above the mountains
whereas the SBO data are acquired from the northern Alpine foothills.
The two instrument sites in the Alpine region, Oberpfaffenhofen
(OPN, 2013–2015) and Sonnblick Observatory (SBO, 2015–2017). Observed areas
at 87 km altitude from OPN (left trapezium) and SBO (right trapezium) are
drawn in blue. Both stations belong to the NDMC.
For the whole observation period the integration time of the camera was set
to 0.5 s. For several nights in the first quarter of 2014, a long
pass filter was used to limit the spectral sensitivity to
1.3–1.65 µm to exclude contributions from O2(0-0) at
1.27 µm. In our later study , we showed that
the contribution of O2 to the recorded signal is negligible. So, the
filter is not required and was therefore removed during later observations.
However, these modifications do not influence the desired wave parameters.
For SBO the settings stayed unchanged for the whole time period. It should be
noted that the entire analysis focusses on the intensity distribution within
each image and not on seasonal or day-to-day changes of airglow intensity.
The number of acquired images per month is depicted in Fig. .
The black bars show the images with clear sky conditions which are used for
analysis. The stacked grey bars correspond to images not analysed due to
cloud cover or moonlight visible in the FOV. The periods with good weather
conditions were carefully selected by manually checking keograms and, where
necessary, inspecting the image sequences of the respective time frames by
eye.
Overview of the data sets for both stations Oberpfaffenhofen (OPN)
and Sonnblick Observatory (SBO). The coverage is grouped by month. The black
bars show the number of images with good observation conditions which are
used for analysis. The grey bars give the portion of images with bad
conditions, e.g. cloud cover or intense moonlight.
For both stations each month is represented by more than a 100 000
images (Fig. ). The maximum number of images in 1 month is
almost 2 million in December 2016. The 7.2 million images (about 1000 h of
airglow observation) of data for OPN are analysed as well as 14.8 million
images (about 2000 h) for SBO. Overall, 28 % of all data are found to be
good and are analysed.
The preprocessing of the images is explained in the following paragraphs.
First, the images are flat fielded to correct them for fixed pattern noise
and camera artefacts. This includes removing dead pixels with interpolation
over neighbouring pixels. Figure shows the steps of
preprocessing for an exemplary image where (a) corresponds to the flat
fielded image. Additionally, a Gaussian blur is applied to the image with a
kernel size of three to reduce high frequency noise. The kernel (a 3×3 matrix) is calculated with k(x,y)=12πσ2⋅exp-x2+y22σ2; x,y∈(-1,1) with
σ=1 and then normalised, so that the sum of all matrix values equals
1. The pixels of the smoothed image (Ismoothed(x,y)) are
calculated as follows, which is commonly called convolution of an image I
with a kernel k:
Ismoothed(x,y)=∑i=-11∑j=-11I(x+i,y+j)⋅k(i,j).
Preprocessing steps explained for one image 26 May 2016
01:52:41 UTC. For display reasons the contrast is adjusted for each image.
(a) Flat-fielded image with Gaussian blur (kernel size 3) applied,
(b) result of star removal, (c) image unwarped to
equidistant grid and flipped around y axis for satellite's view,
(d) image cropped to square size, (e) middle section of
point-symmetric FFT magnitude spectrum (red line: significant level, blue
dots: identified maxima), (f) superposition of the four
reconstructed plane waves, and (g) the four plain waves side by
side.
Figure 3b shows the result of the star removal. Due to the high spatial
resolution, stars usually cover several pixels (in individual cases up to
50 pixels/star) and are present in every image showing airglow. Therefore,
an optimal star removal requires a sophisticated approach, otherwise too many
pixels will be modified. The star removal algorithm consists of three steps.
Identification of stars based on a threshold value: first a median blur is
applied to image (a). The blurred image is subtracted from image (a). The
stars remain as the high frequency parts of the image. All pixels with values
above a pre-defined start threshold are considered to be stars. This normally
identifies more than one but not all pixels influenced by the star, so
further characterisation is needed.
The characterisation of each star is performed by finding its centre
(denoted as star seed) and radius in order to get all pixels influenced by
the star (denoted as star pixels): the maximum value of each star is searched
and taken as the centre of the star. The radius of the star is determined by
looking at the four main directions from the star seed (top, bottom, left,
right). Starting at the star seed, the distance in each direction is
increased individually for as long as the intensity value is lower than the
former value in the respective direction. The maximum distance of the
directions is taken as the star radius. All pixels in a circle around the
star seed within the star radius are considered as star pixels.
All star pixels are interpolated by its nearest (non-star pixel) neighbours.
To overcome the arbitrary threshold in step 1, steps 1 and 2 are
repeatedly applied on the original image with a slightly increased or
decreased threshold for each iteration. This is done for as long as the
number of overall star pixels is between 2 % and 7 % of all pixels.
The final threshold is then used for the actual star removal.
The starless image (Fig. b) is then unwarped to an equidistant
grid resulting in a trapezium shaped FOV. The unwarping depends on the zenith
angle of 55∘ (45∘ for OPN) following the procedure described
in . Furthermore, the image is flipped around the
vertical axis to get a satellite's view, which can be projected onto a map
(east is on the right side of the image). Figure c shows
the result of this step. The largest possible square-shaped area within the
trapezium is taken from the unwarped image (c) in order to prevent
directional differences due to the shape of the analysed area. The result is
shown in Fig. d.
Both mean intensity and linear trend (i.e. a plane calculated by a
two-dimensional linear regression) are subtracted from Fig. d.
The image is then multiplied with a Kaiser–Bessel window (alpha =4,
) so that the edges fade to zero. The Kaiser–Bessel
window has been chosen for its high side-lobe damping. Additionally,
zero-padding is applied to further improve and resolve the peaks of the
following Fourier analysis more clearly.
Analysis
Due to the huge amount of data, an automatic approach is necessary for the
analysis. The two-dimensional fast Fourier transform (2D-FFT, or even 3D-FFT)
is often used to extract wave parameters from airglow images (e.g.
and others). The focus
of this article is on the automatic analysis of the Fourier spectra, i.e. on
identifying peaks, combining the information of consecutive images, grouping
the results in wave events, and investigating them individually.
The previously described preprocessing steps are performed for each image and
the FFT is calculated subsequently. The resulting two-dimensional complex
spectra are then analysed for the major peaks. Figure e shows
the Fourier magnitude spectrum (Mag=Re2+Im2). The point of origin of kx and ky is at the centre of
the spectrum. In Fig. e just the middle part of the spectrum is
shown as the rest is not significant.
In order to identify the peaks in the Fourier spectrum a significance level
is estimated. Accordingly, for each preprocessed image 100 random matrices
based on the standard deviation of the preprocessed image are created and the
2D-FFT is applied to them in the same manner. For each of the 100 random
spectra the magnitude is calculated and the respective maximum values are
extracted. The 95 % percentile of these maxima is then taken as
significance level. Values lower than the significance level (red contour in
Fig. e) are excluded from further analysis.
Now the peaks in the spectrum have to be identified and distinguished
correctly. Therefore, a local maximum search is accomplished: a sliding
window of 5×5 pixels is shifted over the spectrum. If none of
the neighbouring pixels are higher than the pixel in the centre of the
window, then this pixel is treated as a local maximum. For all local maxima
at position (x′,y′) the amplitude A is determined by
A=Re(x′,y′)2+Im(x′,y′)2.
The FFT reconstructs the image by superposing plane waves. If a wave signal
is not adequately described by a plane wave (e.g. the wave crests and valleys
are curved or show other irregularities) it will be composed of additional
plane wave components. This leads to the fact that in some cases more than 10
different wave components are found in one image. Before further simplifying
these cases, all signals are kept if they have at least 10 % of the
intensity of the signal with the highest amplitude.
For visualization purposes Fig. f shows the reconstruction of
the original image based on the four (point-symmetric) local maxima
identified in the spectrum and their derived parameters (marked as blue dots
in Fig. e). Panel (g) shows the four individual wave signatures
as plane waves separately.
For each of these maxima, the wave parameters horizontal wavelength
λ, angle of propagation α, and phase φ are determined
as follows:
λ=1kx2+ky2,α=arctankykx,andφ=arctan(Im(x′,y′)Re(x′,y′)).
In order to bring together the signals extracted from each image
individually, the wave signatures (waves with identical wavelength and
identical angle of propagation) are grouped into so-called wave events. It is
assumed that a wave (band) or an instability feature with a wave-like
appearance (ripple) will last for more than just a few seconds and should
therefore be detected in several consecutive images, possibly with gaps of a
few images. These groups of identical signatures in a given time interval are
henceforth denoted as wave events. A new signature is attributed to a
previously identified wave event if it occurs within less than 30 s after
the last known signature of the (known) wave event.
Each wave event with more than two occurrences of the respective wave
signature (wave signature found in more than two images) is analysed in order
to derive the overall time of occurrence, phase speed, period, and unambiguous
direction of propagation. Through linear regression the mean phase shift with
time is determined from the phase information contained in the FFT. Phase
jumps are considered in order to get the correct slope of the linear
regression. The reciprocal absolute value of the slope gives the period T
of the wave event. With the phase shift with time φ˙=ΔφΔt and the horizontal wavelength the horizontal
phase speed v can be calculated as follows:
v=λ⋅|φ˙|.
The sign of φ˙ also provides the unambiguous direction of
propagation.
Further statistical values are determined for each wave event such as the
length of the time interval, in which the respective wave is observed,
referred to as presence time of the event, and the number of occurrences
within the presence time, which is an important indicator for the persistence
of the wave event. These parameters are used as indicators to decide whether
an event can be considered as an “important” wave. To be considered any
further, an event has to be present for at least 2 min (240 images) in which
the respective wave signature has to be found at least 100 times. The derived
horizontal phase speed should be larger than 3 m s-1 and the residual
standard error of the linear regression less than 7∘. These values
were empirically derived from extensive testing.
Through this kind of filtering, very small-scale waves or instability
features (smaller than 5 to 10 km horizontal wavelength) are
under-represented as these signals change rapidly and are present for only a
small amount of time. This has to be considered when interpreting the
results. In this study, we focus on the more persistent wave events.
Regarding the FOV, the side length of the analysed regions is 47 km for OPN
and 61 km for SBO. FFT results with larger horizontal wavelengths (e.g. due
to the wave being arranged along the diagonal line of the analysed region)
are excluded from further interpretation.
Results
For investigating the results, the data sets are split into the summer (April to
September) and winter (October to March) seasons. The predominant propagation
direction in summer is similar for both stations (OPN and SBO) and is towards
the north-east (NE) direction (Fig. ). More than 46 % (OPN)
and 55 % (SBO) of the waves propagate in this direction. During winter,
the main propagation direction derived from OPN data is south-west (NE:
15 %, SE: 21 %, SW: 38 %, NW: 26 %). At SBO, the main
propagation direction during winter is north-west and south-west (NE:
16 %, SE: 22 %, SW: 27 %, NW: 36 %).
Directions of gravity wave propagation at Oberpfaffenhofen
(a, 48.09∘ N, 11.28∘ E) and Sonnblick (b,
47.05∘ N, 12.95∘ E). The top (bottom) panels show the data
for the summer (winter) season. The colours refer to the horizontal
wavelengths and are separated into small-scale waves (<15 km),
medium-scale waves (≥2/3 of the FOV: 35 and 46 km, respectively) and larger-scale
waves (up to the side length of the respective FOV; 47 and 62 km,
respectively). The numbers next to the wavelength legend represent the
proportion of the respective bin.
Integrated over summer and winter, about 48 % (OPN) and 26 % (SBO) of
the waves have less than 15 km horizontal wavelength. As mentioned above,
the data filtering process underestimates small-scale waves
(Fig. ). Therefore, these small-scale waves are obviously quite
persistent. Medium-scale waves with wavelengths from 15 km to two thirds of
the respective FOV represent 34 % (OPN) and 55 % (SBO) of the
detected waves. Larger-scale waves up to the side length of the FOV represent
about 20 % of all waves.
For OPN, the amount of small-scale waves tends to be larger in winter with
51 % in comparison to 45 % in summer (within the above mentioned
uncertainties caused due to the filter process). For medium- and large-scale
waves (wavelengths larger than 15 km) the situation reverses (49 %
during winter and 55 % during summer), however, this seasonal difference
is quite low. At SBO, the situation is qualitatively similar. The occurrence
rate is about 31 % during winter and 21 % during summer for the
small-scale waves and 70 % during winter and 79 % during summer for
the medium- and large-scale waves.
The number of wave events normalised to the amount of available airglow
observation-hours by season at OPN shows a density of 6.1 events per hour
during summer and 3.6 events per hour during winter. At SBO, the density is
7.1 events per hour in summer and 4.2 events per hour in winter. Thus, the
density in winter is only 60 % as high as in summer.
In order to investigate the intra-diurnal variation of the direction of
propagation, all wave events are binned according to the time of day of their
occurrence (Fig. ). The directions are grouped into the four
quadrants NE, SE, SW, NW. Panels (a), (b), (e), and (f) show the
distribution of wave events with time. Mainly due to the variation of the
length of night, the maximum of wave events is around midnight (approx. 22:00
to 01:00 UTC). The relative distribution of the different directions
(Fig. c, d, g, h) reveals considerable intra-diurnal
variations. Obviously, the propagation towards the north-east (NE) direction
(red) is dominant with more than circa 40 % of the wave events for almost
all hours (Fig. c and d). In winter, the SW direction (green)
is prevalent at OPN and the NW and SW direction (blue and green) at SBO
(Fig. g and h), as already seen from Fig. . In
general, there is a notable anti-correlation for opposing directions (NE to
SW and NW to SE). However, the correlation coefficients underlie high
uncertainties due to only few available data points and – more importantly –
an unequal distribution of wave events which leads to an overestimation of
the early and late night hours. The propagation direction towards NE at SBO
summer (Fig. d), shows an oscillation-like pattern with a
maximum at 23:00 UTC. A shorter period oscillation may be seen at SBO winter
NE direction (Fig. h) with a maximum at 01:00 UTC and a
minimum at 21:00 UTC. However, the derivation of specific periods is
impossible here, because the length of the night is of the order of the
periods.
Number of gravity wave events and distribution of propagation
directions as a function of time for OPN (a, c, e, g) and
SBO (b, d, f, h). The top panels (a)–(d) refer to the
summer season and the bottom panels (e)–(h) to the winter
season. The data are grouped into north-east (NE), south-east (SE),
south-west (SW) and north-west (NW) quadrants.
The distributions of observed horizontal phase speeds are shown in
Figs. and .
Figure depicts the absolute values. During OPN winter,
the maximum of the distribution is around 9 m s-1 with a secondary
peak at about 25 m s-1. For OPN summer, the distribution is not as
smooth as during winter (with peaks at about 5, 10, 18, 23, 30, 38, and
42 m s-1). The SBO distributions reveal peaks at 7 and 13 m s-1
in winter and 23 and 35 m s-1 in summer. The 95 % quantile of
phase speed is 46 and 52 m s-1 considering both seasons and all
directions, thus just a few of the observed wave events propagate faster than
that.
Absolute values of horizontal phase speed of the gravity wave events
for OPN and SBO for the summer and winter seasons in
m s-1.
Horizontal phase speed of the gravity waves at OPN and SBO for
summer and winter season in m s-1 separated by zonal and meridional
components. Negative phase speed indicates westward or southward direction,
and
positive phase speed eastward or northward direction. The dashed grey line
marks zero phase speed and the red and blue dotted lines refer to the mean
values over the respective summer and winter distribution,
respectively.
Table shows the mean values and standard deviation of the
phase speeds as a function of the direction. For both stations, the mean
values are higher during summer than during winter with an increase of about
7 % (OPN, 22.2 to 20.7 m s-1) and 9 % (SBO, 25.5 to
23.5 m s-1). The observed horizontal phase speeds in the SW direction
during summer and NW direction during winter are significantly lower compared
to the other directions (Table ). Especially in summer, the
phase speeds are higher in eastward direction (NE and SE together) than in
westward direction (SW and NW together). During winter at SBO it is similar,
but for OPN winter, the behaviour is different (NE lower phase speed than
SW).
Mean (standard deviation) values of absolute horizontal phase speed
as a function of the station, season, and direction.
Figure shows the distributions separated into
zonal and meridional phase speeds. The dashed lines give the mean values of
the distributions. For zonal and meridional phase speeds, respectively, and
for both stations, the mean values are higher in summer than in winter. The
mean values of all SBO distributions are higher (more toward positive
numbers) than the respective equivalents of OPN distributions.
Table gives additional information about the
distributions. These are approximately symmetric with skewness values smaller
than 0.37 except for the zonal directions in winter where the distributions
are right-skewed with values 0.61 and 0.95 (the standard error of the
skewness, defined as 6N where N is the
number of wave events, is 0.04 (OPN) and 0.02 (SBO)). The standard deviation
at SBO is higher than for OPN. For the zonal direction in winter this
difference is highest with 28 % increase (20.9 (SBO) to 16.3 (OPN)).
Statistical moments of horizontal phase speed distributions for
station, season, and direction. Negative phase speed indicates westward or
southward direction, positive phase speed eastward or northward direction.
The units of mean, median, standard deviation, and peak are m s-1, the
skewness is unitless. The positions of the peaks are determined on basis of
the splines overlying the histograms in Fig. .
The observed wave periods are shown in Fig. . The 10 %
and 90 % quantiles of the distribution of periods range from about 6 to
50 min (OPN) and from 7 to 70 min (SBO). More than 60 % of the wave
events have periods between 10 and 60 min. Around 20 % of the waves show
periods close to the Brunt–Väisälä period with 5 up to 10 min.
Note, that several events have periods smaller than 5 min, but extensive
investigation of these events is beyond the scope of this study. The
distributions of periods have maxima at about 8 and 10 min at OPN for summer
and winter, respectively. At SBO, the maxima are at 13 and 7 min. The
distributions are highly right-skewed with values 3.3 and 3.1 at OPN in
summer and winter, and values of 3.3 and 2.8 at SBO.
Top: the same as Fig. , but for the observed wave periods.
Bottom: histograms of observed wave period for summer and winter
season.
The main results are summarised in the following list:
The main zonal propagation direction is eastwards during summer and westwards
during winter. The main meridional propagation direction is northwards during
summer. During winter, the meridional propagation direction is southwards at
OPN and northwards as well as southwards at SBO (Fig. ).
We found an intra-diurnal variability of the propagation directions. The
opposing directions seem to be anti-correlated (NE to SW and NW to SE).
Oscillations seem to be present in the data, but cannot be precisely
determined (Fig. ).
The number of wave events per observation-hour, the means of zonal and
meridional phase speeds, and the means of absolute horizontal phase speeds
are higher in summer than in winter (Figs. ,
, and ).
Discussion
In order to understand the results it is essential to know which part of the
gravity wave spectrum is actually observed by our instruments. The most
important constraints on the observations are the OH layer thickness and the
FOV of the instruments. The former is for example discussed in
who argue that the observed vertical wavelengths have to
be larger than the OH layer width. showed in their Fig. 9c
the reduction of the observed amplitude of a wave depending on the vertical
wavelength for different values of the OH layer thickness. The horizontal
wavelength is limited by the FOV (OPN: 47 km, SBO: 61 km).
Figure indicates that most periods are smaller than 1 h.
Therefore, the waves contained in the data set are referred to small
horizontal wavelength, high-frequency gravity waves which are known to be
important for momentum transport (see ) and which are of
major importance for the mesospheric circulation
.
The propagation directions of the gravity waves show a clear pattern of
seasonal dependency. This behaviour for mesospheric gravity waves is not
limited to the two Alpine stations OPN and SBO, but it is well known. For
example and compared several airglow
observations of many research groups around the globe and find a meridional
propagation towards the summer pole for many stations. The zonal component of
the eastward propagation during summer and westward propagation during winter
is also dominant at many stations.
The seasonal variation of the zonal propagation direction can be explained by
zonal stratospheric wind fields when assuming that the observed waves
originate from lower atmospheric layers or are directly influenced by waves
from lower layers, for example by wave–wave interactions. There is a strong
westward wind in summer and eastward wind in winter in the stratosphere
filtering gravity waves which propagate with a lower
speed in the same zonal direction. Thus, mostly eastward propagating gravity
waves will be observed in summer and westward propagating gravity waves in
winter. This is also confirmed for example by and
.
In winter, waves with positive zonal phase speed should consequently be
generated in situ or above the stratospheric jet (e.g. by wind shear),
propagate from above down to the airglow layer, or pass the stratospheric jet
when it is unusually weak. The stratospheric wind filtering could also
explain the skewness of the zonal phase speed distributions, because the
filtering is mainly affecting the slower eastward propagating waves in winter
which are less likely to be observed in the mesosphere. Therefore, there is a
bump in the distribution at these phase speeds.
The seasonal variation of the meridional propagation direction
(Fig. ) for summer season towards the north, for winter towards
the south at OPN and towards the south and the north at SBO could be due to
the meridional circulation in the mesosphere. According to
showing Na lidar data and global circulation model runs, the meridional
circulation reverses in summer and winter (in summer: to the south, in
winter: to the north); additionally, it is much weaker in winter than in
summer (about 10 to 14 m s-1 southwards in summer and 0 to
6 m s-1 northwards in winter strongly depending on the model and the
parameters used; the lidar data at 41∘ N, 105∘ W with tides
removed show higher values of up to 18 m s-1 in summer and up to
14 m s-1 in winter). Therefore, the filtering effect can be regarded
as being stronger during summer than during winter. This could be an
indication of the southward propagating summer waves to be more influenced
and filtered out by the stronger meridional circulation while the winter
waves are less influenced by the weaker meridional circulation and do not
suffer such a strong filtering effect. This would explain the clear pattern
during summer and the more arbitrary meridional propagation during winter,
especially at SBO. However, there are alternative hypotheses concerning the
seasonal dependency of meridional wave propagation. Vargas et al. (2015) lay
out that neither the meridional circulation, which is too weak from their
point of view, nor tides can explain this seasonal behaviour on a global
scale. They suggest an interaction of the seasonally dependent (and strong)
zonal wind with the lower thermosphere duct as described by
.
At Oberpfaffenhofen, horizontal wavelengths and phase speeds were already
derived in 2011 and 2015 (:
February to July 2011; : July to November
2015; our data were acquired at SBO during the second time period). In these
cases, a combination of three spectrometers and one scanning spectrometer,
respectively, were used instead of an imaging system. The spectrometers are
sensitive to larger horizontal wavelengths, which are related to a higher
possible maximum of intrinsic phase speed (compare e.g.
). This could explain our phase speeds which are lower
than in the above mentioned studies.
We find the wave event density in winter to be only 60 % of that in
summer. Results from (data from imager at 40.7∘ N,
104.9∘ W) coincide with our results. An explanation for this
behaviour could be that the typical altitude for gravity wave breaking is
lower in winter than in summer . This would decrease the
number of observed wave events in the airglow layer in winter.
The mean value of horizontal wavelengths in is roughly
35 km, which is larger than presented here with 20 km (OPN) and 28 km (SBO)
and the distribution of periods they determined has a peak around 10 min
and a high right-skewness. The peak at the periods we determined is in the
same range with 7 to 13 min. The observed phase speed of
has a peak at 50 m s-1 and is therefore much higher than in our
observations with 22 m s-1 (OPN) and 24 m s-1 (SBO).
find a major propagation direction to the south in winter
which we could determine just for OPN winter. The differences of the stations
OPN and SBO could be due to geographical or time-conditioned differences. The
former difference could be induced by the underlying orography (the Alps in
our measurements), the latter one due to the change of prevailing wind
structures. It is interesting to note here that the FOV of SBO is located at
the foothills of the Alps (compare Fig. ). One might suggest
here a link to the northward propagating gravity waves in SBO winter which is
not present at the other station OPN within the Alpine region.
Intra-diurnal variation can for example be induced by atmospheric tides which
change the direction and absolute value of the wind vector within the night
and exhibit a period of about 24, 12, and 8 h. However, the 12 h solar tide
shows the largest change, at least in the zonal wind .
showed that the influence of the 12 h solar tide can be up
to 40 m s-1 for mid-latitudes (42∘ N). In order to
investigate the possible influence of such phenomena, the relationship
between time of day and the direction is determined (see
Fig. ). We find an anti-correlation between the opposing
directions (NE and SW, NW and SE). This could be an indication for a tide
filtering out waves propagating in one direction and therefore preferring the
opposing direction. From theory and observations (e.g.
), we know that the diurnal and
semi-diurnal tide are supposed to have the strongest amplitudes, however,
also the terdiurnal tide is prominent. Our data show systematic oscillatory
patterns of the same order of magnitude, so that we suggest mesospheric tides
to be of major influence on the intra-diurnal variability of gravity waves. A
more detailed analysis with highly resolved wind data for the Alpine region
is needed to verify the presence of tides in the data, but it is beyond the
scope of this study.
Summary
We have shown two airglow observation data sets with high
spatio-temporal resolution. The instrument FAIM was located in the Alpine
region first at Oberpfaffenhofen, Germany (OPN, 48.09∘ N,
11.28∘ E) and then at Sonnblick Observatory, Austria (SBO,
47.05∘ N, 12.96∘ E). The preprocessing as well as the
analysis technique based on the two-dimensional fast Fourier transform with
automatic peak extraction and grouping into wave events are explained
extensively. Combining the phase information of consecutive images allows the
derivation of additional parameters related to time, especially horizontal
phase speed and wave period. In general, observing the OH airglow layer with
our imager allows us to characterise the spectrum of high-frequency gravity
waves. The horizontal propagation directions of gravity waves show a clear
seasonal dependency to the NE in summer. In winter, they are to the SW at OPN and to
the SW or NW at SBO. The zonal directions can be well explained by stratospheric
wind filtering while the meridional propagation towards the summer pole (OPN
and SBO summer) is not yet completely understood. We suggest the meridional
circulation itself to be the reason for the meridional preferential direction
which is faster in summer when we observe a stronger filtering than in
winter. We assume the generally lower height of gravity wave breaking in
winter to be the reason that the gravity wave event density in winter is just
60 % of that in summer. Concerning the observed horizontal phase speeds
we find 7 %–9 % higher phase speeds in summer than in winter. The
mean phase speeds are 22 m s-1 at OPN and 24 m s-1 at SBO. Very
few events with absolute phase speeds higher than 50 m s-1 were found.
The intra-diurnal variability is investigated by grouping the gravity waves
according to their occurrence time within the night. We find an
anti-correlation between opposing directions (NE and SW, NW and SE) and see
oscillatory patterns. We assume the reason for it to be the influence of
mesospheric tides on gravity wave generation and propagation.
The investigated data are archived at WDC-RSAT (World Data
Center for Remote Sensing of the Atmosphere, https://wdc.dlr.de/, last
access: 10 January 2019).
The conceptualisation of the project, the funding acquisition
as well as the administration and supervision was done by MB and SW. The
operability of the instrument was assured by RS, CS and PH. Fruitful
discussions between all authors led to significant improvements of the
algorithms and results. The software for the analyses and the visualisation
as well as the original draft was written by PH. All co-authors essentially
contributed to the investigation process and reviewed the draft carefully.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Layered phenomena in
the mesopause region (ACP/AMT inter-journal SI)”. It is a result of the LPMR
workshop 2017 (LPMR-2017), Kühlungsborn, Germany, 18–22 September 2017.
Acknowledgements
This work received funding from the Bavarian State Ministry of the
Environment and Consumer Protection by grant no. TUS01UFS-67093 and grant no.
TKP01KPB-70581. The observations are part of the NDMC (Network for the
Detection of Mesospheric Change; https://ndmc.dlr.de). The image
processing, data analysis, and visualisation were done by using the image
processing library OpenCV and the programming language R
especially using the packages data.table
and ggplot2 . The authors thank the anonymous referees for
their valuable comments and the editor for his efforts.
Edited by: Lars Hoffmann
Reviewed by: two anonymous referees
References
Baker, D. J. and Romick, G. J.: The rayleigh: interpretation of the unit in
terms of column emission rate or apparent radiance expressed in SI units,
Appl. Optics, 15, 1966–1968, 1976.
Baker, D. J. and Stair Jr., A. T.: Rocket Measurements of the Altitude
Distributions of the Hydroxyl Airglow, Phys. Scripta, 37, 611–622, 1988.Becker, E.: Sensitivity of the Upper Mesosphere to the Lorenz Energy Cycle of
the Troposphere, J. Atmos. Sci., 66, 647–666, 10.1175/2008JAS2735.1,
2009.
Bradski, G.: The OpenCV Library, Dr. Dobb's Journal of Software Tools, 2000.
Coble, M. R., Papen, G. C., and Gardner, C. S.: Computing Two-Dimensional
Unambiguous Horizontal Wavenumber Spectra from OH Airglow Images, IEEE
T. Geosci. Remote, 36, 368–382, 1998.Conte, J. F., Chau, J. L., Laskar, F. I., Stober, G., Schmidt, H., and Brown,
P.: Semidiurnal solar tide differences between fall and spring transition
times in the Northern Hemisphere, Ann. Geophys., 36, 999–1008,
10.5194/angeo-36-999-2018, 2018.Dowle, M. and Srinivasan, A.: data.table: Extension of “data.frame”,
r package version 1.11.8, available at:
https://CRAN.R-project.org/package=data.table (last access:
10 January 2019), 2018.Fleming, E. L., Chandra, S., Barnett, J., and Corney, M.: Zonal mean
temperature, pressure, zonal wind and geopotential height as functions of
latitude, Adv. Space Res., 10, 11–59, 10.1016/0273-1177(90)90386-e,
1990.Fritts, D. C. and Alexander, M. J.: Gravity wave dynamics and effects in the
middle atmosphere, Rev. Geophys., 41, 1003, 10.1029/2001RG000106, 2003.Fritts, D. C. and Vincent, R. A.: Mesospheric Momentum Flux Studies at
Adelaide, Australia: Observations and a Gravity Wave–Tidal Interaction
Model, J. Atmos. Sci., 44, 605–619,
https://doi.org/10.1175/15200469(1987)044<0605:MMFSAA>2.0.CO;2, 1987.
Garcia, F. J., Taylor, M. J., and Kelley, M. C.: Two-dimensional spectral
analysis of mesospheric airglow image data, Appl. Optics, 36, 7374–7385,
1997.Garcia, R. R. and Solomon, S.: The effect of breaking gravity waves on the
dynamics and chemical composition of the mesosphere and lower thermosphere,
J. Geophys. Res., 90, 3850, 10.1029/JD090iD02p03850, 1985.
Gardner, C. S. and Taylor, M. J.: Observational limits for lidar, radar and
airglow imager measurements of gravity wave parameters, J. Geophys. Res.,
103, 6427–6437, 1998.Gardner, C. S., Coble, M., Papen, G. C., and Swenson, G. R.: Observations of
the unambiguous 2-dimensional horizontal wave number spectrum of OH intensity
perturbations, Geophys. Res. Lett., 23, 3739–3742, 10.1029/96gl03158,
1996.Hannawald, P., Schmidt, C., Wüst, S., and Bittner, M.: A fast SWIR imager
for observations of transient features in OH airglow, Atmos. Meas. Tech., 9,
1461–1472, 10.5194/amt-9-1461-2016, 2016.
Hecht, J. H., Fricke-Begemann, C., Walterscheid, R. L., and Höffner, J.:
Observations of the breakdown of an atmoshpreic gravity wave near the cold
summer mesopause at 54N, Geophys. Res. Lett., 6, 879–882, 2000.Holton, J. and Alexander, M.: The role of waves in the transport circulation
of the middle atmosphere, in: Atmospheric Science Across the Stratopause,
Geophysical Monograph, American Geophysical Union, 10.1029/GM123p0021, 2000.Holton, J. R.: The Influence of Gravity Wave Breaking on the General
Circulation of the Middle Atmosphere, J. Atmos. Sci., 40, 2497–2507,
10.1175/1520-0469(1983)040<2497:TIOGWB>2.0.CO;2, 1983.Kaehler, A. and Bradski, G.: Learning OpenCV3 – Computer Vision in C++
with the OpenCV Library, O'Reilly Media, Sebastopol, USA, 2017.Kaiser, J. and Schafer, R.: On the Use of the I0-Sinh Window for Spectrum
Analysis, IEEE T. Acoust. Speech, 28, 105–107,
10.1109/TASSP.1980.1163349, 1980.Matsuda, T. S., Nakamura, T., Ejiri, M. K., Tsutsumi, M., and Shiokawa, K.:
New statistical analysis of the horizontal phase velocity distribution of
gravity waves observed by airglow imaging, J. Geophys. Res.-Atmos., 119,
9707–9718, 10.1002/2014jd021543, 2014.McLandress, C.: On the importance of gravity waves in the middle atmosphere
and their parameterization in general circulation models, J. Atmos.
Sol.-Terr. Phy., 60, 1357–1383, 10.1016/S1364-6826(98)00061-3, 1998.Meinel, A. B.: OH Emission band in the spectrum of the night sky I, American
Astronomical Society, 111, p. 555, 10.1086/145296, 1950.Mukherjee, G. K., R, P. S., Parihar, N., Ghodpage, R., and Patil, P. T.:
Studies of the wind filtering effect of gravity waves observed at Allahabad
(25.45∘ N, 81.85∘ E) in India, Earth Planets Space, 62,
309–318, 10.5047/eps.2009.11.008, 2010.Nakamura, T., Higashikawa, A., Tsuda, T., and Matsushita, Y.: Seasonal
variations of gravity wave structures in OH airglow with a CCD imager at
Shigaraki, Earth Planets Space, 51, 897–906, 10.1186/BF03353248, 1999.Oberheide, J., Hagan, M. E., and Roble, R. G.: Tidal signatures and aliasing
in temperature data from slowly precessing satellites, J. Geophys. Res., 108,
1055, 10.1029/2002JA009585, 2003.
Pautet, D. and Moreels, G.: Ground-based satellite-type images of the
upper-atmosphere emissive layer, Appl. Optics, 41, 823–831, 2002.Pautet, P.-D., Taylor, M. J., W. R. Pendleton, J., Zhao, Y., Yuan, T.,
Esplin, R., and McLain, D.: Advanced mesospheric temperature mapper for
high-latitude airglow studies, Appl. Optics, 53, 5934–5943,
10.1364/AO.53.005934, 2014.Peterson, A. W.: Airglow events visible to the naked eye, Appl. Optics, 18,
3390–3393, 10.1364/ao.18.003390, 1979.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.:
Numerical Recipes, 3rd edn.: The Art of Scientific Computing, Cambridge
University Press, New York, NY, USA,
2007.R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
https://www.r-project.org/ (last access:
10 January 2019), 2017.
Rousselot, P., Lidman, C., Cuby, J.-G., Moreels, G., and Monnet, G.:
Night-sky spectral atlas of OH emission lines in the near-infrared, Astron.
Astrophys., 354, 1134–1150, 2000.Sandford, D. J., Muller, H. G., and Mitchell, N. J.: Observations of lunar
tides in the mesosphere and lower thermosphere at Arctic and middle
latitudes, Atmos. Chem. Phys., 6, 4117–4127, 10.5194/acp-6-4117-2006,
2006.Sedlak, R., Hannawald, P., Schmidt, C., Wüst, S., and Bittner, M.:
High-resolution observations of small-scale gravity waves and turbulence
features in the OH airglow layer, Atmos. Meas. Tech., 9, 5955–5963,
10.5194/amt-9-5955-2016, 2016.Silber, I., Price, C., Schmidt, C., Wüst, S., Bittner, M., and Pecora,
E.: First ground-based observations of mesopause temperatures above the
Eastern-Mediterranean Part I: Multi-day oscillations and tides, J. Atmos.
Sol.-Terr. Phy., 155, 95–103, 10.1016/j.jastp.2016.08.014, 2017.Tang, Y., Dou, X., Li, T., Nakamura, T., Xue, X., Huang, C., Manson, A.,
Meek, C., Thorsen, D., and Avery, S.: Gravity wave characteristics in the
mesopause region revealed from OH airglow imager observations over Northern
Colorado, J. Geophys. Res.-Space, 119, 630–645, 10.1002/2013JA018955,
2014.Taylor, M. J., Ryan, E. H., Tuan, T. F., and Edwards, R.: Evidence of
preferential directions for gravity wave propagation due to wind filtering in
the middle atmosphere, J. Geophys. Res., 98, 6047–6057,
10.1029/92JA02604, 1993.Taylor, M. J., Bishop, M. B., and Taylor, V.: All-sky measurements of short
period waves image in the OI(557.7 nm), Na(589.2 nm) and near infrared OH
and O2(0,1) nightglow eemission during the ALOHA-93 campaign, Geophys.
Res. Lett., 22, 2833–2836, 10.1029/95GL02946, 1995.Vargas, F., Swenson, G., and Liu, A.: Evidence of high frequency gravity wave
forcing on the meridional residual circulation at the mesopause region, Adv.
Space Res., 56, 1844–1853, 10.1016/j.asr.2015.07.040, 2015.von Savigny, C.: Variability of OH(3-1) emission altitude from 2003 to 2011:
Long-term stability and universality of the emission rate-altitude
relationship, J. Atmos. Sol.-Terr. Phy., 127, 120–128,
10.1016/j.jastp.2015.02.001, 2015.Wachter, P., Schmidt, C., Wüst, S., and Bittner, M.: Spatial gravity wave
characteristics obtained from multiple OH(3-1) airglow temperature time
series, J. Atmos. Sol.-Terr. Phy., 135, 192–201,
10.1016/j.jastp.2015.11.008, 2015.Walterscheid, R., Hecht, J., Vincent, R., Reid, I., Woithe, J., and Hickey,
M.: Analysis and interpretation of airglow and radar observations of
quasi-monochromatic gravity waves in the upper mesosphere and lower
thermosphere over Adelaide, Australia (35∘ S, 138∘ E),
J. Atmos. Sol.-Terr. Phy., 61, 461–478, 10.1016/s1364-6826(99)00002-4,
1999.Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New
York, available at: https://ggplot2.tidyverse.org/ (last access:
10 January 2019), 2016.Wüst, S., Wendt, V., Schmidt, C., Lichtenstern, S., Bittner, M., Yee,
J.-H., Mlynczak, M. G., and Russell III, J. M.: Derivation of gravity wave
potential energy density from NDMC measurements, J. Atmos. Sol.-Terr. Phy.,
138–139, 32–46, 10.1016/j.jastp.2015.12.003, 2016.Wüst, S., Bittner, M., Yee, J.-H., Mlynczak, M. G., and Russell III, J.
M.: Variability of the Brunt-Väisälä frequency at the OH*
layer height, Atmos. Meas. Tech., 10, 4895–4903,
10.5194/amt-10-4895-2017, 2017.Wüst, S., Offenwanger, T., Schmidt, C., Bittner, M., Jacobi, C., Stober,
G., Yee, J.-H., Mlynczak, M. G., and Russell III, J. M.: Derivation of
gravity wave intrinsic parameters and vertical wavelength using a single
scanning OH(3-1) airglow spectrometer, Atmos. Meas. Tech., 11, 2937–2947,
10.5194/amt-11-2937-2018, 2018.Yuan, T., She, C.-Y., Krueger, D. A., Sassi, F., Garcia, R., Roble, R. G.,
Liu, H.-L., and Schmidt, H.: Climatology of mesopause region temperature,
zonal wind, and meridional wind over Fort Collins, Colorado (41∘ N,
105∘ W), and comparison with model simulations, J. Geophys. Res.,
113, D03105, 10.1029/2007JD008697, 2008.Zhang, S. D., Huang, C. M., Huang, K. M., Yi, F., Zhang, Y. H., Gong, Y., and
Gan, Q.: Spatial and seasonal variability of medium- and high-frequency
gravity waves in the lower atmosphere revealed by US radiosonde data, Ann.
Geophys., 32, 1129–1143, 10.5194/angeo-32-1129-2014, 2014.