PROBING ATMOSPHERIC GRAVITY WAVES ON MARS’ ATMOSPHERE
USING MARS EXPRESS OMEGA DATA.
F. Brasil [1,2], P. Machado [1,2], G. Gilli [3,1], A. Cardesín-Moinelo [4,1], J. E. Silva [1,2], D. Espadinha [1,2],
R. Rianço-Silva [2], [1] Instituto de Astrofísica e Ciências do Espaço (IA), Lisbon, Portugal (fbrasil@oal.ul.pt), [2]
Faculdade de Ciências da Universidade de Lisboa (FCUL), Lisbon, Portugal, [3] Instituto de Astrofísica de Andalucía
(IAA-CSIC), Granada, Spain, [4] European Space Astronomy Centre, Spain.

Abstract
We report the detection and characterisation of atmospheric gravity waves on the atmosphere of Mars using
images from the OMEGA spectrometer onboard Mars
Express space mission. These waves have already been
detected by MEx (Määttänen et al., 2010; Spiga et al.,
2012), although there is still an important dataset of
unexploited atmospheric observations (Gondet and Bibring, 2018) to be analysed. In this study we started
to build the first catalogue of atmospheric gravity waves
and morphological parameters using OMEGA/MEx data
by adapting the technique that was already used successfully for Venus (Silva et al., 2021). Each image was
navigated and processed for optimal detection of wave
features and accurate characterisation of wave properties and parameters such as time, spatial coordinates,
horizontal wavelength, packet length, packet width and
orientation of the wave packet, to be analysed in correlation with mars topography, illumination conditions,
local time and Mars seasonal climate variability.

Introduction
Atmospheric gravity waves are mesoscale atmospheric
oscillations in which buoyance acts as the restoring
force, being a crucial factor in the circulation of planetary atmospheres since they transport momentum and
energy, which can dissipate at different altitudes and
force the dynamics of several layers of the atmosphere
(Fritts and Alexander, 2003).
The source of these waves can be associated with the
topographic features (orographic gravity waves) of the
surface, or with jet streams and atmospheric convections
(non-orographic gravity waves). Recent modelling studies showed the strong role of gravity waves on diurnal
tides on Mars atmosphere (Gilli et al., 2020), however
their characteristics are still not well constrained by observations.
We present here follow-up results (Brasil et al., 2021)
on the detection and characterisation of atmospheric
gravity waves on Mars using data from the OMEGA
(Bibring et al., 2004) imaging spectrometer onboard the
European Mars Express (MEx) space mission (Chicarro
et al., 2004).
The OMEGA (Observatoire pour la Minéralogie,

l’Eau, les Glaces et l’Activité) instrument is a mapping spectrometer onboard of Mars Express, consisting on two co-aligned grating spectrometers, the Visible
and Near-Infrared (VNIR) and the Short Wavelength Infrared (SWIR) that produced hyperspectral data QUBEs
covering the wavelengths from 0.38 to 5.1 µm.
The data set covers the Mars Express nominal mission of the OMEGA instrument from January 2004
(Martian year 26) to January 2006 (Martian year 27)
and from June to July 2007 (Martian Year 28), constituted by 27 orbits and 4072 hyperspectral data QUBES.
We used image navigation and processing techniques
based on contrast enhancement and geometrical projections to characterise the morphological properties of the
detected waves.
Method
We set out for a systematic analysis of the OMEGA data
QUBEs in which each image is processed and inspected
individually in order to identify gravity waves, where
they are seen as quasi-periodical sequences of bright
crests and darker throughs (Figure 1).

Figure 1: Atmospheric gravity waves detected on the atmosphere of Mars with the OMEGA camera on June 2007.
Credit: Gondet

For the detection, we used the 0.5 and 1.7 µm since
at these wavelengths, we are probing the surface and the
lower atmospheric layer of Mars (Figure 1).

OMEGA data QUBEs and its IDL routines were
retrieved through the PSA archive from ESA, to produce OMEGA images which were later navigated and
processed individually using python scripts and ENVI
software for optimal detection of wave features and accurate characterisation of wave properties, such as the
horizontal wavelengths, packet width, packet length, location and orientation as shown in Figure 2.
In a given image, the oscillations produced by the
presence of a wave produces a small brightness variation when compared to the background, therefore the
first step to identify these features is to increase the contrast of the image. The resulting image highlights the
morphological characteristics of a wave, which can then
be analysed.
Figure 3: Typical wave-packets detected and characterised in
a given OMEGA image, where it can be seen three different
wave packets (1), (2) and (3).

Figure 2: Wave-packet in detail with the morphological properties gathered: Horizontal wavelength, packet length, packet
width and orientation.

Morphological characterisation usually entails measuring the distance between two points of interest in a
wave-packet. For example, to measure the Horizontal
Wavelength (Figure 2), we take into account the distance between two consecutive crests. The calculation
of this distance (Dist in 1 represents the distance measurement) is performed using equation 1 with the radius
of the planet (a) plus the altitude of the cloud deck we
are observing (h). However, to guarantee an accurate
measure of horizontal wavelength, packet length and
packet width, we measure several times from one crest
to the other, maintaining the perpendicularity between
oscillations.

π
Dist. =

q

π
(λ2 − λ1 )2 cos2 (ϕ̄ 180
) + (ϕ2 − ϕ1 )2

180

(a + h)
(1)

Packet length and packet width are retrieved in the
same form as horizontal wavelength. This process is
repeated for each crest on several locations (depending
on the size and morphology of the wave) within the wave
packet to examine the consistency of measurements. In
Figure 3 is shown three different wave-packets detected
in a OMEGA image.

The example in Figure 3 show some of the difficulties
that arise when charaterising the wave train, such as
the irregularity in its form, i.e., the distance between
different crest may not be the same for every crest on the
wave-packet or the shape of the waves makes it harder
to measure the exact location of consecutive crests and
precise perpendicularity.
Other difficulties associated with the caracterisation
can be related to poor contrast of the image, which makes
it impossible to resolve each crest individually and also
depending on the distance between the planet and the
satellite, the OMEGA track can be very narrow to detect
and characterise the wave-packets that could be present
in these images.
The orientation of the wave-packet is measured alongside the packet length, taking advantage of the known
latitude and longitude values between the origin and the
endpoint of measurement. The following equation 2
gives us the orientation of the wave-packet:

Orientation θ = arctan

∆ϕ
∆λ


(2)

Where ∆ϕ is the difference between the maximum
and minimum latitude values for the wave-packet and
∆λ is the difference between the maximum and minimum values of the wave-packet. This equation gives
the angle that the wave fronts make with a parallel with
latitude equal to ϕ1 .
Some difficulties occur when measuring the orientation of the wave-packet that are related to its morphology,
since in most of the cases the waves can be broken or the
orientation can change from crest to crest. For that we
take into account the direction of the propagating wave
when measuring the orientation.

Preliminary results
During the observing periods (4072 data QUBES analysed from January 2004 to January 2006 and from June
to July 2007), we detected and characterised fifty four
wave-packets at 0.5 and 1.72 µm. The distribution of
the gravity waves detected are present in the topographic
map of Mars in figure 4.

Figure 4: Distribution of characterised wave packets represented by black crosses on Mars during the period of observation. The topography map was made from data from the Mars
Orbiter Laser Altimeter onboard Mars Express (Smith et al.,
2001).

We found that around 10% of the images had gravity
waves present and that most of the waves characterised
were located on the northern hemisphere of Mars. It
should be noted that the most part of the inspected
images were sensing the northern hemisphere, which
means that we could have a bias due to the location
of the detected waves. The detected wave packets on
Figure 4 represent the total number of characterisations,
including the same wave packet detected at different
times.
Also, it should be noted that some of the gravity
waves detected are visible within a range of observing
wavelengths (from 0.5 to 1.72 µm, which will depend
if the waves can be seen in the visible or in the nearinfrared, and the choice of a different wavelength on
several cases is due to better visibility of the feature on
that particular filter (better contrast or less instrumental
artifacts). Most of the waves detected and characterised
are in the wavelength range of 1.7-2.3 µm.
The histograms on Figure 5, show the variation of the
morphological properties taken from the wave-packets.
We see a large variability of values in each histogram,
and it is possibly due to the small amount of characterised waves which makes the statistical distribution of
the detected morphological properties to be very large.
This Figure shows that the horizontal wavelength,
i.e. the distance between crests retrieved from the wavepackets, can go from around 10 to 70 km, with a large
concentration of waves detected for smaller horizontal wavelengths. This can be an indication that smaller

wave-packets are more common in the Mars atmosphere,
and larger wave-packets can be associated with the topography of Mars and non-orographic effects that generate gravity waves.

Figure 5: Histograms showing the values of several morphological properties of the wave-packets and how they are distributed. a) Horizontal wavelength; b) Packet length; c) Packet
width; d) Orientation from the parallel.

Regarding the distribution of the morphological properties in respect of their latitude, Figure 6 show also the
same variability of values for each propertie and also the
concentration of values on the northern hemisphere.

Figure 6: Distribution of the morphological properties of the
wave-packets related to the latitude. a) Horizontal wavelength; b) Packet length; c) Packet width; d) Orientation from
the parallel.

We found that the detected waves were present at
the solar longitudes between 240-250◦ and 330-340◦ ,
which corresponds almost to the beginning and the end
of the dust storm seasons. This can indicate that exists a
relationship between the presence of gravity waves and
the dust storm events, already mentioned by Gondet and
Bibring (2019).
Conclusions and Future Perspectives
We had a clear detection and characterisation of atmospheric gravity waves on Mars atmosphere at visible
and near-infrared wavelengths (0.5 and 1.72 µm), using OMEGA data QUBEs from the Mars Express space
mission. Using the PSA archive from ESA, we retrieved
the data to detect gravity waves which were previously
detected by Määttänen et al. (2010) and Spiga et al.
(2012). The images were navigated and processed using IDL scripts and ENVI software for both detection
and further characterisation purposes. We characterised
several properties of detected wave-packets including
horizontal wavelength, packet width, packet length and
orientation. Preliminary results show that around 10%
of the images have gravity waves present and that most
of the waves present were located on the northern hemisphere of Mars. Further characterised packets could help
in explaining the origin of such waves and their possible relation with dust storm events on the atmosphere of
Mars and constrain current models.
Acknowledgements
We acknowledge support from ESA Faculty Science Exchange MWWM - Mar Wind and Wave Mapping project
and EXPRO RFP/3-17570/22/ES/CM. We acknowledge
support from the Portuguese Fundaçªo Para a Ciência e a
Tecnologia of reference PTDC/FIS - AST/29942/2017,
through national funds and by FEDER through COMPETE 2020 of reference POCI - 01 - 0145 - FEDER 007672, and through a grant of reference 2021.05455.BD.
We would like to thank the late Dr. Brigitte Gondet for
her considerable help that made this work possible.
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