# Atmospheric Gravity Waves in Mars' Lower Atmosphere: Nadir Observations From OMEGA/Mars Express Data

F. Brasil, P. Machado, G. Gilli, A Cardesín-moinelo, J E Silva, D. Espadinha, L. Riu, J. Carter, C. Wilson

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► To cite this version:

F. Brasil, P. Machado, G. Gilli, A Cardesín-moinelo, J E Silva, et al., Atmospheric Gravity Waves in Mars’ Lower Atmosphere: Nadir Observations From OMEGA/Mars Express Data. Journal of Geophysical Research. Planets, 2025, 130 (3), pp.e2024JE008726. 10.1029/2024je008726. hal-05468884

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# JGR Planets

## RESEARCH ARTICLE

10.1029/2024JE008726

**Key Points:**

* Atmospheric gravity waves were detected in the Martian atmosphere using OMEGA hyperspectral data set onboard Mars Express

* A detailed morphological characterization of 125 wave packets was performed on water-ice clouds

* Findings reveal temporal, seasonal and geographical patterns, enhancing our understanding of Martian atmospheric waves distribution

**Correspondence to:**

P. Machado,
machado@oal.ul.pt

**Citation:**

Brasil, F., Machado, P., Gilli, G., Cardesín-Moinelo, A., Silva, J. E., Espadinha, D., et al. (2025). Atmospheric gravity waves in Mars' lower atmosphere: Nadir observations from OMEGA/Mars Express data. *Journal of Geophysical Research: Planets*, 130, e2024JE008726. https://doi.org/10.1029/2024JE008726

Received 24 SEP 2024

Accepted 12 FEB 2025

Correction added on 30 OCT 2025, after first online publication: The copyright line has been changed and B-ON funding statement has been added.

**Author Contributions:**

**Conceptualization:** F. Brasil, P. Machado

**Data curation:** F. Brasil, P. Machado, A. Cardesín-Moinelo, J. E. Silva, D. Espadinha, L. Riu, J. Carter

**Formal analysis:** F. Brasil, P. Machado, J. E. Silva, D. Espadinha, L. Riu

**Investigation:** F. Brasil, P. Machado, G. Gilli, A. Cardesín-Moinelo, J. E. Silva

**Methodology:** F. Brasil, P. Machado, A. Cardesín-Moinelo, J. E. Silva

**Resources:** F. Brasil, P. Machado, A. Cardesín-Moinelo, J. E. Silva, L. Riu, J. Carter, C. Wilson

**Software:** F. Brasil, A. Cardesín-Moinelo, J. E. Silva, L. Riu, J. Carter

**Supervision:** P. Machado, G. Gilli

© 2025. The Author(s).

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Atmospheric Gravity Waves in Mars' Lower Atmosphere: Nadir Observations From OMEGA/Mars Express Data

F. Brasil¹,² ID, P. Machado¹,² ID, G. Gilli¹,³ ID, A. Cardesín-Moinelo¹,³,⁴ ID, J. E. Silva¹,⁵ ID, D. Espadinha¹,² ID, L. Riu⁴ ID, J. Carter⁶,⁷ ID, and C. Wilson⁸ ID

¹Institute of Astrophysics and Space Sciences, Observatório Astronómico de Lisboa, Lisbon, Portugal, ²Faculty of Sciences, University of Lisbon, Lisbon, Portugal, ³Instituto de Astrofísica de Andalucía (IAA-CSIC), Granada, Spain, ⁴European Space Astronomy Centre (ESAC), Madrid, Spain, ⁵Planetary Atmospheres Group Institute for Basic Science, Daejeon, South Korea, ⁶Institut d'Astrophysique Spatiale, CNRS, Paris-Saclay University, Orsay, France, ⁷Aix Marseille University, CNRS, CNES, LAM, Marseille, France, ⁸European Space Agency, ESTEC, Noordwijk, The Netherlands

**Abstract** We present the detection and characterization of mesoscale waves on the lower clouds of Mars (20–40 km) using hyperspectral images from the Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité (OMEGA) onboard the European Mars Express space mission. We used image navigation and processing techniques based on contrast enhancement and geometrical projections to semi-manually detect and manually characterize morphological properties of the detected waves, such as horizontal wavelength or packet length. Our study covers 3 Martian years, spanning from January 2004 (Mars Year 26) to January 2010 (Mars Year 29). We detected 263 wave packets, of which we characterized 125, revealing an average horizontal wavelength of 21 km, with detected waves spanning horizontal wavelengths between 6 and 83 km. Wave activity exhibited spatial and temporal variability, with larger wave packets concentrated in the northern hemisphere and most detections occurring during daytime. Seasonal patterns revealed higher wave activity during northern spring and autumn and southern winter, linked to regional topography, atmospheric density perturbations, and diurnal heating cycles. These findings provide insights into Martian atmospheric gravity waves and demonstrate the OMEGA data set's value for future studies of Mars's atmospheric dynamics.

**Plain Language Summary** Atmospheric gravity waves (AGW) are wave-like disturbances that travel through a planet's atmosphere, much like waves moving across water's surface. These waves influence weather patterns and the overall behavior of the atmosphere. Studying them on Mars helps us understand not only the Martian atmosphere but also provides insights that could be applied to Earth's atmospheric science. In our research, we set out to detect and analyze AGW in the lower-clouds of Mars. We used data captured by the Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité instrument aboard the Mars Express spacecraft. By enhancing the contrast of these images and accurately mapping their geometry, we identified wave patterns and measured their characteristics, such as the distance between waves (wavelength) and the size of wave groups (wave packets). We detected 263 groups of waves and conducted a detailed analysis of 125 of them. We also observed that the waves were wider during Mars's dust storm season, suggesting that their properties change with the seasons and locations on the planet. Understanding these waves enhances our knowledge of how the Martian atmosphere operates. This information is crucial for improving atmospheric models of Mars, which can aid future exploration missions and deepen our understanding of atmospheric processes on other planets.

## 1. Introduction

Atmospheric gravity waves (hereafter GW) are mesoscale atmospheric oscillations that propagate vertically in stable stratified planetary atmospheres (Fritts & Alexander, 2003; Sutherland, 2010). They occur when air parcels are displaced from their equilibrium positions, with buoyancy forces acting as the restoring mechanism. GWs are an effective mechanism for transporting energy and momentum throughout the atmosphere, influencing weather patterns and climate by redistributing these quantities across different atmospheric layers (Holton et al., 1995; Medvedev et al., 2011; Yiğit & Medvedev, 2015). Generating mechanisms for these waves can include flow over topography (orographic waves), jet stream circulation, or convection (non-orographic waves) (Fritts & Alexander, 2003). The distinction between orographic and non-orographic sources is crucial for understanding the mechanisms driving atmospheric dynamics on Mars. Orographic GWs play a significant
**Validation:** P. Machado, G. Gilli,
J. Carter, C. Wilson

**Visualization:** F. Brasil, P. Machado,
G. Gilli, J. E. Silva, D. Espadinha

**Writing – original draft:** F. Brasil,
P. Machado, G. Gilli

**Writing – review & editing:** F. Brasil,
G. Gilli, A. Cardesin-Moinelo, J. E. Silva,
L. Riu, J. Carter, C. Wilson

role in the redistribution of energy and momentum across different atmospheric layers, influencing weather patterns and climate (Medvedev et al., 2011). The study of the morphological and dynamical properties, as well as the spatial and temporal distribution of GWs on Mars, is crucial for properly understanding the behavior of the present-day atmosphere.

Previous signature detections of these waves on the lower and middle atmospheric layers were found through thermal infrared sounding and radio occultation, with the Mars Climate Sounder (MCS) onboard Mars Reconnaissance Orbiter (MRO) (Heavens et al., 2020; Wu et al., 2022) and the Thermal Emission Spectrometer onboard Mars Global Surveyor (Ando et al., 2012; Creasey, Forbes, & Hinson, 2006; Heavens, Pankine, Battalio, & Wright, 2022). For the upper atmosphere, only in situ measurements such as aerobraking measurements (Fritts et al., 2006; Vals et al., 2019) and mass spectrometer measurements (Creasey, Forbes, & Keating, 2006; England et al., 2016) were possible to measure the GW activity. By analyzing the small-scale variability, the sources and dynamics of GW activity can be addressed, and its effects on atmospheric structure and composition can be better understood through the help of general circulation models (Forget et al., 1999; Spiga & Forget, 2009). Previous works already mentioned the presence of GW in OMEGA (Bibring et al., 2004) images (Määttänen et al., 2010; Spiga et al., 2012) on board Mars Express' (MEx) spacecraft (S/C) (Cardesin-Moinelo et al., 2024), and in this study, we intend to analyze the data set that was unexplored until now to characterize the morphological properties of these waves and study their spatial and temporal distribution in the atmosphere of Mars.

The OMEGA imaging spectrometer, has been conducting mapping operations from Mars orbit since December 2003. Although the OMEGA investigation is directed primarily toward surface geology, in this work, we take advantage of the extensive data from the OMEGA to detect and characterize the wave patterns present in the thin atmosphere of Mars.

In this paper, we present the results of a semi-manual detection and manual characterization of wave-like features on the lower clouds of Mars (estimated to be in a region between 20 and 40 km above the surface) using imaging data from the OMEGA instrument. The morphological characterization was adapted from the method described in Silva et al. (2021) to characterize AGW on Venus' atmosphere. In Section 2, we describe the instrument and observations used. The method for the waves' detection and morphological characterization is shown in Section 3 and the analysis of the data biases in Section 4. In Section 5, we present the results and the analysis of their spatial and temporal distributions. We then extend our discussion of gravity waves' spatial and temporal distribution and the conclusions in Section 6.

## 2. Data Acquisition

The OMEGA instrument is a visible and near-infrared mapping spectrometer onboard the Mars Express orbiter with three different channels: the visible "V" (0.38–1.05 µm), and two near-infrared channels, the "C" (0.93–2.73 µm) and "L" (2.55–5.1 µm), as described in Bibring et al. (2004). The data set is composed of hyperspectral data cubes, with a spectral range from 0.38 to 5.1 µm divided into 352 wavelength slits, and an average spatial resolution of 1 km, which can vary between 0.3 and 4 km, depending on Mars Express's position in its elliptical orbit around Mars. The instantaneous field of view (iFOV) is 0.4 mrad for the visible and near-infrared (VNIR) channel, and 1.2 mrad for the short wavelength infrared (SWIR) channel. Mars Express features a highly elliptical near-polar orbit, with an apocenter at 10,142 km from the planet and a pericenter as close as 250 km. The pericenter latitude changes throughout the mission. The orbital period is approximately 7 hr.

Our study covers the first three Martian years (MY) of OMEGA data, starting in January 2004 (MY26, $L_s = 330°$) and continuing until January 2010 (MY29, $L_s = 102°$). A total of 8,320 data cubes covering four MY, from MY26 until MY29 were used. Each datacube was manually examined at two target wavelengths (0.5 and 1.72 µm), because these wavelengths provide optimal contrast and spatial resolution for observing gravity wave patterns in water-ice clouds within the OMEGA data (see Section 3 for details). Regarding the characterization of the wave packets, we selected only nadir observations between a minimum height of 252 km (corresponding to a spatial resolution of 105 m/pix) and a maximum height of 3,748 km (corresponding to a spatial resolution of 1,499 m/pix). For altitudes below 252 km the speed of the instrument was too high and above 3,748 km the spatial resolution became too low.”
<figure><img src="image_4.png" /><figcaption>**Figure 1.** This raw image is a composite of three different wavelengths, resulting in the colored image A (ORB4369_6) in the northern hemisphere with a clear visible wave packet. Panel (b) shows the same image after being processed and cylindrical projected using OMEGA-Py at a wavelength of 0.5 µm. Panle (c) shows a diagram of the morphological proprieties measured on the wave packets. The blue, pink and yellow colored lines represent how the horizontal wavelength (λ), packet width and packet length are measured, respectively. The angle (θ) is the orientation of the wave packet relative to the parallel.</figcaption></figure>

### 3. Methodology

We performed a systematic search for periodic features on the lower cloud of Mars by visual inspection of each image. OMEGA data are handled using the Python module OMEGA-Py (Stcherbinine et al., 2024a, 2024b). Each image was manually navigated and processed to increase the contrast on cloud features. The images were then mapped into cylindrical or polar projections depending on the area present in the image. Figure 1 shows the image before (Figure 1a) and after (Figure 1b) the processing and projection.

Taking advantage of the spectral information of the OMEGA images, we developed a pre-selection method for images with any clouds present by using the reversed ice cloud index (ICIR). The original ice cloud index (ICI), defined by Langevin et al. (2007); Madeleine et al. (2012) identified the slope of the 3.1 µm water ice absorption band by the following equation:

$$ICI = \frac{R_{3,38}}{R_{3,52}} \qquad (1)$$

$R_{3,38}$ and $R_{3,52}$ correspond to the reflectances in the water ice absorption band at 3.38 and 3.52 µm. In the scope of this work, we used the reversed ICI ($ICIR = 1 - ICI$), defined by Szantai et al. (2021) that enables easier identification of clouds.

We used a minimal threshold for detecting water-ice clouds of $ICIR_{threshold} = 0.28$, defined by the authors, indicating that for values above this threshold, water-ice clouds are expected to be present in the image. The method calculates the ICIR for each pixel of the data set. If the number of pixels with an ICIR above the $ICIR_{threshold}$ is greater than 10% of the total pixels of the image, the image will be pre-selected, and a mask will be applied to define the zones with an ICIR above the threshold. After this pre-selection, we inspected each image at both target wavelengths of 0.5 µm and 1.72 µm to detect gravity waves. These two wavelengths were chosen because they provide optimal contrast and spatial resolution for observing gravity wave patterns in water-ice clouds within the OMEGA data. Specifically, the 0.5 µm wavelength, in the visible range, allows for high sunlight reflectance by water-ice clouds, enhancing the visibility of wave structures. The 1.72 µm wavelength, in the near-infrared range, is less affected by surface albedo variations and can reveal cloud features due to scattering effects. Following the criteria used in previous studies of Venus (Silva et al., 2021) we considered a wave packet detected when at least three bright crests and dark troughs appeared in each selected image, as illustrated in Figure 2. Morphological properties of the gravity waves were measured only at the wavelength that provided the best contrast for that specific image. This approach ensured the accuracy and reliability of our measurements.
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Examples of waves detected on OMEGA data between the MY26 to MY29 using the OMEGA-Py python module. Each image was processed and projected, and the 0.5 µm wavelength was selected to detect the wave packets. Images A, B, D, F, and G are at Ls ≈ 150°; Image C at Ls ≈ 285°; Image E at Ls ≈ 245°. Image G is an example of narrow images covering a wide range of latitudes but a very tiny range in longitude, between 334.41°–334.53° for latitude = 11.51°). The color bars represent the reflectance of the selected images, calculated as I/F, cos(i), which accounts for radiance normalized by the Solar flux and the incidence angle.</figcaption></figure>

## 3.1. Characterization of Wave Properties

In this work, we focus the study on the morphological characterization of gravity waves detected on water ice clouds. Our study examines the morphology, as well as the spatial, temporal and seasonal variations of these wave packets. The detection and characterization of GW in the lower atmosphere of Mars using OMEGA follows the work by Silva et al. (2021). It was necessary to adapt the tools to the OMEGA instrument's specificities and Mars's unique atmospheric conditions. Each wave packet was analyzed by manually gathering the position (latitude and longitude) values in each position of the wave packet. For that, properties such as horizontal wavelength, packet width (PW), packet length (PL) and orientation (Figure 1c) are extracted by calculating the distance (Dist.) between an origin and destination target points in the wave packet using the expression:

$$Dist. = \frac{\pi \sqrt{(\lambda_2 - \lambda_1)^2 \cos^2(\bar{\phi}_{180}) + (\phi_2 - \phi_1)^2}}{180} (a + h) \quad (2)$$

The distance calculation is applied between two pixels on top of the visible disc of Mars after being navigated and projected. The values of $\lambda_1$, $\lambda_2$ and $\phi_1$, $\phi_2$ are the longitudinal and latitudinal coordinates of the origin and destination points, $\bar{\phi}$ is the average latitude between measured points, a is the radius of Mars ($a = 3389.5$ km) and h is the altitude of the observed cloud layer (we used an average value of 25 km although the altitude will depend on the season and location of the clouds, as will be described on the next Section 3.1.1). Each morphological property was measured manually several times for the same packet and then averaged in order to examine the consistency of the measurements made.

The orientation of the packet is measured alongside the PL, that is, the angle between the general axis perpendicular to the wavefront alignment and the local parallel at the origin point, given by the following expression:

$$\theta = \arctan\left(\frac{\Delta\phi}{\Delta\lambda}\right) \quad (3)$$
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Example of High-Resolution Stereoscopic Camera limb images with clouds present for the orbits 13,185 (image A) and 13,263 (image B) from the MY32, $L_s = 133^\circ$. In both images, we can see a green line that represents the surface geoid of Mars and a red perpendicular line that is converted in radiances as function of altitude on the right panel, for images 13,185 and 13,263, taken using SPICE kernels for the period of observations. Both images were taken at the nadir channel (wavelength 675 ± 90 nm).</figcaption></figure>

The $\Delta\phi$ is the difference between the wave packet's maximum and minimum latitude values, and $\Delta\lambda$ is the difference between the packet's maximum and minimum longitude values. Since we used a cylindrical projection for the morphological characterization of gravity waves, we note that at high latitudes, the length associated with an interval of longitudes is smaller than the length associated with the same interval of latitudes. Thus, for the case of the orientation of the wave packets, Equation 3 indicates the angle in the cylindrical projection, which may not be the actual angle relative to the latitudinal circle. For wave packets at high latitudes (above 80° N/S), we only considered the position, local time, solar longitude and number of crests for their spatial, temporal, and seasonal characterization. The errors of each measurement were calculated using the propagation of errors in Equations 2 and 3 for the measurement of the distances and the orientation of the wave packets, respectively.

The OMEGA data set allowed us to perform a detailed morphological study of AGW in the Mars atmosphere. However, a dynamical study (e.g., characterization of phase velocity and lifetime) of these waves was not possible because the local background wind is not retrievable from OMEGA data, unlike similar studies on Venus using Venus Express data (Silva et al., 2021).

### 3.1.1. Estimation of Cloud Altitudes

The altitude of water ice clouds on Mars varies with latitude and season. Recent observations by the atmospheric chemistry suite on board the Trace Gases Orbiter found that the typical altitude of clouds varies by 20–40 km between the summer and winter, and it extends up to 80 km altitude during summer in the midlatitudes (Stcherbinine et al., 2022). In this study, we used limb images from the High-Resolution Stereoscopic Camera (HRSC) instrument (Neukum & Jaumann, 2004) which is a stereo-imaging camera on board Mars Express that provides images of the Martian surface and atmosphere through different channels. HRSC limb images offer valuable data for estimating the typical altitude of water ice clouds, serving as an average reference value for the wave packet analysis conducted with OMEGA.

We use SPICE kernels to determine the precise coordinates of the clouds (Acton et al., 2018; Costa, 2018). To measure the cloud's distance from the surface of Mars (red line in Figures 3a and 3b), we analyzed the radiance profiles along the atmosphere. This analysis provides a profile of the radiance, allowing us to assess the altitude of the clouds present on the limb images. The altitude varies depending on the type of cloud being probed. Two
<caption>**Table 1**</caption>

Table Showing the Estimated Measurement Biases on the Distance Defined in Equation 2, by Using Different Clouds' Altitude

<table><thead><tr><th>File name</th><th>Altitude (km)</th><th>λ (km)</th><th>λ<sub>err</sub> (km)</th><th>PW (km)</th><th>PW<sub>err</sub> (km)</th><th>PL (km)</th><th>PL<sub>err</sub> (km)</th></tr></thead><tbody><tr><td>ORB6380_5</td><td>20</td><td>21.4</td><td>1.9</td><td>336</td><td>2.4</td><td>386</td><td>3.2</td></tr><tr><td>ORB6380_5</td><td>25</td><td>21.4</td><td>1.9</td><td>336</td><td>2.4</td><td>386</td><td>3.2</td></tr><tr><td>ORB6380_5</td><td>40</td><td>21.5</td><td>1.9</td><td>337.5</td><td>2.4</td><td>388</td><td>3.2</td></tr><tr><td>ORB6380_5</td><td>80</td><td>21.9</td><td>1.9</td><td>343.4</td><td>2.4</td><td>394.5</td><td>3.2</td></tr></tbody></table>

Note. The columns represent: File Name (the identifier of the image analyzed), altitude (the assumed cloud altitude in kilometres), λ (the measured horizontal wavelength in kilometres), λ<sub>err</sub> (the uncertainty in the horizontal wavelength in kilometres)PW (the packet width in kilometres), PW<sub>err</sub> (the uncertainty in the packet width in kilometres), PL (the packet length in kilometres, and PL<sub>err</sub> (the uncertainty in the packet length in kilometres).

examples are provided in Figure 3, and the graph on the right shows the radiance profiles for both limb images A and B. Figure 3a profile clearly shows two different regions of the clouds, the region between 20 and 40 km mainly associated with the water ice clouds detected with the OMEGA instrument (Szantai et al., 2021; Vincendon et al., 2011) and a higher region between 60 and 70 km, the presence of CO₂ ice clouds (Määttänen et al., 2010; Montmessin et al., 2006; Vincendon et al., 2011). Through the radiance profiles, we estimated water ice cloud altitudes with altitudes between 15 and 35 km. For the morphological characterization of the wave packets, we used an average value of 25 km for the altitude of the clouds and calculated the error on the morphological parameters induced by the uncertainty of the altitude of the clouds. Considering that water ice clouds can reach an altitude of 100 km during global dust storms (Stcherbinine et al., 2020), we took this into account to calculate the effect of underestimating the altitude in Equation 2. As listed in Table 1, the errors on the horizontal wavelength and PL are about 1.9 and 3.2 km, respectively, using 20 and 40 km altitude (instead of an average value of 25 km) and about 3.2 km if the clouds' altitude were at 100 km. The errors on the PW is 2.4 km at 20 and 40 km altitude and 2.4 at 100 km altitude. The bias in the measurement of altitudes of water ice clouds is more significant for broader and lengthier wave packets, as for the case of an altitude of 100 km, with a variation in the length of almost 2% of the value taken at an altitude of 25 km. Note that, given that the height difference is minimal (about 1% of the radius of the planet), the errors in the wavelength of the waves in Table 1 (of the order of 8%) may be due to the error in latitude and longitude instead. Although the pointing error is generally more significant than the correction introduced by altitude (h) in Equation 2, as shown in Table 1, it is still relevant to consider the altitude in the case of very long wave trains, such as the one with 14 crests.

## 4. Data Coverage Bias Analysis

The OMEGA instrument is influenced by the spacecraft's elliptical orbit, which constrains the longitude range of captured images. At pericenter, when the spacecraft is at its lowest altitude of approximately 250 km, it captures long, narrow images due to increased speed, covering extensive latitudinal areas, such as the one illustrated in Figure 2g. OMEGA operates in nadir, limb, and target-pointing modes. We focused on nadir pointing for its direct view, better spatial resolution, optimal coverage and reduced geometric distortion for wave packet analysis. We selected, when possible, images that fully captured wave packets, requiring visibility of at least three crests, and preferred those spanning more than three degrees of longitude.

During the three MY analyzed, 84% of the 8,307 images were captured in nadir pointing mode, which is essential for the detailed characterization of wave packets (Figure 4, top panel). The spatial resolution of the OMEGA images, expressed in kilometres per pixel (km/pix), is determined using the equation *Spatial Resolution* = *IFOV* × *Altitude of S/C*. At the apoapsis altitude of 10,530 km, with the IFOV (instantaneous field of view) of the VNIR channel at 0.4 mrad, the calculated spatial resolution is 4.212 km/pix. We selected only nadir observations with a field of view between 0.1 and 3.7 km/pix, depending on the S/C altitude. Images spanning more than 40° were not considered for characterization, due to long span in longitudes and limitations in the projected images. For waves with smaller horizontal wavelengths (e.g., 6 km), only image with resolutions finer than approximately 2 km/pix were used to ensure sufficient sampling, while coarser-resolution images were valuable for studying larger-scale waves. Among the nadir images, only 37% (3,075 images) fulfill this condition, and 35% (2,983 images) have a spatial resolution below 1.5 km/pix, with a
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Latitude/Longitude coverage maps for the OMEGA data set used in this work during the observation period (from January 2004 to January 2010 corresponding to the MY26-MY29) Top panel: coverage map of all the data set. Middle panel: coverage map of selected nadir images with a longitude span between 3 and 40° and a spatial resolution below 1.5 km/pix. Bottom panel: coverage map of the percentage of detected waves relative to the selected images on the Middle panel. In all panels, a binning of 10° latitude and longitude was used, gray area indicates the locations with zero counts.</figcaption></figure>
mere 15% achieving a resolution smaller than 0.6 km/pix. Figure 4 (middle panel) displays these available observations in nadir pointing mode, with a field of view between 3 and 40° longitude and a spatial resolution below 1.5 km/pix. Images spanning more than 40° were not considered for characterization due to limitations in the projected images. These images are usually taken at high latitudes with a long span in longitudes, making it difficult to resolve and characterize the wave packets.

We used an average cloud height of 25 km in Equation 2 to calculate the morphological properties of wave packets detected in the OMEGA data. Table 1 illustrates the influence of cloud altitude variation on these properties, using an example of a wave packet with 14 crests, as shown in Figure 2f. We computed the horizontal wavelength, PW, PL, and orientation for cloud altitudes of 20 km, 40 km (outside the dust season), and an extreme altitude of 80 km (during the dust season). The error induced by cloud height in the morphological properties measured for the case of 2-F is generally less than 2 km. The pointing error of the instrument varies between a few hundred meters to 1 km, depending on the spacecraft's altitude relative to the planet. The error in the horizontal wavelength measurements for the case of 2-F has a pointing error below 1.8 km, although the error difference considering different altitudes is minimal (below 1 km). For the length and width of the wave packet, the measurement error (1.5 km for PW and 1.8 km for PL) is similar to the difference induced by the highest and lowest heights considered (4–5 km). For smaller wave packets, this difference is below the measurement error. The packet orientation remains independent of the altitude, as shown in Equation 3.

## 5. Results

We identified 263 potential wave packets based on the presence of at least three bright crests and dark troughs within OMEGA's field of view. However, some of the detected wave packets were partially outside the image or presented some turbulence along the packet, making morphological characterization difficult. It was possible to characterize morphologically only 125 wave packets from those initially detected. We selected only the observations in the nadir pointing mode, which spans longitudes from 3 to 40° and has a spatial resolution below 1.5 km/pix, as discussed in Section 4.

Figure 4 (middle panel) illustrates the regions observed under specific conditions (selected nadir images with a longitude span between 3 and 40° and a spatial resolution below 1.5 km/pix) from MY26 to MY29, showing that the southern hemisphere of Mars is observed more frequently than the northern hemisphere. The frequency of wave packet detections is closely linked to the availability and size of nadir-pointing images. Larger images, particularly those covering latitudes between 30°N–70°N and 30°S–70°S, allow for better monitoring of clouds and gravity wave activity, leading to higher detection rates in these regions, as shown in Figure 5 (top panel). In some images, more than one wave packet was detected and, when possible, characterized. These multiple detections contribute to a higher density of observations in certain regions, indicating enhanced wave activity. Figure 5 (top panel) includes all detected and characterized wave packets, even when the same packet appears across multiple images. In contrast, Figure 5 (bottom panel) displays only the characterized waves, providing a more refined view of their spatial distribution.

Detection and characterization are also influenced by observation density and image size. As shown in Figure 4 (bottom panel), the number of available observations decreases significantly at mid-latitudes compared to higher latitudes. Smaller image sizes at mid-latitudes, sometimes spanning less than 0.3° in longitude, further limit the ability to confidently characterize wave packets. For example, Figure 2 (image G) illustrates a typical OMEGA image with a longitudinal coverage below three degrees, constraining wave characterisation in such regions. The overall pattern of gravity wave detections reflects these observational constraints. The higher detection frequency observed in both hemispheres, particularly in the 30°–70° latitude bands, is a direct result of the availability of larger images at these latitudes and their suitability for wave detection and characterisation. Conversely, the lower detection rates at mid-latitudes can be attributed to limited observation coverage and image size constraints, as shown in Figure 4.

The higher number of images in the northern hemisphere, particularly around Acidalia Planitia (46°N, 22°W), is due to optimized observations aimed at cloud monitoring. However, a significant percentage of images in the southern high latitudes is likely due to regular monitoring of the southern polar cap. These optimized observations may introduce a bias in our data, influencing the distribution of observed regions.
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Top panel: Distribution of detected and characterized wave packets on Mars in this study. Each color represents the Martian year in which the waves were detected: red diamonds-shaped for MY26, Yellow circles for MY27, Blue squares for MY28, and Green triangles for MY29. Bottom panel: The same as in the upper panel, but only the locations of characterized wave packets are indicated.</figcaption></figure>

We then analyzed the percentage of gravity waves detected based on images with these constraints, as shown in Figure 4 (bottom panel). Despite the number of selected nadir images in the southern hemisphere being almost twice as large as those in the northern hemisphere, we observe a significantly lower percentage of gravity wave detections in the southern hemisphere. The maximum detection rate in the southern hemisphere is approximately 15%, in stark contrast to the northern hemisphere, where it reaches about 35%. Notably, a higher wave occurrence between 25% and 35% is found in the locations of Arcadia Planitia (47°N, 175°W), above Alba Fossae (49°N, 106°W), in the Chryse Planitia region (28°N, 40°W), and Acidalia Planitia (49°N, 20°W). The average wave occurrence on the data set is 8%.

One possible explanation for detecting more waves in the northern hemisphere is the relatively sparse topography, which facilitates the formation of orographic waves on a larger scale and with more spatial distribution. This could also allow these waves to have a longer lifespan as they encounter fewer obstacles. In contrast, the southern hemisphere shows a concentration of waves in specific regions, particularly where the surface is smoother and has fewer topographic features when compared to the average roughness of the southern hemisphere's topography.
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Histogram of the number of crests present on the wave packets detected between January 2004 and January 2010 (MY26-MY29).</figcaption></figure>

Although this discrepancy might also be related to observational bias, the physical characteristics of the hemi-spheres likely play a significant role in the differing wave detection rates.

5.1. Morphological Properties

Figure 6 shows the number of crests on the characterized wave packets. A minimum of three crests was required to distinguish mesoscale waves from other cloud patterns that might share a similar morphology when considering a wave packet. In our observations, we found that wave packets consisting of 3-6 crests are more commonly detected compared to those with a larger number of crests (greater than 12). This distribution strongly depends on OMEGA's image size and instantaneous FOV that may capture only a part of the wave packet. In fact, for images covering a wider range of longitudes, we could identify wave packets with up to 19 crests, predominantly located in the northern hemisphere. The distribution shown in Figure 6 provides a lower limit for the number of crests. Therefore, both the number of crests and the wave length are probably underestimated due to instrument's observations constraints.

Figure 7 shows the histograms for the horizontal wavelength, PW, PL and orientation. For the case of horizontal wavelength, we find a predominance of values between 6 and 30 km. In some images, we detected atmospheric formations known as “cloud streets”, as described by Clancy et al. (2017). These are formed as the warm surface radiates heat into the colder atmosphere above in the late summer/early autumn at high latitudes. Notably, within these clouds streets, patterns characteristic of gravity waves were observed. Although we were able to morphologically characterize some of these wave packets, distinguishing their precise boundaries and full morphological details proved challenging. This difficulty arose primarily because the wave packets are blended into the cloud streets, limiting our analysis to their horizontal wavelengths. We noted that the horizontal wavelengths of these wave packets are shorter, averaging around 6 km, compared to those of other wave packets.

As we can see from the number of crests detected, the PL, and in some cases, the PW, can be affected by the configuration of the image taken. The PW can be affected if the wave is partially outside the image, leading to potential underestimation. On the other hand, the PL is affected by the limited longitude span of the images. Some wave packets exhibited changes in their length due to the limited span, resulting in significant deviations from the mean PL. Additionally, some wave packets did not have a clear boundary from where the crests emerged, which might further impact the accuracy of the width measurement. Despite these challenges, we have made efforts to account for these factors in our analysis to provide an accurate characterization of the wave packets.
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Histogram plots of the morphological properties of identified waves on images from the OMEGA instrument.</figcaption></figure>

Regarding the orientation of the wave packets in Figure 7 (bottom right panel), values are concentrated between -10° and 10°. The orientation is defined as the angle relative to the parallel (line of constant latitude parallel to the equator), with positive values indicating an increase to the north and negative values otherwise. We observed that wave packets near prominent topographic features, such as craters, often have orientations close to zero in most cases and exhibit higher horizontal wavelengths compared to wave packets with many crests (above 10 crests). Additionally, a significant percentage of wave packets in the northern hemisphere have, in general, more positive angles than those in the southern hemisphere. This suggests that wave packets in the northern hemisphere tend to have a northward inclination. This pattern may be possibly related to the specific dynamical regime during the observed season or other atmospheric conditions.

Considering the estimated altitude range (20–40 km) of the detected and characterized GW, we suggest that the majority of these waves likely have an orographic origin. In contrast, non-orographic sources, such as jet streams and atmospheric convection front systems, are known to produce GWs that propagate and break at higher altitudes (Gilli et al., 2020; Liu et al., 2023; Medvedev et al., 2015). This is supported by the correlation between wave detection and regions with significant topographic features, such as Alba Mons and Tharsis Montes. These waves extend zonally, as indicated by their orientations close to zero. The larger positive angles of the wave packets observed in the northern hemisphere may be related to seasonal atmospheric dynamics or other regional factors, but further investigation is needed to understand fully this trend.

Due to the MEx spacecraft's approximately 7 hr revisit time and the requirement for shorter observation intervals (30 min–2 hr) to track wave progression, we cannot determine the direction of propagation or compute the phase velocity of the wave packets in OMEGA images; this work is limited to the manual morphological characterization of AGW.

A summary of retrieved value for the morphological properties of wave packets by solar longitude (from 0°-90°; 90°-180°; 180°-270°; and 270°-360°), and also all the data set is shown in Table 2.
<caption>**Table 2**</caption>

Table With the Morphological Parameters Gathered From the Characterization of Gravity Waves Using Data From the OMEGA Instrument by Solar Longitude

<table><thead><tr><th>Solar longitude (°)</th><th>λ<sub>x</sub>(km)</th><th>λ<sub>xerr</sub></th><th>PW(km)</th><th>PW<sub>err</sub></th><th>PL(km)</th><th>PL<sub>err</sub></th><th>θ(deg)</th><th>θ<sub>err</sub></th></tr></thead><tbody><tr><td>0–90</td><td>20 (7–83)</td><td>3.42</td><td>100 (17–392)</td><td>2.26</td><td>93 (26–267)</td><td>4.1</td><td>7 (–35–87)</td><td>5.87 × 10<sup>-3</sup></td></tr><tr><td>90–180</td><td>29 (13–80)</td><td>3.88</td><td>187 (71–441)</td><td>2.04</td><td>178 (67–386)</td><td>4.24</td><td>–6 (–27–18)</td><td>3.16 × 10<sup>-3</sup></td></tr><tr><td>180–270</td><td>26 (10–45)</td><td>3.04</td><td>166 (48–540)</td><td>2.36</td><td>132 (63–266)</td><td>3.14</td><td>5 (–39–58)</td><td>4.19 × 10<sup>-3</sup></td></tr><tr><td>270–360</td><td>13 (6–33)</td><td>2.07</td><td>63 (22–140)</td><td>1.88</td><td>55 (22–113)</td><td>2.08</td><td>–7 (–73–29)</td><td>9.76 × 10<sup>-3</sup></td></tr><tr><td>All data</td><td>21 (6–83)</td><td>2.92</td><td>123 (17–540)</td><td>1.81</td><td>107 (22–386)</td><td>3.16</td><td>3 (–72–87)</td><td>5.71 × 10<sup>-3</sup></td></tr></tbody></table>

Note. The average value for each of these properties is shown, and the minimum and maximum values are between parentheses. The error is measured by doing the propagation of errors in both Equations 2 and 3.

We also analyzed the variation of the morphological properties of detected waves with the geographical latitude of Mars, as seen in Figure 8. A distinct pattern in the size of the characterized wave packets between the northern and southern hemispheres is evident. In the southern hemisphere, the morphological properties are concentrated around the average values. The horizontal wavelength in the southern hemisphere ranges from 13 to over 43 km, while in the northern hemisphere it ranges from below 6 km to over 83 km, with a higher concentration at lower wavelengths. As previously described, wave packets with shorter horizontal wavelengths (6–8 km) are present within the cloud streets, facilitating the formation of larger wave packets. The PW has higher values in the northern hemisphere, with a maximum of 540 km, compared to an average of 150 km in the southern hemisphere, although with a maximum of 442 km. This difference can be attributed to the broader images available in the northern hemisphere. The spread of values at higher latitudes may also be due to the diversity in generating mechanisms, including topographic forcing in the Tharsis region and convective forcing in the Planitia regions. For PL and orientation, the southern hemisphere shows more dispersed PL values, while orientations are more concentrated toward negative values compared to the northern hemisphere. These morphological properties are detailed in Table 2, including the average, maximum and minimum values and their associated errors.

## 5.2. Seasonal and Diurnal Variability

In order to investigate the diurnal and seasonal variability of GW, if any, we study their distribution in local time and season in which they were detected. Figure 9a shows the Local True Solar Time (LTST) distribution of the detected waves. A large percentage of the waves appear during daytime (between 10 and 18 hr, yellow circles and blue squares in Figure 9a), predominantly detected in the northern hemisphere. However, we also detected some waves in the southern hemisphere during this period, mostly between 30°S and 70°S longitude. At lower latitudes, the LTST period ranges between 6 and 12 hr near the Tharsis Region. Detections outside the 6–18 LTST period are less common but still occur, particularly at high latitudes during the spring and summer seasons when the illumination angle is favorable. For instance, in the northern hemisphere's Acidalia Planitia region and some sparse detections in the southern hemisphere, gravity waves were found between 0–6 hr and 18–24 hr LTST. These observations at high latitudes are possible due to the extended daylight hours in these regions during their respective spring and summer seasons.

Figure 9b shows the distribution of gravity waves by latitude and LTST across MY. Detections are consistent within the same LTST range each year, with notable concentrations between 13 and 17 hr LTST in the northern hemisphere during MY 28 and 29, likely due to multiple wave packets in the same image. The higher frequency of morning detections aligns with previous findings on water ice cloud formation (Atwood et al., 2024; Kloos et al., 2018). Although there appears to be a trend of increasing detections toward higher latitudes between 10 and 18 hr LTST, this is heavily influenced by the orbital dynamics of MEx, affecting the number and distribution of available observations. The observed frequency correlates with LTST ranges of 12–18 hr in mid-latitudes (40–70°N and 30–60°S) and 10–11 hr LTST at lower latitudes (10°S–10°N). While these patterns suggest diurnal variation, they are significantly shaped by the availability of observations, precluding definitive trend conclusions (Figure 9c).

The distribution of wave packets with seasons of Mars is shown in Figure 10a on a latitude-longitude map. As expected during the northern hemisphere's spring (L<sub>s</sub> = 0°–90°, red diamond-shaped), more wave packets are
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Morphological properties of the characterized waves in relation to latitude. Top-left: Horizontal Wavelength; top-right: Packet Width; bottom-left: Packet Length; bottom-right: Orientation.</figcaption></figure>

detected in the northern hemisphere compared to the southern hemisphere and other solar longitudes. During this period, more than 80% of the wave packets were detected in the northern hemisphere when compared with the ones in the southern hemisphere. The increase in atmospheric activity during spring, driven by rising temperatures and the sublimation of polar caps, likely contributes to this higher detection rate and may also lead to the formation of water ice clouds. During the northern summer (L$_{s}$ = 90°–180°, yellow circle), more wave packets are detected in the southern hemisphere than in the northern hemisphere. Notably, there is a significant concentration of packets around the Lowell crater (51°S, 81°W) in the Thaumasia Fossae region (47°S, 91°W) representing 30% of the detections for this period.

In the northern hemisphere's autumn (L<sub>s</sub> = 180°–270°, blue squares), detections are sparse in the southern hemisphere, representing only approximately 10% of the detections for this period. In contrast, the northern hemisphere shows a high concentration of packets, with more than 80% of the detections accounted for during this time. As the northern hemisphere cools, the atmosphere can become more turbulent, enhancing the presence of gravity waves. Additionally, the disparity in wave packet detection between the hemispheres is also influenced by the geometry of the orbit, which provides larger images and a FOV that favor the detection of gravity waves in the northern hemisphere.
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Coverage maps of OMEGA images during the period of observation (MY26-MY29) in the nadir pointing mode, with a longitudinal span between 3 and 40° and a spatial resolution below 1.5 km/pix. (a) Distribution of the detected wave packets in latitude/longitude relation to their Local True Solar Time (LTST). LTST = 0–6 hr (red diamonds-shaped); LTST = 6–12 hr (yellow circles); LTST = 12–18 hr (blue squares); and LTST = 18–24 hr (green triangles). (b) Distribution of the detected wave packets in latitude and LTST. The data are also distributed by Martian years: MY26 in red diamonds-shaped, MY27 in yellow circles, MY28 in blue squares, and MY29 in green triangles. (c) Maximum number of observations per bin as a function of LTST and Latitude. A binning of 10° latitude and 1 hr LTST was used, and for the locations with zero counts we used the color gray.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Coverage maps of OMEGA images during the period of observation (MY26-MY29) in the nadir pointing mode, with a longitudinal span between 3 and 40° and a spatial resolution below 1.5 km/pix. (a) Distribution of the detected wave packets in latitude and longitude. In red diamond-shaped observations at solar longitudes $L_s = 0°-90°$; in yellow circle $L_s = 90°-180°$; in blue square $L_s = 180°-270°$; and in green triangle $L_s = 270°-360°$. (b) Distribution of the detected wave packets in latitude and solar longitude. The data are also distributed by Martian years: MY26 in red diamonds-shaped, MY27 in yellow circles, MY28 in blue squares, and MY29 in green triangles. (c) Maximum number of observations per bin as a function of $L_s$ and Latitude. A binning of 10° latitude and 10° $L_s$ was used, and for the locations with zero counts we used the color gray.</figcaption></figure>
In the analysis of the distribution of wave packets across latitude and solar longitude, as illustrated in Figure 10b, we observe a widespread distribution across all seasons, predominantly in the northern hemisphere. Figure 10c shows the available observations, taking into account constraints such as pointing mode, FOV, and spatial resolution for GW detection. Two primary regions exhibit the highest number of available observations: in the northern hemisphere (40°N–70°N) between $L_s = 90°–120°$ (representing 20% of the available observations for wave detection), and in the southern hemisphere (30°S–60°S) between $L_s = 230°–280°$ (representing 30% of the available observations for wave detection). These latitude/solar longitude sectors partially overlap with areas near the seasonal polar cap edges, regions noted for their dynamic conditions in studies of surface-atmosphere interactions (Stcherbinine et al., 2023). While such conditions could potentially influence local atmospheric stability and thus gravity wave occurrence, the filtering of high-latitude data and the inherent limitations in our data set preclude definitive conclusions. Nevertheless, the correspondence between areas of enhanced observation availability and the vicinity of polar cap boundaries warrants further investigation.

However, in regions such as $L_s = 0°–30°$ in the northern hemisphere, we observed many wave packets during the MY29. This concentration can be related to the start of spring in this hemisphere and possibly due to the seasonal variations (end of winter and start of spring) in the zonal mean winds that can play a role in the generation of gravity waves. For almost the same period, we also detected a significant number of waves in the southern hemisphere. For the southern hemisphere, half of the wave packets detected (50 wave packets detected during the 4 MY) were found between $L_s = 120°–160°$ for MY27. This period corresponds to the winter in the southern hemisphere, in which the density perturbations in the atmosphere are significantly stronger compared with other seasons (Creasey, Forbes, & Keating, 2006). For MY28, a significant increase in detections was observed mainly in the northern hemisphere between $L_s = 240°–270°$, corresponding to the beginning of a global dust storm that occurred that year.

We still can see a large dispersion of detections for mid-latitudes, but for specific periods, like in the MY27 and MY28, the images captured have a narrow longitudinal coverage, sometimes less than 0.5°. This limited coverage negatively affects the reliability of our detection and characterisation methods. Consequently, it is challenging to confidently identify and analyze possible wave packets present in these regions. We have to point out that for the case of Martian Year 26, we only cover the end of the year since Mars Express only started to acquire data from January 2004.

While Figures 9 and 10 show all the wave packets detected with OMEGA in this work, Figures 11 and 12 are focused on the characterized wave packets only. As observed in Figure 11, the distribution of morphological properties of characterized wave packets varies significantly with LTST, particularly between 12 and 17 hr. The lack of a clear relationship between the size of the wave packets (including horizontal wavelength, PW, and PL) and LTST suggests a complex interaction between wave generation mechanisms and local Martian environmental conditions. However, a trend indicating larger wave packets around 14hr LTST, particularly during MY28, hints at solar heating's role in wave formation and development. This pattern of wave activity could imply a diurnal cycle in gravity wave generation, possibly influenced by atmospheric heating and cooling dynamics. The concentration of larger wave packets in the afternoon hours could be attributed to increased atmospheric instability or convective activity driven by solar heating. However, gravity waves also develop in a statically stable atmosphere and can be quite common at night when the atmosphere is more stable. While we cannot detect these waves at night due to the limitations of OMEGA's visible imaging capabilities, their presence cannot be discounted.

The analysis of the morphological properties across various solar longitudes, as shown in Figure 12, suggests that wave packets with smaller horizontal wavelengths, packet widths and lengths are concentrated between $L_s = 0°–30°$ and between $L_s = 280°–340°$. These periods correspond to the spring and winter in the northern hemisphere respectively. Figure 12 also shows larger PW values during MY28, during dust storm season.

## 6. Summary and Conclusions

In this study, we present a systematic study of AGW on Mars with data from the OMEGA instrument onboard the Mars Express space mission from the MY26 to MY29. The apparent wave packets in the images were morphologically characterized, being interpreted as AGW propagating on Mars' lower atmosphere. Previous works already found GW in OMEGA/MEx images (Määttänen et al., 2010; Spiga et al., 2012), and in this study our goal was to take advantage of the unexplored data set to conduct a search and analysis of gravity waves and their properties, and examined their spatial, temporal and seasonal distribution.
<figure><img src="image_14.png" /><figcaption>**Figure 11.** Distribution of the morphological properties of the characterized wave packets with the local true solar time for the Martian Years: MY26 (in red diamond-shape), MY27 (in yellow circle), MY28 (in blue square), and MY29 (in green triangle). Top-left panel: horizontal wavelength; top-right panel: packet width; bottom-left panel: packet length; bottom-right panel: orientation.</figcaption></figure>

For the detected wave packets on the data set used in this work, only the clouds composed of water ice were considered for characterization, taking advantage of the method previously developed by Langevin et al. (2007); Madeleine et al. (2012); Szantai et al. (2021) and fine-tuned for this work.

Atmospheric waves were mainly detected in the northern hemisphere of Mars, due to the geometrical observation bias of OMEGA images since a large percentage of these images did not meet the requirements for detecting the presence of these waves. We semi-manually identified a total of 263 wave packets, 125 of which were manually morphologically characterized. To critically evaluate the impact of these observational constraints, we performed an explicit analysis of how the selection bias affects our study's outcomes. By prioritizing images with a spatial resolution finer than 1.5 km and a longitudinal FOV between 3 and 40°, we inherently limit our data set to observations that are optimally positioned, especially for wave packet characterization. This approach, while necessary for ensuring the reliability of our observations, may lead to an underrepresentation of atmospheric phenomena that occur outside these ideal conditions. The distribution of detected wave packets varies across different regions of Mars, with some areas exhibiting a maximum of 35% detection rate, while the average detection rate is 8% for the filtered data set considering only images in a nadir pointing mode, with a field of view between 3 and 40 degrees of longitude and a spatial resolution below 1.5 km/pix. Although the data coverage is denser in the southern hemisphere due to the geometry of the observations (see Section 4 and Figure 4), wave packet detections are more frequent in the northern hemisphere. This disparity arises from the observation geometry and conditions more favorable for wave detection in the northern hemisphere.

The morphological properties of the gravity waves in this work, namely the horizontal wavelengths, range from 6 to 83 km, which is consistent with previous observations Heavens et al. (2020) made with the MCS/MRO. Their study reported meridional propagating GWs with horizontal wavelengths between 10 and 30 km in nadir observations at an altitude of 25 km. In the present work we found lower horizontal wavelengths on large structures
<figure><img src="image_15.png" /><figcaption>**Figure 12.** Distribution of the morphological properties of the characterized wave packets with respect to the solar longitude in the x-axis for the Martian Years: MY26 (in red diamond), MY27 (in yellow circle), MY28 (in blue square), and MY29 (in green triangle). Top-left panel: horizontal wavelength; top-right panel: packet width; bottom-left panel: packet length; bottom-right panel: orientation.</figcaption></figure>

called cloud streets. Some of these wave packets have horizontal wavelengths from 6 to 8 km. No relevant literature explicitly addresses the statistics of PW and PL measurements for gravity waves on Mars atmosphere. However, it is important to note that our results are biased by the observational constraints of OMEGA images, and the geometry and FOV introduce an important source of errors, as discussed in Section 4.

From Figure 5 bottom panel, we observed that the spatial distribution of gravity waves is predominantly found in the northern hemisphere, in regions such as Alba Mons, Tempe Terra, Chryse Planitia, Utopia Basin, Elysium Mons, and the Tharsis Montes. Previous results from Ogohara and Ro (2023) pointed out that cloud trains on the eastern side of Phlegra Montes can be formed of mountain lee waves in the northern winter season and have horizontal wavelengths between 20 and 40 km. However, our measurements show no significant wave activity in that region.

The detected waves exhibit spatial and temporal variability, with their occurrence predominantly found in regions with significant topographic features during the winter season of both hemispheres and between 13 and 17 hr LTST. Notably, the wave activity demonstrates a higher frequency during daytime hours, particularly within the 14–16 hr LTST range, as shown in Figure 9. Those observations align with previous studies that reported higher
wave activity during daytime hours (Heavens, Pankine, Battalio, Wright, Kass, et al., 2022). Additionally, our results show wave activity is more prevalent during the winter season in both hemispheres, suggesting a relationship between seasonal variations and gravity wave generation. During MY28 we observed that the large majority of waves detected on that year was just before the onset of the global dust storm that took place that year, at $L_s = 260°$.

Our detection of waves predominantly during the winter season and their association with daytime hours further suggests that seasonal atmospheric circulation patterns and solar heating may enhance the generation and observability of orographic GWs. Moreover, the observed increase in wave activity just before the onset of the global dust storm in MY28 at $L_s = 260°$ points to the potential for orographic GWs to interact with and possibly be modulated by large-scale atmospheric events.

Overall, our study contributes to the understanding of Martian gravity waves and their potential impact on Mars' lower atmosphere. Our results provide insights into gravity waves' spatial and temporal distribution and their morphological properties. In this study, the morphological properties of the wave packets were addressed with the limitations inherent in OMEGA images, and the spatial, temporal and seasonal variations were investigated. Future studies with the HRSC instrument will help to retrieve the phase velocity of the waves detected in the present study, which is crucial for determining the nature of these waves, allowing for a better interpretation and understanding of the atmosphere dynamics of Mars.

## Data Availability Statement

A list of observations used in this paper is provided in the Mendeley Data repository. All observations are available in ESA PSA (PSA, 2023a; PSA, 2023b, 2023c) and NASA PDS (PDS, 2025). We uploaded the raw images and the ones as they were used in this study to a repository publicly available (Brasil, 2024). We acknowledge the use of data from the Mars Express mission.

## Acknowledgments

This work was supported by the Portuguese Fundação 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 2020.06389.BD and 2021.05455.BD. GG acknowledges support from Junta de Andalucia through the program EMERGIA 2021 (EMC21_00249) and the 'Center of Excellence Severo Ochoa' award to the IAA (SEV-2017-0709). IAA is also supported by grant PID2022- 137579NB-100 funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe." Funded by the ESA Faculty Research Contract and Science Exchange Programme, which is in the frame of the MWWM - Mars Wind and Wave Mapping project of reference ESA RFP/3-17570/22/ES/CM. We would like to thank the Mars Express Team for their support and encouragement to start this work. We would like to thank the DLR team, especially Dr Klaus-Dieter Matz, Dr Daniela Tirsch, and Dr Thomas Roatsch, for their support on HRSC data and software. We would like to thank Aurélien Stcherbinine for his help with the OMEGA-Py Python scripts. We also would like to thank the late Dr Brigitte Gondet for her considerable help that made this work possible. Open access publication funding provided by FCT (b-on).

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