JuanCastillo29/space-mission-intelligence-data huggingface dataset:
We observe that the particle size decreases on average with increasing altitude (cf. figure 13), as previously reported by Clancy et al. (2019). This global trend is also apparent in the majority of individual profiles: particle size is observed to decrease with altitude within a given cloud as we can see in figure 14. This is true regardless of the average altitude of the considered cloud (cf. figure 14). The distribution of particle size as a function of altitude is globally shifted to higher altitudes during the GDS (cf. figure 13), i.e., a given size of particles can go higher in the atmosphere during the GDS. Clancy et al. (2019) also observe a similar trend during non-GDS years, with a given class of particle size that increases in altitude during the perihelion season (Ls = 180 −340◦).
While trends can be identified on a global scale, some variability is also evident, as seen in figure 15. As the main clouds altitude increases, water ice particles become distinctly smaller in the storm, with a median effective radius of 0.28 µm before the dust storm and ≤0.1 µm during it. This narrower range of smaller particles, with a strong peak for reff ≤0.2 µm, is typical of the mesospheric clouds (Clancy et al., 2019, figure 12).
Additionally, we also observe the appearance of a small population of larger water ice particles, with reff ≥1.5 µm, at surprisingly high altitude (between 55 km and 64 km) in view of previous observations and studies (e.g. below 40 – 50 km for Guzewich & Smith, 2019). Their identification as water ice particles is robust, i.e., attempts to fit using dust properties are unable to reproduce the observed wavelength dependence of the derived kext (cf. figure 16). However, we have to keep in mind that our understanding of the spectral behaviour of the martian dust still needs to be improved, in particular about the hydration properties. Nevertheless, even updated dust optical properties are not expected to be able to reproduce the spectral bump observed in figure 16, as large water ice particles can do (cf. figure 6). These detections are found below smaller water ice particles (cf. figure 16, 3rd column). Although turbulence during dust storms is expected to enable the lifting of larger particles, the exact mechanism by which these large-grained water ice clouds form or are bring at high altitude remains to be investigated. In addition to turbulence, condensation nuclei properties could also play a role (Gooding, 1986; Michelangeli et al., 1993; Montmessin et al., 2004; Hartwick et al., 2019). One possibility is that these particles are not primarily composed of water ice but could host a relatively large dusty core. Incidentally, large dust particles have been reported from the surface during this GDS, (Lemmon et al., 2019), which could play a role as condensation nuclei for the water ice. Clancy et al. (2010) indeed observed during a previous GDS (2001) that large dust particles can propagate to high altitude (sizes between 1 µm and 2 µm above 70 km).
In this paper, we present our analyses of the Martian water ice clouds in the period before and during the MY 34 GDS using ACS infrared SO observations. This novel observational geometry in the mid-infrared allows us to monitor the 3 µm water ice absorption band, and subsequently constrain the sizes of the detected water ice particles. Indeed, the shape of the wavelength profile of the 3 µm absorption band is a sensitive function of the particle size. The sensitivity of this feature combined with the high resolution of ACS-MIR allows us to derive useful constraints on the particle size distribution of the clouds between reff ≤0.1 µm and 2 µm. Moreover, the temporal range of this dataset, from Ls = 165◦to Ls = 243◦, offers an unique opportunity to observe the impact of such a dust storm on the distribution and composition of the water ice clouds in the Martian atmosphere.

Altitude [km]
Ls [°]
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 reff [µm]
ice clouds, measure their opacity, and estimate their particle size. The method
makes it possible to identify cloud for particle size smaller than 2 µm.
2. We have derived vertical profiles of water ice clouds opacity and average particle
3. We detect water ice clouds in most of the observations across the planet except
during the onset of the GDS (4/5 of our profiles show at least one detection
of water ice, and half of the non-detections are located within the onset of the
GDS).
4. We retrieve very high haze top altitudes during the GDS, notably at solar lon-
gitudes close to the onset with evidence for mesospheric clouds at altitudes
≥ 90 km, and even up to 100 km for the maximum haze top altitude. This
suggests that GDS may elevates peak haze altitudes by 10 to 20 km compared
to the typical perihelion storm season values.
5. We observe that there is a general trend of decreasing particle size with altitude
for the whole dataset, and more specifically that the particle size within a given
cloud/profile also decreases with altitude, typically from 1 to 2 µm at the bottom
of the cloud down to less than 0.2 µm at the top.
6. We note a decrease in the mean water ice particle size during the GDS, with
≤ 0.1 µm during the dust storm. This is related to an increase of the average
altitude of clouds during the GDS, with a higher proportion of mesospheric,
small-grained water ice clouds.
unexpectedly high altitudes during the GDS (between 50 km and 70 km).

Ls = 168° lat = 51°S
Ls = 170° lat = 77°N
Ls = 200° lat = 39°N
Ls = 229° lat = 28°S
2.00
1.75
1.50
Altitude [km]
1.25
reff [µm]
1.00
0.75 0.50
0.25
0 1 reff [µm]
0 1 reff [µm]
0 1 reff [µm]
0 1 reff [µm]
Typical vertical profiles of water ice clouds particle size before (a & b) and during

[Figure: Figure 14.]

(c & d) the GDS. Each panel represents the same profile in two manners: first the vertical line on the left where reff is represented using a color scale (same as figure 10); and second the green lines with reff on the x-axis and the uncertainties shown by the shadowed regions. We observe that the size of the water ice particles of the cloud decreases as the altitude increases, regardless of the mean altitude of the cloud. The GDS is characterized by a shift in altitude of the particle size distribution (∼30 km higher, see profile c compared to a and b), along with a decrease of particles with size between 0.5 µm and 1 µm (cf. figure 15). But some GDS profiles show evidence of larger-grained layers (> 1 µm) as we can see at 60 km in the profile d.
The ACS experiment is led
by IKI Space Research Institute in Moscow. The project acknowledges funding by
Roscosmos and CNES. Science operations of ACS are funded by Roscosmos and ESA.
Science support in IKI is funded by Federal agency of science organization (FANO).
MJW acknowledge the support of the D’Alembert Fellowship program. Raw ACS data
are available on the ESA PSA at https://archives.esac.esa.int/psa/#!Table%
20View/ACS=instrument. Derived opacity and particles sizes can be foud at http://
dx.doi.org/10.17632/w7yff8r97s.1.
Mars with ACS onboard TGO

Aur´elien Stcherbinine1,2, Franck Montmessin2, Mathieu Vincendon3, Michael J. Wolff4, Margaux Vals2, Oleg Korablev5, Anna Fedorova5, Alexander Trokhimovskiy5, Gaetan Lacombe2, Lucio Baggio2
1Department of Astronomy and Planetary Science, Northern Arizona University, Flagstaff, AZ 86011, USA 2LATMOS/IPSL, UVSQ Universit´e Paris-Saclay, Sorbonne Universit´e, CNRS, 78280 Guyancourt, France 3Institut d’Astrophysique Spatiale, Universit´e Paris-Saclay, CNRS, 91405 Orsay, France 4Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, CO, 80301, USA 5Space Research Institute (IKI), 84/32 Profsoyuznaya, 117997 Moscow, Russia
• Cloud altitude varies by 20 to 40 km between winter and summer, and between polar and midlatitudes.
• The GDS in MY 34 increases the altitude of the clouds by 10 to 20 km compared
to the same season in MY 35.
• Clouds are predicted to be lifted at lower altitudes in the Mars PCM compared to ACS detections.
Corresponding author: Aur´elien Stcherbinine, aurelien.stcherbinine@nau.edu The middle infrared (MIR)
channel of the Atmospheric Chemistry Suite (ACS) instrument onboard the ExoMars Trace Gas Orbiter (TGO) ESA-Roscosmos mission has performed Solar occultation measurements of the Martian atmosphere in the 2.3– 4.2 µm spectral range since March 2018, which now covers two Martian Years (MY). We use the methodology previously developed for the study of the MY 34 Global Dust Storm (GDS) (Stcherbinine et al., 2020) to monitor the properties (effective radii, extinction, altitude) of the Martian water ice clouds over the first two Martian years covered by ACS-MIR. The observations encompass the period Ls = 163◦in MY 34 to Ls = 181◦in MY 36. We determine that the typical altitude of the clouds varies by 20 to 40 km between the summer and winter, with a maximum extension up to 80 km during summer in the midlatitudes. Similarly, we also note that for a limited temporal range, the altitude of the clouds also varies by 20 to 40 km between the polar regions and the midlatitudes. We also compare observations acquired during the MY 34 GDS to observations from the same period in MY 35, using that latter as a reference to characterize the effects of this GDS on the clouds’ properties. In addition, we compare our retrievals with the predictions of the Mars Planetary Climate Model (PCM), which shows a reasonable agreement overall for the altitude of the clouds, although the model usually predicts lower altitudes for the top of the clouds.
We use data from the Middle InfraRed (MIR) channel of the Atmospheric Chemistry Suite (ACS) instrument onboard the ExoMars Trace Gas Orbiter mission, which has been probing the Martian atmosphere for two Martian years (MY), to study the properties of the water ice clouds in the Martian atmosphere. We observe that the clouds’ altitude increases by 20 to 40 km during Summer and that they are also observed 20 to 40 km higher around the equator compared to the polar regions. This highlight the spatial and temporal diversity of the Martian water ice clouds, along with the large amplitude of the annual variations of the atmospheric structure. In addition, we contrast observations acquired during the Global Dust Storm (GDS) event of Summer 2018 (MY 34) with data from the following MY that we use as a reference to understand the effects of the GDS on the clouds, which reveals that the presence of the GDS increases the altitude of the clouds by 10 to 20 km. Finally, we compare our results to the predictions of the Mars Planetary Climate Model which shows a reasonable agreement overall, although the model tends to predict lower altitudes for the top of the clouds.
Even though the role of water ice clouds in the Martian atmosphere was not well understood for many years after the Viking missions, they are now considered to play a major role in Martian climate and weather (Richardson, 2002; Montmessin et al., 2004; Navarro et al., 2014; Montmessin et al., 2017; Clancy et al., 2017; Vals et al., 2022; Rossi et al., 2022). Clouds perform an important role in the Martian water cycle as they are a major actor in the inter-hemispheric water exchange (Clancy et al., 1996; Montmessin et al., 2004, 2017). Similarly to atmospheric dust particles, water ice crystals also absorb and scatter incoming solar radiation, thus impacting the atmospheric structure and temperature (Wilson et al., 2007, 2008; Haberle et al., 2011; Madeleine et al., 2012; Navarro et al., 2014). In addition, the formation of clouds affects the ability of water (or hydrogen) to be further mobilized and to escape from the planet. However, more observational data are needed to better characterize the properties of water ice clouds in order to understand and model the evolution of the Martian atmosphere (Vals et al., 2018).
120 km above the surface, which is free from atmospheric absorption in this spectral
range. Indeed, we observed in Stcherbinine et al. (2020) (where transmittances were
computed relative to 150 km) that the haze top altitude only reaches 100 km during
an extreme event like the MY 34 GDS, but without extending over 105 km. Data
calibration and geometry calculations are described in Trokhimovskiy et al. (2020)
and Olsen et al. (2021).
acquire new data in the grating position 12, allowing us to observe the atmospheric
3 µm absorption band by covering the 3.1–3.4 µm spectral range (Korablev et al.,
2018). We do not consider the observations acquired in the so-called ”partial mode”
(i.e., with a smaller number of diffraction orders) around the conjunction of Mars
and Earth (when the downlink from the instruments is very limited). A total of 514
in this study. The spatial and temporal distribution of these observations is shown in
Figure 1. Due to the SO geometry, observations naturally occur in the periods near
dawn and twilight.
we extract the spectral continuum for each observed altitude from the 20 spectral
segments (i.e., diffraction orders) that compose the ACS-MIR observation (Korablev
et al., 2018; Trokhimovskiy et al., 2015). For each diffraction order, we use successive
iterations of a median filter and a second-degree polynomial fit on the 200 centered
points to determine the transmission level of the spectral continuum at the center of the
diffraction order, while minimizing the effects of contamination by the gas absorption
bands or the broad instrumental curvature that affects the orders. This results (for
each observed altitude) in a spectrum with one point per diffraction order, with a spectral resolution of ∆λ ∼20 nm.

To avoid detector edges effects, we only consider the center of each diffraction order to process the extraction. We also ignore the outer regions of the extracted spectra, that is, the spectels corresponding to 3.10, 3.12, and 3.44 µm.
We have updated the uncertainties estimation for the ACS-MIR transmittances. Thus, as we are considering the center of each diffraction order, we include an uncertainty of ±5% of the transmission value to account for the uncorrected straylight that affects the data in position 12 (Trokhimovskiy et al., 2020).
We compute the haze top altitude (i.e., the highest altitude at which aerosols can be detected along the line of sight), defined here as the first altitude for which the transmission is greater than 0.98 over the entire spectrum, along with the extinction coefficient kext for each wavelength (forming an extinction spectrum) and altitude from the total optical depth (i.e., integrated along the line of sight, τ = −log(Tr)) using a vertical profile inversion algorithm based on the onion-peeling method (Goldman & Saunders, 1979) as described in sections 2.2 and 2.4 of Stcherbinine et al. (2020).
To identify the Martian water ice clouds in our observations, and constrain their particle sizes, we compare each retrieved extinction spectrum with models of the extinction coefficient’s wavelength dependence for either water ice or dust spherical particles (Stcherbinine et al., 2020). These theoretical extinction coefficients are computed using a public domain Mie code (Toon & Ackerman, 1981) and assuming a gamma size distribution (Hansen & Travis, 1974) with an effective variance of 0.1 (e.g., Wolff et al., 2017, and references contained within).
To avoid the problem of the presence of local minima for reff during the fitting process, we generate models on a grid of radii of 0.01 between 0.1 and 8 µm, and for each one we compute the χ2 and ∆2 values defined in equations (2) & (3), which quantify the deviation between the data and the model. Finally, we have slightly adjusted the criterion for the detection and characterization of the atmospheric water ice particles compared to Stcherbinine et al. (2020). This modification results from a better understanding of detection biases thanks to the new larger dataset, and also accounts for the new uncertainty estimate (section 2.2). The new criterion is as follows:
χ2 ν, ice < 1 � & �∆2
dust
�
> 4 � (1)
∆2
ice
Where
N
(datai −modelreff, i)2
�
χ2 ν(reff) = 1 N −2
(2)
σ2
i
i=1
and
N
(datai −modelreff, i)2
�
∆2(reff) = 1 N −2
(3)
i=1
datai The ith spectel of the kext spectra from the ACS-MIR observation. modelreff , i The ith spectel of the model extinction spectra for a particle size of reff. σi The uncertainty on the value of datai. N The number of spectral points in the considered spectrum.
–5–

Specifically, we require that:
1. the modeled extinction spectrum using water ice provides a good fit to the observational data, including the spectrum uncertainties �
χ2 ν, ice < 1 � . 2. the water ice model provides a significantly better solution than the best fit that can be obtained with a dust model. So we consider only water ice models with a mean square difference to the data lower than the one of the best dust model by at least a factor 4. This factor 4 has been determined experimentally by visual comparison between the models and the data for many spectra, to retain only those which match within the errorbars.
For each spectrum where water ice crystals are detected, we retrieve the optimal reff as the one corresponding to the model associated with the lower χ2 ν, ice that
verifies equation (1). Then, the lower (respectively upper) bound for the particle size uncertainties corresponds to the minimum (respectively maximum) value of reff in the set of models associated with χ2 ν, ice and ∆2 ice that prove equation (1).
In sections 3.1 and 3.2, we present the results of the analysis of the new data, as described in Stcherbinine et al. (2020). They cover a complete year without a GDS, from Ls = 140◦(MY 35) to Ls = 182◦(MY 36). This range corresponds to ACS-MIR operating in full-observation mode (cf. Figure 1). Then, we compare this new dataset to the previous one for the GDS year MY 34 in section 3.3.
In the following, a ”profile” refers to one set of ACS-MIR observations from the surface to the haze top, while a ”cloud” corresponds to contiguous water ice detections within a profile. Additionally, as we observe the presence of water ice clouds up to the haze top altitude in most of the profiles, we consider the haze top equivalent to the maximum altitude of the water ice clouds. Also, our water ice clouds detections extend typically over ∼20 km below their maximum altitude, thus we will only discuss in this section the maximum altitude of the water ice clouds as it is representative of the overall behavior of the water ice clouds (and the maximum altitude of the aerosols in general).
We show seasonal variations for three distinct latitude ranges in Figure 2 in order to delineate the seasonal variations from the latitudinal dependency of the clouds that will be discussed in paragraph 3.2.
As reported in previous studies (e.g., Clancy et al., 2019; Stcherbinine et al., 2020; Luginin et al., 2020; Liuzzi et al., 2020), we find a decrease in the size of the water ice crystals as altitude increases, both from an individual cloud and from a global perspective (cf. Figures 2, 3 & 4). In addition, clouds are usually found at the top of the profiles, capping the layers composed of other types of aerosols (dust or large ice crystals below).
We also observe in Figure 2 that if the maximum altitude of the clouds fluctuates with season (Jaquin et al., 1986; Forget et al., 1999; Montmessin et al., 2006; Heavens et al., 2011; M¨a¨att¨anen et al., 2013; Smith et al., 2013), the observed variations depend on the latitude. Indeed, at equatorial latitudes (45◦S ≤lat ≤45◦N, panel b), we observe the presence of water ice crystals up to 83 km around the perihelion (Ls ∼270◦), while the maximum altitude of the clouds does not exceed 50 km at Ls ∼80◦. Plus, for latitudes southward 45◦S (panel c) we note an increase of the maximum altitude of the clouds that goes from 30 km at Ls ∼160◦to 80 km at Ls ∼260◦, followed by a 45°N lat 90°N

2.0
a
1.5
Altitude [km]
reff [µm]
1.0
0.5
0.1
45°S lat 45°N
2.0
b
1.5
Altitude [km]
reff [µm]
1.0
0.5
0.1
90°S lat 45°S
2.0
c
1.5
Altitude [km]
reff [µm]
1.0
0.5
0.1
150° 180° 210° 240° 270° 300° 330° 0° 30° 60° 90° 120° 150° 180° Ls
presented in section 2.3. Each panel represents the profiles grouped by ranges of latitude
(North/Equatorial/South). Observations without water ice detections are in gray.

decrease from Ls ∼330◦that brings it back to 30 km at Ls ∼20◦. Symmetrically, in the Northern hemisphere, between 45◦N and 90◦N (panel a) the maximum altitude of the clouds moves from 35 to 20 km between Ls = 145◦and Ls = 215◦, and from 20 to 40 km between Ls = 30◦and Ls = 100◦before coming back to 25 km at Ls = 160◦.
In addition to these variations of the water ice clouds altitude with Ls, we observe in the Northern hemisphere (for latitudes greater than 45◦N; Figure 2a) a sudden increase of their maximum altitude between Ls = 236◦and Ls = 323◦, which indicates the presence of a regional dust storm in this region. This dust storm and its impact on the altitude of the water ice clouds have also been observed by the NOMAD instrument and are reported in Streeter et al. (2022). Indeed, from Ls = 225◦to Ls = 240◦the maximum altitude of the clouds goes from 35 to 80 km. That is to say, there is an increase in the altitude of the water ice clouds of about 40 km while the latitude of the observations remains similar (between 60◦N and 65◦N, cf Figure 1). The clouds remain in these high atmospheric layers (with crystals observed up to 80 km) until Ls = 325◦, even though we can observe a progressive decrease of the maximum altitude of the clouds over the storm, which goes from 80 to 65 km between Ls = 235◦and Ls = 305◦while the observed latitudes oscillate between 41◦N and 63◦N. Then, the maximum altitude of the water ice clouds suddenly decrease between Ls = 323◦and Ls = 328◦, going from 71 to 28 km. However, as 10◦of latitude separate these two observations (61.5◦N for Ls = 323◦and 71.5◦N for Ls = 328◦, plus, 71.5◦N is above the northernmost latitude of the observations of the storm), it is challenging to determine precisely the end date of the dust storm with our observations. For example, the northern limit of the MY 34 GDS was very abrupt and located under similar latitudes (Stcherbinine et al., 2020). In addition, 71.5◦N is also close to the typical boundary of the north polar vortex at this period (Ball et al., 2021), which may act as an efficient barrier to the latitudinal transport of atmospheric particles (Toigo et al., 2020).
Regarding the temporal behavior of this dust storm, we observe that it occurs in the second half of the ”dust storm season”, which illustrates the diversity of Martian dust storms and the necessity to observe and study a large number of these events (H. Wang & Richardson, 2015; Battalio & Wang, 2021). We also observe that this storm does not push clouds to altitudes as high as what has been observed during the MY 34 GDS (80 km here vs 100 km during the GDS), and that no water ice crystals larger than 1.5 µm are observed above 40 km, unlike during the GDS where such large crystals have been detected between 50 and 65 km.