JuanCastillo29/space-mission-intelligence-data huggingface dataset:
��𝑎𝑑(𝜆) = ∫ 𝑘(𝜆, 𝑧)𝑑𝑧
𝐻2 𝐻1
of the whole detached layer at 3708 cm−1 (2.7 μm, MIR) and 13109 cm−1 (0.8 μm, NIR) equals, respectively, 1.7·10-2 and 2.5·10-2. A list of all CO2 detection cases with corresponding observation parameters and retrieved aerosol properties is gathered in Table 2.

From the TIRVIM extinction data, the higher detached layer spans from 63 to 80 km, although aerosol extinction coefficient at 3708 cm-1 is detected only starting from 65 km (Panel e of Figure 7). At altitudes 63−65 km, both dust and CO2 ice unimodal distribution models provide χ2 < 1, and, according to our retrieval procedure, are disregarded. Two sublayers inside the higher detached layer can be extracted from the TIRVIM data. Effective radius is in the 0.2−0.5 μm range with median value of 0.4 μm. Optical depth of the whole detached layer at 3708 cm−1 (TIRVIM) equals 10-2.
The lower detached layer is composed of both H2O ice and CO2 ice particles. The presence of two sublayers of carbon dioxide aerosols is revealed by MIR, the lower one being faint and barely detectable (Panels a and c of Figure 7). Effective radius of CO2 ice particles in the lower detached layer is in 0.1−0.4 μm range with a median value of 0.2 μm. In case of TIRVIM, SNR of extinction coefficient at 3708 cm-1 in this detached layer is slightly above unity, and, as a consequence, CO2 ice particles are only detected but their properties are not retrieved. Instead, TIRVIM detects H2O ice particles with effective radius 0.3−1.2 μm at 51−59 km. Optical depth of the detached layer at 3708 cm−1 (MIR) and 13109 cm−1 (NIR) equals, respectively, 4·10-3 and 3·10-3.

[Table: Table 2. Summary of TIRVIM and MIR occultations with detected CO2 clouds and retrieved microphysical properties of the CO2 ice particles. Columns with reff, N and M contain minimal-median-maximal values. Smax contains maximal value of saturation ratio within the extended by 5 km altitude boundaries calculated from NIR and MIR pressure and temperature data, respectively. Column with τ
shows nadir optical depth of the CO2 cloud layer at 3708 cm−1 and 13109 cm−1. H2O ice column indicates simultaneous detections (✓)] of CO2 ice and H2O ice particles from TIRVIM data, possible detections (?) from MIR data, and no detections (✘) of H2O ice particles.

Loc. time [h] z [km] reff [μm] N [cm-3] M [10-14
Smax NIR;
Occ. Channel LS [°] (MY) Lat. Eastern
g/cm3]
Long.
Smax MIR τ×10-3 H2O ice 3502_E TIRVIM 244.4 (34) 27°S −65° 5.2 72−82 0.6−0.7−0.8 0.02−0.1−0.3 5−20−30 0.08; −
✓
3; 4
49−55 0.1−0.2−0.4 0.7−6−200 20−60−200 [7; 9]·10-4 4; 3 ? 61−79 0.1−0.2−0.6 0.1−10−90 7−40−300 0.07, 0.03 17;25 ✘
MIR
5401_E
337.6 (34) 50°N −104° 6.8
47−56 —1 —1 —1 —1
49−55 0.1−0.2−0.4 0.7−6−200 20−60−200 [7; 9]·10-4 4; 3 ? 61−79 0.1−0.2−0.6 0.1−10−90 7−40−300 0.07, 0.03 17;25 ✘
MIR
5401_E
337.6 (34) 50°N −104° 6.8
47−56 —1 —1 —1 —1

✓
TIRVIM
65−80 0.2−0.4−0.5 0.4−2−20 20−50−100 0.07; 0.03 10; 22 ✘ 6452_I TIRVIM 20.7 (35) 1°S 152° 18.0 59−78 0.2−0.8−0.9 0.03−0.3−5 6−80−200 10; − 18; 21 ✘ 6602_E TIRVIM 26.4 (35) 6°S 7° 6.1 76−86 0.1−0.15−0.21 10−40−500 20−100−200 1; − 7; 9 ✘ 9192_I MIR 121.0 (35) 5°S 19° 17.9 55, 572 2.2 ± 0.1, 1.01 ± 0.052 0.03, 0.42 200, 2002 0.03; 0.02 4; 3 ?
62−70 0.1−0.8−1.9 0.1−2−600 70−500−1000 0.8; 0.02 40; 37 ?
50−65 0.1−0.1−0.3 3–20–60 20−30−40 [8; 3]·10-3 3; 6 ✘
10738_E MIR 186.5 (35) 21°N 140° 6.1
72−74 0.2−0.25−0.3 0.2−0.2−0.6 2−2−3 0.2; 0.06 [5; 8]·10−2 ✘ 79−90 0.1−0.1−0.3 0.3−30−70 2−10−40 1; 0.3 3; 0.6 ✘ 14059_E MIR 349.1 (35) 30°S −91° 5.8 45−62 0.13−0.16−0.4 0.4−10−30 6−20−60 [2; 2]·10-3 7; 20 ✘ 48−52 0.1−0.2−0.3 5−13−70 30−50−60 [5; 5]·10-3 1; 3 ? 58−65 0.1−0.11−0.23 5−40−100 8−40−50 0.2; 0.3 2; 1 ✘
14501_I MIR 7.3 (36) 12°N 13° 18.1
71−79 0.1−0.16−0.3 0.3−4−20 3−5−10 4; 4 0.5; 0.8 ✘ 15965_E MIR 62.1 (36) 42°S −46° 7.4 53−59 0.29−1−1.1 0.02−0.2−70 15−25−80 0.1; 0.07 3; 3 ✘
) 12°N 13° 18.1
71−79 0.1−0.16−0.3 0.3−4−20 3−5−10 4; 4 0.5; 0.8 ✘ 15965_E MIR 62.1 (36) 42°S −46° 7.4 53−59 0.29−1−1.1 0.02−0.2−70 15−25−80 0.1; 0.07 3; 3 ✘

| 16825 E _ | MIR | 92.9 (36) | 54°S | 143° | 8.7 | 58−63 | 0.1−1.9−2.2 | 0.1−0.3−70 | 4−500−2000 | 0.3; 20 | 29; 31 | ✘ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 19076 E _ | MIR | 184.1 (36) | 49°N | −23° | 6.1 | 39−54 | 0.13 | 40−50−603 | 20−30−403 | [3; 4]·10-4 | 5; 3 | ✘ |
|  |  |  |  |  |  | 69−76 | 0.13 | 30−70−1003 | 20−40−603 | 0.05; 0.09 | 5; 4 | ✘ |
|  |  |  |  |  |  | 80−84 | 0.13 | 16−20−40 | 10−13−20 | 1; 0.3 | 1; 0.5 | ✘ |

1 Properties of CO2 clouds were not retrieved from TIRVIM data at these altitudes due to the lower SNR compared to MIR. Instead, TIRVIM detected presence of H2O particles.
2 Two MIR measurements were made in this CO2 cloud layer. Here for reff, N and M, we give exactly the values at probed altitudes instead of minimal-median-maximal values throughout the layer.
3 All best fits in these cases give the same reff = 0.1 μm, see Section 4.2.
2.5 cm-1 (both for reff = 0.01 μm and for reff = 0.1 μm with veff = 0.1) compared to 1.3 cm-1 from the MIR data. This fact indicates that the model poorly represents extinction cross section for very small CO2 ice particles due to a low spectral resolution of refractive index, and that the actual effective radius of the particle could be <0.1 μm. A high-resolution (0.1 cm-1) absorption spectra of solid-state CO2 ice gives 1.1 cm-1

FWHM of 3708 cm-1 band at 75 K (Isokoski et al., 2013) which is close to our experimental data. Hence, a better resolved CO2 ice refractive index (better than 0.1 cm-1 and, preferably, for a wide range of temperatures) is needed for a proper modeling of small CO2 particles and retrieval of their microphysical properties from the 2.7 μm band. Our results are in a good agreement with most previous observations: reff =0.1−0.3 μm from the Pathfinder lander images (Clancy & Sandor, 1998), reff ≤ 1.5 μm from the TES data (Clancy et al., 2007), reff = 1–3 μm with median values of 2.0–2.3 μm from the OMEGA observations (Määttänen et al., 2010), reff = 0.5–2 μm and reff = 0.3−2.2 μm from the CRISM measurements (Vincendon et al., 2011 and Clancy et al., 2019, respectively), reff = 0.08–0.13 μm from the SPICAM UV data (Montmessin et al., 2006), and reff = 0.09–0.11 μm from the UIVS observations (Jiang et al., 2019). From the NOMAD data, Liuzzi et al. (2021) has reported retrievals of effective radii from 0.1 up to 4.5 μm; such large values exceed both previous studies and our observations.
Retrieved mass loading was distributed in range 2×10-14–2×10-11 g/cm3 with a median value of 2×10-13 g/cm3. Nadir optical depth of the CO2 clouds at 3708 cm-1 (2.7 μm) and 13109 cm-1 (0.8 μm) was in the range 5×10-4–4×10-2 with median values 5×10-3 at both wavenumbers. Montmessin et al. (2006) retrieved comparable τnad = 6×10-3–5×10-2 at 0.2 μm from the SPICAM UV stellar occultations, while τnad = 0.01–0.5 at 1 μm from the OMEGA observations (Määttänen et al., 2010) and typical value τnad = 0.2 at 0.5 μm from the CRISM measurements (Vincendon et al., 2011) were larger than our results, as expected from the nadir observations.
An extremely thin cloud was once detected on the ingress occultation #9192 when only two MIR measurements at 55 km and 57 km were made within the CO2 cloud layer. It was composed of quite large particles with reff = 2.2 ± 0.1 μm and reff = 1.01 ± 0.05 μm, M = 2×10-12 g/cm3 at both altitudes and extremely small nadir optical depth of 5×10-5 and 8×10-5 at 3708 cm-1 and 13109 cm-1.

[Figure: Figure 8. Map of the CO2 clouds detections from the 2.7 μm absorption band from the ACS instrument. Top: Seasonal and latitudinal distribution of solar occultation observations. Circles]
reff = 2.2 ± 0.1 μm and reff = 1.01 ± 0.05 μm, M = 2×10-12 g/cm3 at both altitudes and extremely small nadir optical depth of 5×10-5 and 8×10-5 at 3708 cm-1 and 13109 cm-1.

[Figure: Figure 8. Map of the CO2 clouds detections from the 2.7 μm absorption band from the ACS instrument. Top: Seasonal and latitudinal distribution of solar occultation observations. Circles]

and triangles correspond to MIR and TIRVIM observations. No CO2 clouds detection is shown in grey, detection at the morning (evening) terminator is in blue (red). Bottom: Latitudinallongitudinal map of the CO2 clouds detections from the ACS instrument with the solar longitude given by the color code. Squares and triangles represent detections made at the morning and at the evening terminators, respectively. Contour lines represent the Mars Orbiter Laser Altimeter (MOLA) altimetry data. Black lines correspond to negative values taken with 1 km step, red lines — positive values with 3 km step.
From the TIRVIM data, two out of five cloud layers were formed by CO2 and H2O ice aerosols. They were located at ~50 km and ~75 km. Effective radius of water ice particles was in range 0.1−0.2 μm and smaller than CO2 particles at the same altitudes in one case, while in the other 0.4−1.2 μm H2O ice particles were larger compared to carbon dioxide ice.
The MIR spectra analyzed in this work does not allow an unambiguous simultaneous detection of H2O and CO2 aerosols due to lack of absorption signatures of mineral dust and H2O ice in the studied spectral region. If both types of clouds are observed by MIR, models with CO2 particles and either dust or water ice produce essentially the same quality of fit and can not be discriminated. There are four such cases (including the one when TIRVIM detected H2O ice particles) out of 16 detected CO2 cloud layers; they are shown as question marks in Table 2. Effective radius of potential water ice particles is in the range 0.1−0.9 μm.
Recently, Määttänen et al. (2022) has coupled a CO2 cloud microphysical model (Listowski et al., 2014, 2013) with the LMD Mars Global Climate Model (Forget et al., 1999) featuring a detailed H2O cloud microphysics (Navarro et al., 2014). Among other improvements, they have included a possibility for CO2 ice to form on water ice crystals in addition to mineral dust as on condensation nuclei. It resulted in a significant increase in the amount of modeled mesospheric CO2 ice clouds at low and middle latitudes. In our analysis, we did not study the question whether the water ice aerosols that were detected simultaneously with carbon dioxide aerosols are in the form of separate particles or encapsulated within the composite H2O/CO2 particles. In order to do that, a dedicated model of composite aerosol particle extinction should be added to the fitting procedure.
We analyzed 1663 ACS solar occultation observations that contain spectra of carbon dioxide ice combination mode ν1 + ν3 at 3708 cm−1 (2.7 μm). This characteristic spectral signature directly seen in our spectra allows us to detect mesospheric CO2 clouds on Mars, unambiguously discriminating them from mineral dust and water ice aerosols. Unlike other approaches that rely on the information of atmospheric thermal conditions to deduce type of the observed aerosols, we use temperature profiles from the same occultation sessions to retrieve CO2 saturation ratio at the location of carbon dioxide clouds, thus providing important for future numerical modeling information on the state of the CO2 atmosphere of Mars. The key results of our study are as follows:
1. Overall, CO2 clouds were detected in eleven solar occultation observations. In five cases, there were two or three layers of CO2 clouds that were vertically separated by gaps. Carbon dioxide ice was detected from 39 km to 90 km, typical gap width was 5 km, reaching 15 km in a single instance.

2. Effective radius of CO2 aerosol particles was in the range of 0.1−2.2 μm with a median value of 0.6 μm. Spectra of ν1 + ν3 combination mode in case of smallest particles indicate a need for a better resolved CO2 ice refractive index. 3. Mass loading ranged from 2×10-14 to 2×10-11 g/cm3 with a median value of 2×10-
13 g/cm3. Nadir optical depth of CO2 clouds at 3708 cm−1 and 13109 cm−1 was in the range 5×10-4–4×10-2 with median values 5×10-3 at both wavenumbers.
4. Diurnal distribution of detections is significantly asymmetrical: only three detections at the evening terminator versus eight at the morning one. A possible explanation is local time variations of temperature induced by thermal tides. 5. Analysis of ACS TIRVIM spectra that include water ice and carbon dioxide ice absorption features revealed that two out of four cases of CO2 clouds detections were accompanied by observations of H2O aerosols. 6. Supersaturation S > 1 was detected in only 5 out of 19 cases of CO2 cloud layers;
extremely low values S < 0.1 were found in 9 out of 19 cases. Interpretation of these observations requires a comprehensive microphysical modeling coupled with a GCM to account for evolution of temperature.
One potential extension to the current work involves investigation of the 4.3 μm carbon dioxide ice band, which may lead to detection of CO2 clouds at higher altitudes due to its stronger absorption. ACS TIRVIM and ACS MIR data can be utilized for this purpose since they both include described spectra. Besides that, a systematic analysis of aerosol and temperature profiles retrieved from a comprehensive dataset of ACS NIR channel gathered during more than three Martian years could reveal climatology of CO2 clouds, along with their spatial and diurnal variations.
storm as observed by the ACS-MIR channel onboard
the Trace Gas Orbiter

A. Stcherbinine1,2, M. Vincendon1, F. Montmessin2, M. J. Wolff3, O. Korablev4, A. Fedorova4, A. Trokhimovskiy4, A. Patrakeev4, G. Lacombe2, L. Baggio2, A. Shakun4
1Institut d’Astrophysique Spatiale, Universit´e Paris-Saclay, CNRS, 91405 Orsay, France 2LATMOS/IPSL, UVSQ Universit´e Paris-Saclay, Sorbonne Universit´e, CNRS, 78280 Guyancourt, France 3Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, Colorado, 80301, USA 4Space Research Institute (IKI), 84/32 Profsoyuznaya, 117997 Moscow, Russia
• Monitoring of Martian water ice clouds and derivation of vertical profiles of particle size using the 3 µm spectral band.
• Observation of mesospheric water ice clouds at altitudes ≥90 km during the MY 34 GDS.
• Evidence of water ice particles ≥1.5 µm between 50 km and 70 km during the GDS.
Corresponding author: Aur´elien Stcherbinine, aurelien.stcherbinine@ias.u-psud.fr The Atmospheric Chemistry Suite (ACS)
instrument onboard the ExoMars Trace Gas Orbiter (TGO) ESA-Roscosmos mission began science operations in March 2018. ACS Mid InfraRed (MIR) channel notably provides solar occultation observations of the martian atmosphere in the 2.3 – 4.2 µm spectral range. Here we use these observations to characterize water ice clouds before and during the MY 34 Global Dust Storm (GDS). We developed a method to detect water ice clouds with mean particle size ≤2 µm, and applied it to observations gathered between Ls = 165◦and Ls = 243◦. We observe a shift in water ice clouds maximum altitudes from about 60 km before the GDS to above 90 km during the storm. These very high altitude, small-sized (reff ≤0.3 µm) water ice clouds are more frequent during MY34 compared to non-GDS years at the same season. Particle size frequently decreases with altitude, both locally within a given profile and globally in the whole dataset. We observe that the maximum altitude at which a given size is observed can increase during the GDS by several tens of km for certain sizes. We notably notice some large water ice particles (reff ≥1.5 µm) at surprisingly high altitudes during the GDS (50 – 70 km). These results suggest that GDS can significantly impact the formation and properties of high altitude water ice clouds as compared to the usual perihelion dust activity.
In this article, we use data from the Atmospheric Chemistry Suite (ACS) infrared spectrometer onboard the European Space Agency (ESA)-Roscosmos Exomars Trace Gas Orbiter (TGO) mission to study water ice clouds in the Martian atmosphere. More specifically, we aim to characterize the evolution of their altitude, geographic distribution and microphysical properties before and during the planet-wide dust storm that occurred during the summer of 2018. In particular, we developed a method to simultaneously detect the water ice clouds and constrain their particle size using simulated spectra of water ice. We observe that the maximal altitude of the clouds increased from 60 km to above 90 km during the storm. Most high altitude clouds have small particle sizes (lower than 0.3 microns) as expected from the low pressure at such altitude. However, we also observe for the first time large (larger than 1.5 microns) water ice particles at unusually high altitude (higher than 60 km), uniquely during the storm. This suggests that the increased atmospheric activity associated with global dust storm significantly impacts water ice clouds formation.
.

Thus, understanding the extent to which high altitude water vapor condenses to form clouds during GDSs is important in assessing the possible propagation pathways of water vapor up to the exobase (Neary et al., 2019).

Because water vapor can exist in a supersaturated state in the Martian atmosphere (Maltagliati et al., 2011) and has been observed in a large amount during the MY 34 GDS (Fedorova et al., 2020), simply supposing that the freezing point can be used to set the limits where water is free to be mobilized is likely to be problematic.

The availability of condensation nuclei, supplied by the ubiquitous atmospheric dust particles, and possibly by interplanetary dust particles and micrometeoric smoke that aggregates as entering the Martian atmosphere (Crismani et al., 2017; Plane et al., 2018; Hartwick et al., 2019), is another key factor controlling the potential for water ice clouds.

It has for example been shown by a number of studies (Michelangeli et al., 1993) that the occurrence of supersaturation is anti-correlated to the availability of condensation nuclei.

The condensation nuclei themselves are expected to be much more abundant during GDS, including with larger sizes (e.g.

Wolff & Clancy, 2003; Clancy et al., 2010; Lemmon et al., 2019), which may impact how and when water ice clouds form.

It is however difficult to predict the distribution and properties of condensation nuclei at high altitude during GDS, which additionally modify the thermal structure of the atmosphere.

In this complex situation, accumulating observational constraints about the actual behavior of water ice at high altitude during GDS is required to provide further constraints on the fate of high altitude water vapor during these events.

The ExoMars Trace Gas Orbiter (TGO), ESA-Roscosmos Atmospheric Chemistry Suite (ACS) and Nadir and Occultation for MArs Discovery (NOMAD) instruments started their science operation phase in March 2018 (Korablev et al., 2018; Vandaele et al., 2018; Korablev et al., 2019; Vandaele et al., 2019), before the MY 34 GDS.

The ACS middle-infrared (MIR) channel is a crossed-dispersion echelle spec∼300 nm wide spectral interval selected between 2.3 and 4.2 µm.
This interval is set
by rotating the spectrometer secondary grating (i.e., the diffraction order separation)
to align the interval of interest with the detector. The cross-dispersion aspect provides
from 10 to 21 diffraction orders (spectral segments) stacked on top of each other. The
separation between consecutive orders is only dependent on the number of orders dis-
played at once and thus on the secondary grating position. Thirteen different positions
can be employed to completely sample the full accessible spectral range of the instru-
ment. With the two hours orbital period of TGO and the performing of SO in the
mid-infrared, ACS and NOMAD provides a totally new and huge dataset of vertical
profiles of the atmospheric extinction (Korablev et al., 2018; Vandaele et al., 2018).
We are using the ACS-MIR channel to study the extinction properties of the Martian
airborne particles in the 3 µm spectral region, which possesses a distinct diagnostic
capability to identify the O-H stretching signature; whether it is due to water ice ab-
sorption or bound water in dust. In the case of water ice, the depth and shape of the
feature depend on both the abundance and the particle size of water ice (Vincendon
et al., 2011; Guzewich et al., 2014; Clancy et al., 2019).
The ACS-MIR occulta-
tion data in this spectral range gives us complementary information to those derived
from previous limb and nadir scattered light observations by OMEGA (Bibring et al.,
2004; Vincendon et al., 2011) and CRISM (Murchie et al., 2007; Clancy et al., 2019;
Guzewich et al., 2014). We focus here on the use of the water ice absorption feature
during the MY 34 GDS that offer a relevant framework for a further characterization
of the interactions between water and dust during extreme dust events.
configuration centered on the 3.1 – 3.4 µm spectral range corresponding to the 12th
secondary grating position. Figure 1 presents the spatial and temporal distribution of
the position 12 MIR observations used in this study, covering a temporal range from
in the periods near local times of 06:00AM and 06:00PM.

Latitude [°]
Ls [°]
150 100 50 0 50 100 150 Longitude [°]
Every ACS-MIR observation is composed of ∼20 spectral segments individually exhibiting broad instrumental shapes that generally prevents contiguous segments from matching precisely in their overlapping wavelength intervals (cf. figure 2) due to instrumental effects (Korablev et al., 2018; Trokhimovskiy et al., 2015). As the aerosol property derivation is intrinsically dependent of the continuum behavior, we have adopted a method to extract the continuum across all displayed fraction orders, taking into account this spectral shape of each order. This allows us to derive the continuum shape across the 3 to 3.4 µm range.
In order to do this, we have chosen to fit the 200 centered spectral points of each diffraction order with a 2nd degree polynomial, and use an iterative method to remove the gas absorption bands that tend to bias the continuum values represented by the fitted polynomial. After the first iteration, for each diffraction order we determine the standard deviation of the transmission values of the considered spectral points σ, and we consider that all the points with transmission lower than the polynomial fit by at least σ (i.e. Tr(λ) < (fit(λ) −σ)) are affected by some gas absorption. So we remove these points as they tend to underestimate the actual continuum. In a second step, we perform the same filtering method from a polynomial fit on each diffraction order for the remaining wavelengths (i.e. the 200 centered points without the ones with transmission values lower than (fit −σ)). Finally, we perform a third fit to the continuum and then only retain the fitted transmission value at the center point of each fitted order (cf. figure 2.b). As a result, the complete spectrum has an effective spectral resolution of one point per diffraction order, which corresponds to a spectral resolution of ∼1 −2 nm. This is a much smaller number of points than the ACS-MIR native sampling, but is sufficient to capture the general shape of the continuum used in the next sections for solid particles characterisation. Next, we estimate the uncertainties of the data as the sum of the standard deviation of the difference between the data and the polynomial fit for each order (random uncertainties), and the maximal gap between the polynomial fit of the order and the extracted continuum (1 point per order), linearly interpolated between each diffraction order (systematic uncertainties due to the curvature of the diffraction orders, see figure 2.c). Additionally, in the following we do not consider the outer regions of the spectra in order to avoid detector edge effects, i.e. the spectels corresponding to 3.10, 3.12 and 3.44 µm.