JuanCastillo29/space-mission-intelligence-data huggingface dataset:
The TIRVIM channel is a Fourier-transform spectrometer (Korablev et al., 2018; Shakun et al., 2018) covering spectral range of 1.7–17 μm and working both in nadir and solar occultation geometry. We use measurements in a so-called “climatology” mode which is characterized by relatively good signal-to-noise ratio (SNR) and low measurement time (0.4 s) at the expense of lower spectral resolution and coarser spectral sampling (≃1 cm−1 and ≃0.5 cm−1 respectively).
We use spectral points obtained within the 1,760–5,200 cm−1 (2–6 μm) spectral range.
Twenty wavenumbers outside of strong gas absorption bands, including those at ~3 μm, are used for general characterization of aerosol microphysical properties and separation of water ice particles from mineral dust and carbon dioxide ice aerosols: 1,760, 2,510, 2,600, 2,700, 2,800, 2,900, 3,027, 3,072, 3,263, 3,400, 3,446, 3,919, 4,033, 4,116, 4,346, 4,478, 4,584, 4,646, 5,030,
and 5,200 cm−1 (Set 1), following the approach from Luginin et al. (2020). To distinguish between dust and CO2 aerosol particles, we analyze 2.7−2.8 μm CO2 absorption feature which includes two aerosol absorption peaks: combination modes 2ν2 + ν3 and ν1 + ν3 located at ~3600 cm−1 and ~3708 cm−1 respectively.

Spectral widths (FWHM) of both peaks are ~1 cm−1 (Isokoski et al., 2013). For CO2 ice detection and retrieval of particles’ properties, we use Set 1, all spectral points that fall within the 8 cm−1 windows centered at CO2 absorption peaks (Set 2) as well as those in 3550−3750 cm−1 range not included in Set 2 and taken with 10 cm−1 sampling
(Set 3). A description of spectral points used in the study is given in Table 1.
To increase the SNR, all spectral points are averaged using a moving average over a 2km altitude window. After this operation, resulting TIRVIM effective field of view (FOV) equals ∼12 km. SNR of Set 2 equals ~100. Additionally, Set 1 is averaged over a 50-cm-1 spectral window, providing ≥ 103 SNR. Spectral points from Set 3 are averaged over a 10-cm-1 spectral window, which gives ~300 SNR.

[Table: Table 1. Description of spectral points used in the study.
Instrument Spectral points
TIRVIM Set 1 Set 2 Set 3
1,760, 2,510, 2,600, 2,700, 2,800, 2,900, 3,027, 3,072, 3,263, 3,400, 3,446, 3,919, 4,033, 4,116, 4,346,
4,478, 4,584, 4,646, 5,030, and
5,200 cm−1.
Spectral points in 3596−3604 and 3704−3712
windows with ≃0.5 cm−1
sampling.
Spectral points in
3550−3750 cm−1 windows with 10 cm−1 sampling excluding Set
2 windows.
MIR Spectral points in 3704−3712 window with ≃0.05 cm−1 sampling.
Spectral points in 3550−3750 cm−1 windows with 2
cm−1 sampling excluding the 3704−3712 window.
NIR
6351 cm−1 7258 cm−1 10126 cm−1 11682 cm−1 13109 cm−1]
The MIR channel is a cross-dispersion echelle spectrometer dedicated to solar occultation measurements in the 2.2–4.4 µm range with a resolving power ~30 000 (Korablev et al., 2018; Trokhimovskiy et al., 2015). The diffraction orders are dispersed by a primary echelle grating to a secondary, steerable diffraction grating, resulting in a fully used 2−D detector array. In this work, we focus on secondary grating position #4 and diffraction orders 215–224, covering wavenumber range 3599–3774 cm−1.
Every diffraction order is recorded on a detector as a stripe consisting of several rows. The number of rows depends on the wavelength range and equals 31−39 for diffraction orders 224−215 respectively.

In this work, for each diffraction order we use a single row corresponding to the center of the stripe. The MIR effective FOV, with account for the integration time, equals ~ 1 km.
Combination modes of solid CO2 2ν2 + ν3 (3600 cm−1) and ν1 + ν3 (3708 cm−1) are measured in diffraction orders 215 and 221 respectfully. We don’t use ~⅙ from each side of each diffraction order due to imperfections of calibrations (‘bending’ of the continuum near the edges of the detector, e.g. see Figure 2 in Stcherbinine et al., 2020). As a consequence, combination mode 2ν2 + ν3, which is located at the edge of diffraction order 215, is disregarded during the MIR data analysis. In the spectral range 3704−3712 cm-1, we use all MIR spectral points with ~0.05 cm-1 sampling; outside of this window, spectral points are taken with 2 cm-1 sampling. We do not apply additional averaging due to much higher MIR SNR (> 103).
The NIR channel is an echelle spectrometer with a selection of diffraction orders by an acousto-optical tunable filter (Korablev et al., 2018). ACS NIR covers the 0.7–1.7 μm spectral range in diffraction orders 101 through 49 with a resolving power of λ/Δλ ≈ 28,000. During an occultation, NIR conducts measurements in 10 preselected diffraction orders in 2 s, allowing to retrieve information on gaseous abundance and aerosol microphysics. The NIR effective FOV is determined by the slit width (1.2 arcmin) and exposure time and is equal to ~1 km.
The approach on using the NIR data to characterize aerosols is the same as in Luginin et al. (2020): the NIR spectral region does not contain strong aerosol absorption bands, therefore NIR data can not be used for aerosol type determination and serves as a supplement to TIRVIM or MIR data for better definition of aerosol microphysical properties. We use “aerosol” orders 78 (10,052–10,170 cm-1) and 90 (11,500–11,734 cm-1) without strong gas absorption bands, as well as orders 49 (6318–6387 cm-1), 56 (7217-7300 cm-1), and 101 (13,016–13,170 cm-1), which contain CO2, H2O, CO, and O2 gaseous absorption bands. From the “gaseous” orders, slant aerosol transmission is obtained as a byproduct of gaseous abundance retrieval (Fedorova et al., 2020, 2023). SNR for NIR solar occultation measurements varied from 600 to 3000 depending on the averaging of detector lines.
During an occultation, the effective FOV of TIRVIM is several times larger than that of the NIR or MIR. When coupling NIR data with TIRVIM data, we apply a moving average over a 10-km window to NIR spectra profiles to simulate a coarser FOV of TIRVIM. This procedure additionally improves the SNR by a factor of ∼2. When analyzing MIR and NIR data, such procedure is not needed.
TGO started its science operations in the spring 2018. As of this writing, MIR and NIR channels are performing nominally, while TIRVIM has stopped its scientific program due to its Stirling cryocooler end of life on 2nd December 2019.
In this work, we analyze 910 TIRVIM “climatology” solar occultation observations measured from 28th April 2018 (LS 166.5° of MY 34) to 2 December 2019 (LS 115.1° of MY 35), and 864 MIR solar occultation observations with secondary grating position #4 received from 23 April 2018 (LS 164.0° of MY 34) to 10 April 2022 (LS 206.4° of MY 36). NIR channel worked together with TIRVIM (MIR) in 826 (791) observations. For the sake of brevity, hereafter we will write “TIRVIM data” instead of “TIRVIM and NIR data” and “MIR data” instead of “MIR and NIR data”.

During 111 observations, TIRVIM and MIR instruments operated simultaneously. Totally, in this work we analyze 1663 different solar occultation observations that are shown in Figure 1.

[Figure: Figure 1. Temporal and latitudinal distribution of solar occultation observations analyzed in the work. Circles and triangles correspond to MIR and TIRVIM observations, respectively. Color code represents local time of observation. Vertical dashed line marks the 2nd of December 2019 when TIRVIM has stopped its scientific operation.]
The algorithm for retrieval of aerosol extinction mostly follows the procedure described in Luginin et al. (2020). Inside the atmosphere at altitude z, the solar radiance I(z, λ) is attenuated by aerosols and gasses while passing along the line-of-sight (LOS). In this work, all altitudes are taken above the areoid. The ratio of I(z, λ) to the reference spectrum I0(λ) recorded outside of the atmosphere defines a spectrum of atmospheric transmission at altitude H: 𝑇𝑟(𝑧, 𝜆) =
𝐼(𝑧,𝜆)
𝐼0(𝜆). Since
the majority of spectral points are located outside of strong gaseous absorption bands, the main
difference from our previous work is the processing of transmission in the region 3550−3750 cm-
1 (2.7−2.8 μm) which is reach on strong CO2 gaseous lines.
Influence of gaseous lines in the transmission spectra is accounted for as follows. First, we make a correction to the wavenumber values using positions of gaseous absorption lines. Next, we calculate forward transmission model of CO2 gas for a number of atmospheric layers that are defined by the geometry of each solar occultation observation. Gaseous absorption crosssections are calculated line-by-line with the use of spectral line parameters from HITRAN 2016 database and pressure and temperature profiles retrieved from NIR (Fedorova et al., 2023) or MIR (Belyaev et al., 2022)

channels. CO2 number density is calculated assuming 95.5% volume mixing ratio. Modeled gaseous transmission at different altitudes Trg(z, λ) is convoluted with gaussian instrument function with FWHM of 0.16 cm-1 for MIR and 0.94 cm-1 for TIRVIM. Figure 2 illustrates Trg in the 3550−3750 cm-1 region using the transmission from the egress occultation #5401 at 33, 50 and 70 km; imaginary part of CO2 ice refractive index from Warren (1986) indicates position of carbon dioxide ice absorption bands. Finally, aerosol transmission is calculated as 𝑇𝑟𝑎(𝑧, 𝜆) =
𝑇𝑟(𝑧,𝜆)
𝑇𝑟𝑔(𝑧,𝜆). The uncertainty of aerosol transmission Tra in the region 2
2 , where δTr is the uncertainty of transmission
𝛿𝑇𝑟
2.7−2.8 μm is estimated as 𝛿𝑇𝑟𝑎= √(
𝑇𝑟𝑔)
+ 𝑆𝑇𝑟 2  is the sample variance of the transmission.
Tr, and 𝑆𝑇𝑟

[Figure: Figure 2. Left axis: Modeled CO2 gaseous transmission spectrum from the egress occultation #5401 at 33 km (green), 50 km (blue) and 70 km (red). Right axis: imaginary part of CO2 ice refractive index from Warren (1986), orange.]

Examples of three typical transmission spectra patterns near the 3708 cm−1 absorption band from MIR data are shown in Figure 3. Relatively large particles (reff = 1−2 μm) produce a very deep and broad minimum (Tra ≃ 0−0.1, Δν ≃ 2−3 cm-1) which is followed by a maximum (Tra ≃ 0.4); transmission outside of the absorption peak is asymmetric, Tra at shorter wavenumbers is lower (Figure 3, panel a). Smaller particles (reff = 0.2−0.5 μm) produce a symmetrical absorption line with FWHM ~ 1 cm-1 and Tra ≃ 0.4-0.6 in the center of the CO2 feature and Tra ≃ 0.90-0.95 outside of it (Figure 3, panel b). The smallest particles (reff ≤ 0.1 μm) produce a similar pattern with lower transmission values: Tra ≃ 0.8-0.9 in the minimum and unity outside of the absorption line (Figure 3, panel c).
). Smaller particles (reff = 0.2−0.5 μm) produce a symmetrical absorption line with FWHM ~ 1 cm-1 and Tra ≃ 0.4-0.6 in the center of the CO2 feature and Tra ≃ 0.90-0.95 outside of it (Figure 3, panel b). The smallest particles (reff ≤ 0.1 μm) produce a similar pattern with lower transmission values: Tra ≃ 0.8-0.9 in the minimum and unity outside of the absorption line (Figure 3, panel c).

[Figure: Figure 3. Examples of MIR transmission spectra in the 3680−3740 cm-1 spectral region for different CO2 ice particles. Measured transmission Tr containing both aerosol and gaseous contribution is shown in blue, modeled CO2 gaseous transmission Trg is shown in black, retrieved aerosol transmission Tra is shown in red. a) Case of relatively large particles (reff = 1−2 μm). Note that in this panel 0.7 offset was subtracted from Trg for clarity. b) Case of particles with reff = 0.2−0.5 μm. c) Case of the smallest particles (reff ≤ 0.1 μm). Error bars correspond to 1−σ uncertainty level.]

The slant optical depth τ with associated uncertainties δτ is deduced from the Beer-
𝛿𝑇𝑟𝑎
Lambert law as 𝜏(𝑧, 𝜆) = −𝑙𝑛𝑙𝑛 (𝑇𝑟𝑎(𝑧, 𝜆)) , 𝛿𝜏=
𝑇𝑟𝑎. Aerosol extinction k with associated
uncertainties δk is retrieved based on the standard “onion peeling” method (Rodgers, 2000), as described in Luginin et al. (2020).
The aerosol extinction is modeled according to the classical Lorenz-Mie theory. We apply the code by Mishchenko et al. (1999) (https://www.giss.nasa.gov/staff/mmishchenko/Lorenz-Mie.html) and obtain extinction crosssection σ produced by the ensemble of particles defined by their size distribution n(r) and refractive index m as 𝜎(𝜆, 𝑚) = ∫ 𝜋𝑟2𝑄(𝜆, 𝑟, 𝑚)𝑛(𝑟)𝑑𝑟
∞ 0
, where Q is the efficiency factor of extinction of a particle with size r and refractive index m at a wavelength λ.
For dust aerosols, we adopt refractive indices from Wolff et al. (2009). For water ice aerosols, we use refractive indices from Warren & Brandt (2008) and Clapp et al. (1995) measured at different temperatures and apply a temperature-wavenumber interpolation scheme described in Luginin et al. (2020), Table 1 in Section 3.3). As input temperature vertical profiles, we adopt data retrieved from the simultaneous NIR (Fedorova et al., 2023) or MIR (Belyaev et al., 2022)

observations, or predictions from the Mars Climate Database v5.3 (Millour et al., 2018) if neither is available.
As seen from the high-resolution (0.1 cm-1) solid-state IR absorbance spectra of pure CO2 ice (Isokoski et al., 2013), optical properties of carbon dioxide ice depend on the temperature in the 2.7 μm region. When moving from 15 K to 75 K, FWHM of the combination modes decreases by ~1 cm−1. For carbon dioxide ice, data on refractive indices retrieved with high spectral resolution (Δν < 1 cm-1) and for different temperatures are quite scarce. For the 2.7 μm band of crystalline CO2, only one paper by Gerakines & Hudson (2020) contains optical constants retrieved with Δν = 0.2 cm−1 at 70 K in the spectral region 500−4000 cm−1. For modeling of CO2 aerosols, we adopt refractive indices from Gerakines & Hudson (2020) in the spectral region 3580−3720 cm−1 and from Warren (1986) at other wavenumbers. Data from Warren (1986) is a compilation of different sources; in the 2.7 μm band, it is based on the data from Wood & Roux (1982) that was measured with Δν = 4 cm-1 at 80 K. Figure 4 shows adopted real and imaginary parts of refractive indices of dust from Wolff et al. (2009), of water ice from Warren & Brandt (2008), and of carbon dioxide ice from Gerakines & Hudson (2020) and Warren (1986).

[Figure: Figure 4. Adopted real (top panel) and imaginary (bottom panel) parts of refractive indices of dust (red lines) from Wolff et al. (2009), of water ice (blue lines) from Warren & Brandt (2008), and CO2 ice from Warren (1986) and from Gerakines & Hudson (2020) (orange solid and dotted lines respectively). Inlet shows spectral region containing CO2 ice combination modes 2ν2 + ν3 at 3600 cm−1 and ν1 + ν3 at 3708 cm−1. Spectral ranges of TIRVIM, MIR and NIR are marked as red, gray and blue areas, respectively.]
of refractive indices of dust (red lines) from Wolff et al. (2009), of water ice (blue lines) from Warren & Brandt (2008), and CO2 ice from Warren (1986) and from Gerakines & Hudson (2020) (orange solid and dotted lines respectively). Inlet shows spectral region containing CO2 ice combination modes 2ν2 + ν3 at 3600 cm−1 and ν1 + ν3 at 3708 cm−1. Spectral ranges of TIRVIM, MIR and NIR are marked as red, gray and blue areas, respectively.]

To describe the particle size distribution of aerosols, we use log-normal distribution
(𝑙𝑛𝑟−𝑙𝑛𝑟𝑔)
), whose parameters rg and σg are directly connected to
𝑛(𝑟) =
√2𝜋𝜎𝑔𝑟𝑒𝑥𝑝(−
2𝑙𝑛2𝜎𝑔
effective radius reff and effective variance veff as 𝑟𝑒𝑓𝑓= 𝑟𝑔𝑒𝑥𝑝(2.5𝑙𝑛2𝜎𝑔), 𝑣𝑒𝑓𝑓= 𝑒𝑥𝑝(𝑙𝑛2𝜎𝑔) − 1.
We have computed look-up tables of σ for ensembles of particles made from mineral dust, water ice and CO2 ice for reff in the range 0.1–20 μm with 0.01 μm step and for veff in the range 0.1–1.0 with 0.1 step plus 0.05 value at all wavelengths chosen for aerosol retrievals. For water ice, the variation with temperature is also accounted for, with values distributed in the range 100–270 К with 5 К step. Extinction cross sections of ensemble of CO2 particles for reff = 0.1−2 μm and veff = 0.1 in the 3580−3720 cm-1 spectral region is shown in Figure 5. Aerosol extinction coefficient k is readily calculated from σ and the total number of particles per unit volume N (number density) as 𝑘(𝜆) = 𝑁𝜎(𝜆).

[Figure: Figure 5. Extinction cross section of CO2 particles with log-normal distribution calculated for veff = 0.1 and different values of reff : 0.1 μm in red, 0.3 μm in green, 0.5 μm in blue, 1.0 μm in pink, 2.0 μm in orange, and 4.0 μm in grey. Refractive index in this spectral range is taken from Gerakines & Hudson (2020).]
3 𝜋𝑁𝑗𝜌𝑗 ∫
𝑟3𝑛(𝑟, 𝑟𝑒𝑓𝑓,𝑗, 𝑣𝑒𝑓𝑓,𝑗)𝑑𝑟
∞

Uncertainties of the retrieved free parameters (𝛿𝑟𝑒𝑓𝑓,𝑗, 𝛿𝑣𝑒𝑓𝑓,𝑗, 𝛿𝛾) are calculated from the
The procedure for aerosol properties retrieval is substantially the same as the one described in Luginin et al. (2020). Measurements at different altitudes are processed independently. To retrieve effective radius, number density, and mass loading profiles, we solve
𝑚𝑜𝑑)
(𝜘𝑖−𝜘𝑖
an optimization problem by finding a minimum of merit function 𝜒2 =
𝑀 𝑖=1
,
𝑀−𝑝∑
𝛿𝜘𝑖 𝑘(𝜆𝑖)

𝑘𝑚𝑜𝑑(𝜆𝑖)
𝑘(𝜆0) and 𝜘𝑖
𝜎𝑚𝑜𝑑(𝜆𝑖)
𝑘𝑚𝑜𝑑(𝜆0) = 𝜎𝑚𝑜𝑑(𝜆0) are experimental and modeled normalized extinction coefficients, p is the number of
.
Composition of aerosols is a priori unknown, therefore we use six competing models in
simultaneous presence of two out of three types of aerosols, forming a bimodal log-normal
distribution. In the latter case, extinction coefficient is composed of two components 𝑘(𝜆) =

𝜎1(𝜆𝑖)+𝛾2𝜎2(𝜆𝑖)
𝜎1(𝜆0)+𝛾2𝜎2(𝜆0), where σi(λ), i = 1,2 is defined
𝑁2
model was necessary to fit the aerosol data. It was true for both instruments, TIRVIM and MIR.
The value of the shift was always towards the shorter wavenumbers. It changed from one
absorption bands toward shorter wavenumbers when moving from 21 K to 180 K. Our observed
shifts suggest temperatures greater than 70 K of Gerakines & Hudson (2020), and are
qualitatively in agreement with observed temperatures.
Example fits for cases of CO ice particles from MIR and TIRVIM are shown in Figure 6.
ingress TIRVIM occultation #6452.

[Figure: Figure 6. Example fits illustrating different observations of CO2 ice particles. Relative extinction ϰ obtained from the experiment is shown in black, best fitting curve is shown in red. (a) MIR egress observation #15965 at 57.9 km, reff = 1.0 μm. (b) The same observation as in (a) but at 59.4 km, reff = 0.1 μm. (c) TIRVIM ingress observation #6452 at 70.9 km, reff = 0.8 μm. Error bars of the experimental data correspond to 1−σ uncertainty level.]
The saturation vapor pressure of CO2 over solid has been measured by Giauque & Egan (1937) and recalculated from their data in form of Antoine’s equation by the National Institute of Standards and Technology (NIST1) as follows
1301.679
𝑇−3.494,
𝑃𝑠𝑎𝑡 (𝑇) = 6.81228 − where P is the pressure in bars, and T is the temperature in kelvin. Azreg-Aïnou (2005) has shown that this formula is valid from 195 K down to 65 K, which is well below the minimal temperatures in the atmosphere of Mars.
𝑃𝐶𝑂2
The saturation ratio of CO2 S is defined as 𝑆=
𝑃𝑠𝑎𝑡. The uncertainties of S are estimated
from the error propagation relation using the uncertainties of the retrieved pressure and temperature.
For calculation of CO2 saturation ratio, we use pressure and temperature profiles retrieved from ACS NIR and/or ACS MIR channel (Belyaev et al., 2022; Fedorova et al., 2023). The comparison and validation of NIR and MIR temperature profiles has been done in a number of works (Alday et al., 2019, 2021; Belyaev et al., 2021, 2022; Fedorova et al., 2020, 2023).
Out of 111 simultaneous TIRVIM and MIR solar occultation observations, only one resulted in detection of CO2 clouds, namely egress occultation #5401 (LS = 338° of MY 34). In Figure 7, we compare vertical profiles of aerosol extinction coefficient k at different wavenumbers, mass loading m, effective radius reff, and number density N of aerosols retrieved independently from TIRVIM and MIR data during this occultation.
1 https://webbook.nist.gov/cgi/cbook.cgi?ID=C124389&Mask=4

[Figure: Figure 7. Detection of CO2 ice clouds at egress occultation #5401 from the MIR (top panels) and from the TIRVIM (bottom panels) data. Vertical profiles of aerosol extinction coefficient (panels a, e), mass loading (panels b, f), effective radius (panels c, g), and number density (panels d, h). Extinction inside the aerosol carbon dioxide ice (water ice) absorption band as well as corresponding to carbon dioxide ice (water ice) retrieved products are shown in orange (blue). Error bars of the experimental data correspond to 1−σ uncertainty level.]

TIRVIM, due to its extremely wide spectral range, which includes H2O ice and CO2 ice absorption bands, can unambiguously distinguish all three types of aerosols on Mars; on the other hand, MIR (in the position #4) can confidently detect CO2 aerosols with much higher accuracy due to its superior SNR. Another big difference between the instruments is their vertical resolution: ~12 km for TIRVIM against ~1 km for MIR. As a result, MIR discovers a lot more details in the aerosol vertical profiles. Finally, borders of the cloud layers retrieved by different instruments are slightly shifted due to different acquisition times.
The aerosol structure at the egress occultation #5401 can be divided in three layers: the main one that is formed by water ice particles and extends up to at least 27 km and two detached layers that are located at 49−55 km and 61−80 km. A clear gap in ~5 km between the two detached layers is detected by both instruments.
The higher detached layer is composed of CO2 ice particles. It is vertically not uniform and in turn can be divided in four sublayers as revealed by MIR (Panels a-d of Figure 7). Inside of each sublayer, effective radius and number density, and, as a consequence, mass loading and extinction show variations around the local maximum. Effective radius lies in 0.1−0.6 μm range with a median value of 0.2 μm (Panel c of Figure 7). Nadir optical depth 𝜏𝑛𝑎𝑑(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.