JuanCastillo29/space-mission-intelligence-data huggingface dataset:
Using this extracted continuum at each observed tangent point, we can determine the haze top altitude for each observation. We define haze top as the highest altitude at which aerosols can be sensed along the line of sight. We calculate haze top by finding the first altitude for which the transmission is greater than a defined threshold (1 −ε). ε introduces the consideration of the artificial non-steady nature of the measured transmission as it approaches unity at high altitude. We observe empirically that this transition occurs above transmissions around 0.99. We have thus selected a conservative value of ε = 0.02 for this threshold. An example is provided in the figure 3.
varies along the x-axis. Each blue line corresponds to the detector pixels used to obtain the
spectrum of one diffraction order. b. Corrections steps for the extraction of the spectral contin-
uum. In blue the initial data, in yellow the polynomial fit of the 200 points from the center of
each order (after the third iteration), and in red the extracted continuum (with the associated
uncertainties in the red shadowed region). c. Zoom on two diffraction orders.

1.04
1.0
1.02
0.8
1.00
0.6
Trmin
Trmin
0.4
0.98
0.2
0.96
λ1

1.0
0.8
0.6
Transmission
0.4
0.2
0.0
3.10 3.15 3.20 3.25 3.30 3.35 3.40 3.45 [µm]
Calculation of the 3 µm Integrated Band Depth (IBD) which can be used as a first

[Figure: Figure 4.]

order proxy for the presence of some water ice clouds. In blue, we show an example of ACS-MIR position 12 spectra, showing a strong 3 µm absorption band. In green is shown the measurement of the 3 µm absorption using the IBD method (cf. equation (1)) with λ1 = 3.15 µm and λ2
= 3.4 µm). This spectrum is compared to the theoretical transmission of 0.5 µm spherical water ice particles in red (see section 3.1) and in black, another example of ACS-MIR spectra with a low 3 µm band, and presumably no water ice.
In order to retrieve the extinction profile and properties of the aerosols, we employ a vertical inversion algorithm based on the onion-peeling method (Goldman & Saunders, 1979), from the total optical depth τ (i.e., as integrated along the line of sight). The result is the the extinction coefficient kext as a function of altitude. Then, we can compute the opacity of a cloud by integrating the extinction coefficient along a vertical line between the boundary altitudes detected for the cloud (see the following sections for a description of the water ice clouds identification).
For each altitude level i, the optical depth is given by:
N
�
�
R2 j+1 −R2 i − �
R2 j −R2
2 × ��
τi(λ) =
kext,j(λ) (2)
i j=i
� �� �
Ai,j
That we can rewrite in a matrix manner:
τ = A kext (3)
The transfer matrix A is upper triangular so it is invertable and we can write :
kext = A−1 τ (4) I.e.
N
�
A−1�
�
kext,i =
i,j τj
(5)
j=i
–7–

Then, according to BIMP et al. (2008), as τ = −log(Tr) and kext given as a function of τ in (5), we have the uncertainties on the optical depth values (6) and their propagation to the extinction coefficient kext throughout the vertical inversion given by (7).
∆τ(λ) = ∆Tr(λ)
Tr(λ) (6)
� � � �
N
(A−1)2 i,j (∆τj)2 (7)
�
∆(kext,i) =
j=i
N The number of atmospheric layers. Tri The measured transmission value at the ith altitude. τi The optical depth observed at the ith altitude (integrated along the line of sight). kext,i The extinction coefficient of the ith atmospheric layer. Ri The radius of the sphere corresponding to the bottom altitude of the ith atmospheric layer, i.e. the altitude of the bottom of the layer added to the Martian radius.

[Figure: Figure 5 presents several examples of retrieved vertical profiles of the extinction coefficient kext from ACS-MIR observations before and during the MY 34 GDS. We can already notice the presence of one or several detached layers in some profiles, which are located at various altitudes. We also observe strong spectral variability between observations, with some layers having a distinctive signature when comparing the transmissions at 3.2 and 3.4 µm (compare e.g. blue and red profiles in panels a and d of figure 5). We will discuss in the next section how this spectral behavior can be modeled to derive information about the presence and properties of water ice clouds.]
to the particle irradiation of the space environment during the
course of the 11-year mission. Dedicated serial CTI calibra-
tion engineering activities, performed periodically on board the
spacecraft, have been used to measure the effect of radiation
damage on the detectors, and to monitor its evolution. A pixel-
based, physically motivated CtiPixel model that adopted the
Shockley-Read-Hall theory of charge traps was developed to
analyse the serial CTI calibration data sets. The results of the
investigation have shown that alongside the manufacturing de-
fects already present at launch, new trap species caused by par-
ticle irradiation have emerged. The newly generated defects are
characterised by longer charge release timescales compared to
the original manufacturing defect. The intermediate trap has a
measured energy level consistent with the known value of the
multi-vacancy defect, while the third species presents a tail in its
release time distribution, suggestive of a dynamical, transitional
state of some radiation-induced traps, with defects not fully set-
tled into their final configuration. The evolution of the radiation-
induced serial damage appears linear, with the exception of cal-
ibration epochs following strong CME directed towards Earth
like the ones occurring in 2017 and 2024, that present step-
increase in damage level. The latest epochs also show the effects
of temperature instabilities of the focal plane on the measured
serial CTI, as well as a further step increase caused by the en-
gineering CCD annealing test carried out after the end of Gaia
science operations.

Acknowledgements. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Gaia mission website is: http://www.cosmos.esa.int/gaia. This work was financially supported by the European Space Agency (ESA) in the framework of the Gaia project; and the United Kingdom Space Agency (UKSA) through the following grants to the University of Edinburgh and the University of Leicester, ST/S000976/1, ST/S001123/1. The authors gratefully acknowledge the use of computer resources from MareNostrum, and the technical expertise and assistance provided by the Red Española de Supercomputación at the Barcelona Supercomputing Center, Centro Nacional de Supercomputación. Diagrams were produced using the astronomy-oriented data handling and visualisation software TOPCAT (Taylor 2005).
In order to identify water ice clouds, and constrain their particle sizes, we have computed the wavelength dependence of the extinction coefficient of pure water ice and dust layers of various particle sizes (figure 6). These extinction coefficients are calculated 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).
We can see in the figure 6 that the spectral properties of water ice and dust differ significantly in most cases, and that there is a strong dependence of the spectral shape on particle size for water ice. This is not true when water ice particles sizes become greater than about 3 µm: for such sizes the absorption become quite flat in the 3.1 – 3.5 µm range whatever the particle size and it is even not possible to differentiate water ice from dust. Particles size lower than 0.1 µm have similar kext spectra corresponding to the Rayleigh diffusion regime. As a consequence, we do not make any distinction between water ice particles with radii of 0.1 µm and lower.
In the following, we fit our retrieved extinction coefficient from ACS data to these models for pure water ice or dust layers in order to identify and characterize water ice rich layers composed of particles lower than ∼2 µm, as results suggesting larger water
–8–

Ls = 170° | lat = 77°
Ls = 181° | lat = 82°
Ls = 200° | lat = 39°
Ls = 240° | lat = 43°
particle (dust and ice) profile. In red the difference between the extinction coefficient at 3.2 µm
and 3.4 µm, which provides a proxy for the presence of band depth associated with small-grained
water ice We can see layers with various behavior, some typical of the presence of small-grained
water ice (e.g., e at about 50 km) and some interpreted as resulting from either large-grained
water ice, or dust (e.g., d at 20 km). See section 4 for discussion. The shadows regions show the
�
�
. However, we decide to leave α as a free

Cext(λ,reff) Cext(λ0,reff) = kext(λ,reff) kext(λ0,reff)

reff = 0.1 µm
reff = 0.2 µm
1.2
reff = 0.3 µm
reff = 0.5 µm
reff = 0.7 µm
1.0
reff = 1.0 µm
reff = 1.5 µm
0.8
reff = 2.0 µm
Cext / Cext,
reff = 3.0 µm
reff = 5.0 µm
0.6
reff = 8.0 µm
0.4
0.2
N The number of spectral points in the considered spectrum. ν The number of degrees of freedom (ν = N −p). p The number of fitting parameters (here p = 2).
Then, in order to quantify the uncertainties for the retrieved particles size, we search for the reff of all the models that will pass our filtering criteria (cf section 3.3) during the fitting process of an ACS-MIR spectrum and verify χ2 ν, ice ≤
� 2χ2 ν, ice, min + 1
� , where χ2 ν, ice, min is the minimal value of χ2 ν, ice (i.e. associated with the optimal reff value). This last criteria that essentially affects low χ2 ν, ice values was add to take into account the goodness of the optimal fit in the uncertainties estimation As all these models can be considered as acceptable, they will define the uncertainties range of the fit. In other words, the optimal reff corresponds to the model with the lower χ2 ν, ice value, and the lower (respectively upper) bounds for the particle size uncertainties corresponds to the minimal (respectively maximal) value of reff in the set of models that prove equation (11) along with χ2 ν, ice ≤
� 2χ2 ν, ice, min + 1
� .
We identify water ice clouds by correlating extinction coefficient spectral behavior compatible with water ice, and incompatible with dust. To do that, we experimentally determined χ2 thresholds that we present in the following. Thus, to be classified unambiguously as a water ice cloud detection, a layer needs to have a χ2 ν ≤9 for water ice, but also a lower quality fit to dust. We then apply the previously described fitting algorithm also for dust and not only for water ice. We eliminate all those observations that the water ice models don’t improve the χ2 ν by at least a factor 4, as well as the ones that it is possible to fit a dust spectra with a χ2 ν ≤1. That is to say, we only consider the fits which verify the condition (11). This last criteria was added as a safeguard and is relevant in the case of a fit associated to a low χ2 ν value, where even if χ2 ν dust ≥4χ2 ν ice, the dust model still provide a good fit of the data that we cannot ignore.
�
�
ν, dust
χ2 ν, ice ≤ χ2
χ2 ν, ice ≤9 � &
χ2 ν, dust > 1 � (11)
�
& �
After this multi-step filtering process, we note that retrieved particle sizes never exceed 2 µm; in essence the fits indicating sizes between 2 µm and 8 µm have been removed, which was expected as the extinction coefficient is not diagnostic of water ice in that size range, as discussed previously. As a consequence, it is appropriate to consider that our method makes it possible to identify and characterize water ice rich layers with mean particle size of ∼2 µm maximum. Other layers can correspond to either dust-dominated or large-grained water ice layers.
The atmospheric 3 µm integrated band depth can be used to obtain a quick look at water ice clouds in our dataset. It is also of interest in itself as orbital observations of Mars surface have to account for atmospheric 3 µm contribution to deliver information
–11–
In addition, we also remove the fits whose error bars exceed a pre-defined threshold of 0.35 µm. An example of such a fit is found in figure 7.j. However, in most cases, such poorly constrained fits were already eliminated through the dust-fitting test (figure 7.k).

Water ice cloud identification and particle size retrievals from ACS-MIR kext spec-

[Figure: Figure 7.]

tra. The two first columns shows the results of the fitting algorithm using the water ice model (left column) or the dust model (center column). The blue area represents the ACS-MIR spectra, while the red and yellow lines represent respectively the best fit using the water ice and dust models. The right column shows for each observation (line) the associated kext vertical profile (in blue the extinction coefficient at 3.2 µm, and in red the difference between the extinction coefficient at 3.2 µm and 3.4 µm). The black arrows indicate the altitude of the extracted spectrum.
We observe that even if all the presented spectra can be reproduced by our water ice model, the fourth case (j) remains ambiguous as the dust model is also able to provide a acceptable fit (k).
Thus, we will accept the three first fits a, d & g while the fit j will be rejected by the filtering process.
about surface hydration (Jouglet et al., 2007; Audouard et al., 2014). As illustrated in figure 8, the 3 µm IBD matches at first order the more elaborated water ice detection scheme presented in section 3. Actually, the 3 µm IBD captures only small-grained (reff ≤1 µm) water ice clouds according to modeling results presented in figure 6, but this corresponds to typically 90% of detected clouds here (see figures 13 & 15). In addition, the IBD does not capture thin clouds that produce a tenuous absorption, whatever their grain size. Thus, thin layers (normal integrated layer optical depth discernible with IBD figures.
ferences in the 3 µm IBD criterion. This probably reflects actual variations between
profiles, associated with longitudinal variations of the water ice clouds (Smith, 2004;
Wolff et al., 2019), as illustrated in table 1. Local time differences (Szantai et al.,
2019) may also contribute to the variability (Table 1), although the dataset is not yet
extensive enough to properly delineate local time effects.

0.30
2.00
1.75
0.25
3 µm Integrated Band Depth
1.50
0.20
Altitude [km]
1.25
reff [µm]
0.15
1.00
0.75
0.10
0.50
0.05
0.25
0.00
169 170 Ls [°]
0.0 0.5 1.0 reff [µm]
0.00 0.01 kext [km 1]
Vertical profile of a water ice cloud (Ls = 169.8◦, lat = 77◦N, lon = 152◦W),

[Figure: Figure 8.]

showing the vertical variations of the IBD (a), particle size (b) and the extinction coefficient (c, with in blue the extinction coefficient at 3.2 µm, and in red the difference between the extinction coefficient at 3.2 µm and 3.4 µm). We observe that the IBD criterion detects the same water ice cloud as the fitting methodology, and that the size of the water ice particles of the cloud decrease as the altitude increases (b): from 1.2 µm at 18 km to 0.2 µm at 31 km.
water ice clouds layers in the Martian atmosphere and retrieve their particle size (see
discussion in section 4.5). Figure 10 presents the resulting vertical profiles of water ice
This overview reveals two main trends in our dataset. First, we detect water ice clouds
in most observations. In more detail, in our dataset of 65 ACS-MIR grating position-12
observations, 11 of them have no water ice cloud detection, i.e., 83% of our profiles show
at least one detection of water ice. Non-detections are localized in time and places:
6 observations) but corresponds to low haze top altitude beyond the northern limit 3 µm water ice absorption band (IBD)

monitoring from the ACS MIR channel in

[Figure: Figure 9.]

the Martian Northern (a) and Southern (c) hemisphere, before and during the 2018 GDS (MY 34). Latitude and local time of observations are indicated in panels b & d .
of the GDS (see detailed discussion in section 4.4). Second, we observe the presence of two distinct types of vertical profiles that correspond to the two periods separated by the sudden onset of the GDS at Ls ∼195◦−200◦(Guzewich et al., 2019). Two main cloud characteristics differ between these two periods: the vertical extent, and the water ice cloud opacity. Most of the detected clouds are between 10 km and 70 km Ls Latitude Longitude Local time Observation 168.1◦ 51◦S

163◦W
17h39 No IBD 168.2◦ 50◦S
139◦E
17h40 IBD ∼0.25 at 40 km 192.6◦ 68◦N
28◦E
17h09 IBD ∼0.26 at 20 km 192.7◦ 68◦N
3◦E
17h09 No IBD 229.4◦ 38◦S
136◦E
18h53 IBD ∼0.18 at 65 km 229.6◦ 37◦S
50◦E
18h52 No IBD 243.1◦ 54◦N
07h50
22h10

No IBD [Table: Table 1.
Identification of consecutive observations with and without 3 µm atmospheric absorp-
tion using the IBD, showing the local aspect of our observed clouds (see the vertical IBD profiles
in figure 9). The first three examples are associated with significant variations in longitude, while
the last example also show strong local time variations.
before the onset of the GDS, while clouds altitudes typically extend higher between 60 km and about 90 km afterward. Figure 12 shows the vertical profiles of kext at 3.4 µm (i.e. the opacity per km). As 3.4 µm is on the edge of the 3 µm water ice band (Vincendon et al., 2011) it provides an estimator of the global atmospheric opacity, whereas the opacity at 3.2 µm is strongly increased by the presence of water ice clouds. We observe that the water ice clouds opacity at 3.2 µm (vertically integrated along the cloud) typically goes from 0.01 to 0.05 before the GDS, but falls to a few 10−3
for the mesospheric clouds during the GDS, with kext(λ = 3.2 µm) ∼10−4 km−1]
around 90 km (and kext(λ = 3.4 µm) ∼10−5 km−1). We discuss in more details the distribution and properties of clouds during these two periods in the next two sections.
Before the dust storm, we observe the presence of water ice clouds in most profiles, with altitudes ranging from 11 to 44 km in the Northern hemisphere, and between 17 and 69 km in the Southern hemisphere, which is consistent with previous observations (Smith et al., 2013). Thus, clouds are found below the mesosphere for most of the cases. However, the latitude coverage of available observations in the North and in the South are not equivalent (cf. figure 10): Northern observations prior to the GDS are restricted to polar latitudes ≥68◦.
During this period, we indeed observe a latitude-dependence of the haze top altitude that increases closer to the equator (up to ∼70 km at 24◦S) and decreases when moving towards polar latitudes (down to ∼40 km at 80◦N, or 60◦S), see also figure 11.a. This trend is consistent with the expected haze top behavior (Jaquin et al., 1986; Forget et al., 1999; Montmessin et al., 2006; Heavens et al., 2011; Smith et al., 2013). However, the lack of data around the equatorial latitudes before the GDS (no observations between 20◦S and 60◦N, see figure 11.a) prevents us to get access to the latitude of the maximum haze top altitude.
Water ice clouds are detected in the upper parts of most profiles, but some profiles also include lower detached layers (e.g. figure 5.f at 30 km) that are not detected either by the fitting process or the IBD (as the kext at 3.2 µm and 3.4 µm are similar). Thus, these layers are likely be either composed primarily of dust or by > 2 µm water ice particles (cf. section 3.3). This suggests that water ice appears to frequently cap the dust layer in our solar longitude range (Smith et al., 2013). Note however that largerVertical profiles of the retrieved size of the water ice particles in the Martian Northern (a)

[Figure: Figure 10.]

and Southern (c) hemispheres, before and during the MY 34 GDS. In grey the observations without water ice detection. Latitude and local time of the observations are indicated in panels b & d.
grained water ice clouds can also be present at lower altitude (Wolff & Clancy, 2003), but remain undetected here due to a lack of a clear signature in our spectral range (as discussed previously). Nevertheless, the altitude distribution of the detected water ice clouds follows a similar latitudinal trend as the haze top itself: the average altitude 2.00
haze top (up to 100 km) and the water ice clouds (≥ 90 km), while geographical
=
=
These behaviors are combined with a decrease of the
measured IBD values (cf. figure 9). We note that the above behavior appears to be
relatively uniform around the planet (i.e., both zonally and meridionally). Specifically,
top altitude and the maximal altitude of the water ice clouds; in agreement with the
simulations presented in Neary et al. (2019, Figure 3).
We nonetheless identify a
temporal trend likely related to the progressive decay of the dust storm (Guzewich Vertical profiles of the measured opacity (kext = dτ/dz)

at 3.4 µm in the Martian

[Figure: Figure 12.]

Northern (a) and Southern (c) hemispheres, before and during the MY 34 GDS. Latitude and local time of the observations are indicated in panels b & d.
et al., 2019): from Ls = 220◦to Ls = 243◦the lower altitude of the detected water ice clouds in the Southern hemisphere decrease from 68 km to 54 km (cf. figure 10) and the main 3 µm atmospheric absorption (i.e. the maximum of IBD for a given profile) decrease from 75 km to 58 km (cf. figure 9). Regardless that the haze top altitude remains at ∼85 – 90 km, and the latitude goes successively towards and from the equator. Nevertheless, we note a slight North/South asymmetry during the dust storm that is represented by the smaller number of high-altitude water ice clouds in the Northern hemisphere (see figure 11.c&d).