__________________________________________________ 3.5.2 Influence of the physico-chemical parameters on IASI spectra _____________ 3.5.

 1Vertical temperature structure of the atmosphere extending from the surface of the Earth to approximately 110-km altitude as given in the US Standard Atmosphere (1976). The principal layers defined by this temperature structure and the interfaces between them are labeled. As indicated, ozone is found principally in the stratosphere. Two vertical coordinates are given: pressure on the left in millibars (1 mb = 100 Pascal) and geometric altitude on the right (km) [START_REF] Schlatter | Vertical Structure of the Atmosphere[END_REF] [START_REF] Di Biagio | Global scale variability of the mineral dust long-wave refractive index: A new dataset of in situ measurements for climate modeling and remote sensing[END_REF]. . 33 Figure 11 Crystal structure of tectosilicates and phyllosilicates [START_REF] Fang | Mineralogical Controls on Frictional Strength, Stability, and Shear Permeability Evolution of Fractures[END_REF]

Figure 17

The spectral radiance of a radiation beam passing thourgh an elementary optical path ds. 𝑅0: incident spectral radiance , 𝑅𝑎𝑏𝑠: absorbed spectral radiance , 𝑅𝑠𝑐𝑎𝑡: scattered spectral radiance , 𝑅𝑒𝑚𝑖𝑠: emission spectral radiance, 𝑅𝑡𝑟𝑎𝑛𝑠: total transmitted spectral radiance. ..... 40

Figure 18

The spectral distribution of a blackbody with temperatres from 300 K to 700 K [START_REF] Campbell | Introduction to Remote Sensing, Fifth Edit[END_REF] [START_REF] Borstad | Hyperspectral Imagery for Safeguards Applications[END_REF] Figure 32 IASI swath angle and field view pixels [START_REF] Clerbaux | Atmospheric Chemistry and Physics Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder[END_REF] Figure 35 An atmosphere divided homogeneous layers [START_REF] Herbin | Infrared Observation of Earth's Atmosphere[END_REF] Australia, on 20 December, 2006. Middle panels: radiative transfer pseudo-transmittance simulations to identify the main absorbing gases (middle panels) and the weaker absorbers compared with the IASI noise (lower panel) [START_REF] Clerbaux | Atmospheric Chemistry and Physics Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder[END_REF] Figure 46 Brightness temperature Gobi Desert spectrum example from 3 May 2017 before and after the LSE correction using a monthly average emissivity value (D. Zhou et al., 2014). For simplicity, the carbon dioxide, ozone, and water vapor wide bands are removed from the IASI spectra in the rest of the of the chapter.. Deschutter, 2022)............................................. 96 Figure 57 Laboratory extinction coefficient by Fourier Transform infrared spectrometer of (a) Gobi dust samples, with arrows indicating extinction peaks, and (b) normalized extinction coefficient to [0, 1] of the pure minerals quartz, illite, and calcite with their molecular assignments [START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF]

Figure 60

The laboratory Gobi dust spectrum with the molecular assignment for each fingerprint is in black [START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF]

ACKNOWLEDGEMENT

I would like to acknowledge both my doctoral school EDMSRE and the Haut-de-France Region for their financial support. It has been an honor to have the opportunity to work these three years having complete academic freedom in this research.

An immense thank you to my PhD supervisor Hervé Herbin. Support and guidance throughout the project from you have been invaluable. Thank you for believing in what we did and giving me motivation to move forward every day, you simplify it when it gets complicated and I think that what makes you an amazing supervisor. Thank you to all the academics who helped me get to this stage, including Antoine Al Choueiry. Your support specially in my hardest phase in this thesis is unforgettable. Thank you for your advices during every stage of this thesis. Also, it has been an amazing experience working between two research groups across our department, thanks to everyone from the PC2A group, Denis Petitprez and Lise Deschutter from whom I've learned so much, especially on the experimental aspect of my work.

A big thank you to my reviewers Pierre Coheur and Sophie Vandenbussche which their judgment was constructive and helped me improve the last version of manuscript. Thank you for your expertise, I learned a lot from you.

I am grateful to the people who stood by me throughout these three years. My family. Dad, I know you're somewhere above looking at me and smiling. This work is dedicated to you. Mom, you're always my number one inspiration. Thank you for your unconditional love and support.

My friends, Adel you've been there day and night throughout the process, we grew up together, wrote articles together, submitted them together, and stayed up late talking and working together. Thank you for your company throughout these years. You are a true brother to me. Joyce, thank you for giving me motivation every day and showing me how doctors are doctors! Georges, it means a lot to me your company, your calls through thick and thin. Samara, thank you for being an amazing childhood friend through these years you always stood by me, and I am grateful for that. Particularly, mineral dust presents one of the most abundant Particulate Matter (PM) originating from natural sources in the atmosphere [START_REF] Washington | Atmospheric controls on mineral dust emission from the Bodélé Depression, Chad: The role of the low level jet[END_REF], since it is the product of wind erosion of the land surface in arid and semi-arid regions [START_REF] Duniway | Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world[END_REF]. Due to its essential impact on the climate system, more attention has been attributed to mineral dust, which plays a fundamental role in air quality (Querol et al., 2019a). Moreover, climate change and desertification amplify global dust emissions in the atmosphere [START_REF] Safriel | The assessment of global trends in land degradation[END_REF]. Dust can directly perturb the radiative flux by interacting with solar and thermal radiation through absorption and scattering processes [START_REF] Bi | Measurement of scattering and absorption properties of dust aerosol in a Gobi farmland region of northwestern China -A potential anthropogenic influence[END_REF]. It can also indirectly affect clouds, ice formation, and nucleation [START_REF] Kanji | Heterogeneous ice nucleation properties of natural desert dust particles coated with a surrogate of secondary organic aerosol[END_REF]. Climate alteration depends on the physico-chemical properties of atmospheric dust since the forcing effect is associated with dust mineralogical composition (Michel et al., 2003).

Satellite remote sensing instruments present a promising tool to observe Earth's atmosphere and are characterized by reliable observations. However, many challenges cause constraints to measure and quantify aerosol properties from space, including the lack of optical properties and the surface emissivity signal effect on satellite observations. For instance, East Asia can produce up to 800 Tg y -1 of mineral dust [START_REF] An | Temporal and spatial variations in sand and dust storm events in East Asia from 2007 to 2016: Relationships with surface conditions and climate change[END_REF], which is uplifted near the surface in the troposphere above the continent [START_REF] Chen | Comparison of dust emissions, transport, and deposition between the Taklimakan Desert and Gobi Desert from 2007 to 2011[END_REF], where the Land Surface Emissivity (LSE) has spectral variability that affects remote sensing detection [START_REF] Masiello | Diurnal variation in Sahara desert sand emissivity during the dry season from IASI observations[END_REF]. This presents a primary error source on the aerosol quantification, as it impacts clear sky and dust layer observation above the land [START_REF] Ackerman | Remote sensing aerosols using satellite infrared observations[END_REF]. Another particularity of this region, is the diversity in its mineralogical composition; the dust is rich in silicates and carbonates, and depends on dust sources (Young [START_REF] Jeong | Mineralogy and geochemistry of Asian dust: Dependence on migration path, fractionation, and reactions with polluted air[END_REF].

In this work, we will use the Infrared Atmospheric Sounding Interferometer (IASI), a high spectral resolution instrument having different advantages in the infrared: continuous spectrum, low radiometric noise, global coverage above lands/oceans, and day/night observations. We apply our work on East Asian dust storms in order to attain the following objectives:

1-Cross the LSE challenge typically faced above lands in this region and correct available LSE datasets from literature to optimize the IASI observations. 2-Use new aerosol optical properties from East Asia measured in the laboratory to establish the link with the chemical properties.

3-Investigate the capability of IASI observations, with the complexity of dust mineralogy in this region, to provide the mineral dust composition using these new optical properties found in the laboratory.

We introduce in Chapter I the different notions used in this work: the microphysical, chemical, and optical properties of mineral dust, remote sensing detections, and the East Asian deserts. In Chapter II, the IASI instrument is presented. We also discuss the false IASI dust observation caused by the LSE and the impact related to the use of a mean monthly emissivity atlas that can over represent or under represent the IASI radiances.

Then, we introduce a new LSE correction method to improve the LSE dataset; a new diagram scheme is illustrated with the correction equations. Next, we determine an equivalent optical thickness spectrum derived from IASI radiances, and we use it to compare spectra before and after the LSE method. In Chapter III, we present new experimental optical properties (i.e., the extinction coefficient spectra) measured using Thermal Infrared (TIR) data for Gobi dust and pure minerals (quartz, illite, and calcite). An effective extinction coefficient is simulated using a linear combination of the pure mineral extinctions spectra to reproduce the Gobi dust spectrum and IASI dust observation. Then, laboratory measurements are compared to the IASI optical thickness, showing the same molecular features. Finally, we couple the IASI optical thickness with a linear combination of pure mineral spectra to compute the mineralogical dust mass weights. We apply the method to a dust storm that occurred between 3-7 May, 2017, using IASI observations based on METOP-A. Finally, we summarize the work by comparing the results with METOP B and a second dust event between 14-22 March 2021. Lastly, we use the same optical properties in a new radiative transfer algorithm (ARAHMIS) developed at the Laboratoire d'Optique Atmosphérique to simulate the forward model of IASI spectra to be used in future work to retrieve the micro-physical properties.

CHAPTER I. ATMOSPHERIC MINERAL DUST

This chapter introduces the context of the thesis by defining the climate system and the atmospheric constituents: gases, aerosols, and hydrometeors. Then, the mineral dust cycle and its impact on the Earth's atmosphere and ecosystems are discussed. Next, aerosol's physical and chemical properties are defined with a complete description of various parameters, i.e., the size distribution and the chemical composition. The aerosol-radiation interaction is also presented by the dust optical parameters (e.g., refractive index, extinction coefficient), giving information about the aerosol physico-chemical properties.

The chapter also includes a definition of remote sensing detection that uses electromagnetic radiation to provide dust information. Furthermore, the main description of active and passive, in-situ, ground-based, and satellite instruments is given. Finally, the geography of deserts in East Asia is designated, and the challenges faced in this region are explained.

ATMOSPHERIC STRUCTURE AND COMPONENTS

The Earth's climate system compromises several components including the atmosphere, surfaces, and marine and terrestrial ecosystems. One of its most critical components is the atmosphere, as it can have an important impact on the climate. The atmosphere is a gaseous envelope that surrounds the Earth and is held by its gravity. Pressure and air density decrease exponentially with height, while the temperature varies linearly with height, inducing stratification into layers (Figure 1).

One can distinguish several atmospheric layers; the troposphere extends from the surface, and the temperature decreases with the altitude to the layer top limit. Earth's surface radiation influences the lower limit termed the boundary layer can extend from 0 to 2 km, in which dust entrainment happens. [START_REF] Schlatter | Vertical Structure of the Atmosphere[END_REF]. The solar wind interacts with the Earth's geomagnetic field creating the magnetosphere. The latter, stores the solar wind energy and dissipates it in geomagnetic storms that accelerate ions which drive large electrical currents diverted down into the ionosphere.

The atmosphere is formed by 99% of dry air that includes molecular nitrogen (N2)

and oxygen (O2), and trace gases, in addition to water vapor (H2O). Greenhouse gases absorb infrared radiation and re-emit energy in all directions. When the temperature of emitted radiation towards the Earth's surface is higher than the temperature of radiation emitted towards the space, the greenhouse effect occurs warming Earth's atmosphere.

The intensity of this effect depends on the gases' concentration, their absorption capacity and their resistance time in the atmosphere. The most absorbent element is water vapor, followed carbon dioxide (CO2), methane (CH4), ozone (O3), nitrous oxide (N2O), and hydrofluorocarbons (gases consisting of fluorine, chlorine, or bromine), such as chlorofluorocarbons (CFCs) and hydrofluorocarbons (HCFCs), which have a destructive effect on the ozone layer. In addition, reactive gases, including volatile organic compounds VOC, nitrogen (NOx, NH3), and sulfur (SO2 and H2S) components can be present in the atmosphere.

Figure 1 Vertical temperature structure of the atmosphere extending from the surface of the Earth to approximately 110-km altitude as given in the US Standard Atmosphere (1976). The principal layers defined by this temperature structure and the interfaces between them are labeled. As indicated, ozone is found principally in the stratosphere. Two vertical coordinates are given: pressure on the left in millibars (1 mb = 100 Pascal) and geometric altitude on the right (km) [START_REF] Schlatter | Vertical Structure of the Atmosphere[END_REF].

The atmosphere is also composed of suspended liquid or solid matter: aerosols and hydrometeors. The term 'aerosols' refers to suspensions of liquid, solid, or mixed particles with highly variable chemical composition and size distribution [START_REF] Putaud | A European aerosol phenomenology -3: Physical and chemical characteristics of particulate matter from 60 rural, urban, and kerbside sites across Europe[END_REF].

Hydrometeors are considered a separate field from aerosols, forming solid or liquid particles composed mostly of water. In parallel to gases, aerosols (e.g., mineral dust, sulphate, nitrate, organic carbon) and hydrometeors mainly scatter solar radiation and have a cooling effect on the climate system, while strongly absorbing aerosols (e.g., black carbon) have a warming effect described by the parasol effect. The Radiative Forcing (RF) is the scientific terminology adopted by the IPCC to describe the influence of atmospheric components that block infrared radiation in Earth's radiation budge. Therefore, we are particularly interested in atmospheric aerosols. They are variable in space and time depending on the emission source and type. We can distinguish aerosols entrained from natural sources (volcanic ash, sea salt, and mineral dust from deserts)

and anthropogenic aerosols (industrial dust, soot, and organic carbon). Mineral dust is the most abundant in mass by emitted natural particulate matter in the atmosphere (1000-3000 Tg per Year) [START_REF] Boucher | Atmospheric Aerosols[END_REF]. Mineral dust is the product of wind erosion of the soil composed of different minerals in arid and semi-arid regions. It undergoes a specific cycle (emission, transportation, and deposition), significantly impacting the climate system and air quality (Querol et al., 2019b). Moreover, climate change and desertification amplify the global dust emission in the atmosphere [START_REF] Safriel | The assessment of global trends in land degradation[END_REF]. Therefore, special attention must be attributed to mineral dust physico-chemical properties to better understand their impact on the atmosphere, climate and ecosystem.

In this work, we focus on atmospheric mineral dust, for which, in the next section, we will describe the process of the dust cycle, detail its impact, and define its microphysical and chemical properties.

DUST CYCLE AND IMPACT

The mineral dust cycle has three main elements: emission, transport, and deposition (Figure 3). First, mineral dust is entrained in the atmosphere from the ground surface, and this process is controlled by wind speed, the nature of the soil, and surface obstacles faced during the formation process. The threshold required to entrain dust depends on the particle size distribution, the sorting characteristics of the particle, the surface cohesion, and the surface roughness. Dust storms are common events in arid and semi-arid regions including deserts. The latter are characterized by a low vegetation cover, frequent repetition of soil, and atmospheric droughts (i.e., low humidity and high wind velocity).

Once emitted in the atmosphere, dust is transported by turbulent flow structures of the air carrying the particles without contact with the surface. Aerosol lifetime varies from a few days in the lower troposphere up to several months in the stratosphere, depending on mineral dust size distribution and level (Figure 4). The transport direction depends on the direction and intensity of the wind fronts. Mineral dust can be transported in the atmosphere at long distances for many days. For instance, in April 2001, the 'Perfect Dust Storm' event was blown by the Siberian winds from North-West

China and traveled across the Pacific Ocean to the North Atlantic Ocean (Figure 5). The dust cloud reduced visibility while passing through the cities and mixed with pollutant particles in the air, i.e., carbon monoxide. These pollutants were then deposited as they cross the ocean. The global radiation budget and hydrological cycle are generally susceptible to long-range transport and aging processes that can impact the dust's physical properties by altering the particle's morphology, size, and scattering properties. Airborne mineral dust can have a direct impact on the radiative budget through absorption and scattering processes. This impact strongly depends on the mineral dust properties, which dictate the fractions of light dust absorbs or reflects as a function of wavelength. On the other hand, it can have an indirect impact on the radiative balance by interacting with clouds through nucleation. For example, calcium carbonate (CaCO3) can interact with HNO3 in the presence of water, leading to calcium nitrate droplets that can cause severe human health risks (Figure 6). Figure 6 Pictorial representation of changes in a composite mineral dust particle as it is processed in the atmosphere (Usher et al., 2003).

Finally, dust can be deposited by dry deposition through the gravitational settling and impaction of the Earth's surface by turbulence on the Earth's surface or by wet deposition, i.e., scavenging through precipitation in the water or ice phase. When deposited, dust plays an important role in trace element biogeochemistry and ocean productivity.

Iron, phosphorus, and nitrogen are the nutrients that control the functionality of marine and terrestrial ecosystems. They are considered an essential fertilizers source for fauna and flora. In addition, depending on dust load concentration in the atmosphere, nitrate particles can interact with calcite particles to form calcium nitrate, a source of nitrogen when finally deposited [START_REF] Fan | Impact of air pollution on wet deposition of mineral dust aerosols[END_REF]. Figure 7 shows the average dust flux deposition per year.

Figure 7 Global annual average of dust deposition flux in g/m 2 /year [START_REF] Jickells | Global iron connections between desert dust, ocean biogeochemistry, and climate[END_REF].

Finally, the mineral dust cycle affects on regional or global scales the continental and marine ecosystems, the Earth's climate and air quality, human health and activity, fauna, and flora. Therefore, it is important to understand the dust's physical and chemical

properties, which we introduce in the next section.

MICROPHYSICAL AND CHEMICAL PROPERTIES

The aerosols' properties are extremely variable in space and time due to the heterogeneity in aerosol sources and their relatively short residence time in the atmosphere.

Hence, one is usually interested in characterizing a population of aerosols rather than individual particles [START_REF] Boucher | Atmospheric Aerosols[END_REF]. The most important characteristics of an aerosol population are the size distribution, number of concentrations, and chemical composition.

Size Distribution

The size distribution (SD) is an important parameter in understanding the different impacts of mineral dust. The aerosol size affects the particle entrainment, suspension, changes with the aging processes and therefore impacts the radiative budget. In this section, we define the particle size distribution equations. Then, we classify the SD based on the aeolian processes by describing the particle mode based on their SD. Finally, we mention some possible techniques for measuring the SD.

An aerosol population is characterized by its SD. Experimental observations show that the aerosol size is fairly distributed, according to a lognormal function (Figure 8) [START_REF] Limpert | As the need grows for conceptualization[END_REF]. The distribution can be in volume, surface, or number size distribution. [START_REF] Limpert | As the need grows for conceptualization[END_REF].

The function describes the number of aerosols per unit volume 𝑛(𝑟) between the radii 𝑟 and 𝑟 + 𝑑𝑟 and it is given by:

𝑛(𝑟) = 𝑁 0 𝑟𝑙𝑛𝜎 𝑔 √2𝜋 𝑒 - 1 2 ( 𝑙𝑛(𝑟 𝑟 𝑔 ⁄ ) 2𝑙𝑛 2 𝜎 𝑔 ) (1)
where 𝑁 0 is the total number of concentration aerosols (in particles.m -3 ), 𝑟 𝑔 the mean geometric radius, 𝜎 𝑔 the geometric standard deviation and 𝑙𝑛𝜎 𝑔 is the dimensionless standard deviation of 𝑙𝑛(𝑟 𝑟 𝑔 ⁄ ).

The entrainment of mineral dust depends on the particle size distribution. In aeolian transport, we can identify three types of soils each depending on the particle size: clay, silt and sand [START_REF] Shao | Parameterization of size-resolved dust emission and validation with measurements[END_REF]. Clays are particles with diameter between 0.01 and 2 μm and follow the suspension process by the wind, they can be transported at long distances in the atmosphere before deposition. Particles with diameter less than 0.01 μm is limited by the adhesive and cohesive forces which tend to form larger particles or aggregates. Silt particles have a diameter between 2 and 60 μm and undergo the saltation process where particles are ejected from the surface for relatively small time before being deposited back to the surface. This process is considered as a prior condition for the initiation of dust storms. Sand particles have a diameter between 60 and 2000 μm, move very slowly in a soil mass following the creep process. Clay and silt are considered dust-like particles and particularly atmospheric dust presents particles with less than 10 μm.

Aerosols can be classified based on their mode: the radius ensemble for which n(r)

presents a maximum [START_REF] Boucher | Atmospheric Aerosols[END_REF]. We can distinguish the nucleation mode (particles < 20 nm), the fine mode (for clay particles) and the coarse mode (for silt particles). Fine mode dominates the aerosol number distribution, while the coarse mode dominates the aerosol volume distribution. Figure 9 summarizes soil types based on aeolian processes and modes associated with the SD. for aerosol fine mode [START_REF] Möhler | The effect of organic coating on the heterogeneous ice nucleation efficiency of mineral dustaerosols[END_REF]. In this study, experimental dust measurements were performed with SMPS and APS: the first counts the particle diameters between 20 nm-600 nm and the second counts the particle diameters between 500 nm-20 µm [START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF]. Furthermore, Scanning Electron Microscopy (SEM) can estimate the particles' diameter and mineral composition [START_REF] Falkovich | Chemical and mineralogical analysis of individual mineral dust particles[END_REF]. However, the result of this method represents the SD based on a portion of the sample of hundreds of individual particles. In contrast, the first two methods typically measure about thousands of particles.

Chemical Composition

Aerosol chemical composition is key element in understanding aerosol processes and their impact on the atmosphere. The radiative forcing is highly associated with dust mineralogical composition changes in the atmosphere [START_REF] Michel | Reactive uptake of ozone on mineral oxides and mineral dusts[END_REF]. In this section, we will elaborate on the mineralogical composition of mineral dust and its mixture state.

Moreover, we will describe the vibrational modes in the infrared (IR) that can be used to characterize the composition of dust samples using different techniques.

Mineralogical Composition

Mineral dust is composed of minerals, especially silicates and carbonates, in crystalline or amorphous form. One of the major mineral families is the phyllosilicates group (e.g., illite, kaolinite, and smectite) which are abundant in dust samples [START_REF] Journet | A new data set of soil mineralogy for dustcycle modeling[END_REF]. Members of the tectosilicates group, i.e., quartz and feldspars, form 75% of the Earth's crust and are found in different desert dust samples from the globe as shown in Figure 10. The carbonates family contains calcite and dolomite, considered tracers of Saharan and East Asian dust [START_REF] Formenti | Recent progress in understanding physical and chemical properties of African and Asian mineral dust[END_REF]. These minerals are detectable in the thermal infrared, where different vibrational modes are present. Mg 2+ , Al 3+ , Fe 2+ , and Fe 3+ forming infinite sheets from which the name sheet silicates [START_REF] Fang | Mineralogical Controls on Frictional Strength, Stability, and Shear Permeability Evolution of Fractures[END_REF].

Tectosilicates Phyllosilicates

Figure 11 Crystal structure of tectosilicates and phyllosilicates [START_REF] Fang | Mineralogical Controls on Frictional Strength, Stability, and Shear Permeability Evolution of Fractures[END_REF].

Minerals are formed of polyatomic molecules having molecular motion at a given frequency, a function of atomic coordinates called vibrational modes. Polyatomic molecules can be visualized from a number of point masses united by springs. If such a system receives energy, it will undertake a complex vibrational movement (stretching, deformation, swinging, wagging, twisting). The fundamental vibrational modes are shown in Dust composition can be determined either for individual particles or a bulk sample. For individual-particle analysis, the SEM has been used for decades to obtain dust particles' chemical elements, shape, and size distribution. This analysis allows a better understanding of the external and internal mixing of mineral dust. The method was used to exploit the quartz and illite/quartz ratio contents and link them to the particle size distribution [START_REF] Shao | Mineralogical characteristics of airborne particles collected in Beijing during a severe Asian dust storm period in spring 2002[END_REF].

A bulk sample is predominately analyzed to obtain the mineralogical composition with X-Ray Diffractometry (XRD). This technique is limited to determining a sample's crystalline compounds while neglecting non-crystalline minerals, which may constitute a significant fraction in some dust samples. XRD was previously used in geochemical tracing methods to link dust mineralogy and sources depending on the geology [START_REF] Chen | Geochemical studies on the source region of Asian dust[END_REF]. In East Asia, this analysis showed that the calcite content decreases from West to East, and conversely, the feldspar content increases [START_REF] Shen | Mineralogical characteristics of soil dust from source regions in northern China[END_REF].

Vibrational spectroscopy is another method that measures the vibrational energy levels associated with the chemical bonds in certain structural fragments of a dust sample. Fourier Transform InfraRed (FTIR) spectrometry is among the most popular techniques. The measured extinction coefficient spectrum of dust exhibits mineralogical fingerprints showing chemical information (Di Biagio et al., 2014a). In-situ spectrometers are typically employed, while satellite sensors are used to map the Earth's surface soil mineralogy, i.e., the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [START_REF] Rajendran | ASTER capability in mapping of mineral resources of arid region: A review on mapping of mineral resources of the Sultanate of Oman[END_REF]. Until today, the absence of the adequate optical properties (i.e., complex refractive indices) restricts dust parameter retrievals from satellite observations. For example, the quartz optical properties used in dust retrieval date from 1969 [START_REF] Peterson | Optical properties of quartz dust particles at infrared wavelengths[END_REF]. As well as the limitations encountered using satellite remote sensing i.e., the land surface signal with its high spectral variability altering the spectral detections.

Mixture State

The knowledge of the mixture types of an aerosol population is important to understand its properties, particularly the chemical interactions involved during the dust cycle.

Aerosols can be grouped based on their mixture state; we typically distinguish between external and internal mixtures. In external mixing, each particle of the population can be differentiated with its proper chemical or mineral composition, e.g., quartz, calcite, and illite. In this type of mixture, we can use different SD for each aerosol type. In internal mixing, each particle of the population can inhomogeneous. In this case, the population is characterized by a single SD.

The two mixtures defined above describe ideal models, and the reality falls in between. Therefore, a real aerosol population is a function of the SD in addition to the mixture type, which can vary in degrees. The different types of aerosol mixtures are represented in Figure 13. Electron microscopy techniques can be used to provide information on the mixture state of individual particles i.e., Transmission Electronic Microscopy (TEM) and SEM [START_REF] Deboudt | Mixing state of aerosols and direct observation of carbonaceous and marine coatings on African dust by individual particle analysis[END_REF], which are also used to identify the particle shape.

Sokolik and Toon, 1999 based their study on external mixture and used a liner combination method to obtain an effective extinction coefficient. In addition, they modeled the radiative effect by considering internal mixtures using the Effective Medium Theory.

By its name, the theory allows to obtain an effective dielectric constant or complex refractive indices of aggregate of minerals forming a particle. The most used theories are the Volume Mixing Approximation (VMA) and Bruggeman's Approximation (BGA).

Particle shape

The shape of mineral dust particles is highly variable. We can find nearly pristine crystals, aggregates/agglomerates, rounded, thin flakes, and even spherical particles.

Like the SD, the shape can modify the particle's surface area interacting with the radiation. However, it was previously proved that the shape factor is not directly linked to the SD [START_REF] Knippertz | Mineral dust: A key player in the earth system[END_REF]). An example in Figure 14 of SEM images shows Saharan dust samples with different shapes.

Figure 14 Electron-microscope images of sample mineral dust particles encountered in a soil sample collected in northern Sahara [START_REF] Nousiainen | Optical modeling of mineral dust particles: A review[END_REF].

OPTICAL PROPERTIES

How the material interacts with radiation is specific for each component, therefore understanding the absorption and emission processes makes it possible to identify these components and quantify their parameters. When measured, the radiation feedback interacting with aerosols through absorption, scattering and emission, defines the optical properties of aerosols. In this section, we will describe the radiation-matter interaction, the measured radiation parameters and the optical constants i.e. the efficiency factor, the extinction coefficient of a single particle then for a layer of particles and the parameters that affect this extinction i.e. the complex refractive indices.

Radiation-Matter Interaction

Particles can interact with solar and terrestrial radiation through absorption and scattering in different directions. The scattering can be elastic: the photon keeps its energy and changes angle with total reflection, with zero transmission and no absorption.

On the other hand, scattering can also be inelastic: the photon gives part of its kinetic energy to the vibrational modes of the atom, and changes angle only with partial reflection and refraction, with a non-zero transmission, and no absorption as there is not enough energy for the atom's electron to move to a higher state of energy. In addition, the process can include absorption and re-emission which depend on the wavenumber of radiation and the properties of the material. First, the photon gives its energy to the atom, and the absorbing electron moves to a higher energy level. Then, the electron will return to its initial state by re-emission of radiation. Particularly, thermal emission reflects the kinetic energy of random movements of atoms and molecules in matter. For example, greenhouse gas emissions contribute through thermal emissions in the temperature forcing and changes in the climate system. Figure 15 illustrates the different processes of radiation-matter interaction.

Figure 15 Reflection, transmission absorption and emission of radiation [START_REF] Hyll | Infrared Emittance of Paper -Method Development, Measurements and Applications[END_REF].

Radiative Transfer Equation

The radiation beam that passes through a particle is attenuated by the absorption and scattering processes. These interactions depend on different parameters: the wavenumber of incident radiation beam, the size and shape of the particle, and its natural ability to absorb or scatter energy. Figure 16 illustrates the aerosol-radiation mechanisms in the case of a spherical homogeneous particle.

Figure 16 Interaction between a radiation beam with a homogeneous aerosol particle with a spherical shape. 𝐼 0 : incident intensity, 𝐼 𝑎𝑏𝑠 : absorbed intensity, 𝐼 𝑠𝑐𝑎𝑡 : scattered intensity, 𝐼 𝑡𝑟𝑎𝑛𝑠 : total transmitted intensity.

The extinction irradiance 𝐼 𝑒𝑥𝑡 (in W/m 2 ) can be expressed as the sum of scattering and absorption processes, and it is written as follows:

𝐼 𝑒𝑥𝑡 = 𝐼 𝑎𝑏𝑠 + 𝐼 𝑠𝑐𝑎𝑡 (2)
where I abs and I scat are the irradiances of absorbed and scattered radiation respectively.

In reality, dust is entrained in the atmosphere and transported, forming a layer of particles. When the distance between the particles is larger than the SD of the layer population, we can assume that total scattered intensity is the sum of the scattered intensities of each particle, termed the single scattering approximation. The latter can be applied if the population of particles is diluted having the mean free path is much larger than the wavelength. In this case, the multiple scattering in which each particle of the ensemble interacts differently can be neglected. Therefore, we conduct this study on non-opaque observations and assume only single scattering interactions. Nevertheless, it is important to define the optical properties of a layer of the aerosol population in the atmosphere. Figure 17 The spectral radiance of a radiation beam passing thourgh an elementary optical path ds. 𝑅 0 : incident spectral radiance , 𝑅 𝑎𝑏𝑠 : absorbed spectral radiance , 𝑅 𝑠𝑐𝑎𝑡 : scattered spectral radiance , 𝑅 𝑒𝑚𝑖𝑠 : emission spectral radiance, 𝑅 𝑡𝑟𝑎𝑛𝑠 : total transmitted spectral radiance.

The variation of the spectral radiance along the elementary path 𝑑𝑠 is written as:

𝑑𝑅(𝑣, 𝑠) = -𝑅(𝑣, 𝑠) 𝑘 𝑒𝑥𝑡 (𝑣, 𝑠)𝑑𝑠 + 𝐵(𝑣, 𝑇) 𝑘 𝑒𝑥𝑡 (𝑣, 𝑠)𝑑𝑠 (3)
The first term of the equation expresses the attenuation of the radiance, which is proportional to the total extinction coefficient 𝑘 𝑒𝑥𝑡 constituting the sum of the atmospheric components' extinctions (gases, aerosols and clouds). The second term represents the increase of the radiance due to the thermal emission along the path.

In the case of spherical particles, the extinction coefficient 𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣) of a layer of aerosols is described by Mie Theory as follows:

𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣) = ∫ 𝜋𝑟 𝑔 2 𝑄 𝑒𝑥𝑡 (𝑟, 𝑣, 𝑚)𝑛(𝑟)𝑑𝑟 (4)
with 𝑄 𝑒𝑥𝑡 (𝑟, 𝑣, 𝑚) is the particle extinction efficiency, m is the complex refractive index, n(r) is the lognormal size distribution.

The efficiency factor expresses the efficiency of the attenuated radiation from a geometric cross-section of a medium. It is expressed as the ratio of the effective cross-section to the geometric cross-section. For spherical particles of diameter D, having a geometric crosssection of 𝜋𝐷 2 , the extinction efficiency 𝑄 𝑒𝑥𝑡 can be written as:

𝑄 𝑒𝑥𝑡 = 4𝜎 𝑒𝑥𝑡
𝜋𝐷 2 (5) where the extinction cross-section 𝜎 𝑒𝑥𝑡 (in m 2 ) describes the radiation attenuated in all directions by the particle. It can also be written as the sum of the absorption and scattering cross-sections and is calculated by integrating the elementary effective crosssection σ (𝜃, 𝜙) over 4π steradians of an elementary solid angle 𝑑Ω , and it is given as follows:

𝜎 𝑒𝑥𝑡 = ∫ 𝜎 (𝜃, 𝜙)𝑑Ω 4𝜋 (6)
The second term of the Equation (3) describes the emitted radiation by the crossed optical path. Due to molecular collisions in the atmosphere, the primary emission comes from thermal emissions. Hence, the atmospheric molecules act like a blackbody, a hypothetical body that absorbs all incident radiation on it and reflects none. As a result, the radiation is re-emitted with perfect efficiency, which only varies with temperature variation. It is described by Planck's Law: a blackbody radiation 𝐵(𝑣, 𝑇) (in W. m 2 . sr. (m -1 ) -1 ) is defined as the amount of energy emitted by a blackbody as function of its thermodynamic temperature 𝑇 (in K) and radiation wavenumber 𝑣 (in cm -1 ):

𝐵(𝑣, 𝑇) = 2 ℎ𝑐 2 𝑣 3 [𝑒𝑥𝑝( ℎ𝑐𝑣 𝑘 𝐵 𝑇 )-1] (7)
The optical properties of the blackbody are independent of the particular material of which the object is made. At a fixed temperature, the radiance versus wavenumber is called spectral distribution: how much in each wavenumber interval is emitted by matter.

Wien Displacement law states that the emission peak decreases with increasing blackbody's temperature (Figure 18), it is given by:

𝜆 = 𝑏 𝑇 (8)
where 𝑏 = 2.897 × 10 -3 m. K is the constant of proportionality termed Wien's displacement constant.

The atmosphere is affected by solar and terrestrial radiation. The sun emits a relatively constant radiation flux towards the Earth with an approximate surface temperature of 6000 K. The total power emitted is composed of wavelengths varying from low to high energy. Terrestrial radiation is generated by thermal infrared emission and has a surface temperature of around 300 K.

Figure 18 The spectral distribution of a blackbody with temperatres from 300 K to 700 K [START_REF] Campbell | Introduction to Remote Sensing, Fifth Edit[END_REF]).

Kirchhoff's Law states that all Blackbodies at the same temperature have the same ratio of emitted radiation to absorbed radiation. Hence, we can define the spectral emissivity; a dimensionless parameter that measures the effectiveness of a body as a radiator with respect to an ideal radiator, and is written as follows:

𝜀 𝑣 = 𝑅(𝑣,𝑇) 𝐵(𝑣,𝑇) (9)
For a blackbody, the emissivity is 1 since it presents an ideal radiator, while for an ideal reflector or white body, the emissivity is 0. As in nature, there is no ideal radiator or reflector, the emissivity lies between 0 and 1, such a body is termed graybody e.g., Earth's surface which has a highly variable emissivity and can pose limitations in aerosol observations.

From Equation (3), we also can define:

𝜏(𝑣, 𝑠) = ∫ 𝑘 𝑒𝑥𝑡 (𝑣, 𝑠)𝑑𝑠 𝑠 2 𝑠 1 (10)
where 𝜏(𝑣) is the optical thickness between 𝑠 1 and 𝑠 2 . It is one of the most used parameters in atmospheric studies since its value contains information about the number of particles and their extinction properties (absorption and scattering) along the path. This parameter does not contain information about emission.

Size parameter

The size parameter 𝑥 is a unitless parameter that establishes the link between the particle size and the wavenumber of radiation. The effect of the size of a particle on scattering is defined by the size parameter, which is the ratio of its perimeter to the wavelength of the incident light such that:

𝑥 = 𝜋𝐷 𝜆 (11)
where 𝑟 is the radius of the particle and 𝜆 is the wavelength of radiation (both expressed in μm ).

The size parameter allows us to define three regimes (Figure 19):

1-Geometric Optics Regime for particles having 𝑥 ≫ 1 or D≫ 𝜆 (D is the diameter of the particle).

2-Mie Regime for particles having the same order of the wavelength or 𝐷 ≈ 𝜆.

3-Rayleigh Regime for particles having 𝑥 ≪ 1 or 𝐷 ≪ 𝜆. Absorption dominates over scattering for sufficiently small absorbing particles (𝐷 ≪ 𝜆). If the particle is about the same size (𝐷 ≈ 𝜆) as or larger than the wavelength (D≫ 𝜆), the extinction is due to both scattering and absorption.

Particle Shape

In the case of mineral dust, the particles are not spherical and have various shapes. [START_REF] Kalashnikova | Modeling the radiative properties of nonspherical soil-derived mineral aerosols[END_REF] showed that deviations from spherical behavior are mostly due to the scattering component of extinction since irregularly shaped particles have larger scattering efficiencies than spheres. In addition, particle absorption is much less sensitive to particle shape, therefore sphericity in thermal infrared was considered and Mie theory was applied Di [START_REF] Di Biagio | Global scale variability of the mineral dust long-wave refractive index: A new dataset of in situ measurements for climate modeling and remote sensing[END_REF]. However, Legrand et al., 2014 has shown that assuming sphericity gives some degrees of uncertainties in the longwave spectra, especially near the resonant peaks. Therefore, non-spherical theories are applied depending on particle symmetry,, i.e., the T-matrix method and Continuous Distribution of Ellipsoid -CDE [START_REF] Reed | Mass extinction spectra and size distribution measurements of quartz and amorphous silica aerosol at 0.33-19 µm compared to modelled extinction using Mie, CDE, and T-matrix theories[END_REF][START_REF] Silvester | Finite Elements for Electrical Engineers[END_REF].

Complex Refractive Index

The complex refractive index (CRI) is a fundamental parameter combined with the SD to characterize aerosols using remote sensing. It can be used to determine the link between aerosols' chemical and optical properties. Many studies used it as a fixed input in inversion processes to solve retrieval problems (Di Biagio et al., 2014b;[START_REF] Ryder | Coarse and giant particles are ubiquitous in Saharan dust export regions and are radiatively significant over the Sahara[END_REF].

The refractive index of a medium is a function of the wavenumber of the radiation under consideration. The refractive index characterizes the way the medium interacts with the electromagnetic radiation, giving a macroscopic description of the propagation of radiation in that medium, and it is written as follows:

𝑚(𝑣) = 𝑛(𝑣) + 𝑖 𝜅(𝑣) (12)
The real part 𝑛(𝑣) is the ratio of the speed of light in the medium to that in the vacuum, while the imaginary part 𝜅(𝑣) characterizes the absorption of the medium. An example of the CRI for pure Quartz is shown in Figure 21. Different techniques were previously used to retrieve the CRI of pure minerals that can constitute an aggregate of mineral dust, and are described as follows: Wavenumber n (cm -1 )

1-The refraction method is typically applied on a thin film or a prism sample. It measures the real refractive index calculated using a prism's minimum angle of deviation [START_REF] Born | Principles of Optics[END_REF],. The imaginary refractive index is considered insignificant for a non-absorbing medium. However, for absorbing medium, the imaginary part can be determined using the optical path through the light absorption prism by solving Maxwell's equation [START_REF] Sasaki | Determination of complex refractive index using prism composed of absorbing medium[END_REF].

2-The reflection method measures the reflection on aerosol powder films. Both real and imaginary parts can be calculated by substituting the reflectivity using two equations derived from Fresnel equations and Kramers-Kronig relations [START_REF] Kitamura | Optical constants of silica glass from extreme ultraviolet to far infrared at nearroom temperature[END_REF].

This method was used by [START_REF] Peterson | Optical properties of quartz dust particles at infrared wavelengths[END_REF][START_REF] Spitzer | Infrared Lattice Bands of Quartz[END_REF][START_REF] Spitzer | Infrared Lattice Bands of Quartz[END_REF] for Quartz crystal and by [START_REF] Querry | Complex refractive index of limestone in the visible and infrared[END_REF] for clay minerals (i.e., Illite, kaolinite, and Montmorillonite).

3-The transmission method measures the transmittance of samples of thin homogeneous films where the reflectivity is negligible. [START_REF] Egan | Optical properties of inhomogeneous materials : applications to geology[END_REF] used this method to calculate the CRI of clay minerals (Illite, Kaolinite…). In this method, the imaginary part is estimated, and the Kramers-Krönig relations are used to obtain the real part.

In the case of a heterogeneous sample, the largest errors come from underestimating the scattering effect [START_REF] Mcpheat | Comparison of aerosol and thin film spectra of supercooled ternary solution aerosol[END_REF]. Another limitation is the thickness of the sample film, which should be reduced to increase the transmittance: its value is very small for imaginary indices greater than 1. Hence, the method modifies the shape, size, and vibrational bounds, reducing a representative sample of atmospheric aerosols [START_REF] Felske | Determination of the Refractive Indices of Soot Particles from the Reflectivities of Compressed Soot Pellets[END_REF].

4-The particle's suspension method overpasses the limitations of the previous method by measuring the transmittance of suspended particles [START_REF] Hubert | New approach for the determination of aerosol refractive indices -Part II: Experimental set-up and application to amorphous silica particles[END_REF]. The CRI is then retrieved using an optimal estimation method to retrieve from a simulated spectrum an experimental spectrum by using Mie theory in the case of spherical particles and T-matrix in the case of non-spherical particles.

Different methods can be used to retrieve the CRI, and the studies remain limited in the covered spectral range. In our study, we use the CRI characterized from a Gobi Desert dust sample and individual minerals (quartz, illite and calcite), using mechanical shaking by the particle suspension method. The apparatus is optimized to decrease the volume of the chamber with a maintained aerosol flux during the extinction and size distribution measurements. First, Mie Theory was used to simulate extinction spectra, and then the optimal estimation method was employed with Kramers-Krönig relations to quantify the CRI at a spectral resolution of 0.5 cm -1 [START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF]. The experimental setup is described in Chapter III.

MINERAL DUST DETECTION USING REMOTE SENSING

In order to better understand mineral dust properties and their impact, a large panel of methods has been developed during the last thirty years using remote sensing instruments. [START_REF] Fischer | Progress in remote sensing[END_REF] defined remote sensing as "the art or science of telling something about an object without touching it.". Technically, remote sensing instruments are sensors that measure, at a distance, the electromagnetic radiation interacting with an object.

Remote Sensing Process

The process of remote sensing consists of having a physical object interacting with the electromagnetic radiation detected by the instrument. The measured parameters are called the sensor data which are examined and analyzed by the scientists. From these datasets, information selection is performed with respect to the aimed application such as geology, meteorology, atmospheric sciences. The process of remote sensing is illustrated in Figure 22.

Figure 22 Remote sensing process [START_REF] Campbell | Introduction to Remote Sensing, Fifth Edit[END_REF].

Key Concepts of Remote Sensing

Remote sensing instruments are characterized by the following key concepts:

1 -The spectral differentiation indicates how to distinguish between two different wavelengths and identifies the instrument's spectral resolution. These sensors can employ panchromatic, multispectral, or hyperspectral resolution measuring the acquired quantity with multiple band combinations. These combinations differ by the number of channels. A comparison of different spectral resolutions is illustrated in Figure 23. In the design of optical remote sensing systems, owing the limited amount of incident energy, there are critical tradeoffs between the spatial resolution, the spectral resolution and signal-to-noise ratio (SNR). For this, optical systems can provide data with a high spatial resolution but with a small number of spectral bands (i.e., multispectral data from HIMAWARI). On the other hand, with a high spectral resolution and a high SNR, the spatial resolution is reduced. For example, the Infrared Atmospheric Sounding Interferometer IASI having a SNR of 500 with a high spectral resolution of 0.5 cm -1 but with a spatial resolution of 12 km.

Figure 24 A comparison of spatial resolution between (a) panchromatic (b) multispectral (c) hyperspectral imagery [START_REF] Borstad | Hyperspectral Imagery for Safeguards Applications[END_REF]. 4-The temporal resolution describes the smallest time needed to detect the same region again and the use of information of the same region acquired over time. Although, for most satellites, two adjacent orbits overlap in the imaging swaths, and this overlap increases with increasing latitude, some areas of the Earth tend to be re-imaged more frequently. Also, satellite systems can point their sensors to image the same area between different satellite passes separated by one to five days. Thus, a sensor's actual temporal resolution depends on various factors, including the satellite/sensor capabilities, the swath overlaps, and latitude.

Active and Passive Remote Sensing

We can distinguish between active and passive instruments according to the radiation source used to detect an object. Active instruments use their light source, while passive instruments use natural radiation from the sun and Earth's surface from UV, Visible, Infrared to Microwave (Figure 26). They can be located on the ground, in situ on balloons or aircrafts, and on satellites depending on the objective of measurement. Using these instruments, we can directly measure the feedback of electromagnetic radiation when interacting with aerosols. Nevertheless, we can quantify different aerosol parameters. Major active and passive sensors are summarized in Table .1. AERONET photometers provide, from ground-based observations, inversion products of aerosol SD.

From satellite remote sensing, the vertical distribution of aerosols in the atmosphere (dust altitude and layer thickness) can be obtained by the spaceborne lidar CALIOP measuring the backscattered radiation [START_REF] Wainker | The global 3-D distribution of tropospheric aerosols as characterized by CALIOP[END_REF]. Daily observations in TIR from high spectral resolutions instruments also have shown the ability to derive the vertical dust plumes distributions originating from the Sahara. Mean monthly altitudes were previously derived from AIRS [START_REF] Pierangelo | Dust altitude and infrared optical depth from AIRS[END_REF] and IASI [START_REF] Capelle | Evaluation of IASI-derived dust aerosol characteristics over the tropical belt[END_REF]. Furthermore, IASI vertical profiles of concentration were retrieved for the Saharan dust plumes [START_REF] Vandenbussche | Retrieval of desert dust aerosol vertical profiles from IASI measurements in the TIR atmospheric window[END_REF].

On the other hand, quantitative estimates of the horizontal distribution of desert dust are done in terms of Aerosol Optical Depth (AOD). The geostationary satellite HIMAWARI 8 RGB [START_REF] Wang | Dust Heterogeneous Reactions during Long-Range Transport of a Severe Dust Storm in May 2017 over East Asia[END_REF] can differentiate natural dust from anthropogenic dust with lower AOD values. MODIS measurements in the near-infrared and visible are used above non-bright surfaces to measure the AODs [START_REF] Ginoux | Identification of anthropogenic and natural dust sources using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue level 2 data[END_REF]. In addition, dust AODs can be retrieved from UV to the near infrared using for example Ozone Monitoring Instrument (OMI) [START_REF] Torres | Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview[END_REF]. Recently, spaceborne spectrometers with high spectral resolution were used to retrieve the AODs of horizontal distribution of Saharan dust plumes from AIRS measurements [START_REF] Desouza-Machado | Infrared retrievals of dust using AIRS: Comparisons of optical depths and heights derived for a North African dust storm to other collocated EOS A-Train and surface observations[END_REF] and IASI [START_REF] Pierangelo | Retrieving the effective radius of Saharan dust coarse mode from AIRS[END_REF]. Moreover, AOD-altitude bins derived from IASI were used to detect daily dust emissions events [START_REF] Chédin | Contribution of IASI to the Observation of Dust Aerosol Emissions (Morning and Nighttime) Over the Sahara Desert[END_REF]. Aerosol retrievals from IASI also allowed to describe the 3D distribution and evolution of dust outbreak over East Asia [START_REF] Cuesta | Three-dimensional distribution of a major desert dust outbreak over East Asia in March 2008 derived from IASI satellite observations[END_REF]. In addition, the retrievals allowed the quantification of microphysical properties of the airborne dust from AIRS [START_REF] Pierangelo | Retrieving the effective radius of Saharan dust coarse mode from AIRS[END_REF], and the coarse dust effective size was derived from IASI on a monthly basis [START_REF] Capelle | Evaluation of IASI-derived dust aerosol characteristics over the tropical belt[END_REF]. IASI have also been used

to detect different types of aerosols (i.e., volcanic ash) and retrieve their AODs and microphysical properties [START_REF] Clarisse | Retrieving radius, concentration, optical depth, and mass of different types of aerosols from high-resolution infrared nadir spectra[END_REF][START_REF] Guermazi | Quantitative Retrieval of Volcanic Sulphate Aerosols from IASI Observations[END_REF]. The percentage mass fraction of clays only were previously obtained from IASI using experimental extinction coefficients (illite, kaolinite and montmorillonite) [START_REF] Klüser | Information content of space-borne hyperspectral infrared observations with respect to mineral dust properties[END_REF]. The latter work opens the perspective to retrieve the dust mineralogical composition from IASI observations which is the goal of this thesis.

Table 1 Different sensors types, functioning, measured and retrieved parameters.

EAST ASIAN DESERTS

East Asia is the second-largest mineral dust source in the world after the Sahara, and it can produce up to 800 Tg per year of mineral dust [START_REF] An | Temporal and spatial variations in sand and dust storm events in East Asia from 2007 to 2016: Relationships with surface conditions and climate change[END_REF]. Strong winds lift and transport large amount of mineral dust every year, mostly in winter and spring [START_REF] Xie | Characteristics of chemical composition and seasonal variations of PM2.5 in Shijiazhuang, China: Impact of primary emissions and secondary formation[END_REF].

To better understand the dust storms in this region, one should look at the topography in East Asia with alternating high mountains and basins. A diverse regional structure

SEVIRI, HIMAWARI

Spectro-Radiometers

Measures the intensity of radiation in multiple wavelength bands Aerosol Optical Depth, Altitude, Effective Radius, and Concentration

MODIS

Spectrometers

Gratings

Spectrometers

Measures the spectral content of incident radiation using the law of interference from the principle of grating diffraction

AIRS, OMI

Fourier Transform Spectrometers

Based on the interference phenomenon of two electromagnetic waves using the principle of an interferometer

IASI

with desert surfaces, i.e., the Gobi and Taklamakan, as well as the Horqin Sandy Land between North China and Mongolia. In contrast, more vegetation covers the eastern regions with more urban and populated cities, such as Shandong (Figure 27).

The Gobi Desert, Taklimakan Desert, and nearby plateaus are considered the primary dust sources in this region [START_REF] Wu | Regional Characteristics of Recent Dust Occurrence and Its Controlling Factors in East Asia[END_REF]. These deserts are affected by both the East Asian monsoons and the global westerlies, and a change in either of these systems influences regional climate. Dust masses are transported downwind from Mongolia and

China [START_REF] Chen | Characteristics of the long-range transport dust events in Chengdu, Southwest China[END_REF], crossing the Korean Gulf and Japan [START_REF] Mikami | Aeolian dust experiment on climate impact: An overview of Japan-China joint project ADEC[END_REF] to the Pacific Ocean until reaching the North American continent [START_REF] Guo | Trans-Pacific transport of dust aerosols from East Asia: Insights gained from multiple observations and modeling[END_REF], and sometimes reaching Europe [START_REF] Grousset | Case study of a Chinese dust plume reaching the French Alps[END_REF]. One of the particularities of this region is the diversity in its mineralogical composition; the dust is rich in silicates and carbonates, which are considered as dust source tracer in East Asia (Young [START_REF] Jeong | Mineralogy and geochemistry of Asian dust: Dependence on migration path, fractionation, and reactions with polluted air[END_REF]. Satellite remote sensing detection above this region encounters many challenges. In particular, East Asian dust is uplifted near the surface in the low troposphere above the continent [START_REF] Chen | Comparison of dust emissions, transport, and deposition between the Taklimakan Desert and Gobi Desert from 2007 to 2011[END_REF], where the Land Surface Emissivity (LSE) variability in the Infrared affects remote sensing detections [START_REF] Masiello | Diurnal variation in Sahara desert sand emissivity during the dry season from IASI observations[END_REF]. This limitation presents a primary error source, as it impacts clear sky and dust layer detection above the land [START_REF] Ackerman | Remote sensing aerosols using satellite infrared observations[END_REF].

Horqin Sandy Land

Airborne dust can alter the Earth radiative budget by changing the atmosphere's absorption and scattering properties. In addition to the impact on the Earth's climate and ecosystem, human activity, health and regional visibility.

OBJECTIVE

We introduced in this chapter, mineral dust cycle and its impact on the climate system. To better understand that impact, remote sensing observations proved the ability to quantify the aerosol chemical and microphysical properties from space. However, the retrieval remains challenging due to the variability and diversity of these properties depending on different dust sources and changes occurring during transport. The objective of this work, is to retrieve aerosol chemical properties of East Asian dust plumes from IASI observations in TIR, using new experimental optical properties measured in the laboratory (extinction coefficients of pure minerals).

The thesis aims first, to overpass the challenge of remote sensing observations above land caused by the LSE that alters dust observations in TIR. This parameter will be discussed in Chapter II, and a developed method will be described to overcome this limitation allowing us to characterize and quantify different airborne mineral dust properties.

Second, after LSE optimization, we use new pure minerals extinction spectra and test them to reproduce experimental Gobi dust extinction spectrum, which allow us to calculate the mass fraction of major mineralogical families composing desert dust. Then, we derive the optical thickness from IASI observations to quantify from its spectral features the mineral composition and plot the evolution of the mineralogical composition during dust events. The final aim is to link the composition results to different dust sources in East Asia (Chapter III).

CHAPTER II. DUST DETEC-TION USING IASI

The chapter introduces the high spectral resolution remote sensing instrument used to detect and characterize mineral dust properties, the Infrared Atmospheric Sounding

Interferometer (IASI). First, an overview of the sensor's satellite MetOp is provided. Then, we briefly explain its optical architecture and the optical principle employed to measure the IR radiation to describe the IASI characteristics, leading us to define the radiative transfer equation. Second, we elaborate on the different atmospheric constituents detected by the IASI: gases, aerosols, and clouds. We will particularly focus on the mineral dust detections having spectral features in the TIR region and study the emitted dust from East Asian deserts in the low troposphere above land. Finally, as the land surface emissivity (LSE) poses large constraints due to its high variability, we discuss how its signal affects the IASI spectra and we introduce an LSE optimization method to enhance mineral dust detections above land and quantify its chemical and microphysical properties (Chapter III).

INSTRUMENT PRESENTATION

MetOp Satellite

The METeorological OPerational Satellite (MetOp) is a satellite series launched as a low-Earth-orbiting sun-synchronous satellite at an altitude of about 800 km. The series delivers high-resolution sounding and high-resolution imagery in global coverage. IASI is one of eight meteorological instruments on MetOp [START_REF] Hilton | Hyperspectral Earth Observation from IASI: Five Years of Accomplishments[END_REF]. resolution and radiometric performance [START_REF] Crevoisier | Towards IASI-New Generation (IASI-NG): Impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables[END_REF]. Figure 29 shows the MetOp payload and IASI optical system.

Figure 29 The MetOp satellite and its payload with a closer look on IASI system (eumetsat.int).

MetOp makes 14 orbits around Earth, ensuring global coverage. IASI is on board with daytime and nighttime detections for a temporal resolution of about 12 h. The satellite scans from a low altitude sun-synchronous orbit, in nadir view -the observation points on the surface Earth that are directly below the satellite trajectory. Figure 30 illustrates the ground track of IASI/MetOp orbits around the globe.

Figure 30 Ground track of MeTop satellite for 24h [START_REF] Herbin | Infrared Observation of Earth's Atmosphere[END_REF].

The Infrared Atmospheric Sounding Interferometry (IASI)

IASI is an Infrared Fourier Transform Spectrometer, based on the Michelson interferometer principle. As shown in Figure 31, the radiation beam is scanned by a scan mirror, then is directed by the entrance mirrors M1 and M2 to a semi-reflective beam splitter that splits the incident beam into two waves into two cubes corners, one fixed, and the other moving parallel to the beam, the reflected beams recombine into one wave with an optical path difference then directed to the exit mirrors M3 and M4. These elements form the Interferometer Hot Optics Subassembly IHOS module and are linked to a cold box which transmits the signal to the detectors. In addition, there is the electronic module that guides the cube-corner mechanism and the laser module that controls loop electronics dynamics. The instrument has a swath of 2200 km with each field of view corresponding to 2 ×2 circular pixels, each with a 12 km diameter footprint in nadir. It observes Earth up to an angle of 48.3° on both sides of the satellite track. Figure 32 shows the IASI swath scan and the field view with the corresponding pixels.

Figure 32 IASI swath angle and field view pixels [START_REF] Clerbaux | Atmospheric Chemistry and Physics Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder[END_REF].

With a continuous spectral radiance 𝑅(𝑣) characterizing the radiation source of a wavenumber 𝑣, the two coherent waves lead to an interference and the amplitude of the output wave depends on the optical path difference 𝑥, leading to the following interferogram:

𝐼(𝑥) = ∫ 𝑅(𝑣)(1 + 𝑐𝑜𝑠 (2𝜋𝛾𝑥))𝑑𝑣 ∞ 0 ( 13
)
The interferogram is the sum of a constant term and the real part of the Direct Fourier Transform of 𝑅(𝑣). The first term is not used in spectroscopy but the second term is used to retrieve the spectral radiance by the Inverse Fourier transform, and it is given by: IASI measures the spectrum of infrared radiation emitted by the Earth and the atmosphere. The sensor covers a continuous infrared spectral range between 645 and 2760 cm -1 (3.62 and 15.5 μm) and provides 8461 channels with a high spectral resolution of 0.5 cm -1 and low radiometric noise [START_REF] Blumstein | IASI instrument: technical overview and measured performances[END_REF]. With this feature, IASI can provide day/night detections and ocean/land measurements at a high spectral resolution and a low radiometric noise (Figure 33).

𝑅(𝑣) = ∫ 𝐼(𝑥)𝑐𝑜𝑠(2𝜋𝑣𝑥) 𝑑𝑥 ∞ 0 (
Figure 34 IASI-C radiometric noise averaged over one field of view of four pixels [START_REF] Serio | Characterization of the observational covariance matrix of hyper-spectral infrared satellite sensors directly from measured earth views[END_REF].

Radiative Transfer Equation

The RTE describes the atmospheric state and the radiative processes absorption, scattering and thermal emission. A realistic representation of the atmosphere is a complex problem since the distribution of atmospheric components and parameters is not homogeneous.

Nevertheless, most radiative transfer codes divide the atmosphere in a finite number of horizontal layers for which homogeneous atmospheric properties can be assumed [START_REF] Clarisse | A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements[END_REF][START_REF] Herbin | Infrared Observation of Earth's Atmosphere[END_REF]. Therefore, we will consider that Earth's atmosphere is highly stratified and can be assumed to be divided into horizontal homogeneous layers (Figure 35). Each layer has its pressure, temperature, and optical properties. In addition, each atmospheric column is discretized and considered independent from another columns.

Figure 35 An atmosphere divided homogeneous layers [START_REF] Herbin | Infrared Observation of Earth's Atmosphere[END_REF].

We consider that the atmosphere is stratified into plane, parallel and homogeneous layers. The integration of Equation (3) from Earth's surface to the top of the atmosphere having an altitude 𝐻. For IASI viewing in nadir it is written as follows: The first term represents the radiance directly emitted by the surface with a skin temperature 𝑇 𝑠 having a blackbody emitted radiation 𝐵(𝑣, 𝑇 𝑠 ), an emissivity 𝜀(𝑣), and attenuated by the atmospheric column above the surface having a total extinction 𝑘(𝑣, ℎ), which expresses the sum of gaseous 𝑘 𝑒𝑥𝑡 𝑔𝑎𝑠 (𝑣, ℎ), aerosols 𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣, ℎ) and clouds 𝑘 𝑒𝑥𝑡 𝑐𝑙𝑜𝑢𝑑 (𝑣, ℎ) extinction coefficients, and it is given as follows:

𝑘(𝑣, ℎ) = 𝑘 𝑒𝑥𝑡 𝑔𝑎𝑠 (𝑣, ℎ) + 𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣, ℎ) + 𝑘 𝑒𝑥𝑡 𝑐𝑙𝑜𝑢𝑑 (𝑣, ℎ) (16)

The second term expresses the down welling radiance from the sun radiance Is and the atmospheric layer at altitude h, describing the radiance contribution emitted from the atmosphere towards surface, that is back-reflected from it and then reaches the satellite.

The third term represents the upwelling radiance directly emitted from the atmosphere along the slant path in the viewing direction, integrated along the whole path (Figure 36). Note that the second term only affects the shortest IASI wavelengths.

Figure 36 Nadir viewing observation of the radiation coming from the Earth's surface (1), the atmosphere (2a and 3) and the sun (2b).

ATMOSPHERIC SPECTRAL DETECTION

IASI captures the Earth's blackbody radiation signal in the longwave infrared. The radiation thermal radiation emitted by land/ocean surfaces and the atmosphere, in addition to the reflected sunlight pass through the atmosphere (gases, aerosols, and clouds), resulting in a modification in the signal received by the sensor along the path. A molecule modifies the spectrum at specific wavenumbers due to its rotation-vibration transitions.

The Brightness Temperature or apparent radiation temperature 𝑇 𝐵 (𝑣) (in K) is defined as the temperature of a blackbody outputting the same radiance as that measured

for its surface at a given wavenumber and calculated from the spectral radiance:

𝑇 𝐵 (𝑣) = ℎ𝑐𝑣 𝑘 𝐵 ln ( 2ℎ𝑐 2 𝑣 3 𝑅(𝑣) +1) (17)
The primary objective of IASI mission is to provide high spectral resolution measurements that estimate humidity and temperature profiles in numerical weather predictions. However, since its launch it has also provided monitoring atmospheric constituents, including gases, aerosol and cloud detections that were not initially expected [START_REF] Hilton | Hyperspectral Earth Observation from IASI: Five Years of Accomplishments[END_REF].

Figure 37 shows how IASI brightness temperature spectra are affected by gas molecular absorptions for a clear sky above the sea; the major difference with a clear sky above the desert is caused the surface emissivity signal that highly alters the spectral detection. Mineral dust and ice cloud detection examples show different spectral features depending on the layer's chemical and microphysical properties. First, we shall describe the absorbing gases that alter the IASI spectra. Then, we will focus on the atmospheric window where mineral dust is detectable. However, the contribution of the land surface effect can be important. Therefore, we elaborate on the attributed spectral change by the surface in the TIR atmospheric window.

Figure 37 Examples of IASI brightness temperature spectra for a clear sky scene, a scene above the desert, a scene containing dust and another containing ice cloud. Spectral channels after 2220 cm -1 were removed due to solar reflectance term that we disregard in our work.

Atmospheric Gases Detection

Although not IASI's primary objective, it has demonstrated its ability to detect different gas extinctions, according to Equation ( 15) and (16). In the infrared, the gases have negligible scattering coefficient, and the absorption is dominant. Hence, the total gas extinction is equivalent to the gas absorption coefficient (𝑘 𝑒𝑥𝑡 𝑔𝑎𝑠 = 𝑘 𝑎𝑏𝑠 𝑔𝑎𝑠 ). Water vapor is the dominant absorber in the IASI spectrum since it constitutes the most abundant gas in the atmosphere [START_REF] Herbin | H216O and HDO measurements with IASI/MetOp[END_REF] The spectrum is also sensitive to Ozone (O3) signature, methane (CH4), and carbon monoxide (CO). In addition, the spectrum includes features from the weak absorbers, nitric acid (HNO3) and chlorofluorocarbons CFC-11 and CFC-12. Other weak absorbers results are seen in concentrated plumes, i.e., sulfur dioxide (SO2) plumes ejected in volcanic explosions [START_REF] Clarisse | Tracking and quantifying volcanic SO2 with IASI, the September 2007 eruption at Jebel at Tair[END_REF] and the detection of emission sources of short-lived trace species, such as ammonia (NH3), methanol ( CH3OH), and formic acid (HCOOH) has been possible [START_REF] Van Damme | Evaluating 4 years of atmospheric ammonia (NH3) over Europe using IASI satellite observations and LOTOS-EUROS model results[END_REF]. Figure 38 shows the main gas species detected by IASI, in total the instrument components are already detected. Middle panels: radiative transfer pseudo-transmittance simulations to identify the main absorbing gases (middle panels) and the weaker absorbers compared with the IASI noise (lower panel) [START_REF] Clerbaux | Atmospheric Chemistry and Physics Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder[END_REF].

Airborne Mineral Dust Detection

In addition to the gas species, the IASI signal can be altered through the aerosol extinction 𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣, ℎ) from Equation ( 16). Due to the high spectral resolution of IASI and its sensitivity to various aerosol spectral signature in TIR, the detection and classification of aerosol types (including volcanic ash, desert dust and biomass burning) within plumes is possible [START_REF] Clarisse | Retrieving radius, concentration, optical depth, and mass of different types of aerosols from high-resolution infrared nadir spectra[END_REF]. Particularly, airborne mineral dust is formed by external or internal mixtures of silicates and carbonates minerals, therefore IASI is sensitive to mineral dust spectral signature in the TIR, having absorption features between 750 and 1250 cm -1 where the atmosphere is mostly transparent. The strongest feature is caused by O-Si-O asymmetrical stretching caused by mineral molecules i.e., illite and quartz [START_REF] Madejová | IR Spectra of Clay Minerals[END_REF]. Other modes can also be detected in the atmospheric window i.e.

the -OH bending in clay minerals [START_REF] Madejova | Baseline Studies of the Clay Minerals Society Source Clays: Infrared Methods[END_REF], and the C-O asymmetrical stretching in carbonates (i.e., calcite, dolomite). However, the C-O symmetrical stretching in carbonates is masked near 1400 cm -1 by the water vapor continuum. The IASI spectrum reaches its maximum transmittance at 781.25 and 1235.25 cm -1 , by these extremities and the dust absorption feature, a V-shaped brightness temperature spectrum is detected (Xuan and [START_REF] Sokolik | Characterization of sources and emission rates of mineral dust in Northern China[END_REF]. Figure 39 shows a forward model simulation of IR spectrum of an empty atmospheres (i.e., empty from gases) with a single layer of Gobi Desert dust (𝑟 𝑔 = 0.25 μm; 𝜎 𝑔 = 2μm) compared with an IASI Gobi dust observation.

Surface Emissivity

One of the most important parameters in the RTE Equation ( 15 [START_REF] Huang | Improved Representation of Surface Spectral Emissivity in a Global Climate Model and Its Impact on Simulated Climate[END_REF].

To test the different surfaces effect, we take two IASI observations from the same scanning track time and date: one of a clear sky above the Sea of Japan and another of a clear sky above the Gobi Desert from the 3 May 2017 shown in Figure 41. An observation of a clear sky above the sea with a negligible LSE contribution shows a flat brightness temperature spectrum in the atmospheric windows (780 to 980 cm -1 and 1070 to 1210 cm -1 ). Conversely, a clear sky above the desert spectrum has high LSE spectral variability that is characterized by a peak at 1159 cm -1 , with a decrease in its brightness temperature between 1069 and 1250 cm -1 . These two features appear due to a high reflectance phenomenon called the Reststrahlen Effect arising from and between coherent oscillations of the vibrational optical phonons [START_REF] Caldwell | Low-loss, infrared and terahertz nanophotonics using surface phonon polaritons[END_REF][START_REF] Griffiths | Fourier Transform Infrared Spectrometry[END_REF]. In particular, it is confined between Transversal Optical (TO) and Longitudinal Optical (LO) phonon modes of Quartz, most dominant mineral in desert surfaces [START_REF] Winta | Second-harmonic phonon spectroscopy of α -quartz[END_REF]. It is triggered by a sudden drop in the real refractive index value. As the real part becomes smaller than the imaginary part, the reflectance increases to a value of 1, which results in a quartz-rich surface decrease in the LSE to values of less than 1 in the Reststrahlen region.

SURFACE IMPACT ON DUST DETECTIONS

We are interested in detecting aerosols from IASI detections from the atmospheric windows where only spectral gas lines are exhibited. However, the LSE signal can affect this spectral region when aerosols are detected above the desert. In this section, we will discuss the impact of the LSE on dust selection during a dust storm and the use of an LSE dataset derived from IASI from the literature to account for its effect. However, its direct use can over represent or under represent the IASI spectrum. Therefore, we introduce a Reststrahlen Feature new LSE optimization methodology that corrects these datasets and optimizes spectral detection.

Dust Selection

We use the 'V-shape' dust criteria to select IASI dust spectra. We do this by computing the difference in the brightness temperature ∆𝑇 𝐵 , calculated by subtracting two brightness temperature values 𝑇 𝐵,𝑣 1 and 𝑇 𝐵,𝑣 2 for two wavenumbers 𝑣 1 and 𝑣 2 respectively, and is written as follows:

∆𝑇 𝐵 = 𝑇 𝐵,𝑣 1 -𝑇 𝐵,𝑣 2 (18)
The choice of the channels in equation ( 18), depends on two factors:

1-Concentration variability: Figure 42 shows the simulated spectra of an empty atmosphere with a single layer of Gobi Desert dust (𝑟 𝑔 = 0.25 μm; 𝜎 𝑔 = 2 μm; 𝐿 = 2 Km),

showing the Aerosol Optical Depth (AOD) as function of the concentration. As the concentration increases, the 'V-shapes' becomes more inclined and the measurement is more sensitive to the concentration variation in the atmospheric windows.

Figure 42 The variation of the AOD as function of the concentration from simulated spectra of an empty atmosphere with a single layer of Gobi Desert dust using a Mie code and Gobi dust CRI. Gray patterns indicate important gases absorption bands from the order of increasing wavenumbers CO2, O3, and H2O. where MODIS shows over the same region a negligible daily average AOD (Figure 44b).

The false detection and selection from the IASI spectra are related to the LSE constraints. To solve the problem of the emissivity signal, many studies have used for their aerosols and gases retrievals, an IASI mean monthly emissivity dataset provided by D. Zhou et al. (2014) with 0.25°× 0.25° latitude-longitude grids [START_REF] Bauduin | IASI's sensitivity to near-surface carbon monoxide (CO): Theoretical analyses and retrievals on test cases[END_REF][START_REF] Clarisse | A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements[END_REF][START_REF] Piontek | The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation[END_REF]. The climatology is produced with an algorithm that retrieves the surface emissivity products from the IASI by comparing and validating them with independent in situ and satellite measurements [START_REF] Zhou | All weather IASI single field-of-view retrievals: Case study -Validation with JAIVEx data[END_REF][START_REF] Zhou | Global land surface emissivity retrieved from satellite ultraspectral IR measurements[END_REF][START_REF] Zhou | Monitoring surface climate with its emissivity derived from satellite measurements[END_REF]. The LSE spectral variability is low at the Sea of Japan and Shandong (Zhou et al., 2014).

Notably, the surface composition is rich in quartz in the Taklamakan and Gobi Deserts,

showing high LSE spectral variability between 1069 and 1250 cm -1 , with a characteristic peak at 1159 cm -1 , which is identical to the spectral desert detection from the IASI illustrated in Figure 41.

Figure 45 Mean monthly surface emissivity of four different surfaces of (latitude ; longitude)

during the month of May (Zhou et al., 2014).The shaded orange box indicates the Reststrahlen region.

The direct use of these fixed climatology can underestimate or overestimate the spectral observation due to the LSE variability caused by diurnal and seasonal cycles, satellite viewing angles, or surface-type variation [START_REF] Huang | An Observationally Based Global Band-by-Band Surface Emissivity Dataset for Climate and Weather Simulations[END_REF]). An example is provided in Figure 46¸the IASI spectral radiance in green of a scene above desert is directly divided by the corresponding climatology LSE pixel. It gives an excessive correction of a desert spectrum, where the brightness temperature in green is strongly altered after applying the correction at 1059 cm -1 , with its neighboring gap displayed, and the brightness temperature between 1069 and 1250 cm -1 highly exceeds that of a flat clear sky detection.

This result indicates that the direct use of the climatology can alter the spectral radiance signature and falsify the atmospheric information in question. Hence, the optimization of the LSE climatology is crucial to finding an optimal aerosol quantification. As a result, establishing the surface contribution and removing it from satellite observations remain challenging.

Figure 46 Brightness temperature Gobi Desert spectrum example from 3 May 2017 before and after the LSE correction using a monthly average emissivity value (D. Zhou et al., 2014). For simplicity, the carbon dioxide, ozone, and water vapor wide bands are removed from the IASI spectra in the rest of the of the chapter.

The LSE Optimization Method

In a first step, IASI pixels are classified into different types (clear sky, aerosols and cloud), we consider only pixel that have 90 % of homogeneity in detection. Then, the pixels are filtered using a principal component analysis (PCA) code developed in Laboratoire d'Optique Atmosphérique (LOA) [START_REF] Herbin | Spectral Detection from IASI using Principal Component Analysis[END_REF]). The intensity of the Reststrahlen feature is proportional to the particle size [START_REF] Simms | Analysis of Long Wave Infrared (LWIR) Soil Data to Predict Reflectance Response[END_REF]. Based on this, the Reststrahlen feature can be used as a surface effect indicator; it is highly displayed on desert surface detections compared to airborne dust. After classification, to optimize the LSE effect on IASI radiances, a new methodology was developed by calculating the Reststrahlen relative value 𝑋 𝑚(𝑣) of a spectrum 𝑚(𝑣) for both the IASI spectral radiance and the mean monthly emissivity value at the same pixel (Note that this value does not correspond to the CRI parameter defined in Chapter I). The method expresses the relative value between the observed spectrum with the Reststrahlen effect 𝑚 𝑤/ (𝑣 𝑅 ) at the Reststrahlen wavenumber 𝑣 𝑅 = 1159 cm -1 and the expected value of the observation spectrum without the Reststrahlen effect 𝑚 𝑤/𝑜 (𝑣 𝑅 ), and it is written as follows:

𝑋 𝑚(𝑣 𝑅 ) = 𝑚 𝑤/ (𝑣 𝑅 )-𝑚 𝑤/𝑜 (𝑣 𝑅 ) 𝑚 𝑤/𝑜 (𝑣 𝑅 ) (19)
where 𝑚 𝑤/𝑜 (𝑣 𝑅 ) is computed by solving a linear function of the form 𝑦 = 𝑎𝑥 + 𝑏 calculated in the neighboring Reststrahlen feature with choosing channels not affected by the narrow gas bands (Figure 48). Then, we define the emissivity correction factor C as follows:

𝐶 = 𝑋 𝑅(𝑣 𝑅 ) 𝑋 𝜀(𝑣 𝑅 ) (20)
with C being the ratio between the Reststrahlen relative value for the spectral radiance detection 𝑋 𝑅(𝑣 𝑅 ) and the Reststrahlen relative value for the mean monthly emissivity spectrum 𝑋 𝜀(𝑣 𝑅 ) for the same detected grid, both calculated by Equation (𝟏𝟗).

The corrected surface emissivity 𝜀 𝐶 (𝑣) is estimated from the mean monthly surface emissivity 𝜀̅ (𝑣) and the correction factor 𝐶, and it is written as follows:

𝜀 𝐶 (𝑣) = 1 + 𝐶(𝜀̅ (𝑣) -1) (21)
The correction in Equation (𝟐𝟏) is calculated when a significant Reststrahlen effect is present. For this, a minimum condition is applied to the 𝑚 𝑤/ (𝑣 𝑅 ) -𝑚 𝑤/𝑜 (𝑣 𝑅 ) value of both the radiance and emissivity spectra. A limit case occurs when the surface is masked and no emissivity correction is taken into consideration, the detections can be concentrated dust layers and opaque clouds. The correction factor C is 0; hence, the corrected surface emissivity 𝜀 𝐶 is 1. The saturated spectra are characterized by having a CO 2 a higher brightness temperature peak at 668 cm -1 than the brightness temperature O 3 peak at 1044 cm -1 (Figure 49). On the other hand, when the LSE variability is small, the Reststrahlen wavenumber 𝜀̅ (𝑣 𝑅 ) approaches 1. In this case, the correction factor C equals 1, and the literature dataset is directly used (𝜀 𝐶 (𝑣) = 𝜀̅ (𝑣)). Finally, if there is a minimal sensitivity from the IASI radiance and the climatology 𝜀̅ (𝑣 𝑅 ) on the Reststrahlen characteristic, the correction factor C is also 0.

After solving Equation ( 21) to obtain the corrected emissivity 𝜀 𝐶 (𝑣), the corrected spectral radiance 𝑅 𝑐 (𝑣) is calculated dividing the observed spectral radiance 𝑅(𝑣) by corrected emissivity 𝜀 𝐶 (𝑣). The dust spectra are then selected by converting the radiance to the brightness temperature 𝑇 𝐵𝑐 (𝑣) following Equation ( 17), then conditions were applied on ∆𝑇 𝐵1 and ∆𝑇 𝐵2 as discussed in Section 2.3.1. Figure 50 illustrates the flowchart of the optimization method described.

Figure 49 IASI spectral examples showing a CO 2 peak at 668 cm -1 higher than the O 3 peak at 1044 cm -1 exceptionally for an opaque detection. The correction method is for illustration applied to two IASI observations shown in Figure 51, detections of a clear sky above the desert (in red) and a mineral dust (in gray).

By applying the LSE method, the effects dominated by the Reststrahlen phenomenon where removed from the first spectrum (corrected spectrum in green) as if a clear sky above the sea (in blue) was detected after the correction. The peak at 1059 cm -1 was minimized and the lower shift between 1069 and 1250 cm -1 was flattened. In contrast, the modification of the second spectrum (in black) was minimal, maintaining the dust spectrum and its 'V-shape' signature. In both cases, only the base line and the Reststrahlen feature were altered and the gases narrow bands remain unchanged. Results indicate that the LSE method corrects the IASI spectra and differentiates between surface and mineral dust detections. In the first case, it removes the surface signal and in the second case it corrects the spectrum without removing dust features. To better understand the LSE method's effect, we derive the IASI optical thickness from the spectral radiance. This parameter gives us information about the dust layer extinction, layer thickness, and dust microphysical properties.

Infrared retrievals neglect atmospheric effects and aerosol scattering [START_REF] Ackerman | Remote sensing aerosols using satellite infrared observations[END_REF]. In addition, if the aerosol layer is optically thin, the surface emission term is the primary contributor to the measured radiance and the radiative transfer equation of the Equation ( 15) can be approximated as:

𝑅(𝑣) = 𝜀(𝑣)𝐵(𝑣, 𝑇)𝑒 -∫ 𝑘(𝑣,ℎ)𝑑ℎ 𝐻 0 (22)
where the spectrum 𝑅(𝑣) is a function of the surface emissivity 𝜀(𝑣), the surface temperature 𝑇 𝑠 described by the Planck function 𝐵(𝑣, 𝑇), and the total extinction 𝑘 𝑣 of the altitude of the top atmosphere H, containing gases, aerosols, and clouds.

Consider a homogeneous and stratified dust layer of thickness L. Hence, we can apply Lambert-Beer's law. For an incident spectral radiance 𝑅 0 (v) at wavenumber 𝑣, the attenuated amount of 𝑅 𝑜𝑏𝑠 (v) passing through a dust layer of thickness 𝐿 has an extinction coefficient 𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣) resulting from the absorption and scattering (Figure 52), and Equation ( 22) can be written as follows:

𝑅 𝑜𝑏𝑠 (𝑣) = 𝑅 0 (𝑣)𝑒 -𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣)𝐿 (23)

The incident spectral radiance 𝑅 0 (𝑣) represents a clear sky spectrum above the sea where 𝑅 0 (𝑣) ≥ 𝑅 𝑜𝑏𝑠 (𝑣). The same clear sky spectrum is used as a reference to IASI observations studied. This spectrum is selected from the first date of a dust event with the nearest sea location to the dust source, and fixed during the following dates of the storm.

The purpose is to obtain a relative optical thickness to disregard the variation in the temperature from observation to another. From IASI radiances, we can calculate an equivalent optical thickness 𝜏(𝑣) [START_REF] Klüser | Information content of space-borne hyperspectral infrared observations with respect to mineral dust properties[END_REF]. It is obtained by the natural logarithm of the ratio of a clear sky spectral radiance 𝑅 0 (𝑣) and a spectral radiance observation 𝑅 𝑜𝑏𝑠 (𝑣):

𝜏(𝑣) = 𝑙𝑛 ( 𝑅 0 (𝑣) 𝑅 𝑜𝑏𝑠 (𝑣) ) (24)
This ratio becomes independent from the Planck function 𝐵(𝑣, 𝑇) by subtracting it in the calculation, and unconstrained by the surface emissivity after applying the LSE optimization method discussed above. What remains in the equation is the aerosol extinction multiplied by the layer thickness 𝐿 with an error 𝜖 𝜏 , which includes noise and the remaining residue of gas present in the spectrum. Thus, Equation ( 24) is written as follows:

𝜏(𝑣) = 𝑙𝑛 ( 𝑒 -𝑘 𝑒𝑥𝑡 𝑔𝑎𝑠𝑒𝑠 (𝑣).𝐻 𝑒 -𝑘 𝑒𝑥𝑡 𝑔𝑎𝑠𝑒𝑠 (𝑣).𝐻-𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣).𝐿 ) = 𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 (𝑣)𝐿 + 𝜖 𝜏 (25)
Equation ( 25) was applied to the example spectra shown in Figure 53. Before the LSE correction of the clear sky detection above the desert, an apparent optical thickness has an extinction coefficient caused by a land surface, e.g., a sand surface contaminated by the Reststrahlen effect (Figure 53a). After the LSE correction, the apparent optical thickness approaches zero with no aerosol features. This indicates that the spectral signal comes entirely from the land surface. However, a minor optimization affected the optical thickness of the mineral dust detection above the desert (Figure 53b) with an aerosol extinction signal remaining. This result verifies that the LSE optimization method corrected only land surface signals without removing the aerosol fingerprints that present crucial data to quantify the aerosol physico-chemical properties.

Conclusion

The IASI instrument detects different atmospheric components with a high spectral resolution: gas, airborne dust, and clouds depending on their optical layer thickness.

However, the LSE can affect detections above land, especially desert surfaces, causing false mineral dust detections with surface detections and affecting quantified parameters.

Previous uses of an LSE monthly dataset from IASI show under-estimation or over-estimation of LSE signal due to the LSE's temporal variability. A new LSE method was developed and used to optimize the use of these datasets. The surface effect was minimized from a clear sky detection above the desert and a mineral dust detection. In addition, the IASI optical thickness was derived to verify the optimization that allowed the total removal of the surface features without removing dust signatures. The method applied on a case study on 4 May 2017 removed the selection of false aerosol detections above the Taklamakan Desert. Results were compared to MODIS and CALIOP for validation. Finally, the LSE optimization is a crucial step toward characterizing the dust's physico-chemical properties.

CHAPTER III. AEROSOL COM-POSITION RETRIEVAL

In this chapter, we introduce different mineral groups and previous mineralogical studies. Then, we present new experimental optical properties (i.e., the extinction coefficient spectra) measured using laboratory FTIR spectra for Gobi dust and pure minerals (quartz, illite, and calcite). An effective extinction coefficient is simulated using a linear combination of the pure mineral extinctions spectra to reproduce the Gobi dust spectrum and IASI dust detections. Then, laboratory measurements are compared to the IASI optical thickness, showing the same molecular features. Finally, we couple the IASI optical thickness with a linear combination of pure mineral spectra to compute the mineralogical dust mass weights. In addition, we apply the method to a dust storm that occurred between 3 and 7 May 2017 using IASI observations based on METOP-A and the results are compared with a second dust event between 14 and 22 March 2021 and METOP B. Finally, we compute the forward model to obtain an a priori on the SD, which will be used in the future to solve the inverse problem.

percentages were given since there is less information available for silt fractions. The airborne dust and parent soil fractions were compared, showing a different composition. On a regional scale, East Asia is known to have a variable regional mineralogical composition, as it depends on the geomorphology and the soil composition. Three broad types of dust sources were identified in East Asia (Xuan and [START_REF] Sokolik | Characterization of sources and emission rates of mineral dust in Northern China[END_REF]. Type I deserts are located in topographic lows, such as the Taklamakan desert; these deserts are associated with fluvial fans [START_REF] Ginoux | Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products[END_REF]. Type II deserts are located on the plateau, with natural dust sources coming from the Gobi Desert of Inner Mongolia. Type III deserts are in dry agricultural areas, e.g., the Horqin Sandy Land, which was shown to have a particular influence on some desert storms [START_REF] Minamoto | Large-scale dust event in East Asia in May 2017: Dust emission and transport from multiple source regions[END_REF]. Figure 55 displays the mineralogical composition data from literature of the three regions I, II and III. We collected mineralogical literature data of the three dust sources: Taklamakan Desert (Di Biagio et al., 2014), Gobi Desert [START_REF] Wang | Interactions of ozone and acetic acid with natural Gobi dust[END_REF] and Horqin Sandy land [START_REF] Shen | Mineralogical characteristics of soil dust from source regions in northern China[END_REF].

The first was derived from Fourier transform infrared spectrometry while the others used XRD method to measure the mineralogical composition collected dust samples. The dust is highly rich in phyllosilicates in all regions. From West to East Asia, the carbonate soil content decreases and the feldspar content increases. Hence, the diversity in the dust constituents allow to trace dust sources. For instance, a study used experimental methods, i.e., geochemical tracing methods, to link dust mineralogy and sources depending on the geology [START_REF] Chen | Geochemical studies on the source region of Asian dust[END_REF]. SEM method was also employed to exploit the quartz and illite/quartz ratio contents and link them to particle size distribution [START_REF] Shao | Mineralogical characteristics of airborne particles collected in Beijing during a severe Asian dust storm period in spring 2002[END_REF]. The quantification of the dust mineralogy remains complex and limited due to the dependency on the particle size fraction and the dust sources, the aeolian transport and mixing, and the aging processes that alter the physic-chemical properties of dust. Moreover, investigating dust mineralogy is still challenging from satellite instruments. It may be due to the challenges posed by the LSE contribution to the measured radiance and the lack of adequate experimental optical properties (i.e., CRI) crucial in aerosol retrievals.

The first limitation was overpassed after applying an LSE correction method fully described in Chapter II. The second urges us to measure the optical properties experimentally to be applied to the IASI spectra, as it will be discussed in the next section.

Gobi Dust Optical Properties

Laboratory Measurements

The laboratory setup and experimental procedure are described in detail in the references [START_REF] Herbin | New approach for the determination of aerosol refractive indices -Part I: Theoretical bases and numerical methodology[END_REF][START_REF] Hubert | New approach for the determination of aerosol refractive indices -Part II: Experimental set-up and application to amorphous silica particles[END_REF], and are only summarized herein. The experimental study was performed by two laboratories: PhysicoChimie des Processus de

Combustion et de l'Atmosphère (PC2A) and Laboratoire d'Optique Atmosphérique (LOA).

Deschutter, 2022 analyzed samples of pure quartz crystal, pure amorphous illite, and pure calcite crystal, in addition to Gobi dust samples that were collected in Yanchi, Ningxia province in China (107.475211°E,36.487333°N) [START_REF] Romanias | Heterogeneous Interaction of Isopropanol with Natural Gobi Dust[END_REF]. Sample particles (around 5 g of mass) were placed in suspension by maintaining mechanical agitation using a magnetic stirrer as illustrated in Figure 56. Using a mass flow controller set at 2 L. min -1 , the particles were carried by pure nitrogen gas into a buffer volume to ensure homogeneous aerosol flow and then into two consecutive spectrometer setups. The first was a Fourier transform infrared spectrometer with a 10 m multipass cell, which measured the extinction coefficient in the spectral range between 650 and 9000 cm -1 . The second was a UV-visible spectrometer with a 1 m cell measuring extinction coefficients between 10,000 and 40,000 cm -1 . In this study, we are interested in the spectral range between 750 and 1250 cm -1 (in the atmospheric window) corresponding to the aerosol detection. At the exit of the cell, a scanning mobility particle sizer (SMPS) and an aerodynamic particle sizer (APS) were simultaneously used to record the size distribution generated by the apparatus between 10 nm and 20 µm. Results of Gobi dust had an average geometric size diameter of 0.5 μm with a standard deviation of 1.95 μm. The most intense bands for quartz from the tectosilicates group are between 1080 and 1177 cm -1 , generated by Si-O asymmetrical stretching. The Si-O symmetrical stretching produces a doublet at 778 and 795 cm -1 [START_REF] Reig | FTIR quantitative analysis of calcium carbonate (calcite) and silica (quartz) mixtures using the constant ratio method. Application to geological samples[END_REF][START_REF] Spitzer | Infrared Lattice Bands of Quartz[END_REF].

This doublet caused by the same Si-O stretching is also found in phyllosilicates or clay minerals (e.g., illite, kaolinite…) [START_REF] Madejova | Baseline Studies of the Clay Minerals Society Source Clays: Infrared Methods[END_REF]. The major peak present in the latter group at 1033 cm -1 is due to the Si-O asymmetrical stretching. This shift difference with respect to the stretching mode in quartz is explained by cation substitutions, particularly Al 2+ for Si 2+ . The shoulder at 916 cm -1 for illite is assigned to the -O-H bending associated with Al, Fe, or Mg cations [START_REF] Madejová | IR Spectra of Clay Minerals[END_REF]. The fundamental vibrations of CO3 anions in carbonates (e.g., calcite, dolomite, etc.) show a spectral feature at 885 cm -1 of carbonates in Thermal InfraRed (TIR) caused by C-O asymmetrical stretching [START_REF] Bishop | Spectral Properties of Anhydrous Carbonates and Nitrates[END_REF][START_REF] Green | Characterisation of carbonate minerals from hyperspectral TIR scanning using features at 14 000 and 11 300 nm[END_REF]. Table 2 summarizes the molecular assignments in the TIR active modes of the abundant minerals of the three main mineral groups in desert dust from previous studies. [START_REF] Bishop | Spectral Properties of Anhydrous Carbonates and Nitrates[END_REF][START_REF] Green | Characterisation of carbonate minerals from hyperspectral TIR scanning using features at 14 000 and 11 300 nm[END_REF]

Linear Combination Method

Airborne dust particles occur as a mixture of pure minerals in the volume of a particle. The distribution of the molecules can be heterogeneous in the case of an internal mixture or homogeneous in the case of an external mixture. By individual-particle analysis, SEM measurements of Gobi sample particles showed an external mixture. Figure 58a shows the micrograph of a particle using the SEM technique, indicating its morphol- [START_REF] Bichon | Complex refractive indices in the TeraHertz domain of samples from atmospheric aerosol sources[END_REF].

In this case, the optical properties of individual minerals can be used to model the optical properties of dust. An effective extinction coefficient 𝑘 𝑒𝑓𝑓 (𝑣) can be obtained from a linear combination (LC) of the individual mineral extinctions [START_REF] Sokolik | Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths[END_REF].

The combination is calculated from the normalized experimental extinction coefficients

(a) (b)
of quartz 𝑘 ̅ 𝑄 , illite 𝑘 ̅ 𝐼 , and calcite 𝑘 ̅ 𝐶𝑎 , with an extinction fraction of the mineral groups in question, 𝑓 𝑇𝑒𝑐 , 𝑓 𝑃ℎ𝑦 , and 𝑓 𝐶𝑎 , respectively, and it is calculated by: 𝑘 𝑒𝑓𝑓 (𝑣) = 𝑓 𝑇𝑒𝑐 𝑘 ̅ 𝑄 (𝑣) + 𝑓 𝑃ℎ𝑦 𝑘 ̅ 𝐼 (𝑣) + 𝑓 𝐶𝑎 𝑘 ̅ 𝐶𝑎 (𝑣) (26) Equation ( 26) is of the form 𝐛 = 𝐀𝐱 where b is the dust measurement spectrum for n wavenumbers, A contains the individual minerals spectra, and x is the unknown mass weight vector. In this case, we have more equations than unknowns and the system is overdetermined. This problem is resolved by the linear approach which uses a least square solution that minimizes 𝐀𝐱 from b, by multiplying both side by the transpose matrix 𝐀 𝐓 ,and it is given by:

𝐀 𝐓 𝐛 = 𝐀 𝐓 𝐀𝐱 (27)
Then the remaining residue is calculated by: ‖𝒃 -𝑨𝒙‖ (28)

Figure 59 shows an LC simulation fitted to the Gobi dust spectrum. The individual mineral spectra reproduced a fit with 13.7% of quartz, 79.8% of illite, and 6.5% of calcite to obtain an external mineral mixture spectrum with the remaining residue ∈ 𝑅𝑒 from the predicted spectrum 𝑘 𝑒𝑓𝑓 (𝑣) and the experimental spectrum 𝑘 𝑒𝑥𝑝 (𝑣). The residue difference between the LC simulated spectrum and the Gobi dust spectrum is 2.5%. In order to compare the results with the data in the literature, it is important to convert the extinction weights to mass weights. This can be performed by obtaining the mass extinction coefficient MEC (in m 2 . g -1 ), which can be calculated from the maximum value of the extinction coefficient 𝑘 𝑖 𝑚𝑎𝑥 (𝑣) divided by the mass concentration 𝑀𝐶 𝑖 for the mineral 𝑖:

𝑀𝐸𝐶 𝑖 = 𝑘 𝑖 𝑚𝑎𝑥 (𝑣) 𝑀𝐶 𝑖 (29)
where 𝑀𝐶 𝑖 is obtained from the particle size distribution recorded experimentally by the APS and the SMPS. The densities used in this study for the mineral components quartz (Q), illite (I), and calcite (Ca) were 𝜌 𝑄 = 2.65 g . cm -3 , 𝜌 𝐼 = 2.8 g . cm -3 , and 𝜌 𝐶𝑎 = 2.71 g . cm -3 , respectively.

Hence, the mass weight 𝑤 𝑖 is obtained the product of the individual mineral extinction weight 𝑓 𝑖 and its mass extinction coefficient 𝑀𝐸𝐶 𝑖 , divided by the sum of the same product over all the minerals, and it is given as follows:

𝑤 𝑖 = 𝑓 𝑖 𝑀𝐸𝐶 𝑖 ∑ 𝑓 𝑖 𝑀𝐸𝐶 𝑖 𝑖 (30)
Then, by applying Equation (29) to the experimental normalized extinction coefficients, the extinction masses of quartz, illite, and calcite were 𝑀𝐸𝐶 𝑄 = 0.189 m 2 g -1 , 𝑀𝐸𝐶 𝐼 = 0.209 m 2 g -1 , and 𝑀𝐸𝐶 𝐶𝑎 = 0.187 m 2 g -1 , respectively. The obtained mass weights from Equation (30) are: 𝑤 𝑇𝑒𝑐 = 15.3%, 𝑤 𝑃ℎ𝑦 = 80%, and 𝑤 𝐶𝑎𝑟𝑏 = 4.7%, respectively.

MINERALOGICAL MAPPING USING IASI

IASI Molecular Features

The optical thickness was derived from the IASI radiance (defined in Chapter II), corresponding to the dust extinction coefficient multiplied by a dust layer (Equation 25).

CASE STUDIES

After testing the LC method on both laboratory measurements and satellites observations, in this section we will apply the method on case studies of dust storms in East Asia. We used IASI-A for the first dust storm in May 2017, and a second dust storm in March 2021. In the two cases, we will link the mineralogical mass weights to regional dust sources. Finally, we compared results between IASI-A and IASI-B.

May 2017 from IASI-A

An intense dust storm occurred from 3 to 8 May 2017, affecting visibility in North China as dust loads crossed the region. The storm started in the southwest of inner Mongolia, where the dust plume moved southeastward, passing through North China, the Korean peninsula, and Japan to dissipate in Russia. Three cold fronts generated dust during this event. The first two fronts lifted dust from the Gobi Desert on 3 May. On 4 May, at night, a third front started and different blown dust loads were generated and merged between 4 and 6 May from both the Horqin Sandy Land and Gobi Desert [START_REF] Minamoto | Large-scale dust event in East Asia in May 2017: Dust emission and transport from multiple source regions[END_REF]. Figure 62 shows the evolution of the dust plume mass weights for tectosilicates, phyllosilicates, and carbonates in May 2017 between 3 and 6 May. The dust plume could not be detected anymore on 7 and 8 May. In addition to the calculation of the RMS of the residue obtained from the LC method. The optical thickness at 1072.5 cm -1 , where the aerosol absorption feature is the strongest. The IASI optical thickness at 1072.5 cm -1

shows a homogeneous temporal evolution between day and night detections.

The LC method was applied to approximately 4000 spectra, from which 8% rejected solutions with negative mass weights. The rejected values can be linked to the difference in the size-fraction between the IASI dust detections and experimental dust used for the LC fitting. This result indicates the importance of the microphysical dust information to better estimate the mineralogical composition.

All the mineral families during this dust event reported wide spatial variability. North-Eastward and passing above the Horqin Sandy Land, where its dust loads are mixed with Gobi dust. In the example of 5 May during the day (5D), the weight of the tectosilicates strengthened again after a third cold front mixed dust from both deserts since the Horqin Sandy Land is rich in feldspars [START_REF] Shen | Mineralogical characteristics of soil dust from source regions in northern China[END_REF].

Generalization March 2021 from IASI-A

The mineralogical mapping method was applied to a more recent dust storm in March 2021. The first dust plume was generated from the Mongolian Gobi Desert on 14

March, with winds blowing on 15 March, uplifting aerosols from the Gobi Desert and Horqin Sandy Land together to travel southeastward. On 16 March, another dust plume was produced from the Taklamakan Desert, mixing with loads already in the atmosphere.

A previous study only investigated emissions from the Gobi Desert between 14 and 15

March [START_REF] Wang | A Comparison of the Different Stages of Dust Events over Beijing in March 2021: The Effects of the Vertical Structure on Near-Surface Particle Concentration[END_REF].

The study applied was applied between 14 March and 18 March during the day, as

shown in Figure 64. The IASI mineralogical weights showed a regular temporal evolution as the first and second dust plumes were carried into the atmosphere. From 3500 spectra, the LC fitting rejected 15% of the values, the double of values rejected for the May 2017 event. The result can be linked to the difference in the size distribution between the emissions for the two cases in the study; the PM10 (suspended particles with a diameter less than 10 μm) concentration emissions were doubled in March 2021 [START_REF] She | Towards a comprehensive view of dust events from multiple satellite and ground measurements: Exemplified by the May 2017 East Asian dust storm[END_REF].

Table 4 shows the IASI mean mineralogical values per half-day, with the RMS and the optical thickness at 1072.5 cm -1 . In this case, the mean mass weight value of the tectosilicates was 25% in region I. Intense dust emissions from this region started on 16

March during the day (16D), and the dust was found to be made up of 66% phyllosilicates and 7.4% carbonates. In region II, the results show consistent mineral weights with the previous case study. However, region III indicates the uplift of heavier tectosilicate particles and fewer phyllosilicate fine particles. The carbonate mean values were close for region I and II, with the highest peak from region I at 9% and the lowest in region III at 5.2%. Large optical thickness values were found in this case as the dust mass concentration emitted reached almost 1 × 10 4 μg/m 3 while the May 2017 concentrations reached a maximum of 5 × 10 3 μg/m 3 [START_REF] She | Towards a comprehensive view of dust events from multiple satellite and ground measurements: Exemplified by the May 2017 East Asian dust storm[END_REF][START_REF] Wang | A Comparison of the Different Stages of Dust Events over Beijing in March 2021: The Effects of the Vertical Structure on Near-Surface Particle Concentration[END_REF]. Therefore, we used the HYSPLIT 24 h backward trajectory on 17D to better understand the origin source (Figure 65). Three locations were selected; the first trajectory, on the left, shows a mass that traveled from Central Asia, having different mineralogy that was particularly rich in tectosilicates (the values of this plume are not shown in Table 4). The second indicates that dust emissions occurred on 16 March day above the Taklamakan Desert (region I), which occurred and traveled in the same direction (Eastward) as the third uplifted masses of region II from the Gobi Desert. Gobi Desert. The 1500 dust spectra were selected from IASI-A, while 2400 spectra were selected from IASI-B due to the overlap in IASI orbits. These results confirm the ability to apply this method using different IASI platforms, in addition to the ability to identify the dust emission source. In conclusion, we were able to apply the LC method and evaluate the spatial and temporal evolution of the chemical composition on two case studies: May 2017 and March 2021, with four years of difference and two different seasons. Results were homogeneous between daylight and night detections, and between two MetOp platforms A and B. Moreover, we were able to link the results with dust sources. [START_REF] Journet | A new data set of soil mineralogy for dustcycle modeling[END_REF] provided airborne dust mineralogical datasets on a global scale using parent soil samples and a transport model. On the other hand, Wang, 2019 used XRD method on a sample from the Gobi Desert to obtain dust mineralogy. In study, we use the LC method to obtain composition information using laboratory spectral measurements and IASI satellite detections. For both cases, the mass weight values of phyllosilicates are closer to the percentages found for clay dust from [START_REF] Journet | A new data set of soil mineralogy for dustcycle modeling[END_REF] than by the regional study from Wang, 2019. On the other hand, the percentage of tectosilicates lies between the values of these two studies. The obtained results can be related to the size fraction.

RESULTS COMPARISON WITH LITERATURE

This work used laboratory spectra having a clay-sized distribution similar to the indirect approach applied on clay fractions studied by [START_REF] Journet | A new data set of soil mineralogy for dustcycle modeling[END_REF]. However, the XRD regional measurement didn't distinguish clays from silt particles which explains a lower content of phyllosilicates and higher content tectosilicates in their study. In addition, the IASI mineralogical values applied on region I displays two scenarios: May 2017 tectosilicates content was high and characterized by short-range aeolian transport, and the second case in March 2021 shows low tectosilicates content characterized by a longtransport.

PERSPECTIVES TOWARDS SOLVING THE RETRIEVAL PROBLEM

Remote sensing instruments capture the solar and terrestrial radiation passing through the atmosphere and interacting with its components. Therefore, these sensors do not give a direct measurement of the aerosol's particle size or concentration but rather their influence on atmospheric radiation. Here lies the importance of the inversion methods, which enable us to solve an inverse problem and retrieve aerosols' microphysical parameters. In this section, we introduce briefly the concept of the microphysical retrievals with a sensitivity study on major dust parameters using ARAHMIS code. In addition, we show present work's results (simulation and inversion) on an example of IASI spectrum.

An inverse problem finds from a set of observations the causal atmospheric state that produced them. It solves a set of linear or non-linear equations in the presence of some errors in the measurements and assumptions made to formulate the equations. A retrieval problem has two components: a forward model that presents the calculation of the measurements from the target quantity and the inverse model which gives an estimation of this quantity with associated errors. In order to solve the inverse problem the main question is : "Given the radiation field, how well may we infer atmospheric parameters from this information?"-Goody and Ynug, 1995. Solving the retrieval problem in remote sensing is mathematically ill-conditioned; it contains more unknowns than the available equations leading to the existence of multiple solutions. It is necessary in such case to restrict the range of possibilities by applying an a priori knowledge of the result.

Forward Model

We consider that the observations (i.e., radiances) measured in the spectral range of an instrument are assembled in the measurement vector y and the unknown parameters into the state vector x. This vector can include the geophysical and atmospheric variables: pressure, temperature, gases profiles, aerosol SD, concentration, altitude, thickness layer, and surface emissivity and temperature. The best possible representation of the atmospheric state is determined by the physics of measurement, termed the forward function f(x):

𝐲 = f(𝐱) (32)
However, one must distinguish between the reality set by y that comes with a measurement error ∈ and the best description of this reality f(x) due to a measuring .

Hence, y is approximated by a forward model F(x), and is written as follows:

𝐲 = F(𝐱)+∈ (33)
In this work, the measurement vector y corresponds to the IASI brightness temperature detection. On the other hand, the state vector x has 2 parameters: the aerosol SD and concentration. The first is the effective radius 𝑟 𝑒 (in m), calculated from the geometric radius and geometric standard deviation, and given by Equation (34) in general and by Equation (35) when the standard deviation is fixed:

𝑟 𝑒 = ∫ 𝑟 3 𝑛(𝑟)𝑑𝑟 ∫ 𝑟 2 𝑛(𝑟)𝑑𝑟 (34)
𝑟 𝑒 = 𝑟 𝑔 𝑒 2.5𝑙𝑛 2 𝜎 𝑔 (35)

The second parameter presents the Volume Mixing Ratio VMR (in ppm): the ratio of the amount of a substance i in a given volume 𝑉 𝑖 (in 𝑚 3 ) to the total amount of all species in that volume 𝑉 𝑡𝑜𝑡𝑎𝑙 , and it is written as follows:

𝑉𝑀𝑅 = 𝑉 𝑖 𝑉 𝑡𝑜𝑡𝑎𝑙 (36)
Reference formulae can be found in detail in Seinfeld and Pandis, 2006 and summarized hereby. The ratio in Equation ( 36) is multiplied by the partial air column 𝑝 𝑎𝑖𝑟 ( in particles.m -2 ) to give information about the number of particles per unit surface 𝑁 𝑆 (in particles.m -2 ), and it is given by: 𝑁 𝑆 = 𝑉𝑀𝑅 × 𝑝 𝑎𝑖𝑟 (37)

Then, the effective number concentration can be calculated by:

𝑁 𝑒 = 𝑁 𝑆 𝑒 -3.0𝑙𝑛 2 𝜎 𝑔 (38)
Finally, one can derive the effective mass volume concentration (in g.m -3 ) with the effective radius 𝑟 𝑒 and the effective number concentration 𝑁 𝑒 , with the knowledge of the aerosol's density 𝜌, and it is written as follows: First, one must consider gases contributions resulting from an additive factor in the absorption cross section. We use the LBL approach to calculate the attenuation coefficient 𝑘 𝑎𝑏𝑠 𝑔𝑎𝑠 of gases species at all atmospheric levels with a very narrow spectral resolution less than 0.01 cm -1 . It is solved by the knowledge of the spectral position lines, their intensity or their width at half maximum. The gases spectral lines are used from the Highresolution Transmission Molecular Absorption (HITRAN).

𝑀 𝑒 = 4 3 𝜋𝜌𝑟 𝑒 3 𝑁 𝑒 (
Second, for simplicity we consider that the particles are spherical. In our case, the parameter factor 𝑥 is close to 1, since IASI detections have a wavelength close to the diameter of airborne dust (𝐷 ≈ 𝜆). Hence, we apply Mie Theory that allows efficient calculation of the aerosol extinction coefficient 𝑘 𝑒𝑥𝑡 𝑎𝑒𝑟𝑜𝑠𝑜𝑙 . To study their influence on the measurement we only vary the SD and concentration parameters. The CRI of Gobi dust spectrum is fixed in this study from Deschutter, 2022.

Figure 68 The influence of the CRI on the IASI spectra of Gobi dust compared with spectrum without aerosol particles (right panel) and the zoom between 800 and 1000 cm -1 .

Sensitivity on the Altitude

Figure 69 illustrates the influence of altitude on IASI spectra. The spectra were simulated by fixing the gases concentrations, the CRI of Gobi dust from Deschutter, 2022, the VMR at 4.30x10 -11 ppm, the mean geometric radius at 0.25 µm. Depending on the altitude at which the dust plume is located, for a given concentration, the thermal contrast between the surface temperature and the temperature of the layer studied will increase as one rise in the different layers of the atmosphere. The altitude can impact the sensitivity of IASI on the optical thickness. We chose an a priori value of the altitude based on the data recorded by CALIOP or measurements taken from the literature. To constrain the algorithm, we fixed this parameter.

SD Impact

One essential data that should be adjusted is the mean geometric radius of the particles in the dust plume. It is a parameter of the state vector that we want to retrieve.

Therefore, it is necessary to establish an a priori value of the mean geometric radius rg of the aerosol population and then give it a variability. Figure 70 represents the extinction of Gobi dust plume for radii of 0.15 to 0.50 µm in steps of 0.05 µm. According to the simulations, the mean geometric radius strongly influences the extinction in the studied wavelength range. We must therefore be careful about the choice of the a priori but especially about the variability of this parameter.

VMR Impact

The four Gobi dust spectra in Figure 71 were simulated for a mean geometric radius of 0.25 µm and a standard deviation of 2 µm at an altitude between 1 and 2 km. We notice that when we increase the VMR, the absorption band of Si-O around 1000 cm -1 deepen.

This means the more the dust layer is concentrated or thick, the more the 'V-shape' is pronounced.

Figure 71

The effect of increasing the VMR the IASI simulated spectra of Gobi dust.

Case Study: May 2017

We apply the forward model on two spectral detections from the case study of May

Conclusion

This chapter introduced a method for computing mineralogical weights during dust storm events using both experimental optical properties and satellite detections from IASI.

First, we considered that dust presents an external mixture of pure minerals and we applied an LC method to the laboratory extinction coefficients of pure minerals to fit the Gobi dust spectrum showing a residue of 2.5%. In addition, after LSE correction, an aerosol optical thickness spectrum from the IASI was derived, with spectral features similar to those from laboratory Gobi dust sample. The LC was also applied to the satellite optical thickness and the residue shifted to 20% due to the presence of narrow gas bands; however, this residue dropped to 5% after smoothing the IASI spectrum.

Second, mineralogical weight maps were obtained for three mineral groups: tectosilicates, phyllosilicates, and carbonates, with the RMS and the optical thickness at 1072.5 cm -1 . The method was applied to two case studies from IASI-A operation by day and night, in two different seasons, with a time gap of four years between them: spring 2017 and winter 2021. The results also showed mean mineralogical weights similar to the claysized literature data, with a high temporal and spatial variability for tectosilicates and carbonates that allowed to trace the dust source. Hence, this method presents a powerful tool to link dust mineralogy to the original sources. Finally, an important link between the LC rejected values and the particle size distribution was attributed. This result leads us to solve the retrieval problem by solving the RTE equation and in the future find an a priori for the effective radius and VMR. First, we briefly studied the sensitivity of aerosol parameters using ARAHMIS code. Then, we reproduced two IASI observations, one above the desert and the other of a mineral dust.

The next step is to inverse the mineral dust fit to retrieve the parameters in question.

GENERAL CONCLUSION AND PERSPECTIVES

The atmosphere is formed by gaseous species, clouds, and aerosols from different sources, primarily from deserts. Recently, more efforts were made to quantify their impacts on the Earth's thermal balance, climate, and human health. Significant progress has been made by satellite remote sensing instruments better to understand aerosols emission, dispersion, and impacts, but many questions remain open, especially in composition quantification. Due to the difficulty to properly represent the spectral surface emissivity, spaceborne sensors remain limited in detecting aerosols above land. To better understand these observations, a perfect knowledge of the optical properties of aerosols is also necessary. This objective has led us to develop a new methodology to correct the LSE and optimize IASI observations. Then, we used new experimental optical properties and linked them with the dust mineralogical composition. This thesis has brought new knowledge about the chemical composition from IASI measurements using laboratory optical properties. We were able to bring quantitative information about airborne dust composition using a spaceborne remote sensing instrument.

First, we found that dust detections above land were highly affected by LSE. Soil rich in quartz generates the Reststrahlen phenomenon, altering the IASI observation and causing false aerosol detection and characterization. A mean monthly emissivity dataset was previously used to solve this problem. However, the direct use of these data can strongly change the spectral detection, losing aerosol signals. To achieve the first objective of this thesis, a new LSE optimization method was developed using the same data and based on the Reststrahlen feature at 1159 cm -1 . A correction factor was calculated and applied to the IASI detection spectra. This method improved the surface emissivity representation and thereby the aerosol detections without altering the mineral dust information from both day and night observations. This was demonstrated on two case studies with different temporal and seasonal characteristics, which shows the ability to apply the LSE correction method to different situations.

Second, a new method was applied to the laboratory optical properties of Gobi dust samples having external mixture state, and new mineralogical mass weights were obtained. The IASI optical thickness was derived from the radiances showing the same spectral features as laboratory measurements. This result leads to applying the method on the IASI optical thickness spectra of two East Asian dust storms, giving us new information about aerosol composition. Results were consistent with the literature data, particularly with the clay fractions' composition. In addition, we could trace dust sources by comparing them with HYSPLIT trajectories. Hence, we attained the second and third objectives by linking new optical properties to the chemical composition and using it in remote sensing detection from IASI to characterize the mineralogical composition of airborne dust.

One of the strengths of this work, is the use of the IASI which has global coverage, day and night detections with a high spectral resolution, owing the capability to detect mineral dust. The correction of IASI spectra from the new LSE methodology leads to reliable quantification of aerosol properties.

Also, we directly used the extinction coefficients in the LC method without making assumptions on the particles' shape, that presents until now a complex parameter to quantify. Typically, mineralogical measurements are performed on dust samples or aerosol particles generated from parent soils in laboratory. We were able to quantify the aerosol composition directly from dust plumes using satellite detections and laboratory spectral measurements. This work fills the gap of the aerosol chemical properties retrieval directly from uplifted dust in the atmosphere.

We considered in this study only major minerals in East Asia without taking into consideration minor minerals i.e., hematite which is known to be an important species in Saharan dust storms. In addition, hematite is not detectable in the TIR band and its spectral features rely in FIR. Therefore, it is interesting in the future to apply the LC method on another satellite sensor e.g., Far-infrared Outgoing Radiation Understanding & Monitoring (FORUM) which will interestingly also cover the far-infrared (from 100 to 667 cm - (https://www.forum-ee9.eu).

This study showed external mixtures of dust samples, however internal mixtures should be taken into consideration in many other cases. The effective medium theory can be used to better estimate the extinction for inhomogeneities in internal mixtures (Liu et al., 2014).

As shown in Chapter III, results are close to literature mineralogy for clay-sized particles. Solving the LC equations lead to have up to 15% of rejected values which were negative values of tectosilicates mass weights, these particles tend to be silt. Hence, it is important to consider the size distribution and retrieve its value. In the future, we will use IASI detections with the laboratory optical properties to retrieve the aerosol microphysical properties (SD, concentration) using ARAHMIS code developed in LOA from the optimal estimation method.

The work done during this thesis is published in Remote Sensing MDPI journal and presented in different international conferences and seminars (see Appendix A and B).

ABSTRACT

East Asia is the second largest source of mineral-type aerosols in the world after the Sahara. These can modify the Earth's radiative balance due to their properties of absorption and diffusion of atmospheric radiation. Therefore, precise information on the physico-chemical properties of desert dust is essential to understand and better quantify their impact on the atmosphere. In this context, we aim to test the potential of a new methodological approach combining laboratory measurements and satellite remote sensing for better characterization of aerosols from the desert and semi-arid surfaces. The application concerns studying desert storm events in East Asia from the IASI spaceborn instrument. One of the major difficulties of this work comes from the fact that mineral dust in East Asia is emitted above the continental surface in the lower troposphere, which poses constraints linked first to a low detection sensitivity due to a lack of thermal contrast and second due to the high influence and variability of the Land Surface Emissivity (LSE). Thus, the first part focused on improving the consideration of LSE by developing a new methodology from a set of average monthly emissivity dataset. Then, experimental extinction coefficients of pure minerals were linearly combined to reproduce a spectrum of Gobi dust, which made it possible to obtain the mineralogical mass fractions. In addition, from the IASI radiances, an optical thickness of Gobi dust was calculated, displaying characteristics identical to those measured in the laboratory. The linear combination of pure mineral spectra was then applied to IASI optical thickness, providing mineralogical mass fractions. Finally, the entire methodology was applied to two dust events during two different seasons and years: May 2017 and March 2021. The method made it possible to obtain the first spatial and temporal distributions of the chemical composition of desert aerosols. The average mass fractions obtained for particles from the Gobi Desert, Taklamakan Desert, and Horqin Sandy Land are close to mass fractions from the literature. In addition, these results on the chemical composition made it possible to link aerosols' spatial variability to dust emission sources, confirmed using a backward trajectory model. In addition, comparing two IASI instruments on MetOp A and B showed good consistency, allowing us to consider the method's applicability to different MetOp platforms having the same instrument type. The results present an important advance in improving the characterization of desert aerosols by space remote sensing, in particular concerning the analysis of the chemical composition.

Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10

 2345678910 Figure 1Vertical temperature structure of the atmosphere extending from the surface of the Earth to approximately 110-km altitude as given in the US Standard Atmosphere(1976). The principal layers defined by this temperature structure and the interfaces between them are labeled. As indicated, ozone is found principally in the stratosphere. Two vertical coordinates are given: pressure on the left in millibars (1 mb = 100 Pascal) and geometric altitude on the right (km)[START_REF] Schlatter | Vertical Structure of the Atmosphere[END_REF]. ......................................................................................................... 23 Figure 2 The radiative forcing estimates from 1750 to 2011, and aggregated uncertainties (horizontal bars) for the main drivers of climate change (https://www.ipcc.ch). .................... 24 Figure 3 Desert dust cycle: emission, transport and deposition. ............................................. 26 Figure 4 Atmospheric lifetimes of different size particles at different levels of the Atmosphere (Jaenicke, 1980). .................................................................................................................. 27 Figure 5 TOMS detection shows transported mineral dust from China to the North Atlantic Ocean 2001 (https://nasa.gov). ...................................................................................................... 27 Figure 6 Pictorial representation of changes in a composite mineral dust particle as it is processed in the atmosphere (Usher et al., 2003). .................................................................. 28 Figure 7 Global annual average of dust deposition flux in g/m 2 /year (Jickells et al., 2005). ..... 28 Figure 8 Density functions of selected log-normal distributions compared with a normal distribution. Log-normal distributions n (𝑟𝑔, 𝜎𝑔) as function of the number of particles are shown for five values of multiplicative standard deviation 𝜎𝑔 (Limpert et al., 2001). ......................... 30 Figure 9 Schematic of different soil types clay, silt and sand, their size distribution and their aeolian processes. ................................................................................................................ 31 Figure 10 Mineralogy of 19 different aerosol samples from the world(Di Biagio et al., 2017). . 33

Figure 12 Figure 13 Figure 14 Figure 15 Figure 16

 1213141516 Figure 11Crystal structure of tectosilicates and phyllosilicates[START_REF] Fang | Mineralogical Controls on Frictional Strength, Stability, and Shear Permeability Evolution of Fractures[END_REF]. ................ 33 Figure 12 Fundamental vibrational modes with quartz molecular structure in the ground state and excited state at 1108 cm -1 having a symmetrical stretching. ............................................. 34 Figure 13 Schematic of mixture state of aerosols. (a) external mixture, (b) perfect internal mixture, (c) realistic mixture. ............................................................................................... 36 Figure 14 Electron-microscope images of sample mineral dust particles encountered in a soil sample collected in northern Sahara (Nousiainen, 2009). ........................................................ 37 Figure 15 Reflection, transmission absorption and emission of radiation(Hyll, 2012). ............ 38

Figure 19 Figure 20 Figure 21 Figure 22

 19202122 Figure 18The spectral distribution of a blackbody with temperatres from 300 K to 700 K[START_REF] Campbell | Introduction to Remote Sensing, Fifth Edit[END_REF]. ........................................................................................... 42 Figure 19 Size parameter 𝑥 as a function of wavelength 𝜆 of the incident radiation and particle radius 𝑟 (Wallace and Hobbs, 2006). ...................................................................................... 44 Figure 20 Extinction spectra of the three scattering regimes for a particle of 1 µm (Deguine, 2018). ........................................................................................................................................... 45 Figure 21 Spectral variation of the real part n and imaginary part 𝜅 of the CRI (Deschutter, 2022). ........................................................................................................................................... 46 Figure 22 Remote sensing process (Campbell and Randolph, 2011). ....................................... 48 Figure 23 A comparison of sensors in terms of the width of bands (Baisantry and Khare, 2016). ........................................................................................................................................... 49 Figure 24 A comparison of spatial resolution between (a) panchromatic (b) multispectral (c) hyperspectral imagery (Borstad et al., 2004). ......................................................................... 49

Figure 25 Figure 27 Figure 28 Figure 29 Figure 30 Figure 31

 252728293031 Figure24A comparison of spatial resolution between (a) panchromatic (b) multispectral (c) hyperspectral imagery[START_REF] Borstad | Hyperspectral Imagery for Safeguards Applications[END_REF]. ......................................................................... 49 Figure 25 Spatial coverage of Central Asia in 19-5-2019 (a) VIIRS imagery with continuous coverage (b) MODIS a sounder sampling with spatial gaps in black (https://worldview.earthdata.nasa.gov)................................................................................ 50 Figure 26 (a) Active sensors and (b) passive sensors detecting radiation signals. .................. 51 Figure 27 Topography of East Asia. ...................................................................................... 54 Figure 28 The central business district in Beijing on the 25 April 2017 (Left) and after a dust storm swept through in the next days. (Credits to Nicolas Asfouri, Agence France Presse-Getty Image). ................................................................................................................................ 55 Figure 29 The MetOp satellite and its payload with a closer look on IASI system (eumetsat.int). ........................................................................................................................................... 60 Figure 30 Ground track of MeTop satellite for 24h (Herbin and Dubuisson, 2015). .................. 61 Figure 31 IASI optical functioning (Cayla and Javelle, 1995). .................................................. 62

Figure 32

 32 Figure32IASI swath angle and field view pixels[START_REF] Clerbaux | Atmospheric Chemistry and Physics Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder[END_REF]. ................................. 62 Figure 32 Example of IASI (a) interferogram; and (b) its spectral radiance using Fourier transform (Reproduced from Grieco et al., 2010). ................................................................... 63

Figure 36 Figure 37 Figure 38

 363738 Figure35 An atmosphere divided homogeneous layers[START_REF] Herbin | Infrared Observation of Earth's Atmosphere[END_REF]. ........... 65 Figure 36 Nadir viewing observation of the radiation coming from the Earth's surface (1), the atmosphere (2a and 3) and the sun (2b). ............................................................................... 66 Figure 37 Examples of IASI brightness temperature spectra for a clear sky scene, a scene above the desert, a scene containing dust and another containing ice cloud. Spectral channels after 2220 cm -1 were removed due to solar reflectance term that we disregard in our work. .................... 67 Figure 38 Top panel: radiance atmospheric spectrum (in normalized units) recorded by IASI/MetOp, over West of Australia, on 20 December, 2006. Middle panels: radiative transfer pseudo-transmittance simulations to identify the main absorbing gases (middle panels) and the weaker absorbers compared with the IASI noise (lower panel) (Clerbaux et al., 2009). ............ 69

Figure 39 Figure 40 Figure 41 Figure 42 Figure 45

 3940414245 Figure 38Top panel: radiance atmospheric spectrum (in normalized units) recorded by IASI/MetOp, over West ofAustralia, on 20 December, 2006. Middle panels: radiative transfer pseudo-transmittance simulations to identify the main absorbing gases (middle panels) and the weaker absorbers compared with the IASI noise (lower panel)[START_REF] Clerbaux | Atmospheric Chemistry and Physics Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder[END_REF]. ............ 69 Figure 39 Top panel: brightness temperature detection of Gobi dust. Low panel: forward model simulation of an empty atmospheres with a single layer (2 Km) of Gobi Desert dust ( 𝑟𝑔 = 0.25 μm; 𝜎𝑔 = 2 μm). Sym Str: symmetrical stretching; Asym Str: asymmetrical stretching; Bend: bending. .............................................................................................................................. 70 Figure 40 Spectral emissivity of water, ice, coarse snow and desert (Huang et al., 2018). ........ 71 Figure 41 IASI detection example of a clear sky above the Sea of Japan (in blue), and a clear sky above the Gobi Desert (in red) in 3 May 2017. The black arrow indicates the Reststrahlen feature. ........................................................................................................................................... 72 Figure 42 The variation of the AOD as function of the concentration from simulated spectra of an empty atmosphere with a single layer of Gobi Desert dust using a Mie code and Gobi dust CRI. Gray patterns indicate important gases absorption bands from the order of increasing wavenumbers CO2, O3, and H2O. ............................................................................................ 73 Figure 43 IASI dust example from Gobi Desert in 3 May 2017 showing spectral position of IASI channels for ∆𝑇𝐵1 (in green) and ∆𝑇𝐵2 (in blue) computations used in this study to select dust spectra. ................................................................................................................................ 74 Figure 44 (a) Difference in the brightness temperature ∆𝐓𝐁𝟐 of the IASI dust selection during the dust storm on 4 May 2017 by day; (b) merged dark target/deep blue aerosol optical depth from MODIS/Terra on 4 May 2017 (https://worldview.earthdata.nasa.gov, last accessed on 1 December 2021). The dashed black circle region presents the false aerosol detection region. .. 75 Figure 45 Mean monthly surface emissivity of four different surfaces of (latitude ; longitude) during the month of May (Zhou et al., 2014).The shaded orange box indicates the Reststrahlen region. ................................................................................................................................. 76

Figure 48 Figure 49 Figure 50 Figure 51 Figure 52 Figure 53 Figure 55 Figure 56

 4849505152535556 Figure 46Brightness temperature Gobi Desert spectrum example from 3 May 2017 before and after the LSE correction using a monthly average emissivity value (D.Zhou et al., 2014). For simplicity, the carbon dioxide, ozone, and water vapor wide bands are removed from the IASI spectra in the rest of the of the chapter.................................................................................. 77

.Figure 59

 59 Figure 57 Laboratory extinction coefficient by Fourier Transform infrared spectrometer of (a) Gobi dust samples, with arrows indicating extinction peaks, and (b) normalized extinction coefficient to [0, 1] of the pure minerals quartz, illite, and calcite with their molecular assignments (Deschutter, 2022). Sym Str: symmetrical stretching; Asym Str: asymmetrical stretching; Bend: bending. .............................................................................................................................. 97 Figure 58 (a) Scanning electron micrograph and (b)the Energy-Dispersive X-ray Spectroscopy chart (low panel) of an individual particle of Gobi dust sample showing the associated minerals (Bichon et al., 2021). ............................................................................................................. 99 Figure 59 Simulation of the linear combination of pure mineral coefficients (red) fitted with the experimental Gobi dust extinction spectrum (black) with the remaining residue from the simulation and experimental spectrum (gray). .................................................................... 101

Figure 61 Figure 62 Figure 63 Figure 64 Figure 65 Figure 66 Figure 68 Figure 69 Figure 70 Figure 71 Figure 72

 6162636465666869707172 Figure 60The laboratory Gobi dust spectrum with the molecular assignment for each fingerprint is in black[START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF]. Sym Str: symmetrical stretching; Asym Str: asymmetrical stretching; Bend: bending. The IASI equivalent optical thickness detection of the dust plume on 4 May 2017 at daytime after LSE correction, with mineralogical family assignment, is in blue. Tec: tectosilicates; Phy: phyllosilicates; Carb: carbonates. ........................................................... 103 Figure 61 Simulation of the linear combination of pure mineral coefficients (red) fitted with the IASI dust optical thickness (blue) with the remaining residue from the simulation and experimental spectrum (gray) and the RMS (light gray envelope). ........................................ 105 Figure 62 Mineralogical mass weight maps of tectosilicates, phyllosilicates, and carbonates during the 3-6 May 2017 dust storm by day (D) and night (N) from IASI detections. The last two column from the left also show the RMS of the spectral residues of the LC method and the optical thickness at 1072.5 cm -1 (graph continues to next page). ...................................... 108 Figure 63 HYSPLIT 24 h backward trajectory for different dust detections in dust storms on 5 May 2017 by night (https://www.ready.noaa.gov/index.php, accessed on 1 December 2021). 111 Figure 64 Mineralogical mass weight maps of tectosilicates, phyllosilicates, and carbonates during the 14-18 March 2021 dust storm by day (D) and night (N) from IASI detections. The last two column from the left also show the RMS of the spectral residues of the LC method and the optical thickness at 1072.5 cm -1 (graph continues to next page). ................................ 113 Figure 65 HYSPLIT 24 h backward trajectory for different dust detections in dust storms on 17 March 2021 by day (https://www.ready.noaa.gov/index.php, accessed on 1 December 2021). ......................................................................................................................................... 117 Figure 66 Mineralogical extinction coefficient maps of tectosilicates, phyllosilicates, and carbonates on 4 May during the 2017 dust storm from IASI detection of METOP A and METOP B. The last two column from the left also show the RMS of the spectral residues of the LC method and the optical thickness at 1072.5 cm -1 . ............................................................................. 118 Figure 67 ARAHIS forward model scheme used to simulate IASI spectra. ............................. 122 Figure 68 The influence of the CRI on the IASI spectra of Gobi dust compared with spectrum without aerosol particles (right panel) and the zoom between 800 and 1000 cm -1 . ............... 124 Figure 69 The influence of the altitude of dust plume on the IASI simulated spectra of Gobi dust. ......................................................................................................................................... 125 Figure 70 The effect of increasing the mean geometric radius on the IASI simulated spectra of Gobi dust. .......................................................................................................................... 126 Figure 71 The effect of increasing the VMR the IASI simulated spectra of Gobi dust. ............. 127 Figure 72 ARAHMIS fits of IASI dust spectrum spectra having different ∆TB2 (top panel) and a clear sky above the sea spectrum (low panel) from 4 May 2017. .......................................... 128

  values and STD of the three dust region sources in the dust storm of May 2017 in East Asia.......................................................................................................... Table 4 The IASI mean mineralogical values and STDs of the three dust region sources in the dust storm of March 2021 in East Asia. .....................................................................................................

  recently developed into a major field of study with farreaching scientific and social attention. Topics such as atmospheric composition were not deemed sufficiently until the last 35 years and now represent major branches of the discipline. Aerosols are solid or liquid particles emitted and transported at different layers in the atmosphere, having interactions with gaseous molecules, solid particles and liquid droplets. They can originate from natural sources i.e., deserts and volcanos and even oceans, or from human sources mainly related to industrial activities. They play an important role in the radiative balance of the Earth, as they have the ability to absorb and scatter solar and terrestrial (or thermal infrared) radiation.

  Emitted components from 1750 to 2011 are shown in Figure 2. The overall RF is positive for gases, while aerosols and clouds have a negative RF. Nevertheless, the uncertainty bar for aerosols RF estimation is large with respect to the estimated itself. This uncertainty comes from the lack of knowledge of the aerosols' microphysical properties and especially chemical properties.

Figure 2

 2 Figure 2 The radiative forcing estimates from 1750 to 2011, and aggregated uncertainties (horizontal bars) for the main drivers of climate change (https://www.ipcc.ch).

Figure 3

 3 Figure 3 Desert dust cycle: emission, transport and deposition.

Figure 4

 4 Figure4Atmospheric lifetimes of different size particles at different levels of the Atmosphere[START_REF] Jaenicke | Atmospheric aerosols and global climate[END_REF].

Figure 5

 5 Figure 5 TOMS detection shows transported mineral dust from China to the North Atlantic Ocean 2001 (https://nasa.gov).

Figure 8

 8 Figure 8 Density functions of selected log-normal distributions compared with a normal distribution. Log-normal distributions n (𝑟 𝑔 , 𝜎 𝑔 ) as function of the number of particles are shown for five values of multiplicative standard deviation 𝜎 𝑔[START_REF] Limpert | As the need grows for conceptualization[END_REF].

Figure 9

 9 Figure 9 Schematic of different soil types clay, silt and sand, their size distribution and their aeolian processes. Different techniques are used to determine the SD and the concentration of aerosols. For instance, the Scanning Mobility Particle Sizer (SMPS) measures the SD of aerosols whose electrical mobility diameter determines their size with a Differential Mobility Analyzer (DMA) and counting particles using a Condensation Particle Counter (CPC). Another example is the aerodynamic particle sizer (APS) that measures the aerodynamic particle diameter based on particle time of flight and the optical diameter based on scattered light intensity. Typically, both methods are used simultaneously to give a total SD

Figure 10

 10 Figure 10 Mineralogy of 19 different aerosol samples from the world (Di Biagio et al., 2017).

Figure 12

 12 Figure 12 with an example of Si-O-Si (quartz) symmetrical stretching mode.

Figure 12

 12 Figure 12Fundamental vibrational modes with quartz molecular structure in the ground state and excited state at 1108 cm -1 having a symmetrical stretching.

Figure 13

 13 Figure 13 Schematic of mixture state of aerosols. (a) external mixture, (b) perfect internal mixture, (c) realistic mixture.

  Solar and terrestrial radiation interact with aerosol particles and gaseous components that make up Earth's atmosphere by absorption or scattering. The Radiative Transfer Equation (RTE) describes the propagation of the radiation in the atmospheric medium taking into account the processes of extinction and thermal emission. It is an equation of transport of the spectral radiance 𝑅(𝑣) (in W. m 2 . sr. (m -1 ) -1 ), which expresses the conservation of the radiative energy of an incident radiation in an elementary optical path of thickness 𝑑𝑠 (Figure17).

Figure 19

 19 Figure 19Size parameter 𝑥 as a function of wavelength 𝜆 of the incident radiation and particle radius 𝑟[START_REF] Wallace | Atmospheric Science: An Introductory Survey: Second Edition[END_REF].

Figure 20

 20 Figure20shows the spectral distribution of extinction in the three scattering regimes for different particle diameters. The extinction is measured experimentally by Fourier Transform Spectrometers in IR, Vis-UV. Particularly, for atmospheric dust (between 10 -2 and 10 μm), Mie Regime covers the most important for thermal infrared region (between 8 and 15 μm). A code is used in our work to simulate extinction spectra from Mie theory by knowing the size distribution and the complex refractive indices of dust and will be discussed in the next chapter.

Figure 20

 20 Figure 20Extinction spectra of the three scattering regimes for a particle of 1 µm[START_REF] Deguine | Propriétés Optiques et Chimiques des cendres volcaniques: Mesures de Laboratoire et Applications à la télédétection Spatiale[END_REF].

Figure 21

 21 Figure 21Spectral variation of the real part n and imaginary part 𝜅 of the CRI[START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF].

Figure 23 A

 23 Figure23A comparison of sensors in terms of the width of bands[START_REF] Baisantry | Pan Sharpening for Hyper Spectral Imagery Using Spectral Mixing-Based Color Preservation Model[END_REF].

  spatial coverage defines if the image coverage is global or local, complete or partial. Particularly, satellite's spatial coverage depends on the scanning, swath and observing cycle of the instrument. The scene can be observed with continuity i.e., the Visible Infrared Imaging Radiometer Suite (VIIRS) or can be sampled partially i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS) as illustrated in Figure 25.

Figure 25

 25 Figure 25 Spatial coverage of Central Asia in 19-5-2019 (a) VIIRS imagery with continuous coverage (b) MODIS a sounder sampling with spatial gaps in black (https://worldview.earthdata.nasa.gov).

Figure 26

 26 Figure 26 (a) Active sensors and (b) passive sensors detecting radiation signals.

  Photometer

Figure 27

 27 Figure 27 Topography of East Asia.

  Figure 28 shows the impact of a desert storm occurred in the beginning of May 2017 on the visibility in Beijing in two days difference.

Figure 28

 28 Figure 28 The central business district in Beijing on the 25 April 2017 (Left) and after a dust storm swept through in the next days. (Credits to Nicolas Asfouri, Agence France Presse-Getty Image).

  Developed by CNES and in cooperation with EUMETSAT, three sun-synchronized METOP satellites (A, B, and C) have been launched in the last 15 years with IASI-A, B, and C on board. In the next 20 years, a new generation of IASI-NG will continue the IASI mission with increased

Figure 31

 31 Figure 31 IASI optical functioning (Cayla and Javelle, 1995).

14 )

 14 The interferogram should be sampled at very stable and regular positions of the moving mirror. Using a stable clock is not sufficient as it affects the speed of the moving corner cube. Instead, a reference zero-crossings laser beam triggers the sampling. Hence the interferogram has a finite number of values 𝑥 limited by the maximum of the optical path difference allowed by the moving corner. The measurement is made with a maximum optical path difference of 𝑥 𝑚𝑎𝑥 = 2 cm . Figure32shows an example of IASI interferogram 𝐼(𝑥) (Panel (a)), and the spectral radiance using the mathematical Fourier Transform (Panel (b)).

Figure 33

 33 Figure 33 Example of IASI (a) interferogram; and (b) its spectral radiance using Fourier transform (Reproduced from Grieco et al., 2010).

  𝑘(𝑣, ℎ)𝐵(𝑣, 𝑇)𝑒 -∫ 𝑘(𝑣,ℎ

Figure 38

 38 Figure 38Top panel: radiance atmospheric spectrum (in normalized units) recorded by IASI/MetOp, over West of Australia, on 20 December, 2006. Middle panels: radiative transfer pseudo-transmittance simulations to identify the main absorbing gases (middle panels) and the weaker absorbers compared with the IASI noise (lower panel)[START_REF] Clerbaux | Atmospheric Chemistry and Physics Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder[END_REF].

Figure 39

 39 Figure 39 Top panel: brightness temperature detection of Gobi dust. Low panel: forward model simulation of an empty atmospheres with a single layer (2 Km) of Gobi Desert dust ( 𝑟 𝑔 = 0.25 μm; 𝜎 𝑔 = 2 μm). Sym Str: symmetrical stretching; Asym Str: asymmetrical stretching; Bend: bending.

  ), is the surface emissivity 𝜀(𝑣), since it can significantly affect the detected IASI signal. It depends on the soil composition, diurnal and seasonal variability.

  Figure40shows the spectral emissivity of four surface types. The desert surface emissivity is highly variable since it is highly rich in Quartz in the thermal infrared, while other types show values close to 1.

Figure 40

 40 Figure 40Spectral emissivity of water, ice, coarse snow and desert[START_REF] Huang | Improved Representation of Surface Spectral Emissivity in a Global Climate Model and Its Impact on Simulated Climate[END_REF].

Figure 41

 41 Figure 41 IASI detection example of a clear sky above the Sea of Japan (in blue), and a clear sky above the Gobi Desert (in red) in 3 May 2017. The black arrow indicates the Reststrahlen feature.

  Dust (L.[START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF] r g = 0.25 mm ; s= 2 mm L= 2 km 2-Gases narrow bands: These bands can alter the radiance spectral value depending on the gas's concentration in the atmosphere. We tested empirically hundreds of mineral dust spectra to find the best channels that minimizes the impact of gases on the ∆𝑇 𝐵 calculation. In this study, we use two ∆𝑇 𝐵 for dust selection: ∆𝑇 𝐵1 = 𝑇 𝐵,811.25 -𝑇 𝐵,988 and ∆𝑇 𝐵2 = 𝑇 𝐵,1191.25 -𝑇 𝐵,1112 (Figure43).

Figure 43

 43 Figure 43 IASI dust example from Gobi Desert in 3 May 2017 showing spectral position of IASI channels for ∆𝑇 𝐵1 (in green) and ∆𝑇 𝐵2 (in blue) computations used in this study to select dust spectra.

Figure 44 (

 44 Figure 44 (a) Difference in the brightness temperature ∆𝐓 𝐁𝟐 of the IASI dust selection during the dust storm on 4 May 2017 by day; (b) merged dark target/deep blue aerosol optical depth from MODIS/Terra on 4 May 2017 (https://worldview.earthdata.nasa.gov, last accessed on 1 December 2021). The dashed black circle region presents the false aerosol detection region.

  the example of four types of surfaces in East Asia: the Gobi and Taklamakan Deserts, vegetation in the eastern regions, and urban and populated cities in Shandong.

  An example is shown in Figure 47a for IASI detection, where clouds (in gray) mixed with the dust plume and separated the detection into four different parts. IASI detection and selection are consistent with the RGB image in Figure 47b showing geostationary HIMAWARI 8 RGB imagery, where thin high clouds (in olive color) mix with the plume dividing it into four different aerosol sections (in pink). The IASI map with a Mercator projection shows homogeneous cloud and aerosol selection with the same morphology but with a different projection than the HIMAWARI 8 imagery having a polar projection. The inverted 'L-shape' cloud selection from the IASI map has the same shape in the imagery but is elongated. This comparison indicates the consistency of the IASI cloud and the dust selections.

Figure 47 (

 47 Figure 47 (a) IASI dust and cloud selection on 15 March 2021; (b) RGB Dust False-like SEVIRI from HIMAWARI (www.icare.univ-lille.fr, last accessed on 1 December 2021).

Figure 48

 48 Figure 48 Example of an IASI (a): spectral radiance and; (b) its corresponding surface emissivity from D. Zhou et al., 2014 with the calculated linear equation in the neighboring of 𝑣 𝑅 (in green) and the value of 𝑚 𝑤/𝑜 (𝑣 𝑅 ) for each spectrum (red point).

Figure 50

 50 Figure 50 LSE correction scheme diagram.

Figure 51

 51 Figure 51 IASI spectral detection example for a clear sky above the sea compared to a clear sky above the desert and mineral dust detection from the 3 May 2017 dust event before and after LSE optimization.

Figure 52

 52 Figure 52 Lambert-Beer's law application on a dust plume detected by IASI/MetOp

Figure 53

 53 Figure 53 Example of (a) the apparent optical thickness spectrum of a clear sky above the desert and (b) the equivalent optical thickness of mineral dust above the Gobi Desert from the 3 May 2017 dust event before and after LSE correction.

Figure 54 (

 54 Figure 54 (a) The difference in the brightness temperature of the IASI dust selection after LSE correction during the dust storm on the 4 May 2017 at daytime, with clouds selection (white pixels). The dashed black circles present the minimized false aerosol detection region. The orange line is the CALIOP orbit track and the blue line is the CALIOP viewing section; (b) CALIOP-attenuated backscatter at 532 nm (www.icare.univ-lille.fr, accessed on 27 November 2021).

Figure 55

 55 Figure 55 Mineralogical composition (percent of mass weights) from literature data of three major desert sources (black) in three region types (red). Tec: Tectosilicates, Phy: Phyllosilicates, Carb: Carbonates, N.D: Non-determined data.

Figure 56

 56 Figure 56 Experimental apparatus parts: aerosol generation, extinction measurement, and size distribution measurement (Reproduced from Deschutter, 2022).Molecular Features

Figure 57

 57 Figure 57Laboratory extinction coefficient by Fourier Transform infrared spectrometer of (a) Gobi dust samples, with arrows indicating extinction peaks, and (b) normalized extinction coefficient to [0, 1] of the pure minerals quartz, illite, and calcite with their molecular assignments[START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF]. Sym Str: symmetrical stretching; Asym Str: asymmetrical stretching; Bend: bending.

  ogy and mixture state. In addition, Energy-Dispersive X-ray Spectroscopy allows the fraction of minerals found in each particle. The study was performed on hundreds of individual particles, from which 80% were composed of 70% of one mineral, as indicated in Figure 58b.

Figure 58 (

 58 Figure 58 (a) Scanning electron micrograph and (b)the Energy-Dispersive X-ray Spectroscopy chart (low panel) of an individual particle of Gobi dust sample showing the associated minerals (Bichon et al., 2021).

Figure 59

 59 Figure 59 Simulation of the linear combination of pure mineral coefficients (red) fitted with the experimental Gobi dust extinction spectrum (black) with the remaining residue from the simulation and experimental spectrum (gray).

Furthermore, the experimentalFigure 61

 61 Figure 61 Simulation of the linear combination of pure mineral coefficients (red) fitted with the IASI dust optical thickness (blue) with the remaining residue from the simulation and experimental spectrum (gray) and the RMS (light gray envelope).

  Table 3 presents for each overpass the mean mass weight values, RMS, and the optical thickness at 1072.5 cm -1 in the three regions (I, II, and III) presented in Figure 55. The mean value of tectosilicates was 48.5% in region I, whereas it was less than half in regions II and III. Conversely, the contents of phyllosilicates in regions II and III were close to 73% and 46% in region I. The contents of carbonates in the three regions had values of around 5.5%. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model has employed to link the mineralogical values to the possible dust sources. Figure 63shows 24 h of backward trajectories on 5 May at 11 UTC, predicting the sources of detected dust masses. From the left to the right, the first (I) comes from the Taklamakan Desert. Dust particles are emitted in the region without long-distance transport, which is explained by the high content of tectosilicates and the lower content of phyllosilicate. The second (II) originates from the Gobi Desert, traveling Eastward with the lowest tectosilicate and the highest carbonate content. The third (III) shows the Gobi load trajectory transported

  Figure 62 Mineralogical mass weight maps of tectosilicates, phyllosilicates, and carbonates during the 3-6 May 2017 dust storm by day

Figure 63

 63 Figure 63 HYSPLIT 24 h backward trajectory for different dust detections in dust storms on 5 May 2017 by night (https://www.ready.noaa.gov/index.php, accessed on 1 December 2021).

  Figure 64 Mineralogical mass weight maps of tectosilicates, phyllosilicates, and carbonates during the 14-18 March 2021 dust storm by day (D) and

Figure 65

 65 Figure 65 HYSPLIT 24 h backward trajectory for different dust detections in dust storms on 17 March 2021 by day (https://www.ready.noaa.gov/index.php, accessed on 1 December 2021). IASI-A and IASI-B METOP A ended its mission in November and December 2021, and only METOP B and C continue the mission. Hence, a comparison between IASI-A and IASI-B is crucial to test the applicability of this study on the present IASI platforms. The comparison was applied to the 2017 case study, where only two METOPs, A and B, were in an orbital phase with a time gap of 45 min, with similar views from both satellites ensuring the maximum daily coverage. The results from 4 May 2017 during the day are shown in Figure 66, with no notable spatial shift between the two platforms as the dust plume traveled slowly from west to east. The plume mineralogical extinction evolution increased consistently in mineral weights after 45 min according to IASI-B, with continuous dust emissions from the

Figure 66

 66 Figure 66 Mineralogical extinction coefficient maps of tectosilicates, phyllosilicates, and carbonates on 4 May during the 2017 dust storm from IASI detection of METOP A and METOP B. The last two column from the left also show the RMS of the spectral residues of the LC method and the optical thickness at 1072.5 cm -1 .

39 )

 39 The forward model operator F(x) describes the RTE (Equation (15) discussed previously in Chapter II) is shown in Figure 67. To calculate the total extinction coefficient 𝑘(𝑣, ℎ), we remove clouds detections.

125 Figure 69

 12569 Figure 69The influence of the altitude of dust plume on the IASI simulated spectra of Gobi dust.

Figure 70

 70 Figure 70The effect of increasing the mean geometric radius on the IASI simulated spectra of Gobi dust.

  2017 described in Section 3.3.1, in order to solve the retrieval problem and quantify the mean geometric radius and the VMR in a future work. Figure72shows forward model simulations applied to dust spectrum (top panel) and a clear sky above the desert detection (low panel) from May 2017 dust storm. The simulations are fitted to IASI measurements where the gas components were computed using the Line-by-Line approach. The surface emissivity was computed usingZhou et al., 2014 and the LSE optimization method. Aerosols were considered spherical, and Mie's theory was applied to the population of aerosols having a mean geometric radius rg, a geometric standard deviation 𝜎, and a Volume Mixing Ratio (VMR). The fittings show an RMS (1.01K) having an order close to the instrument noise (0.25 K). This result was obtained with fixing gases parameters and can be improved, which will allow us to solve the inverse problem in the future and retrieve the effective radius and the VMR.

Figure 72

 72 Figure 72 ARAHMIS fits of IASI dust spectrum spectra having different ∆T B2 (top panel) and a clear sky above the sea spectrum (low panel) from 4 May 2017.

  These results imply the temporal stability of IASI-A in a time gap of five years and show the ability to use IASI detection in different seasons. In addition, IASI-A and IASI-B were compared giving consistent mineral weights, which allow for the application of this work using different IASI platforms in the future. Moreover, laboratory optical properties exploited with IASI measurements allowed for obtaining new mineralogical dust composition in the East Asian deserts. Hence, the laboratory optical properties (individual minerals and Gobi dust extinctions) used are suitable. No previous work used satellite remote sensing to differentiate dust mineralogical families. This study crossed the different challenges posed by LSE and the mineralogical variability, and obtain the mineral dust composition.

TABLE OF CONTENT

 OF

	COVER ___________________________________________________________________
	TABLE OF CONTENT __________________________________________________________

TABLE OF FIGURES___________________________________________________________

 OF

TABLE OF

 OF

	1.5.3 Active and Passive Remote Sensing ___________________________________
	1.6 East Asian Deserts __________________________________________________
	1.7 Objective _________________________________________________________
	CHAPTER II. DUST DETECTION USING IASI _______________________________________
	2.1 Instrument Presentation _________________________________________________
	2.1.1 MetOp Satellite ____________________________________________________
	TABLES __________________________________________________________ 2.1.2 The Infrared Atmospheric Sounding Interferometry (IASI) _________________
	ACRONYMS _______________________________________________________________ 2.1.3 Radiative Transfer Equation (RTE) ____________________________________
	INTRODUCTION ____________________________________________________________ 2.2 Atmospheric Spectral Detection __________________________________________
	CHAPTER I. ATMOSPHERIC MINERAL DUST _______________________________________ 2.2.1 Atmospheric Gases Detection ________________________________________
	1.1 Atmospheric Structure and Components _________________________________ 2.2.2 Airborne Mineral Dust Detection ______________________________________
	1.2 Dust Cycle and Impact _______________________________________________ 2.2.3 Surface Emissivity _________________________________________________
	1.3 Microphysical and Chemical Properties _________________________________ 2.3 Surface Impact on Dust Detections ________________________________________
	1.3.1 Size Distribution __________________________________________________ 2.3.1 Dust Selection _____________________________________________________
	1.3.2 Chemical Composition _____________________________________________ 2.3.2 The LSE Optimization Method _______________________________________
	1.3.3 Particle shape ____________________________________________________ Conclusion ______________________________________________________________
	1.4 Optical Properties ___________________________________________________ CHAPTER III. AEROSOL COMPOSITION RETRIEVAL _________________________________
	1.4.1 Radiation-Matter Interaction ________________________________________ 3.1 Experimental Laboratory Data ___________________________________________
	1.4.2 Radiative Transfer Equation _________________________________________ 3.1.1 Dust Mineralogy ___________________________________________________
	1.4.3 Size parameter ___________________________________________________ 3.1.2 Gobi Dust Optical Properties _________________________________________
	1.4.4 Particle Shape ____________________________________________________ 3.2 Mineralogical Mapping using IASI_______________________________________
	1.4.5 Complex Refractive Index __________________________________________ 3.2.1 IASI Molecular Features ___________________________________________
	1.5 Mineral dust detection using remote sensing ______________________________ 3.2.2 Linear Combination Method: Application on IASI Observations ____________
	1.5.1 Remote Sensing Process ____________________________________________ 3.3 Case Studies ________________________________________________________
	1.5.2 Key Concepts of Remote Sensing ____________________________________ 3.3.1 May 2017 from IASI-A ____________________________________________

TABLE OF FIGURES

 OF Figure

TABLE OF TABLES

 OF

Table 1

 1 Different sensors types, functioning, measured and retrieved parameters. .............................

Table 2

 2 Molecular assignments of some minerals in TIR. ...................................................................

Table 3

 3 The IASI mean mineralogical

Table 2

 2 Molecular assignments of some minerals in TIR.

	Mineral Group	Mineral Name	Molecular Assignment	Central Wavenumber (𝐜𝐦 -𝟏 )	References
	Tectosilicates	Quartz	Si-O Symmetrical Stretching Si-O Asymmetrical Stretching	778, 795 1080, 1102, 1177	(He et al., 2019; Reig et al., 2002; Spitzer and Kleinman, 1961)
	Phyllosilicates	Illite	-O-H Bending Si-O Asymmetrical Stretching	916 1033	(Madejová et al., 2017; Madejova and Komadel, 2001)
	Carbonates	Calcite	C-O Asymmetrical Stretching	879 to 904

Table 3

 3 The IASI mean mineralogical values and STD of the three dust region sources in the dust storm of May 2017 in East Asia.

												Mean			Mean
			Mean Mass Weight per Half-Day (%)
												RMS			𝝉 𝟏𝟎𝟕𝟐.𝟓 𝐜𝐦 -𝟏
			Tec			Phy			Carb
	Date
		I	II	III	I	II	III	I	II	III	I	II	III	I	II	III
	3D	52.0 23.5	-	43.0	70.4	-	5.0 6.1	-	0.029 0.018	-	0.357	0.706	-
	3N	46.0 10.8	-	48.5	82.3	-	5.5 6.9	-	0.017 0.018	-	0.477	0.631	-
	4D	55.4 20.2	-	39.5	74.3	-	5.1 5.5	-	0.029 0.018	-	0.368	0.555	-
	4N	50.8 12.5	-	46.1	81.2	-	3.1 6.3	-	0.014 0.015	-	0.574	0.456	-
	5D	37.9 20.3 27.5 55.4	74.1	67.3 6.7 5.6 5.2 0.013 0.021 0.033 0.667	0.603 0.515
	5N	43.9 17.2 17.2 50.8	76.2	78.6 5.3 6.6 4.5 0.015 0.014 0.008 0.406	0.539 0.529
	6D	57.0 23.5 23.4 38.4	69.8	72.9 4.6 6.7 3.7 0.045 0.015 0.016 0.325	0.702 0.916
	6N	45.1 23.6	-	51.1	69.5	-	3.8 6.9	-	0.014 0.016	-	0.407	0.516	-
	Mean	48.5 19.0 22.7 46.6	74.7	72.9 4.9 6.3 4.4 0.022 0.017 0.019 0.448	0.589 0.653
	STD	6.0	4.7	4.2	5.6	4.6	4.6 1.0 0.5 0.6 0.011 0.002 0.011 0.111	0.083 0.186

  On 15 March 2021, dust mass III had high tectosilicate content having a mixture of the Gobi and Horqin sources, a similar scenario to 5 May 2017, with the Horqin Sandy land source causing the increase in tectosilicate weight. The mass load II shows lower tectosilicate content as it originated only from the Gobi Desert. In addition, the weight of phyllosilicates was higher in II than III, suggesting that dust emissions in II were from the Gobi Desert. By16 and 17 March (16N and 17N), clouds covered the region, masking dust plumes detection. On 17 March by day (17D), the mineralogical weights had heterogeneous variability in the three different regions, each related to a different source. We used the HYSPLIT 24 h backward trajectory that shows on 17D the presence of dust emission in region I above Taklamakan Desert, originating from 16 March by day and traveling in the same direction (Eastward) as the uplifted masses of region II from the Gobi Desert. In addition, another mass traveled from Central Asia, showing different mineralogy that was particularly rich in tectosilicates (the values of this plume are not shown in Table4).On 15 March 2021, dust mass III had high tectosilicate content having a mixture of the Gobi and Horqin sources, a similar scenario to 5 May 2017. On the other hand, mass load II shows lower tectosilicate content as it originated only from the Gobi Desert. Hence,

the Horqin Sandy land is an important dust source rich in tectosilicate. In addition, the weight of phyllosilicates was higher in II than III, suggesting that dust emissions in II were from the Gobi Desert. By 16 and 17 March (16N and 17N), clouds covered the region, masking dust plumes detection. On 17 March by day (17D), the mineralogical weights had heterogeneous variability in the three different regions, each related to a different source.

), never before sounded in its entirety from space. The mission will start in

Appendix B: Presentations __________________________________________________ REFERENCES _____________________________________________________________ ABSTRACT _______________________________________________________________

EXPERIMENTAL LABORATORY DATA

Dust Mineralogy

Mineral dust is composed of silicates, carbonates, and oxides forming the Earth's crustal components from which pure minerals can be divided into different groups based on their structure. Hence, we can distinguish:

1-Phyllosilicates (e.g., illite, kaolinite, chlorite and smectite) are formed of sheets of tetrahedra silicates. They are abundant in clay-sized dust (discussed in Section 1.3.1) and hence termed "clay minerals", while chlorite is the only specie that can be found in both clay and silt particles [START_REF] Griffin | The distribution of clay minerals in the World Ocean[END_REF].

2-Tectosilicates (e.g., quartz and feldspars) have an interlocking tetrahedra structure.

They are minor in clay-sized particles and major in silt fraction since they have strong covalent bonds hard enough to be broken by wind erosion. They form 75% of the Earth's crust.

3-Carbonates (e.g., calcite and dolomite) are found in both clay [START_REF] Abtahi | Soil Genesis as Affected by Topography and Time in Highly Calcareous Parent Materials Under Semiarid Conditions in Iran[END_REF] and silt particles.

4-Minor minerals include iron oxides (e.g., goethite and hematite) and sulfates (e.g., gypsum) mostly present in clay-sized fractions.

Until now, information about dust mineralogy on large spatial scale is still limited.

Due to the lack of airborne dust composition, [START_REF] Journet | A new data set of soil mineralogy for dustcycle modeling[END_REF] used an indirect approach to obtain dust mineralogy on a global scale. The study estimated the global soil mineralogy from 211 soil units. The average mineralogy for the clay and silt fractions from surface topsoil from seven continental dust sources. A general circulation model was used with an aerosol scheme (LMDZ-INCA) for dust emission and transport. The study found that phyllosilicates dominate the clay-sized particles with averages between 63-71%; tectosilicates presented only 3-5 %, while carbonates are between 6-9%.

Whereas the silt-sized are most abundant with tectosilicates and carbonates, no for the two groups. Therefore, using an experimental dust extinction coefficient allows the identification of the mineralogical composition from the IASI optical thickness.

Figure 60 The laboratory Gobi dust spectrum with the molecular assignment for each fingerprint is in black [START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF]. Sym Str: symmetrical stretching; Asym Str: asymmetrical stretching; Bend: bending. The IASI equivalent optical thickness detection of the dust plume on 4 May 2017 at daytime after LSE correction, with mineralogical family assignment, is in blue. Tec: tectosilicates; Phy: phyllosilicates; Carb: carbonates.

Linear Combination Method: Application on IASI Observations

Dust sample collection can be critical as the mineral composition is highly variable in source and time, which can pose a challenge when dust sampling is difficult. To remove this dependency, the linear combination of the pure mineral extinctions of quartz, illite, and calcite, described in Section 3.1.2, was used to obtain the mineralogical extinction fractions. The equality between the IASI spectral optical thickness 𝜏(𝑣) from Equation (25) and the linear combination from Equation ( 26) is solved as follows:

where the total error 𝜖 is the sum of the residue error 𝜖 𝑅𝑒 of gases narrow bands and the error of having other minor families not taken into consideration 𝜖 𝑚𝑖𝑛𝑜𝑟 . The layer thickness L value does not affect the result, as it is considered a constant parameter for each pixel.

In Figure 61, Equations ( 29) and (30) were applied to an example from the May 2017 dust event. The LC method gives 16.8% for tectosilicates, 78.3% for phyllosilicates, and 4.9% for carbonates. The IASI optical thickness shows intense narrow bands of gases, which increase the residue up to 20%.

To remove gases' narrow bands, the Savitzky-Golay method is applied. It performs a polynomial regression to the data points in the moving window [START_REF] Press | Numerical Recipes in C ++ The Art of Scientific Computing Second Edition, Second Edi[END_REF].

Smoothed window points are chosen in a way so it does not remove the IASI sensitivity to aerosols. The smoothing is computed using MATLAB software developed by Math-Works (MathWorks, 2019).

The residue dropped to 5% by removing the gas effect to smooth the fitting, indicating a significant error caused by the gases present in the atmosphere. In addition, the calculated values are close to the percentages obtained in the experimental spectrum.

Therefore, the LC method allowed us to obtain new mineralogical mass weights from satellite IASI detections using laboratory optical properties of major pure minerals. The plume altitude and thickness layer are determined by lidar measurements e.g., CALIOP/CALIPSO shown in Figure 53b.

Figure 67 ARAHIS forward model scheme used to simulate IASI spectra.

123

Influence of the physico-chemical parameters on IASI spectra

Sensitivity on the CRI

The CRI is a crucial parameter because it determines both the contribution of absorption and scattering of the studied particles. Moreover, we have seen previously that, in the spectral domain of thermal IR, the CRI varies according to the chemical composition of the particles (see Section 3.3). Figure 68 shows several simulated IASI spectra of the Gobi dust spectrum with complex refractive indices from the literature (Di Biagio et al., 2014) and [START_REF] Deschutter | Propriétés optiques des poussières désertiques de Gobi et de ses composés purs : spectres d'extinction expérimentaux et détermination des indices complexes de réfraction[END_REF] used in our work. It is important to specify that the spectra were simulated by fixing the gases concentrations, the altitude of the dust layer between 1 and 3 km, the VMR at 4. 30x10 -11 ppm, the mean geometric radius at 0.25 µm. We see a difference in the shape of the spectrum as a function of the CRI. More precisely, if we are interested in the spectral range between 800 and 1000 cm -1 , we observe that the behavior of the spectra is different depending on the complex index of refraction used (left panel). The choice of the optical properties, impact the simulated spectra, and can alter the retrieval results. Therefore, it is important to choose the adequate CRI representing the mineralogical composition of the IASI observation.

Appendix A:

Abstract

L'Asie de l'Est est la deuxième source d'aérosols de type minérale au monde après le Sahara. Celles-ci peuvent modifier le bilan radiatif de la Terre de par leurs propriétés d'absorption et de diffusion du rayonnement atmosphérique. Par conséquent, une information précise des propriétés physico-chimiques des poussières désertiques est essentielle pour comprendre et mieux quantifier leur impact sur l'atmosphère.

Dans ce contexte, l'objectif de cette thèse est de tester le potentiel d'une nouvelle approche méthodologique combinant mesures de laboratoire et de télédétection satellitaire pour une meilleure caractérisation des aérosols issus des surfaces désertiques et semi-arides. L'application de celle-ci porte sur l'étude d'évènements de tempêtes désertiques dans l'Est Asiatique à partir de l'instrument spatial IASI.

Une des difficultés majeures de ce travail vient du fait que la poussière minérale émise dans cette région réside majoritairement au-dessus de la surface continentale et dans la basse troposphère, ce qui pose des contraintes liées d'une part à une faible sensibilité par manque de contraste thermique et d'autre part dues à la grande influence et l'importante variabilité de l'émissivité de la surface terrestre (EST). Ainsi, la première partie de ce travail s'est attachée à améliorer la prise en compte de l'EST en développant une nouvelle méthodologie à partir d'un ensemble de données d'émissivité mensuelle moyenne.

Ensuite, des coefficients d'extinction expérimentaux des minéraux purs ont été combinés linéairement pour reproduire un spectre de poussières de Gobi, ce qui a permis d'obtenir les fractions massiques minéralogique. De plus, à partir des luminances IASI, une épaisseur optique de poussières de Gobi a été calculée, affichant des caractéristiques identiques à celle mesurée en laboratoire. La combinaison linéaire des spectres de minéraux purs a ensuite été appliquée à l'épaisseur optique IASI, fournissant des fractions massiques minéralogiques.

Enfin, l'ensemble de la méthode a été appliqué à deux événements de poussières au cours de deux saisons et années différentes : Mai 2017 et Mars 2021. Ce qui a permis d'obtenir les premières distributions spatiales et temporelles de la composition chimique d'aérosols désertiques. Les fractions massiques moyennes obtenues pour les particules provenant du désert de Gobi, du désert de Taklamakan et de Horqin Sandy Land sont proches des fractions massiques de la littérature. De plus, ces résultats sur la composition chimique ont permis de lier la variabilité spatiale des aérosols aux sources d'émission de poussières, confortée à l'aide d'un modèle de rétro-trajectoires. De plus, une comparaison entre deux instruments IASI sur Metop A et B a montré une bonne cohérence, permettant d'envisager l'applicabilité de la méthode à différentes plates-formes Metop, voir à d'autres instruments du même type. Les résultats exposés dans ce manuscrit représentent une avancée importante quant à l'amélioration de la caractérisation des aérosols désertiques par télédétection spatiale, notamment concernant l'analyse de la composition chimique.