This PDF file contains the front matter associated with SPIE Proceedings Volume 12730, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The EUMETSAT Central Facility retrieves and disseminates several near-real time geophysical products from both geostationary and polar VIS/IR imagers. The primary scope of these missions is to serve Numerical Weather Prediction (NWP), nowcasting and climate monitoring. In this contribution, we focus on the cloud and Water Vapor (WV) imaging products from the new generation EUMETSAT imagers i.e., the Flexible Combined Imager (FCI) on board of Meteosat Third Generation (MTG-I, launched in Dec 2022) and METimage on board EUMETSAT Polar System Second Generation (EPS-SG, expected 2024+). These instruments provide unprecedented spatial resolution (down to 500 m at Nadir), temporal sampling (ten minutes for the geostationary FCI), and wider spectral range (approximately 0.4 μm to 13 μm) including WV (approximately 0.9 μm, 1.38 μm, approximately 6.7 μm, approximately 7.3 μm), O₂ A-band (0.762 μm), and CO₂ (approximately 13.3 μm) absorption channels. We present the retrieval and validation approach chosen for these products and the challenges presented by the near-real time operational processing. We explore, in particular, the expected improvements based on the enhanced instrument’s capabilities (i.e., more accurate cloud detection, layering, altitude and spatial inhomogeneity), while maintaining continuity with the legacy products from their predecessor satellites. In particular, the new approximately 0.9μm channel allows improved daytime estimates of WV amount near the surface. We show preliminary cloud and WV products retrieved from early FCI measurements, including their validation strategy against independent cloud observations from the ground-based ACTRIS network and humidly measurements from IGRA radiosondes.

The Geostationary Environment Monitoring Spectrometer (GEMS) on the GEO-KOMPSAT 2B satellite is the first UV-visible instrument on geostationary platforms for providing optical, chemical, and physical properties of aerosols and atmospheric gases including nitrogen dioxide, sulfur dioxide, ozone, and formaldehyde. The National Institute of Environmental Research (NIER) of the Korean Ministry of Environment operates GEMS and produces, applies, and distributes GEMS data. NIER obtains data from GEMS observations and provides air quality information in temporal resolution of one hour and spatial resolution of 3.5 km × 8 km for most products over Asia. This presentation focuses on air quality studies using GEMS data, which analyze variations in aerosol and gaseous species concentrations over the recent years. The use of GEMS for monitoring exceptional events such as volcanoes and forest fires is also addressed. Additionally, results of applying GEMS Aerosol Optical Depth (AOD) were discussed to calculate aerosol motion vectors and assimilate with Chemistry Transport Model (CTM) data. This presentation highlights the potential of GEMS data for improving air quality monitoring and forecasting.

Cloud detection holds significant importance when analyzing satellite imagery, notably in the visible domain. A wide variety of detection tools already exists for this type of application, using radiometric information. With the aim of enhancing cloud detection, recent advances in deep learning have made it possible to create tools based on pattern recognition while leveraging radiometric data. This paper aims to present a method focused on machine learning, giving details of its construction process, from the creation of the datasets to overall performance. A sample use case is also presented to demonstrate the promising outcomes obtained. Using over 600,000km² of ground-labeled satellite imagery and a U-Net based architecture for our machine learning algorithm, we achieved encouraging performances over various land types. Our results showed significant results when evaluated on the three most frequently used metrics for Image Segmentation. Our product gives interesting results in certain areas that present challenging ground types, such as snowy tops, when using Infrared imagery.

We have developed a new forward model for all sky radiative transfer calculations in the spectral range 10 to 2760 cm⁻¹ . This new code, which we call σ-IASI/F2N, allows us to calculate in all-sky based on an original parametrization of the optical depth of atmospheric gases and clouds. Clouds are represented through the atmospheric profiles of Liquid Water Content (LWC), Ice Water Content (IWC), and effective radii of both water droplets (re) and ice crystals (De). The cloud parametrization relies on suitable scaling laws, which make the radiative transfer equations for a cloudy atmosphere identical to those for a clear atmosphere. Therefore, the difficulties in applying a multiple-scattering algorithm to a partly cloudy atmosphere are avoided, and the computational efficiency is practically the same as that for a clear atmosphere. The new radiative transfer code has been coupled with an inverse scheme based on the Optimal Estimation methodology. The problem of dimensionality of the data and parameter space is handled by considering suitable transforms, which allow the representation of the radiances (data space) and the atmospheric state vector (parameter space) through a set of reduced components. The dimensionality is diminished through the random Projections transform for the radiance space, whereas we use the usual Principal Component Analysis for the parameter space. The scheme’s performance has been evaluated using the Infrared Atmospheric Sounder Interferometer (IASI) spectral radiances. The soundings are collocated with analyses from the European Centre for Medium-Range Forecasts (ECMWF) model. The ECMWF analyses provide the basic information, i.e., the first guess state vector to initialize the inverse scheme. The forward/inverse technique uses the whole IASI spectral coverage (645 to 2760 cm⁻¹ ). As such, it is the first scheme for all sky using the full IASI spectrum to retrieve clouds and atmospheric parameters simultaneously. This new forward/inverse model is exemplified through the analysis of a set of IASI soundings over the Antarctica continent on 9 September 2021 at the onset of the ozone hole. We will show that infrared retrievals add information to assess ozone’s spatial extent and depletion.

Aerosols are significant atmospheric constituents that modulate radiation and cloud processes. We evaluated 17-year aerosol profile trends in Barcelona, Spain, from lidar measurements. In summer aerosol reaches 5 km, while in the other seasons it exhibits clear exponential decay. Sahara dust transport affects all seasons, with winter layers above and others penetrating the boundary layer. This study informs the formation of haze and urban preservation strategies in the Mediterranean. The analysis puts in evidence that the averaged net radiative effect is of cooling at both surface level and top of the atmosphere.

The Freezing Level Height (HFL) is an essential parameter in meteorology, aviation, and in studying the rain-induced attenuation and cross-polarization. It is the level in the troposphere, where the temperature is 0 °C. The knowledge of HFL is also necessary to know the position of the radar bright band, which is of immense importance in radar meteorology and satellite communication. The measured HFL at several locations shows disagreement with those estimated using the ITU-R model. In this study, the authors wish to establish a model for the HFL. To perform this study, they obtained these values from the Integrated Global Radiosonde Archive (IGRA) over several locations in 35N-35S. To find the parameters that control the HFL, they chose a few location-centric parameters, viz. latitude (θ), height from the mean sea level (H), surface pressure (SP), surface temperature (ST), and relative humidity (RH). They investigated their correlations. They performed the study in three steps. At first, they studied the dependence of HFL on θ alone. Next, they studied its dependence as a function of θ and H. Next, they investigated its dependence under the simultaneous presence of ST, SP, RH, θ, and H. The paper presents the functional relationships of HFL with these parameters and suggests the most significant relations as the location-centric models of HFL. The models so established are used to estimate the HFL at some other geolocations and validated against the measured values obtained from the IGRA. The authors compare the estimated HFL values with those obtained using the ITU-R model. The authors compare the HFL values obtained from the precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite with those obtained from the ITU-R model over a few locations in India.

The mixing of air layers, wind shear components, mountain waves, aerosol particles, and other pollutants causes rotational turbulence that affects the lowest and densest layer of the earth’s surface troposphere. This turbulence results in a significant change in the height of the Convective Boundary Layer (CBLH) over a day. To analyze the behavior of the convective boundary layer, a statistical technique is used to observe the variation in peak positions of Light Detection and Ranging (LiDAR) back-scatter signals. Furthermore, a statistical method is provided to obtain the best-fit distribution to demonstrate how the result leads to the physical observation of the data. This method involves selecting a suitable distribution for the dataset and defining the percentile bins to use a specific range of the data. The observed frequencies and expected frequencies are then calculated to formulate the chi-square statistics, which are used to determine the fitness of the distribution. Finally, a histogram with the best-fit distribution curve is plotted to examine whether the formulation of the function is appropriate. This approach provides a comprehensive understanding of the behavior of the entire boundary layer and can be used to inform further analysis and interpretation of the data.

Thermal infrared data of Sustainable Development Goals Satellite 1 (SDGSAT-1) with 30-m spatial resolution was one highest resolution data. The accurate recognition of cloud on SDGSAT-1 thermal infrared data is becoming vitally important for thermal infrared target detection in lager areas. In this paper, a new cloud detection method was proposed using SDGSAT-1 thermal infrared data. First, a new cloud index was devised based on SDGSAT-1 thermal infrared data and muti-features were extracted. Then, cloud samples and non-cloud samples were set up using SDGSAT-1 thermal infrared data and high-resolution satellite remote sensing data. Lastly, Support Vector Machine (SVM) was introduced to detect cloud mask. The results show that the new method can detect cloud mask on SDGSAT-1 thermal infrared data.

A purpose of this paper is to present comparison of unfiltered radiances measured by CERES scanners when their scanning is aligned in the plane orthogonal to the solar plane and targeting Greenland around the time of summer solstice. Since the summer solstice in 2002, FM1 on Terra and FM4/FM3 on Aqua have been scanning in the minor plane for the month of June to collect comparison data. Measurements of FM5 on S-NPP and FM6 on NOAA20 have been added to this dataset starting in 2012 and 2018, respectively, with the data collection extended from May to July due to orbit characteristics. This unique dataset presents one-of-a-kind opportunity to compare radiances measured from four different platforms to assess CERES calibrations, particularly the consistency of their shortwave channel, as the solar zenith angles are the smallest for high latitudes producing relatively strong SW signal over ice/snow/clouds of Greenland. Results of comparison reported in this paper will be used in work to release new and updated versions, Edition 5, of ERBE-like (ES8) data products, for Terra and Aqua scanners.

Exploiting the Infrared Atmospheric Sounder Interferometer (IASI) profiling capability for surface parameters, atmospheric temperature, and water vapour we have designed a new Water Deficit Index (wdi) to monitor drought and heatwaves. Because of climate change at a global level, drought is becoming a strong emergency also in countries which never experienced it, such as the Mediterranean mid-latitude area and, in particular, Italy. The last two years strongly affected the northern part of Italy, i.e. the Po Valley, causing high vegetation and soil water stress. Satellite data can provide a large spatial coverage (locally and globally) as well as a continuous data supply and are an important help to ground monitoring stations, especially in remote regions with dense vegetation. In this paper, we used the wdi to investigate the 2022 intense drought over the Po Valley region. We integrated the study considering both the Surface Soil Moisture (SSM) from Copernicus Sentinel-1 C-SAR and the Normalized Difference Moisture Index (NDMI) from Sentinel-2 images. We also considered the Fractional Vegetation Cover (FVC), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and the Leaf Area Index (LAI) data from the Drought & Vegetation Data Cube (D&V Data Cube) from the European Organization for the Exploitation of Meteorological Satellites - Satellite Application Facilities (EUMETSAT SAFs). Overall, we found that the wdi compares well to other indices related to vegetation stress and can be used as a tool for risk assessment of forest fires and agriculture productivity.

Surface and vegetation monitoring is a key activity in analyzing and understanding how climate change is impacting natural resources. Moreover, identifying vegetation stress using remote-sensed data has proven to be essential in assessing said understanding, as well as in the effort to prevent or act upon extreme phenomena, such as premature land and forest dryness due to summer heatwaves in the Mediterranean area. Typically used satellite indices for this purpose are the well-known NDVI, followed by Leaf Area Index (LAI) and Surface Soil Moisture (ssm), together with physical parameters such as surface and air temperature close to the surface (the latter retrieved by both remote-sensed data and in situ measurements). However, it is a known fact that NDVI is not able to differentiate between barren soil and suffering vegetation, while surface temperature and air temperature correlate poorly with soil moisture. The analysis carried out in this paper is aimed at proving the effectiveness of two newly designed thermodynamical indices, ECI and wdi, in assessing vegetation stress and woodland degradation in southern Italy between 2014 and 2022. ECI is based on infrared surface emissivity, which is closely related to land cover, while wdi directly measures surface water loss. Said indices have been estimated from both ECMWF operational analysis and IASI L2 data, the latter upscaled and remapped on a regular grid using an optimal interpolation scheme. Moreover, a comparison with other traditional indices is presented, further validating the applied methodology.

Understanding the tropospheric diabatic heating is essential for predicting Earth’s weather and climate. To reach that goal, information on Water Vapor (WV) in the atmosphere is mandatory. Unfortunately, monitoring WV is challenging, because of its high variability in both space and time. Particularly, advanced knowledge and modeling of its fine scale behavior would be beneficial to improve forecasting applications. In this contribution, we propose to compare and discuss the spectral content of dataset from two instruments recording WV content of the atmosphere, focusing on small scales: Ocean Land Color Instrument (OLCI) on board of Copernicus Sentinel-3 and Zenith Wet Delay (ZWD) retrieved from Global Navigation Satellite System (GNSS) observations. Kolmogorov‘s theory states that the structure function of passive scalar, or equivalently the temporal power spectrum (assuming Taylor frozen turbulence), should follow a given power law within the inertial range. Using the von Karman modelling, it is possible to assess the outer scale length of turbulence defined as the frequency where the spectrum saturates. For our analysis on WV small scales, we have selected a region around Lindenberg in Germany. We will discuss the spectral content of the retrieved observations and their specificity. We will highlight the potential of ZWD from GNSS observations to study daily variations of turbulence parameters.

High-resolution humidity observations in the lower atmosphere are crucial to an accurate regional weather forecast. However, current remote sensing technologies are unable to adequately capture the full four-dimensional distribution of water vapour. For this reason, we propose the use of simple interferometers to measure the bending due to atmospheric refraction of Automatic Dependent Surveillance-Broadcast (ADS-B) radio signals routinely broadcast from commercial aircraft for air traffic control purposes. Variations in atmospheric refraction are strongly influenced by changes in humidity, which could allow detailed profiles of atmospheric water vapour to be constructed from numerous bending angle measurements. We present some early results from a prototype interferometer and explore how existing methods currently used to assimilate bending angles derived using radio occultation sounding could be adapted to assimilate observations of ADS-B refraction events. With thousands of flights across UK airspace every day and potentially modest instrument costs, there is the opportunity for millions of bending angle observations daily.

We study the spatial spectra of aerosol density fluctuation from the spectra of the fluctuations of the scattered signals acquired in experimental flights of the all-European scientific project DELICAT (DEmonstration of LIdar based Clear Air Turbulence detection). We selected flight segments with constant altitude, direction and speed of the aircraft. The signal is represented as a function of two coordinates: the distance from the aircraft to the scattering volume – the path of the aircraft relative to the air mass, obtained by integrating the airspeed. Then aerosol clouds are visualized as bands inclined at an angle of 45 degrees. We evaluate two-dimensional spatial spectra of aerosol density fluctuations. In these spectra pronounced ridges along the diagonal are observed. The diagonal cross-section of the spectra corresponds to one-dimensional spatial spectra along the line of sight. A possible relation between the atmospheric density spectra and the aerosol density spectra will be the subject of the further study.

In this work, we will show the potential of a nonlinear statistical regressor method based on a Deep Neural Network (DNN) scheme for retrieving XCO₂. Toward this objective, we set up a training exercise based on simulated IASI observations using the state-of-the-art radiative transfer mode (RTM) σ-IASI/F2N. A nine-year-long record from 2014 to 2022 of atmospheric state vectors using CAMS reanalysis dataset from ECMWF related to one day of each month at four synoptic hours (00-06-12-18 UTC) has been processed to capture typical seasonal and diurnal cycles, resulting in about 400,000 of IASI-L1 synthetic spectral radiances. In order to provide the regression scheme with the most representative information on the CO₂ signature, we implemented principal component analysis (PCA) of different regression features. Specifically, the PCA transform was applied to IASI band-1 (645-1210 cm^-1), which is most affected by CO₂ absorption, and to atmospheric temperature profiles. For IASI measurements the base of 90 principal components from the EUMETSAT IASI Level one Principal Component Compression (PCC) has been considered. Finally, different locations at various latitudes were selected to validate and evaluate the retrieval scheme's performance. In terms of validation, a set of real IASI soundings was matched with in situ measurements collected at Mauna Loa station, renowned as a background site with minimal regional impact. Preliminary findings demonstrate a high level of accuracy in extracting growth rate, trend, and seasonality from the predictions, showing a correlation greater than 0.9 with the in-situ data.

We report on the design of a dual comb-based fiber lidar system for Integrated Path Differential Absorption (IPDA) measurements. The system relies on the Dual Comb Spectroscopy (DCS) technique using the lidar return from a hard target. Frequency combs are generated by means of electro-optic modulators. The working principle and architecture of the dual comb lidar are detailed. We present a proof of concept of the lidar system and demonstrate the measurement of a water-vapor absorption line, utilizing a diffuse (non-cooperative) hard target located 200 meters away from the emitter.

A limb-viewing spatial heterodyne interferometer is developed to observe temperature in the mesosphere and lower thermosphere. This can be used to measure atmospheric waves with small vertical wavelengths. The instrument measures the O₂ atmospheric A-band airglow emission in the near-infrared. The emission is visible during day- and night-time, allowing for a continuous observation. The image is taken by a 2d detector. The optical system conserves the 2d spatial temperature information. The spectral information is superimposed in horizontal detector direction. The usual processing thus uses the horizontal detector dimension to resolve the spectral while averaging the underlying spatial information. The altitude coverage is given by the vertical detector direction, resulting in a finely resolved vertical temperature profile for one image. In light of this, we explore a novel processing approach that exploits the spatial information along the horizontal axis as well. We propose to split the interferogram into two halves, mirror it around the center and perform a retrieval on both sides separately, obtaining two spatial cross tracks of independent temperature data. Assuming that the instrument views backward, consecutive measurements give along track sampling. Combining this with the split interferogram method and the usual fine vertical resolution of the instrument, it provides 3d information on the atmospheric temperature field which allows to obtain some information on 3d propagation characteristics of atmospheric waves. In our research, we delve into the viability, advantages and constraints of the split interferogram approach. We will discuss the impact of horizontal temperature variation onto the retrieval result. We show the impact of background temperatures on the retrieval. Furthermore, we discuss the influence of apodization onto the retrieval of split interferograms.

Cloud coverage constitutes a huge problem for Earth observation satellites constellations. It leads to a significant proportion of unusable images that have to be rescheduled, which represents both a waste of time and money. Agile targeting systems combined with satellite planning optimization and weather forecasting allow to minimize the number of cloudy images. As demonstrated earlier by the authors, the computational efficiency of optical flow forecasting approaches allows to build plans with up-to-date forecasts with good spatial and temporal resolutions. This approach, developed and implemented in the field of view of a unique geostationary satellite, is in this work evaluated at worldwide scale by fusion of several geostationary satellites’ fields of view. Using a specific simulation framework, we evaluated the efficiency of this method against a more classical Numerical Weather Prediction model for 24 hours scenarios. Results showed that the optical flow method allows to reduce the rejection proportion of such scenario from thirty to forty percents.

Wind speed measurement with on-board system has many applications in aeronautics (Gust Load alleviation, Haps, etc.) and space (Weather forecast). The molecular wind lidar is developed for those purposes as it sent laser pulses into the atmosphere to determine, with a spectral analyzer, the wind speed from the Doppler shift induced by the molecules of the atmosphere. In this paper we present the lidar architecture developed at ONERA, that uses a Quadri Mach-Zehnder (QMZ) as a spectral analyzer and a UV fiber laser, designed for gust load alleviation application. We discuss about the advantages of such architecture for wind measurement from space. Simulations of the performances have been performed in the case of Calibration/Validation (Cal/Val) of Aeolus, showing standard deviation on wind speed measurement less than 2 m/s up to 17 km of altitude for the optimized hybrid fiber laser of 10 W laser average power and a Pulse Repetition Frequency (PRF) of 5 kHz. Simulations that evaluate the performances for Aeolus measurement with minor changes in the lidar architecture have been computed, with results showing that requirements are fulfilled up to 22.5 km of altitude with the optimized hybrid fiber laser of 10 W and 3 kHz PRF.

The paper presents a method for investigating temperature fields in space and transparent media using off-axis digital holographic interferometry. This interferometric technique is particularly well-suited for measuring dynamically changing temperature fields due to its capability to automatically evaluate temperature from a single interferogram. The experimental validation of this technique involved measuring temperature variations within a burning candle's flame. To establish a reference point, an initial hologram was captured without a burning candle, followed by subsequent holograms during the burning process. This allowed the observation of changing refractive index gradient states in the surrounding air. The reconstruction of the complex amplitude facilitated the calculation of the distribution of phase changes. By establishing a relationship between phase changes, thermal coefficients of the air's refractive index, and temperature fluctuations, temperature measurements were achieved at these distinct states. The accuracy of the measurement was estimated to be less than 1 °C in our experimental setup, showing the high precision achievable with this technique. In summary, the paper offers analysis of digital holographic interferometry as a tool for temperature measurements in transparent media. Its potential applications extend to combustion studies, space exploration, atmospheric research, and various other scientific disciplines.

Open burning of biomass occurs in many parts of the world and is a major environmental problem. This is because biomass combustion is a major source of greenhouse gases, reactive trace gases, and particulate matter emissions into the atmosphere. Emissions from combustion of biomass have the potential to impact local, regional, and global air quality issues and climate change. Satellite information on fire activity and vegetation productivity has been combined to create a data set of gas and aerosol emissions from fires. We used these emission data to obtain aerosol distributions of open burning origin by using a regional chemical transport model simulation. This study targets severe biomass burning aerosols in Sumatra Island in September 2019. We simulated the meteorological fields required for offline calculations of chemical transport models with the SCALE (Scalable Computing for Advanced Library and Environment) regional model. Simulation results were validated with biomass burning aerosol distributions derived from JAXA/GCOM-C/Second Generation Global Imager (SGLI) and aerosol optical thickness from the NASA/AErosol RObotic NETwork (AERONET). The biomass burning aerosol distribution was found to be well reproduced, but there was an underestimation in aerosol volume.

The Japanese space mission, JAXA/GCOM (Global Change Observation Mission-Climate)-C (SHIKISAI in Japanese), was launched in 2017, carrying the Second-Generation Global Imager (SGLI). The SGLI performs wide-swath multispectral measurements in 19 channels from near-ultraviolet to thermal infrared (IR), including red (674 nm designated as the PL1 band) and near-IR (869 nm; PL2 band) polarization channels. This work presents retrieval of Severe Biomass Burning Aerosols (SBBAs) generated by severe wildfires using the advantage of SGLI features. Namely, it is shown that simultaneous observation of polarization and radiance is useful not only for retrieval of optical properties but also vertical variation of SBBAs. The obtained results are validated by comparison with a meteorological regional model CTM.

The increase in concentrations of major atmospheric pollutants such as NO₂, CO, CH₄ as result of human activities is one of the main causes of the dynamic climate changes observed in recent years. These rapid changes have a strong influence on air quality at local and global levels and directly affect human health. This is one of the main reasons for faster global warming. The concentration of methane in the atmosphere is increasing at an accelerating rate. Three sectors are responsible for most anthropogenic CH₄ emissions: fossil fuels, waste and agriculture. Locating, tracking and quantifying all these emissions is an important step towards a more accurate inventory. The use of satellite observations rises at a new label the monitoring process and improves the accuracy of emissions reporting. Medium-resolution satellite data, such as that provided by the TROPOMI sensor on the European Sentinel-5P satellite, is a powerful tool for detecting and tracking large emissions of air pollutants. The methodology presented here enables us to determine background concentrations of CH₄, NO₂, CO relatively quickly and efficiently. It improves our ability to quickly detect periodic or occasional emissions from unregulated sources, track seasonal and annual variations in concentrations of these air pollutants, etc. Hundreds of cases of high methane, NO₂ and CO emissions in coal mining areas have been registered using this methodology. The method is also applicable to lower-intensity emission sources, such as landfills, agriculture or recording methane emissions from wetlands.

In recent years a steady trend of increasing concentrations of major air pollutants is observed. The nature and dynamics of this trend vary according to the type of pollutant, source of emissions, and location. Because of these differences, it is important to comprehensively analyze the spatial and temporal behavior of the most important air pollutants using satellite and ground-based measurement data. An important step in this process is locating, tracking, and quantifying the emissions. This paper presents the results of air pollution monitoring based on the analysis of data obtained from 32 Ground-based Automatic Measuring Stations (GAMS) located throughout Bulgaria. The spatial and temporal behavior of major air pollutants such as NO, NO₂, SO₂, CO, and benzene for the period 2015 - 2022 was investigated. However, not all GAMS have data for all types of pollutants. The largest amount of information is available for SO₂ and NO₂, while small numbers of GAMS provide data for CO. For pollutants such as NO₂, SO₂, and CO an analysis with satellite data from the European Sentinel-5P satellite was performed. Due to the uneven distribution of the available information from ground measurements, the spatial behavior of the pollutants studied is presented using a unified methodology for selected regions. Monthly and annual average data were also analyzed in our study.

Clouds are essential in climate, especially to evaluate the radiative balance in the Earth atmosphere and, their contribution depends on the type of cloud. In addition, cloud classification plays an important role in the development of different research and technological fields such as solar photovoltaic energy. We use ground-based zenith observations of Cloud Optical Depth (COD) and Cloud Base Height (CBH), at one-minute intervals, to develop a clustering algorithm. It is based on non-supervised machine learning using k-means function. Due to the intrinsic characteristics of the measuring instruments, high-altitude clouds with large COD are not accurately represented. For this reason, a classification into six categories is performed. Regarding to COD, our machine learning method detects three COD clusters separated at 3.2 and 24.5. On the other hand, the three CBH clusters well identify low-, mid- and high-clouds, with centroids around 1500 m, 5399-6240 m, and 9589 m, respectively. A slight increase in these CBH boundaries with COD is also observed. Our clustering method is consistent and robust since it does not present any sensitivity regarding to the temporal window used to perform the clustering. The resulting clusters are consistent and in line with the cloud classification established by the WMO.

Aerosols and clouds have been scientifically considered distinct from theoretical point of view. The two components share the same physical meaning, namely particles suspended in the air, even if the chemical or physical features can differentiate. Form the radiometric view it has been a challenging task to separate the two atmospheric components in an exact way. Recently scientists are also discussing about a continuum between aerosols and clouds. In this study we use calibrated images of an all-sky camera with the aim of exploring the features that can differentiate the two regions in terms of radiometric magnitudes. An intense smoothing is applied, and the spatial derivatives are performed on the red channel radiances. These derivatives are almost zero in the cloud-free area and sensibly different from zero in the rest of the image. Applying dynamic thresholds to Blue-to-Red Ratio (BRR), we further determine the cloudy region of the sky. Then, we define aerosol-cloud transition zone as the non-cloudy sky zone with intense directional derivatives of the radiances in the red channel. This transition zone shows different radiometric characteristics with respect to cloud and cloud-free regions, for example in terms of BRR distribution of the pixels.