Remote Sensing of Clouds and Aerosols: Techniques and Applications


Remote sensing of clouds and aerosols is of central importance for studying climate system processes and changes. Reliable information is required on climate-relevant parameters such as aerosol and cloud optical thickness, layer height, particle size, liquid or ice water path and vertical particulate matter columns. A number of challenges and unsolved problems remain in algorithms and their application. This includes remote sensing of clouds and aerosols with respect to 3D effects, remote sensing of polluted and mixed clouds, combination of ground-based and satellite-based systems, and the creation of long-term uniform global records. This session is aimed at the discussion of current developments, challenges and opportunities in aerosol and cloud remote sensing using active and passive remote sensing systems.

Convener: Jan Cermak | Co-conveners: Virginie Capelle, Gerrit de Leeuw, Alexander Kokhanovsky
vPICO presentations
| Thu, 29 Apr, 14:15–17:00 (CEST)

vPICO presentations: Thu, 29 Apr

Chairpersons: Jan Cermak, Virginie Capelle
Cloud system analysis
Robert Levy, Lorraine Remer, Kerry Meyer, and Yaping Zhao

The global aerosol system of today is not the same as it was two decades ago when Terra and Aqua were launched.  As a result of a changing climate (natural and anthropogenic) convolved with changes in human activity (deliberate and accidental), some regions have experienced significant changes to their total aerosol burden (increases or decreases of total loading) or their aerosol composition (as defined by relative size or source type).  Other regions have had less or no significant changes.   At the same time, changes in aerosol amount and composition affect clouds through direct and indirect microphysical and radiative processes.  We can theoretically predict what might happen to clouds when you add aerosol to an otherwise pristine environment. Conversely, there is the situation of removing aerosol from a polluted environment. 

Over the past two decades, sensors on both Earth Observing Satellites (Terra and Aqua) have observed the radiative signatures of aerosols and clouds, as well as their trends.   Via massive efforts by their respective characterization teams, the resulting data records appear to have a minimum of artificial drifts.  Therefore, we are trying to assess, region by region, our 20-year records of aerosols and clouds, along with meteorological variables. Where have been the most significant changes of aerosols and/or clouds?  Where do the changes in clouds conform with expectations based on changes of aerosols and meteorology and where do they not?  In addition to separate ‘aerosol’ and ‘cloud’ retrievals from the Moderate Resolution Imaging Spectrometer (MODIS), there is a ‘twilight zone’ that is not accounted for in either product. What are these regions, and have they changed over the past two decades?  We will present our early efforts at characterizing the MODIS view of aerosol and cloud changes, while also relating to changes in radiative fluxes at the top-of-atmosphere (TOA) from corresponding observations by the Clouds and the Earth's Radiant Energy System (CERES).  

How to cite: Levy, R., Remer, L., Meyer, K., and Zhao, Y.: Relating changing aerosols to changing clouds: two decades of Terra and Aqua observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8509,, 2021.

Vasileios Tzallas, Anja Hünerbein, Hartwig Deneke, Martin Stengel, Jan Fokke Meirink, Nikos Benas, and Jörg Trentmann

The improvement of our understanding of the spatiotemporal variability of cloud properties and their governing processes is of high importance, given the crucial role of clouds in the climate system. The availability of long-term and high-quality satellite observations together with mature remote sensing techniques has made feasible the creation of multi-decadal climate data records for this purpose.

Various cloud classification techniques have been developed and applied in the past, each with distinct advantages and disadvantages, allowing studying clouds from different perspectives. One of these techniques is the creation of cloud regimes which provides information on the prevalence of simultaneously occurring cloud types over a region. This study uses the k-means clustering method, applied to 2-dimensional histograms of cloud top pressure and optical thickness, in order to derive and analyze cloud regimes over Europe during the last decade. Europe is selected for this work because it is an appropriate region for studying cloud regimes since the prevailing atmospheric circulation patterns and its diverse geomorphology, result in a mixture of diverse cloud types. In order to achieve that, the CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), is used as basis for the derivation of the cloud regimes. In particular, pixel-level Cloud Optical Thickness (COT) and Cloud Top Pressure (CTP) products of CLAAS-2.1, from 2004 to 2017, are used in order to compute 2D histograms on a 1°×1° spatial resolution. Then the k-means clustering algorithm is applied, treating each 2D COT-CTP histogram of each grid point and time step as an individual data point. Various sensitivity studies on the subsampling of the data and the selection of the cloud regimes were carried out, in order to test the robustness of the method and of the results.

In contrast to the previous studies and taking advantage of the geostationary orbit of Meteosat Second Generation (MSG), on which SEVIRI is aboard, a better sampling of the diurnal cycle of clouds is thus included in the derivation process of cloud regimes. Furthermore, the annual cycle of the produced cloud regimes is examined. In addition, for each regime, the time step with its highest spatial frequency of occurrence is selected for a visual comparison with the corresponding RGB image. Finally, a comparison of the cloud regimes against the synoptic large scale weather pattern classification is investigated. The weather pattern classification consists of 29 typical defined patterns of the daily synoptic circulation and it is produced by the German Weather Service (DWD).

How to cite: Tzallas, V., Hünerbein, A., Deneke, H., Stengel, M., Meirink, J. F., Benas, N., and Trentmann, J.: Cloud regime analysis over Central Europe based on 14 years of satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5961,, 2021.

Jan H. Schween, Ulrich Löhnert, and Sarah Westbrook

Marine stratocumulus clouds of the eastern Pacific play an essential role in the Earth's energy and radiation budget. Parts of these clouds off the west coast of South America form the major source of water for the Atacama, a hyper-arid area at the northern coast of Chile. Within the DFG collaborative research center 'Earth evolution at the dry limit', for the first time, a long-term study of the vertical structure of clouds and their environment governing the moisture supply to the coastal part of the Atacama is available.

Three state of the art ground based remote sensing instruments were installed for one year at the airport of Iquique/Chile (20.5°S, 70.2°W, 56m a.s.l.) in close cooperation with the Centro del Desierto de Atacama (Pontificia Universidad Católica de Chile). The instruments provide vertical profiles of wind, turbulence and temperature, as well as integrated values of water vapor and liquid water. The cloudnet algorithm is used to exploit instrument synergy and provides vertical cloud structure information.

We observe a land-sea circulation with a super-imposed southerly wind component. Highest wind speeds can be found during the afternoon. Clouds show a distinct seasonal pattern with a maximum of cloud occurrence during winter (JJA) and a minimum during summer (DJF). Clouds are higher and vertically less extended in winter than in summer. Liquid water path shows a diurnal cycle with highest values during night and morning hours and lowest values during noon. Furthermore, the clouds contain much more liquid water in summer. The turbulent structure of the boundary layer, together with the temperature profile, can be used to characterize the mechanism driving the cloud life cycle.

How to cite: Schween, J. H., Löhnert, U., and Westbrook, S.: Stratocumulus Clouds at the West Coast of South America: Observations of Diurnal and Seasonal Cycle, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9532,, 2021.

Torsten Seelig, Felix Müller, Hartwig Deneke, and Matthias Tesche

In our study, we track shallow/warm marine cumulus clouds in the trade wind zone centred around the Canary Islands in August 2015. Tracking was performed in the CLAAS-2 data record (CM SAF CLoud property dAtAset using SEVIRI, [1]) which is based on time-resolved geostationary measurements with the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation. The retrieval of cloud trajectories allows for the calculation of the cloud lifetime distribution, the horizontal cloud size distribution and to characterize temporal changes in cloud properties. Cloud physical properties are available in the daytime. Filtering for daytime and low-level clouds we found about 65 thousand trajectories. For the considered period and domain, the lifetime distribution follows a power law. Most frequent are clouds which live on a time scale of tens of minutes. In the horizontal cloud size distribution, we detected two intervals following an exponential law but with different scaling. The first interval includes cloud sizes smaller than 30 km2 and the second interval includes cloud sizes equal to or larger than 30 km2 but smaller than 1000 km2. Clouds having a mean horizontal cloud size of approximately 30 km2 are most frequent. Furthermore, we present time series’ of cloud physical properties, as cloud droplet effective radius at cloud top re, cloud optical thickness, cloud water path and cloud droplet number concentration. For comparison of the trajectories, we choose re as a measure. If re reaches a certain value the trajectories have been centred at this specific relative time.

[1] Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., Meirink, J. F.: The MSG-SEVIRI-based cloud property data
record CLAAS-2. Earth System Science Data 9(2), 415–434 (2017). DOI 10.5194/essd-9-415-2017

How to cite: Seelig, T., Müller, F., Deneke, H., and Tesche, M.: Quantifying development of shallow/warm marine cumulus clouds from geostationary observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10068,, 2021.

Jihu Liu, Minghuai Wang, Daniel Rosenfeld, and Yannian Zhu

Proper observation of global warm rain and understanding of its formation processes can significantly advance our understanding on aerosol-cloud-precipitation interactions. Previous study shows that due to smaller cloud effective radii (Re), rain from liquid clouds over land is sharply reduced compared to oceans (Mülmenstädt, 2015). However, in our study, we use A-Train satellite observations to show that there should be smaller land-sea difference on probability of precipitation (POP) of warm clouds between land and oceans. The discrepancy is probably because the algorithm bias in CloudSat precipitation flag products over land, which may mistakenly treat drizzle as no rain. We also find that if Re is smaller than 14 mm, no matter how thick the warm cloud is it can hardly produce significant precipitation (here defined as radar reflectivity factor lager than 0dBZ), which can generate dynamic feedback on the development of clouds.

How to cite: Liu, J., Wang, M., Rosenfeld, D., and Zhu, Y.: Smaller land-sea contrast on probability of precipitation (POP) of warm clouds over globe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14533,, 2021.

Alyson Douglas and Philip Stier

Cloud processes are the leading source of uncertainty in our current global climate models. Therefore understanding cloud formation, lifetime, and decay remains pivotal in order to reduce uncertainty in our global climate models future projections. Exploiting over ten years of satellite observations, the relationships between cloud properties and environmental factors, including aerosols, can be better understood and clustered into environmental regimes. We cluster regimes based on the regional strength of the relationships between the environment and cloud properties revealed using a random forest. Numerous processes, such as stratocumulus to cumulus transitions, may be constrained by the environmental regimes revealed by our analysis. Our results show that depending on the region, aerosol and the environment work to determine the baseline cloud properties. These observation based regimes can be compared to regimes derived from global climate models to understand how well model parameterizations capture the cloud controlling factors.

How to cite: Douglas, A. and Stier, P.: Using the learnings of machine learning to distill cloud controlling environmental regimes from satellite observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16443,, 2021.

Pragya Vishwakarma, Julien Delanoë, Christophe Le Gac, Fabrice Bertrand, Jean-Charles Dupont, Martial Haeffelin, Pauline Martinet, Frédéric Burnet, Christine Lac, Alistair Bell, Damien Vignelles, Felipe Toledo, Susana Jorquera, and Jean-Paul Vinson

Transportation especially aviation sector all around the world is severely hindered due to Fog and hence observations and specific research for fog is necessary. The SOFOG3D (SOuth west FOGs 3D) experiment took place in South-West of France which is particularly prone to fog occurrence, during the period between November 2019 to March 2020 with primary objective to advance our understanding of fog processes and to improve fog forecast. Simultaneous measurements from various remote sensing instruments like BASTA: a 95 GHz cloud radar with scanning capability, HATPRO Microwave radiometer (MWR), doppler lidar, and balloon-borne in-situ measurements were collected to characterize the spatio-temporal evolution of Fog. On the supersite, detailed measurements of meteorological conditions, aerosol properties, fog microphysics, water deposition, radiation budget, heat, and momentum fluxes are collected to provide 3D structure of the boundary layer during fog events. The improvement in the retrieval of fog parameters and understanding of fog dynamics based on cloud radar and microwave (MWR) synergy will be addressed. We will present our work on the retrieval of key fog parameters like dynamics and microphysics using a combination of cloud radar and MWR observations. The retrievals will be validated with the tethered-balloon and radio-sounding observations. In-situ measurements and remote-sensing retrievals of fog microphysical properties will be compared. We will show a detailed analysis of retrieved LWP derived from BASTA radar only with LWP derived from HATPRO microwave radiometer, considering instrumental uncertainty and sensitivity. A closer analysis of the in-situ data (measured by granulometer) will be presented in order to assess and improve the retrieval derived with cloud radar in vertically pointing mode. Radar attenuation will be quantified by measuring the backscattered radar signal on well-known calibrated reflectivity metallic targets installed at the top of 20 m mast. The integrated attenuation along the radar beam path will be measured by the cloud radar and used as a new constraint to improve the microphysical properties.

How to cite: Vishwakarma, P., Delanoë, J., Le Gac, C., Bertrand, F., Dupont, J.-C., Haeffelin, M., Martinet, P., Burnet, F., Lac, C., Bell, A., Vignelles, D., Toledo, F., Jorquera, S., and Vinson, J.-P.: Fog Analysis during SOFOG3D Experiment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9941,, 2021.

Chairpersons: Gerrit de Leeuw, Alexander Kokhanovsky
Cloud retrievals
Andrzej Kotarba and Mateusz Solecki

Vertically-resolved cloud amount is essential for understanding the Earth’s radiation budget. Joint CloudSat-CALIPSO, lidar-radar cloud climatology remains the only dataset providing such information globally. However, a specific sampling scheme (pencil-like swath, 16-day revisit) introduces an uncertainty to CloudSat-CALIPSO cloud amounts. In the research we assess those uncertainties in terms of a bootstrap confidence intervals. Five years (2006-2011) of the 2B-GEOPROF-LIDAR (version P2_R05) cloud product was examined, accounting for  typical spatial resolutions of a global grids (1.0°, 2.5°, 5.0°, 10.0°), four confidence levels of confidence interval (0.85, 0.90, 0.95, 0.99), and three time scales of mean cloud amount (annual, seasonal, monthly). Results proved that cloud amount accuracy of 1%, or 5%, is not achievable with the dataset, assuming a 5-year mean cloud amount, high (>0.95) confidence level, and fine spatial resolution (1º–2.5º). The 1% requirement was only met by ~6.5% of atmospheric volumes at 1º and 2.5º, while more tolerant criterion (5%) was met by 22.5% volumes at 1º, or 48.9% at 2.5º resolution. In order to have at least 99% of volumes meeting an accuracy criterion, the criterion itself would have to be lowered to ~20% for 1º data, or to ~8% for 2.5º data. Study also quantified the relation between confidence interval width, and spatial resolution, confidence level, number of observations. Cloud regime (mean cloud amount, and standard deviation of cloud amount) was found the most important factor impacting the width of confidence interval. The research has been funded by the National Science Institute of Poland grant no. UMO-2017/25/B/ST10/01787. This research has been supported in part by PL-Grid Infrastructure (a computing resources).

How to cite: Kotarba, A. and Solecki, M.: Accuracy assessment of the joint CloudSat-CALIPSO global cloud amount based on the bootstrap confidence intervals, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7736,, 2021.

Yi Zeng, Yannian Zhu, Jiaxi Hu, Minghuai Wang, and Daniel Rosenfeld

Cloud top thermodynamic phase (liquid, or ice) classification is critical for the retrieval of cloud properties such as cloud top particle effective radius, cloud optical thickness and cloud water path. The physical basis for phase classification is the different absorption and scattering properties between water droplets and ice crystals over different wavelengths. Passive sensors always use the hand-tuned phase classification algorithms such as decision trees or voting schemes involving multiple thresholds. In order to improve the accuracy and universal applicability of phase classification algorithms, this study uses unsupervised K-means clustering method to classify phase using Himawari-8 (H8) multi-channel RGB images (multi-channel image algorithm, MIA). In order to evaluate the phase classification obtained by MIA, H8-CLP (H8 official product), we use CALIOP phase product as a benchmark. Through the evaluation of cloud top phase of cases from April to October in 2017, the hit rate of liquid and ice phase from H8-MIA is 88% and 65% respectively, and the total hit rate of H8-MIA algorithm is 72%. The hit rate of liquid and ice phase from H8-CLP is 81% and 62% respectively, and the total hit rate of H8-CLP algorithm is 68%. The hit rate of H8-MIA is higher than that of H8-CLP in both liquid and ice phases. It shows that the application of MIA algorithm to H8 satellite can provide more accurate and continuous cloud top phase information with high spatial and temporal resolution.

How to cite: Zeng, Y., Zhu, Y., Hu, J., Wang, M., and Rosenfeld, D.: Multi-channel Imager Algorithm (MIA): A novel cloud top phase classification algorithm applied to Himawari-8 Geostationary Satellite, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5149,, 2021.

masada Tzabari, Vadim Holodovsky, Omer Shubi, Eshkol Eitan, Orit Altaratz, Ilan Koren, Anna Aumann, Klaus Schilling, and Yoav Schechner

 Significant climate uncertainties are associated with insufficient understanding of small warm clouds, due to the nature of their 3D structure and radiative transfer. It is desirable to improve understanding of such clouds and their sensitivity to environmental changes. This requires sensing platforms that are suitable for 3D sensing, and signal analysis tuned to 3D radiative transfer. We approach these challenges in the CloudCT project, funded by the ERC. It is a mission that develops and aims to demonstrate 3D volumetric scattering tomography of clouds. This will be facilitated by an unprecedented large formation of ten cooperating nanosatellites. The formation will simultaneously image cloud fields from multiple directions, at approximately 20m nadir ground resolution. Based on this data, scattering tomography will seek the 3D volumetric distribution of droplet effective radius, liquid water content and optical extinction. In addition to advancement of the technology, CloudCT will yield a global database of 3D macro and microphysical properties of warm cloud fields.

In this talk, we present advances made on several fronts of the project: modeling, payload, algorithm, and operation. Regarding cloud modeling, we performed LES simulations (using the SAM model with bin microphysics) of warm convective cloud fields (at different environments), at high spatial resolution. Using the simulated clouds properties, several imager and waveband possibilities have been quantitatively considered for the mission. Major consideration criteria are tomographic quality in the face of sensor and photon noise, calibration errors and stray light. Additional criteria are technological availability, platform constraints, calibration requirements and cost.

We investigated specifically possibilities of visible light (VIS, 463nm, 545nm, 645nm, and 705nm) short wave infra-red (SWIR, 1641 nm), and polarized imagers (POL, 463nm, 545nm, 645nm, and 705nm).  These examinations relied on physical modeling of 3D radiative transfer and the sensing processes. Due to platform constraints in CloudCT, each platform will carry a single camera exclusively (either VIS/NIR or SWIR). Hence, we describe the tradeoff of introducing SWIR cameras and various POL architectures.  

While CloudCT is mainly designed for simultaneous imaging of each cloud field, it is possible to tolerate a lag of several seconds, as small warm clouds hardly evolve in this time scale (at the 20 meter spatial scale). We exploit this, to add more view-points, using the same number of platforms (10). The added viewpoints correspond to single-scattering angles, where polarization yields enhanced sensitivity to the droplet microphysics. These angles require sampling of <1° in the fogbow region. This dictates requirements for the platform attitude control.  

On the algorithmic front, we advanced the retrieval to yield results that (compared to the simulated ground truth) have smaller errors than the prior art. Elements of our advancement include initialization by a parametric horizontally-uniform microphysical model. The parameters of this initialization are determined by a fast optimization process.  The optimized initialization is particularly strong, when relying on the detected degree of linear polarization, instead of radiance.

How to cite: Tzabari, M., Holodovsky, V., Shubi, O., Eitan, E., Altaratz, O., Koren, I., Aumann, A., Schilling, K., and Schechner, Y.: CloudCT 3D volumetric tomography - mission advances, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9585,, 2021.

Goutam Choudhury and Matthias Tesche

Aerosols interact with atmospheric radiation either directly through scattering and absorption or indirectly by acting as cloud condensation nuclei (CCN) and ice nucleating particles (INP), thereby altering cloud properties. The latter aerosol-cloud interaction (ACI) effects are still poorly understood and believed to be one of the key uncertainties in climate models. In the present scenario, the observations of CCN are still sparse as in-situ measurements are expensive and often restricted to specific locations and limited time periods. An alternative is to turn to satellite observations for ACI studies. The Cloud Aerosol Lidar with Orthogonal Polarisation (CALIOP) is a spaceborne lidar aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. It provides high-resolution vertical profiles of aerosol related parameters such as the aerosol extinction coefficient, backscatter coefficient, aerosol subtypes, and depolarization ratio. In order to estimate the CCN concentrations, we use these parameters along with the normalised lognormal bimodal volume size distributions and complex refractive indices of different aerosol subtypes given in the CALISPO aerosol model.

                The normalised size distribution, the refractive index and the relative humidity are first used to compute the extinction coefficient using the MOPSMAP package. For this, all the aerosol types are treated as spherical particles except the dust which is treated as spheroid. The size distribution is then modified until the estimated extinction agrees with that measured by the CALIPSO. The modified size distribution is integrated to compute the number concentration of aerosols that form the favourable CCN reservoir. To estimate the uncertainty in the retrieval algorithm, we performed the sensitivity analysis by varying the initial normalised volume size distribution by up to +/- 50 % for each mode (fine and coarse). The results are presented as case studies with some preliminary validation against in-situ measurements. The purpose of this work is to obtain a global 3D CCN climatology for use in ACI studies and improving the performance of the global climate models.

How to cite: Choudhury, G. and Tesche, M.: Retrieving cloud condensation nuclei concentrations from spaceborne lidar measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2598,, 2021.

Marta Luffarelli and Yves Govaerts

The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA Aerosol Climate Change Initiatiave (CCI) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations.  It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space.


Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This “twilight zone” can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap.


The CISAR algorithm aims at overcoming the need of an external cloud mask, discriminating internally between aerosol and cloud properties. This approach helps reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations.  


Global maps obtained from the processing of S3A/SLSTR observations will be shown. The SLSTR/CISAR products over events such as, for instance, the Australian fire in the last months of 2019, will be discussed in terms of aerosol optical thickness, aerosol-cloud discrimination and fine/coarse mode fraction.

How to cite: Luffarelli, M. and Govaerts, Y.: Consistent retrieval of cloud/aerosol single scattering properties and surface reflectance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12608,, 2021.

Laura Gómez Martín, Daniel Toledo, Margarita Yela, Cristina Prados-Román, José Antonio Adame, and Héctor Ochoa

Ground-based zenith DOAS (Differential Optical Absorption Spectroscopy) measurements have been used to detect and estimate the altitude of PSCs over Belgrano II Antarctic station during the polar sunrise seasons of 2018 and 2019. The method used in this work studies the evolution of the color index (CI) during twilights. The CI has been defined here as the ratio of the recorded signal at 520 and 420 nm. In the presence of PSCs, the CI shows a maximum at a given solar zenith angle (SZA). The value of such SZA depends on the altitude of the PSC. By using a spherical Monte Carlo radiative transfer model (RTM), the method has been validated and a function relating the SZA of the CI maximum and the PSC altitude has been calculated. Model simulations also show that PSCs can be detected and their altitude can be estimated even in presence of optically thin tropospheric clouds or aerosols. Our results are in good agreement with the stratospheric temperature evolution obtained through the ERA5 data reanalysis from the global meteorological model ECMWF (European Centre for Medium Range Weather Forecasts) and the PSCs observations from CALIPSO (Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations).

The methodology used in this work could also be applied to foreseen and/or historical measurements obtained with ground-based spectrometers such e. g. the DOAS instruments dedicated to trace gas observation in Arctic and Antarctic sites. This would also allow to investigate the presence and long-term evolution of PSCs.

Keywords: Polar stratospheric clouds; color index; radiative transfer model; visible spectroscopy.

How to cite: Gómez Martín, L., Toledo, D., Yela, M., Prados-Román, C., Adame, J. A., and Ochoa, H.: Polar  Stratospheric Clouds detection at Belgrano II Antarctic station from DOAS measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3054,, 2021.

Aerosol retrievals
Thomas Wagner, Steffen Dörner, Sebastian Donner, Steffen Beirle, Janis Puķīte, and Stefan Kinne

Absorption of solar radiation by atmospheric aerosols is an important heat source in the atmosphere. The absorption potential by aerosols (usually quantified by the co single scattering albedo) can vary strongly, depending on the aerosol composition. The absorption potential can be captured by in-situ sample analyses or retrieved by remote sensing techniques (e.g. by sun/sky photometry). For a global view, advanced satellite sensors with polarization and especially multi-viewing would be required (e.g. 3MI). However, sensor data at different UV wavelengths (e.g. TOMS) already inform qualitatively on the presence of (elevated) absorbing aerosol (i.e. from mineral dust, wildfires) via the so-called UV absorbing aerosol index (UVAI).

In this study, we propose an UVAI similar approach for ground-based observations of scattered sun light. We first performed radiative transfer simulations. Based on these simulations we found that absorbing aerosols can indeed be identified from ground-based measurements. We could in particular show that the detection of absorbing aerosols is possible in the presence of clouds (except optical very thick clouds), which will be of special importance, because existing remote sensing measurements of the aerosol absorption are only possible for clear sky conditions.

We also derived the UVAI from ground based measurements during a ship cruise in April and May 2019 over the tropical Atlantic. Clearly enhanced values of the UVAI could be detected when the ship crossed air masses which were contaminated by desert dust aerosols from the Sahara.

We present these early results and discuss possible future improvements.

How to cite: Wagner, T., Dörner, S., Donner, S., Beirle, S., Puķīte, J., and Kinne, S.: Detection of absorbing aerosols from ground based observations of scattered sun light, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3064,, 2021.

Masanori Saito and Ping Yang

Airborne atmospheric aerosol impacts the Earth’s energy budget through their radiative effects and interactions with clouds. Among various aerosol species, mineral dust particles are the most dominant aerosols over desert areas.Continuous monitoring of the global distributions of the mineral dust aerosol is essential to the assessment of their local and climatic impacts. Satellite observations in conjunction with remote sensing techniques have been playing an essential role in the understanding of the global distribution of dust aerosol properties. However, the satellite-based retrievals of mineral dust aerosol properties may involve systematic biases and large uncertainty partly because their optical properties that are fundamentally determined by particle sizes, chemical compositions, and complex morphologies of aerosol particles are not adequately modeled. This presentation will introduce a recently developed comprehensive database for the single-scattering properties of irregular aerosol particles (so-called TAMUdust2020 database) for various remote sensing applications including both passive and active-sensor observations. The TAMUdust2020 database was developed based on an ensemble of various irregular particle shape models that mimic realistic mineral dust particle shapes and their diversity, and was developed with the state-of-the-art light scattering computational capabilities including the physical-geometric optics method (PGOM) and the invariant-imbedding T-matrix (II-TM) method. Comparisons of the scattering properties between laboratory measurements and the present simulations based on TAMUdust2020 database show reasonable consistency. Furthermore, we apply the dust aerosol scattering properties to simulate various spaceborne satellite observations, including multiangle polarimetric observations, thermal infrared observations, and lidar observations. In this presentation, we will demonstrate the capability of current satellite observations with the scattering property database to infer aerosol optical depth and particle effective radius.

How to cite: Saito, M. and Yang, P.: A comprehensive database of the optical properties of mineral dust aerosol particles for spaceborne remote sensing applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6603,, 2021.

Angela Benedetti, Samuel Quesada Ruiz, Julie Letertre Danczak, Marco Matricardi, and Gareth Thomas

The ESA-funded Aerosol Radiance Assimilation Study (ARAS) has provided ground-breaking research in using visible radiance data from satellite to estimate the concentration of aerosols.

Satellite observations in the infrared and microwave parts of the spectrum have long been assimilated into forecasting systems to help estimate the best possible initial conditions for global weather predictions. Assimilating radiances in the visible part of the spectrum, on the other hand, continues to pose many challenges.The reason lies in the complex interactions of cloud and aerosol particles with radiation at those wavelengths and in the complex characteristics of the surface as a reflector of visible light. These factors make it difficult to develop an observation operator, which converts model values into satellite observation equivalents.

One of the key achievements of ARAS is to have developed an observation operator for aerosol reflectances in the visible part of the spectrum. This operator was comprised of two elements: a fast radiative transfer code based on a Look-Up-Table approach developed by RAL Space for aerosol retrievals (Thomas et al, 2009) and adapted to the ECMWF’s Integrated Forecast System as well as a surface reflectance model for ocean and land.

This enabled the first-ever experimental assimilation of reflectances into the 4D-Var assimilation system of ECMWF’s Integrated Forecasting System (IFS) to help estimate aerosol concentrations. The assimilation experiments were very successful. The performance was remarkable considering that this was a new development rolled out over the course of just two years. The observations used in the ARAS project were cloud-cleated aerosol reflectances from the MODIS instrument on board the Aqua and Terra satellites. Experiments were carried out to compare the impact of assimilating these observations with the impact of assimilating traditional satellite-derived AOD observations. The results show that the performance of reflectance assimilation is broadly comparable to that of satellite AOD assimilation. However, it varies depending on the metrics used and the period analysed.

While assimilating aerosol reflectances is still experimental, the results show great potential for future operational implementation in atmospheric composition forecasts. Such forecasts are routinely produced by the EU‐funded Copernicus Atmosphere Monitoring Service (CAMS) implemented by ECMWF. However, the scope for future applications is much wider than that. Many of the tools developed in ARAS for aerosol visible reflectance assimilation could be adapted to clouds. This could open the way towards a fuller exploitation of visible radiances to improve numerical weather prediction.


Thomas G.E., Carboni E., Sayer A.M., Poulsen C.A., Siddans R., Grainger R.G. (2009) Oxford-RAL Aerosol and Cloud (ORAC): aerosol retrievals from satellite radiometers. In: Kokhanovsky A.A., de Leeuw G. (eds) Satellite Aerosol Remote Sensing over Land. Springer Praxis Books. Springer, Berlin, Heidelberg.

How to cite: Benedetti, A., Quesada Ruiz, S., Letertre Danczak, J., Matricardi, M., and Thomas, G.: Outcomes of the Aerosol Radiance Assimilation Study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10986,, 2021.

Fani Alexandri, Torsten Seelig, Peter Braeuer, and Matthias Tesche

Aerosol particles affect the climate directly through interaction with radiation. They also can cause a radiative forcing due to aerosol-cloud interactions (ACI), by acting as cloud condensation nuclei (CCN) in the formation of warm clouds or as ice nucleating particles (INP) during the phase change in mixed-phase clouds. Spaceborne remote sensing is a promising approach for quantifying ACI at a global scale and a useful technique for assessing and improving the performance of climate models. A more than 14-year data set of height-resolved measurements of aerosol optical properties from the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite (Winker at al., 2009) is utilized for estimating the vertical distributions of cloud-relevant particle concentrations. More specifically in this satellite-based study, conversion factors are applied to extinction coefficient observations to obtain vertical profiles of dry particle number and surface area concentration (Mamouri and Ansmann, 2015; 2016; Marinou et al., 2019). The last two are then used as input in measurement-based INP parameterizations in order to retrieve the INP active fractions for different aerosol types. Second part of this methodology is to combine images of geostationary sensors which provide continuously the history of cloud development with polar-orbiting observations on aerosol and cloud parameters that will allow the quantification of ACI. The spaceborne-based findings are crucial for identifying the effects of changes in aerosol concentration on the glaciation of warm and cold clouds.

How to cite: Alexandri, F., Seelig, T., Braeuer, P., and Tesche, M.: Retrieval of ice nucleating particle concentrations from spaceborne lidar measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13625,, 2021.

Anu Kauppi, Antti Kukkurainen, Antti Lipponen, Marko Laine, and Johanna Tamminen

In this presentation we consider uncertainty in Look-up table (LUT) based technique for retrieving aerosol optical depth (AOD) and aerosol type using TROPOMI/S5P measurements.
The LUTs are multi-dimensional tables containing aerosol microphysical properties and they have been constructed using libRadtran simulations. 
Especially we study difficulty in aerosol microphysical model selection that reflects the retrieval uncertainty. As a source of uncertainty we have also acknowledged so called model discrepancy originating from imperfect forward modeling. 
The methodology considered is based on Bayesian inference where the retrieved AOD estimate is given as maximum a posterior (MAP) value and uncertainties are described as posterior density functions. We have also combined statistically the most appropriate aerosol microphysical models by Bayesian model averaging when the selection of single best-fitting model is not clear.
The motivation is to consider difficulty in aerosol model selection and obtain realistic uncertainty estimates.
We have applied this methodology to OMI/Aura measurements in our earlier studies. Here we present results when used higher resolution measurements from TROPOMI/S5P and studied the methodology covering various aerosol conditions including wild fire and dust events.

How to cite: Kauppi, A., Kukkurainen, A., Lipponen, A., Laine, M., and Tamminen, J.: Studying uncertainty in LUT-based aerosol retrieval employing Bayesian statistical approach applied to TROPOMI/S5P measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9644,, 2021.

Aerosol system analysis
Vasilis Margaritis, Nikolaos Hatzianastassiou, Marios Bruno Korras Carraca, and Maria Gavrouzou

After the outbreak of SARS-CoV-2 in December 2019 and its spread worldwide in the following months and seasons, the governments around the world were forced, one by one, to impose lockdown measures in their countries during the ‘Covid Year’ of 2020, trying to slowdown or even stop the spread of the virus. These nationwide lockdowns, included measures that led to the reduction of human movement, such as transportation, in urban areas, while they also diminished the industrial activity. Since transportation and industrial activity are among the major sources of emission of anthropogenic aerosols, it is possible that a change, namely a decrease, of the atmospheric aerosol loading is observed during the year 2020. 

In this study, we examine and quantify the possible effect of worldwide Covid19-related lockdowns on air quality, and more specifically on the aerosol optical depth, which is a good measure of aerosol loading. The analysis is done at global scale using Collection 6.1 Level-3 daily 1°x1° latitude-longitude gridded spectral Aerosol Optical Depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS) on AQUA satellite during the period 2003-2020. We assess the possible anomaly in AOD values during 2020 by comparing their annual, seasonal and monthly mean values with the corresponding climatological ones for the period 2003-2019. A trend analysis is also performed using time series of deseasonalized AOD anomalies during the period 2003-2020. Special emphasis is given to specific great urban areas, as well as to areas where stricter measures were taken for limiting the virus’ spread. For these areas of interest, a further analysis using higher resolution (10km x 10km) MODIS Level-2  AOD data was made in order to capture local changes in AOD that could be hindered by the coarser resolution Level-3 data. Finally, for these regions, the AOD changes estimated using MODIS Level-2 data are intercompared with the corresponding ones using data from local AERONET (AErosol RObotic NETwork) stations. Preliminary results show a clear reduction in AOD values, mainly starting from April 2020 and becoming more clear in late spring and early summer (May and June) of 2020.

How to cite: Margaritis, V., Hatzianastassiou, N., Korras Carraca, M. B., and Gavrouzou, M.: The effect of SARS-CoV-2 on atmospheric particulate matter (AOD) as observed by satellites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7128,, 2021.

Perla Alalam and Hervé Herbin

Large desert lands such as Sahara, Gobi or Australia present main sources of atmospheric mineral dust caused by intense dust storms. Transported dust particles undergo physical and chemical changes affecting their microphysical and optical properties. This modifies their scattering and absorption properties and alters the global atmospheric radiative budget.

Currently, remote sensing techniques represent a powerful tool for quantitative atmospheric measurements and the only means of analyzing its evolution from local to global scale. In order to improve the knowledge of atmospheric aerosol distributions, many efforts were made particularly in the development of hyperspectral infrared spectrometers and processing algorithms. However, to fully exploit these measurements, a perfect knowledge of Complex Refractive Index (CRI) is required.

In that purpose, a new methodology based on laboratory measurements of mineral dust in suspension coupled with an optimal estimation method has been developed. This approach allows getting access to CRI of several desert samples with various chemical compositions.

Here, we present the first results of the physical parameters (effective radius and concentration) retrievals using Infrared Atmospheric Sounding Interferometer IASI data, during dust storm events. The latter use the CRI of different desert samples obtained in laboratory and a new radiative transfer algorithm (ARAHMIS) developed at Laboratoire d’Optique Atmosphérique LOA.

How to cite: Alalam, P. and Herbin, H.: Tropospheric Mineral Dust Study by High Spectral Resolution Infrared Satellite during intense dust storms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2851,, 2021.

Sara Herrero, David Mateos, Carlos Toledano, Roberto Román, Ramiro González, Chistopher Ritter, Juan Carlos Antuña-Sanchez, Daniel González-Fernández, Abel Calle, Victoria E. Cachorro, and Ángel M. de Frutos

Atmospheric aerosols are an important forcing agent in the estimation of radiative budget, being the Arctic an area of special weakness. The Group of Atmospheric Optics, University of Valladolid and the Alfred Wegener Institute for Polar and Marine Research, installed in 2017 a CE-318T Sun-sky-Moon  photometer (Cimel Electronique S.A.S) in the Arctic station Ny-Ålesund (79ºN, 12ºE). This study presents an inventory of all high-turbidity aerosol episodes recorded in the period 2017-2020 (data of level 1.5-validated from AERONET). This inventory is based on the separate analysis of coarse and fine mode aerosol optical depth. Aerosol episodes are attributed to coarse, fine or mixture of aerosols. Complementary information provided by HYSPLIT air mass back trajectories, MODIS images, forecast aerosol models, CALIOP/CALIPSO satellite data, and other collocated instruments on the station are also used. Special focus is given to long-range transport of aerosols from big forest fires in Canada, United States and Russia.

How to cite: Herrero, S., Mateos, D., Toledano, C., Román, R., González, R., Ritter, C., Antuña-Sanchez, J. C., González-Fernández, D., Calle, A., Cachorro, V. E., and de Frutos, Á. M.: Inventory of aerosol episodes in Ny-Ålesund (Svalbard) in the period 2017-2020 by sun photometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15194,, 2021.

Ramiro González Catón, Carlos Toledano, Roberto Román Diez, David Mateos, Eija Asmi, Edith Rodriguez, Juan Carlos Antuña-Sánchez, Sara Herrero, Victoria E. Cachorro, Abel Calle, and Ángel M. de Frutos

Long range transported aerosol from biomass burning affects polar regions, especially the Arctic. The frequency and intensity of bushfires in the context of a warming climate has been pointed out in the last report of the Intergovernmental Panel on Climate Change. In high latitudes, these events impact large areas through long-range transport of the smoke particles in the troposphere or even the stratosphere. The lifetime and radiative impact are related with the height of the plumes and the processes that modify particle size and absorptive properties during the transport. Several recent publications have shown the impact of the Australian smoke in the southern hemisphere, including Antarctica, in January-March 2020. The tools that were used to monitor that extraordinary event can be used in the Arctic to investigate similar effects in the frequent biomass burning events that generate smoke plumes in boreal regions. In this work, we present the results derived from ground-based instrumentation as well as satellite and model data. The change of the smoke properties after several days of transport is also provided, namely an increase in the fine mode particle size and the single scattering albedo, as well as a decrease in the coarse mode particle concentration. These features are relevant for radiative forcing calculations and therefore the impact of long range transported smoke in the radiative balance over polar regions.

How to cite: González Catón, R., Toledano, C., Román Diez, R., Mateos, D., Asmi, E., Rodriguez, E., Antuña-Sánchez, J. C., Herrero, S., Cachorro, V. E., Calle, A., and de Frutos, Á. M.: Monitoring of long-range transported smoke in polar regions with remote sensing instruments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15699,, 2021.

Shuyun Yuan and Ying Li

A new remote sensing method for PM2.5 based on coupling semi-empirical and numerical model

Shuyun Yuan1,2,Ying Li1,2 *,

1 Department of Ocean Sciences and Engineering, Southern University of Science and Technology, Shenzhen, China

2 Center for Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen, China


Fine particulate matter (PM2.5), as a major kind of air pollution, is composed of a complex mixture of solid or liquid airborne particles, including sulfate, nitrate, ammonia, black carbon, mineral dust, and water, which may cause heart disease and lung cancer, as well as both chronic and acute respiratory diseases, such as asthma. According to the World Health Organization’s report in 2016, 91% of the world’s population was living in places where the WHO air quality guideline levels were not reached. Therefore, it is important to monitor ground PM2.5 concentrations with high resolution at a large scale, which is fundamental to understanding its tempo-spatial distribution, transport paths, formation mechanism, mitigation strategies, etc.

In the previous research, the semi-empirical method (SEM) of physical mechanism based on the physical mechanism between PM2.5 and AOD has been developed ( Lin et al. 2015). The results show the method's capacity to identify PM2.5 spatial distribution with high-resolution at national, regional, and urban scales and to provide useful information for air pollution control strategies, health risk assessments, etc.

However, the double parameters (K and ) of aerosol characteristics are obtained based on long-term observational data regression in a low spatial and temporal resolution. In the high-resolution PM2.5 concentration inversion (1km), it is usually difficult to establish such a dense ground-based observation network. Therefore, although the inversion results above the station have high accuracy, the inversion accuracy in the area far away from the station is limited, which underscores the need to incorporate the variations in aerosol characteristics in this model.

Numerical chemical transport model (CTM) can provide a more complete spatial distribution and solve the problem of insufficient ground observation data to a certain extent. Although the uncertainty of PM2.5 absolute concentration simulation at individual stations is high, the overall aerosol characteristics pattern simulated are relatively more reliable with emission information in high spatial-temporal resolution driven by reasonable meteorology. Thus, in order to improve the SEM and assess the effect of the variations in aerosol characteristics on satellite, in this study, we try to incorporate the SEM with the CTM together by simulating the double parameters with the concept in the SEM by using the numerical data. The results showed better agreements between satellite-retrieved and ground-observed PM2.5(with daily averages of 0.87) compared with that of the previous SEM (with daily averages of 0.69) in the same study region. This new method not only can take the advantages from both the SEM and CTM but also be suitable for operations with a quite low computation cost than the CTM itself.

How to cite: Yuan, S. and Li, Y.: A new remote sensing method for PM2.5 based on coupling semi-empirical and numerical model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2569,, 2021.