AS3.3

The study of ice clouds properties is of central importance to further understand the role of ice clouds in climate system processes. Therefore, it is crucial to perform accurate ice cloud retrievals in satellite-based systems in order to provide reliable information about the cloud microphysical, macrophysical and optical properties. Current and future satellite missions like Sentinel-5 Precursor (S5P), Sentinel-4 (S4), and Sentinel-5 (S5) are designed to monitor the air quality and greenhouse gases. The cloud retrieval algorithm used operationally for these missions is ROCINN (Retrieval Of Cloud Information using Neural Networks) which retrieves the cloud top height (CTH), cloud optical depth (COD) and cloud albedo (CA) from measurements in the NIR in the O₂ A-band (755-771 nm). ROCINN considers two cloud models: Clouds as Reflecting Boundaries (CRB) and Clouds As scattering Layers (CAL). In this work we will present the latest developments including the ice cloud retrieval performed using the VLIDORT radiative transfer (RT) model containing ice cloud parametrization. This study investigates the performance of ROCINN for ice cloud retrieval for several test scenarios adapted from Level 2 operational data. The selected datasets contain partially and fully cloudy scenarios for ice clouds placed at different CTH and for different COD. The retrieved CTH and COD for ice clouds are evaluated for the TROPOMI/S5P and S4 satellites.

How to cite: del Águila, A., Lutz, R., Molina García, V., Romahn, F., and Loyola, D.: Retrieval of Ice Cloud Properties from Sentinel-5 Precursor and Sentinel-4 Measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9552, https://doi.org/10.5194/egusphere-egu22-9552, 2022.

This study aims at improving an empirical cloud masking approach for the high-resolution analysis of land surface effects on boundary layer clouds.

The observation of boundary layer clouds with high-resolution satellite data can provide comprehensive insights into spatiotemporal patterns of land surface-driven modification of cloud occurrence, such as the diurnal variation of the occurrence of fog holes and cloud enhancements attributed to the impact of the urban heat island. High-resolution satellite-based cloud masking approaches are often based on locally-optimized thresholds that are compared against satellite-observed reflectances to separate cloudy from clear-sky observations that can be affected by the local surface reflectance. Therefore, spatial differences in surface albedo, as found in and around urban areas or forests, can introduce spatial biases in the detected cloud cover that may impede the analysis of spatial pattern changes due to land surface influences. In this study, two approaches for cloud masking using the High Resolution Visible channel of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation are developed and validated for the region of Paris to show and improve applicability for analyses of urban effects on clouds. Firstly, a local approach that uses an optimized threshold to separate the distribution of visible reflectances into cloudy and clear sky for each individual pixel accounting for its locally specific brightness. Secondly, a regional approach that uses visible reflectance thresholds that are independent of surface reflection at the observed location. While the first approach is representative for the widespread usage of locally-optimized approaches, derived cloud masks result in regional biases that are caused by the differences in surface reflectance. This makes the regional approach a more appropriate choice for the high-resolution satellite-based analysis of cloud cover changes over different surface types and the interpretation of locally induced cloud processes.

How to cite: Fuchs, J., Andersen, H., Cermak, J., Pauli, E., and Roebeling, R.: High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12813, https://doi.org/10.5194/egusphere-egu22-12813, 2022.

Clouds are an essential component in our Earth system because of their importance for the weather, the water cycle and the Earth radiation budget. To better understand the climate, its past and future evolution, the development of long coherent time series of cloud properties is needed. In addition, as the clouds strongly impact the radiance at the top of the atmosphere, the detection of clear-sky scenes is a major preprocessing step for most climate and atmospheric satellite applications, such as trace gas retrieval or to derive the Earth Outgoing Longwave Radiation.  

The Infrared Atmospheric Sounding Interferometer (IASI), flying on board the suite of Metop satellites for more than 15 years, has shown an excellent stability over its entire lifespan and a very good consistency between the three instruments (on board Metop-A, -B and -C). This makes the IASI dataset an excellent climate data record. For the detection and the characterization of clouds, the current IASI operational Level 2 product is highly performant. However, since it was first released in 2007, the L2 cloud data have undergone a series of updates which have not yet been reprocessed back in time. This leads to discontinuities in the data record which makes it very difficult for use in long-term studies. Even in the event of a complete reprocessing of the L2, there would also be no guarantee on the homogeneity of the futures versions. Other cloud products exist (e.g. the AVHRR-L1C, the cloud_cci, the CIRS-LMD) but those are usually either less accurate or sensitive to cloud detection or are not available in near-real-time. These limitations in the existing products triggered the development of a sensitive and coherent IASI cloud detection dataset.

Here we present a new cloud detection algorithm for the IASI measurements based on a Neural Network (NN). The input data consists of a set of 45 IASI channels. Those were selected outside the regions affected by CO₂, CFC-11 and CFC-12 absorptions to avoid any long-term bias in the detection as their concentrations are evolving over time in the atmosphere. As a reference dataset, we use the current version (v6.6) of the IASI L2 cloud product. The IASI-derived NN cloud product appears to be both accurate in the cloud detection and coherent over the whole IASI period and between the three versions of the instrument. To illustrate this, we show global distributions and time series of the cloud fractions and we assess the quality of the cloud mask by comparing the NN product against several other cloud products. We also evaluate the capabilities of our NN cloud detection product to correctly distinguish cloud from dust plumes.

How to cite: Whitburn, S., Clarisse, L., Coheur, P., and Clerbaux, C.: Cloud detection from IASI radiance for climate analysis purposes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7589, https://doi.org/10.5194/egusphere-egu22-7589, 2022.

Ice crystal formation and growth processes in mixed-phase clouds (MPCs) are not sufficiently understood. This leads to uncertainties of atmospheric models in representing MPCs. This presentation is centered around riming, which occurs when liquid water droplets freeze onto ice crystals. While it is challenging to observe riming directly, we retrieve a proxy for riming from airborne radar measurements using data collected during the (AC )3 aircraft campaign ACLOUD performed in 2017. For this campaign, two closely collocated aircraft were flying in formation for obtaining collocated in situ and remote sensing observations. We aim to quantify the normalized riming fraction 𝑀 by matching measured to simulated radar reflectivities 𝑍𝑒 . For the latter we use the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA) to calculate 𝑍𝑒 from the in situ observed particle size distributions. Liquid droplets are assumed to be spheres and Mie scattering is applied, while we use the self-similar Rayleigh Gans approximation (SSRGA) for ice crystals. We present an Optimal Estimation algorithm to obtain ice crystal mass size - as well as SSRGA parameters from measured 𝑍𝑒 and in situ parameters. We exploit the fact that mass size and SSRGA parameters depend on 𝑀. We evaluate including empirical relationships derived via model calculations done by an aggregation and riming model as forward operators in the algorithm. Also we use the model calculations directly to restrict the prior information. We validate the obtained 𝑀 values by looking at in situ ice crystal images for selected time periods. We compare our findings to macrophysical cloud properties and meteorological conditions to understand external drivers and variability of riming. This will lead to a better understanding of riming as a key process occurring in arctic MPCs.

How to cite: Maherndl, N., Maahn, M., Tridon, F., and Dupuy, R.: Retrieving riming in arctic mixed phase clouds from collocated remote sensing and in situ aircraft measurements during ACLOUD, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13359, https://doi.org/10.5194/egusphere-egu22-13359, 2022.

Tracking clouds in satellite data has multiple applications. It is used for short-term weather forecasting as well as long-term weather and climate analyses. Our long-term goal is to investigate cloud lifecycles under different conditions, such as marine or continental areas, over deserts, or in areas with increased anthropogenic aerosols. This is a key element in understanding cloud radiation effects and the human influence on the cloud lifecycle.

To identify clouds and their trajectories, we are using Particle Image Velocimetry (PIV) which is well-known for measuring velocities in fluid dynamics. The algorithm works on the cloud mask from CLAAS2 (Cloud property dataset using SEVIRI v2) by EUMETSAT (2014 Stengel et al, “CLAAS: the CM SAF cloud property dataset using SEVIRI”). The mask is created with a multi-channel approach using satellite data from SEVIRI. However, the presented algorithm can be adapted to work on any geostationary satellite data set. It identifies clouds in the satellite data and computes a velocity field with the next timestep using cross correlation. This velocity field is interpolated onto the individual clouds and the virtual positions (old positions adjusted with velocity field) are then compared against the next timestep of clouds via a matching criterion. Previously only the distance of the centroids was used for this criterion. Now the overlapping area is used as well in sequence with the distance. This improves the capability to track large clouds immensely because they are more likely to have large shifts in their centroid due to a change in shape.

The presented results are twofold. Firstly, we will show a comparison of individual cloud trajectories between both methods to establish a deeper understanding of the methodology. Secondly, we will look at the distributions of the cloud sizes and trajectory lengths for both methods to see the overall improvement that can be gained from the updated matching criterion.

How to cite: Müller, F. and Seelig, T.: Cloud tracking in geostationary satellite data: Comparison of two Matching Methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4633, https://doi.org/10.5194/egusphere-egu22-4633, 2022.

Atmospheric aerosol particles originating either from natural (i.e. dust, volcanic ash, smoke, sea salt) or anthropogenic (i.e. pollution, agricultural activities) sources, affect the Earth’s climate through absorption and scattering of the incoming solar radiation and cloud property modifications. Aerosols can further significantly deteriorate air quality and result in adverse human health problems.

Aerosols multifarious effects depend on their intrinsic optical properties and their load, as well as the radiative characteristics of the underlying surface. The quantification of the aerosol net effect on the Earth’s radiative budget is subject to large uncertainties owing to the rapid temporal and spatial changes of the aerosol field and the aerosol properties. Multi-angular polarimetric remote sensing can provide detailed information on aerosol microphysical and optical properties in order to better constrain the aerosol radiative forcing and chemical composition.

The Compact Multi-Angle Polarimeter (C-MAP) is an airborne sensor that will provide highly accurate measurements of intensity and polarization at 7 measurement wavelengths (410, 443, 490, 555, 670, 753 and 865nm) and 5 different viewing angles (0, ±15 and ±40°). C-MAP is currently being developed by Thales Alenia Space-UK in collaboration with the University of Leicester. The project aims to incorporate the MAP technology into a compact airborne MAP that will fly on board a UK demonstrator flight in late 2022. The instrument design is based on the upcoming MAP sensor on-board the CO2M mission (Sierk et al., 2021; Spilling et al., 2021), also developed by TAS-UK.

Herein we illustrate the performance of C-MAP in terms of aerosol and surface property retrievals using the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm (Dubovik et al., 2011; 2021). Our analysis is carried out using simulated radiances generated by GRASP for various synthetic scenes characterized by pre-assumed atmospheric conditions in terms of aerosol content (shape, size, composition and load), solar zenith angle and surface albedo.  The series of sensitivity tests developed, aims to verify the C-MAP capability to derive a set of aerosol optical and microphysical properties along with surface characteristics. Here, microphysical properties include the aerosol size distribution, complex refractive index and fraction of spheres for coarse mode, while optical properties consist of the aerosol optical depth (AOD) and single scattering albedo (SSA). Surface reflectance is described through retrievals of Bidirectional reflectance distribution function (BRDF) and Bidirectional Polarization Distribution Function (BPDF) parameters.

How to cite: Gialitaki, A., Dhillon, R., Panagi, M., Lodge, A., Lloyd, S., Di Noia, A., Boesch, H., Tsekeri, A., and Vande Hey, J.: Aerosol property retrievals with the use of an airborne compact multi-angle polarimeter (C-MAP), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10464, https://doi.org/10.5194/egusphere-egu22-10464, 2022.

Arctic amplification, the phenomenon that the Arctic is warming faster than the global mean is still not fully understood. The Transregional Collaborative Research Centre TR 172 -- Arctic Amplification: Climate Relevant Atmospheric and Surface Processes (AC³) funded by the DFG contributes towards this research topic.

This excessive Arctic warming is both a consequence and a driver of rapid changes in the Arctic and in part created by aerosol feedbacks. Since different aerosol types have different climate effects, the observation of aerosols is urgently needed in the Arctic. Thus, for the purpose of measuring aerosols in the troposphere, a Fourier-Transform InfraRed spectrometer (FTS) for measuring down-welling emission measurements and a Raman-Lidar are operated at the AWIPEV research base in Ny-Ålesund, Spitsbergen (78°N).

The height of the aerosol layer, aerosol backscatter, extinction, depolarization, the lidar ratio and the color ratio are measured by the Raman-Lidar. Based on that information, a retrieval algorithm, LBLDIS, for aerosol types (dust, sea salt, black carbon and sulfate), optical thickness and effective radius is modified and used for analyzing the emission spectra measured by the FTS.

Combining the two observations, the aerosols can be observed more comprehensively. The most probable origin of the dominant aerosol types is explored by tracking the origin of air masses through back-trajectory calculations using the FLEXPART atmospheric transport model.

How to cite: Ji, D., Palm, M., Ritter, C., Richter, P., Buschmann, M., and Notholt, J.: Ground-based remote sensing of aerosol properties using the Emission FTIR NYAEMFT and the Raman-Lidar KARL in Ny-Ålesund, Spitsbergen (78°N), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5345, https://doi.org/10.5194/egusphere-egu22-5345, 2022.

Due to its geographical location, Cyprus is often affected by dust storms arriving from the largest deserts of the planet, the Sahara, the Arabian Peninsula and  the Syrian. In order to characterize dust properties, the Cyprus Atmospheric Observatory (CAO) and the Unmanned Research Laboratory (USRL) of the Cyprus Institute (CYI), in collaboration with the Cyprus Atmospheric Remote sensing Observatory (CARO) of the ERATOSTHENES Centre of Excellence (ECoE) of the Cyprus University of Technology (CUT), performed a research campaign in Fall 2021. Measurements were performed with ground-based aerosol remote sensing systems (lidars, ceilometers and sunphotometers), and UAV based in-situ instruments (OPCs, backscatter sondes, and impactors able to collect dust samples). As part of the  remote sensing observations, two depolarized lidars performed measurements from different locations, one from CYI premises in Nicosia and the second one from ECoE-CUT premises in Limassol. The lidar signals provide information about the vertical aerosol profile at the two locations, which can be used to derive the optical properties of dust at different altitudes. Here, we will present first results on the synergy between the continuous vertically extended measurements of lidars and the in-situ measurements from UAV instrumentation during the periods of dust outbreaks. Two dust events occurred from 25 October to 1 November and from 13 to 18 November 2021. During these dust events, the lidars observed depolarized aerosol layers from ground up to 5 km above sea level. The lidar measurements provided the temporal and spatial development of these dust layers, and were also used in real-time for planning the UAV flight schedule. According to backward trajectory analyses, the two dust events had different origins with the first arriving from the Sahara and the second one from the Middle East.

How to cite: Papetta, A., Kezoudi, M., Marenco, F., Keleshis, C., Mamouri, R.-E., Nisantzi, A., and Sciare, J.: Characterizing dust aerosols with lidar and UAV based measurements (Cyprus Fall campaign 2021)., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7591, https://doi.org/10.5194/egusphere-egu22-7591, 2022.

Mineral dust is the most abundant natural dust in the atmosphere. It has direct and indirect effects on the radiative budget altering climate and air quality. These effects are directly dependent of the mineralogical composition and microphysical properties of the transported dust in the atmosphere.

High spectral resolution Infrared remote sensing technology has shown the ability to characterize different atmospheric components from local to global scale. In particular, the atmospheric aerosols are quantified using hyperspectral infrared spectrometers and processing algorithms since to achieve these measurements, a perfect knowledge of mineral dust optical properties is required i.e. extinction coefficient and complex refractive indices.

East Asia presents the second largest dust source in the world after Sahara. The atmospheric dust in this region has a diversity in its mineralogical composition; rich in silicates but also in carbonates that present a tracer of this region. On the other hand, the dust is uplifted in the low troposphere leaving satellite remote sensing detections with Land Surface Emissivity (LSE) constraints.

To cross these challenges, Infrared Atmosphere Sounding Interferometer (IASI) observations were used with all its advantages: continuous spectrum, day and night, ocean and land detections, high spectral resolution and low radiometric noise. A new LSE optimization method was developed to correct the IASI spectra. Then, a semi-quantitative method was applied based on laboratory measurements of suspended mineral dust coupled with optimized spectral detections, to obtain new mineralogical dust extinction weights. These weights depend on the chemical composition, the size distribution and the concentration, by this means a retrieval of the latter parameters was performed using a new radiative transfer algorithm (ARAHMIS) developed at Laboratoire d’Optique Atmosphérique (LOA).

Therefore, we present the results of dust chemical and physical parameters (mineralogy, effective radius and concentration) obtained using Infrared Atmospheric Sounding Interferometer IASI data with laboratory optical properties, during dust storm events in East Asia.

How to cite: Alalam, P. and Herbin, H.: Aerosol Mineralogical and Microphysical Study from Laboratory to Satellite Remote Sensing IASI Measurements: Application to East Asian Deserts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2668, https://doi.org/10.5194/egusphere-egu22-2668, 2022.

The open burning of biomass fuels is an important source of aerosols because they contribute significantly to the pre-industrial radiative forcing budget and they are a large source of aerosol in the modern era that is anticipated to increase due to climate change. However, the optical properties of smoke have been shown to be complex and variable, which in turn complicates a) the retrieval of aerosol properties using remote measurements and b) the estimation of the direct radiative forcing caused by smoke.

During the FIREX-AQ aircraft campaign, we measured the angular distribution of light (i.e. scattering phase function) scattered by smoke in situ using the NOAA Laser Imaging Nephelometer. We then used collocated measurements of the particle size distribution and literature values of the complex refractive indices to calculate expected phase functions using Mie theory. When comparing the measured versus calculated phase functions, we see there is more backscattered light in the measurements.

This enhanced backscatter has two important repercussions. First, when the measured phase function is used with an open source algorithm (GRASP) to retrieve the particle mode size, we find that the algorithm tends to undersize the particles by about 10%. Second, when the enhanced backscatter is included in a simple radiative transfer model, we observe an additional 20% cooling effect from fresh smoke.

How to cite: Ahern, A. T., Wagner, N. L., Brock, C. A., Lyu, M., Moore, R. H., Wiggins, E. B., Winstead, E. L., Robinson, C. E., and Murphy, D. M.: Angularly-resolved measurements of light scattering by smoke from wildfires during FIREX-AQ, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3116, https://doi.org/10.5194/egusphere-egu22-3116, 2022.

Volcanic eruptions are one of the most impressive natural phenomena to which our planet is subjected and which over the years have influenced human life. During explosive eruptions a great amount of volcanic particles are ejected in atmosphere and can remain suspended for days, also creating aviation traffic impairments.

Orbiting satellite observations can provide a large amount of daily data. The global perspective offered by Geosynchronous Earth Orbit (GEO) and Low Earth Orbit (LEO) satellite systems is of vital importance for the monitoring of volcanoes, especially those in remote and inaccessible areas. Data from LEO satellite visible-infrared (VIS-IR) spectroradiometers (e.g., VIIRS, AVHRR), but also from microwave radiometers (MHS, ATMS), can be used. Although the LEO thermal-infrared (TIR) data analysis represents the classic approach in the study of volcanic eruptions, given their remarkable spatial resolution and sensitivity to ash clouds, their brightness temperature (BT) difference signatures saturate because of large amount of tephra mass as well as the presence of volcanic particles having sizes bigger than 10 µm within the expanding plume.

Microwave (MW) and millimeter (MMW) passive sensors can be also exploited because they are more sensitive to larger tephra particles (i.e., sizes bigger than 10-100 µm) so that the near-source plume does not typically extinguish the MW and MMW signals, especially in the first hours after the eruptive event. Satellite-based detection of volcanic eruptions, using infrared radiometric data from LEO spectroradiometers, may lead to an ambiguous detection in the proximity of the volcanic vent during sub-Plinian volcanic events. The thermal-infrared (TIR) brightness-temperature difference signatures saturate because of the large tephra particle within the expanding plume. In this respect, the use LEO spaceborne millimeter-wave (MMW) radiometric observations can help since plumes at millimeter wavelength are less optically opaque than at micron ones.

In order to demonstrate this MMW-TIR synergy, we show the analysis of the 2014 Kelud and 2015 Calbuco eruption case studies, considering LEO radiometric measurements and algorithms based on physical-statistical approaches as well as machine learning techniques. Eruptions of the Etna volcano in 2018 and 2021 are also considered to evaluate the detection capability of TIR methods. Results are compared with literatures in terms of volcanic cloud mapping as well as with available other validated satellite estimates for the tephra columnar loading in the near source and distal areas. Detection split windows approaches are compared with random forest (RF) models. During the learning phase, the RF models were trained to give more importance to the false alarms. The mass loading retrieval is done by inverting the forward problem. Effective radius and mass loading are two related quantities. Due to this aspect, a neural network model is developed to solve a multiple regression problem.

How to cite: Romeo, F., Mereu, L., Scollo, S., Papa, M., Corradini, S., Merucci, L., and Marzano, F. S.: Volcanic cloud satellite retrieval: an infrared and millimeter-wave multisensor approach using statistical and machine learning methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10650, https://doi.org/10.5194/egusphere-egu22-10650, 2022.

Aerosol absorption is an important parameter for assessing the climatic impact of aerosols. In this study, we present a multi-sensor algorithm to generate global maps of single scattering albedo (SSA) 550 nm using the concept of 'critical optical depth.' Global maps of SSA were generated following this approach using spatially and temporally collocated data from Clouds and the Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board Terra and Aqua satellites. Limited comparisons against airborne observations over India and surrounding oceans were generally in agreement within ±0.03. Global mean SSA estimated over land and ocean is 0.93 and 0.97, respectively. Seasonal and spatial distribution of SSA over various regions are also presented. The global maps of SSA, thus derived with improved accuracy, provide important input to climate models for assessing the climatic impact of aerosols on regional and global scales.

How to cite: Devi, A. and Satheesh, S. K.: Global Maps of Aerosol Single Scattering Albedo Using Combined CERES-MODIS Retrievals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12518, https://doi.org/10.5194/egusphere-egu22-12518, 2022.