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AS3.3

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: Alexander Kokhanovsky | Co-conveners: Jan Cermak, Virginie Capelle, Gerrit de Leeuw
Presentations
| Fri, 27 May, 08:30–11:50 (CEST)
 
Room M1

Fri, 27 May, 08:30–10:00

Chairpersons: Jan Cermak, Irène Xueref-Remy

08:30–08:35
Introduction

08:35–08:45
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EGU22-9552
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ECS
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solicited
Ana del Águila et al.

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.

08:45–08:52
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EGU22-10793
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ECS
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On-site presentation
William Cossich et al.

Downwelling radiance spectra in the 100-1500 cm-1 interval, measured by the Radiation Explorer in the Far Infrared-Prototype for Applications and Development (REFIR-PAD) spectroradiometer on the Antarctic plateau since 2012, are ingested by an automatic machine learning algorithm, named cloud identification and classification (CIC), to detect and classify the Antarctic clouds. 

The CIC algorithm is a modified version of the one chained in the End-2-End Simulator (EES) of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, the next ESA 9th Earth Explorer. CIC sits at the center of the decision tree of the FORUM EES for retrieving clear-sky atmospheric products or cloud properties. 

Co-located lidar measurements are exploited to define training sets composed of radiance spectra in presence of clear sky, ice clouds or mixed-phase clouds. The CIC is initially tested on a controlled verification subset for optimization. It is demonstrated that the information content in the far infrared (FIR) part of the spectrum is critical for the improvement of the performances of the algorithm to identify thin clouds and for cloud phase classification. A test set of 1726 spectra is then used to estimate the classification error. Unprecedented cloud occurrence statistics concerning more than 4 years of data are provided for multiple time scales and related to meteorological parameters such as surface air temperature and wind direction. 

The results indicate a clear sky mean annual occurrence of 72.3%, while ice and mixed-phase clouds are observed in 24.9% and 2.7% respectively, with an inter-annual variability of a few percent. The seasonal occurrence of clear sky shows a minimum in winter (66.8%) and maxima (75-76%) during intermediate seasons. In austral winter the mean surface temperature is about 9°C colder in clear conditions than when ice clouds are present. Mixed-phase clouds are observed only in the warm season (November-March). In austral summer they amount to more than one third of total observed clouds. Their occurrence is correlated with warmer surface temperatures. In the summer, the mean surface air temperature is about 5°C warmer when clouds are present than in clear sky conditions.  

A comparison of the CIC classification with available satellite Level-2 (L2) and Level-3 (L3) products, is provided. Passive (Infrared Atmospheric Sounding Interferometer - IASI, and Moderate Resolution Imaging Spectroradiometer - MODIS), and active (Cloud-Aerosol LiDAR with Orthogonal Polarization - CALIOP, and the Cloud Profiling Radar - CPR) sensors are considered.   

For selected case studies, a direct comparison between co-located L2 satellite products and parameters retrieved from ground-based observations (REFIR-PAD, lidar) is performed. In case of cloudy scenes, retrieved cloud parameter are used to simulate FORUM like observations and to evaluate the impact of the additional FIR part of spectrum in satellite cloud retrievals from infrared passive measurements. 

For L3 satellite products, the monthly gridded data are used for comparison. The differences observed among the considered products and the CIC results are analysed in terms of footprint sizes and sensors' sensitivities. The comparison highlights the ability of the CIC/REFIR-PAD to identify multiple cloud conditions from high spectral resolution radiances. 

How to cite: Cossich, W., Maestri, T., Martinazzo, M., Di Natale, G., Palchetti, L., Bianchini, G., and Del Guasta, M.: Cloud occurrence on the Antarctic plateau: ground-based detection and satellite products , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10793, https://doi.org/10.5194/egusphere-egu22-10793, 2022.

08:52–08:59
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EGU22-12813
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ECS
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Virtual presentation
Julia Fuchs et al.

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.

08:59–09:06
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EGU22-2992
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ECS
Analysis of TGE-producing clouds using satellite data
(withdrawn)
Ekaterina Svechnikova et al.
09:06–09:13
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EGU22-4375
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ECS
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Virtual presentation
Genica Liliana Saftoiu Golea et al.

Stratocumulus clouds represent one of the key components of the Earth's radiative balance because it generally reflects incident solar radiation. The aim of the study is to understand the cloud occurrence and characteristics of stratocumulus clouds using satellite data collected from Dec 2019 to Feb 2021. The time series for cloud characteristics contained 10944 hourly profiles of which 1513 were for Stratocumulus clouds. We used a statistical cloud classification model based on data on cloud optical depth and cloud pressure top. Of the total clouds measured, Cumulus clouds were the most frequently detected (25 %), followed by Altocumulus clouds (17.48 %), Cirrus clouds (17.01 %) and Stratocumulus clouds (13.82 %). We focused on the Stratocumulus clouds. They represent a higher percentage of the total number of clouds detected, especially, in the winter months. A series of macrophysical and microphysical stratocumulus cloud parameters (cloud cover fraction, cloud top temperature, cloud top pressure, cloud height, cloud optical depth, liquid water path) were extracted from the Clouds and the Earth's Radiant Energy System (CERES) database for Magurele, a region in south west Bucharest, Romania. The highest median value for liquid water path was observed in winter 2020–2021 (61.4 g m-2), reflecting the large number of Stratocumulus cloud observations during this period. The lowest median value for liquid water path was 35.14 g m-2 in summer 2020. The cloud water radius of the liquid particles has similar median values ​​(8.67 - 8.92 μm) during the study period except for the winter 2020–2021, when the median value of the radius had the maximum value (9.69 μm). We calculated cloud geometric depth of the stratocumulus clouds, whose median value varied between 141.7 m (summer) and 187.3 m (winter). All these characteristics help us better understand the climatology of stratocumulus clouds [1].

Acknowledgment

GLSG work was supported by the Romanian Nucleu Programme - Project PN 19 06 03 03. GLSG work was also supported by the University of Bucharest, PhD research grant. SS, GI and TH acknowledge the support from NO Grants 2014-2021, under Project EEA-RO-NO-2019-0423, contract no 31/01.09.2020.  BA work was supported by a grant of the Romanian Ministry of Education and Research, CNCS-UEFISCDI (Project No. PN-III-P1-1.1-TE-2019-0649) within PNCDI III. BA work was also supported by the Romanian National Core Program (Contract No. 18N/2019). Present research [1] was accepted for publication and is currently in press at Romanian Reports in Physics (http://www.rrp.infim.ro/IP/AP601.pdf).

How to cite: Saftoiu Golea, G. L., Stefan, S., Antonescu, B., Iorga, G., and Hriscan, T.: Cloud Classification and Characteristics Analysis of Stratocumulus Clouds over Bucharest-Magurele, Romania, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4375, https://doi.org/10.5194/egusphere-egu22-4375, 2022.

09:13–09:20
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EGU22-3315
Jonathan Jiang et al.

High-altitude clouds play a key role in Earth’s weather and climate which is crucial to life on Earth. However, many aspects of high-altitude cloud formation and evolution are not well understood and poorly modelled in climate simulations. Earth’s NexT-generation ICE mission (ENTICE) has been proposed to help solve this problem. ENTICE’s scientific objective is to advance our fundamental understanding of clouds by identifying how anvil clouds interact and evolve with ambient thermodynamic conditions. Combining a 94 GHz radar and multi-frequency sub-millimeter microwave radiometers, ENTICE would measure diurnally resolved ice water content, vertical profiles of cloud ice particle size, and in-cloud temperature and humidity from space. This in turn will help reduce uncertainties in cloud climate feedback and improve both climate and weather modelling. Building off previous work on the orbital characteristics required to fulfill ENTICE’s science goals, this paper attempts to improve the accuracy of past simulations. In this study, the atmospheric radiative transfer simulator (ARTS) software is used to enhance the fidelity of the simulated radar and radiometer retrievals from the previous study. ARTS is a radiative transfer software developed by the University of Hamburg and Chalmer University. The study looks at both a multi frequency radiometer and radar at 94 GHz. The results of these simulations will be used to enhance future satellite missions that study high clouds.

How to cite: Jiang, J., Johnson, K., Yue, Q., and Palo, S.: ENTICE Satellite Orbital Simulator Enhanced with ARTS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3315, https://doi.org/10.5194/egusphere-egu22-3315, 2022.

09:20–09:27
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EGU22-7589
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ECS
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Virtual presentation
Simon Whitburn et al.

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.

09:27–09:34
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EGU22-10929
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ECS
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Virtual presentation
Siyao Liu and Chuanfeng Zhao

 

This study investigates the size distribution, the mean diameter and the concentration of ice particles within stratiform clouds by using in-situ observations from 29 flights in Hebei, China. Furthermore, it examines the empirical fitting of ice particle size distributions at different temperatures using Gamma and exponential functions. Without considering the first three bins of ice particles, the mean diameter of ice particles (size range 100 – 1550 µm) is found to increase with temperature from -15 ℃ to -9 ℃ but decrease with temperature from -9 ℃ and 0 ℃. By considering the first three bins of ice particles using the empirical Gamma fitting relationship found in this study, the mean diameter of ice particles (size range 25 – 1550 µm) shows similar variation trend with temperature, while the turning point changes from -9℃ to -10℃. The ice particle number concentration increases from 13.37 L-1 to 50.23 L-1 with an average of 31.27 L-1 when temperature decreases from 0 ℃ to -9 ℃. Differently, the ice concentration decreases from 50.23 L-1 to about 22.4 L-1 when temperature decreases from -9 ℃to -12 ℃. The largest mean diameter of ice particles at temperatures around -9 ℃ and -10 ℃ is most likely associated with the maximum difference of ice and water supersaturation at that temperature, making the ice particles grow the fastest. These findings provide valuable information for future physical parameterization development of ice crystals within stratiform clouds.

How to cite: Liu, S. and Zhao, C.: Multi-case analysis of ice particle properties of stratiform clouds using in-situ aircraft observations in Hebei, China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10929, https://doi.org/10.5194/egusphere-egu22-10929, 2022.

09:34–09:41
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EGU22-13359
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On-site presentation
Nina Maherndl et al.

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.

09:41–09:48
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EGU22-4903
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Virtual presentation
Artem Feofilov and Hélène Chepfer

Clouds play an important role in the energy budget of our planet, and their response to climate warming is the largest source of uncertainty for model-based estimates of climate sensitivity and evolution. Understanding the Earth's energy budget requires knowing the cloud coverage, its vertical distributions and optical properties. Predicting how the Earth climate will evolve requires understanding how these cloud variables respond to climate warming. Documenting how the cloud’s detailed vertical structure evolves on a global scale over the long-term is a necessary step towards understanding and predicting the cloud’s response to climate warming.

Satellite observations have been providing a continuous survey of clouds over the whole globe. Infrared sounders have been observing our planet since 1979. Despite an excellent daily coverage and daytime/nighttime observation capability, the height uncertainty of the cloud products retrieved from the observations performed by these space-borne instruments is large. This precludes the retrieval of the cloud’s vertical profile with the accuracy needed for climate relevant processes and feedback analysis. This drawback does not exist for active sounders, which measure the altitude-resolved profiles of backscattered radiation with an accuracy on the order of 1−100 meters.

All active instruments share the same measuring principle – a short pulse of laser or radar electromagnetic radiation is sent to the atmosphere and the time-resolved backscatter signal is collected by the telescope and is registered in one or several receiver channels. However, the wavelength, pulse energy, pulse repetition frequency (PRF), telescope diameter, orbit, detector, or optical filtering are not the same for any pair of instruments. These parameters define the active instruments’ capability of detecting atmospheric aerosols and/or clouds for a given atmospheric situation and observation conditions (day, night, averaging distance). In merging different satellite data, the difficulty is to build a multi-lidar record accurate enough to constrain predictions of how cloud evolve as climate warms.

In this work, we discuss the approach to merging the measurements performed by the relatively young space-borne lidar ALADIN/Aeolus, which has been orbiting the Earth since August 2018 and operating at 355nm wavelength with the measurements performed since 2006 by CALIPSO lidar, which is operating at 532nm and is near the end of its life-time.

The approach consists of:

(a) developing a cloud layer detection method for ALADIN measurements, which complies with CALIPSO cloud layer detection;

(b) comparing/validating the resulting cloud ALADIN product with the well-established CALIOP/CALIPSO cloud data set;

(c) developing an algorithm for merging the CALIOP and ALADIN cloud datasets;

(d) applying the merging algorithm to CALIOP and ALADIN data and build a continuous cloud profile record;

(e) adapting this approach to future missions (e.g. ATLID/EarthCare).

In the presentation, we show the results of preliminary analysis performed for the first two steps and discuss the future development of this approach.

How to cite: Feofilov, A. and Chepfer, H.: Incorporating ALADIN/Aeolus lidar observations into a climate record of cloud profile, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4903, https://doi.org/10.5194/egusphere-egu22-4903, 2022.

09:48–09:55
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EGU22-4633
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ECS
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On-site presentation
Felix Müller and Torsten Seelig

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.

Fri, 27 May, 10:20–11:50

Chairpersons: Virginie Capelle, Nina Mahendrl

10:20–10:30
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EGU22-10464
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ECS
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solicited
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Virtual presentation
Anna Gialitaki et al.

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.

10:30–10:37
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EGU22-4647
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ECS
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On-site presentation
Gabriel Calassou et al.

According to the European Environmental Agency, industrial fine particles emissions have represented respectively 15% and 6.5% of PM10 and PM2.5 emissions in Europe between 2013 and 2018. Stack emissions are a significant contributor to the atmospheric PM burden. Satellite imagery is a proven technique for stack plume detection although the quantitative retrieval of aerosol properties within the plume remains a challenge. We propose a new method to detect stack plume aerosol properties from hyperspectral satellite imagery.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is a medium-resolution (30 m) hyperspectral imaging mission launched in 2019 and carrying a camera with 239 spectral channels between 0.4 and 2.5 µm. Additionally to PRISMA data, SENTINEL-2/MSI observations within a few days delay from PRISMA acquisition are used in the proposed method to better constrain the surface reflectance conditions over the targeted scenes.

 Three industrial sites have been observed: a coal-fired power plant in Kendal, South Africa (on 25/09/2021), a steel plant in Wuhan, China (on 24/03/2021), and gas flaring at a gas extraction site in Hassi Messaoud, Algeria (on 09/07/2021).The Sentinel-2 acquisitions are set to the PRISMA spectral resolution thanks to a fusion method called the Coupled Non-Negative Matrix Factorisation (Yokoya et al., TGRS, 2011).

Then, the aerosol optical depth and the particulate radius  are  retrieved using an optimal estimation method (Calassou et al., RS, 2020). The retrieved radii range from 0.15 to 0.3 µm with an uncertainty of 5 to 20 nm for the flare emission, from 0.3 to 0.7 µm with an uncertainty of 15 to 40 nm for the steel site emission and from 0.4 to 1.25 µm with an uncertainty of 0.05 to 0.2 µm for the coal plant. The retrieved AOTs vary from 0.2 to 1 for the flaring site, from 0.5 to 3.4 for the steel site plume and from 0.6 to 2.45 for the coal plant emission. The retrieved aerosol radii are of the same order of magnitude as literature data for the flares, while retrieved radii for the coal plant and the steel site are higher due to the potential contribution of a coarse aerosol mode than is not accounted for in the procedure.

The proposed case studies demonstrate the ability of a coupled hyper/moderate spectral satellite imagery for stack plume analysis and open a way to estimate particulate flux emission from stack using space remote sensing. 

How to cite: Calassou, G., Foucher, P.-Y., and Léon, J.-F.: Characterization of the physical properties of industrial plume aerosols from PRISMA hyperspectral images., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4647, https://doi.org/10.5194/egusphere-egu22-4647, 2022.

10:37–10:44
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EGU22-4816
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ECS
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Virtual presentation
Bijoy Krishna Gayen

The Aerosol Optical Depth (AOD) from several to tens of kilometres coarse resolution satellite images such as MODIS and VIIRS sensors is a standard geophysical product that has gained significant importance for atmospheric pollution and climate studies. In regional studies, coarser-resolution limits the application of these products. AOD retrieval is highly challenging due to subjected high complex surface characteristics and dynamics aerosol properties. Many methods have been developed based on the state-of-the-art radiative transfer (RT) model or the Look-Up-Table (LUT) approach, which is very time-consuming. Therefore, in this paper, we proposed the integration of two simplified algorithms for retrieving AOD from Landsat 8 Images; one is Simplified and Robust Surface Reflectance Estimation Method (SREM), and the second is Simplified Aerosol Retrieval Algorithm (SARA). SREM has been used for the estimation of LSR from top-of-atmospheric reflectance (TOA) with support of geolocation information, which is one of the key input in SARA for AOD retrievals from TOA. Both simplified algorithms are developed based on the RT equation without using a LUT approach, which makes them fast and robust in their inherent retrieval processes. The method is validated using Aerosol Robotic Network (AERONET) measurements over two distinct locations, Beijing (China) and Indo Gangetic Plain (IGP) (India, Bangladesh and Nepal foothills). For cross-evaluation of SREM LSR in AOD retrieval, available LSR products LaSRC (Landsat 8 Surface Reflectivity Code) have been taken as input to SARA. Also, the results of the SREM-SARA algorithm have been evaluated with a collection of 6 (C6) MODIS MOD04_3k products at 3 km spatial resolution. The performance of this algorithm is evaluated with four statistical metrics: correlation coefficient (R2), root means square error (RMSE), mean absolute error (MAE) and expected error (EE). The 30 m AOD retrieved from the SREM-SARA algorithm showed high consistency with AERONET AOD measurements, with R2  ~ 0.98, and that approximately 97.44% of the retrievals fall within the EE with a low RMSE of 0.072 and MAE of 0.037 over the Beijing area. However, in the IGP region, surface features distributed consist of various land covers with high reflective surfaces and complex aerosol type distribution; SREM-SARA-derived AOD showed relatively high agreement with AERONET measurements, with an R2~ 0.90, RMSE~0.168, and MAE~0.141 compared to the LaSRC corrected LSR, where LaSRC-SARA showed R2 ~ 0.61, RMSE~0.298, and MAE~0.250. Comparison of SREM-SARA retrieved AOD with MOD04_3k revealed that the retrieved AOD agree quite well with MODIS C6 products (spatial  R2<= 0.80). In terms of spatial coverage, the MODIS product has not as good as the SREM-SARA AOD. These results suggested the robustness of the combination of SREM-SARA and the potential for effective in retrieving AOD at the finer scale resolution that holds the impression of the localized process in the atmospheric pollution and thereby lays out a way to study the atmosphere and climate interaction at the finer scale. 

How to cite: Gayen, B. K.: Aerosol Optical Depth Retrieval from Landsat 8 OLI Images using  SREM and SARA algorithms over complex surfaces, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4816, https://doi.org/10.5194/egusphere-egu22-4816, 2022.

10:44–10:51
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EGU22-3126
Irène Xueref-Remy et al.

Since 2018, the continuous monitoring of atmospheric aerosols by remote sensing technics was developed in the Aix-Marseille area, south-east of France. Two complementary sites located about 70 kilometers from each other, the first one at the Observatoire de Haute Provence (OHP) in a rural area and the second one in an urban environment at Longchamp site in the Marseille city center, were equiped with automatic aerosols Lidars (CIMEL CE376) and photometers (CIMEL CE318-T). The OHP site is part of the ACTRIS-France infrastructure for the long-term monitoring of aerosols, water vapor and reactive trace gases. The Longchamp one, that belongs to the regional air quality agency ATMOSUD, should join this infrastructure soon as well and is supported by the ANR COoL-AMmetropolis project for the present study. The ACTRIS-Fr data are hosted in the national AERIS/ICARE database. Furthermore, two other sites are equiped with remote sensing facilities : a ceilometer (Vaisala CL31) at Marignane, 25 km west of Marseille center, and radiosoudings at Nimes, about 70 km away. The datasets collected at the four sites allow us to study the boundary layer height variability in this coastal area, which is characterized by complex atmospheric dynamics and a tortuous topography. The boundary layer height is a key parameter to understand the variability of greenhouse gases and pollutants and its determination will be of great help for air quality and climate related studies. Also, our Lidars datasets are exploited to study the long-range transport of aerosols plumes outcoming from different sources (pyrogenic, volcanic, desertic…) and to  characterise the optical properties of such aerosols, which play a role on air quality and climate that needs to be better characterized. An overview of the results obtained so far will be presented.

How to cite: Xueref-Remy, I., Riandet, A., Bellon, C., Khaykin, S., Blanc, P.-E., Gomez, F., Armengaud, A., Gille, G., Popovici, I., Pascal, N., Podvin, T., and Goloub, P.: Continuous monitoring of atmospheric aerosols by LIDAR remote sensing technics in the south-east of France at the Observatoire de Haute Provence and Marseille Longchamp sites in the framework of ACTRIS-France and of the ANR COoL-AMmetropolis project., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3126, https://doi.org/10.5194/egusphere-egu22-3126, 2022.

10:51–10:58
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EGU22-5345
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ECS
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On-site presentation
Denghui Ji et al.

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.

10:58–11:05
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EGU22-7591
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ECS
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Alkistis Papetta et al.

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.

11:05–11:12
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EGU22-1592
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ECS
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Virtual presentation
Xingchuan Yang and Chuanfeng Zhao

Wildfires are an important contributor to atmospheric aerosols in Australia and could significantly affect regional and even global climate. This study investigates the impact of fire events on aerosol properties along with the long-range transport of biomass burning aerosol over Australia using multi-year measurements from Aerosol Robotic Network (AERONET) at ten sites over Australia, satellite dataset derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), reanalysis data from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and back-trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). The fire count, FRP, and AOD showed distinct and consistent interannual variations with high values during September-February (Biomass Burning period, BB period) and low values during March-August (non-Biomass Burning period, non-BB period) every year. Strong correlation (0.62) was found between fire radiative power (FRP) and aerosol optical depth (AOD) over Australia. Furthermore, the correlation coefficient between AOD and fire count was much higher (0.63-0.85) during October-January than other months (-0.08-0.47). Characteristics of Australian aerosols showed pronounced difference during BB period and Non-BB period. AOD values significantly increased with fine mode aerosol dominated during BB period, especially in northern and southeastern Australia. Carbonaceous aerosol was the main contributor to total aerosols during BB period, especially in September-December when carbonaceous aerosol contributed the most (30.08-42.91%). Aerosol size distributions showed a bimodal character with both fine and coarse aerosols particle generally increased during BB period. The mega fires during the BB period of 2019/2020 further demonstrated the significant impact of wildfires on aerosol properties, such as the extreme increase in AOD for most southeastern Australia, the dominance of fine particle aerosols, and the significant increase in carbonaceous and dust aerosols in southeastern and central Australia, respectively. Moreover, smoke was found as the dominant aerosol type detected at heights 2.5-12 km in southeastern Australia in December 2019 and at heights roughly from 6.2 to 12 km in January 2020. In contrast, dust was detected more frequently at heights from 2 to 5 km in November 2019, January, and February 2020. A case study emphasized that the transport of biomass burning aerosols from wildfire plumes in eastern and southern Australia significantly impacted the aerosol loading, aerosol particle size, and aerosol type of central Australia.

How to cite: Yang, X. and Zhao, C.: Optical, physical and chemical characteristics of Australian aerosols associated with fire events from 2002 to 2019, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1592, https://doi.org/10.5194/egusphere-egu22-1592, 2022.

11:12–11:19
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EGU22-2668
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On-site presentation
Perla Alalam and Hervé Herbin

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.

11:19–11:26
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EGU22-3116
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ECS
Adam T. Ahern et al.

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.

11:26–11:33
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EGU22-10650
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ECS
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Virtual presentation
Francesco Romeo et al.

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.

11:33–11:40
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EGU22-12518
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ECS
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Archana Devi and Sreedharan K Satheesh

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.

11:40–11:50
Conclusions