The aim of this session is to discuss current developments, challenges and opportunities in the characterisation of aerosols and clouds, in the study of aerosol-cloud interactions and their long-term effects, using ground-based, airborne and spaceborne active and passive remote sensing systems. We invite submissions of theoretical, methodological and empirical studies to advance the field of aerosol/cloud remote sensing, and to improve our understanding of aerosol-cloud interactions and their effect on the climate.
Orals: Mon, 4 May, 14:00–17:35 | Room D1
The Multi-Viewing Multi-Channel Multi-Polarisation Imager (3MI) on the METOP-SG A platform is a 2D wide field of view radiometer dedicated to aerosol and cloud observation for atmospheric composition, air quality, numerical weather prediction and climate monitoring. Leveraging on three missions of its precursor instrument POLDER, the 3MI will provide multi-spectral (from 410 to 2130 nm), multi-polarisation (-60°, 0°, and +60°), and multi-angular (10 to 14 views) observation of the Earth reflectance. Although primarily dedicated to aerosol monitoring, the multiangle and polarisation 3MI observation will provide advanced capabilities for monitoring of cloud properties and water vapour from an operational meteorological mission. In particular, 3MI will allow for detailed observation of cloud parameters that are crucial in understanding the complex interactions between aerosols and clouds, sources of large discrepancies among climate models.
Among other objectives, 3MI will enable better characterisation of cloud microphysics (phase, particle size distribution) and provide access to cloud vertical extent (geometrical thickness), all of which being provided in near-real time by so-called Day-1 algorithm.
In addition of the EUMETSAT official Day-1 clouds product, an optimal-estimation-based algorithm that makes use of the high information content of 3MI observations is being developped to retrieve vertical profile of cloud properties (LWC, extinction coefficient). While the retrieval is performed assuming an idealized but physically based distribution of LWC/IWC and extinction coefficient, the major challenge in this approach remains the computational burden involved by the iterative optimization.
To partly overcome this problem, we take advantage of the higher spatial resolution provided by the MetImage, also aboard METOP-SG A, and propose a synergistic approach based on statistical learning to improve the a priori and initial state vectors used by the optimal-estimation-based algorithm.
We will describe here the status and recent progress made for the development of the research cloud products leveraging the synergy of 3MI and MetImage observation.
How to cite: Riedi, J., Hioki, S., Compiegne, M., Labonnote, L., Henriot, N., Thieuleux, F., Ferlay, N., Truffier, A., Penide, G., and Cornet, C.: Towards retrieval of vertical cloud profiles from synergistic use of multi-angle, mult-ispectral, multi-polarimetric and multi-resolution observations of the 3MI and MetImage., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21882, https://doi.org/10.5194/egusphere-egu26-21882, 2026.
The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is providing dense global observation of vertical air motion and aerosol-cloud-precipitation properties at 100m vertical resolution. This study introduces the Level 2 cloud-precipitaion microphysics products of the Japanese Aerospace Exploration Agency (JAXA). Several versions of the JAXA Level 2 standard cloud-precipitation and air-motion products were released in 2025. Further update of these radar-only (CPR_CLP), radar-lidar (AC_CLP), and radar- lidar-imager synergy (ACM_CLP) products, as well as the release of JAXA L2 precipitation rate products (CPR_RAS, AC_RAS, ACM_RAS ) are planned at the beginning of 2026 for better understanding of the cloud-precipitation processes.
How to cite: Sato, K., Okamoto, H., Nishizawa, T., and Yoshitaka, J.: Overview of EarthCARE JAXA Level 2 Cloud-Precipitation Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21346, https://doi.org/10.5194/egusphere-egu26-21346, 2026.
Doppler velocity inside clouds is measured from space for the first time by Cloud Profiling Radar (CPR) onboard Earth Clouds, Aerosols and Radiation Explorer (EarthCARE). The EarthCARE JAXA Level 2 cloud products are derived from CPR, 355-nm high-spectral-resolution lidar (ATLID) and Multi-Spectral Imager (MSI). The CPR standalone cloud product (CPR_CLP) and the CPR-ATLID synergy product (AC_CLP) were released to the public in March 2025. Version up of these two products as well as CPR-ATLID-MSI synergy product (ACM_CLP) were released in December 2025. These products include cloud mask, height resolved cloud particle types, cloud particle habits, cloud/precipitation microphysics, terminal velocity of cloud and precipitation particles and vertical air motion.
This paper presents global analysis of cloud microphysics and vertical velocity by using EarthCARE data. We demonstrate how such information is used to enhance our understanding of cloud formation. Comparisons with existing global products were also conducted. The retrieved cloud and vertical motion were also used to evaluate high resolution numerical models.
How to cite: Okamoto, H., Sato, K., Nishizawa, T., Jin, Y., Zhang, H., and Shaik, A.: Global analysis of vertical motion and cloud properties by using EarthCARE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21270, https://doi.org/10.5194/egusphere-egu26-21270, 2026.
Observational data for cloud processes are captured at a huge range of spatial resolutions, from particle processes resolved in micrometres, to mesoscale systems measured with horizontal sampling distances of kilometres. However, between these resolution ranges, at the scale of tens of metres, few observational constraints exist. Nevertheless, there is an increasing understanding that processes (e.g. phase heterogeneity) are occurring at scales which fall between the observable length-scales of current sensing paradigms, and can drastically alter cloud evolution and their resulting radiative effects.
Motivated by this relative lack of observational data, we have developed and deployed a suite of physical property retrieval tools for Sentinel-2 imagery, a 10 m/pixel multispectral satellite, as part of the Clouds Decoded project (funded by the UK’s Advanced Research and Invention Agency). In this presentation, we will provide a tour of the algorithms and techniques developed---and released open-source---for cloud type classification, cloud height, optical depth, ice/liquid phase, and particle effective radius. These involve a mix of radiative transfer modelling and inversion, computer vision techniques, and machine learning, and are validated against ground-based measurements from ACTRIS sites. In addition to describing the methods themselves, we will also provide an overview of the large, open dataset we have produced, which comes as both individual products and in a regridded, parameterised format, and which will also be made open to the community. We will highlight the potential uses of this data and hope to encourage the community to adopt it as a source of high-resolution information about clouds that can complement and enhance existing data sources and modelling efforts.
How to cite: Francis, A., Bertozzi, B., Borne--Pons, P., Campbell, J., and Czerkawski, M.: Clouds Decoded: High Resolution Cloud Property Retrievals in Sentinel-2, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19138, https://doi.org/10.5194/egusphere-egu26-19138, 2026.
Cloud condensation nuclei (CCN) play a critical role in aerosol–cloud interactions (ACI). It has been shown that the column number of aerosol particles exceeding a predetermined threshold radius (NCCN) is a suitable CCN proxy. Previously this CCN proxy has been estimated from PARASOL using Level-2 aerosol microphysical and/or optical property retrievals. With the launch of SPEXone on Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, further improvements in NCCN retrievals can be achieved. In particular, retrieved refractive index can enable estimation of the volume fraction of aerosol-water, facilitating the derivation of dry aerosol size distibution and correspondingly dry CCN. In addition, retrieved aerosol layer height (ALH) can be used to estimate the boundary layer (BL) contribution of NCCN (NCCN(BL)).
We developed a deep neural network (NN) algorithm as an extension of the Remote sensing of Trace gas and Aerosol Products (RemoTAP)-NN framework to directly retrieve dry NCCN, and NCCN(BL) from SPEXone measurements. The algorithm was trained on synthetic SPEXone measurements generated from a three-mode aerosol representation including fine mode, insoluble coarse/dust mode, and soluble coarse mode. Initial validation was performed using independent synthetic measurements, based on the ECHAM-HAM global aerosol-climate model.
For validating NCCN from real SPEXone observations, we use collocated AERONET data, for which both dry and ambient NCCN are computed. On the log base 10 scale, the NN algorithm achieved RMSDs of 0.33 (dry) and 0.21 (ambient) over land, and 0.21 (dry) and 0.20 (ambient) over ocean. The slightly higher RMSD for dry NCCN is attributed to the cases in which the AERONET derived refractive index reaches its upper limit of 1.6. In comparison, CCN proxies derived using the classical RemoTAP algorithm exhibited RMSDs approximately 20% higher.
Ongoing work focuses on validating the retrieved fraction of aerosols within the boundary layer using EarthCARE ATLID Level-2 observations.
How to cite: Hannadige, N., Fu, G., van Diedenhoven, B., Jia, H., Yuan, Z., and Hasekamp, O.: Estimation of Cloud Condensation Nuclei (CCN) from SPEXone on PACE using a neural network retrieval algorithm: Comparison to AERONET and ATLID/EarthCARE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14047, https://doi.org/10.5194/egusphere-egu26-14047, 2026.
In this work, we describe a newly established network of fisheyes sky cameras (SKYCAM) at a dozen of locations worldwide for continuous cloud monitoring. Each location is equipped with two cameras at about a 100m distance from each other. This dual view enables the retrieval of the altitude of the cloud base. The cameras acquire a picture (1000 x 2000) of the sky every minute in three different wavelength (Red, Green and Blue) and the data are directly sent to a central facility for processing.
The data are calibrated both for precise geometry (using a variety of techniques including systematic observation of the sun) and radiometry (using Radiative transfer and aerosol information). Using the cloud base information derived from stereo, calibrated radiances and radiative transfer, additional properties of the cloud can be derived (cloud thickness and top height) that can be used to re-construct observations from satellite data. We apply this technique to validate cloud observations from Sentinel 2 /3. This method enables an objective analysis of the remotely sensed cloud mask performances and possible improvements by providing a large range of surface conditions (vegetation, snow, bright surfaces, urban area) and seasons as the system operates continuously.
At some locations, this system is complemented by surface reflectance measurements over a 100m x 200m area performed from a multispectral camera (CAMSIS) mounted a high tower and/or measurements from AERONET which enable the development/validation of more advanced products (aerosol spatialization, incoming shortwave and photosynthetically active radiation, satellite derived surface reflectance).
How to cite: Vermote, E., Santamaria Artigas, A., and Skakun, S.: A sky camera network (SKYCAM) for the validation of the remote sensing of clouds from Sentinel 2/3, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1631, https://doi.org/10.5194/egusphere-egu26-1631, 2026.
We present the latest developments in SPEXone aerosol products. Firstly, the RemoTAP-Hybrid algorithm is developed, combining Multiple Collaborated Neural Networks (MCNN) with a full physics inversion. RemoTAP-Hybrid improves SPEXone global aerosol retrievals regarding accuracy and speed. Secondly, following an analysis on AERONET data, we extend the aerosol description to a parametric 4 mode by adding a fine non-spherical mode, in addition to a fine spherical, coarse non-spherical, and coarse spherical mode. The new aerosol description extends retrieval capability for scenes dominated with non-spherical fine particles. Thirdly, higher level products are developed for aerosol composition (black carbon, brown carbon, dust, fine/coarse water/non-absorbing components, fine non-spherical non-absorbing component) based on the retrieved complex refractive index and aerosol volume per mode. We will present the global and seasonal distribution of chemical composition and aerosol microphysics retrieved from SPEXone.
We validate the retrieved aerosol properties with AERONET data for AOD, Angstrom Exponent, absorption AOD, and SSA, showing unprecedented accuracy. The RMSE for AOD is 0.051 (0.037) over land (ocean), and 78.3% (80.8%) of the AOD retrievals are within the Global Climate Observing System (GCOS) requirement. The RMSE for Angstrom Exponent is 0.186 (0.207), for absorption AOD is 0.023 (0.014), and for SSA is 0.038 (0.044) over land (ocean). Finally, we present our latest results on aerosol retrievals above cloud from SPEXone.
How to cite: Fu, G., Lu, S., Gao, M., Rietjens, J., and Hasekamp, O.: New developments on SPEXone aerosol products on the NASA PACE mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11277, https://doi.org/10.5194/egusphere-egu26-11277, 2026.
3MI is a new mission launched in August 2025 on board of the EPS-SG A satellite. The purpose of 3MI is to provide multi-spectral, multi-polarisation, and multi-angular images of the Earth TOA outgoing radiance to characterise the microphysical properties of the atmosphere.
The design consists of two detectors (SWIR and VNIR) and a rotating filter and polariser wheel. The instrument acquires images under 12 spectral bands ranging from 410 nm to 2130 nm. Nine of the bands acquire polarised images at 60°, 0° and -60°. The multi-view is achieved by several successive overlapping acquisitions of the same Earth-Atmosphere target under 14 different angles, thanks to the large FOV of 3MI.
At L1, 3MI provides two different products to users: L1B, which represent the acquisitions as the satellite orbits the Earth; and L1C, which contain, for each point on the surface, the multiple angles under which it is observed.
After a successful SIOV, the instrument is undergoing the commissioning phase. Here we will present the first assessment of the 3MI performance at L1 in both the radiometric and geometric aspects, the lessons learned with respect to the ground calibration and, briefly, the potential for the L2 aerosol and cloud products.
How to cite: Guermazi, H., Vazquez-Navarro, M., Fougnie, B., De Bartolomei, M., Landier, L., and Ouvrard, A.: Overview of first images of the EPS-SG/3MI Polarimeter Instrument and Level-1 Performances , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20674, https://doi.org/10.5194/egusphere-egu26-20674, 2026.
Tha Canadian High altitude Aerosol Water vapour and Cloud (HAWC) mission is built upon innovative Canadian optical remote sensing technology and will provide important information related to water vapour, aerosols and cloud microphysics in the upper troposphere and lower stratopshere. HAWC is made up of three Canadian passive optical instruments, the Spatial Hetrodyne Observations of Water (SHOW), the Aerosol Limb Imager (ALI) and the Thin Ice Cloud and Far infraRed Emissions (TICFIRE) where the first two look at scattered sunlight in the atmospheric limb from low earth orbit and the latter instrument measures thermal emission in the nadir, also from low earth orbit. This presentation will outline the technology, the measurements and the expected scientific return of the HAWC mission that is expected to launch in the first part of the next decade.
How to cite: Degenstein, D., Bourassa, A., Walker, K., Huang, Y., and Blanchet, J.-P.: The Canadian High altitude Aerosol Water vapour and Cloud (HAWC) Mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15306, https://doi.org/10.5194/egusphere-egu26-15306, 2026.
Shipping activities emit aerosols that can modify the microphysical and optical properties of low-level marine clouds. In the framework of the ESA ACtIon4Cooling (Aerosol Cloud Interactions for Cooling) project, marine clouds influenced by ship-track emissions are investigated as natural analogues to assess the monitoring capabilities of various Solar Radiation Modification (SRM) approaches, including Marine Cloud Brightening (MCB).
In this study, we combine high-resolution satellite observations from SUOMI-NPP/VIIRS (Visible Infrared Imaging Radiometer Suite) and Sentinel-5p/TROPOMI (TROPOspheric Monitoring Instrument) with vessel density data from EMODNET (European Marine Observation and Data Network) to detect cloud anomalies in the shipping corridors and quantify the ship-relevant cloud perturbations. VIIRS-derived cloud variables include cloud top height, cloud top emissivity, effective radius, liquid water path, and optical depth, while TROPOMI provides similar cloud information in the Oxygen A-band. Additional TROPOMI L2 products such as the absorbing aerosol index, aerosol type, and tropospheric NO₂ columns can also provide suitable proxies for ship emissions. The detection of the ship-tracks can be further improved when actual AIS (Automatic Identification System) data are used instead of the monthly aggregated EMODNET vessel density maps.
Cargo and tanker ships dominate the upper range of ship lengths, often between 150 and 300 meters, with some exceeding 400 meters, while passenger ships also include very large vessels over 200 meters, corresponding to cruise liners. Since ship length serves as a proxy for vessel capacity and engine power, larger ships generally consume more fuel and emit greater amounts of aerosol precursors. As a result, cargo, tanker, and passenger ships are more important for atmospheric emissions and ship track formation, even though smaller vessels might be more numerous.
Perturbations of the cloud parameters due to ship emissions are detected using machine learning classifiers with Logistic Regression being the baseline and more advanced models like Random Forest Regressor and Gradient Boosting (XGBoost). To quantify the ship-relevant cloud perturbations, the detected perturbations are fed directly to the Radiative Transfer Model pyDOME, which returns the full radiance field together with TOA (top-of-the atmosphere) forcing, surface irradiance and heating‑rate profiles for every perturbation. In order to synthesize the observations-based results and to explore the large-scale implications of the perturbations in marine low-level clouds, simulations are conducted with the state-of-the-art atmospheric general circulation model ICON (the ICOsahedral Non-hydrostatic model).
How to cite: Argyrouli, A., Hedelt, P., Seo, S., Lutz, R., Efremenko, D., Quaas, J., Luo, H., Marinou, E., Voudouri, K. A., Tsichla, M., and Amiridis, V.: Investigating the Shipping Effect on Marine Clouds Using Satellite Observations and Vessel Density, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20255, https://doi.org/10.5194/egusphere-egu26-20255, 2026.
As different aerosol components have different effects on the climate, it is important to retrieve their global distribution over the longest feasible period. For this reason, we develop an aerosol composition retrieval based on a combination of three different satellite-based instruments which cover with their precursor and planned successor instruments the time from 1995 until 2030. The current algorithm is working with SLSTR (Sea and Land Surface Temperature Radiometer) aboard Sentinel 3A and 3B, the Infrared Atmospheric Sounding Interferometer (IASI), and the Global Ozone Monitoring Experiment-2 (GOME-2), both on METOP A/B/C. These instruments provide complementary information content due to combining measurements in the UV and VIS from GOME-2 with measurements in the TIR from IASI and the added value of the second viewing direction from the dual-view of SLSTR.
The new retrieval algorithm ROCAS (Retrieval Of Composition of Aerosols from Satellite) will be presented. ROCAS combines the preprocessed Level 1 data form the three instruments in so called super-pixels and performs an Optimal Estimation based retrieval after cloud masking and spectral consistency filtering. Retrieved parameters are the surface albedo at different wavelengths, the surface temperature, atmospheric column relative humidity, aerosol optical depth (AOD) and the individual AOD contributions for five aerosol components (black carbon, organic carbon, sulphate, sea salt and mineral dust).
With ROCAS, we can observe the expected patterns of the individual aerosol components, such as mineral dust over the deserts and their outflow regions, and black and organic carbon where smoke by large fires is transported. We can also observe sulphate over industrial regions in India, the USA and Europe.
ROCAS has the potential to quantitatively monitor aerosol composition and with this additional information to refine our understanding of their climate impact. In this study we show initial retrieval results for the individual aerosol components including a case study, a first validation and a comparison to other datasets including retrieval results from active instruments (EarthCare) and model data. The presentation will conclude with a discussion of the unique capabilities / additional information content for aerosol composition monitoring and remaining limitations of ROCAS.
How to cite: Stöffelmair, U., Popp, T., Vountas, M., and Bösch, H.: Aerosol Composition Retrieval from a combination of three satellite based instruments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4795, https://doi.org/10.5194/egusphere-egu26-4795, 2026.
Aerosols play an important role in governing the Earth's radiation budget. In addition to scattering and absorbing radiation themselves, they affect the formation and properties of clouds. In both of these processes, and especially in the latter, are affected by the uptake of water. Nevertheless, the particulars of the uptake of water by aerosols remain poorly understood. The efficiency of water uptake (i.e., hygroscopicity) for a given aerosol is highly sensitive to its composition, history, and mixing state, making it a difficult property to model or predict. This leads to stark disagreements between models using different aerosol prescriptions, which greatly contributes to the large uncertainties in the resulting radiative forcing estimates. A better understanding of aerosol water uptake is therefore crucial for accurate warming predictions.
This understanding is currently held back by a lack of data. In most cases, in-situ measurements of aerosol properties are preceded by a drying step that removes any information about water content, so hygroscopicity data is only available for the small subset of studies where it is explicitly targeted. As such the spatial and temporal coverage of these data are very limited. To properly inform and constrain model choices, then, a satellite dataset would be incredibly valuable.
While the aerosol water uptake is a difficult property to measure from space, the rich information content of multi-angle polarimeter (MAP) nstruments such as POLDER-PARASOL and SPEXone-PACE presents new opportunities. We aim to use these instruments to produce a satellite dataset of the aerosol water content, and use it to assemble a first-of-its-kind global climatology on aerosol hygroscopicity. To retrieve a volume water fraction we compare the retrieved real component of the refractive index to an average refractive index for dry material and the known refractive index of pure water, assuming a linear scaling with the volume fraction. Here, refractive indices are retrieved from MAP measurements using the RemoTAP algorithm. The resulting water content measurements can then be used in combination with ambient relative humidity data from reanalysis products to estimate the hygroscopicity.
Recent years have seen efforts to validate these volume water fraction retrievals using both airborne (campaign) and ground-based in-situ measurements, with promising results. We now feel sufficiently confident to begin assembling the data into a global climatology, beginning with the POLDER era. We investigate regional and seasonal trends in the data, and compare them to the corresponding average relative humidities from ERA5 reanalysis to get an indication of hygroscopicity. Initial findings include a clear land/ocean divide, as well as a north-south contrast consistent with pollution patterns. Regions known for biomass burning are additionally investigated for seasonal patterns.
We briefly review the results of the aforementioned validation process, and present a first look at a global climatology on water uptake for the years 2006 through 2009.
How to cite: Mens, J., van Diedenhoven, B., and Hasekamp, O.: Remote Sensing of Aerosol Water: First Look at a Climatology on Water Uptake, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21195, https://doi.org/10.5194/egusphere-egu26-21195, 2026.
Characterizing aerosol dynamics in coastal urban areas remains a challenge due to the interplay between complex topography and diverse emission sources. This study presents a framework integrating ground-based lidar observations with high-resolution Weather Research and Forecasting (WRF) simulations to resolve the three-dimensional structure of the Eastern Mediterranean (EM) boundary layer. We validate a high-resolution WRF model using diverse ground-based measurements. By evaluating distinct synoptic regimes such as long-range dust transport and complex multi-source pollution layering, we demonstrate how numerical modelling complements lidar-derived profiles of aerosols, humidity, and thermodynamics. A key finding of this integrated approach is the WRF capacity to provide relatively high accuracy estimates during daytime periods when solar background noise typically limits lidar signal, enabling continuous, 24-hour characterization of complex urban vertical profile. In particular, the WRF model successfully simulated key atmospheric features observed by lidar, supporting its application as a validated, complementary tool for refining urban air quality representation, especially during periods when continuous observational data are limited or unavailable. Our analysis also shows that surface-level monitoring largely underestimates the vertical complexity of pollution transport in regions like Haifa and Tel Aviv. This study presents a transferable methodology for refining aerosol and moisture distribution assessments in urban areas, where pollution layering conditions are difficult to predict.
How to cite: Chudnovsky, A., Ohneiser, K., Ansmann, A., Avisar, D., Berkovic, S., Roter, F., and Rostkier-Edelstein, D.: Synergistic lidar and modelling frameworks for pollution monitoring: assessing model reliability between diverse urban sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8266, https://doi.org/10.5194/egusphere-egu26-8266, 2026.
Posters on site: Mon, 4 May, 10:45–12:30 | Hall X5
Clouds play a crucial role in the Earth’s energy balance, thereby influencing its climate system. Cloud fraction (CF) is one of the important Essential Climate Variables. The discrepancies among satellite CF products are due to four effects: the Resolution effect, the View angle effect, the ability of the sensor to detect clouds, and the difference in satellite overpass time. Additionally, the reanalysis data is not a direct observation but rather depends on the model parameters. To understand the diurnal variation of CF, we need to use data from a geostationary satellite after bias correction, if any. To understand the cloud processes that are inevitable in the climate system, we need to compare and study existing cloud products and understand the CF data and biases.
This study leverages INSAT 3D geostationary satellite data to monitor cloud fraction changes over the Indian region from 2014 to 2024, providing high temporal and spatial resolution insights. We examine diurnal and seasonal patterns in CF and compare them against bias-corrected MODIS, MISR data, and study diurnal variation using ERA5 reanalysis datasets. Preliminary analysis reveals systematic biases in INSAT-3D CF, with differences in amplitude and phase relative to ERA5. Unless the biases in INSAT 3D are quantified and corrected, the diurnal pattern in CF cannot be understood robustly over the Indian region.
To overcome the Resolution effect, we employ a pattern recognition technique having feature vector to correct the CF bias in the INSAT 3D data using the CF from high spatial resolution satellites such as Sentinel 2 (QA60 cloud mask band). The optimized feature vector includes - Ae (standard method estimate of CF), Aedge (fraction of cloudy pixels that border a clear pixel on at least 1 of their eight sides or vertices), the first moment invariant (Hu moment), Mean, Variance and entropy of the grey levels in the scene, which makes it a six dimensional vector. The radiance from the thermal band of Landsat 8/9 can be used as an extra dimension during nighttime. The cloud masks that have similar spatial features will have similar true CFs and the degree of correction depends upon ratio of cloud size to pixel size and distribution of true cloud area.
How to cite: Ramachandran, A. and Dey, S.: Understanding and Correcting Biases in INSAT 3D Cloud Fraction Using High-Resolution Satellite Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1132, https://doi.org/10.5194/egusphere-egu26-1132, 2026.
Aerosols play a critical role in Pakistan’s atmospheric environment by influencing air quality, public health, weather systems, and regional climate dynamics. Given the country’s diverse geography ranging from arid deserts and fertile plains to high-altitude mountainous regions. Pakistan experiences complex aerosol compositions arising from both natural sources (desert dust, sea salt, biogenic particles) and anthropogenic activities (industrial emissions, vehicular exhaust, biomass burning, and urban pollution). This review synthesizes the current state of aerosol remote sensing research in Pakistan, with a particular focus on satellite-based observations, ground-based networks, and integrated modeling approaches. We examine the application of major remote sensing platforms and products for retrieving aerosol optical depth, aerosol type, spatial–temporal variability, and long-term trends across key regions such as the Indo-Gangetic Plain, major urban centers, and transboundary dust corridors. The review highlights how aerosol remote sensing has advanced understanding of seasonal pollution episodes, dust transport mechanisms, monsoon–aerosol interactions, and aerosol radiative effects in Pakistan. Despite notable progress, significant challenges remain, including limited ground validation, data gaps in mountainous and rural areas, uncertainties in aerosol characterization, and insufficient integration with health and climate impact assessments. The paper concludes by outlining future research priorities, emphasizing the need for enhanced ground-based monitoring, high-resolution satellite data assimilation, and interdisciplinary frameworks to support evidence-based air quality management and climate policy in Pakistan.
How to cite: Aslam, M. U.: Aerosol Remote Sensing in Pakistan: Current Status, Challenges, and Future Directions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2438, https://doi.org/10.5194/egusphere-egu26-2438, 2026.
Cloud condensation nuclei (CCN) concentrations indicate the ability of aerosols to activate into cloud droplets and therefore play an important role in aerosol–cloud interactions and their associated radiative forcings. Reliable observational constraints on CCN are essential for improving the representation of aerosol–cloud processes in climate models, yet remain challenging to obtain at adequate spatial and temporal scales.
The GRASP synergetic retrieval framework is designed to integrate complementary information from multiple satellite sensors, i.e., Sentinel-3/OLCI and Sentinel-5P/TROPOMI, enabling the retrieval of aerosol microphysical properties with enhanced spatial resolution and temporal coverage. In this study, we derived total CCN, as well as the CCN of each aerosol species, from the GRASP retrieved aerosol microphysical properties by defining CCN as the number of dry aerosol particles with radii exceeding 0.12 µm.
Using data over 2022, we focus on Europe – the Mediterranean – Western Asia – Northern Africa as a testbed region characterized by diverse CCN distributions over both land and ocean. The obtained CCN values are evaluated against a CAMS reanalysis-derived CCN dataset (Block et al., 2024), and compared to the MODIS cloud droplet number concentration for a consistency check. The results show a robust agreement in both spatial patterns and magnitudes, particularly for biomass-burning and sulfate-dominated CCN over oceanic regions, which are especially relevant for aerosol–cloud interaction studies.
Our generated CCN dataset has been realistically applied to a case study of marine cloud perturbations associated with the 2018 Kīlauea volcanic eruption. The analysis demonstrates the capability of the dataset to capture coherent variability among sulfate aerosols, CCN, and low-level marine clouds in response to volcanic degassing, highlighting its potential for applications such as Marine Cloud Brightening research and broader evaluation and constraint of aerosol–cloud interactions in regional and global atmospheric models.
How to cite: Wang, X., Litvinov, P., Lopatin, A., Momoi, M., and Dubovik, O.: Cloud condensation nuclei concentrations derived from GRASP synergetic aerosol products for Sentinel-3/OLCI and Sentinel-5P/TROPOMI , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5597, https://doi.org/10.5194/egusphere-egu26-5597, 2026.
Anthropogenic aerosols offset a poorly quantified fraction of greenhouse gas warming. Moreover, poorly understood aerosol impacts on clouds limit our ability to better constrain the sensitivity of Earth’s climate to anthropogenic radiative forcing. Recently, natural experiments have become a state-of-the-art approach for studying the causal impacts of aerosols on clouds. Here, we identify localised anomalies in cloud properties as recorded in long-term satellite climatologies from MODIS, AVHRR and SEVIRI satellite instruments. We identify aerosol-impacted cloud areas around megacities, near volcanoes and near shipping corridors as regions with reduced cloud droplet size in satellite climatologies of liquid-water clouds. The contrast in cloud properties between the polluted hot spot and the nearby unpolluted area depends on the horizontal resolution of a cloud climatology. Such resolution-dependence highlights the need to analyse localised cloud property anomalies in high-resolution climatologies of clouds. The natural experiments of aerosol impacts on clouds documented here can be used to better understand cloud responses to aerosols.
How to cite: Aun, M., Luhamaa, A., Keernik, H., and Toll, V.: Hotspots of Aerosol Pollution Identified in Satellite Climatologies of Clouds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6993, https://doi.org/10.5194/egusphere-egu26-6993, 2026.
Aerosol-cloud interactions (ACI) are a significant source of uncertainty in climate projections. Nitrogen-dominated aerosol episodes are emerging over the Netherlands, strongly influencing local air quality and climate, but our understanding of aerosol-cloud interactions under these nitrogen-dominated conditions is still not well quantified. Ground-based remote-sensing instruments like cloud radars can provide us high temporal and spatial resolution data for cloud microphysics, like cloud droplet number concentration, and aerosol properties can be obtained using lidar measurements. In this study, we quantify how these aerosol particles in nitrogen-polluted episodes affect low-level clouds by combining remote-sensing observations with aerosol speciation measurements at the Ruisdael Observatory in the Netherlands.
Generally, column aerosol optical depth (AOD) from sun photometers and vertically resolved attenuated backscatter (ATB) from ceilometers are used as aerosol proxies. A key difference is that AOD represents extinction integrated over the full atmospheric column, whereas ATB is a vertically resolved backscatter profile, and ATB must therefore be vertically integrated for a meaningful comparison. However, both respond differently to meteorological parameters, aerosol loadings, and the instrument’s configurations. Therefore, understanding the variation of ATB and AOD in response to meteorology is essential. Overall, our framework will provide consistent conditions under which ceilometer ATB can be used as an aerosol proxy along with the column AOD during nitrogen-dominated episodes.
Here, we use a Mie model framework to investigate how ATB and AOD behave under different aerosol compositions, loadings, and meteorological conditions. Further, using a long-term observation from Cabauw (the Netherlands) as a case site, we focus on periods when nitrate clearly dominates the aerosol composition. Surface data from aerosol mass spectrometry and size-distribution measurements are combined with ceilometer profiles, sun-photometer retrievals, and meteorological data. Together, these measurements allow nitrogen-dominated episodes to be grouped by composition, relative humidity, and boundary-layer conditions, providing a consistent way to quantify aerosol-cloud interactions.
Our initial results indicate that, during nitrate-dominated episodes, hygroscopic aerosol particles build up in the boundary layer and strongly enhance light extinction. Extinction, backscatter, and other related aerosol optical properties respond strongly to RH-driven particle growth, making the growth factor a key control on the observed signals. We will investigate these relationships in more detail using measurements from both the RITA-2021 and the CAINA-2025 campaign datasets. These nitrate-rich aerosols act as cloud condensation nuclei (CCN), and they are expected to increase cloud droplet number concentration with more but smaller cloud droplets, which can be detected by ground-based cloud radar observations.
The resulting framework provides insight into how nitrogen-rich aerosol pollution affects clouds' microphysical properties and strengthens the understanding of aerosol-cloud interactions in nitrate-dominated environments.
How to cite: Sinha, N., Russchenberg, H., Steinke, I., Maherndl, N., Biskos, G., R. Nursanto, F., and Dusek, U.: Characterizing Aerosol-Cloud Interactions during Nitrogen-Dominated Episodes over the Netherlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8022, https://doi.org/10.5194/egusphere-egu26-8022, 2026.
We present a retrieval algorithm based on the optimal estimation method for estimating the integrated water vapor above clouds using shortwave infrared radiance observations (Peroni et al., 2025). Water vapor plays a crucial role in cloud formation and evolution, particularly in convective systems where exchanges between clouds and their surrounding environment strongly modulate the local variability of atmospheric humidity. Improved knowledge of the water vapor distribution above and around clouds is therefore essential for better understanding cloud/water vapor interactions and for constraining Large-Eddy Simulations and weather prediction models.
The retrieval algorithm is developed in the framework of the Cluster for Cloud evolution, ClImatE and Lightning (C3IEL) space mission, scheduled for launch in 2028. C3IEL aims to advance our understanding of convective cloud dynamics by providing observations of three-dimensional cloud development velocities, electrical activity, and the water vapor distribution above and around clouds.
Results obtained for idealized atmospheric conditions with vertically homogeneous cloud profiles demonstrate the feasibility of retrieving the integrated water vapor above clouds from three shortwave infrared radiances. Absolute retrieval errors are found to be below 2 kg.m⁻² for optically thick clouds or for integrated water vapor contents below 20 kg.m⁻², and below 1 kg m⁻² for very thick clouds (COT > 150). For more realistic cases, from the ECMWF-IFS dataset, the retrieval performs well for water clouds, with RMSE generally below 1 kg.m⁻². Retrieval accuracy is found to mainly depend on cloud vertical penetration, with degraded performance for optically thin and low-level clouds (COT < 50 and cloud top height < 2 km).
For very low water vapor contents encountered mainly above high deep convective clouds, the algorithm tends to systematically overestimate the retrieved values due to an overestimation of the cloud extinction profile in the upper cloud layers within the inversion model. These results demonstrate the strong potential of shortwave infrared observations for retrieving integrated water vapor above clouds and provide guidance for further improvements of the retrieval algorithm in preparation for the C3IEL mission.
How to cite: Penide, G., Peroni, R., Cornet, C., Zemb, A., Pujol, O., and Pierangelo, C.: Integrated Water Vapor retrieval under cloudy sky conditions from SWIR satellite measurements in the context of C3IEL space mission project., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10005, https://doi.org/10.5194/egusphere-egu26-10005, 2026.
The Copernicus Sentinel-3 mission, carrying the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), provides critical observations for monitoring atmospheric composition. Operational Level-2 algorithms for aerosol and cloud retrievals rely on accurate information from the Sentinel-3 Land Surface Reflectivity (LSR) Auxiliary Product to stabilize the inversion process. To address this requirement, this static LSR product is being developed at 1–2 km spatial resolution using a hybrid GRASP retrieval approach.
The hybrid GRASP approach is designed to provide stable full BRDF retrievals by reducing the number of parameters to be inverted. In this framework, aerosol properties at coarse resolution are not retrieved but are instead reused from existing multi-year datasets, such as PARASOL/GRASP and VIIRS. Furthermore, coarse-resolution surface products from the PARASOL/POLDER-3 satellite are utilized as a priori information for the high-resolution retrieval. Within this framework, OLCI benefits from its dense spectral sampling in the visible to near-infrared range, which is well suited for characterizing surface reflectance and BRDF spectral dependence. SLSTR complements this capability through its dual-viewing geometry, which provides additional constraints on surface anisotropy and helps reduce ambiguities between atmospheric and surface contributions. In addition, SLSTR’s short-wave and thermal infrared bands enhance cloud and snow screening and support more robust atmospheric correction.
In this work, we evaluate the generated LSR auxiliary product derived from Sentinel-3 OLCI and SLSTR measurements. The assessment follows a hierarchical strategy: first, the impact of the generated LSR is demonstrated by validating retrieved aerosol properties (AOD, Ångström exponent) globally against AERONET ground observations. Second, the retrieved surface BRDF is validated regionally against the GROSAT reference dataset, which provides synergetic AERONET and OLCI-A/B retrievals. Third, the products are compared with MODIS MCD43A3 surface albedo at the global scale, demonstrating strong spatial and radiometric consistency. The impact of the generated LSR dataset on aerosol property retrievals, aerosol layer height (ALH), water vapor (WV) and cloud properties is also discussed.
How to cite: Liu, Z., Lytvynov, P., Matar, C., Zhai, S., Panda, S., Fuertes, D., Lopatin, A., Dubovik, O., Kokhanovsky, A., Kirches, G., Brockmann, C., Lanzinger, V., Lehner, A., and Chimot, J.: Quality Assessment of the Sentinel-3 Land Surface Reflectivity (LSR) Auxiliary Product from OLCI and SLSTR for Improved Aerosol and Cloud Retrievals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11824, https://doi.org/10.5194/egusphere-egu26-11824, 2026.
Cloud amount is a key meteorological variable that directly affects surface solar radiation and precipitation variability. However, cloud amount observations from the Korea Meteorological Administration’s Automated Surface Observing System (ASOS) rely on subjective judgments by human observers and suffer from low temporal resolution. Satellite-based cloud products also face limitations in adequately capturing point-scale cloud variability due to spatial resolution constraints.
To address these limitations, this study developed an automated cloud amount estimation system by integrating a sky imager with deep learning techniques. A dataset was constructed using sky imager images collected from January 2020 to July 2025 and corresponding concurrent ASOS cloud amount observations. A CNN-based classification model and a U-Net–based segmentation model were independently developed. The CNN model estimates cloud amount at the image level, while the U-Net model performs pixel-level cloud segmentation using cloud masks generated by a normalized Red–Blue Ratio (nRBR) algorithm as ground truth data.
Validation results show that the CNN model achieved a correlation coefficient (R) of 0.95 and an RMSE of 1.27 when compared with ASOS observations, while the U-Net model achieved a cloud detection accuracy of approximately 0.97, demonstrating stable reproduction of cloud distributions. The proposed system enables rapid cloud amount estimation from high–temporal-resolution continuous observations and suggests its potential applicability to photovoltaic power forecasting as well as agricultural and meteorological applications.
How to cite: Park, M., Park, S., Jeong, D., Kim, C. K., and Lee, Y. G.: A Deep Learning–Based Automated Cloud Amount Estimation Method Using Sky Imager Images, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17952, https://doi.org/10.5194/egusphere-egu26-17952, 2026.
The Optimal Cloud Analysis (OCA) algorithm developed by EUMETSAT retrieves cloud optical thickness (COT), cloud effective radius (CRE), and cloud-top pressure (CTP) at pixel level using optimal estimation techniques with pre-computed Look-Up Tables (LUTs). Current operational CTP products from GK2A tend to underestimate cloud-top height when validated against CALIPSO, with larger errors observed in lower atmospheric layers and ice cloud regions. To address these limitations, we adapt the OCA algorithm for the Advanced Meteorological Imager (AMI) aboard the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A). Since OCA was originally designed for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard Meteosat Second Generation, direct application is limited due to differences in spectral response functions (SRFs) between sensors. In this study, we regenerate sensor-specific LUTs for 11 channels covering visible to thermal infrared wavelengths (VI006, VI008, NR016, SW038, WV063, WV073, IR087, IR096, IR105, IR112, IR133) using the libRadtran radiative transfer model. Radiative transfer calculations employ the DISORT solver with Mie scattering for liquid water clouds and parameterized ice crystal optical properties, covering comprehensive ranges of cloud optical thickness, effective radius, and viewing geometries. A total of eight radiative parameters (Rbd, Rd, Rfd, Tb, Td, Tfbd, Tfd, Em) for both solar and thermal channels are recalculated with GK2A/AMI SRFs. Comparison between original EUMETSAT LUTs and newly generated GK2A LUTs reveals systematic differences across all channels, demonstrating the necessity of sensor-specific LUT adaptation rather than direct algorithm porting. The adapted algorithm will be validated against CALIPSO products over the East Asian region.
Acknowledgements: This work was funded by the Korea Meteorological Administration Research and Development Program under Grant (RS-2025-02221093).
How to cite: Yu, J. and Lee, Y. G.: Adaptation of OCA Algorithm for GK2A/AMI: Sensor-Specific Look-Up Table Regeneration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17960, https://doi.org/10.5194/egusphere-egu26-17960, 2026.
Understanding aerosol chemical composition is essential for quantifying aerosol impacts on climate, air quality, and human health, as well as for improving the representation of aerosols in chemical transport and climate models. The chemical composition of aerosols directly determines their optical, microphysical, hygroscopic properties. The aim of this study is to investigate how different assumptions regarding aerosol chemical composition and size distribution and, employed within GRASP Chemical Component approach, affect the accuracy and consistency of retrieved aerosol optical properties.
In this approach, aerosols are represented as internal mixtures of predefined chemical species based on Maxwell–Garnett or linear volume mixing rules, instead of retrieving flexible, spectrally varying complex refractive indices. Aerosol size distributions are parameterized using lognormal functions with 5 to 9 bins. The baseline retrieval configuration assumes a Maxwell–Garnett mixture with aerosol components distributed between two modes: a fine mode consisting of Black Carbon, Brown Carbon, and Quartz mixed with water and soluble species, and a coarse mode composed of Iron Oxide and Quartz with water and soluble species, using a five-bin lognormal size distribution. A series of validation experiments was conducted to assess the impact of alternative modeling assumptions, including increasing the number of size bins, incorporating organic matter into the coarse mode, separating Sea Salt and Dust into two distinct coarse modes, and replacing Brown Carbon refractive indices with CAMS values.
GRASP Chemical Component approach were applied to ground-based AERONET observations of direct Sun radiance and sky-scanning diffuse radiation at wavelengths primarily between 440 and 1020 nm for different aerosol types (UV and SWIR channels for several sites), and were validated against standard AERONET products (AE, SSA, refractive index, and size distribution). The results demonstrate good agreement between retrieved AE and AERONET reference products for both dust- and smoke-dominated sites. The assumptions regarding brown carbon refractive indices improve the spectral dependence of SSA, particularly during biomass-burning events. Furthermore, separating sea salt and dust into distinct coarse modes yields a more physically realistic representation of aerosol chemical composition across different AERONET sites. Overall, the proposed configuration changes have the potential to improve the interpretation of aerosol type and enhance the consistency between remote sensing data and chemical composition models of aerosols. For a more qualitative assessment, it is planned to use extensive statistical information with a larger volume of AERONET measurements.
How to cite: Berdina, L., Lytvynov, P., Herrera, M. E., Behera, A. K., Dubovik, O., Lapyonok, T., and Tishkovets, V.: Advancing GRASP Aerosol Chemical Component approach using AERONET measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19383, https://doi.org/10.5194/egusphere-egu26-19383, 2026.
Entrusted by both the European Commission (EC), and Member states, EUMETSAT is on the verge of implementing the comprehensive portfolio of operational Near Real Time (NRT) L2 cloud products for the Copernicus Sentinel-3 mission.
To that end, several preparatory studies were conducted in the last years to assess the scientific feasibility and potential expected quality. These include: i.e., Cloud Top Pressure (CTP) from the OLCI O2 spectral bands, Synergy Cloud Mask and obstruction (from both OLCI & SLSTR). Additionally, besides the already-existing cloud-related algorithms (e.g., the Basic Cloud Mask and Bayesian Cloud Mask in L1 SLSTR, the IdePix algorithm among which for OLCI data, etc.), EUMETSAT has also developed two operational NRT L2-related processors for cloud masking and cloud tracking applications:
- The SLSTR Naïve Probabilistic Cloud and Aerosol detection algorithm (initiated in 2022), tailored for enhanced cloud and aerosol discrimination, and preserving pixels contaminated by dust / ashes / smoke. It is now running within the S3 NRT AOD processor.
- The SLSTR Atmospheric Motion Vectors (AMVs), which are mesoscale estimations of the wind (speed, direction and height), obtained by tracking cloud features across sequences of images.
EUMETSAT is now preparing the phase 2 of Sentinel-3 NRT L2 cloud products and application developments. These will be summarised in this presentation, notably:
- A cloud mask validation framework of the Naïve Probabilistic Cloud and Aerosol detection algorithm, relying on the comparison of S3 LEO products vs. GEO products (e.g., from MTG/FCI) and machine learning techniques to improve tests, thresholds and decision trees, in partnership with the Laboratoire d’Optique Appliqu
- The extension of the Naïve Probabilistic Cloud and Aerosol detection algorithm to future NRT L2 aerosol processors, such as the S3 NRT Aerosol Layer Height (ALH). Additionally, thanks to the current internal development of a SLSTR/OLCI L1 synergy product, .
- And the development of operational NRT cloud products suite that could support not only operational meteorological agencies but also the Sentinel-3 AMV processor, in a similar way as done for the operational AMV products on GEO sensors (MSG/SEVIRI and MTG-I/FCI).
How to cite: Martins, E., Chimot, J., Spezzi, L., Bozzo, A., Riedi, J., Barbieux, K., and Fougnie, B.: Developments of NRT Level-2 Cloud products in Copernicus Sentinel-3, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20661, https://doi.org/10.5194/egusphere-egu26-20661, 2026.
The dynamics of the polar vortex in the Northern Hemisphere play a crucial role in shaping the composition and distribution of stratospheric aerosols. This study investigates the temporal evolution of the Junge layer within the vortex, emphasizing its interaction with aerosol characteristics. Utilizing a novel frequency-scanning lidar system, high-resolution vertical profiles of stratospheric aerosols (15–30 km) were obtained in February 2023. The ground-based lidar measurements were also validated with satellite data. These observations captured a significant polar stratospheric cloud (PSC) event on February 11, 2023, at Kühlungsborn (54°, 11°) at an altitude of 22 km, providing insight into the relationship between aerosol distribution and vortex stability.
How to cite: Eixmann, R., Baumgarten, G., Ernst, F., Froh, J., Höffner, J., Löns, C., Lüke-Mense, T. H., Mauer, A., and Saavedra Garfias, P.: Polar Vortex Dynamics and Stratospheric Aerosol Evolution: Observations with a Novel Frequency-Scanning Lidar, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20899, https://doi.org/10.5194/egusphere-egu26-20899, 2026.
Early identification of fires and reliable monitoring of particulate matter in industrial areas are challenging due to complex emission sources and the limitations of passive optical sensors. We present a dual-wavelength (532/1064 nm) scanning LiDAR system designed for simultaneous fire smoke detection and particulate monitoring in industrial environments. The system operates horizontally at approximately 55 m above ground level, with full azimuthal scanning and kilometer-scale range coverage, enabling near-source observation of aerosol plumes at stack height. Elastic backscatter signals at both wavelengths are used to retrieve aerosol extinction coefficients, from which the Ångström exponent (AE) is derived to characterize particle size. In parallel, polarization-resolved measurements provide the linear depolarization ratio (δ), indicating particle shape and non-sphericity. By jointly analyzing extinction (α), AE, and δ, the system discriminates between fine soot-dominated combustion aerosols, ash-laden near-source smoke, and non-combustion industrial particulates in real time. Field deployment in the Siheung Industrial Complex (Republic of Korea) captured an actual fire event on 22 July 2024. During the early combustion phase, the smoke plume exhibited moderate AE and elevated depolarization, consistent with coarse, irregular ash particles. As the fire stabilized, the aerosol signature transitioned to higher AE and low depolarization, indicating fine soot-dominated smoke. Additional observations revealed clear contrasts between daytime and after-hours particulate regimes, with nighttime conditions showing expanded hotspots associated with higher extinction and coarser particle characteristics. These results demonstrate that horizontally scanning, dual-wavelength polarization LiDAR provides a robust and practical approach for integrated fire detection and particulate monitoring in complex industrial environments, offering enhanced situational awareness for air-quality management and early fire warning.
Acknowledgements
This work was supported by the Ministry of the Interior and Safety (MOIS), Republic of Korea, through the Joint Cooperation R&D Program (Project No. 2023-MOIS-20024324), and by the Advanced Institute of Convergence Technology (AICT), Seoul National University.
How to cite: Kim, K., Kim, S.-M., Kim, S.-J., Oh, S., Sung, M., and Park, J.-M.: Dual-Wavelength Scanning LiDAR for Fire Smoke and Aerosol Monitoring in Industrial Areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21973, https://doi.org/10.5194/egusphere-egu26-21973, 2026.
Clouds and aerosols can obstruct the solar radiation propagating through the atmosphere before it reaches the Earth's surface due to the scattering and absorption processes. The impact of this obstruction on Earth observation is related to the degree of obstruction along the optical path, and the remote sensing application in question. Usually, such obstruction is accounted for by applying cloud and shadow masking for the observed pixels or by performing simultaneous atmosphere/surface retrieval. Estimation of the atmospheric signal (clouds and aerosol obstructions) from the top of atmosphere measurements can be used to identify clouds, cloud shadows or the presence of aerosol in the atmosphere. In ACOM this is done by extracting surface signal from atmospheric one and then separating clouds and aerosol features from each other using multi-dimensional spectral thresholds and spatial variability tests.
The concept applied in ACOM allows a quantitative estimation of the atmospheric obstruction which results in the distinction of different clouds and aerosol classes varying from low to high levels of aerosol and cloud loading in addition to cloud vicinity, cloud shadow and aerosol plumes shadow classes. ACOM shows robust results with ENVISAT/MERIS and Sentinel-3/OLCI and the algorithm can be easily extended to any other optical instruments with spectral channels in the blue and infrared ranges.
How to cite: Matar, C., Litvinov, P., Chen, C., Momoi, M., Gomez, J., Liu, Z., Dubovik, O., and Goryl, P.: Versatile Aerosol and Cloud Obstruction Mask (ACOM) for Diverse Remote Sensing Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22012, https://doi.org/10.5194/egusphere-egu26-22012, 2026.
Short-term forecasting of cloud position is essential for improving solar irradiance nowcasting, the management of photovoltaic systems, and atmospheric monitoring. In this work, we evaluate the impact of replacing RGB all-sky images with semantically segmented sky masks as an input representation for multi-frame cloud motion prediction, assuming the availability of a sky segmentation model. To this end, we have adapted a ConvLSTM (Shi et al., 2015) backbone to operate on five-class segmentation masks (cloud-free, cloud, thin cloud, sun, other), enabling a controlled comparison with an RGB-based ConvLSTM. The training and evaluation are performed using the SKIPP’D dataset (Nie et al.,2023): around 58,000 videos with 1-min resolution. To ensure consistent evaluation, all predictions and ground-truth frames are processed through a common segmentation model. Thus, model performance is evaluated in the segmentation label space, using segmenter-derived masks as a proxy reference rather than physical ground truth.
Operating on the semantic mask space improves temporal stability and agreement with reference masks across standard segmentation metrics. On average, it increases the Intersection over Union by 0.49%, and the Dice coefficient by 0.94%, relatively to the RGB baseline. Improvements are most notable for the dominant classes cloud and cloud-free, while performance on thin-cloud and sun pixels remains limited, due to their lower frequency, intrinsic semantic ambiguity, and the reduced spatial resolution of the dataset. The results also show a trade-off between recall reduction and precision improvement.
These results indicate that introducing semantic information as an intermediate representation simplifies the prediction task and strengthens the model’s ability to capture cloud evolution patterns within a segmentation-based evaluation framework. While the present study does not provide end-to-end validation against irradiance measurements, it highlights the potential of segmentation-based approaches for future cloud nowcasting systems and motivates further work at higher spatial resolutions, with direct radiative validation, and with different network architectures.
This work was supported by the Ministerio de Ciencia e Innovación (MICINN), with the grant no. PID2024-157697OB-I00 and TED2021-131211BI00375. Financial support of the Department of Education, Junta de Castilla y León, and FEDER Funds is acknowledged (CLU-2023-1-05). This work was funded by European Comision through the EUBURNRISK project (INTERREG-SUDOE; S2/2.4/F0327). The authors acknowledge the support of COST Action CA21119 HARMONIA and the Spanish Ministry for Science and Innovation to ACTRIS ERIC
Shi, Z. Chen, H. Wang, D.-Y. Yeung, W. kin Wong, W. chun Woo, Convolutional LSTM Network: A machine learning approach for precipitation nowcasting (2015). arXiv: 1506.04214
Nie, X. Li, A. Scott, Y. Sun, V. Venugopal, A. Brandt, Skipp’d: A sky images and photovoltaic power generation dataset for short-term solar forecasting, Solar Energy 255 (2023) 171–179.
How to cite: Gatón, J., Román, R., Guzman, C., González-Fernández, D., Longarela, B., Herrero del Barrio, C., Herrero-Anta, S., González, R., and Toledano, C.: Multi-frame cloud prediction from all-sky images: RGB vs segmented masks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9705, https://doi.org/10.5194/egusphere-egu26-9705, 2026.
The retrieval of cloud properties from satellite measurements has wide-ranging applications, including light path correction for atmospheric composition, assessment of Earth's radiation budget, studies of aerosol-cloud interactions, and meteorology. One parameter that has received relatively little attention to date is the height of the cloud base and the derived geometrical thickness. This is largely due to the significant attenuation of light when tropospheric clouds are highly opaque at optical wavelengths. After briefly presenting a solution to the radiative transfer problem in the molecular oxygen absorption band measured by the TROPOMI instrument aboard the Sentinel-5P satellite, this study applies three independent algorithms to the same set of measurements and derives one year of global cloud base altitude, from which the geometrical thickness can be inferred. The validation of the derived cloud parameters, including top and bottom altitude, cloud phase, and optical thickness, sets the stage for the potential creation of a long-term data record for climate research, considering that future missions such as Sentinel-4 on MTG and Sentinel-5 on EPS-SG will provide similar spectral coverage.
How to cite: Lelli, L., Sayer, A., Bramstedt, K., Vountas, M., Molina García, V., Argyrouli, A., and Loyola, D.: Global assessment of cloud geometrical thickness from TROPOMI on Sentinel 5P, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21193, https://doi.org/10.5194/egusphere-egu26-21193, 2026.
Despite recent advances, modeling convective clouds remains an important source of uncertainties in climate and weather modeling. Their development is largely dependent on the amount of water vapor available in the atmosphere, which will increase with global warming. It is therefore necessary to better understand the spatial and temporal variability of water vapor in the atmosphere to improve our understanding of the interaction between this gas and clouds. To address this need, the C3IEL (Cluster for Cloud Evolution, Climate and Lightning) mission, a joint effort between CNES and ISA is developed and scheduled for 2028. This mission will use the differential absorption of water vapor in three Short-Wave infrared (SWIR) channels to retrieve the amount of integrated water vapor above and around convective clouds at a high spatial resolution of about 100 m. Recent studies have demonstrated the feasibility of using the optimal estimation method to perform such retrieval, based on the assumption of a plane-parallel cloud. However, despite accurate retrievals with RMSE less than 1kg /m², this method is computationally expensive and does not take into account the spatial context of the scene (pixel wise retrievals). This work presents another method based on convolutional neural networks – a computer vision deep learning architecture – to retrieve integrated water vapor above clouds and in clear sky areas. An attention mechanism and physical constraints are implemented to ensure the physical accuracy of the retrievals. The training of the presented model is based on synthetic C3IEL observations generated using the Meso-NH numerical atmospheric model and the ARTDECO 1D radiative transfer model. The first results are encouraging, with very fast retrievals inferior than 0.9 kg/m² RMSE on synthetic data and a real improvement brought by the attention mechanism and physical constraints. However, available training data are still limited due to computational costs of generating new cloudy scenes and new radiative transfer simulations, and current work aims to provide more diversity in training examples to really demonstrate the ability of the algorithm to generalize to new cases.
How to cite: Zemb, A., Penide, G., Cornet, C., Thuylie, N., Thieuleux, F., Riedi, J., and Devigne, E.: Development of an Inversion Method based on Convolutional Neural Networks for the retrieval of integrated water vapor above clouds in the context of the C3IEL mission., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18813, https://doi.org/10.5194/egusphere-egu26-18813, 2026.
The Dead Sea, a hypersaline terminal lake at the lowest place on Earth, has undergone significant environmental changes in recent decades, most notably is the reduction to the lake. In this work, we investigate the factors influencing evaporation in this rapidly changing hydrological system. We combine in-situ eddy-covariance evaporation measurements from Ein Gedi (2014-2017; DESERVE data) with Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD satellite-based retrievals and local meteorology to quantify diurnal, seasonal, and aerosol-related variability. Despite an expected diurnal cycle, our analysis shows that in most months no statistically significant difference exists between morning (10:30 local time Terra overpass) and noon (13:30 for Aqua) evaporation, with January and March being the only exceptions (p < 0.01). Seasonal patterns are more pronounced, with maximum evaporation in spring and summer and minimum rates in winter. Our results identified several high-evaporation outlier clusters which coincided with extreme weather, particularly heavy rainfall events (e.g., January 2015; March 2014). These events occur during or immediately after synoptic disturbances, suggesting that non-typical meteorology can temporarily enhance evaporation via changes in salinity, vapor pressure deficit, and surface-atmosphere interactions. Analysis of the evaporation-AOD relationship shows a weak but statistically significant negative correlation in summer morning (Terra) measurements (r = 0.255, p = 0.0007), the season with the most stable atmospheric conditions. Multiple regression indicates that temperature is the dominant predictor of evaporation in all models, while wind speed, wind direction, and upwelling longwave radiation are significant only during morning overpasses. Notably, the region has complex pollution regimes, as is reflected by the relationship between both parameters, whereby a dust player can impact the interaction. Meaning that dust events may suppress evaporation by reducing incoming solar radiation and altering the surface energy balance. These results provide the first quantitative evidence of aerosol-evaporation interactions at the Dead Sea using co-located in situ and satellite datasets.
How to cite: Sever, L., Vullers, J., Corsmeier, U., Alpert, P., and Chudnovsky, A.: Can we relate high resolution satellite-based aerosol optical depth (AOD) measurements to instantaneous evaporation rates in complex Dead Sea environs?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3546, https://doi.org/10.5194/egusphere-egu26-3546, 2026.
Aerosol vertical structure across marine-to-inland transition regions is influenced by the interaction of synoptic forcing, mesoscale circulation, and boundary-layer processes, yet remains insufficiently documented in subtropical island environments. During February–March 2024, coordinated airborne and ground-based remote sensing observations were conducted over southern Taiwan as part of the NASA ASIA-AQ/Kao–Ping Experiment (KPEx-2024), focusing on a compact coastal–inland transition affected by complex terrain and episodic continental outflow. This study examines the vertical structure of aerosols along the marine–coastal–inland pathway using synergistic ground-based Micro Pulse Lidar (MPL) and NASA G-III airborne High Spectral Resolution Lidar (HRSL-2) observations, which provide complementary temporal continuity and three-dimensional spatial coverage. Two contrasting pollution episodes were selected to examine the vertical characteristics of aerosols under different dynamical conditions, including a locally influenced event under weak synoptic forcing and a long-range transport event associated with persistent northeasterly flow. The observations reveal notable differences in aerosol vertical distributions and layering between the two regimes, reflecting the combined influence of local accumulation processes and background-flow-driven transport. These results point to how synergistic multi-platform lidar observations can provide new insight into aerosol vertical structure and transport behavior across complex coastal transition regions.
How to cite: Wang, Y.-C., Wang, S.-H., Tu, C.-C., Lai, H.-C., Soong, W.-K., and Lin, N.-H.: Marine-to-Inland Aerosol Vertical Structure over Taiwan during the 2024 ASIA-AQ and KPEx: Synergistic Observations from Ground-based MPL and NASA G-III Airborne HRSL-2, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10085, https://doi.org/10.5194/egusphere-egu26-10085, 2026.
Clouds play a key role in the Earth’s radiative balance and atmospheric dynamics, yet large uncertainties persist in their representation in weather and climate models. These uncertainties are partly related to the limited availability of continuous, high-resolution observations of cloud geometry. In this context, ground-based imaging networks provide a valuable opportunity to observe cloud fields with high temporal and spatial detail. In this work, we present a general framework for cloud detection and cloud-height retrieval using a network of 25 all-sky cameras distributed across the city of Valladolid (Spain) and its surrounding areas.
All instruments are identical OMEA-3C all-sky cameras operated and geometrically calibrated within the GOA-SCAN infrastructure of the Group of Atmospheric Optics. The proposed methodology combines image preprocessing, cloud-pixel segmentation, identification of matching cloud pixels, and stereoscopic reconstruction to derive instantaneous cloud-height fields. Cloud heights are retrieved through stereoscopic triangulation from planar-projected image pairs (Nguyen and Kleissl, 2014; Beekmans et al., 2016; Blum et al., 2021). The system provides continuous observations every five minutes, allowing the monitoring of cloud spatial structure and short-term evolution.
For each acquisition time, every camera is paired with all other cameras in the network, producing multiple independent cloud-height estimates based on row-wise correlation techniques. These estimates are filtered using geometric constraints, correlation quality metrics, and physical plausibility criteria. The use of multiple camera distances enables sensitivity to different cloud layers and ensures a spatially consistent coverage of the urban area and its surroundings.
A key component of this study is the validation of the retrieved cloud heights using independent ground-based observations. Cloud-base heights derived from the all-sky camera network are compared with measurements from a co-located ceilometer, allowing an objective assessment of the retrieval accuracy under different cloud conditions. This comparison provides insight into the performance of the stereoscopic approach and its limitations, particularly for low and multi-layer cloud scenes.
The presented framework establishes a robust basis for future developments, including extended validation with additional remote-sensing instruments and satellite products, as well as improvements in retrieval accuracy and operational applicability.
This work was supported by the Ministerio de Ciencia e Innovación (MICINN), with the grant no. PID2024-157697OB-I00. This work is part of the project TED2021-131211B-I00375 funded by MCIN/AEI/10.13039/501100011033 and European Union, “NextGenerationEU”/PRTR and is based on work from COST Action CA21119 HARMONIA. Financial support of the Department of Education, Junta de Castilla y León, and FEDER Funds is gratefully acknowledged (Reference: CLU-2023-1-05). This work was funded by European Comision through the EUBURN-RISK project (INTERREG-SUDOE; S2/2.4/F0327). The authors acknowledge the support of the Spanish Ministry for Science and Innovation to ACTRIS ERIC and the Marie Sklodowska-Curie Staff Exchange Actions with the project GRASP-SYNERGY (grant no. 10 101131631).
Beekmans, C., Schneider, J., Läbe, T., Lennefer, M., Stachniss, C., and Simmer, C. (2016) Atmospheric Chemistry and Physics, 16, 14231–14248.
Blum, N. B., Nouri, B., Wilbert, S., Schmidt, T., Lünsdorf, O., Stührenberg, J., Heinemann, D., Kazantzidis, A., and Pitz-Paal, R. (2021) Atmospheric Measurement Techniques, 14, 5199–5224.
Nguyen, D. A. and Kleissl, J. (2014) Solar Energy, 107, 495–509.
How to cite: Herrero del Barrio, C., Román, R., Herrero-Anta, S., González-Fernández, D., Carracedo, R., González, R., Longarela, B., Gatón, J., Mateos, D., Calle, A., Toledano, C., Cachorro, V., and de Frutos, Á.: Cloud-height mapping from all-sky camera network , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10719, https://doi.org/10.5194/egusphere-egu26-10719, 2026.
Total and altitude-dependent cloud cover are important characteristics of the present weather and used in nowcasting, numerical weather prediction and climate applications as well as for scientific studies. While cloud cover is still widely observed by humans, MeteoSwiss is in the process to automate the procedure using a variety of sensors. In this effort, the estimation of the cloud amount per cloud layer appeared to be particularly challenging as algorithms based exclusively on ceilometers could not satisfy all quality requirements. The main shortcomings are limitations in the representativity and the long integration time of 15 minutes, which prevents the delivery of timely and accurate values for the present weather.
In this context, we investigate how a well-calibrated hemispheric infrared camera combined with the ceilometer cloud base measurements can improve the cloud information. In the different investigated synergetic algorithms the ceilometer is the predominant source of cloud height information whereas the infrared camera provides information on the cloud amount. The more basic algorithm uses a threshold on the infrared brightness temperatures to distinguish cloudy from clear-sky pixels. A more elaborate algorithm matches cloud-base hits of the ceilometers with infrared camera pixels to produce cloud cover estimates for each cloud layer. This approach does not require a clear sky reference and is to a large extent insensitive to calibration inaccuracies. It further allows us to exploit the infrared image at high airmasses, i.e. far down towards the horizon, what in turn further improves spatial representativity. In this work, we evaluate both algorithms with respect to human observations and the reference algorithm based on ceilometers only.
How to cite: Rüfenacht, R., Hervo, M., Hartmann, N., Juda, P., Foresti, L., Vogt, F. P. A., Gröbner, J., and Haefele, A.: Synergetic retrieval of altitude-dependent cloud cover from ceilometers and infrared camera, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11749, https://doi.org/10.5194/egusphere-egu26-11749, 2026.
Observations from CloudSat and CALIPSO have demonstrated that the interpretation of cloud radiances derived from passive measurements must be reconsidered in light of vertically resolved profile information. The ESA Cloud, Aerosol and Radiation Explorer (EarthCARE) mission provides a unique opportunity to continue this reinterpretation by combining active and passive measurements from a single satellite platform, enabling a direct linkage between nadir profiling observations and swath-based imagery.
EarthCARE carries an active backscatter lidar (ATLID) and a cloud profiling radar (CPR), which provide high–spatial-resolution vertical profiles of cloud and aerosol properties along the satellite track. These active instruments operate in nadir view, while the passive multispectral imager (MSI) observes a 150 km wide swath with a spatial resolution of 500 m. In combination with the broadband radiometer (BBR), the passive MSI measurements enable the assessment of cloud radiative impacts and cloud feedbacks through their influence on radiative fluxes at the top of the atmosphere. The active radar–lidar synergy provides complementary information on cloud vertical structure, including cloud base altitude and estimates of liquid and ice water content, thereby contributing to the characterization of vertical profiles of cloud changes. To quantify the representation of cloud types from passive observations, the cloud classification framework introduced by the International Satellite Cloud Climatology Project (ISCCP) is applied. Cloud types are characterized using radiometric brightness temperature, interpreted as cloud-top height, and visible reflectance, interpreted as optical thickness, as retrieved from MSI. This cloud type histogram is analysed using vertically resolved cloud information from active measurements. The availability of active profile measurements makes it possible to augment the traditional two-dimensional ISCCP cloud-type histograms with vertically resolved cloud information. This combined perspective allows a more detailed understanding of how specific cloud types and regimes contribute to radiative fluxes at the top of the atmosphere and how changes in cloud vertical structure influence cloud feedbacks.
How to cite: Hünerbein, A., Burzik, A., Bley, S., Madenach, N., and Walter, G.: Evaluating of passive cloud-type classification using active-passive EarthCARE measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18395, https://doi.org/10.5194/egusphere-egu26-18395, 2026.
The Mediterranean basin is recognized as a climate change hotspot, characterized by strong variability in clouds and aerosols driven by its special location, the combination of land-sea surfaces and the convergence of air masses of different origin. Aerosol-cloud interactions (ACI) in this region remain poorly investigated, thus they represent a major source of uncertainty in regional climate models. Spaceborne active remote sensing provides the capability to simultaneously observe the vertical structure of clouds and aerosols, enabling the study of their physical properties and interactions.
In this work, observations from CloudSat’s Cloud Profiling Radar (CPR) and the CALIPSO Lidar are used to investigate aerosol-cloud interactions over the Mediterranean. As a first step, an 11-year CloudSat dataset (2007-2017) is analyzed in order to comprehend the climatology of the Mediterranean cloud properties. Characteristics such as cloud occurrence, cloud types, seasonality, thermodynamic phase and cloud-top and cloud-base heights are examined to provide a foundation for subsequent ACI research.
A synergistic analysis of CloudSat and CALIPSO observations for the period 2007-2009 is conducted to investigate aerosol-cloud interactions. Specific filtering criteria are applied to ensure that selected cloud layers are influenced by distinct aerosol types, allowing a meaningful correlation between them. Particular emphasis is placed on mineral dust, a dominant aerosol species in the Mediterranean, frequently transported from lower latitudes and affecting both land and sea regions.
The spatial distributions of aerosols and clouds are analyzed and intercompared to identify coherent patterns that indicate their interaction. Results show a pronounced association between pure dust layers and high-level cloud types, particularly cirrus and altostratus, which present higher frequency of occurrence over land compared to marine regions. These findings suggest a link between dust presence and the formation or modification of ice and mixed-phase clouds in the Mediterranean region.
This work explores the potential to describe aerosol-cloud interactions through mathematical relationships, aiming to contribute toward a more quantitative representation of ACI in regional studies. The results highlight the value of active remote sensing for understanding aerosol-cloud processes in a climatically sensitive region such as the Mediterranean.
How to cite: Koutsoupi, I., Giannakaki, E., Marinou, E., Battaglia, A., Kollias, P., and Amiridis, V.: Aerosol-Cloud Interaction over the Mediterranean using Active Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19785, https://doi.org/10.5194/egusphere-egu26-19785, 2026.
The accurate retrieval of aerosol properties from space is a cornerstone of our ability to quantify climate forcing and monitor global air quality. Multi-angle polarimeters (MAPs), such as PARASOL-POLDER and the recently launched PACE-SPEXone and Metop-SG-3MI, offer unprecedented information content, disentangling complex aerosol microphysics from surface scattering. However, the efficacy of retrieval algorithms, such as RemoTAP, is often constrained by post-processing quality controls. Traditional methods rely on static goodness-of-fit thresholds or "one-size-fits-all" post-processing filters (e.g., Χ2 < 5), which enforce a rigid trade-off between data coverage and accuracy. Our recent analysis reveals that such static thresholds often fail to account for systematic biases over complex surfaces, leading to unnecessary data loss or effectively allowing high-error retrievals to silently contaminate climate records. In this work, we present a paradigm shift in quality assessment: a Predictive Dynamic Quality Filter powered by a Physics-Aware Deep Learning Framework. Unlike generic "black-box" approaches, our architecture is designed to explicitly decouple the competing influence of atmospheric state variables from complex surface reflectance signatures. By processing these distinct physical signals alongside a rich set of spectral multi-directional total and polarized reflection signatures of the surface, the model dynamically constructs a pixel-level error profile that adapts to the underlying scene, robustly handling diverse conditions ranging from bright surfaces to the intricate directional reflectance of heterogenous vegetation. This Surface-Aware framework effectively learns to identify the "trustworthiness" of a retrieval based on its physical context, rather than a fixed goodness-of-fit cost. Here we present results applying this framework to POLDER RemoTAP retrievals. To ensure robust generalization and address potential overfitting, we employed a rigorous validation strategy using a comprehensive dataset from 477 global AERONET sites spanning four years (2006-2009). The model was trained on a strategically stratified subset of these observations while its performance was evaluated against a strictly independent, hold-out validations group. Unlike static filtering, our dynamics framework adapts to local conditions, substantially increasing the volume of valid observations data while simultaneously driving a significant reduction in error. By optimizing the selection of high-quality retrievals without discarding valuable data, this method significantly refines the inputs available for climate models. The primary outcome of this framework is the ability to predict pixel-level compliance with Global Climate Observing System (GCOS) standards, offering a metric directly applicable to climate studies. This "Predictive Dynamic Quality Filter" transforms aerosol retrieval quality filtering from a passive estimation task into an active, self-assessing framework. By unlocking the full statistical potential of the RemoTAP algorithm, we provide a robust pathway for generating climate-quality datasets from historical POLDER archives, current instruments as SPEXone and 3MI and future missions like and CO2M, significantly refining our constraints on aerosol-cloud interactions and radiative forcing.
How to cite: Patel, P., Diedenhoven, B. V., Hasekamp, O., and Fu, G.: Advancing RemoTAP: A Deep Learning Framework for Predictive Dynamic Quality Assessment in Multi-Angle Polarimetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20103, https://doi.org/10.5194/egusphere-egu26-20103, 2026.
Aerosols and high altitude clouds in the Arctic have an important role in the radiative energy budget by influencing the net gain or loss of radiative heat. Similarly, aerosols are the main nucleation source for the formation of high altitude clouds and responsible to alter cloud properties. Ground-based lidar systems are well-suited for the investigation of aerosol and optically-thin clouds due to their temporal scales and high vertically-resolved information. In remote locations with challenging environments like the Arctic, however, ground-based lidars are scarce mainly due to their typical high maintenance requirements. To overcome this limitation, a compact mobile Rayleigh-Mie-Resonance lidar has been recently developed as part of the European Lidar Array for Atmospheric Climate Monitoring (EULIAA) project. The EULIAA-IR1 is a Doppler infrared (770 nm wavelength) mobile, compact (1 m3) and highly autonomous lidar. The EULIAA-IR1 lidar senses the atmosphere by three line of sights and is capable to cover the atmosphere from 4 km to at least 50 km. The lidar’s high resolution Doppler spectra at three field of views has been conceived to allow the retrieval profiles of the wind components, particle (Mie scattering) and molecular (Rayleigh scattering) unattenuated backscatter coefficient, with depolarization capabilities for zenith observations, as well as stratospheric temperature profiles.
The EULIAA-IR1 system has been deployed to the first of a series of field campaigns ranging from high latitudes to the tropics within the framework of the EULIAA project. The high latitude campaign is taken place at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) located at 69.3° North and 16.0° East on top of the Ranman montain (379 m.a.s.l.) at Andøya, Norway. The EULIAA-IR1 lidar started operation at ALOMAR since end of October 2025, where persistent aerosol layers up to about 20 km and optically thin clouds up to 10 km have been systematically observed. The EULIAA-IR1 observations of aerosols and thin clouds are here presented to assess thresholds of backscatter coefficient and depolarization ratio (δ) to characterize and distinguish aerosols and thin clouds which have been previously reported by other lidar systems. We use the lidar slant observations to extend the characterization of the aerosol layers and their dynamics based on the meridional and zonal wind components obtained by the lidar. Moreover, we show how the EULIAA-IR1’s retrieved vertical wind component provides an insight on the interaction at the cloud mixing layer (top and bottom edges), where the complex interplay between aerosols and clouds takes place.
With this contribution we demonstrate the EULIAA-IR1system data pipeline to provide near-real-time retrieval products to different data dissemination platforms and meteorological agencies for data assimilation purposes, which is one of the goals of the EULIAA project.
How to cite: Saavedra Garfias, P., Billault-Roux, A.-C., Höffner, J., Froh, J., Ernst, F., Baumgarten, G., Mauer, A., Lüke-Mense, T., Rüfenacht, R., Strotkamp, M., and Flügge, M.: Characterization of Arctic aerosol and optically-thin clouds from the middle troposphere to the lower stratosphere by means of lidar backscatter and depolarization ratio, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20926, https://doi.org/10.5194/egusphere-egu26-20926, 2026.
To this date, most of the algorithms used to retrieve aerosol properties from multi-angular satellite images use a forward radiative transfer model with a baked-in 1D assumption. Among other things, this means neglecting the effects of surface heterogeneity, such as adjacency (the fact that neighbouring surface reflection properties perturb the diffuse component of incident radiation) and topography (the fact that the surface is not smooth and flat). This introduces bias in the radiative transfer simulation, and thus in the retrieved aerosol properties.
This project aims to investigate the impact of neglecting these heterogeneities on aerosol and surface retrievals from multi-angular satellite observations. For that purpose, a series of benchmarking cases was designed to assess the performance of the GRASP retrieval algorithm (currently state-of-the-art for the processing of EPS-SG/3MI observations) against a known reference created using Eradiate (an accurate 3D radiative transfer model). Benchmarking cases range from simple 1D setups aimed at verifying the alignment of the GRASP forward model and Eradiate, to complex, plausible 3D scenes generated after actual locations on Earth. All assume a 1D atmosphere to focus on the effects of surface heterogeneity.
The complex scenes incorporate topography and land cover information, with varied dominant land cover setups: agricultural, urban, coastal, mountain, in-land water (lake). Locations are situated near key AERONET stations, and the simulated instrument is derived from actual satellite specifications (i.e. geometries, wavelengths) based on PARASOL/POLDER.
In this presentation, we introduce our approach and discuss our conclusions on both the benchmarking approach and the results.
How to cite: Leroy, V., Momoi, M., Zhai, S., Marton, N., Luffarelli, M., Govaerts, Y., and Litvinov, P.: The 3DREAMS project: A study of the effect of 3D surface heterogeneity on aerosol retrieval based on synthetic images, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10287, https://doi.org/10.5194/egusphere-egu26-10287, 2026.
Harmonising aerosol approaches between global atmospheric models and satellite remote sensing retrievals is essential for improving the consistency and reliability of aerosol products. Within the CAMEO framework, this study investigates the impact of aerosol chemical composition and size distribution (SD) parameterisation on satellite aerosol retrievals using POLDER/PARASOL polarimetric observations, with validation against AERONET measurements over the full 2008 year. The baseline retrieval configuration consists of five lognormal size bins and a predefined aerosol chemical composition model (Fine mode: Black Carbon, Brown Carbon, Quartz and soluble species and Coarse mode: Iron Oxide, Quartz and soluble species). While this configuration provides robust performance for aerosol optical depth (AOD) and Ångström exponent, limitations remain in reproducing spectral single scattering albedo (SSA) and detailed SD structure.
To address these issues, a series of sensitivity experiments were conducted. These include harmonising the complex refractive index with CAMS aerosol chemistry, incorporating CAMS organic matter in both fine and coarse modes, increasing SD flexibility through an eight-bin representation, and adjusting inversion constraints under different aerosol loading conditions. Results demonstrate that harmonisation between CAMS and satellite retrieval assumptions improves agreement with AERONET, particularly for SSA, highlighting the mutual advantages of harmonisation.
The analysis shows that increasing SD complexity beyond five bins has only a minor impact on retrieved optical properties from PARASOL. This indicates that the information content of PARASOL measurements does not fully support highly complex SD characterisations, and that retrieval model complexity should be adapted to sensor capabilities. Similarly, including coarse-mode dust produces limited changes in optical retrievals, suggesting a reduced sensitivity of PARASOL to coarse aerosol properties.
These findings highlight that future multi-angle polarimetric sensors, such as 3MI, with extended spectral coverage from the visible to shortwave infrared, are expected to provide stronger sensitivity to coarse particles and improved aerosol characterisation. Overall, this study provides evidence that harmonisation between CAMS and remote sensing approaches strengthens the consistency of aerosol retrievals, while emphasizing the need to balance model complexity with observational information content.
How to cite: Herrera, M., Litvinov, P., Behera, A., Berdina, L., Dubovik, O., Matar, C., and Lapyonok, T.: Optimizing GRASP retrieval configurations for space-borne multi-angular polarimetric measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19437, https://doi.org/10.5194/egusphere-egu26-19437, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot 5
EGU26-1290 | ECS | Posters virtual | VPS2
Variation of atmospheric properties during a dust episode over central Himalayan region using Lidar observation and auxiliary dataMon, 04 May, 14:57–15:00 (CEST) vPoster spot 5
This work investigates a prominent dust event that occurred over Nainital, Uttarakhand, during 14-18 May 2025. Continuous micro pulse lidar (MPL) observations provided evidence of significant aerosol enhancement during the event. Distinct elevated aerosol layers were observed between 1 and 2.5 km above ground level, where backscatter coefficients increased to approximately 5×10⁻³ km⁻¹ sr⁻¹ and extinction values ranged between 0.8 and 1.2 km⁻¹. The persistence of these layers indicated long-range transport rather than local sources. Satellite-based aerosol optical depth (AOD) data from MODIS confirmed these enhancements, showing values doubling from 1.1-1.5 to above 2.2 during the peak dust intrusion. Meteorological observations documented elevated daytime temperatures between 20.1 ± 1.3 and 26.8 ± 1.6 °C and a marked reduction of relative humidity to below 50%, suppressing aerosol scavenging. Wind speeds intensified, with nocturnal maxima up to 5.6 ± 1.1 m/s and predominantly westerly to northwest directions (230°- 265°), favoring dust transport from western source regions. Synoptic-scale 850 mb wind analyses further corroborated persistent strong westerlies guiding mineral aerosols from the Thar Desert and Indo-Gangetic plains into the Himalayan foothills. The results highlight the importance of integrating lidar measurements with meteorological and reanalysis datasets to capture both vertical and horizontal characteristics of dust intrusions in mountainous regions.
How to cite: Singh, S. K., Singh, N., Rawat, V., Chauhan, M., and Debnath, S.: Variation of atmospheric properties during a dust episode over central Himalayan region using Lidar observation and auxiliary data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1290, https://doi.org/10.5194/egusphere-egu26-1290, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 5
EGU26-5443 | Posters virtual | VPS3
Automated nighttime contrail detection using spatio-temporal clustering of Raman lidar measurementsTue, 05 May, 14:24–14:27 (CEST) vPoster spot 5
We present an automated procedure that combines lidar measurements, ADS-B flight tracks and ECMWF ERA5 meteorological data to detect and characterise nighttime aircraft contrails. Measurements have been carried out at the Observatory of Haute-Provence (OHP) in France. Lidar scattering-ratio profiles were processed with a sensitivity-driven spatio-temporal discrimination algorithm to identify contrail “spots” and aggregate them into contrail signatures. A parameter score identifies an optimal discrimination threshold set that balances sensitivity and false positives. In our case, these thresholds took these values: scattering ratio SR ≈ 2.1; temporal aggregation ≈ 7.2 min; vertical separation ≈ 0.3 km. Applied to five nighttime events, the method yields mean contrail altitudes of 8.7–10.3 km, geometrical thicknesses of 0.1–1.1 km, horizontal widths 2–3 km, and optical depths (COD) of ≈0.05–0.40. Persistent contrails are associated with ice-supersaturated layers and temperatures below −41 °C. Contrail optical depth resulted well correlated with both vertical thickness and horizontal extent. We have demonstrated that combining lidar with ADS-B and ERA5 substantially improves detection and discriminates contrails from natural cirrus at night, a regime where passive satellite retrievals are limited. This approach is automatic, transferable and reproducible, offering robust validation data for satellite algorithms and improved contrail parameterizations in climate models.
How to cite: Mandija, F., Keckhut, P., Alraddawi, D., Irbah, A., Sarkissian, A., Khaykin, S., Peyrin, F., and Baray, J.-L.: Automated nighttime contrail detection using spatio-temporal clustering of Raman lidar measurements , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5443, https://doi.org/10.5194/egusphere-egu26-5443, 2026.
EGU26-23052 | ECS | Posters virtual | VPS3
CALIPSO validation of an apparent MODIS AOD spike over the Central Himalayas (2011)Tue, 05 May, 14:27–14:30 (CEST) vPoster spot 5
Remote sensing of aerosol variability over the Central Himalayas remains challenging because of complex terrain, strong elevation gradients in surface reflectance, and frequent cloud and snow contamination, which can bias passive aerosol optical depth (AOD) retrievals. In this study, we examine an apparent MODIS-derived AOD enhancement in 2011 over the Central Himalayas that deviates from the expected seasonal pattern of reduced aerosol loading during the monsoon and post-monsoon periods. The central objective is to determine whether this apparent anomaly represents a physically meaningful aerosol enhancement or is influenced by retrieval limitations in high-relief environments. We evaluate the MODIS anomaly using collocated CALIPSO observations, including vertically resolved aerosol extinction profiles and aerosol-layer optical depths. CALIPSO measurements show no evidence of persistently elevated aerosol layers corresponding to the MODIS enhancement, and aerosol extinction remains vertically shallow, indicating that the observed AOD anomaly is not associated with strong free-tropospheric aerosol intrusion. These results suggest that the apparent MODIS “spike” likely reflects a column-integrated enhancement dominated by near-surface aerosol and/or terrain–cloud–snow-related retrieval effects rather than a sustained elevated aerosol event. This study highlights the importance of integrating active lidar profiling with passive satellite retrievals to improve the interpretation of aerosol anomalies over mountainous regions and strengthens the basis for aerosol–cloud interaction assessments in the Himalayas.
How to cite: Bello, A., Bhardwaj, A., and Sam, L.: CALIPSO validation of an apparent MODIS AOD spike over the Central Himalayas (2011), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23052, https://doi.org/10.5194/egusphere-egu26-23052, 2026.
EGU26-15531 | ECS | Posters virtual | VPS3
Cloud Detection over Snow- or Ice-covered Surfaces Using Oxygen A-Band ObservationsTue, 05 May, 15:18–15:21 (CEST) vPoster spot 5
Cloud detection over snow- or ice-covered (S/IC) surfaces remains a critical challenge in satellite remote sensing. The cloud-like high surface albedo and ice-cloud-like brightness temperatures of these surfaces often lead to systematic misclassification in visible- and infrared-based algorithms, including the threshold-based cloud detection applied to Sentinel-5P atmospheric composition retrievals. Misclassified clear-sky scenes can introduce biases in the retrieved total columns of ozone, sulfur dioxide, and nitrogen dioxide, while misclassified cloudy scenes reduce the spatial coverage of satellite products.
To address this challenge, we develop a global cloud detection algorithm based on absorption images derived from Sentinel-5P oxygen A-band observations. The algorithm exploits the reduction of oxygen absorption in cloudy pixels, as cloud layers reflect solar radiation before it reaches the underlying surface, thereby shortening the radiative transfer path in the atmosphere and reducing absorption along the path. In addition, spatial texture information extracted from oxygen absorption images is incorporated to enhance sensitivity to optically thin and broken clouds, enabling more robust discrimination between clouds and bright underlying surfaces. This physical mechanism makes the algorithm insensitive to surface type, rendering it particularly suitable for global cloud detection, including over S/IC surfaces.
Validation against CALIPSO demonstrates a marked improvement in cloud detection performance across diverse surface and cloud conditions. The proposed algorithm achieves an overall accuracy of 91%, compared with 85% for the Suomi-NPP product and 48% for the operational Sentinel-5P product. Improvements are particularly pronounced over S/IC surfaces, where detection accuracy increases by 15% relative to Suomi-NPP. Additionally, detection accuracy for optically thin clouds improves by 20% globally, with the largest gains (up to 52%) observed over S/IC surfaces. These results demonstrate the value of oxygen absorption and spatial texture features for cloud detection, especially over S/IC surfaces, and support improved quality and consistency of satellite-based atmospheric observations over polar and other bright-surface regions.
How to cite: Liu, H. and Li, S.: Cloud Detection over Snow- or Ice-covered Surfaces Using Oxygen A-Band Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15531, https://doi.org/10.5194/egusphere-egu26-15531, 2026.
