On February 8, 2024 the NASA Plankton, Aerosol, Cloud & ocean Ecosystem (PACE) mission has been launched with onboard the SPEXone Multi-Angle Polarimeter. SPEXone is designed to deliver unprecedented information on aerosol properties, such as size, shape, absorption (Single Scattering Albedo), and amount (Aerosol Optical Depth, number concentration), and complex refractive index. From the complex refractive index, size and shape, chemical composition can be derived in terms of volume fractions of the main aerosol components. The launch of PACE brings an end to a 10 year gap in the availability of space-based multi-angle polarimeter data, which are essential to understand and quantify the role of aerosols and clouds in climate change. In this contribution, we present the first year of aerosol data from SPEXone. As we will show, the first version of SPEXone aerosol data shows already very good agreement with ground-based AERONET observations. The presentation includes a global view on aerosol composition in terms of volume fractions of Dust, Sea Salt, Black Carbon, Organic Carbon, fine mode inorganics (Sulphate, Nitrate), and aerosol water. SPEXone shows expected patterns of high fractions of sulphates/nitrates over industrial regions and cities in Asia and (North+South) America, Black Carbon over biomass burning regions in Africa and North America, Dust over desert regions and as outflow over the ocean, and hydrated sea salt over the open ocean. Finally, we discuss the capability of SPEXone to provide a Cloud Condensation Nuclei (CCN) product from the retrieved aerosol properties (number concentration, size distribution, water content).
How to cite: Hasekamp, O., Fu, G., Laasner, R., van Diedenhoven, B., Hannadige, N., Yuan, Z., van der Schaaf, L., van Hees, R., Smit, M., and Rietjens, J.: Polarimetric Remote Sensing of atmospheric aerosols: The first year of SPEXone PACE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1538, https://doi.org/10.5194/egusphere-egu25-1538, 2025.
The presentation discusses the approaches to model aerosol properties realized in GRASP (Generalized Retrieval of Aerosol and Surface Properties) algorithm (Dubovik et al., 2021). GRASP algorithm is developed based heritage of earlier efforts on the AERONET retrieval development (Dubovik and King, 2000, Dubovik et al., 2000) with idea to use the same algorithm for different applications. Thus, at present, GRASP is a versatile algorithm that could be applied diverse observations including laboratory, passive and active remote sensing measurements from ground and space. All those observations have different sensitivities to details of aerosol properties and, therefore, analysis of each type of observations requires adequate approach to model aerosol properties. For example, aerosol model used for interpretation of AERONET observations includes many more paraments than aerosol model used for interpretation of satellite observations from single view satellite imager such a MODIS or OLCI. At the same time, the aerosol models used for different observations should be consistent and compatible. Following this concept, GRASP aerosol forward model can be adequately adjusted for applications to very different observations ranging from in situ nephelometers, ground-based AERONET radiometers to satellite polarimetric and radiometric imagers, as well as, to ground-based and satellite lidar observations. The aerosol model describes all aerosol properties: size distribution, complex index of refraction, or composition, particle shape, rules of for several component mixing, vertical profile description, etc. The presentation overviews historical evolution of all details of aerosol model, the assumptions made for adaptation to different observations of different types of and the rational used for the current specific design od aerosol model. Finaly, the comparative analysis will be done to outline the differences and agreements with other common approaches used in aerosol remote sensing.
Dubovik, O., A. Smirnov, B. N. Holben, etc., “Accuracy assessments of aerosol optical properties retrieved from AERONET Sun and sky-radiance measurements”, J. Geophys. Res.,105, 9791-9806, https://doi.org/10.1029/2000JD900040, 2000.
Dubovik, O. and M. D. King, “A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements”, J. Geophys. Res., 105, 20,673-20,696, https://doi.org/10.1029/2000JD900282, 2000.
Dubovik, O., D. Fuertes, P. Litvinov, et al. , “A Comprehensive Description of Multi-Term LSM for Applying Multiple a Priori Constraints in Problems of Atmospheric Remote Sensing: GRASP Algorithm, Concept, and Applications”, Front. Remote Sens. 2:706851. doi: 10.3389/frsen.2021.706851, 2021.
How to cite: Dubovik, O., Litvinov, P., Lapyonok, T., Torres, B., Lopatin, A., Fuertes, D., Derimian, Y., Chen, C., Li, L., Lesueur, P., Momoi, M., Lin, W., Sinyuk, A., and Lind, E.: Aerosol Modelling & Lessons Learned from GRASP Aerosol Remote Sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14820, https://doi.org/10.5194/egusphere-egu25-14820, 2025.
The Particulate Observing Scanning Polarimeter (POSP) is a cross-track multispectral polarimetric scanning imager onboard both GaoFen-5(02) and DQ-1 satellites. POSP has a field of view +/- 50 degree with a nadir resolution of ~6.4 km and a swath of ~1850 km, and it measures the stokes vector (I, Q, U) at nine spectral bands from UV to SWIR, specifically 380, 410, 442, 490, 670, 865, 1610, and 2250 nm. Previous study has shown the UV-VIS-NIR-SWIR single-view polarimetric measurements from POSP/GF-5(02) could provide reliable information content for aerosol and surface characterization. In this study, we will describe the synergetic utilization of POSP measurements from GF-5(02) morning and DQ-1 afternoon satellites in order to enhance the capability to retrieve aerosol and surface properties.
How to cite: Chen, C., Lei, X., Liu, Z., Litvinov, P., Zhai, S., Dubovik, O., Cao, Y., Yu, H., Xiao, K., Wang, Y., Li, Z., and Hong, J.: Synergetic retrieval of aerosol and surface properties from cross-track polarimetric scanner POSP onboard GF-5(02) and DQ-1 satellites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14419, https://doi.org/10.5194/egusphere-egu25-14419, 2025.
The Copernicus carbon dioxide monitoring (CO2M) mission is the space component of the European integrated monitoring and verification support capacity dedicated to monitoring anthropogenic CO2 emissions. Due to the high accuracy requirements of this measurement, it is critical to measure and characterise cloud and aerosol in support of the CO2 retrieval. The Cloud Imager (CLIM) and the Multi-Angle Polarimeter (MAP) are therefore included as a payloads on the CO2M mission and the measured cloud and aerosol properties are used to correct for scattering and transmission in the downstream greenhouse gas (GHG) retrieval. The synergistic use of the cloud and aerosol products within the CO2M ground segment will be described.
How to cite: Phillips, P., Bauch, H., Böttcher, M., Dubovik, O., Fougnie, B., Lang, R., Matar, C., Preusker, R., Quast, R., and Sasi, S.: The (CLIM) Cloud Imager and the (MAP) Multi-Angle Polarimeter in the Context of Atmospheric Correction for CO2 Retrievals, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19194, https://doi.org/10.5194/egusphere-egu25-19194, 2025.
The space-borne C3IEL (Cluster for Cloud evolution, ClImate and Lightning) mission, jointly developed jointly by the French and the Israeli space agency, aims at providing new insights on convective clouds, at high spatial and temporal resolutions, close to the scales of the individual convective eddies. The mission will simultaneously characterize the convective cloud dynamics, the interactions of clouds with the surrounding water vapor, and the lightning activity.
The C3IEL mission consists in a short-baseline (~150 km) train of 2 synchronized small satellites. Each satellite carries a visible camera (670 nm) for cloud imagery at a spatial resolution of ~20 meters, near-infrared water vapor imagers (1.04, 1.13 et 1.37 µm ; ~125 m at nadir) measuring in and near the water vapor absorption bands, a lightning imager (777.4 nm ; ~140 m at nadir) and at two photometers (337 and 777.4 nm).
The scientific objectives of the C3IEL mission will be first reminded. They consist in documenting the convective cloud development through their 3D evolution and their environment with the retrieval of water vapor surroundings the clouds and the use of other observations such as geostationary satellite to characterize aerosol properties. In addition, the lightning activities created by such clouds will be observed. We will introduce the satellite train configuration, the different sensors of the mission and the innovative and different observational strategy that will be applied during daytime and nighttime. We will then detail the expected observations and products. Finally we will discuss the current status of the mission and the way forward.
How to cite: Cornet, C., Rosenfeld, D., Defer, E., Holodovsky, V., Penide, G., Peroni, R., Price, C., Ricard, D., Rimboud, A., Schechner, Y., Yair, Y., Cheymol, C., Deschamps, A., Frid, A., Gillot, L., Kader, A., and Aviad, S.: C3IEL, the Cluster for Cloud evolution ClImatE and Lightning Mission to Study Convective Clouds at High Spatial and Temporal Resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11912, https://doi.org/10.5194/egusphere-egu25-11912, 2025.
The cloud phase is expected to change in the warming world. Knowledge of the cloud phase is an initial and essential step in retrieving cloud optical parameters. Cloud optical parameters are generally retrieved by single-viewing remote sensing spectrometers. Identifying mixed-phase clouds (MPC), pure ice and liquid clouds simultaneously for cloud retrievals remains a research challenge. This study addresses this issue and uses the SCIATRAN radiative transfer model (RTM) to understand the sensitivities of the cloud phases and their varying optical and microphysical properties in the solar radiation spectrum in nadir and near-nadir viewing geometries. We use the dual-view measurements of the Surface Land and Sea Temperature Radiometer (SLSTR) to determine cloud phases. For the MPC, we introduce the parameter Ice Fraction (IF), defined as the fraction of the total extinction of solar radiation attributed to ice. To classify the cloud phases, we use two indices: a) the NIR ratio of 1.64 µm to 2.25 µm backscattered intensities at the top of the atmosphere, which is sensitive to spectral absorption in the cloud, and b) the dual-view ratio, using nadir and near-nadir intensities at 0.87 µm, which exploits the angular scattering variation of clouds. These indices form the NIR-dual view ratio (multiplication of the NIR and the dual-view ratio), enhance discrimination across the cloud phases, and especially allow MPC identification over ocean and snow surfaces. This ratio typically ranges from less than 2.75 for ice clouds and more than 3.50 for water clouds. The values in between attribute to the presence of MPC, except for MPC, having an ice contribution of > 80%. The NIR-dual view ratio is similar to water clouds for lower IF (e.g., IF < 20%). To test theoretical RTM results, we validated the NIR-dual view ratio calculated from SLSTR onboard Sentinel-3A and comparing with the CloudSat product (2B-CLDCLASS-LIDAR). Results demonstrate that our approach identifies all three cloud phases with more than 80% accuracy. This research highlights the potential of dual-view satellite observations to improve cloud phase classification, advancing the capabilities of cloud retrieval algorithms.
How to cite: Vinjamuri, K. S., Vountas, M., Rozanov, V., Lelli, L., Burrows, J. P., and Bösch, H.: Sensitivity and Identification of Cloud Phases in the Solar Spectrum Using Dual-View Satellite Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14162, https://doi.org/10.5194/egusphere-egu25-14162, 2025.
Global distribution of cloud properties and vertical motions are investigated by using the Japan Aerospace Exploration Agency (JAXA) level2 cloud algorithms for Earth Clouds, Aerosols and Radiation Explorer (EarthCARE), which is the JAXA and the European Space Agency (ESA) joint satellite mission. Cloud profiling radar (CPR)-only and CPR-atmospheric lidar (ATLID) synergy algorithms are based on the heritages from our algorithms for CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) but several major improvements are made. The algorithms are evaluated using ground-based observations and statical comparisons are also conducted by CloudSat and CALIPSO and Aqua/MODIS data. Algorithms consist of cloud mask, cloud type(phase), cloud particle category (ice habit), cloud and precipitation microphysics, terminal velocity and vertical air motion schemes.
How to cite: Okamoto, H. and Sato, K.: Global characterization of cloud properties and vertical motions by using EarthCARE observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19666, https://doi.org/10.5194/egusphere-egu25-19666, 2025.
The precise retrieval of aerosol properties from satellite data is pivotal for advancing our understanding of their impacts on climate and air quality. The RemoTAP (Remote Sensing of Trace Gas and Aerosol Products) algorithm represents a significant leap forward, leveraging data from multi-angle polarimeters (MAPs), such as the past PARASOL-POLDER instrument, the current PACE-SPEXone and the future Metop-SG-3MI and CO2M-MAP instruments. A unique ability of these instruments to measure both the intensity and polarization of sunlight across multiple wavelengths and viewing angles offers an unparalleled dataset for aerosol characterization, including number concentrations, size distributions, and refractive indices. We have substantially enhanced the RemoTAP results by integrating improved cloud fraction values derived from MAPs using a neural network approach, ensuring more accurate aerosol retrievals through better cloud filtering techniques. To further elevate data quality, advanced quality filters utilizing multiple key metrics were developed, effectively enhancing data integrity, resulting in a more refined aerosol dataset essential for precise atmospheric analysis. The validation of these enhancements involved comparisons with ground-based AERONET (Aerosol Robotic Network) observations over 284 sites, demonstrating the reliability of RemoTAP-derived aerosol properties. Furthermore, a pixel-level cross-comparison was carried out with GRASP-derived PARASOL-based aerosol data, as RemoTAP and GRASP are similar kind of algorithms for polarimetric measurements. The scientific implications of these advancements are profound, as the improved retrieval of aerosol size and composition using advanced polarimetric observations directly refines the estimation of cloud condensation nuclei (CCN) (proxy) concentrations and consequently the global CCN-Nd (cloud droplet number concentration) relationship. This refined relationship is crucial for understanding aerosol-cloud interactions, allowing for more accurate quantification of aerosol-induced cloud albedo changes, thereby reducing uncertainties in radiative forcing estimates due to aerosol-cloud interactions (RFaci). Such improvements contribute to a more precise representation of aerosol impacts in climate models, ultimately enhancing predictions of climate sensitivity and future warming scenarios. By advancing the RemoTAP algorithm, our findings underscore the transformative potential of these methodologies in delivering accurate and reliable aerosol climatology, driving forward the frontier of atmospheric science and climate research.
How to cite: N Patel, P., van Diedenhoven, B., Hasekamp, O., and Fu, G.: Advanced Aerosol Retrievals with RemoTAP and PARASOL: Enhancing Understanding of Aerosol-Cloud Interactions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14494, https://doi.org/10.5194/egusphere-egu25-14494, 2025.
Aerosol-Cloud Interactions (ACIs) remain a major uncertainty in climate predictions. While satellites provide valuable climatology to constrain ACIs and estimate their radiative forcing (e.g., Twomey effect), discrepancies among studies and measurement limitations persist. Passive sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) cannot simultaneously retrieve aerosol and cloud properties, and biases arise when absorbing aerosols above clouds (AACs) affect cloud retrievals. Such biases are particularly evident during extreme events like wildfires, dust storms, or volcanic eruptions, where AACs distort measurements of Cloud Effective Radius (CER) and Cloud Optical Thickness (COT) (e.g., Haywood et al., 2004; Alfaros and Contreras, 2013; Constantino and Bréon, 2010-2013).
Hitherto, existing AAC studies focus on biomass burning aerosols (BBAs) over the Southeast Atlantic, limiting their scope.
Then, the objective of this work is to globalize the AAC study to several types of absorbing aerosols all over the world. To do so, we created a new database combining aerosols and clouds properties as well as new aerosols products based on specific aerosol-cloud scenarios. We used L3 and L2 data from TROPOMI (on board the Sentinel-5P satellite) and MODIS respectively, as well as reanalysis from CAMS: ERA5 and EAC4. We covered the period from 2019 to 2023, containing noticeable events such as the Australian and Californian Wildfires (2019/2020) or regular Saharan dust storms.
In a second time, we conducted statistical analyses on COT, CER and Cloud Droplets Number Concentration (Nd), over specific regions where Nd retrievals are reliable (McCoy, 2017). The primarily results show strong responses to AAC on cloud properties during strong events. The indirect aerosol effect is not as visible as expected, but still, this work is encouraging. The next objective is to combine satellite observations with Radiative Transfer simulations (on RTTOV) to reproduce the AAC scenes and confirm our observations and better quantify the AAC biases on cloud properties as well as ACIs more generally.
How to cite: Devigne, E., Sourdeval, O., and Waquet, F.: Disentangle Aerosol-Cloud-Interactions (ACIs) using a new Aerosol-Cloud Data Base from Passive Remote Sensors and Reanalysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5636, https://doi.org/10.5194/egusphere-egu25-5636, 2025.
In the period of Arctic Amplification, with surface temperatures at high northern latitudes rising faster than those at the mid-latitudes, the Arctic is undergoing significant changes. Among these changes are the retreat of sea ice and the melting of glaciers, processes that affect the spectral reflectance of solar radiation at the surface. By detecting subtle changes in reflectance at the top of the atmosphere (RTOA) as measured by the GOME-2 scanning spectrometer, we have identified significant negative trend (at 95% confidence) over the Greenland ice sheet (GrIS) for the period 2007-2024. We analyze the causes behind this RTOA drop through a combination of the AVHRR-based CLARA-A3 CM SAF’s Cloud, Radiation and Surface Albedo data record, ECMWF Reanalysis v5 (ERA5), and ground based measurements from Summit Station, Greenland. The primary focus of this combined spatiotemporal analysis is to better understand the impact of clouds on RTOA as well as on the surface conditions over the GrIS. Moreover, we aim to deduce how the atmospheric conditions over Greenland are responding to a warming Arctic climate system and what that means for GrIS in the years to come. At EGU25, we will present our most recent findings from our multi-dataset study and share our conclusions on the role of clouds in the observed drop in RTOA above Greenland.
This research is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Transregional Collaborative Research Center TRR 172 “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3.
How to cite: Mchedlishvili, A., Vountas, M., and Bösch, H.: Identifying the Role of Clouds in the Recent Decrease in Top of the Atmosphere Reflectance Over Greenland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4196, https://doi.org/10.5194/egusphere-egu25-4196, 2025.
We present work and results of the XBAER4EnMAP project, which focuses on the adaptation and further development of the mature eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm for the HyperSpectral Imager (HSI) instrument on board the Environmental Mapping and Analysis Program (EnMAP) satellite mission.
The XBAER algorithm retrieves aerosol optical thickness (AOT), surface reflectance (SRF) and cloud parameters and was previously developed using radiance data from both the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel 3, both of which provide radiance data at a spatial resolution of 300m to 1.2km.
We aim to update XBAER to utilize EnMAP's higher spatial resolution radiance data of 30m, and to produce equivalent spatial-resolution data products with it. We present a comparison of OLCI and EnMAP top of atmosphere reflectances (RTOA) for co-located pixels, demonstrating that EnMAP is well calibrated enough for use in the XBAER algorithm. We then present results of comparison of XBAER-derived surface reflectance (SRF) and AOT using the same co-located pixels, i.e. at OLCI spatial resolution. Furthermore, we present first results of high-spatial resolution XBAER AOT, including a preliminary comparison with AErosol RObotic NETwork (AERONET).
Finally we discuss remaining challenges, such as updating XBAER's cloud mask and surface treatment for high spatial resolution data, and how to best compare AOT retrieved for the the small EnMAP scene size to AOT retrieved from point sites.
How to cite: Laffoy, S., Vountas, M., Mei, L., and Bösch, H.: First Retrievals of Aerosol Optical Thickness and Surface Reflectance using EnMAP Radiance Data with the XBAER Algorithm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4701, https://doi.org/10.5194/egusphere-egu25-4701, 2025.
Northeast China is influenced by a diverse range of aerosol sources, including industrial emissions, biomass burning, dust storms, and the transport of mineral dust from the Gobi and Taklamakan deserts. These varying aerosol types interact with solar radiation through scattering and absorption processes. The AERONET sun-sky-lunar CE318-T photometer was recently installed in Changchun in October 2024 to study and monitor aerosol characteristics in this region. This installation marks the beginning of systematic and accurate measurements of aerosol properties in the area, providing a valuable dataset for understanding aerosol behavior. Given the complex interactions between aerosol particles and solar radiation, it is essential to know how different aerosol types influence radiation patterns. For this study, AERONET measurements were compared with data obtained from the set of instruments installed at the SOLYS2 sun tracker. The SOLYS2 is equipped with pyranometers, pyrgeometer, pyrheliometer, and shading ball assembly. This set of sensors captures radiation from the entire hemisphere, including both direct and diffuse radiation from the sun and sky. It allows the measurement of global horizontal irradiance, direct normal irradiance, and diffuse normal irradiance to be provided, enabling a comprehensive analysis of aerosol-radiation interactions in the region. In this work, we investigate how varying aerosol loading impacts solar radiation in this region, focusing on the influence on radiation patterns and overall atmospheric conditions.
How to cite: Gao, X., Yukhymchuk, Y., Milinevsky, G., and Sun, X.: Impact of aerosol load on solar radiation in the northeast China region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5535, https://doi.org/10.5194/egusphere-egu25-5535, 2025.
Clouds influence Earth's radiative budget, with high-level clouds playing a critical role in atmospheric warming. Accurate cirrus cloud characterization is crucial and can be achieved using different data sources. Active satellite sensors are presently the most accurate source for cirrus data, but their usefulness in climatological studies is limited. In contrast, passive data, available for the past 40 years, offers sufficient temporal resolution but struggles to detect cirrus clouds effectively. This study evaluates MODIS cloud masking algorithms for cirrus detection, comparing their performance to CALIOP data. Specifically, we aim to assess whether MODIS cloud detection tests used to generate MYD35 operational data can be re-used for masking of cirrus.
Using CALIOP data as the reference, we evaluated six tests for cirrus detection considered in MODIS cloud masking algorithm and their combination (ATC). Additionally we applied two ISCCP-originating tests: ISCCP3.6 and ISCCP23 tests.
Our results showed that the ATC method outperforms others, with 72.98% accuracy during the day and 59.50% at night (probability of detection: 80.87% and 25.46%, false alarm rate of 34.86% and 6.90%, and Cohen’s kappa coefficient of 0.46 and 0.19 respectively). The ATC test offers a reliable option for creating high-level cloud masks.
How to cite: Nguyen Huu, Ż., Kotarba, A. Z., and Wypych, A.: Cloudy Planet: Cirrus Detection with MODIS Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8784, https://doi.org/10.5194/egusphere-egu25-8784, 2025.
Earth's climate system and weather are affected by clouds, as they regulate the global radiative budget, depending on their altitude, structure and composition. Therefore, accurate cloud information is crucial, particularly above the Mediterranean, which is considered as a climate hotspot.
In this work we utilize space-based radar products from the CloudSat mission to provide statistics on the properties of the clouds observed above the Mediterranean during the period 2007 – 2017. CloudSat’s payload, the Cloud Profiling Radar (CPR), is the first spaceborne 94-GHz (W-band) radar producing vertical cloud profiles over the globe. Three domains are selected in the Mediterranean to study the geometrical properties and the cloud types by month and altitude.
Our results reveal that low-level clouds are dominant above the Mediterranean region especially during winter and spring periods, mainly appearing at altitude up to 4 km, while high clouds prevail throughout the year at altitudes between 9 and 14 km, except in July and August above the East Mediterranean, where they are nearly absent. In the East Mediterranean, a higher frequency of low-level clouds is observed during the summer period. The majority of the deep convective clouds are observed above the West and Central Mediterranean, indicating the influence of the Atlantic systems and the mid-latitude cyclones on the Mediterranean weather conditions. Additionally, a cloud climatology is constructed in order to examine trends in each cloud type.
The results from this intercomparison will be used to derive a better understanding of the model's limitations to accurately predict cloud geometrical and microphysical properties in the region, and to improve the aerosol-cloud interaction model representation.
The research work was supported by the Hellenic Foundation for Research
and Innovation (H.F.R.I.) under the “Basic Research Financing
(Horizontal support for all Sciences), National Recovery and Resilience
Plan (Greece 2.0)” (Project Number: 015144)
How to cite: Koutsoupi, I., Marinou, E., Voudouri, K. A., Tsikoudi, I., Paschou, P., Amiridis, V., Battaglia, A., Kollias, P., and Giannakaki, E.: Analysis of Cloud Types and their Geometrical Properties over the Mediterranean using CloudSat Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16774, https://doi.org/10.5194/egusphere-egu25-16774, 2025.
Atmospheric aerosols of both natural and anthropogenic origin have a significant impact on the Earth's climate and on human health by degrading air quality. To study the influence of aerosols on climate and to monitor air pollution, it is important to know the characteristics of aerosol particles. In the case of smoke particles, their microphysical, chemical and optical properties are complex and strongly depend on the combustion sources, the aging process and the meteorological conditions.
This study is devoted to the analysis of optical properties of the particles that can be adopted as a model of smoke particles. The main attention has been paid to the analysis of the spectral dependence of the backscattering linear depolarization ratio (LDR), in particular, the strong wavelength dependence of the LDR, that is observed for some biomass burning events in lidar measurements. Among other light scattering models of smoke particles that can describe such spectral dependence and reproduce the optical properties of smoke particles, the most realistic model in the form of fractal-like clusters of carbonaceous spherules (monomers) was selected. The analysis was carried out on the basis of the created database of optical characteristics of fractal-like clusters calculated for a certain set of cluster parameters differing in their structure, size and morphological characteristics of individual monomers. Our results show that the LDR is a complex function of various factors such as particle size and shape, refractive index, fraction and composition of the core and monomer coating material, etc., and for a certain choice of morphological characteristics, a fractal-like model can reproduce the observed spectral dependence of the LDR. In the framework of such a model, the values of the LDR measured by lidars can be used to estimate the monomer size of the cluster particles, more precisely, the product of the monomer size and the real part of the refractive index of the monomer.
The integration of fractal aggregate morphology into aerosol models can improve the retrieval accuracy of microphysical and chemical properties of smoke particles. And combined analysis of Sun photometer and lidar measurements can provide complementary information for clarifying morphological characteristics of the monomers, such as size, refractive index, and cluster structure and size distribution.
How to cite: Berdina, L., Tishkovets, V., Lytvynov, P., Dubovik, O., Lapyonok, T., Hu, Q., and Goloub, P.: Optical properties of smoke particles: modeling and interpretation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12956, https://doi.org/10.5194/egusphere-egu25-12956, 2025.
Discrepancies in aerosol representation in chemistry-transport models (CTMs) arise from uncertainties in emission sources. These are exacerbated by nonlinear physico-chemical processes and transport. Assumptions about optical properties and limitations in satellite retrievals further contribute to the problem. Despite their significance, such disparities remain largely unaddressed. Under the European Commission’s CAMS Model Evolution (CAMEO) project, we conducted an intercomparison of aerosol physical properties between CAMS reanalysis and POLDER/GRASP products. POLDER, a multi-angular polarimeter onboard PARASOL, operated from 2008 to 2013. This study examines adjustments made to the GRASP retrieval algorithm to address these disparities in the CAMS CTM.
Our findings show that CAMS aerosol optical depth (AOD) at multiple wavelengths closely aligns with POLDER/GRASP data when CAMS refractive indices are incorporated into the retrieval algorithm. While CAMS assimilates MODIS 550 nm AOD data, it also produces reliable AODs across other wavelengths. Validation at AERONET stations, focusing on the year 2008, demonstrated reasonable agreement for biomass-burning aerosols, desert dust, and anthropogenic pollutants. However, uncertainties remain in estimating single scattering albedo (SSA). Furthermore, CAMS fine-mode AOD aligns well with POLDER/GRASP retrievals. In contrast, CAMS coarse-mode AOD shows a weak positive linear correlation with POLDER/GRASP. From July to September, CAMS reanalysis exhibits a non-negligible bias at sites dominated by biomass burning and anthropogenic emissions, confirmed by AERONET ground data. These results highlight persistent uncertainties in representing black carbon, brown carbon, and organic matter in both CTMs and satellite retrievals. Over dust-dominated stations, fine-mode dust exhibits light-absorbing properties, while coarse-mode dust shows minimal spectral variation. Such findings, which vary by location and season, provide crucial insights for CTM development.
This work supports future advancements in aerosol modelling and lays the groundwork for exploring the capacities of upcoming multi-angular, multi-viewing polarimetric missions, such as PACE/HARPOL-2, PACE/SPEX, and 3MI. These efforts will improve data assimilation and enhance the accuracy of CAMS reanalysis datasets.
How to cite: Herrera, M., Behera, A. K., Matar, C., Litvinov, P., Dubovik, O., Berdina, L., Tishkovets, V., Lapyonok, T., Ducos, F., and Fuertes, D.: Evaluation of CAMS Reanalysis Aerosol Optical Properties Against POLDER/GRASP Retrievals: Insights into Fine and Coarse Mode Aerosol Characteristics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18561, https://doi.org/10.5194/egusphere-egu25-18561, 2025.
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., Ltivinov, 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 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18672, https://doi.org/10.5194/egusphere-egu25-18672, 2025.
Weather radar plays a crucial role in the monitoring, forecasting, and early warning of severe convective weather. With the widespread application of artificial intelligence technology in the meteorological field, the demand for high-quality, long-sequence weather radar product is becoming increasingly urgent. To support the development of domestic independent severe convective weather models, enhance the identification and monitoring technology for severe convective weather, and promote in-depth research in numerical forecasting and mechanisms, the Meteorological Observation Center of China Meteorological Administration has applied techniques such as feature analysis and recognition, multi-source data collaborative quality control, inspection evaluation, and diagnostic error correction. Through a hierarchical processing approach involving single-station quality control, network quality control, and inspection evaluation, coarse data errors are eliminated. This process has resulted in the formation of a long-sequence, high-quality, and high spatiotemporal resolution weather radar basic product dataset (V1.0) covering the years from 2011 to 2023. This dataset incorporates post-quality control base data from nationwide weather radars, along with four types of two-dimensional mosaic products: composite reflectivity, hybrid scan reflectivity, echo top height, and vertically integrated liquid water content, as well as three-dimensional mosaic products of constant altitude plan position indicator (CAPPI). The post-quality control base data amounts to roughly 200TB, encompassing data from 247 stations with a temporal resolution of approximately 6 minutes. The two-dimensional mosaic product data totals approximately 1.2TB, featuring a temporal resolution of 6 minutes. These two-dimensional mosaic products cover a horizontal spatial range from 73.0° to 135.0°E and from 12.2° to 53.2°N, with a spatial resolution of 0.01° × 0.01°. The three-dimensional mosaic product data totals approximately 27.5TB, sharing the same temporal and horizontal resolution as the two-dimensional mosaic product data. In terms of vertical spatial coverage, it spans from 0.5 km to 16 km, consisting of 24 layers. The vertical resolution is 0.5 km for altitudes up to 8 km and 1 km for altitudes above 8 km. Currently, this dataset has played a crucial role in severe convective weather model training and the development of reanalysis data in the Chinese region.
How to cite: Wu, L.: Weather Radar Long-sequence Product Dataset in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5963, https://doi.org/10.5194/egusphere-egu25-5963, 2025.
Ambient fine particulate matter (PM2.5, mean aerodynamic radii less than 2.5µm) is a leading cause of millions of premature deaths annually, linked to lung cancer, pulmonary inflammation, and cardiopulmonary mortality. Therefore, accurate global measurements of PM2.5 are essential for epidemiological studies, designing air quality control strategies, and improving air quality forecasting.
The POLDER-3/GRASP multi-angular polarimetric products enable advanced retrievals of aerosol properties, such as size distribution, refractive index, particle composition, and aerosol layer height (ALH), facilitating the estimation of ground-level PM2.5. However, POLDER has limited sensitivity to ALH and relies on assumed aerosol vertical profiles, which may introduce uncertainties that propagate into PM2.5 estimations. Complementarily, the CAMS reanalysis data provides global coverage with high temporal resolution, offering detailed information on tropospheric aerosol distributions and vertical structures.
In this study, we first validate global ground-level PM2.5 estimations from POLDER/GRASP products using detailed columnar aerosol properties, such as size distribution, refractive index or chemical composition and ALH, against observational data from the US Environmental Protection Agency (EPA). Next, CAMS reanalysis aerosol products are integrated into the POLDER-3/GRASP framework to enhance the estimation accuracy. This integration combines columnar aerosol properties retrieved from POLDER/GRASP at higher spatial resolution with detailed vertical profiles from CAMS, notably ground-level aerosol fractions. Finally, we analyze the improvements achieved by comparing the integrated results with US EPA ground-based measurements. This synergistic approach investigates the potential of combining POLDER/GRASP retrievals with CAMS comprehensive vertical aerosol structures to improve global air quality assessments, paving the way for advanced monitoring capabilities from future multi-angular polarimeter missions, such as PACE/SPEX, PACE/HARPOL-2, and 3MI.
How to cite: Liu, Z., Lopatin, A., Behera, A., Herrera, M., Kuznetsov, K., Momoi, M., Giralda, M., Matar, C., Zhai, S., Lytvynov, P., Fuertes, D., Lapyonok, T., Derimian, Y., and Dubovik, O.: Estimation of Global PM2.5 from Polarimetric Remote Sensing (POLDER-3/GRASP) and CAMS Reanalysis Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11285, https://doi.org/10.5194/egusphere-egu25-11285, 2025.
Atmospheric aerosols are solid mass and liquor particles levitation in the air,
which have a significant impact on the Earth 's radiation balance of energy and world
climate, achievement of the "carbon-2 goal," and even human health..In addition,
while domestic satellites are booming, there is less satellite data available for
download and of high quality. Therefore, it is an urgent demand of the country to use
FY-3D remote sensing data to retrieve aerosol characteristics in the field of air
satellite remotely sensed.This paper describes the inverse validation of AOD before and after the epidemic in the Yangtze River Delta using the Deep Blue algorithm and the study of its spatial and temporal distribution characteristics.The inversion results show that the MOD04 DB products are significantly correlated with the resampled FY-3D/MERSI-II AOD with a correlation coefficient of 0.810. In addition, the inverted AOD have a better continuity in spatial and temporal distributions in the Yangtze River Delta region.In addition, we comprehensively analyzed the inversion AOD of FY- 3D/MERSI-II and the aerosol optical thickness of Taihu station on the ground in February-June 2018 in the Yangtze River Delta, and make out that the inversion AOD was close to the ground-based observation data and the trend was consistent, and the correlation coefficient between the two was greater than 0.75.Based on the analysis of the temporal and spatial distribution of AOD in the Yangtze River Delta region before and after the epidemic, we found that the AOD values of the entire Yangtze River Delta study area showed a decreasing trend in several cities, and the AOD of several cities decreased more significantly in 2020. From a temporal perspective, the average AOD values showed a decreasing trend from 2018 to 2020, with the largest average AOD value in 2018 and the smallest average AOD value in 2020.
How to cite: bian, X. and xiao, C.: Study on the inversion of aerosol optical parameters and their spatial and temporal distribution characteristics in Yangtze River Delta region based on FY-3D, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18028, https://doi.org/10.5194/egusphere-egu25-18028, 2025.
Marine aerosols are crucial for ocean-atmosphere interactions. However, there is a lack of analysis on the long-term aerosol optical characteristics and relative humidity (RH) based on large-scale ocean regions. This paper addresses this issue by analyzing the spatiotemporal variations of aerosols in China's offshore waters from 2007-2022 using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) data. The results show that in the sea area, the aerosol backscatter coefficient at 532nm and extinction coefficient at 532nm increase with increasing RH; the aerosol depolarization ratio decreases with increasing RH. The optical properties of aerosols are generally lower in summer, while they fluctuate with rising RH in other seasons. Notably, the values recorded in 2014-2019 were markedly lower than those in 2007-2013. Nevertheless, from 2020 to 2022, the proportion of aerosol types remained largely stable compared to 2014-2019, suggesting a negligible impact of the COVID-19 pandemic on emissions, but the near-shore waters' aerosol optical properties were more sensitive to minor changes than those of far-off waters. After dividing the China's offshore waters, the overall optical properties of aerosols decreased in order of the Bohai Sea and Yellow Sea, the East China Sea, and the South China Sea, due to the proportion of marine aerosols increase towards the south. After dividing the altitude layer in the sea area, within the range of 0-7 km, the higher the altitude, the less oceanic aerosols and the more terrestrial aerosols. The aerosol depolarization ratio rises, while the aerosol backscatter coefficient at 532nm and extinction coefficient at 532nm alternate downwards. We speculate this is linked to the steep decline in total aerosol content as altitude rises. Our results could provide theoretical references for further deepening the understanding of aerosol generation mechanisms in China's offshore waters.
How to cite: He, J., Li, Z., and Jiang, W.: Long-term spatiotemporal features of aerosol optical characteristics and relative humidity in China's offshore areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15328, https://doi.org/10.5194/egusphere-egu25-15328, 2025.
The uncertainties in the cloud radiative properties are the main cause of biases in the radiative fluxes both at the top of the atmosphere and at the surface (Di Natale et al. 2022). This study aims at providing an in-depth characterisation of clouds occurrence and properties on the Antarctic Plateau by analyzing an extensive dataset of spectrally resolved downwelling radiances in the far- and mid-infrared region of the spectrum (200 − 1000 cm-1). Observations were performed by the REFIR-PAD (Radiation Explorer in the Far Infrared—Prototype for Applications and Development) spectroradiometer at the Concordia research station on the Antarctic Plateau, during the period from 2013 to 2020. An improved version of the Cloud Identification and Classification (CIC) algorithm (Maestri et al. 2019; Donat et al. 2024) is utilized for the identification of cloud layers and their classification in terms of phase (ice or mixed phase). An extended dataset, comprising about 75000 cloudy spectra, is then analysed to derive geometrical, optical, and microphysical properties of the observed layers. First, the Polar Threshold (PT) algorithm (Van Tricht et al. 2014) is applied to collocated Lidar backscatter profiles obtained from a Lidar system (INO-CNR Istituto Nazionale di Ottica 2024) to derive cloud base and top altitude. Then, the geometrical information is exploited by the Simultaneous Atmospheric and Cloud Retrieval (SACR) physical inversion algorithm (Di Natale et al. 2020), which, applied to the REFIR-PAD radiances, enables the derivation of cloud optical depth, effective dimensions, and atmospheric vertical profiles of water vapor and temperature. Statistics of clouds optical and microphysical properties for different cloud types are derived. Results show that the mean optical depth and effective diameter of ice clouds are 0.58 and 25 μm, respectively. It is found that 90% of the data indicate effective diameters smaller than 52 μm. During the austral summer, both optical depth (0.34) and effective diameter (19 μm) are at their lowest values, while maxima are found in the winter season (0.73 for optical depth and 30 μm for effective diameter). The ice clouds mean temperature is 236 K, with a seasonal cycle showing the highest temperatures in summer. Mixed-phase clouds exhibit a significantly higher mean optical depth of 2.35. Their averaged effective diameter is 8.55 μm, and the mean cloud temperature is 244 K. The base height of mixed-phase clouds is found at mean value of 0.29 km above ground level (agl) which is significantly lower than the one for ice clouds found at 0.62 km agl. Finally, a new parametrization of cloud microphysical properties based on the thermodynamic conditions of the layer is proposed for potential applications in climate and numerical weather prediction models. The effective diameter of ice crystals is parameterized as a function of the mean cloud temperature and ice water content, whereas the effective diameter of water droplets for mixed-phase clouds is expressed as a univariate function of the mean cloud temperature. A comparison with parametrizations widely used in climate and numerical weather prediction models is also provided.
How to cite: Fabbri, E., Maestri, T., Donat, F., Martinazzo, M., Masiello, G., Liuzzi, G., Palchetti, L., Di Natale, G., Del Guasta, M., and Bianchini, G.: Statistics of Optical and Microphysical Properties of Ice and Mixed-Phase Clouds on the Antarctic Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13530, https://doi.org/10.5194/egusphere-egu25-13530, 2025.
The Southern Ocean (SO) is one of the cloudiest places on Earth, with distinct cloud properties including a high prevalence of multilayer clouds. Previous research has found that multilayer clouds contribute to net cloud radiative effect biases. In our previous paper,we compared and validated different LEO passive sensor retrievals (AVHRR-Patmosx, CMSAF, MODIS collection 6) over the SO against active sensor retrievals (CloudSAT- CALIOP). In the comparison of cloud top height, we found that a mean absolute bias of 0.65 km (AVHRR CMSAF), 1.03 km (MODIS), and 1.31 km (AVHRR PATMOS) was observed for single-layer cloud scenes cases. This mean bias increased to 1.86 km (AVHRR CMSAF), 3.22 km (MODIS), and 3.34 km (AVHRR PATMOS) for multilayered cloud scenes. One of the significant factors for the observed differences is the presence of multilayer clouds.
Given the results of the comparison and a need for more accurate cloud retrieval for multi layer clouds in particular, we developed a new multilayer retrieval algorithm for CTH from MODIS data over the SO region using an artificial neural network (NN) approach. The retrieval algorithm employs MODIS radiances and reanalysis datasets. The algorithm's performance for the topmost cloud layer demonstrates a significant improvement compared to the traditional retrieval approaches. The MODIS CTHs mean bias error against the CloudSAT- CALIOP merged dataset was reduced to approx 0.02 km with an RMSE of 0.84 km. In multilayer scenarios,the CTHs of the top layer were retrieved with a MBE of 0.08km and RMSE of 0.98 km and the CTHs of the second layer with a MBE of 0.01km and RMSE of 1.54 km. The results were analysed to understand the influence on latitude, solar zenith angle, sensor zenith angle, cloud optical depth and surface temperature on the ANN algorithm. The research successfully demonstrated the usefulness of NN in retrieval algorithms.
How to cite: A Kurup, A., Poulsen, C., and T Siems, S.: Multilayer Retrieval of Cloud Top heights from MODIS over the Southern Ocean , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1551, https://doi.org/10.5194/egusphere-egu25-1551, 2025.
NASA's Plankton, Aerosol, Clouds, and ocean Ecosystem (PACE) mission was successfully launched on February 8, 2024. A key instrument aboard the PACE platform- the Hyper-angular Rainbow Polarimeter-2 (HARP2), a state-of-the-art multi-angular polarimeter, collects data from multiple viewing angles spanning -54.5° to +54.5° across 4 channels ranging from visible to near-infrared wavelengths, with measurements of linear polarization at three directions. Thus, HARP2 has very high sensitivity to aerosol characteristics, including particle size, type, and absorption etc.
In this study, the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm was utilized to retrieve aerosol and surface parameters simultaneously from HARP2 measurements collected over the past half year. Validation of aerosol properties was conducted by comparing HARP2/GRASP products with ground-based AERONET observations, while surface property retrievals were evaluated against MODIS surface products. The results have demonstrated encouraging performance: spectral AOD shows a high correlation with AERONET data with correlation coefficients ~0.9and a mean bias of -0.01. The Ångström Exponent (AE) and single scattering albedo (SSA), also showed reasonable agreement, with AE and SSA achieving correlation coefficients of 0.54 and 0.9. Surface property retrievals exhibited robust correlations with MODIS data, demonstrating the effective decoupling of surface and atmospheric signals from the satellite observations.
This research highlights the potential of HARP2/GRASP products in providing accurate aerosol and surface parameters, leveraging HARP2’s polarimetric capabilities. In the future, we will also explore the synergistic potential of integrating observations from multiple PACE instruments to enhance the information content and improve retrieval coverage and accuracy.
How to cite: Li, C., Dubovik, O., Puthukkudy, A., Lytvynov, P., Lopatin, A., Fuertes, D., García Gómez, A., and Antuña Sánchez, J. C.: Retrieval and validation of aerosol and surface properties from HARP2/PACE using GRASP algorithm , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6443, https://doi.org/10.5194/egusphere-egu25-6443, 2025.
During the last years there have been a great advance in the development of satellite missions for Earth Observation. Most of them rely on passive remote sensing measurements, particularly on multiwavelength multi-angular polarimetry measurements (MAP). Upcoming missions such as Sentinel-5 will also deploy MAP and there are even private initiatives to expand space MAP measurements. Although MAP measurements have been proven to be ideal for expanding our knowledge in aerosol optical and microphysical properties (Dubovik et al., 2019), they provided very limited information of the aerosol properties vertically-resolved. On the other hand, multiwavelength lidar measurements are capable of providing accurate aerosol vertical profiles but face with limitations in the retrieval of aerosol optical and microphysical properties because of the limited information content of the stand-alone lidar measurements (Perez-Ramirez et al., 2019). Here we explore the potentiality of inverting aerosol microphysical properties vertically-resolved by combining in the Generalized Retrieval of Atmosphere and Surface Properties (GRASP – Dubovik et al., 2021) space lidar and polarimetry measurements.
We present extensive simulations of aerosol optical and microphysical properties vertical-profiles retrievals combining multiwavelength MAP and lidar measurements in GRASP, with the enhanced capability of differentiating between aerosol fine and coarse mode properties. The retrieval is pushed forward by trying to obtain 22 bins size distribution, similar to those provided by AERONET. In the simulations different mixtures of fine and coarse mode were used, varying refractive indexes from low to high absorption. We have used the HARP-like polarimetry configuration that uses the heritage of POLDER space polarimetry and is deployed in the NASA PACE mission. For lidar measurements, multiwavelength configurations are tested, from single backscattering measurements to adding additional extinction measurements. Our results show full capacity of GRASP to retrieve aerosol properties vertically-resolved differentiating between fine and coarse properties, although the more accurate results are obtained when using all lidar information. However, we found out that optimized retrieval needs of constraining surface properties, particularly because of the impact of BPDF in coarse mode retrieval. Limitations in surface retrievals can be solved with the multi-pixel approach in GRASP when applied to real missions. Finally, we present case-study of synergy retrievals from airborne measurements obtained during NASA field campaigns when AirHARP + lidar flew together. The results of the simulations serve as baseline for future space mission that will combine space lidar + polarimetry or to the synergy of different satellite missions.
References
Dubovik, O., et al., 2019: Polarimetric remote sensing of atmospheric aerosols: instruments, methodologies, results, and perspectives. J. Quant. Spectrosc. Radiat. Transfer, 224, 474-511,
Dubovik, O, et al., 2021: A comprehensive description of multi-term LSM for applying multiple a priori constraints in problems of atmospheric remote sensing: GRASP algorithm, concept and applications. Front. Remote. Sens., 2, 706851.
Perez-Ramirez, D, et al., 2019: Retrievals of aerosol single scattering albedo by multiwavelength lidar measurements: Evaluations with NASA Langley HSRL-2 during DISCOVER-AQ field campaigns. Remote. Sens. Environ., 222, 144-164.
How to cite: Perez-Ramirez, D., Litvinov, P., Lopatin, A., Rodriguez-Navarro, O., Muñiz-Rosado, J., Fuertes, D., Dubovik, O., Puthukuddy, A., and Martins, V.: Synergy in GRASP of space multiwavelength multi-angle polarimetry and lidar measurements for vertical profiles of aerosol optical and microphysical properties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18896, https://doi.org/10.5194/egusphere-egu25-18896, 2025.
While traditional thermal infrared retrieval algorithms based on radiative transfer models (RTM) could not effectively retrieve the cloud optical thickness of thick clouds, machine learning based algorithms were found to be able to provide reasonable estimations for both daytime and nighttime. Nevertheless, stand-alone machine learning algorithms are occasionally criticized for the lack of explicit physical processes. In this study, RTM simulations and a machine learning algorithm are synergistically utilized using the optimal estimation (OE) method to retrieve cloud properties from thermal infrared radiometry measured by Moderate Resolution Imaging Spectroradiometer (MODIS). In the new algorithm, retrievals from a machine learning algorithm are used to provide a priori states for the iterative process of OE method, and an RTM is used to create radiance lookup tables that are used in the iteration processes. Compared with stand-alone OE, the cloud properties retrieved by the new algorithm show an overall better performance by using statistic a priori information obtained by machine learning algorithm. Compared with stand-alone machine-learning based algorithm, the radiances simulated based on retrievals from the new method align more closely with observations, and physical radiative processes are handled explicitly in the new algorithm. Therefore, the new method combines the advantages of RTM-based cloud retrieval methods and machine-learning models. These findings highlight the potential for machine-learning-based algorithms to enhance the efficacy of conventional remote sensing techniques.
How to cite: Huang, H., Wang, Q., Liu, C., and Zhou, C.: Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1161, https://doi.org/10.5194/egusphere-egu25-1161, 2025.
Proper proxies for CCN are vital to provide accurate constraints for Aerosol-Cloud Interactions (ACI) in climate models. An effective proxy for CCN is the column number of aerosol particles that surpasses a predetermined threshold radius (Nccn). 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) satellite, further improvements on the Nccn retrievals are expected. For example, retrieved refractive index can be used to estimate the volume fraction of aerosol-water, which can help deduce the dry aerosol size distibution and subsequently dry CCN. Further, the retrieved Aerosol-Layer Height (ALH) can be used to estimate the boundary layer (BL) contribution of Nccn (Nccn (BL)) which is better suited for quantifying ACI as it is more related to CCN at cloud base than the total column.
The estimation of Nccn from physics based MAP algorithms can be challenging given its dependance on multiple retrieved aerosol parameters. We have implemented a deep neural network (NN) algorithm as an extension for the Remote sensing of Trace gas and Aerosol Products (RemoTAP)-NN algorithm to directly retrieve dry Nccn and Nccn (BL) from SPEXone measurements. The algorithm is trained on synthetic SPEXone measurements based on 3 aerosol modes which are fine mode, insoluble coarse/dust mode and soluble coarse mode. It has been validated using synthetic SPEXone measurements, simulated based on the 7 mode aerosol model from the ECHAM-HAM global aerosol-climate model. The performance of the NN algorithm was compared with RemoTAP classical algorithm.
The NN algorithm retrieved dry Nccn has a relative RMSE of 0.197 over the ocean and 0.301 over the land whereas dry Nccn estimated by RemoTAP level-2 retrievals for the same synthetic measurements has a relative RMSE of 0.382 over ocean and 0.559 over land. Nccn (BL) retrieved from the NN algorithm has a relative RMSE of 0.349 and 0.825 over the ocean and the land respectivey. The relative RMSE of Nccn (BL) derived from the RemoTAP classical algorithm is 1.039 and 1.233 over the ocean and land respectively. Our study demonstrates that the NN algorithm can accurately retrieve Nccn, outperforming the capabilities in classical algorithms.
How to cite: K. Hannadige, N., Fu, G., van Diedenhoven, B., Jia, H., and Hasekamp, O.: Estimation of Cloud Condensation Nuclei (CCN) from SPEXone on PACE using a deep neural network retrieval algorithm , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10514, https://doi.org/10.5194/egusphere-egu25-10514, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 5
EGU25-14363 | Posters virtual | VPS3
One-and-half Decade Long Global Retrieval Dataset of UV-VIS Spectral Optical Depth and Single-scattering Albedo of Absorbing Aerosols above Clouds from A-train Active-Passive SynergyWed, 30 Apr, 14:00–15:45 (CEST) | vP5.14
Active and passive sensors onboard satellites and suborbital measurements have shown frequent aerosol-cloud overlapping situations over several regions worldwide on a monthly to seasonal scale. However, retrieving the optical properties of aerosols lofted over clouds poses challenges. Primarily, the assumption of aerosol single-scattering albedo (SSA) in the satellite-based algorithms is known to be one of the largest sources of uncertainty in quantifying the above-cloud aerosol optical depth (ACAOD). On the radiative forcing aspect, the sign and magnitude of the aerosol radiative forcing over clouds are determined mainly by the aerosol loading, the absorption capacity of aerosols (SSA), and the brightness of the underlying cloud cover.
We contribute to addressing the uncertainties surrounding the absorbing aerosols-cloud radiative interactions by offering a novel, NASA’s A-train-centric, one-and-half decade long (2006-2022) global retrieval product of aerosols above cloud that delivers 1) spectral ACAOD, 2) spectral SSA of light-absorbing aerosols lofted over the clouds, and 3) aerosol-corrected cloud optical depth (COD). The synergy algorithm combines lidar retrievals of ACAOD derived from the ‘De-polarization Ratio’ method applied to CALIOP and the top-of-atmosphere (TOA) spectral reflectance from OMI (354-388 nm) and MODIS (470-860 nm) sensors to deduce the joint aerosol-cloud product. The availability of accurate ACAOD accompanied by a marked sensitivity of the TOA measurements to ACAOD, SSA, and COD allow retrieval of SSA for above-cloud aerosols scenes using the ‘color ratio’ algorithm applied to UV and VIS sensors.
We will present multiyear (2006-2022), regional retrievals of UV-VIS spectral aerosol SSA above clouds, and it’s a comparison against ORACLES airborne in situ and remote sensing measurements and ground-based AERONET inversions. A preliminary uncertainty analysis suggests that an uncertainty of 20% in ACAOD can result in an error of ~0.02 at 388 nm and ~0.01 at 470 nm in the retrieved SSA from OMI and MODIS, respectively. Furthermore, the presented aerosol-cloud remote sensing algorithm assumes implications for the recently launched EarthCARE and PACE missions with potential synergy of ATLID lidar and OCI imager. The availability of the global aerosol-cloud joint product will reenergize the community by offering 1) an improved representation of aerosol extinction and absorption properties over clouds and 2) much-needed observational estimates of the radiative effects of aerosols in cloudy regions for constraining the climate models.
How to cite: Jethva, H., Torres, O., Kayetha, V., and Hu, Y.: One-and-half Decade Long Global Retrieval Dataset of UV-VIS Spectral Optical Depth and Single-scattering Albedo of Absorbing Aerosols above Clouds from A-train Active-Passive Synergy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14363, https://doi.org/10.5194/egusphere-egu25-14363, 2025.
