Big variety of different satellites on Earth orbit are dedicated to aerosol studies. However, due to limited information content, the main aerosol products of the most of satellite missions is AOD while the accuracy of aerosol size and type retrieval from space-borne remote sensing still requires essential improvement. . The combination of measurements from different satellites essentially extends their information content and, therefore, can provide new possibility for much better retrieval of extended set of both aerosol and surface properties.

In the frame of ESA SYREMIS project GRASP algorithm was adapted for synergetic retrieval from combined space-borne instruments: (i) synergy from polar-orbiting (LEO) satellites (in particular, synergy of Sentinel-5p/TROPOMI, Sentinel-3A, -3B/OLCI instruments) and (ii) synergy of LEO and geostationary (GEO) satellites (in particular, synergy of Sentinel-5p/TROPOMI, Sentinel-3A, -3B/OLCI and HIMAWARI/AHI sensors). On one hand such synergy constellation extends the spectral range of the measurements. On another hand it provides unprecedented global spatial coverage with several measurements per day which is crucial for global climate studies and air-quality monitoring.

In this talk we discuss physical basis and concept of the LEO-LEO and LEO-GEO synergies used in GRASP retrieval. It will be demonstrated that SYREMIS/GRASP synergetic approach allows transition of information from the instruments with richest information content  to the instruments with lower one. This results in increased performance of AOD, aerosol size and absorption properties retrieval and more consistent surface BRDF characterization. 

How to cite: Litvinov, P., Chen, C., Dubovik, O., Zhai, S., Matar, C., Fuertes, D., Lopatin, A., Lapionak, T., Dornacher, M., Lehner, A., and Retscher, C.: Multi-instrument synergetic retrieval of aerosol and surface properties with GRASP algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20841, https://doi.org/10.5194/egusphere-egu24-20841, 2024.

Aerosols affect climate in several ways. Their effect depends not only on the aerosol abundance and geospatial distribution but also on the aerosol types present. Therefore, there is an important need for the retrieval of aerosol types from satellite measurements.

By combining data from different satellite instruments, information on the composition of aerosols in the atmosphere shall be determined with an optimal estimation retrieval algorithm. We make use of data from three different instruments measuring with different observation characteristics, different spectral ranges (UV, VIS, thermal IR), different viewing geometries (nadir, oblique). The included instruments are dual-view instrument SLSTR (Sea and Land Surface Temperature Radiometer) onboard Sentinel 3A and 3B; and the Infrared Atmospheric Sounding Interferometer (IASI) and the Global Ozone Monitoring Experiment-2 (GOME-2), both onboard Metop A/B/C.

In preparation for the information content analysis and future aerosol retrieval, the data from the different instruments are homogenized to a common grid of 40x80 km², the coarsest instrument resolution (GOME-2), within a temporal matching window of 60 minutes.  A cloud masking algorithm (APOLLO_NG) is then applied to the highest resolution radiometer data (1x1 km²) from SLSTR and in addition to the Advanced Very High Resolution Radiometer (AVHRR) onboard Metop A/B/C to take into account the temporal variation of the clouds.

For the information content analysis, a set of those observations is simulated with the SCIATRAN radiative transfer model for different observing conditions / geometries, surface types, aerosol types and aerosol amounts. With these data an analysis of the combined information content is then conducted which focuses on capabilities for the determination of aerosol abundance (total Aerosol Optical Depth - AOD) and aerosol types (as contributions to total AOD of fine / coarse mode, mineral dust, absorbing aerosols) in a cloud-free atmosphere over different ground surface types. The information content analysis will help to identify those instrument channels / spectral windows that carry most of the information which is then used to develop a retrieval algorithm for AOD and aerosol types.

How to cite: Stöffelmair, U., Popp, T., Vountas, M., and Bösch, H.: Information content analysis for aerosol type from a combination of three satellite instruments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1486, https://doi.org/10.5194/egusphere-egu24-1486, 2024.

The height of aerosols is important for many applications, such as the Earth’s radiation budget, transport of aerosols, aviation, retrieval of aerosol optical thickness and the atmospheric correction. Active instruments, such as the lidars on Caliop and Aeolus have provided important insight in the vertical distribution of aerosols, but these missions had a small spatial coverage and have now ended. EarthCare will provide an important replacements for these instruments, but the daily, global coverage provided by passive instruments, such as TROPOMI, remains essential.

In 2019, the first operational, global Aerosol Layer Height (ALH) product was released, retrieved from near-infrared measurements by TROPOMI on Sentinel-5P.  The operational algorithm uses a machine learning technique in the forward model, to quickly and accurately simulate the around 4000 spectral absorption lines in the O₂-A band around 760 nm, and the inversion problem is solved iteratively using an optimal estimation routine, in order to have a proper error estimation.

Since its release, many important improvement have been implemented. First focused on single, selected layers of absorbing aerosols, the processor now provides the ALH for all cloud-free scenes, including scattering aerosol layers. The algorithm performs well over oceans (within the 1 km accuracy requirement) but not over land surfaces. The surface albedo is an important error source, especially over bright surfaces and for thin aerosol layers. In order to improve the retrieval over land, the surface albedo can be fitted, yielding highly improved results. However, for the operational processor this required the retraining of the neural network, since the derivatives to the fit parameters are needed in the optimal estimation routine. Therefore, the derivatives to surface albedo at two wavelengths in the continuum (outside the  O₂-A band) were added to the algorithm forward model. This yielded improved accuracy over land and a large increase in the number of successful retrievals. The latest version of the S5P/TROPOMI ALH (version 2.6.0, including all these improvements and the surface fit) was released in November 2023. We will present the algorithm and the latest validation results, including ALH estimates from various instruments in space and from ground-based lidar networks. 

The TROPOMI ALH algorithm is developed and maintained within the EU Copernicus program. Its developments are important for Sentinel-3 OLCI, for which a similar O2-A band retrieval is being developed, and the upcoming geostationary mission Sentinel-4 and the successor missions for S5-precursor (Sentinel-5), which will also have a similar ALH product. 

How to cite: de Graaf, M., Sneep, M., Tilstra, G., ter Linden, M., and Veefkind, P.: Latest developments of the Aerosol Layer Height retrieval from S5P/TROPOMI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2364, https://doi.org/10.5194/egusphere-egu24-2364, 2024.

Aerosol vertical profiles in the lowest 1 km of the atmosphere are not very well studied due to technology limitations (e.g. LIDARs) and air space restrictions (e.g. manned and unmanned aircrafts). This study investigates sensitivity of the radiance measurements to the aerosol profiles from the standard columnar almucantar and direct sun measurements combined with low elevation sky scanning (from the horizon to zenith direction). Sensitivity studies are conducted for the sun-sky radiometer filter bands deployed within AERONET. AErosol RObotic NETwork (AERONET) is a network of sun-sky-moon photometers that measure solar radiation at 9 wavelength bands (centered at 340, 380, 440, 500, 675, 870, 937, 1020, 1640 nm, 2-10 nm except 25 nm for 1640 nm FWHM filter transmission). The main AERONET products are columnar aerosol optical depth, Angstrom exponent (from direct sun measurements), single scattering albedo and size distribution (from Almucantar and hybrid scans). The additional products investigated in this study are vertical volume density and aerosol extinction coefficient profiles. GRASP (with the plane parallel radiative transfer model) is initially used to simulate scattered sky radiances and conduct aerosol profile inversions. Several aerosol models and loadings are investigating as well as aerosol volume density profiles.

How to cite: Lind, E., Herreras-Giralda, M., Momoi, M., Eck, T., Sinyuk, A., and Dubovik, O.: Aerosol vertical profile retrievals using passive radiometric measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12449, https://doi.org/10.5194/egusphere-egu24-12449, 2024.

Aerosols may vary spatially quite rapidly in an urban environment, but present aerosol products fail to detect them due to limitations in coarse spatial resolution. Aerosol dispersal can be mapped at local scales using Sentinel-2 with relatively high spatial (10, 20, and 60 m) and temporal (5 days) resolutions. A new high-resolution (60 m) coupled aerosol-surface reflectance retrieval algorithm Modified Sensor Invariant Atmospheric Correction (MSAIC) has been developed to address the urban air pollution problem from Sentinel-2. Sentinel-2 retrieved AOD products validated against Aerosol Robotic Network (AERONET) (R²= 0.830, and RMSE= 0.156) and MODIS (R²= 0.655, and RMSE= 0.240) AOD products. For light to medium aerosol loading (AOD < 0.2), it was largely successful in extracting AOD with uncertainties <0.10. Additionally, MSIAC produces precise surface reflectance estimation at 60 m resolution across the 13 band of Sentinel-2. This is important as the accuracy of satellite-retrieved AOD is determined by surface reflectance correctness. Thus, it is crucial also to create an accurate estimation of surface reflectance. In the absence of in-situ observations and a Radiometric Calibration Network (RadCalNet) over India, two indirect approaches were used to verify Sentinel-2 retrieved surface reflectance products: (a) Sensitivity analysis , and (b) the use of invariant targets. Little or no change in surface reflectance was observed for different aerosol concentrations, and insignificant change in surface reflectance was observed for invariant targets. Additionally, Sentinel-2 retrieved surface reflectance was validated using observations obtained from the radiometric calibration network RadCalNet over La Crau (France) with uniform landscape and low AOD. Results for this validation will be presented to demonstrate the quality of this Sentinel-2 analysis compared to previous results. For further improvement of the algorithm, more investigation is required over the in-situ sites with varying (high) AOD concentration, less uniform and low reflectance landscapes, and not just desert sites like Gobabeb and Psedo-Invariant Calibration Sites (PICS). We argue that co-located global networks of continuous ground monitoring stations are required simultaneously characterising surface reflectance and aerosol over a range of surface and atmospheric conditions. Such a network would allow thorough quality evaluation of satellite retrieved products conducted over land.

How to cite: Mantri, S., Remedios, J., Yin, F., Vande Hey, J., and Carboni, E.: High spatial resolution aerosol and surface reflectance retrieval and validation using Sentinel-2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-939, https://doi.org/10.5194/egusphere-egu24-939, 2024.

Atmospheric Research Expedition to Abu Dhabi (AREAD) is a ship campaign that sailed from Vigo, Spain to Abu Dhabi, UAE between 26/11/2022 and 19/12/2022, aimed at the characterization of the atmospheric composition and at identifying pollutants transport over the Mediterranean, Red Sea and Arabian Sea. In this study, we present preliminary results from the remote sensing observations acquired during the cruise. The area of interest, surrounded by deserts and anthropogenic sources, has been recognized as a climate change hotspot due to extreme temperature increases and an important contribution to greenhouse gas emissions. Limited studies focus on the area because of limited and few observational data are available.

AREAD’s main objective was to contribute to the knowledge of trace gases and aerosol concentrations in the region and to complement with wintertime observations the AQABA campaign (24/06/2017-03/09/2017) performed in the same region during the summer season. The research vessel’s voyage included observations in the Suez Canal, one of the most heavily used navigational hubs in global trade routes. The on-board instrumentation included in-situ observations for trace gases (NO, NO2, O3, SO2, CO2, CH4) and aerosol optical, physical and chemical properties (PM1, PM10, particle sizes, aerosol spectral absorption and scattering). In addition, remote sensing of aerosol, clouds and boundary layer height (BL) was obtained with a VAISALA CL51 ceilometer and an automatic ship-photometer (CIMEL CE318T, modified for marine applications) included in AERONET.

The preliminary results suggest a change of regime between the Mediterranean and the Suez Canal. AOD levels remained below 0.1 for the first part of the cruise and increased to more than 0.2 after the entrance of the ship into the Suez Canal. Even though there was no significant variation in BL height which remained below 1km for most of the cruise, increased particle backscatter is observed within the BL and in elevated layers after the Suez Canal. Desert dust, trade ship emissions and pollution from Middle East fossil energy production plants could be some of the species contributing to the higher aerosol loading observed in the latter leg of the cruise.

Acknowledgment: The datasets presented in this research were acquired during the Atmospheric Research Expedition to Abu Dhabi (AREAD) on-board research vessel Jaywun, operated by the Environment Agency of Abu Dhabi (EAD), which is gratefully acknowledged. The ship-photometer is developed in the frame of AGORA-Lab joint laboratory (Laboratoire d’Optique Atmospherique from CNRS/University of Lille and CIMEL Electronique company).

How to cite: Papetta, A., Marenco, F., Pikridas, M., Blarel, L., Dubois, G., Goloub, P., Torres, B., Mohamed, R., and Sciare, J.: Remote sensing aerosol observations from AREAD ship campaign in the Mediterranean and Middle East, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3068, https://doi.org/10.5194/egusphere-egu24-3068, 2024.

The potential presence of super coarse desert dust aerosol particles  with volume radii larger than 15 microns in the atmosphere has recently become one of hot topics intensively discussed in the modeling and observation community. Such large particles may represent an essential part of aerosol mass in the atmosphere and while their contribution to atmospheric radiation is rather moderate. Therefore, the characterization of super coarse aerosols is very challenging while they are responsible for sizable overall contributions in aerosol effects environment and climate dynamics. Indeed, AERONET network, that arguably can be considered as the most comprehensive source of information about ambient columnar aerosol properties does not consider aerosol particles with radius larger  than 15 microns. In contrast, most chemical transport models do consider super course particles and a number of in situ campaigns have reported the presence of such particles. This presentation describes efforts to test and evaluate the capabilities and limitations of detecting the super coarse dust particles from AERONET like measurements. It also considers possibilities to detect such particles using other remote sensing methods including lidar active observation and measurements in IR spectral range. Several modifications of the retrieval approaches that allow for optimizing remote sensing of super coarse ambient aerosol are proposed and discussed.

How to cite: Lopatin, A., Dubovik, O., Sinuyk, A., Lind, E., Lapyonok, T., Eck, T., Smirnov, A., Herreras Giralda, M., Momoi, M., Litvinov, P., and Perez, C.: Potential and limitation of remote sensing observations to monitor super coarse particles of ambient aerosol, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15941, https://doi.org/10.5194/egusphere-egu24-15941, 2024.

The new-generation of satellites will revolutionize Earth observation with advanced sensors, including polarimetric observation capabilities like EUMETSAT's Metop-SG. Equipped with instruments like the Multi-Viewing Multi-Channel Multi-Polarisation Imaging (3MI), these satellites will offer unprecedented insights into the aerosol and cloud components of Earth's atmosphere. However, the indirect nature of satellite observations requires validation through in-situ measurements. In response, we are developing an in-situ cloud and aerosol imaging polarimeter, designed for operation in cloud chambers, at mountain-top stations as well as aboard research aircraft. This innovative approach aims to bridge the gap between satellite data and in-situ measurements, enhancing validation studies and ensuring the accuracy of next-generation satellite observations in climate change research.

Our polarimeter features an innovative design, highlighting a high-resolution camera chip combined with a wide-angle lens for capturing laser light scattered by cloud and aerosol particles at a high angular resolution of better than 0.05° and for a wide backscattering angular range from about 101° to 169°. A directed laser beam is imaged over several meters at a specific observation angle in an open path arrangement. Light scattered from particles transforms into a line on the camera chip, with each pixel corresponding uniquely to a different scattering angle. The optical design incorporates an adjustable polarization filter set-up within the imaging system, enabling seamless measurement of the full Stokes polarization vector of the angular light scattering function. This advanced imaging polarimeter concept offers high accuracy in measuring cloud drop size distribution, as well as linear and circular polarization and depolarization ratios. Notably, our in-situ polarimeter is "active," utilizing a polarized laser beam, distinguishing it from the "passive" approach of satellite polarimeters relying on sunlight.

The presentation will delve into the detailed concept of this innovative polarimeter, offering insights into its optomechanical design. Results from comprehensive optical simulations will showcase the expected measurement capabilities, concluding with findings from initial laboratory tests of the prototype instrument.

How to cite: Schnaiter, M., Hamel, A., Wagner, S., and Järvinen, E.: A novel in-situ cloud and aerosol imaging polarimeter for atmospheric research - Bridging the gap between satellite data and in-situ measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10144, https://doi.org/10.5194/egusphere-egu24-10144, 2024.

The current advanced geostationary imagers including the GOES-R ABI and MTG FCI instruments offer significant improvements in terms of spatio-temporal resolution compared to previous instruments, featuring pixel sizes for solar channels down to 500x500m², and scan frequencies up to 1 per min. While these capabilities enable us to better resolve small-scale variability in clouds and radiation, our understanding of the practical benefits for monitoring cloud development and retrieving surface solar irradiance remains limited. One key reason is the limited representativity of many ground-based remote sensing observations serving as potential reference, which are point-like in nature. In contrast, satellite-derived quantities correspond to extended spatial domains.

To improve our knowledge about the small-scale structure and variability of clouds and its influence on solar radiation, the Small-Scale Variability of Solar Radiation (S2VSR) campaign was conducted at the ARM Southern Great Plains (SGP) site in summer 2023. A unique sensor network consisting of 60 autonomous pyranometer stations developed at the Leibniz Institute for Tropospheric Research was deployed at the SGP site for a 12-week period. Stations were distributed across a 6x6 km2 domain centered around the ARM SGP Central Facility. Together with operational ARM measurements including cloud profiling and a stereo-photogrammetric 4D reconstruction of clouds, this campaign offers an unprecedented dataset for studying cloud-induced small-scale variability in solar irradiance, resolving fluctuations down to the second- and decameter-scale.

In the present contribution, a preliminary analysis of the benefits of 500m-resolution retrievals based on the GOES-R ABI imager using the S2VSR data will be given. Specifically, the deviation of satellite retrievals of surface solar irradiance from single-site measurements caused by the limited representativity will be quantified. An estimate of the instantaneous retrieval uncertainty will be given for different cloud situations. Also, the effects of navigation accuracy and the impact of two different parallax correction strategies will be quantified.

How to cite: Deneke, H., Flynn, C., Foster, M., Heidinger, A., Kalesse-Los, H., Macke, A., Meirink, J. F., Redemann, J., Sengupta, M., Walther, A., Wiltink, J., and Witthuhn, J.: Assessing the benefits of improved spatiotemporal resolution of current geostationary imagers for surface solar irradiance retrievals based on the S2VSR campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18834, https://doi.org/10.5194/egusphere-egu24-18834, 2024.

Since 2014, EUMETSAT is leading the establishment for the European Near Real Time (NRT) and operational satellite constellation dedicated to the monitoring of aerosols and some of their sources, like wildfires. On top of that, EUMETSAT is developing the Level-2 (L2) NRT satellite Aerosol Optical Depth (AOD) products and intensively works with the European Centre for Medium-Range Weather Forecasts (ECMWF) to prepare the future operational assimilation by the Copernicus Atmospheric Monitoring Service (CAMS).

Our presentation will focus on the current status of the NRT aerosol products from the Copernicus Sentinel-3 mission, the challenges, and their evolution. It will notably address the current quality of the AOD data from the Collection 3 as derived and operationally disseminated by EUMETSAT from the Optimized Simultaneous Surface Aerosol Retrieval (OSSAR-CS3) processing chain, the planned evolution with the Day-2/Day-3 developments with notably the NRT SYNergy items, and also the new Aerosol Layer Height (ALH) developments under preparation from the O2-A Sentinel-3 bands.

The NRT Aerosol products are jointly produced with the NRT Fire Radiative Power products from Sentinel-3, hence providing key information on some important aerosol emission sources. Consistency across these two datasets will be addressed. These products provide the foundation of the upcoming NRT aerosol and fire developments under preparation for the Flexible Combined Imager (FCI) instrument onboard the Meteosat Third Generation (MTG) platform.

How to cite: Chimot, J., Meraner, A., Martins, E., Joro, S., Fougnie, B., and Bojkov, B.: EUMETSAT efforts to establish the European (NRT) satellite constellation for aerosol & fire monitoring. Current status and upcoming developments with Sentinel-3 and MTG., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19551, https://doi.org/10.5194/egusphere-egu24-19551, 2024.

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles.  Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures.  We have developed two algorithms which can consistently derive these products from multiple IR sounders.  The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm.  The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS.  The ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

How to cite: Liu, X.: Remote Sensing of Atmospheric Temperature, Water Vapor, Trace Gases, Cloud, and Surface Properties on Daily and Decadal Time Scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2925, https://doi.org/10.5194/egusphere-egu24-2925, 2024.

The Earth’s atmosphere contains suspended particles and molecules with a wide range of characteristics. Their interaction with radiation (in both solar and thermal spectral regions) affects the transfer of energy as well as its spatial distribution in the atmosphere, affecting the weather at any moment and climate in the long term. Multi-Angular Polarimetric (MAP) observations have a great potential for quantifying the properties (e.g., size, concentration, etc.) of aerosol particles at a high accuracy. For this reason, a MAP is included on the Copernicus Carbon Dioxide Monitoring satellite mission (CO2M; intended launch date: 2026) to provide a correction of the light path to meet the mission’s stringent requirements for CO2 column retrievals. However, for both trace gas and aerosol retrievals it is also essential to filter out any cloud-contaminated measurements, because clouds strongly interact with radiation and cover between 60-70% of the Earth’s surface at any given time. This study presents an algorithm designed for detecting clouds based on the MAP instrument on CO2M. The algorithm is an adaptation of an approach that was newly developed at SRON Netherlands Institute for Space Research for the MAP instrument onboard the Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) platform (i.e., POLarization and Directionality of Earth Reflectances; POLDER) and we are working towards making it applicable to other MAP instruments. This algorithm consists of an Artificial Neural Network model that is trained based on synthetic measurements with realistic geometry, aerosol, and cloud inputs. The synthetic measurements correspond to a wide range of atmospheric conditions and were produced for using the Remote Sensing of Trace Gases and Aerosol Products (RemoTAP) forward radiative transfer model developed at SRON Netherlands Institute for Space Research. This algorithm is designed to predict the cloud fraction based on the observed multi-angular polarization and radiance data, plus the instrument specifications and the corresponding viewing- and solar- geometry parameters. Here we focus on the efficacy of the approach for the CO2M mission. Furthermore, the sensitivity of the algorithm’s performance as a function of instrument characteristics (e.g, viewing angles, wavelengths, accuracy) will be discussed.

How to cite: Jahani, B., Yuan, Z., van Diedenhoven, B., Hasekamp, O., Fu, G., and Lu, S.: A Machine Learning Algorithm for Cloud Detection Based on the CO2M Multi-Angular Polarimetric Satellite Measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18224, https://doi.org/10.5194/egusphere-egu24-18224, 2024.

TROPOMI on board of Sentinel-5 Precursor (S5P) provides continuous daily distribution of several cloud properties, which are required as input for trace-gas retrievals. The operational TROPOMI cloud retrieval is a two-step algorithm. At first, the OCRA (Optical Cloud Recognition Algorithm) computes a radiometric cloud fraction using a broad-band UV/VIS color space approach and later the ROCINN (Retrieval of Cloud Information using Neural Networks) retrieves the cloud height, cloud optical thickness and cloud albedo from NIR measurements in and around the oxygen A-band (~760nm). Within the ROCINN algorithm two different models are possible; the Clouds-as-Reflecting-Boundaries (CRB), where the cloud is a simple Lambertian reflector and the Clouds-as-Layers (CAL), where the cloud is a homogeneous layer of scattering liquid-water spherical particles. There is evidence that some TROPOMI cloud retrievals are contaminated by aerosols. This is particularly true in the following cases: (a) when there is co-existence of clouds and aerosols in the same TROPOMI footprint and (b) when there is a pure aerosol layer, appearing in the TROPOMI cloud product. The latter is usually the case of OCRA deriving an elevated radiometric cloud fraction corresponding to the given aerosol conditions. Then, ROCINN is triggered and returns two additional cloud parameters. Often, the false alarms of elevated OCRA cloud fraction can be identified when ROCINN retrieves a cloud height at the surface level. However, there are cases in which ROCINN cloud outputs do not refer to the surface properties of the scene, but to aerosol layers present in the same TROPOMI footprint. Especially for dust aerosols, which are usually large particles and comparable to the cloud droplet size, we expect more frequently those mixed retrievals. In particular, dust layers with large concentrations (i.e., high aerosol optical depth (AOD)) are better candidates for erroneously retrieved clouds in the TROPOMI L2 product. The TROPOMI aerosol algorithm (TropOMAER) makes use of the L1b reflectances in the UV to derive aerosol information in cloud-free and above-cloud aerosol scenes. With the use of ground-based active and passive remote sensing instruments, we are able to characterize well the vertically resolved cloud and aerosol layers in the lower troposphere. In this work, synergistic ground-based measurements from a PollyXT multiwavelength-Raman-polarization lidar and an AERONET sun-photometer are used to discriminate dust aerosols from clouds in TROPOMI measurements. We have selected ground-based observation sites over which the atmospheric column frequently contains large contributions of desert dust particles.

How to cite: Argyrouli, A., Lutz, R., Romahn, F., Molina García, V., Lelli, L., Loyola, D., Torres, O., Marinou, E., and Amiridis, V.: Distinguishing between cloud and aerosol layers in the TROPOMI/Sentinel-5P measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11696, https://doi.org/10.5194/egusphere-egu24-11696, 2024.

Accurate cloud properties retrievals from passive instruments are particularly challenging in presence of vertically highly in-homogeneous and/or multiple cloud layers because of the inherent lack of observational constraints for the vertical profile below the cloud top. The Optimal estimation algorithm for Cloud Analysis (OCA) developed at EUMETSAT is capable of retrieving cloud properties of up to two overlapping layers using radiances from imaging instruments. Example of such instruments are the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG), the Flexible Combined Imager (FCI) aboard the Meteosat Third Generation and METimage aboard the Metop-Second Generation. Since 2013 OCA retrievals have been operational as demonstrational product for MSG-SEVIRI and are now ready to be disseminated as operational products for MTG-FCI.

A number of improvements to the baseline OCA algorithm have been implemented recently including a better initialisation of cloud phase and multi-layer flag and a new forward model to enable the use of solar channels not only in single-layer but also in multi-layer cloud conditions. We also introduced a cloud model with a more complete representation of the vertical inhomogeneity in optical properties.

Using observations from the MSG-SEVIRI and MTG-FCI in the visible/near-infrared and infrared channels, we tested the updated algorithm to retrieve simultaneously a set of cloud microphysical and optical properties. To evaluate the accuracy of the retrieval we employ a number of retrieved cloud vertical profiles from Lidar/Radar measurements from both collocated A-Train orbits and in-situ data from the ACTRIS-Cloudnet network. The use of the solar channels in both single and multi-layer clouds enables a more consistent retrieval of their microphysical properties (effective radius and optical thickness). The addition of the vertical inhomogeneity has a significant impact on the retrieved cloud top pressure, bringing it closer to the estimates from the cloud Lidar.

The new version of OCA allows for a more complete and consistent retrieval of single- and two-layer cloud profiles and provides some further insights on the vertical distribution of cloud parameters. This in turn can be helpful for various applications such as the height assignment of Atmospheric Motion Vectors and specific visualisations of cloud products for forecasters.

How to cite: Bozzo, A., Spezzi, L., Watts, P., and Jackson, J.: The quest for an accurate retrieval of vertically complex cloud layers from passive instruments., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15702, https://doi.org/10.5194/egusphere-egu24-15702, 2024.

DISAMAR (determining instrument specifications and analysing methods for atmospheric retrieval) is a computer model developed to simulate the retrieval of properties of atmospheric trace gases, aerosols, clouds, and the ground surface from passive remote sensing observations in a wavelength range from 270 to 2400 nm. It is being used for the TROPOMI/Sentinel-5P and Sentinel-4/5 missions to derive Level-1b product specifications. It is also used  in some research to obtain aerosol and trace gas properties, but its application to cloud properties retrieval is limited. This study presents the retrieval of cloud pressure and cloud optical thickness as well as surface pressure for cloud free based on TROPOMI Oxygen-A (Band 6) and Oxygen-B (Band 5) band measurements, and compares the results with FRESCO and NPP-Suomi Level 2 cloud property data. Different cross section datasets including JPL2008, HITRAN 2008 and HITRAN2020 are also tested in this study. In conclusion, for surface pressure retrieval, using O₂-A band gives more reliable results than O₂-B band and is easier to converge in the calculation, especially over land surface. But while over sea surface, using O₂-B band in retrieval performs better than O₂-A band. Secondly, the retrieval based on the cross section file JPL2008 shows better results when using O₂-A band, but HITRAN2020 gives better results when using O₂-B band. Thirdly, setting appropriate a-priory value in DISAMAR and removing some of the wavelengths with high residual simulated reflectivity can significantly improve the results , both in terms of convergence and reduction of validation error. The cloud pressure correlation coefficient between the retrieval and NPP or FRESCO data is 0.85 and 0.99 respectively, while the cloud optical thickness has a correlation coefficient of 0.77 between retrieval and NPP COT datasets.

How to cite: Zhang, X., Wang, P., Stammes, P., Xie, T., Lu, F., and Sneep, M.: Cloud property retrieval based on DISAMAR: implications of differences between Oxygen-A and Oxygen-B band data from TROPOMI on Sentinel 5P, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1880, https://doi.org/10.5194/egusphere-egu24-1880, 2024.

The cloud droplet number concentration (CDNC) is one of the most important microphysical properties of liquid clouds for understanding and quantifying the effective radiative forcing by aerosol-cloud interactions (ERFaci). Indeed, CDNC is linked to the relevant processes of the cloud formation and evolution. CDNC is closely related to the chemical composition of the condensation nucleation nuclei and the cloud droplet size distribution. Nevertheless, this key parameter remains poorly known. CDNC is not yet operationally provided from current standard satellite retrievals. Our approach relies on an innovative determination of CDNC from satellite observations in combination with atmospheric cloud-resolving modelling. We introduce our new, community-based tool: the Satellite Simulator and Sandbox for Cloud Observation and Modelling (S3COM). Briefly, S3COM aims to simulate realistic satellite observations and cloud products from model outputs, to quantify the sensitivity of radiative quantities to cloud parameters, and to assist the development of retrieval algorithms using output fields from high-resolution models. We use realistic cloud situations (stratocumulus, cumulus, marine and continental clouds) obtained from the ICOsahedral Nonhydrostatic Large Eddy Model (ICON-LEM) to simulate top of atmosphere radiances with the Radiative Transfer for TOVS (RTTOV), from visible to infrared, observed by the Moderate Resolution Imaging Spectroradiometer (MODIS). Performance of MODIS-type algorithms coupled with ICON-LEM simulations is described and characterization of error sources is given. Results on CDNC retrievals for warm liquid water stratocumulus clouds are presented and discussed for the study case of the 02 May 2013.

How to cite: Siméon, A., Gonzalez, J., and Sourdeval, O.: Cloud Droplet Number Concentration: Satellite Retrievals Improved by Advanced Atmospheric Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9506, https://doi.org/10.5194/egusphere-egu24-9506, 2024.

Utilizing solar-independent thermal infrared (TIR) radiances, a convolutional neural network (CNN)-based framework (TIR-CNN) is developed to consistently retrieve cloud properties from passive satellite observations during both daytime and nighttime conditions. This framework enables the retrieval of diverse cloud properties, including cloud mask, cloud optical thickness (COT), cloud effective radius (CER), cloud top height (CTH), cloud base height (CBH), column cloud phase, and the identification of single/multi-layer clouds. The TIR-CNN framework primarily consists of two branches. In the first branch, the inputs include TIR radiances, viewing geometry, and altitude, producing outputs such as cloud mask, COT, CER and CTH. The network is trained using daytime Moderate Resolution Imaging Spectroradiometer (MODIS) products over a full year, and the results are validated and evaluated using passive and active products in an independent year. The evaluation results demonstrate that the retrieved cloud properties are well consistent with available MODIS daytime (cloud mask, COT, CER, and CTH) and nighttime (cloud mask and CTH) products. The retrieved COT and CTH also show robust agreements with active sensors during both daytime and nighttime, indicating that the algorithm performs stably across the diurnal cycle. The second branch of the TIR-CNN framework receives inputs including TIR radiances, altitude, landcover, lifting condensation level, and the retrieved cloud products from the first branch. It generates outputs such as CBH, cloud phase, and single/multi-layer cloud identifications. The comprehensive training, validation, and testing procedures are conducted using radar-lidar products from CloudSat/CALIPSO. The estimation of global CBH results in root-mean-square errors of 1.19 km and 1.91 km for single- and multi-layer clouds, respectively. The cloud classifier achieves total accuracies of 82% for single-layer clouds and 85% for multi-layer clouds. In addition, the model has remarkable accuracy in identifying cloud phase within each pixel's vertical column, particularly in distinguishing mixed-phase clouds with an ice cloud top.

How to cite: Wang, Q., Zhou, C., Husi, L., Zhu, Y., Zhuge, X., Liu, C., Weng, F., and Wang, M.: Retrieval of Cloud Properties from Thermal Infrared Radiometry Using Convolutional Neural Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1258, https://doi.org/10.5194/egusphere-egu24-1258, 2024.

The Polarized Submillimeter Ice-Cloud Radiometer (PolSIR): Observing the diurnal cycle of ice clouds in the tropics and sub-tropics

In May 2023 NASA has selected PolSIR as the latest addition to its Earth Venture Instrument class missions. PolSIR addresses key research priorities related to uncertainties in our current understanding in high clouds and cloud feedbacks as formulated in NASA’s latest Decadal Survey and in the latest Intergovernmental Panel on Climate Change (IPCC) Assessment. In this context, PolSIR will address the following objectives:

• Constrain the seasonally influenced diurnal cycle amplitude, form, and timing of the ice water path (IWP) and particle diameter in tropical and sub-tropical ice clouds
• Determine the diurnal variability of ice clouds in the convective outflow areas and understand relation to deep convection
• Determine the relationship between shortwave and longwave radiative fluxes and the diurnal variability of ice clouds
• Enable improvement of climate models by providing novel observations of the diurnal cycle of ice clouds, ultimately leading to improved climate modeling skills and increased fidelity of climate forecasts in support of critical decision-making.

The PolSIR mission consists of two 12U CubeSats, each equipped with a cross-track scanning polarized submillimeter radiometer in the spectral range of 325–680 GHz. The two PolSIR satellites fly in separate, 52-degree inclination, non-sun-synchronous orbits, taking science measurements between ±35 degrees latitude enabling monthly sampling of the diurnal cycle of ice clouds and their microphysical properties in the tropics and sub-tropics. PolSIR’s observation concept provides significant benefits over the Program of Record (PoR) as well as synergies with future missions which will either be in sun-synchronous orbits, thus not sampling the diurnal cycle, or lack the observation frequencies needed to fully observe ice water path (IWP).

How to cite: Bennartz, R. and Wu, D. and the PolSIR Science Team: The Polarized Submillimeter Ice-Cloud Radiometer (PolSIR): Observing the diurnal cycle of ice clouds in the tropics and sub-tropics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5886, https://doi.org/10.5194/egusphere-egu24-5886, 2024.