Aerosols injected into the stratosphere can persist for months to years and influence the radiative properties, stratospheric chemistry and large scale atmospheric dynamics. Despite their importance, accurate retrieval of their optical and microphysical properties using satellite observations, remains challenging. On one hand, while active remote sensing provides valuable information on stratospheric aerosol vertical distribution, retrievals of the full suite of particle properties are often limited by instrumental constraints and the need for strong a priori assumptions. On the other hand, passive remote sensing relying on intensity-only measurements, cannot differentiate between different aerosol types coincidentally present in the atmospheric column. Furthermore, passive aerosol retrieval algorithms are mainly designed for clear-sky conditions, limiting their applicability in the presence of clouds.Multi-angle polarimetric observations offer extended capabilities for deriving the optical and microphysical properties of aerosols, also in presence of underlying liquid clouds. Previous studies [Wanquet et al., 2009; 2013, Hasekamp, 2010] have demonstrated the potential of retrieving aerosol properties above clouds, with best performance reported for fine-mode absorbing aerosols located above liquid clouds, while the retrievals for coarse mode non-spherical aerosols presents larger uncertainties.Stratospheric particles represent a particular category of ‘aerosols above clouds’, since in the presence of tropospheric clouds they are always situated above them. In this context, we present a first attempt to extend the polarimetric above-cloud retrieval techniques for stratospheric aerosols originating from a moderate volcanic eruption. To this end, we exploit synergistic observations from EarthCARE and PACE satellite missions following the eruption of Ruang volcano in Indonesia in April 2024.Approximately one month after Ruang eruption, sulfate-rich stratospheric aerosol layers were detected by the EarthCARE/ATLID high-spectral-resolution lidar at altitudes between 20 and 25 km. These observations provide information on layer vertical distribution and aerosol optical depth (AOD) at 0.355 um, while collocated PACE/HARP2 hyper-angular polarimetric measurements, in combination with optical modeling [Gasteiger and Wiegner, 2018] and radiative transfer simulations [Mayer and Kylling, 2005] are used to infer particle size and AOD at 0.67 um. It is worth mentioning that our methodology, while building on previous studies, needs to adapt them to non-simultaneous satellite overpasses.First results demonstrate the feasibility of retrieving stratospheric AOD and particle size (i.e. effective radius) using the collocated EarthCARE and PACE observations, highlighting the potential of their synergy towards advanced stratospheric aerosol characterization.This study contributes to the ACtIon4Cooling (Aerosol Cloud Interactions for Cooling) project, which investigates key mechanisms that have the potential to efficiently modify the Earth’s solar radiation budget using natural analogues, including volcanic eruptions as a proxy for stratospheric aerosol injection.

Shipping activities emit aerosols that can modify the microphysical and optical properties of low-level marine clouds. In the framework of the ESA ACtIon4Cooling (Aerosol Cloud Interactions for Cooling) project, marine clouds influenced by ship-track emissions are investigated as natural analogues to assess the monitoring capabilities of various Solar Radiation Modification (SRM) approaches, including Marine Cloud Brightening (MCB).In this study, we combine high-resolution satellite observations from SUOMI-NPP/VIIRS (Visible Infrared Imaging Radiometer Suite) and Sentinel-5p/TROPOMI (TROPOspheric Monitoring Instrument) with vessel density data from EMODNET (European Marine Observation and Data Network) to detect cloud anomalies in the shipping corridors and quantify the ship-relevant cloud perturbations. VIIRS-derived cloud variables include cloud top height, cloud top emissivity, effective radius, liquid water path, and optical depth, while TROPOMI provides similar cloud information in the Oxygen A-band. Additional TROPOMI L2 products such as the absorbing aerosol index, aerosol type, and tropospheric NO₂ columns can also provide suitable proxies for ship emissions. The detection of the ship-tracks can be further improved when actual AIS (Automatic Identification System) data are used instead of the monthly aggregated EMODNET vessel density maps.Cargo and tanker ships dominate the upper range of ship lengths, often between 150 and 300 meters, with some exceeding 400 meters, while passenger ships also include very large vessels over 200 meters, corresponding to cruise liners. Since ship length serves as a proxy for vessel capacity and engine power, larger ships generally consume more fuel and emit greater amounts of aerosol precursors. As a result, cargo, tanker, and passenger ships are more important for atmospheric emissions and ship track formation, even though smaller vessels might be more numerous.Perturbations of the cloud parameters due to ship emissions are detected using machine learning classifiers with Logistic Regression being the baseline and more advanced models like Random Forest Regressor and Gradient Boosting (XGBoost). To quantify the ship-relevant cloud perturbations, the detected perturbations are fed directly to the Radiative Transfer Model pyDOME, which returns the full radiance field together with TOA (top-of-the atmosphere) forcing, surface irradiance and heating‑rate profiles for every perturbation. In order to synthesize the observations-based results and to explore the large-scale implications of the perturbations in marine low-level clouds, simulations are conducted with the state-of-the-art atmospheric general circulation model ICON (the ICOsahedral Non-hydrostatic model).

Volcanic emissions of sulfur dioxide (SO2) affect the environment, climate, and society. Their detection and quantification rely extensively on remote sensing techniques, which are used to track SO2 and monitor volcanic activity worldwide. In particular, nadir-viewing satellites measuring total SO2 vertical column densities (VCDs) have provided valuable insights into volcanic emissions for decades. However, the determination of the SO2 layer height (LH) is more challenging. In this study, we present an improved SO2 LH (and VCD) retrieval algorithm, applicable to the second UV spectral band (BD2) of the TROPOspheric Monitoring Instrument (TROPOMI). This band exhibits a stronger SO2 absorption than the third band (BD3) that is traditionally used for SO2 retrievals. To assess the impact of various spectral, atmospheric, and observation conditions, we conducted sensitivity analyses from a set of synthetic spectra representative of TROPOMI measurements using the Look-Up Table COvariance-Based Retrieval Algorithm (LUT-COBRA). Our results demonstrate that BD2 retrievals result in more accurate estimates of the SO2 heights and columns, particularly in the upper troposphere and lower stratosphere (UTLS), with LH errors reduced by at least a factor of 2. The algorithm was applied to real TROPOMI observations of volcanic eruptions and degassing episodes, and compared to BD3 retrievals. BD2 shows an improved sensitivity, with less noise, and a detection limit as low as 2 DU, surpassing the current operational TROPOMI SO2 product by an order of magnitude. Furthermore, our plume height estimates align closely with independent measurements from the Infrared Atmospheric Sounding Interferometer (IASI) and Microwave Limb Sounder (MLS), confirming the reliability of the approach.

Volcanic emissions from the Tajogaite volcano, located on the Cumbre Vieja edifice on the island of La Palma (Canary Islands, Spain), caused significant public health and aviation disruptions throughout the eruption (19 September-13 December 2021, officially declared over on 25 December). Nonetheless, it is considered the most significant volcanic event in Europe over the past 75 years due to the substantial amount of SO2 released into the atmosphere. The Instituto Geogr & aacute;fico Nacional (IGN), the authority responsible for volcano surveillance in Spain, implemented extensive operational monitoring to track volcanic activity and to provide a robust estimation of the volcanic plume height using a video-surveillance network. In parallel, the State Meteorological Agency of Spain (AEMET), in partnership with other Spanish ACTRIS (Aerosol, Clouds, and Trace Gases Research Infrastructure) members and collaborating institutions, conducted an unprecedented instrumental deployment to evaluate the impacts of this volcanic event on atmospheric composition. This effort included a network of aerosol profilers surrounding the volcano. A total of four profiling instruments were installed on La Palma: one MPL-4B lidar and three ceilometers. Additionally, a pre-existing Raman lidar on the island contributed valuable data to this study. These efforts are undertaken due to the importance of monitoring volcanic plume height in terms of air quality (necessary for the implementation of effective civil protection policies), volcanic activity surveillance (for tracking and forecasting eruptive behaviour), and, from a scientific perspective, for improving our understanding of the climatic and radiative impacts of this type of aerosol.In this study, the eruptive process was characterised in terms of the altitude of the dispersive volcanic plume (hd), measured by both IGN and AEMET-ACTRIS, and the altitude of the eruptive column (hec), measured by IGN. Modulating factors such as seismicity and meteorological conditions were also analysed. The consistency between the two independent and complementary datasets (hd,IGN and hd,AEMET) was assessed throughout the eruption (mean difference of 258.6 m).Our results confirmed the existence of three distinct eruptive phases, encompassing a range of styles from Strombolian explosive to effusive activity. While these phases have been characterised in previous studies, the results of the present work provide complementary information and novel insights from an alternative observational approach, which may be of use in future volcanic crises and will be applied to operational surveillance during such events.A subsequent comparison of hd,AEMET with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol layer height product (ALHCALIOP) revealed a systematic underestimation by the satellite product, with a mean difference of 392.2 m.Finally, the impact of using hec in estimating SO2 emissions from the NASA MSVOLSO2L4 satellite-based product was evaluated. When a fixed (standard) plume altitude of 8 km was used instead of the observed hec, the total SO2 emission was significantly underestimated by an average of 56.2 %, and by up to 84.7 %. These findings underscore the importance of accurately determining the volcanic plume height when deriving SO2 emissions from satellite data.

Volcanic eruptions impact human populations and the environment, with volcanic ash contributing to these effects through its influence on air quality, agriculture, and air transportation. Traditional satellite observation methods for monitoring volcanic ash encounter several challenges, including distinguishing ash from other aerosols, coverage area limitations, frequency of observations, and the impact of adverse weather and atmospheric conditions. These issues highlight the need for supplementary satellite-based approaches to improve volcanic ash monitoring. This project introduces a new method that utilizes machine learning techniques to analyze UV and visible satellite data for detecting and classifying volcanic ash. The research focuses on exploring how satellite UV and visible light observations can be used to identify volcanic ash in the atmosphere. A classifier was developed using simulations from a radiative transfer model, which represents various atmospheric scenarios. This classifier is then applied to analyze spectral measurements obtained by the TROPOspheric Monitoring Instrument (TROPOMI) on the ESA Sentinel-5p satellite. The complexity of detecting and classifying volcanic ash arises not only from the presence of other aerosols in the atmosphere but also from the changing characteristics of the ash, influenced by the type of magma and ongoing alterations as the ash remains airborne. This research presents progress in tackling these challenges and in developing a complex algorithm that incorporates a wide range of parameters. The application of this method to the Raikoke eruption case study enables the identification of some volcanic ash, illustrating the potential of this approach. However, this case study also reveals the presence of misclassifications, highlighting the need for continuous improvement in the classifier. This research offers valuable insights into the detection and classification of volcanic ash, contributing to the enhancement of monitoring strategies for hazard mitigation.

The ESA Climate Change Initiative (CCI) Ozone and Aerosols Precursors project is developing long-term climate data records (CDRs) of the Global Climate Observing System (GCOS) Precursors for Aerosol and Ozone Essential Climate Variables. These precursors include short-lived atmospheric trace gases such as formaldehyde (HCHO), glyoxal (CHOCHO), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), and ammonia (NH3). The project aims to create consistent and harmonized CDRs from multiple satellite missions, including GOME, SCIAMACHY, GOME-2, OMI, TROPOMI, IASI, and MOPITT. This work presents selected findings of a round robin exercise conducted for UV-VIS retrievals. We focus on two key factors that influence HCHO air mass factor determination: the surface albedo climatology and the model a priori profiles. The impact of these factors on the HCHO vertical columns is evaluated by comparing the use of recent auxiliary datasets. Results are presented for TROPOMI HCHO columns and compared to the operational product. The recent reprocessing of TROPOMI Level 1 data has enabled the development of new albedo climatologies in the UV, offering a finer spatial resolution than the previously used OMI albedo climatology. Additionally, we evaluate the use of a priori vertical profiles from the CAMS reanalysis dataset (spanning the 2003-2022 period) instead of the current TM5-MP profiles used in the TROPOMI operational product. We assess the impact of these alternative datasets on the TROPOMI HCHO vertical columns and on their validation towards ground-based data. The generation of the ESA CCI HCHO CDR will be based on these findings. This comprehensive assessment not only contributes to the ongoing improvement of TROPOMI data quality but also provides deeper insights into the factors influencing HCHO vertical columns.

Nitrogen oxides play an important role in many atmospheric chemistry processes in both the stratosphere and troposphere. In this context, over the past few decades, NO2 column measurements have been provided from polar sun-synchronous low-earth orbit (LEO) satellite instruments. These space-borne remote sensing measurements have contributed to our understanding of the global distribution of tropospheric NO2 levels, their changes over time and estimates of emissions. However, the LEO instruments only observe NO2 once per day at a specific local time, limiting the monitoring of diurnal variation in NO2 due to variations in emissions and chemical reactions throughout the day. To address the shortcomings of the current atmospheric composition monitoring by LEO and to capture the diurnal variation of air quality processes at the local scale, the Geostationary Air Quality (Geo-AQ) constellation mission, consisting of three geostationary satellite sensors (i.e. Geostationary Environment Monitoring Spectrometer (GEMS) for Asia, Tropospheric Emissions: Monitoring of Pollution (TEMPO) for North America, and Sentinel-4 (S4) for Europe), has been launched.In this study, we present a tropospheric NO2 retrieval algorithm designed for geostationary satellites using GEMS measurements. The GEMS NO2 retrieval algorithm is based on a heritage of NO2 retrieval from previous LEO satellites, following a common approach consisting of three steps: (1) the spectral retrieval of total NO2 slant columns using Differential Optical Absorption Spectroscopy (DOAS) technique, (2) the separation of slant columns into stratospheric and tropospheric contributions, and (3) the conversion of tropospheric slant columns to tropospheric vertical columns using air mass factors. However, to account for the characteristics of the geostationary satellite, such as hourly sampling, limited geographical coverage, and larger zenith angles, we developed and implemented a number of improvements in the DLR GEMS NO2 retrieval algorithm. To estimate the stratospheric contribution and describe the diurnal variation of stratospheric fields, an improved stratosphere-troposphere separation approach was developed using the CAMS global forecast (IFS cycle 48r1) data and evaluated by comparing it to results obtained using the STREAM scheme. For the improved tropospheric AMF calculation, sensitivity tests were performed using different surface reflectance and cloud products. Notably, a cloud correction using cloud parameters from the DLR Optical Cloud Recognition Algorithm (OCRA) based on Loyola et al. (2018) improves the tropospheric NO2 column retrievals for clear-sky scenes.Our GEMS tropospheric NO2 retrieval results show good agreement with various reference datasets including ground-based and satellite measurements. Furthermore, the hourly sampling and high spatial resolution of GEMS tropospheric NO2 columns demonstrate the capability for a detailed analysis of the diurnal evolution of NO2 burden and emission strengths over Asia from space.

The knowledge of the surface reflectance is essential for the retrieval of atmospheric trace-gases from satellites. It is required in the conversion of the observed trace gas slant column to the total vertical column by means of a so-called air-mass factor. Although there exists climatological databases based on UV satellite data (e.g. OMI, GOME-2), these have a low spatial resolution and are not appropriate for current and future UV satellite missions like Sentinel-5p/TROPOMI or MTG-S/UVN (Sentinel-4) due their significantly higher spatial and spectral resolution. Current climatologies which are used in operational retrievals provide the Lambertian Equivalent Reflection (LER, e.g. OMI, GOME-2, TROPOMI, see [1,2,3]) and Directional-LER (DLER, e.g. GOME-2, TROPOMI see [3,4]) for selected wavelength in the UV-VIS range and are based on the so-called minimum LER approach, i.e. determine the minimum surface reflectance in the measurement timeframe.We present here a new technique called GE_LER (Geometry-dependent Effective Lambertian Equivalent Reflectivity) based on Machine Learning, which retrieves the DLER from UV satellites in a wavelength range as opposed to the single wavelength approaches of existing climatologies. In this way, dedicated surface reflectivities for specific trace gas retrieval wavelength ranges can be determined. We train a Neural Network with simulated UV spectra, which have been calculated with (V)LIDORT (see [5]). This radiative transfer model is also used for the generation of Air Mass Factors in the operational TROPOMI trace gas retrieval. In this way we reduce the influence of using different radiative transfer models with respect to trace gas retrievals.First results of our GE_LER retrieval for several trace-gases based on TROPOMI data will be shown. References[1] Kleipool (2010), OMI/Aura Surface Reflectance Climatology L3 Global Gridded 0.5 degree x 0.5 degree V3, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC),, 10.5067/Aura/OMI/DATA3006[2] Tilstra et al. (2017), Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME-2 and SCIAMACHY, J. Geophys. Res. Atmos. 122, 4084-4111, doi:10.1002/2016JD025940[3] Tilstra et al. (2021), Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments, Atmos. Meas. Tech. 14, 4219-4238, doi:10.5194/amt-14-4219-2021[4] Tilstra et al. (2023), A directional surface reflectance climatology determined from TROPOMI observations, Atmos. Meas. Tech. Discuss. [preprint], doi:10.5194/amt-2023-222, in review[4] Spurr et al. (2008), LIDORT and VLIDORT: Linearized pseudo-spherical scalar and vector discrete ordinate radiative transfer models for use in remote sensing retrieval problems. Light Scattering Reviews, Volume 3, ed. A. Kokhanovsky, Springer

Owing to its high spatial resolution, the TROPOspheric Monitoring Instrument (TROPOMI) launched in 2017 onboard the Sentinel-5 Precursor (S5P) platform provides important information on global volcanic and anthropogenic SO2 emissions, with an unprecedented level of details.In a recent study (Theys et al., 2021), we proposed an approach called Covariance-Based Retrieval Algorithm (COBRA), different from the classical Differential Optical Absorption Spectroscopy (DOAS). Application of COBRA to TROPOMI SO2 column retrievals leads to a significant reduction of the retrieval noise and biases as compared to the TROPOMI operational (DOAS-based) SO2 product. COBRA even reveals new emission sources in long-term averaged SO2 maps.In view of a future operational deployment (planned end 2024), the COBRA SO2 scheme is being implemented as part of the Copernicus S5-P Product Algorithm Laboratory (PAL). In this poster, we give an update of TROPOMI COBRA SO2 results. The latest developments of COBRA S5P-PAL v2 algorithm are presented and discussed. The pre-operational S5P-PAL environment enables a full reprocessing of TROPOMI data. For several examples, we illustrate the COBRA data set for the long-term monitoring of SO2 columns over both anthropogenic and volcanic scenes. Finally, possible future developments of COBRA are discussed. Theys, N., Fioletov, V., Li, C., De Smedt, I., Lerot, C., McLinden, C., Krotkov, N., Griffin, D., Clarisse, L., Hedelt, P., Loyola, D., Wagner, T., Kumar, V., Innes, A., Ribas, R., Hendrick, F., Vlietinck, J., Brenot, H., and Van Roozendael, M.: A Sulfur Dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources, Atmos. Chem. Phys., 21, 16727–16744, https://doi.org/10.5194/acp-21-16727-2021, 2021.

In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar-orbiting satellites Global Ozone Monitoring Experiment-2 (GOME-2) and Tropospheric Monitoring Instrument (TROPOMI), but several improvements are implemented to account for specific features of geostationary satellites. The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere-troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) Integrated Forecast System (IFS) cycle 48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations. For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of the GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to the GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close to or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and the advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements. The retrieved DLR GEMS tropospheric NO2 columns show good capability for capturing hotspot signals at the scale of city clusters and describe spatial gradients from city centres to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products shows overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open-ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 50 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak.

The retrieval of cloud parameters from the atmospheric Sentinel missions requires Earth reflectance measurements from a set of spectral bands. The ground pixels of the involved spectral bands should be fully aligned, but when they are not, special treatment is required within the operational algorithms. This so-called inter-band spatial misregistration of passive spectrometers is present when the Earth reflectance measurements in different spectral bands are captured by different spectrometers. The cloud retrieval algorithm requires reflectance measurements in the UV(ultraviolet)-VIS (visible) band, where the first cloud parameter (i.e., radiometric cloud fraction) is retrieved from OCRA (Optical Cloud Recognition Algorithm). In addition, Earth reflectances in the NIR (near-infrared) band are needed for the retrieval of two additional cloud parameters (i.e., cloud height and cloud albedo or cloud-top height and optical thickness) from the ROCINN (Retrieval of Cloud Information using Neural Networks) algorithm. In the former TROPOMI (TROPOspheric Monitoring Instrument)/S5P (Sentinel-5 Precursor) retrieval, a coregistration scheme of the derived cloud parameters from the source band to the target band based on pre-calculated mapping weights from UV-VIS to NIR and vice versa is applied. In this paper we present a new scheme for the coregistration of the TROPOMI cloud parameters using collocated VIIRS (Visible Infrared Imaging Radiometer Suite)/SNPP (Suomi National Polar-orbiting Partnership) information. The new coregistration scheme based on the VIIRS data improves the TROPOMI cloud product quality and allows the addition of cloud information for the first (westernmost) TROPOMI UVIS ground pixel. In practice, the latter means that a significant number of valid data points are included in the TROPOMI cloud, total ozone, SO2 and HCHO product since 26 November 2023 (orbit 31705), when the new coregistration scheme became operational. From a comparison analysis between the two techniques, we found that the largest differences mainly appear for inhomogeneous scenes. From a validation exercise of TROPOMI against VIIRS in the across-track direction, we found that the old coregistration scheme tends to smooth out cloud structures along the scan line, whereas such structures can be maintained with the new scheme. The need to implement a similar inter-band spatial coregistration scheme is foreseen for the Sentinel-4/MTG-S (Meteosat Third Generation - Sounder) and Sentinel-5/MetOp-SG (Meteorological Operational Satellite - Second Generation) missions. In the case of the Sentinel-4 instrument, the external cloud information will originate from collocated data captured by the FCI (Flexible Combined Imager) on board the MTG-I (Meteosat Third Generation - Imager) satellite.

<p>As part of its Climate Change Initiative extension program (CCI+), the European Space Agency started a new activity aiming to develop space-based long-term climate data records for precursors gases involved in the formation of aerosols and ozone (https://climate.esa.int/en/projects/precursors-for-aerosols-and-ozone/about/). The targeted trace gases are NO<sub>2</sub>, SO<sub>2</sub>, HCHO, CHOCHO, CO and NH<sub>3</sub>.</p> <p>For SO<sub>2 </sub>in particular the project relies on recent developments for Sentinel-5 Precursor/TROPOMI based the Covariance-Based Retrieval Algorithm (COBRA). This algorithm will be used to build consistent column retrievals from a series of four satellite sensors (GOME, SCIAMACHY, OMI and TROPOMI), covering nearly three decades of observations.</p> <p>Here, we give an overview of the SO<sub>2</sub> development activities in the Precursors CCI+ project. To achieve the project objectives, an extensive comparison of results is performed in the form of a round Robin exercise. This includes comparison of different algorithms, but also testing of various processing options for ancillary data such as cloud and surface reflectance parameters. First results from the round Robin exercise are presented with a focus on OMI, in particular the comparison between COBRA and Principal Component Analysis (PCA) column retrievals. Preliminary results using COBRA for the historical sensors GOME and SCIAMACHY are also shown.</p> <p> </p>

We introduce the new Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 product of total column ozone (O-3), total and tropospheric column nitrogen dioxide (NO2), total column water vapour, total column bromine oxide (BrO), total column formaldehyde (HCHO), and total column sulfur dioxide (SO2) (daily products , ; monthly products , ). The GOME-2 level-3 products aim to provide easily translatable and user-friendly data sets to the scientific community for scientific progress as well as to satisfy public interest. The purpose of this paper is to present the theoretical basis as well as the verification and validation of the GOME-2 daily and monthly level-3 products.The GOME-2 level-3 products are produced using the overlapping area-weighting method. Details of the gridding algorithm are presented. The spatial resolution of the GOME-2 level-3 products is selected based on the sensitivity study. The consistency of the resulting level-3 products among three GOME-2 sensors is investigated through time series of global averages, zonal averages, and bias. The accuracy of the products is validated by comparison to ground-based observations. The verification and validation results show that the GOME-2 level-3 products are consistent with the level-2 data. Small discrepancies are found among three GOME-2 sensors, which are mainly caused by the differences in the instrument characteristic and level-2 processor. The comparison of GOME-2 level-3 products to ground-based observations in general shows very good agreement, indicating that the products are consistent and fulfil the requirements to serve the scientific community and general public.

The Western Balkan region is known for emitting alarmingly high sulphur dioxide amounts from coal-fired power plants. Though a number of environmental regulations have been introduced in recent years (e.g. desulphurisation installations, construction of modern power plants), the pollution burden is still much higher than recommended by the authorities. A number of different montoring systems are required to observe the growing pollution situation in the Western Balkan region, partly caused by a high energy demand from outside (e.g. Western Europe).Several of the top ten SO2 polluters in Europe are located in Bosnia-Herzegovina and Serbia. Here we present the first in situ measurements of sulphur dioxide in this region conducted with a German research aircraft in cooperation with local scientists in Bosnia-Herzegovina and Serbia. Two of the strongtest emitting coalfired power plants were selected for the measurements in autumn 2020: Tuzla in Bosnia-Herzegovina and Nikola Tesla in Serbia (Nikola Tesla). The measurements were mainly conducted in the boundary layer (below ~1 km altitude in winter). Downwind of the power plants, extremely high SO2 mixing ratios exceeding 100 parts per billion (ppb = nmol mol-1 ) were measured at a distance of ~20-40 km from the sources. The SO2 plumes from the power plants were trapped in well-defined inversion layers between ~500-1000 m altitude. The airborne measurements can be used to validate synchronous spaceborne SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite. A first intercomparison indicates some problems with dense smoke clouds frequently covering these countries in the winter months. However, it turned out that the Nikola Tesla flight is to some extent suited for a TROPOMI-SO2 validation, since it was obtained during cloud-free conditions with a well-defined vertical extension of the probed SO2 plume (needed to estimate the Vertical Column Density, VCD, measured by the satellite). In addition, these airborne measurements accompanied by model simulations can be used to determine the SO2 emission strength of the power plants and to compare it to the source strength reported by the power plant operators. The results indicate a reasonable agreement between the airborne measurements, model results, emission inventories, and satellite measurements for the Nikola Tesla power plants.

Volcanic eruptions eject large amounts of ash and trace gases such as sulfur dioxide (SO2) into the atmosphere. A significant difficulty in mitigating the impact of volcanic SO2 clouds on air traffic safety is that these gas emissions can be rapidly transported over long distances. The use of space-borne instruments enables the global monitoring of volcanic SO2 emissions in an economical and risk-free manner. Within the European Space Agency (ESA) Sentinel-5p+ Innovation project, the S5P SO2 layer height (S5P+I: SO2LH) activities led to the improvements of the retrieval algorithm and generation of the corresponding near real-time S5P SO2 LH products. These are currently operationally provided, in near real-time, by the German Aerospace Center (DLR) within the framework of the Innovative Products for Analyses of Atmospheric Composition (INPULS) project. The main aim of this paper is to present its extensive verification, accomplished within the S5P+I: SO2LH project, over major recent volcanic eruptions, against collocated space-borne measurements from the IASI/Metop and CALIOP/CALIPSO instruments as well as assess its impact on the forecasts provided by the Copernicus Atmospheric Monitoring Service (CAMS). The mean difference between S5P and IASI observations for the Raikoke 2019, the Nishinoshima 2020 and the La Soufrière-St Vincent 2021 eruptive periods is ∼ 0.5 ± 3 km, while for the Taal 2020 eruption, a larger difference was found, between 3 ± 3 km and 4 ± 3 km. The comparison of the daily mean SO2 LH further demonstrates the capabilities of this near real-time product, with slopes between 0.8 and 1 and correlation coefficients ranging between 0.6 and 0.8. Comparisons between the S5P SO2 LH and the CALIOP/CALIPSO ash plumes revealed an expected bias at −2.5 ± 2 km, considering that the injected SO2 and ash plume locations do not always coincide over an eruption. Furthermore, the CAMS assimilation of the S5P SO2 LH product led to much improved model output against the non-assimilated IASI LH, with a mean difference of 1.5 ± 2 km, compared to the original CAMS analysis, and improved the geographical spread of the Raikoke volcanic plume following the eruptive days.

The Copernicus Atmosphere Monitoring Service (CAMS), operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission, provides daily analyses and 5 d forecasts of atmospheric composition, including forecasts of volcanic sulfur dioxide (SO2) in near real time. CAMS currently assimilates total column SO2 products from the GOME-2 instruments on MetOp-B and MetOp-C and the TROPOMI instrument on Sentinel-5P, which give information about the location and strength of volcanic plumes. However, the operational TROPOMI and GOME-2 data do not provide any information about the height of the volcanic plumes, and therefore some prior assumptions need to be made in the CAMS data assimilation system about where to place the resulting SO2 increments in the vertical. In the current operational CAMS configuration, the SO2 increments are placed in the mid-troposphere, around 550 hPa or 5 km. While this gives good results for the majority of volcanic emissions, it will clearly be wrong for eruptions that inject SO2 at very different altitudes, in particular exceptional events where part of the SO2 plume reaches the stratosphere. A new algorithm, developed by the German Aerospace Centre (DLR) for GOME-2 and TROPOMI, optimized in the frame of the ESA-funded Sentinel-5P Innovation–SO2 Layer Height Project, and known as the Full-Physics Inverse Learning Machine (FP_ILM) algorithm, retrieves SO2 layer height from TROPOMI in near real time (NRT) in addition to the SO2 column. CAMS is testing the assimilation of these products, making use of the NRT layer height information to place the SO2 increments at a retrieved altitude. Assimilation tests with the TROPOMI SO2 layer height data for the Raikoke eruption in June 2019 show that the resulting CAMS SO2 plume heights agree better with IASI plume height data than operational CAMS runs without the TROPOMI SO2 layer height information and show that making use of the additional layer height information leads to improved SO2 forecasts. Including the layer height information leads to higher modelled total column SO2 values in better agreement with the satellite observations. However, the plume area and SO2 burden are generally also overestimated in the CAMS analysis when layer height data are used. The main reason for this overestimation is the coarse horizontal resolution used in the minimizations. By assimilating the SO2 layer height data, the CAMS system can predict the overall location of the Raikoke SO2 plume up to 5 d in advance for about 20 d after the initial eruption, which is better than with the operational CAMS configuration (without prior knowledge of the plume height) where the forecast skill is much more reduced for longer forecast lead times.

Sulfur dioxide (SO2) is known as a major air pollutant harmful to human health. Furthermore, it is a precursor gas of sulfate aerosol, which exerts a direct negative radiative forcing and thus leads to climate cooling. Anthropogenic SO2 sources are primarily associated with the combustion of sulfur-rich fossil fuels. While the operation of flue gas desulfurization devices has led to large SO2 reductions in western Europe, a hotspot of anthropogenic SO2 sources remains in the Balkan region as recently observed from space by the TROPOMI instrument on the Sentinel-5P satellite. Large coal-fired power plants with no or only incomplete SO2 removal cause these high emissions. Targeting these strong emitters, the DLR Falcon 20 aircraft was equipped with an isotopically on-line calibrated Chemical Ionization Ion Trap Mass Spectrometer (CI-ITMS) to obtain detailed in situ SO2 observations during the METHANE-To-Go-Europe aircraft campaign in autumn 2020. These SO2 measurements were complemented by in situ observations of greenhouse gases (CO2, CH4), aerosol number concentrations, and other short-lived pollutants (CO, NO, NOy). Two flights, on November 2nd and 7th 2020, focused on characterizing the pollution plumes downwind of two coal-fired power plants located in Bosnia-Herzegovina (Tuzla) and Serbia (Nikola Tesla), respectively. These power plants belong to the ten strongest SO2 emitters in Europe, and according to the World Health Organization, both countries are among the most polluted ones in Europe. We present a detailed analysis of the two DLR Falcon flights with strongly enhanced SO2 mixing ratios (exceeding 50 ppb), which were observed at low flight altitude (<1 km). Respective flight patterns were designed to allow for the evaluation of the TROPOMI vertical SO2 column densities, and both flights were performed during cloud-free conditions. The airborne measurements and satellite data will also be complemented by hourly ground-based SO2 measurements near both power plants. In addition, measurements are combined with state-of-the art model simulations from (i) the regional atmospheric chemistry climate model MECO(n); (ii) the atmospheric transport and dispersion model HYSPLIT; and (iii) the chemistry coupled Weather Research and Forecasting model WRF-Chem to improve the emission quantification of these power plants.

Sensitive and accurate detection of sulfur dioxide (SO2) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO2 columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO2-free radiance variability, so that the SO2 slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO2 retrievals. The performance of this technique is also benchmarked against that of the principal component algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO2 accurately is further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO2 map, considering 2.5 years of TROPOMI data. In addition to the known sources, we detect many new SO2 emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO2 emission rates. Results are comparable to other recently published TROPOMI-based SO2 emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO2 emissions with a detection limit of about 8 kt yr−1, a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO2 column data set at a global scale will allow missing sources to be identified and quantified and help improve SO2 emission inventories.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Volcanic SO2 height retrieval from UV satellite measurementsAuthorsPascalHedeltiDNikitaFedkinMariLizaKoukouliiDDmitryEfremenkoKonstantinosMichailidisiDDimitriosBalisiDNickolayKrotkoviDDiegoLoyolaSee all authors Pascal HedeltiDCorresponding Author• Submitting AuthorGerman Aerospace Center DLR OberpfaffenhofeniDhttps://orcid.org/0000-0002-1752-0040view email addressThe email was not providedcopy email addressNikita FedkinUniversity of Maryland College Parkview email addressThe email was not providedcopy email addressMariLiza KoukouliiDLaboratory of Atmospheric Physics, Aristotle University of ThessalonikiiDhttps://orcid.org/0000-0002-7509-4027view email addressThe email was not providedcopy email addressDmitry EfremenkoGerman Aerospace Center (DLR)view email addressThe email was not providedcopy email addressKonstantinos MichailidisiDAristotle University of ThessalonikiiDhttps://orcid.org/0000-0002-8691-6874view email addressThe email was not providedcopy email addressDimitrios BalisiDAristotle University of ThessalonikiiDhttps://orcid.org/0000-0003-1161-7746view email addressThe email was not providedcopy email addressNickolay KrotkoviDNASA Goddard Space Flight CenteriDhttps://orcid.org/0000-0001-6170-6750view email addressThe email was not providedcopy email addressDiego LoyolaGerman Aerospace Center (DLR)view email addressThe email was not providedcopy email address

Information about the height and loading of sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for aviation safety and for assessing the effect of sulfate aerosols on climate. While SO2 layer height has been successfully retrieved from backscattered Earthshine ultraviolet (UV) radiances measured by the Ozone Monitoring Instrument (OMI), previously demonstrated techniques are computationally intensive and not suitable for near-real-time applications. In this study, we introduce a new OMI algorithm for fast retrievals of effective volcanic SO2 layer height. We apply the Full-Physics Inverse Learning Machine (FP_ILM) algorithm to OMI radiances in the spectral range of 310–330 nm. This approach consists of a training phase that utilizes extensive radiative transfer calculations to generate a large dataset of synthetic radiance spectra for geophysical parameters representing the OMI measurement conditions. The principal components of the spectra from this dataset in addition to a few geophysical parameters are used to train a neural network to solve the inverse problem and predict the SO2 layer height. This is followed by applying the trained inverse model to real OMI measurements to retrieve the effective SO2 plume heights. The algorithm has been tested on several major eruptions during the OMI data record. The results for the 2008 Kasatochi, 2014 Kelud, 2015 Calbuco, and 2019 Raikoke eruption cases are presented here and compared with volcanic plume heights estimated with other satellite sensors. For the most part, OMI-retrieved effective SO2 heights agree well with the lidar measurements of aerosol layer height from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and thermal infrared retrievals of SO2 heights from the infrared atmospheric sounding interferometer (IASI). The errors in OMI-retrieved SO2 heights are estimated to be 1–1.5 km for plumes with relatively large SO2 signals (>40 DU). The algorithm is very fast and retrieves plume height in less than 10 min for an entire OMI orbit.