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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. This approach offers a promising prospect of using physics-based machine learning applications to other instruments.

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

Ozone data derived from the Tropospheric Monitoring Instrument (TROPOMI) sensor on board the Sentinel-5 Precursor satellite show exceptionally low total ozone columns in the polar region of the Northern Hemisphere (Arctic) in spring 2020. Minimum total ozone column values around or below 220 Dobson units (DU) were seen over the Arctic for 5 weeks in March and early April 2020. Usually the persistence of such low total ozone column values in spring is only observed in the polar Southern Hemisphere (Antarctic) and not over the Arctic. These record low total ozone columns were caused by a particularly strong polar vortex in the stratosphere with a persistent cold stratosphere at higher latitudes, a prerequisite for ozone depletion through heterogeneous chemistry. Based on the ERA5, which is the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis, the Northern Hemisphere winter 2019/2020 (from December to March) showed minimum polar cap temperatures consistently below 195 K around 20 km altitude, which enabled enhanced formation of polar stratospheric clouds. The special situation in spring 2020 is compared and discussed in context with two other Northern Hemisphere spring seasons, namely those in 1997 and 2011, which also displayed relatively low total ozone column values. However, during these years, total ozone columns below 220 DU over several consecutive days were not observed in spring. The similarities and differences of the atmospheric conditions of these three events and possible explanations for the observed features are presented and discussed. It becomes apparent that the monthly mean of the minimum total ozone column value for March 2020 (221 DU) was clearly below the respective values found in March 1997 (267 DU) and 2011 (252 DU), which highlights the special evolution of the polar stratospheric ozone layer in the Northern Hemisphere in spring 2020. A comparison with a typical ozone hole over the Antarctic (e.g., in 2016) indicates that although the Arctic spring 2020 situation is remarkable, with total ozone column values around or below 220 DU observed over a considerable area (up to 0.9 million km2), the Antarctic ozone hole shows total ozone columns typically below 150 DU over a much larger area (of the order of 20 million km2). Furthermore, total ozone columns below 220 DU are typically observed over the Antarctic for about 4 months.

Tropospheric NO2 columns retrieved from OMI are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work introduces a framework for reconstructing gaps in the OMI NO2 data over China by using machine learning and an adaptive weighted temporal fitting method with NO2 measurements from GOME-2B, and surface measurements. The reconstructed NO2 has four important characteristics. First, there is improved spatial and temporal coherence on a day-to-day basis, allowing new scientific findings to be made. Second, the amount of data doubled, with 40% more data available. Third, the results are reliable overall, with a good agreement with MAX-DOAS measurements (R: 0.75-0.85). Finally, the mean of reconstructed NO2 vertical columns during 2015 and 2018 is consistent with the original data in the spatial distribution, while the standard deviation decreases in most places over mainland China. This novel finding is expected to contribute to both air quality and climate studies.

The accurate determination of the location, height, and loading of sulfur dioxide (SO2) plumes emitted by volcanic eruptions is essential for aviation safety. The SO2 layer height is also one of the most critical parameters with respect to determining the impact on the climate. Retrievals of SO2 plume height have been carried out using satellite UV backscatter measurements, but, until now, such algorithms are very time-consuming. We have developed an extremely fast yet accurate SO2 layer height retrieval using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm. This is the first time the algorithm has been applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform. In this paper, we demonstrate the ability of the FP_ILM algorithm to retrieve SO2 plume layer heights in near-real-time applications with an accuracy of better than 2 km for SO2 total columns larger than 20 DU. We present SO2 layer height results for the volcanic eruptions of Sinabung in February 2018, Sierra Negra in June 2018, and Raikoke in June 2019, observed by TROPOMI., SCOPUS: ar.j, info:eu-repo/semantics/published

In this work, we address the image geolocation issue that is present in the imagery of EPIC/DSCOVR (Earth Polychromatic Imaging Camera/Deep Space Climate Observatory) Level 1B version 2. To solve it, we develop an algorithm that automatically computes a registration correction consisting of a motion (translation plus rotation) and a radial distortion. The correction parameters are retrieved for every image by means of a regularised non-linear optimisation process, in which the spatial distances between the theoretical and actual locations of chosen features are minimised. The actual features are found along the coastlines automatically by using computer vision techniques. The retrieved correction parameters show a behaviour that is related to the period of DSCOVR orbiting around the Lagrangian point L 1 . With this procedure, the EPIC coastlines are collocated with an accuracy of about 1.5 pixels, thus significantly improving the original registration of about 5 pixels from the imagery of EPIC/DSCOVR Level 1B version 2.

In October 2017, the Sentinel-5P satellite with the TROPOMI instrument was launched into space. Total ozone columns from TROPOMI instrument are retrieved in near real time using the well established DOAS approach, and a subsequent conversion into vertical column densities using an iterative air mass factor calculation. The total ozone algorithm has already been successfully applied to the GOME, SCIAMACHY and GOME-2 missions. To account for the high spatial resolution of the TROPOMI instrument, the treatment of clouds in the total ozone retrieval has been improved. Besides the total column, a tropospheric ozone column is also provided from TROPOMI. The cloud convective differential method is used to separate the tropospheric and stratospheric ozone column. Deep convective clouds shield the tropospheric ozone from the satellite based observers. Therefore the ozone columns above deep convective clouds are good approximations of the stratospheric ozone column. These data are corrected for the variety in the cloud top altitudes and averaged over a 5 days time period and a clean area. In a last step the stratospheric column are subtracted from the total ozone columns for cloud free observations. After a short introduction to the retrieval algorithms, first results will be presented for both total and tropospheric ozone columns from TROPOMI. First comparisons to ozone column data from GOME-2 and ground-based observations will be shown as well.

n October 2017 the Sentinel-5P (S5P) was launched into space. The total ozone data of the TROPOspheric Monitoring Instrument (TROPOMI) on S5P are retrieved in near-real-time using the well established DOAS approach, and a subsequent conversion of the retrieved slant column densities into vertical column densities using an iterative air mass factor calculation. This algorithm has already been successfully applied to the previous missions GOME, SCIAMACHY and GOME-2. However, the high resolution of S5P requires a more sophisticated cloud product and the ozone algorithm is adapted to the new cloud model. Besides the total column, a tropospheric ozone column is also included in the official ozone data products. The cloud convective differential method is used to separate the tropospheric and stratospheric ozone column. Deep convective clouds shield the tropospheric ozone from the satellite based observers. Therefore the ozone columns above deep convective clouds are good approximations of the stratospheric ozone column. These data are corrected for the variety in the cloud top altitudes and averaged over a 5 days time period and a clean area. In a last step the stratospheric column are subtracted from the total ozone columns for cloud free observations. After a short introduction to the algorithms, the first results will be presented for both tropospheric and total ozone. First comparisons to similar data based on GOME-2 on MetOp A&B observations look very good, initial validation results using ground-based or sonde data will be presented.

Since the launch of GOME onboard ERS-2 in 1995 total and tropospheric ozone have been derived from European satellite instruments. In the framework of the ESA CCI and the EU ECMWF C3S projects, BIRA generates total ozone products from the satellite sensors GOME, SCIAMACHY, OMI, and GOME-2 using the GODFIT algorithm and DLR is responsible for harmonizing the total column data from all these sensors and generating a merged product, which encompasses more than two decades of global total ozone observations. Additionally, tropospheric ozone columns form the European sensors are generated by DLR using the convective cloud differential algorithm. Total and tropospheric ozone from GOME-2 onboard MetOp-A and -B are operational products from the EUMETSAT AC-SAF and within the ESA CCI project the tropical tropospheric ozone products from GOME, SCIAMACHY, OMI, and GOME-2 were harmonized and a merged data product was delivered and has been updated regularly. On a global scale a slight increase in total ozone columns is observed over the years since 1995 until today indicating that the total ozone starts to emerge into the expected recovery phase. Tropospheric data from the last 22 years show a slightly increasing trend with strong regional variations especially in the tropical eastern Pacific and Atlantic Ocean. These unique ozone datasets will be extended during the next two decades with measurements from the EU Copernicus missions Sentinel-5 Precursors (successfully launched in October 2017) and the future Sentinel-4 and Sentinel-5 missions.

We present time-series of the global distribution of water vapor columns over more than two decades based on measurements from the satellite instruments GOME, SCIAMACHY, and GOME-2 in the red spectral range. Particular focus is the consistency amongst the different sensors to avoid jumps from one instrument to another. This is reached by applying robust and simple retrieval settings consistently. Potentially systematic effects due to differences in ground pixel size are avoided by merging SCIAMACHY and GOME-2 observations to GOME spatial resolution, which also allows for a consistent treatment of cloud effects. In addition, the GOME-2 swath is reduced to that of GOME and SCIAMACHY to have consistent viewing geometries. Remaining systematic differences between the different sensors are investigated during overlap periods and are corrected for in the homogenized time series. The resulting "Climate" product allows to study the temporal evolution of water vapor over the last 20 years on global scale.

Identifying ozone profile shape is a critical yet challenging job for the accurate reconstruction of vertical distributions of atmospheric ozone that is relevant to climate change and air quality. Motivated by the need to develop an approach to reliably and efficiently estimate vertical information of ozone and inspired by the success of machine learning techniques, this work proposes a new algorithm for deriving ozone profile shapes from ultraviolet (UV) absorption spectra that are recorded by satellite instruments, e.g. GOME series and the future Sentinel missions. The proposed algorithm formulates this particular inverse problem in a classification framework rather than a conventional inversion one and places an emphasis on effectively characterizing various profile shapes based on machine learning techniques. Furthermore, a comparison of the ozone profiles from real GOME-2 data estimated by our algorithm and the classical retrieval algorithm (Optimal Estimation Method) is performed.

Tropospheric ozone columns are retrieved using the convective cloud differential (CCD) method from GOME_2 as operational products within the O3M-SAF project. This algorithm is also adapted to other sensors like GOME or SCIAMACHY. The data from these three European satellites are combined to a long time series. However, the CCD method is limited to the tropics, therefore a different approach is used to extend the tropospheric ozone data to mid latitudes. Comparable to the OMI-MLS approach a GOME_2-MLS approach was tested, and showed promising results. A description of the new algorithm as well as a comparison with the well established CCD method will be presented. Ozone sondes are the standard tool for validating tropospheric ozone columns, here only a short comparison might be given.

The operational processing of remote sensing data requires high-performance radiative transfer model (RTM) simulations. To date, considerable success has been achieved in dimensionality reduction techniques as well as in heterogeneous multi-CPU/GPU computing for highly intensive parallel computations. We have developed several techniques for accelerating the radiative transfer solver. They include (1) analytical methods which allow to compute set of atmospheric scenarios in one RTM call; (2) dimensionality reduction of the datasets, and (3) GPU-computing using CUDA framework. These techniques provide almost 300x cumulative speed-up for the RTM with respect to the original single-threaded CPU code. In this paper, we analyze the applicability of the proposed methods to a practical problem of total ozone column retrieval from UV-backscatter measurements.

With the convective cloud differential algorithm (CCD) the tropospheric ozone column (below 10 km) is calculated as the difference between the total ozone column for cloud free observations and the stratospheric column given by the vertical column above high reaching convective clouds. In this study our CCD algorithm is applied to the total ozone column product from GOME, SCIAMACHY, OMI and GOME-2. The resulting timeseries includes 20 years of tropospheric ozone data from 1995 to 2015. Here the same retrieval was applied to all instruments both for the total ozone column (GODFIT) as for the tropospheric column (CCD). This results in a very consitent data set. For the trend analysis individual ozone sonde stations as well as certain latitude bands were considered. Together with the linear term we fitted an El Niño index, a QBO index and a combination of several sinus and cosinus functions to simulate the seasonal variations as well as the phase shift. Despite the fact that the same algorithms were used, the observed trends still depend on the satellite instrument. While GOME-2 tropospheric ozone data showed a trend of 0.22 DU/yr in the 10°S to 0° latitude band between 2007 and 2014, the data from OMI increased by 0.037 DU/yr in the same latitude band and period. Assuming that the atmospheric trends are the same for the two overpass times of the instruments, we looked for instrumental effects as one possible explanation for the different trends. Therefore the data were compared to the SHADOZ ozone soundings. The partial column below 10 km was subtracted from the satellite insturments' data. The difference to the soundings showed temporal drifts for all instruments varying between -1.08 DU/yr and 0.06 DU/yr. The different trends in both the tropospheric satellite data and the validation indicates the need of further harmonized CCD data before analysing long-term trends.

In the framework of ESA’s “GOME Evolution Project”, a reprocessing will be made of the entire 16 year GOME Level 1 dataset. The GOME Evolution Project further includes the generation of a new GOME water vapour product, and a public outreach programme. In this paper we will describe the reprocessing of the Level 1 data, carried out with the latest version of the GOME Data Processor at DLR. The change most visible to the user will be the new product format in NetCDF, plus supporting documentation (ATBD and PUM). Full-mission reprocessed L1b data are expected to be released in the 4th quarter of 2015

We present here the results of the Bardarbunga eruption monitored by the GOME-2 instrument aboard MetOp-A & -B. After increased seismic activity in August, the Icelandic volcano Bardarbunga (Bárðarbunga) erupted on 31 August 2014. Since 1 September the GOME-2 instruments aboard the MetOp-A and –B satellites detect a continuous emission of sulphur-dioxide (SO2) emitted from the Holuhraun fissure at the flanks of the Bardarbunga volcano. At the beginning the emitted SO2 was mainly transported to the north-eastern direction over Scandinavia and Russia. However, on September 22 an SO2 cloud was even moving over Europe and could be detected at the Hohenpeissenberg and Schneefernerhaus observatories. So far no volcanic ash was ejected due to the so far effusive nature of the eruption. However, an explosive eruption of the volcano is still possible, which will probably produce large amounts of volcanic ash and could affect the air traffic over Europe. SO2 emissions are a good indicator for volcanic activity, since besides weak anthropogenic emissions there are no other known sources for atmospheric SO2, which can cause respiratory problems in the local population and the aircraft passengers. Furthermore in form of acid rain it increases the oxidation of aircraft components. It was found that for some volcanic eruptions SO2 can be a good proxy for the much harder to detect volcanic ash. Volcanic ash can be hazardous not only for the local population but also for aviation since it can cause total engine failure if it melts and then congeals in the engine. Furthermore ash is highly abrasive to engine turbine vanes and propellers. Under the leadership of IMF, DLR-EOC provides operational trace gas measurements, including total SO2 columns, in near-real-time (i.e., within 2 hours of recording) in the framework of EUMETSAT’s Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF).

In order to guarantee high signal-to-noise ratios, the design of spectrometers aboard spaceborne platforms is a tradeoff between spectral and spatial resolution. Since molecular absorption is highly spectrally dependent, atmospheric composition instruments favor spectral over spatial resolution. The TROPOMI instrument aboard Sentinel 5 Precursor (S5P) will have a spatial resolution of about 7x7 km2 at nadir, which clearly outperforms the resolution of previous atmospheric missions (320x40 km2 for OME/ERS-2, ~120x30 km2 for SCIAMACHY/ENVISAT and 80x40 km2 for GOME-2/Metop-A,B). However, inside a TROPOMI ground pixel there may still be a considerable amount of unresolved cloud variability. In this work, we present a sensitivity study of measured reflectivities as a function of the unresolved cloud variability. For this purpose, we simulate virtual measurements of a TROPOMI-like instrument in cloudy scenes at different spatial resolutions by means of the three-dimensional (3D) radiative transfer model MoCaRT (Monte Carlo Radiative Transfer). The reference inside-pixel cloud microphysical properties are provided by the PArallelized Large-Eddy-Simulation Model (PALM) at a spatial resolution of 10x10 m2 in a 6.4x6.4 km2 domain. The retrieval algorithms of both, atmospheric molecules and cloud properties rely on one-dimensional radiative transfer (RT) models which do not account for neither cloud variability nor 3D RT effects. Moreover, the quality of atmospheric gas retrievals strongly depends on the accuracy of the cloud information. Accordingly, we first analyze the 3D effects on the cloud products by ingesting the 3D simulated spectra (around the oxygen A-band) to the ROCINN_CAL cloud algorithm. In a second step, the impact of the cloud variability on the ozone product is assessed (which also include the indirect impact on cloud properties).

Current and future satellite sensors provide measurements in and around the oxygen A-band on a global basis. These data are commonly used for the determination of cloud and aerosol properties. In this paper, we assess the information content in the oxygen A-band for the retrieval of macrophysical cloud parameters using precise radiative transfer simulations covering a wide range of geophysical conditions in conjunction with advance inversion techniques. The information content of the signal with respect to the retrieved parameters is analyzed in a stochastic framework using two common criteria: the degrees of freedom for a signal and the Shannon information content. It is found that oxygen A-band measurements with moderate spectral resolution (0.2 nm) provide two pieces of independent information that allow the accurate retrieval of cloud-top height together with either cloud optical thickness or cloud fraction. Additionally, our results confirm previous studies indicating that the retrieval of cloud geometrical thickness (CGT) from single-angle measurements is not reliable in this spectral region. Finally, a sensitivity study shows that the retrieval of macrophysical cloud parameters is slightly sensitive to the uncertainty in the CGT and very sensitive to the uncertainty in the surface albedo.

The European Space Agency’s [ESA] Ozone Climate Change Initiative [CCI] project aims at producing and characterizing a number of high quality ozone data products generated from multiple satellite sensors. Improved Level-2 total ozone data sets based on the European sensors GOME/ERS-2, SCIAMACHY/Envisat and GOME-2/MetOp-A covering more than 17 years have recently been released. GOME-type Direct FITting [GODFIT] provides consistent results for all three sensors which are manifested via a similar seasonality, albeit with reduced amplitude, and similar solar zenith angle dependency. The validation of total ozone columns hence demonstrates a high level of inter-sensor consistency and an excellent long-term stability of individual instruments. One concludes that the accuracy of the CCI total ozone data sets is at the percent level during the complete time period from 1996 until 2012.

In order to promote the use of atmospheric composition products derived from MetOp/GOME-2 in a wider community, DLR operates processing and delivery systems as well as spatial data services based on Open Geospatial Consortium (OGC) standards. Through the Web Map Service (WMS) and the Web Coverage Service (WCS) different data layers, projections and views can be portrayed for interactive inspection and the full resolution primary data can be downloaded for further processing. Complementary to the data delivery services provided by DLR in the framework of the Ozone and Atmospheric Chemistry Monitoring Satellite Application Facility (O3M-SAF), DLR operates spatial data services for total and tropospheric column products for a number of trace gases (O3, NO2, BrO, SO2, H2O, OClO and HCHO) as well as cloud properties (cloud fraction, cloud-top pressure and cloud optical thickness) derived from GOME-2. These products are available in near-real-time (less than 2 hours after sensing) and offline. During more than one year of test and productive operations for a continuously growing number of product types, DLR gained practical experience with operational and configuration aspects of providing atmospheric composition datasets through standard business-to-business spatial data services. Taking into account the various data species (trace gases and cloud properties), product quality information, different levels of temporal and spatial aggregation and assimilation, as well as different data timeliness, the spatial data services were configured, optimized and presented with the goal of best serving a wide range of applications ranging from short-term air quality monitoring and chemical weather forecasting to long-term climate research. This contribution gives an overview of the full GOME-2 operational data processing chain from reception to the access service front-ends. The focus is on operational experience gained in the configuration, optimization, presentation and performance of atmospheric data access services. In addition we introduce the EOWEB® GeoPortal, the new DLR multi-mission web portal for interactive access to earth observation data. EOWEB® GeoPortal integrates the classical catalogue-and-order services with the novel browse-and-download data access paradigm. Finally, a short outlook on future developments planned by DLR to extend the spatial data services is presented.

Satellite-based remote sensing measurements of atmospheric sulphur dioxide (SO2) provide valuable information on anthropogenic pollution and volcanic activity. Sensors like GOME-2 on MetOp-A make it possible to monitor SO2 emissions on a global scale and daily basis. SO2 total column amounts are retrieved in near-real time using the UV range of backscattered sunlight making it possible to detect and track volcanic eruption plumes as a valuable tool for aviation warning. For aviation safety the correct determination of the plume height is a central issue. Therefore a novel method has been developed for the determination of the plume height in near-real time based on the operational DOAS retrieval combined with an iterative look-up table (LUT) approach. The method has been applied to the eruption of Eyjafjöll volcano, April - May 2010, and to the eruption of Kilauea, July 2008.

Within the distributed EUMETSAT EPS ground segment, DLR acts as an integral part of the Ozone and Atmospheric Chemistry Monitoring Satellite Application Facility (O3M-SAF) for the processing, archiving and delivery of GOME-2 atmospheric products containing the total column density of different trace gases such as O3, NO2, BrO, SO2, OClO and HCHO. Based on the continuous reception of GOME-2 level 1B data, different processing chains are triggered for the generation and dissemination of level 2 products. The challenging requirements of the EPS ground segment for the SAFs concerning processing throughput, system reliability, guaranteed near-realtime capability, data consistency and activity traceability are met at DLR with an integrated O3M-SAF processing and product management system implemented with the DLRs Data Information and Management System DIMS. DIMS is operated in DLRs multi-mission earth observation ground segment facility. This production environment integrates the EUMETCast receiving station and the GOME-2 processing system hosting the Universal Processor for Atmospheric Spectrometers (UPAS), thus enabling various data services ranging from online dissemination in near-realtime to the publishing and ordering of products through EUMETSATs central UMARF catalogue. This poster presents the data services provided by DLR in the O3M-SAF. The data services include the online delivery of products to subscribed users in near-realtime, the dissemination via the EUMETCast system, the upload of product information to the UMARF catalogue and the processing of orders for archived products, including online delivery per FTP or on offline media. In addition information and products are published in the World Data Center for Remote Sensing of the Atmosphere (WDC RSAT) and in DLRs multi-mission web gateway EOWEB®. The poster gives an overview of the deployment of the operational facility at DLR and concludes with the presentation of first operational experiences after entering the O3M-SAF continuous development and operations phase.