In this presentation, the potential of the recently launched Tropomi instrument to detect bromine monoxide (BrO) in volcanic plumes is investigated. So far BrO in volcanic plumes has been successfully retrieved from satellite only during major eruptions. The higher spatial resolution of S5-P/Tropomi (3.5x7km) and the daily coverage allows for an investigation of volcanic BrO during smaller eruptions and even during continuous passive degassing. The continuous observation of passive degassing volcanoes yields the potential for long-term monitoring of volcanoes from satellite. Also, it is expected that the volcanic plumes can be tracked over larger distances.
BrO is a halogen radical altering – inter alia – the atmospheric ozone chemistry. BrO and in particular the molar BrO/SO2 ratios in volcanic gas emissions have been suggested as proxy for monitoring volcanic activity on several accounts.
In this study, we present BrO column densities as well as SO2 column densities retrieved using Differential Optical Absorption Spectroscopy (DOAS) and BrO/SO2 molar ratios in volcanic plumes with varying emission strength from Tropomi data. By deriving a time series, we investigate the variation of the BrO/SO2 molar ratio of various volcanoes, in order to investigate plume chemistry and emission composition.

Glyoxal (CHOCHO) and formaldehyde (HCHO) are intermediate products in the atmospheric oxidation process of most volatile organic compounds (VOC). Similar to other VOC, glyoxal and formaldehyde are mainly emitted from natural and anthropogenic processes, but also have a significant contribution from fires. Formaldehyde and glyoxal can be used as tracers of VOCs, and by computing the ratio of CHOCHO to HCHO (RGF), information on the type of source can be obtained. Both, glyoxal and formaldehyde can be retrieved from space-borne observations by using the Differential Optical Absorption Spectroscopy (DOAS) method. Since many years, the number of studies about formaldehyde and glyoxal retrievals has been increasing, mainly focusing on improving the retrieval from different satellite measurements in order to reduce the uncertainties in their columns and in estimating VOC emissions.
Here, glyoxal and formaldehyde retrievals developed for measurements from the TROPOMI instrument, which was recently launched on the Sentinel 5 Precursor (S5P) platform are presented. These retrievals are an updated version of the in-house retrieval algorithm developed for the SCIAMACHY, GOME-2 (A and B) and OMI instruments at IUP-Bremen. Overall, the retrieved formaldehyde and glyoxal column amounts from the S5P algorithm show similar seasonal behavior for both species over selected regions during the first year of data which is now available. In addition, the high spatial resolution of S5P offers new information for identification of local sources over selected hot-spots with large amounts of VOCs. By computing the RGF ratios and also by computing a linear correlation between species and indicators of vegetation, fires and anthropogenic emissions, this novel data set is applied for investigation of sources and atmospheric fates of VOCs.

Emissions of non-methane volatile organic compounds (NMVOCs) from vegetation play a critical role in atmospheric chemistry and composition. However, modeling these emissions has large uncertainties and there are few surface observations to provide constraints on the model emission estimates, given that the largest sources are in the tropics. Satellite measurements of trace gases provide one of the best datasets to evaluate and test model parametrizations for biogenic emissions. So far, this evaluation has been mostly limited to using observations of formaldehyde (H2CO) to infer top-down emissions of isoprene (C5H8) using data from Aura/ Ozone Monitoring Instrument (OMI). Here we show a new method to re-partition top-down estimates of carbon monoxide (CO) from different source sectors, including CO that is chemically produced from biogenic emissions. For most top-down inversion estimates, fluxes are partitioned into source sectors by scaling the total flux with the apriori sector emission fractions. We apply a Markov Chain Monte Carlo (MCMC) approach to find a Bayesian estimate that uses both posterior flux and a priori uncertainties and obtains an optimal partitioning of the flux estimates with characterized errors. We start with a total CO flux estimate obtained from an inversion of Terra/Measurements Of Pollution In The Troposphere (MOPITT) CO observations with the GEOS-Chem model, a global chemical transport model driven by assimilated meteorology from the NASA Goddard Earth Observing System (GEOS). After partitioning the CO flux into biomass burning (BB), fossil fuel (FF) and biogenic (BG) source sectors, we compare the BG results to the GEOS-Chem prior information for CO from biogenic NMVOCs, which uses the Model of Emissions of Gases and Aerosols from Nature (MEGAN) for biogenic emissions, and to top-down estimates of isoprene emissions using OMI formaldehyde observations. We find similar seasonality and regional patterns in the posterior CO and top-down isoprene estimates globally. For the African savanna region, both top-down CO and ispoprene seasonality vary significantly from the MEGAN apriori. This method for estimating biogenic sources of CO will provide an independent constraint on the MEGAN model and has the potential for diagnosing decadal scale changes in emissions due to land-use change and climate variability.

Carbon monoxide (CO) is an important trace gas for understanding air quality and atmospheric composition. It is a good tracer of pollution plumes and atmospheric dynamics.

With three IASI instruments flying on the Metop-A, Metop-B and Metop-C satellites, any location on Earth is now observed at least six times per day in the infrared spectral range. All cloud free observations are analysed in near real time mode.

IASI CO concentrations are retrieved from the radiance data using the Fast Operational Retrievals on Layers for IASI (FORLI) algorithm, based on the Optimal Estimation theory. The operational production is performed at EUMETSAT and the products are distributed in NRT via EUMETCast under in the framework of AC SAF.

We present an analysis of 11 years of global distributions of CO. Comparison with MOPITT CO data (v7T/v8T, record starting in 2000) will be shown. IASI and MOPITT data are jointly assimilated in the Copernicus Atmospheric Monitoring Service (CAMS) to generate CO pollution forecasts. We will also focus on the pollution event that occurred in Europe in October 2017, where dust from Sahara and smoke from Portugal/Spain wildfires were transported by hurricane Ophelia accross Europe. 3D observations by ground-based, aircraft, and satellite data compared with CAMS atmospheric composition analyses will be presented.

Since 2006, the SAMPO service, formerly known as OMI Very Fast Delivery service, has deliver satellite observations of atmospheric composition, including volcanic SO2 and aerosols, over Arctic areas. The service started with OMI Direct Readout (DR) data received and processed to images in Sodankylä, northern Finland. Since launch of the Suomi-NPP satellite, also OMPS images has been available as well as the data files. For OMPS, the service use also data received in Alaska ensuring Arctic spatial coverage. The service provides data and images within 20 minutes after the satellite overpass, which reflects the leading idea behind the service – provide data as soon as possible.

The weather forecast communities as well as episode monitoring groups have realized the usefulness of this kind of early DR data source to monitor aerosol and SO2 emissions from volcanic eruptions. Thus, to fulfill the operational needs, the dissemination of SAMPO SO2 and aerosols via EUMETCast started in late 2018. EUMETCast is EUMETSAT’s multicast dissemination system, which uses commercial telecommunication geostationary satellites to deliver the data to the users. The NOAA-20 (former JPSS-1) OMPS data will be implemented into the SAMPO system in about mid 2019 when the SW packages are ready.

The interesting question is if there will be an European contribution for the DR community in future. The EPS-SG platform will have a direct broadcast (DB) capability and thus, the S5 data, for example, could be an important data source for the DR community.

In this talk we will show the resent improvements of the SAMPO service and it’s products. The audience has a possibility to give feedback as well as propose new improvements. Furthermore, we will speculate on availability of S5 DR data and set the general requirements for the S5 DB SW so that the S5 DR services are feasible in future.

The Copernicus Atmosphere Monitoring Service (CAMS) implemented by ECMWF on behalf of the European Commission is one of the major users of Sentinel-5p/TROPOMI data. TROPOMI provides high resolution spectral measurements in the ultra-violet (UV), visible (VIS), near infrared (NIR) and shortwave-infrared (SWIR) part of the spectrum. This wide spectral range allows several atmospheric pollutants species to be retrieved, e.g. O3, NO2, SO2 and Formaldehyde (HCHO) from the UVVIS, and CO and Methane (CH4) from the SWIR part of the spectrum at unprecedented horizontal resolution of about 3.5 km x 7 km in the UVVIS and 7 km x7 km in the SWIR. CAMS is using all the currently retrieved chemical species as input to its global forecasting system for atmospheric composition.

Before the data could be used in the CAMS operational global forecasting system, the CAMS system was used to passively monitor the TROPOMI data and to give ESA feedback on the data quality. A global data assimilation system is a powerful tool to monitor the quality of satellite data because it provides continuous global coverage and allows us to build up statistics quickly. The model fields are interpolated in time and space to the location of the observations, statistical analyses of the differences between the model's first guess or analysed fields and the observations (the so called departures) are calculated and their time evolution is monitored. If the model fields are stable the departures normally show a relatively smooth behaviour from day to day. A sudden jump on a global scale, which is larger than the instrument noise, is an indication of possible problems in the data or the model. Long term monitoring statistics can also detect biases between the model and the data and between different satellite retrievals.

After the quality of the TROPOMI data was assess with initial passive monitoring experiments, assimilation tests with the new data followed and the impact of the data on the CAMS analyses and forecasts was assessed.

In this talk we will show results from the monitoring and assimilation tests of several TROPOMI species in the CAMS system, illustrate the power of a global data assimilation system to monitor satellite data and show the impact that TROPOMI data have in the CAMS system.