Atmospheric Composition

In the Satellite Observations Department at KNMI we study the global and regional atmospheric composition using satellite observations of trace gases, aerosols, clouds and winds. The observations contribute to monitoring and research of Climate, Ozone, and Air Quality. The main satellite instruments used in our division are OMI, GOME, GOME2, SCIAMACHY, SEVIRI and ASCAT. We develop calibration and retrieval algorithms for these instruments, and process and distribute the satellite data to users, e.g. via TEMIS, in collaboration with international partners. To validate the satellite observations and to provide local monitoring we also operate several ground-based instruments, like the Brewer, the ozone sonde and the NO2 sonde. Our division has the Principal Investigatorship for the Dutch-Finnish instrument OMI, launched in 2004 on NASA's EOS-Aura satellite, and for the Dutch-ESA instrument TROPOMI, to be launched early in 2016 on ESA's Sentinel-5 Precursor satellite.
A thirty year time series of the ozone hole (left) and a global air pollution map of NO2 (middle). A brochure about our department (right).

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2017-04-13: Surface reflectivity climatologies from UV to NIR determined from observations by GOME-2 and SCIA

The primary goal of this paper is to introduce two new surface reflectivity climatologies. The two databases contain the Lambertian-equivalent reflectivity (LER) of the Earth’s surface, and they are meant to support satellite retrieval of trace gases and of cloud and aerosol information. The surface LER databases are derived from the Global Ozone Monitoring Experiment (GOME)-2 and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instruments and can be considered as improved and extended descendants of earlier surface LER climatologies based on the Total Ozone Mapping Spectrometer (TOMS), GOME-1, and Ozone Monitoring Instrument (OMI) instruments.

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2017-04-12: A geostationary product for near-real-time identification of high ice water content environments

We developed a satellite data product for identification of atmospheric environments with a high chance of the presence of high ice water content (IWC > > 1 g/m3). This High IWC mask is based on measurements of cloud properties using the cloud physical properties (CPP) algorithm applied to the geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI).

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2017-03-13: Space-based NOx emission estimates over remote regions improved in DECSO

We improve the emission estimate algorithm DECSO (Daily Emission estimates Constrained by Satellite Observations) to better detect NOx emissions over remote areas. The new version is referred to as DECSO v5. The error covariance of the sensitivity of NO2 column observations to gridded NOx emissions has been better characterized. This reduces the background noise of emission estimates by a factor of 10. An emission update constraint has been added to avoid unrealistic day-to-day fluctuations of emissions. We estimate total NOx emissions, which include biogenic emissions that often drive the seasonal cycle of the NOx emissions. We demonstrate the improvements implemented in DECSO v5 for the domain of East Asia in the year 2012 and 2013. The emissions derived by DECSO v5 are in good agreement with other inventories like MIX. In addition, the improved algorithm is able to better capture the seasonality of NOx emissions and for the first time it reveals ship tracks near the Chinese coasts that are otherwise hidden by the outflow of NO2 from the Chinese mainland. The precision of monthly emissions derived by DECSO v5 for each grid cell is about 20%.

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