We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.

In 2018, the Aeolus satellite of the European Space Agency (ESA) was launched to improve weather forecasts through global measurements of wind profiles. Given the novel lidar technique onboard, extensive validation efforts have been needed to verify the observations. For this reason, we performed long-term validation measurements in Germany and Chile. We found significant improvement in the data products due to a new algorithm version and can confirm the general validity of Aeolus observations.

A synergistic retrieval method of aerosol components (water-soluble, light-absorbing, dust, and sea salt particles) from CALIOP and MODIS observations was developed. The total global 3-D distributions and those for each component showed good consistency with the CALIOP and MODIS official products and previous studies. The shortwave direct radiative effects of each component at the top and bottom of the atmosphere and for the heating rate were also consistent with previous studies.

Commercial microwave links as rainfall sensors have been investigated and evaluated in numerous studies with gauge-adjusted radar used for reference for rainfall observations. We evaluate collocated commercial microwave links, which are thus exposed to identical atmospheric conditions. This set-up enables the exploration of inconsistencies in observations of independent sensors using data from a real telecommunication network. The sensors are in agreement and are homogeneous in their behaviour.

Clouds play a key role in Earth’s climate by influencing the surface energy budget. Certain types of atmospheric aerosols, called ice-nucleating particles (INPs), induce the formation of ice in clouds and, thus, often initiate precipitation formation. The Portable Ice Nucleation Chamber 2 (PINCii) is a new instrument developed to study ice formation and to conduct ambient measurements of INPs, allowing us to investigate the sources and properties of the atmospheric aerosols that can act as INPs.

Aeolus is a satellite equipped with a Doppler wind lidar to detect global wind profiles. This study evaluates the impact of Aeolus winds on surface wind forecasts over tropical oceans and high-latitude regions based on the ECMWF observing system experiments. We find that Aeolus can slightly improve surface wind forecasts for the region > 60° N, especially from day 5 onwards. For other study regions, the impact of Aeolus is nearly neutral or limited, which requires further investigation.

Snow layer melting and melt pond formation on Arctic sea ice are important seasonal processes affecting the surface reflection and energy budget. Sea ice reflectivity was surveyed by airborne imaging spectrometers in May–June 2017. Adapted retrieval approaches were applied to find snow layer liquid water fraction, snow grain effective radius, and melt pond depth. The retrievals show the potential and limitations of spectral airborne imaging to map melting snow layer and melt pond properties.

We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.

A first evaluation of the Geostationary Environmental Monitoring Spectrometer (GEMS) NO₂ was carried out via comparison with the NO₂ data obtained from the ground-based Pandora direct-sun measurements at four sites in Seosan, Republic of Korea. Comparisons between GEMS NO₂ and Pandora NO₂ were performed according to GEMS cloud fraction. GEMS NO₂ showed good agreement with that of Pandora NO₂ under less cloudy conditions.