We developed a new efficient algorithm leveraging orbital dynamics to collocate radio occultation soundings with microwave radiance soundings. This new algorithm is 99 % accurate and is much faster than traditional collocation-finding approaches. Speeding up collocation finding is useful for calibrating and validating microwave radiometers and for data assimilation into numerical weather prediction models. Our algorithm can also be used to predict collocation yield for new satellite missions.

The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.

As methane (CH₄) contributes to global warming, more CH₄ measurements are required to better characterise source emissions. Hence, we tested a cheap CH₄ sensor for 338 d of landfill sampling. We derived an excellent CH₄ response model in a stable environment. However, different types of air with the same CH₄ level had diverse sensor responses. We characterised temperature and water vapour response but could not replicate field sampling. Thus, other species may cause sensor interactions.

Methane is powerful greenhouse gas; thus, to reduce methane emissions, it is important that the methods used to measure methane are tested and validated. The static chamber method is an enclosure-based technique that directly measures methane emissions; however, it has not been thoroughly tested for the new variety of methane sources that it is currently being used for. We find that the static chamber method can accurately measure methane emissions under a wide range of methane emission rates.

We describe the stand-alone retrieval algorithm used to derive aerosol properties relying on measurements of the Multi-Spectral Imager (MSI) aboard the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite. This aerosol data product will be available as M-AOT after the launch of EarthCARE. Additionally, we applied the algorithm to simulated EarthCARE MSI and Moderate Resolution Imaging Spectroradiometer (MODIS) data for prelaunch algorithm verification.

We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.