All JOYCE observations come with a distinct challenge: the desired meteorological parameters such as temperature and humidity profile or cloud liquid or ice water content are only indirectly contained in the measurements. This holds especially true for the identification as well as the  quantification of the physical processes at work, e.g. in-cloud droplet growth, turbulent mixing or radiative cooling/heating.

This is why our research is specified in inverse methods, meaning that we find ways of extracting the desired quantities from the indirect remote sensing observations by means of statistical and physical approaches. This can be achieved through variational retrieval or AI (neural networks) methods.

To find out more contact: Maria Toporov (Inverse methods), Bernhard Pospichal (JOYCE) and Ulrich Löhnert