The Arctic is experiencing rapid changes which result from complex feedback mechanisms which are not well understood. A better understanding of the Arctic climate system is thus a key challenge which we address by exploiting ground-based and satellite remote sensing observations. In particular, we want to gain insight into water vapor, clouds, precipitation and radiative effects in the Arctic. Detailed observations are crucial here and available from long-term measurements at Ny-Ålesund but also from campaign based activities, e.g. MOSAiC. more...

Find out more from Kerstin Ebell

Airborne observations fill the gap between local, single point, but temporarily high resolved ground based and satellite borne wide area, but spatialy and temporarily coarse resolved observations. We use them to take a closer look on clouds, precipitation, and the surface in the Arctic and tropics. Often instruments on aircraft are used to test future techniques for satellite applications. more...

Find out more from Mario Mech

Clouds in the tropics are crucial for the climate. Many studies showed that climate predictions' largest uncertainties depend on how these small puffy cumulus clouds will respond to global warming. Will they be shallower? Or deeper? Will they rain more? Precipitation in the tropics is also essential for redistributing water vapor in the sub-cloud layer. Rain and cloud in tropical regions live over an immense blue ocean full of eddies that mix water from different areas or big rivers. Does the ocean influence cloud formation? And if so, how? Exploiting the unique multiscale dataset collected during the EUREC4A campaign we try to answer some of these questions.

Satellite instruments provide unique views into our climate system with strongly increasing capabilities in the future. For example, the Ice Cloud Imager (ICI) as part of the upcoming MetOp-Second Generation will for the first time provide submillimeter measurements for atmospheric monitoring. The Meteosat Third Generation (MTG) will include sounding instruments for 3D probing of the atmosphere. However, extracting relevant information to infer cloud properties or the atmospheric composition is not straight forward. In this respect, new techniques involving methods from artificial intelligence provide new opportunities, such as deriving fog climatologies.