Student projects

MSc topics

 

Classification of cloud structures

Clouds consist of myriads of small particles. However, on larger scales pattern are formed via the interaction of different processes in an emergent way. It is not clear how this structure formation process really works, but obviously the environmental conditions (e.g. winds, temperature, humidity, aerosols) have a strong impact on the formation of different pattern as can be seen in observations. In this project, cloud structures as seen from satellite should be classified. Using machine learning algorithms, a large data set of satellite images (e.g. Meteosat) will be investigated in order to identify different classes of cloud pattern automatically. The results should be investigated statistically in order to obtain a first estimate about the frequency of occurrence of distinct cloud pattern. In a second step, the identified cloud structures should be combined with meteorological data sets (reanalysis data from ECMWF) in order to provide information about preferential conditions for different cloud pattern.

Contact: Prof. Peter Spichtinger

Target group: MSc Computer Sciences/Atmospheric Physics

 

Determination of radiative forcing for ice clouds using machine learning

Clouds crucially influence the energy budget of the Earth-Atmosphere system. They partly reflect or scatter incoming solar radiation back to space, thus less energy as compared to clear sky scenarios is transferred into the system (cooling, albedo effect). On the other hand, infrared radiation as emitted from Earth’s surface is partly absorbed by clouds and re-emitted, leading to warming of the system (greenhouse effect). For ice clouds, both effects are of comparable size. Thus, the resulting net effect depends on some parameters characterizing the environmental conditions. In this project, radiative transfer calculations using a very simple and idealized setup should be carried out for an ensemble of several parameter settings. From the results a new but simple model should be developed using machine learning techniques to describe the net radiative effect of ice clouds as a function of parameters. This new model then can be used for first estimations of the radiative effect of ice clouds for similar environmental settings.

Contact: Prof. Peter Spichtinger

Target group: MSc Computer Sciences/Atmospheric Physics