Dies ist eine alte Version des Dokuments!
Bei Interesse an Masterarbeiten im Lehrstuhl für Theoretische Meteorologie bitte bei Prof. G. Craig, Prof. T. Birner, Priv.Doz. T. Janjic oder Dr. C. Keil nachfragen. Nachfolgend eine Auswahl derzeit offener Themen:
Although convective clouds are much smaller than large-scale weather systems, they can be a major source of error in weather forecasts because errors grow rapidly. Studying this error growth requires large ensembles of forecasts to accurately represent the many ways that the weather situation can develop. The aim of this project is to develop a simple numerical model that can represent the error growth processes, but is inexpensive to run in large ensembles. Different model formulations will be designed, programmed in Python and evaluated using advanced verification measures.
For more detailed information please contact George Craig.
Topics broadly in the area of stratosphere-troposphere and climate dynamics are available upon request. Recent research topics in our group include: variability and long-term trends in the width of the tropical belt, processes that govern the temperature structure of the tropical tropopause layer, the dynamics of sudden stratospheric warmings and their coupling to the troposphere, transport processes in the upper troposphere / lower stratosphere. Interested candidates are asked to look through research topics on our group's website, in particular our recent publications: https://www.meteo.physik.uni-muenchen.de/~Thomas.Birner/pubs.html.
For more detailed information please contact Thomas Birner.
The assimilation of cloud-related observations is challenging as traditional error metrics (e.g. RMS error) are not really suitable for the evaluation of intermittent fields as clouds or precipitation (double penalty problem). To overcome this deficiency, various feature-based scores have been developed for the verification of forecast precipitation fields, but these scores have not yet been applied in the context of data assimilation.
The goal of this thesis ist to test the use of feature-based metrics for the assimilation of cloud-affected satellite observations in the emsemble data assimilation system KENDA for the regional weather forecast model COSMO-DE. Different approaches shall be tested in an idealized setup of KENDA that is used by several people in the HErZ data assimilation group.
Contact: Leonhard Scheck
Observations and high-resolution numerical simulations both show that tropospheric humidity affects the development of deep moist convection, with a drier troposphere typically limiting or delaying the apparition of the deepest clouds. This phenomenon is generally explained by the fact that a dry environment can quickly erode the cloud core through lateral mixing (called entrainment). Although convective cloud parameterizations usually account for entrainment, the influence of environmental humidity is generally not well captured, in part because the host model (typically a weather prediction or climate model) cannot resolve all the small spatial humidity fluctuations.
In this project, a Cloud Resolving Model (CRM) operating at about 100m resolution will be used to examine the connections between the development of deep clouds and small scale moisture fluctuations in the free troposphere. The analysis of high-resolution model data should constitute the first step towards a parameterization of convection that could explicitly account for unresolved moisture variability.
Contact: Julien Savre
The storage space requirements for output of numerical weather and climate prediction is growing faster than the cost of storage space is decreasing. Model resolution is continuously increased to overcome issues with parameterized physical processes like convection. At the same time the ensemble size (e.g. the number of model runs performed to create one forecast) is also increased to improve the assessment of uncertainty within the forecast. Both in combination results in really big data sets that are not only difficult to process but also very expensive to store.
One way to reduce the amount of output is to rely more on online diagnostics and not to save large fractions of the output. While this approach appears promising in operational setups of weather services, it is only a partial solution in research. Visualization of arbitrary aspects of a model run would no longer be possible and experiments would have to be carried out again if changes are made to the online diagnostic.
Another way is to store model output with reduced precision. File formats currently in use support only lossless compression or if lossy compression is possible only spatial correlation between neighboring data points is used (e.g., JPEG compression). The temporal correlation is ignored. In contrast, video compression algorithms are essentially based on the temporal correlation between successive time steps. Without reducing the quality, this results in a compression ratio that is by one order of magnitude higher than that of individual images. Central ideas of video compression should be directly transferable to the compression of model output. Examples are differential coding (only differences between time steps are stored) and motion compensation (for unchanged but moved parts of an image only a displacement vector is stored). On the other hand, assumptions about the perception by the human eye are not applicable (e.g., changes in brightness and color are not equally important).
The following questions should be addressed in this thesis:
Which compression algorithms are best suited for meteorological model output? Candidates are video compression and general purpose algorithms. The plan is not to develop new algorithms, but to asses existing ones.
What are the characteristics of errors produced by the analyzed algorithms?
Which degree of compression is acceptable for a set of different meteorological applications?
Contact: Robert Redl
Further themes are possible, please talk to Prof. G. Craig, Prof. T. Birner, Priv.Doz T. Janjic or Dr. C. Keil.