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Theses Topics

Students are welcome at IAP. Topics for bachlor or master theses can be found in the following list. If you are interested or have any questions, please contact the persons mentioned below each project.

  1. Dependence of mid-latitude noctilucent clouds (NLC) on ambient winds: NLC are thin ice clouds in the mesopause region. Especially mid-latitude data can help understanding their role in a changing climate. This study aims on quantifying the occurrence of NLC above Kühlungsborn in relation to the wind in the mesopause region. (Dr. M. Gerding, Optics)
  2. Testing of a low-cost laser based cloud detector: Availability of human operators often limits operating times of middle-atmosphere lidars. Cloud detectors can help by automatically triggering the startup process. In this study, a low-cost, laser-based solution is tested for cloud detection capability and compared with other optical methods. (Dr. M. Gerding, Optics)
  3. High-resolution volumetric radar observations of polar mesospheric summer echoes (PMSE) with the Middle Atmosphere Alomar Radar System (MAARSY): visualization, analysis and characterization of 4D radar images of polar mesospheric summer echoes. (Prof. J. L. Chau and Dr. M. Urco, Radar)
  4. Statistical analysis of the mesopause dynamics at different latitudes: Multi-year wind data from 8 to 100 km in height are available from IAP radar measurements. They cover the latitudes: 49 °S, 12 °S, 5 °S, 54 °N, 69 °N. The project includes the analysis of mean winds and dominant planetary waves and tides at these latitudes and their comparisons (Dr. J. F. Conte, Radar)
  5. Identification, characterisation, and classification of atmospheric patterns in radar data.IAP radars distributed along the globe produce a huge amount of high dimensional data daily. However, only a small subset of data/phenomena can be examined with traditional methods. In order to identify, characterise, and classify known (KHIs and vertical drafts) and unknown atmospheric patterns in the large radar dataset, we will implement a machine learning approach capable of identifying patterns in the radar data The main challenge of this project is the lack of labeled data and the unknown number of classes (atmospheric patterns), which will be overcome by using modern machine learning techniques such as active learning, statistical classification, and dimensionality reduction.(Dr. J. F. Conte, Radar)
  6. DNS simulations of Kelvin-Helmholtz instabilities with Dedalus: In this project, numerical investigations of mesoscale instabilities at different Reynolds numbers and stratification rates are to be carried out. (Prof. J. L. Chau, Radar)
  7. The derivation of electron number densities from cross-polarized radar measurements: The Saura MF radar with its operating frequency (3MHz), a modular structure and polarization configuration is able to determine the electron density of the lower ionosphere by the measurement of absorption and Faraday rotation. This project aims to combine both absorption and Faraday information with regularization techniques to derive improved electron density profiles. (Dr. T. Renkwitz, Radar)
  8. Subseasonal prediction with machine learning: Use machine learning to predict/reconstruct some easy variables such as minimum/maximum temperature and wind speed some weeks ahead using reanalysis data, model simulations and instrumental observations. (Dr. M. Amiramjadi, Modelling)
  9. Statistical properties of sudden stratospheric warmings: The statistical properties of these large-scale circulation patterns are determined from reanalysis data – a valuable information source for local observations. (Dr. Ch. Zülicke, Modelling)
  10. Energy cascades in the wave turbulence: Wave turbulence theory allows us to characterize energy cascades in atmospheric flows. We will analyze the double energy cascades (direct and inverse) in gravity wave systems. (Dr. V. Avsarkisov, Modelling)
  11. Optimization of background determination for improvement of lidar measurements: A time series of a point measurement in Kühlungsborn or Alomar is artificially generated from wind/temperature fields of the global model KMCM. With them, different approaches for the determination of background fields are tested, which can be used in lidar measurements.(Dr. Urs Schaefer Rolffs, Modelling, und Dr. Irina Strelnikova, Optics)

If you are interested in these theses or in student research projects or internships, you can also contact the heads of the departments directly: