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studium:themen_fuer_masterarbeiten_theo [2020/01/10 14:13] keil |
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| ===== Topics for master theses in the theory group ===== | ===== Topics for master theses in the theory group ===== | ||
| - | 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: | + | Bei Interesse an **Masterarbeiten** im Lehrstuhl für Theoretische Meteorologie bitte bei Prof. G. Craig, Prof. T. Birner, oder Dr. C. Keil nachfragen. Nachfolgend eine Auswahl derzeit offener Themen: |
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| + | ==== Analyzing the “butterfly effect” in operational forecast data ==== | ||
| + | The European Centre for medium-range weather forecasting (ECMWF) has accidentally run a one year-long butterfly experiment, with their deterministic and control forecast initially differing only by tiny truncation errors. This data set, together with the regular ensemble forecasts, provides a great opportunity to study uncertainty growth in weather prediction and to address related questions on forecast improvement potential and forecast busts. The thesis requires good python coding skills and interest in data analysis and visualization. | ||
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| + | For more detailed information please contact Tobias Selz or George Craig. | ||
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| + | ==== Development of an AI-based algorithm for potential vorticity inversion ==== | ||
| + | Piece-wise potential vorticity (PV) inversion is a method to understand spatial interactions in the atmosphere. It has been used to investigate the reasons for uncertainty growth or extreme weather. However, running the numerical inversion algorithm on forecast data is expensive and often leads to convergence issues. The goal of the thesis is to investigate, if AI-based methods could be used for the PV inversion instead. Therefore, a training data set has to be created first, applying the conventional method. After that, various neuronal network designs should be developed, trained and tested. The thesis required excellent python coding skills and working independently with neural networks. | ||
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| + | For more detailed information please contact Tobias Selz or George Craig. | ||
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| + | ==== Spatial variability budget of convective-scale precipitation forecasts ==== | ||
| + | Short-range precipitation forecasts often exhibit overconfidence in ensemble forecasts, leading to discrepancies with observed precipitation extending beyond the predicted area. Incorporating model uncertainty schemes may mitigate this issue by introducing greater spatial variability. | ||
| + | Spatial variability can be assessed using spatial skill scores such as the Fractions Skill Score (FSS) and Structure-Amplitude-Location (SAL) score, applied to different combinations of ensemble members. Since these scores involve squared differences between ensemble members, they can be reformulated in a form of variance. Analyzing how this variance is modulated by including model uncertainty schemes can provide insights for improving ensemble forecasts. | ||
| + | This study examines how the spatial variability of precipitation forecasts is modified by a physically based stochastic perturbation scheme, with a particular focus on hourly precipitation during the summer over Germany. The variance budget framework proposed by Matsunobu et al. (2025) will be extended to incorporate FSS- and SAL-based spread metrics. | ||
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| + | For more detailed information please contact Takumi Matsunobu or Christian Keil. | ||
| + | |||
| + | ==== Analyzing the “butterfly effect” in operational forecast data ==== | ||
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| + | For more detailed information please contact Tobias Selz or George Craig. | ||
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| + | ====Dynamics-transport coupling in the stratosphere==== | ||
| + | The stratospheric circulation is projected to change in response to a warming climate. A changing circulation can impact the transport of trace gases (ozone, water vapor and ozone destroying substances) in the middle and upper atmosphere. The project will focus on developing a better understanding of the dynamics-transport coupling in the stratosphere. | ||
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| + | Changes in the large-scale stratospheric dynamics and transport of trace gases will be investigated using a combination of theory, observations and numerical modeling. Key topics of investigation will be the subseasonal to climatological impact of stratospheric dynamical processes on the global distribution of tracers in the stratosphere. A hierarchy of climate models will be used. | ||
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| + | For more detailed information please contact Thomas Birner. | ||
| ====Large-scale impacts of convective clouds==== | ====Large-scale impacts of convective clouds==== | ||
| Zeile 44: | Zeile 72: | ||
| Contact: Leonhard Scheck | Contact: Leonhard Scheck | ||
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| + | /** | ||
| ====Tropospheric moisture variability and the development of tropical convection==== | ====Tropospheric moisture variability and the development of tropical convection==== | ||
| Zeile 51: | Zeile 81: | ||
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| Contact: Julien Savre | Contact: Julien Savre | ||
| + | **/ | ||
| ==== Applicability of lossy compression methods to meteorological applications==== | ==== Applicability of lossy compression methods to meteorological applications==== | ||
| Zeile 67: | Zeile 97: | ||
| Contact: Robert Redl | Contact: Robert Redl | ||
| - | \\ | ||
| - | Further themes are possible, please talk to Prof. G. Craig, Prof. T. Birner, Priv.Doz T. Janjic or Dr. C. Keil. | + | |
| + | ============================================== | ||
| + | |||
| + | Further themes are possible, please talk to Prof. G. Craig, Prof. T. Birner, or Dr. C. Keil. | ||
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| Zeile 454: | Zeile 486: | ||
| Tel: 08153/282553 | Tel: 08153/282553 | ||
| simon.unterstrasser@dlr.de | simon.unterstrasser@dlr.de | ||
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| + | ==== Investigation of tropical predictability in simulations using a stochastic convection scheme==== | ||
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| + | In a series of model experiments (ICON) a stochastic convection scheme has been used to investigate the limits of atmospheric predictability that originate from the fast error growth that happens in moist convection and the spreading of this error upscale. However, although global simulations were performed this analysis has been restricted to the midlatitudes. | ||
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| + | The main idea behind this master thesis is to first re-apply the diagnostics that were used before to the tropics in addition with a basic evaluation of model performance and biases. In a second step diagnostics and phenomena that are specific to the tropics (eg. Kelvin waves, MJO) should be considered and their role in the error growth process could be investigated. | ||
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| + | This project requires interest in dynamical meteorology and modeling as well as a substantial amount of data analysis using python. | ||
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| + | Further reading:\\ | ||
| + | Selz, 2019: Estimating the intrinsic limit of predictability using a stochastic convection scheme. (dataset description, midlatitude analysis)\\ | ||
| + | Judth, 2020: Atmospheric Predictability of the Tropics, Middle Latitudes, and Polar Regions Explored through Global Storm-Resolving Simulations (Investigation of tropical predictability with a high-res model) | ||
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| + | Contact: Tobias Selz or George Craig | ||
| **/ | **/ | ||