students
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students [2020/08/14 21:40] – adding links chylik | students [2024/01/26 19:14] – moving some projects to past projects chylik | ||
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- | ====== Student projects ====== | + | ======= Student projects |
- | === Studying cloud and total water statistics in high resolution models simulations === | ||
+ | ==== A year in LES (AYIL): Large eddy simulation of the complete MOSAiC drift ==== | ||
- | As part as our work developing pdf cloud parametrizations we use large amounts | + | From September 2019 to October 2020 the Polarstern Research Vessel drifted with the sea ice through the central Arctic, as part of the [[https:// |
- | {{ :cinema_barb.png?400 | Cumulus clouds as simulated | + | {{::mosaic_1.png?nolink& |
+ | {{: | ||
+ | {{:: | ||
- | === 3D cloud structure in high resolution data === | ||
- | The shape and arrangement of clouds have a large effect on how clouds interact with radiation and therefore effect climate sensitivity. | ||
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- | === Detecting plumes and downdrafts in 3D simulations === | ||
- | {{ : | ||
- | Convection (i.e. buoyancy driven vertical movements) occurs on scales commonly too small for global models to capture. The vertical transport of heat, moisture, and momentum via convection plays a critical role in the boundary layer, yet is difficult to determine how much of the boundary layer transport is caused by unstructured turbulence versus organized and structured convection. As a thesis a student would modify already existing code to detect such structures, evaluate the importance and sensitivity of various assumptions, | ||
+ | ==== Modeling convective cloud population dynamics and organization as observed during EUREC4A ==== | ||
- | === Classic cloud fraction parametrizations vs machine learning === | ||
- | We have millions of samples from high-resolution simulations over Germany comparing cloud fraction to other meteorological variables. These samples are a side product of the paper Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations (https:// | ||
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- | === Explaining observed cloud asymmetry | ||
- | {{ : | ||
- | One of the results of our recent paper was that the asymmetry observed in clouds by looking at them from below might mostly be an artifact of the observational setup (Griewank, Heus, Lareau, Neggers (2020): Size-dependence in chord characteristics from simulated and observed continental shallow cumulus, Atmospheric Chemistry and Physics, https:// | ||
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- | === Modeling convective cloud population dynamics and organization as observed during EUREC4A === | ||
The spatial organization of moist atmospheric convection has been studied intensively in recent years. This effort is motivated by the recent insight that such organization plays an important role in feedbacks between clouds and Earth' | The spatial organization of moist atmospheric convection has been studied intensively in recent years. This effort is motivated by the recent insight that such organization plays an important role in feedbacks between clouds and Earth' | ||
- Perform experiments with the BiOMi population model for conditions observed during the EUREC4A campaign | - Perform experiments with the BiOMi population model for conditions observed during the EUREC4A campaign | ||
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{{: | {{: | ||
- | === The effect of humidity inversion in the Arctic | ||
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- | Humidity inversion describes the situation where the specific humidity in some layer of air is increasing with altitude instead of decreasing. Humidity inversion atop a cloudy boundary layer are relatively common [[arcticclouds|in the Arctic]]. The entrainment at the top of the top of the cloud layer then often leads to transport of humidity from the free atmosphere into the boundary layer. This can then effect both the precipitation from the clouds and the thermodynamic properties of the clouds. | ||
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- | Does the presence of humidity inversion lead to warming of cooling of the cloud layer? And does it lead to increase the turbulence in the boundary layer? These questions will be investigated with both conceptual mixed-layer model and large eddy simulation. If you are interested in this topic please contact [[https:// | ||
+ | ==== Stochastic and scale-aware parameterization of atmospheric convection using EDMF ==== | ||
- | === Stochastic and scale-aware parameterization of atmospheric convection using EDMF === | ||
Atmospheric convection and associated cloud processes are not resolved by most numerical models used for weather forecasting and climate prediction. As a result, their impact on the larger-scale flow and climate has to be represented through parameterization. Recently the ever increasing power and efficiency of supercomputers have for the first time allowed | Atmospheric convection and associated cloud processes are not resolved by most numerical models used for weather forecasting and climate prediction. As a result, their impact on the larger-scale flow and climate has to be represented through parameterization. Recently the ever increasing power and efficiency of supercomputers have for the first time allowed | ||
- Shallow convection at the [[https:// | - Shallow convection at the [[https:// | ||
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{{:: | {{:: | ||
- | === Time variability vs. spatial variability - Testing Taylor' | ||
- | Measurements are often taken at one point and then interpreted as a spatial variability by taking the wind into account. But how good is this approach? Can we find a strong correlation between the temporal and spatial variability? | ||
- | === How does changing ice microphysics affect cloud formation? === | + | ===== Other possible projects ===== |
+ | |||
+ | (A full description will follow shortly) | ||
+ | |||
+ | * Confronting fine-scale models with ACLOUD field campaign data on Arctic clouds (Vera Schemann, Jan Chylík, Roel Neggers) | ||
+ | * Comparing various cloud sampling approaches (Jan Chylík) | ||
+ | * Analyze resolution dependency of eddy representation (Jan Chylík) | ||
+ | |||
+ | |||
+ | ===== Past student projects ===== | ||
+ | |||
+ | === Studying cloud and total water statistics in high resolution models simulations === | ||
+ | |||
+ | |||
+ | As part as our work developing pdf cloud parametrizations we use large amounts of high resolution simulations to test assumptions and evaluate performance ([[cloudscheme|PDF cloud scheme]]). This work offers Students the chance to work with state of the art data while being able to choose from a wide range of themes to fit their interests. For example the Student could adapt our previous analyses to work on new small domain simulations conducted over Spitsbergen, | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | |||
+ | === 3D cloud structure in high resolution data === | ||
+ | The shape and arrangement of clouds have a large effect on how clouds interact with radiation and therefore effect climate sensitivity. | ||
+ | |||
+ | |||
+ | === Detecting plumes and downdrafts in 3D simulations === | ||
+ | {{ : | ||
+ | Convection (i.e. buoyancy driven vertical movements) occurs on scales commonly too small for global models to capture. The vertical transport of heat, moisture, and momentum via convection plays a critical role in the boundary layer, yet is difficult to determine how much of the boundary layer transport is caused by unstructured turbulence versus organized and structured convection. As a thesis a student would modify already existing code to detect such structures, evaluate the importance and sensitivity of various assumptions, | ||
+ | |||
+ | |||
+ | === Classic cloud fraction parametrizations vs machine learning === | ||
+ | We have millions of samples from high-resolution simulations over Germany comparing cloud fraction to other meteorological variables. These samples are a side product of the paper Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations (https:// | ||
+ | |||
+ | |||
+ | |||
+ | ==== How does changing ice microphysics affect cloud formation? | ||
Clouds at freezing temperatures can contain various ice particles. There are a few different type of ice crystals that can grow in supersaturated conditions. The differences in shape between | Clouds at freezing temperatures can contain various ice particles. There are a few different type of ice crystals that can grow in supersaturated conditions. The differences in shape between | ||
- | That said, an important question is whether the changes in optical properties and precipitation also lead to differences in the vertical structure of a cloud. Based on the observational data from recent campaigns in the Arctic, the problem will be investigated on large-eddy simulations in [[http://gop.meteo.uni-koeln.de/ | + | That said, an important question is whether the changes in optical properties and precipitation also lead to differences in the vertical structure of a cloud. Based on the observational data from recent campaigns in the Arctic, the problem will be investigated on large-eddy simulations in [[http://atmos.meteo.uni-koeln.de/ |
If you are interested in this topic please contact [[https:// | If you are interested in this topic please contact [[https:// | ||
- | |||
{{ : | {{ : | ||
- | === Other possible projects === | ||
- | (A full description will follow shortly) | ||
- | * Confronting fine-scale models with ACLOUD field campaign data on Arctic | + | ==== The role of humidity inversions in the Arctic |
- | * Comparing various cloud sampling approaches (Jan Chylík) | + | |
- | * Analyze resolution dependency of eddy representation (Jan Chylík) | + | |
+ | Humidity inversion describes the situation where the specific humidity in some layer of air is increasing with altitude instead of decreasing. Humidity inversion atop a cloudy boundary layer are relatively common [[arcticclouds|in the Arctic]]. The entrainment at the top of the top of the cloud layer then often leads to transport of humidity from the free atmosphere into the boundary layer. This can then effect both the precipitation from the clouds and the thermodynamic properties of the clouds. | ||
+ | |||
+ | Does the presence of humidity inversion lead to warming of cooling of the cloud layer? And does it lead to increase the turbulence in the boundary layer? These questions will be investigated with both conceptual mixed-layer model and large eddy simulation. If you are interested in this topic please contact [[https:// | ||
+ | |||
+ | |||
+ | |||
+ | === Explaining observed cloud asymmetry | ||
+ | {{ : | ||
+ | One of the results of our recent paper was that the asymmetry observed in clouds by looking at them from below might mostly be an artifact of the observational setup (Griewank, Heus, Lareau, Neggers (2020): Size-dependence in chord characteristics from simulated and observed continental shallow cumulus, Atmospheric Chemistry and Physics, https:// | ||
students.txt · Last modified: 2024/05/21 14:13 by neggers