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--- students [2022/09/05 10:43] – impving headlines chylik
+++ students [2024/03/16 16:06] (current) – [Other possible projects] removing no longer active projects chylik
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 ======= Student projects =======
+==== A year in LES (AYIL): Large eddy simulation of the complete MOSAiC drift  ====
+From September 2019 to October 2020 the Polarstern Research Vessel drifted with the sea ice through the central Arctic, as part of the [[https://mosaic-expedition.org/|MOSAiC]] field campaign. Based on the observational data collected on the ship and by the instrumentation on the sea ice (MetCity), we performed daily Large-Eddy Simulations (LES) of the atmospheric domain surrounding the ship. The spatial and temporal resolutions were high enough to resolve small scale turbulence and clouds. The computational burden of this effort has been considerable, and performing these kind of simulations for a full year in the central Arctic has never been achieved before. With the production runs now completed, the model output can be evaluated against independent measurements. In addition, the simulated turbulence and clouds can be used to gain deeper insight into the ongoing rapid warming of the Arctic. This process is often referred to as Arctic Amplification, which is the central focus of the ongoing [[https://www.ac3-tr.de/|AC3]] project. If you are interested in working with these data, for example to evaluate the LES against measurements or to use the high resolution model output as a virtual laboratory, please contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/neggers|Roel Neggers]] or [[https://geomet.uni-koeln.de/institut/beschaeftigte/chylik|Jan Chylík]]
+{{::mosaic_1.png?nolink&200|MOSAiC logo}}
+{{:20170525_ps106017_sschoen-720px-4132655360.jpeg?direct&200|The Polarstern RV during MOSAiC, surrounded by instrumentation on the sea ice (MetCity)}}
+{{::mosaic_routine_20200419_crop_small.png?direct&240|Ray tracing rendering of simulated low level mixed-phase clouds during MOSAiC}}
 ==== 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's climate. In addition, the spatial structure of convective fields should be taken into account when preparing global circulation models for the "grey zone" of resolutions at which convection becomes partially resolved. What exactly causes the observed spatial patterns in convective cloud fields, and how to represent those in weather and climate models, is not yet fully understood. At InScAPE we recently developed a conceptual model for populations of convective objects that live on a two-dimensional grid. This model, which is named [[https://doi.org/10.1002/essoar.10503592.1|BiOMi]] (Binomials on Microgrids), uses a Bernoulli process to represent object births, movement and life-cycles. This significantly enhances the computational efficiency. In addition, rules of interaction are prescribed that allow objects to cluster and form long-lived convective structures. These rules reflect known physics of cumulus clouds, introduce convective memory in the system, and make convective structures act like cellular automata. The work in this project includes the following activities.
   - Perform experiments with the BiOMi population model for conditions observed during the EUREC4A campaign
@@ Line 11: / Line 21: @@
 {{:pre_eurec4a_20190121_modis_aqua_corr.refl_500m_snapshot_small2.png?400|MODIS image of cloud populations in the Caribbean on 21 January 2019}}
-==== The effect of humidity inversion in the Arctic   ====
-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.
+==== Stochastic and scale-aware parameterization of atmospheric convection using EDMF ====
-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://geomet.uni-koeln.de/institut/beschaeftigte/chylik|Jan Chylík]]
-==== 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  resolutions in Earth simulations at which convection is partially resolved. This situation is often referred to as the "grey zone problem" of cumulus parameterization. Solving this problem requires a total scientific rethink of the design of convective parameterizations for next-generation weather- and climate models. A potential way forward is the development of Eddy Diffusivity Mass Flux (EDMF) parameterizations that are formulated in terms of [[https://doi.org/10.1002/2015MS000502|size distributions]] of convective objects. An advantage of these schemes is that they are inherently scale-aware, while stochastic behavior reflecting cloud population dynamics can easily be introduced through population statistics. In this project the student will work with an EDMF scheme that is currently being developed by the InScAPE group. More information about EDMF is also provided [[EDMF|here]]. We implemented it as a subgrid scheme in one of our Large-Eddy Simulation ([[models|DALES]]) codes. The work consists of conducting microgrid LES experiments for selected convective cases to test the behavior of the scheme for known conditions. Cases include:
   - Shallow convection at the [[https://www.arm.gov/capabilities/observatories/sgp|ARM SGP]] site in the United States,
@@ Line 29: / Line 34: @@
 {{::bjorn_dsc_6243_narval1_web.jpg?direct&300|Cumulus clouds in the EUREC4A area}}
-==== Time variability vs. spatial variability - Testing Taylor's hypothesis of frozen turbulence in LES ====
-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? And does it depend on the synoptic situation? Or the environment? In this project the hypothesis will be tested in the model world by comparing temporal and spatial variability at different sides (e.g. JOYCE and Barbados) and under varying conditions. We will only look at model output - for spatial and temporal variability. Can we confirm the hypothesis in this clean and consistent model world? To discuss the topic or get more information please contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/schemann|Vera Schemann]]
-==== 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  different ice crystals lead to differences in fall velocities, optical properties, but also in their ability to rime liquid droplets or aggregate with other ice crystals.
-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/inscape/doku.php?id=models|DALES]].
-If you are interested in this topic please contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/chylik|Jan Chylík]]
-{{ :difprofiles_tke_b.png?500 | The differences in TKE in model runs with different concentration of ice particles. }}
 ===== Other possible projects =====
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 (A full description will follow shortly)
-  * Confronting fine-scale models with ACLOUD field campaign data on Arctic clouds (Vera Schemann, Jan Chylík, Roel Neggers)
+  * Confronting fine-scale models with ACLOUD and AFLUX 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)
-===== No longer available =====
+===== Past student projects =====
 === Studying cloud and total water statistics in high resolution models simulations ===
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 === 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://doi.org/10.1029/2018MS001421), and allow a student to very easily and quickly apply various machine learning techniques to derive empirical cloud fraction parameterizations. The first technical steps are already laid, the scope and complexity of the project can be easily adjusted to suit either a Bachelor or Masters thesis. Requirement: Basic Python. To discuss this topic feel free to contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/griewank|Philipp Griewank]].
+==== 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  different ice crystals lead to differences in fall velocities, optical properties, but also in their ability to rime liquid droplets or aggregate with other ice crystals.
+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/inscape/doku.php?id=models|DALES]].
+If you are interested in this topic please contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/chylik|Jan Chylík]]
+{{ :difprofiles_tke_b.png?500 | The differences in TKE in model runs with different concentration of ice particles. }}
+==== The role of humidity inversions in the Arctic climate system  ====
+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://geomet.uni-koeln.de/institut/beschaeftigte/chylik|Jan Chylík]]