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--- students [2024/03/16 15:06] – [Other possible projects] removing no longer active projects chylik
+++ students [2024/05/21 12:13] (current) – [A year in LES (AYIL): Large eddy simulation of the complete MOSAiC drift] neggers
@@ Line 4: / Line 4: @@
 ==== 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]]
+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]]
 {{::mosaic_1.png?nolink&200|MOSAiC logo}}
@@ Line 22: / Line 22: @@
-==== 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:
+==== The spatial organization of convective thermals during transitions from shallow to deep convection over land ====
-  - Shallow convection at the [[https://www.arm.gov/capabilities/observatories/sgp|ARM SGP]] site in the United States,
-  - Continental deep precipitating convection in Brazil as observed during the [[https://www.arm.gov/research/campaigns/amf2014goamazon|Go-Amazon]] field campaign,
-  - Marine subtropical convection as observed during the recent [[http://eurec4a.eu/|EUREC4A]] field campaign.
-To further discuss this topic please contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/neggers|Roel Neggers]].
-{{::49946157427_41a6ade8ab_k.jpg?direct&300|Shallow convective clouds over the ARM SGP site}}
+Due to the continuing increase in resolution of atmospheric circulation models, convective processes that used to be fully parameterized are gradually becoming partially resolved, a situation referred to as the convective "grey zone". This necessitates the development of parameterizations that are scale-aware and scale-adaptive, an area of intense current research.  A recently pursued way of achieving this goal is to formulate population-dynamical models that adopt the convective thermal as the smallest unit or building block of convection. To help develop and improve such next-generation convective parameterizations, in this project we analyze large-eddy simulations of diurnal cycles of shallow-to-deep convective transitions observed at the ARM Southern Great Planes site. Based on previous algorithms from the literature, a tracking algorithm has been developed to gain insight into the behavior of populations of such thermals. Thermal characteristics including life-time, trajectory and geometric information, and kinematic and thermodynamic properties are investigated. Of particular interest is the spatial distribution of thermals, to gain insight into what drives their clustering and organization. Various metrics expressing the degree and type of spatial organization among thermals are explored to this purpose.
-{{:43527220314_aa58f45df1_k.jpg?direct&200|Convective clouds during the GoAmazon campaign}}
-{{::bjorn_dsc_6243_narval1_web.jpg?direct&300|Cumulus clouds in the EUREC4A area}}
-===== Other possible projects =====
-(A full description will follow shortly)
-  * Confronting fine-scale models with ACLOUD and AFLUX field campaign data on Arctic clouds (Vera Schemann, Jan Chylík, Roel Neggers)
-===== 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, Juelich, or the Zugspitze. Or the Student could expand on our previous analyses by implementing already published parametrizations, applying machine learning approaches, or developing and testing their own ideas. For more information get in contact with [[https://geomet.uni-koeln.de/institut/beschaeftigte/griewank/|Philipp Griewank]] or [[https://geomet.uni-koeln.de/institut/beschaeftigte/schemann|Vera Schemann]].
-{{ :cinema_barb.png?400 | Cumulus clouds as simulated by ICON-LEM over Barbados}}
-=== 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.  Recently run super-large domain simulations offer a unique glimpse into 3D cloud structures which are impossible to measure. As a thesis a Student could evaluate various aspects of the cloud structure in recently conducted super-large simulations over the tropical Atlantic and Germany. Possible topics are the relation of cloud fraction to total cloud cover or how cloud structure changes when the horizontal resolution changes from 600 to 150 meters. To discuss this topic feel free to contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/griewank|Philipp Griewank]] or [[https://geomet.uni-koeln.de/institut/beschaeftigte/schemann|Vera Schemann]].
-=== Detecting plumes and downdrafts in 3D simulations ===
- {{ :poster_inscape.png?110| 2D slices of liquid water and vertical velocity through a 3D convective plume}}
-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, and visualize their work in 3D. To discuss this topic feel free to contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/griewank|Philipp Griewank]].
-=== 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]]
-=== Explaining observed cloud asymmetry  ===
-{{ :beard_panel_small-1.png?400 |}}
-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://doi.org/10.5194/acp-2020-338). The interested student could use the same data used for the publication to quantify the asymmetry and relate it to various atmospheric variables, such as wind shear, to understand how much of the observed asymmetry is due to the cloud evolving as it is sampled. Depending on the level and technical skills of the student a Bachelor thesis version is feasible that would only include model data. For a Master thesis I would expect the student to either include observations into the analysis, or to complete a more in depth model analysis. Requirement: Basic/Intermediate python. To discuss this topic feel free to contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/griewank|Philipp Griewank]].
+If you are interested in this topic please contact [[https://geomet.uni-koeln.de/institut/beschaeftigte/neggers|Roel Neggers]].
+{{ ::new_snap_t040530_bird3.png?direct&600 | Ray tracing rendering of deep convective clouds in an LES simulation of of 30 August 2016 at the ARM SGP site during the HI-SCALE campaign}}