Waves to Weather
print


Breadcrumb Navigation


Content

PhD position (B3b)

Sources of uncertainty for convective-scale predictability

This PhD position contributes to project B3 "Sources of uncertainty for convective-scale predictability".

Primary supervisor: Dr. Christian Barthlott
Other project leader: Dr. Christian Keil - Faculty advisor: Prof. Dr. Corinna Hoose

The Cloud Physics group at the Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), offers a PhD position within Phase 2 of the collaborative research center "Waves to Weather" (W2W). The Karlsruhe Institute of Technology (KIT) is a distinguished research university that combines three core tasks – research, education and innovation – into a single mission. With more than 9,000 employees and 25,100 students, it is one of
the largest institutions of research and higher education in natural sciences and engineering in Europe.

Forecasting convective precipitation remains one of the key challenges in weather prediction. The predictability of convection depends on many factors including uncertainties in the synoptic-scale flow, inaccuracies in the state and description of the atmosphere and the underlying land surface, and approximations in the representation of key physical processes in weather models. In project B3 of W2W, we will identify sources of uncertainty for convective-scale predictability by investigating the mutual link between land surface and microphysical uncertainties, to better understand the mechanisms for convection initiation, cloud formation and subsequent precipitation. Phase 1 addressed the different uncertainties in an univariate approach by perturbing these components independently in perturbed-parameter experiments to study their relative influence on precipitation in different weather regimes. In Phase 2 we will address the more challenging question of how the collective impact of different uncertainties affects convective precipitation on different spatiotemporal scales for different weather regimes. We will additionally consider uncertainties
in the parameters of the drop size distribution influencing a number of microphysical processes. The numerical simulations will be conducted with the new Icosahedral Nonhydrostatic (ICON) forecasting system. The use of a full ensemble forecasting system including data assimilation further allows to examine how the structural model error of cloud microphysical uncertainties affects predictability and how it compares to the uncertainty introduced by the physically-based stochastic boundary layer perturbation scheme (PSP). Finally, the role of orography in controlling land surface–atmosphere and aerosol–cloud interactions is addressed and the most important scales at which the terrain impacts precipitation predictability shall be determined.

The project is designed as a twin project of KIT and LMU, where another PhD position is open. The PhD student at KIT will investigate the individual and collective effects of land surface and microphysical uncertainties with deterministic model runs to elucidate meaningful combinations to be considered later on in the full ensemble system. This student will also conduct a process-based analysis of individual and collective effects of these uncertainties in the full ensemble system and investigate if and how the terrain modulates precipitation predictability. The PhD student at LMU will examine the relative importance of land surface and microphysical uncertainties in the context of a full ensemble forecasting system (ICON-D2-KENDA) allowing a comprehensive view on predictability of precipitation. There will be a close cooperation between the PhD students at KIT and LMU, namely a selection of common cases, the use of the same forecasting model ICON with identical perturbations, common analysis of the same ICON ensemble dataset, common use of shared ensemble tools for diagnostics, and joint and regular discussions about the model results.

The ideal candidate holds a MSc in Meteorology, Physics, or other Geosciences and has already some experience in numerical modeling and/or skills to evaluate huge data sets. Besides very good knowledge of English, experience with Fortran, Unix, and statistical/visualisation software (e.g. Python, R, ncl, GrADS) would be an advantage.

The position is initially funded for 3 years with possible extension until June 2023 and should start as soon as possible. It is remunerated according to TV-L E13 (75%). Applicants are asked to specify their desired starting date and to give the contact details of two academic referees.

W2W features an innovative program for the development of early career researchers based on self-government. In addition to self-organized activities such as workshops, trainings and a guest program, the successful candidate will have the opportunity, if desired, to pursue an international research visit of at least one week. The consortium conducts an ambitious program to gradually enhance gender equality on all career levels within the academic fields combined in W2W.

KIT is an equal opportunity employer. Women are especially encouraged to apply. Applicants with disabilities will be preferentially considered if equally qualified.

If you are interested, please apply online here!


Service