Clear Air Turbulence (CAT) is an atmospheric phenomenon that is socio-economically relevant (aircraft safety), important for understanding atmospheric composition (cross-tropopause tracer exchange, STE), and in particular it is a complex and challenging fundamental process in atmospheric dynamics.
This project is based on a unique dataset of worldwide and multi-year recordings of Eddy Dissipation Rate (EDR) on commercial aircrafts.
It allows for an observation-driven project on CAT events, leading to novel insight about the meteorological processes that are triggering CAT, the impact of CAT on the larger-scale flow and STE, and eventually the predictability of CAT.
In this PhD project, CAT events are first categorized in terms of their basic characteristics (altitude, intensity) and their linkage to distinct weather systems (e.
g., tropopause folds, jet streams), as determined in operational ECWMF forecasts and analyses. This will lead to a CAT typology based on observations, and a characterization of the considered weather systems in terms of how prone they are to CAT.
In a second part, the impact of CAT on the tropopause temporal evolution and the relevance for STE are determined. Several observed CAT events near the tropopause are identified and quantified in their EDR intensity.
Dedicated IFS-ECMWF simulations are performed that allow for a detailed analysis of the processes modifying potential vorticity near the tropopause, the larger-scale flow in the vicinity of the considered CAT events, and a quantification of how much the CAT events contribute to STE.
Finally, CAT predictability as a function of forecast lead time is assessed for different categories in the CAT typology.
It will be quantified how many hours in advance a severe CAT event can in principle be predicted, and whether this forecast horizon depends on the type of CAT.
We are looking for a highly motivated and curious PhD candidate with a MSc degree in meteorology or physics. A solid background in atmospheric dynamics and modelling is required, ideally complemented with experience in analysing and combining observational and reanalysis data.
You should have a passion in conducting complex data analyses, creativity in studying processes and compiling climatologies of CAT, an interest in the practical utility of own developments, and good programming skills (ideally in Python / Fortran).
Very good spoken and written English language skills are required for the presentation of results at international conferences, and for the reading and writing of scientific papers.