University Assistant (prae doc): Data-driven methods to reduce model errors in numerical weather prediction

Supervision: Stefano Serafin and Martin Weissmann (both Department of Meteorology and Geophysics)  


Project outline:

Despite decades of progress, numerical weather prediction remains plagued by significant model errors, which cause biased simulations, inaccurate estimates of forecast uncertainty and suboptimal assimilation of observations. Model errors arise primarily from parameterizations, i.e., simplified representations of small-scale unresolved processes. The traditional approach to parameterization design balances theoretical guidance, empirical evidence and tuning based on trial and error. Today, advanced data assimilation methods and ever-increasing computing resources make theoretically-grounded and objective parameter estimation (PE) practically feasible as an alternative approach.

The specific topic of this doctoral project is PE in an ensemble data assimilation framework. Within the data assimilation cycle, observations of the atmosphere are used to adjust the values of model state variables and uncertain model parameters. The adjustment relies on ensemble estimates of the covariance between model equivalents of the observations and state variables or parameters. PE is well established in the data assimilation community, but seldom employed with the purpose of improving the model formulation. This innovative project aims at filling this gap.

The doctoral candidate will work on PE in idealized Observing System Simulation Experiments, where the assimilated observations are synthetic and derived from a high-resolution numerical simulation (nature run). Since the methodology is general, the application domain can be tailored to the scientific background and interests of the candidate. The focus can be on parameterizations of the boundary layer over mountains or of gravity wave drag. The primary working tools are the WRF weather model and the DART data assimilation testbed.


Job Description:               

The position aims to deepen and extend the professional and scientific education targeting a doctoral degree. Tasks and responsibilities include:

  • Independent research and development of an academic profile targeting a doctoral degree. We expect the successful candidate to sign a doctoral thesis agreement within one year.
  • Participation in research, teaching and administration of the research group/department
  • Participation in examination activities
  • Participation in evaluation activities and in quality assurance
  • Supervision of students
  • Participation in teaching and independent teaching of courses as defined by the collective agreement

The candidate who is selected for this position joins VISESS as a PhD student member.



  • Meteorology / Environmental Engineering / Physics / Mathematics / Environmental Sciences / Earth Sciences: Master’s Degree or equivalent
  • Excellent command of written and spoken English
  • Essential qualifications: Basic experience with numerical weather prediction codes, familiarity with Linux/UNIX environments, strong motivation, ability to work in a team.
  • Assets: Knowledge of mesoscale meteorology and/or ensemble-based data assimilation, demonstrated proficiency in Python and Fortran programming, knowledge of software version control systems, familiarity with high-performance computing.


We offer:

  • Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) with relevant work experience determining the assignment to a particular salary grade
  • An active and inspiring research environment, a vibrant PhD community and many ways to connect with peers from home and abroad on a social and professional level
  • A broad range of interdisciplinary training possibilities and school activities, such as leadership skill trainings, workshops, seminars, mobility and summer schools


Expected starting date: (ideally) spring 2023


Duration of employment: 3 years with the possibility of extensions up to 4 years 
(The employment relationship is initially limited to 1.5 years and automatically extended to a total of 3 years unless the employer submits a declaration of non-renewal after a maximum of 12 months. In exceptional, well argumented cases and on the condition that budget is available, there is the possibility of extensions up to 4 years)


Extent of employment: 30 hours/week


Application: Interested students can apply until the deadline of 30 November 2022 via:      


Further information: For further information, please contact


The University pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity ( The University lays special emphasis on increasing the number of women in senior and in academic positions. Given equal qualifications, preference will be given to female applicants.