Numerical Modelling of the Atmosphere II
Syllabus, Master's level, 1ME416
- Code
- 1ME416
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Earth Science A1F, Physics A1F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 7 March 2019
- Responsible department
- Department of Earth Sciences
Entry requirements
Numerical Modelling of the Atmosphere, 10 credits
Learning outcomes
On completion fo the course, the student shall be able to:
- describe dataassimilation methods used in forecast models
- summarize the fundamentals of ensemble modelling
- apply methods to evaluate ensemble forecasts
- explain methods for seasonal forecasts and to critically analyse the skill of these forecasts
Content
Empirical methods for data assimilation: successive correction, nudging, multivariate statistical dataassimilations methods: optimal interpolation, 3D-var, 4D-var, Kalman filtering
Fundamental theory for ensemble modelling: early methods and operational, error growth in forecasts and limit of predictability. Seasonal forecasts.
Methods to evaluate probability forecasts: spread and bias, Brier score, Brier skill score, spread-reliability, attribute/reliaility diagram
Instruction
Lectures, computer labs and seminars.
Assessment
Written exam (3 hp). Lab with hand in assignment (1 hp). Seminars (1 hp).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.