HUOM! OPINTOJAKSOJEN TIETOJEN TÄYTTÄMISTÄ KOORDINOIVAT KOULUTUSSUUNNITTELIJAT HANNA-MARI PEURALA JA TIINA HASARI
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1. Course title
Numeerisen meteorologian laboratoriokurssi
Laborationskurs i numerisk meteorologi
Laboratory Course in Numerical Meteorology
2. Course code
Aikaisemmat leikkaavat opintojaksot 53655 Numeerisen meteorologian laboratoriokurssi, 5 op.
3. Course status: optional
-Which degree programme is responsible for the course?
Master's Programme in Atmospheric Sciences
-Which module does the course belong to?
ATM300 Advanced Studies in Atmospheric Sciences
- Study Track in Aerosol Physics
- Study Track in Meteorology
TCM300 Advanced Studies in Theoretical and Computational Methods
-Is the course available to students from other degree programmes?
4. Course level (first-, second-, third-cycle/EQF levels 6, 7 and 8)
Master’s level, degree programmes in medicine, dentistry and veterinary medicine = secondcycle
degree/EQF level 7
Doctoral level = third-cycle (doctoral) degree/EQF level 8
-Does the course belong to basic, intermediate or advanced studies (cf. Government Decree
on University Degrees)?
5. Recommended time/stage of studies for completion
-The recommended time for completion may be, e.g., after certain relevant courses have
6. Term/teaching period when the course will be offered
The course will be lectured every year in spring term (III-IV periods).
7. Scope of the course in credits
8. Teacher coordinating the course
Prof. Heikki Järvinen
9. Course learning outcomes
At end of the course, students should have
- basic skills in high-performance computing in meteorology
- practical experience on using large-scale atmospheric models
- researcher skills in thermo-dynamic, synoptic, and numeric reasoning about meteorological research questions
- transferable skills in scientific problem solving as a research group member and publicly presenting scientific results in a focused research question
At practical level, students should be able to modify, build, compile, and run on a parallel high-performance computer the OpenIFS atmospheric model, as well as post-process, diagnose, and visualize the model results.
10. Course completion methods
The course is a "learning-by-doing" -type course where all lectures are short and introductory. Students are in the focus and well-supervised.
There is a pre-exercise prior attending the course, an elimination step of technical skills before entering the group work phase (to ensure all group members have sufficient skills to contribute to the group task), and a final public seminar. The grading is pass/fail.
It is recommended to take this course during the last year of master studies since rather comprehensive knowledge of meteorology (or, oceanography, hydrology, glaciology, terrestrial ecosystems - or any major Earth system component) is needed. The course has a different topic each year and can be taken more than once, e.g. in doctoral studies.
12. Recommended optional studies
Good understanding of meteorology (or, oceanography, hydrology, glaciology, terrestrial ecosystems - or any major Earth system component) helps to complete the group work phase.
13. Course content
1. Super-computer architectures and operating systems, 2. Linux command line and bash-scripting, 3. Compiling and running the OpenIFS, 4. Post-processing, diagnosing and plotting the output, 5. Group work on selected research topics in dynamic meteorology, 6. Reporting the findings orally in a public seminar
14. Recommended and required literature
The OpenIFS model is used as a prediction tool on the course. It is recommended to familiarize with the OpenIFS documentation before/during the course at https://software.ecmwf.int/wiki/display/OIFS/OpenIFS+Home
15. Activities and teaching methods in support of learning
There is a weekly meeting in the computer class, and Moodle discussion at other times. All physical meetings are supervised hands-on sessions. In the group work phase, groups are expected to make progress in their assigned research questions.
16. Assessment practices and criteria, grading scale
There is a pre-exercise, elimation test (on Linux command line and bash-scripts), and the final seminar. Grading is pass/fail.
17. Teaching language