Duration
30h Th
Number of credits
| Master in oceanography (120 ECTS) | 3 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
* Purpose of data assimilation and inverse methods
* Expressing uncertainty
* Origin of model and observation errors
* Reminder of static concepts: random variable, expectation, error covariance
* Sequential assimilation methods (nudging, successive corrections, optimal interpolation, 3D-Var, Kalman filter, Kalman smoother)
* Non-Sequential assimilation (4D-Var, representer method)
Learning outcomes of the learning unit
* understand various data assimilation methods
* able to conceptually define state vector, observation operator, observation vector and error covariances for a given problem
Prerequisite knowledge and skills
Prerequisites: http://progcours.ulg.ac.be/cocoon/cours/OCEA0036-1.html
Programming skills in Julia, Matlab/Octave, Python or similar programming languages
Planned learning activities and teaching methods
A serie of lectures with exercises
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Additional information:
face-to-face if possible
Recommended or required readings
Evensen, G. (2009) Data Assimilation, The Ensemble Kalman Filter, Springer http://dx.doi.org/10.1007/978-3-642-03711-5
Written work / report
Additional information:
Written report on application of a data assimilation method on a simple model
If a Large Language Model (LLM, like ChatGPT, LLaMa, GitHub Copilot...) is used to generate or rewrite some part of the report or some computer code, these parts should be clearly marked in the report. You should consult with the lecturer beforehand if the use of LLMs is appropriate.
This provision does not apply to the use of translation tools and spell checkers.
In any case, it is the student's responsibility to ensure that the content of the report is accurate and is not plagiarism (including unintended plagiarism).
Work placement(s)
Organisational remarks and main changes to the course
Contacts
a.barth@ulg.ac.be