Duration
30h Th
Number of credits
| Master in oceanography (120 ECTS) | 3 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
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
Planned learning activities and teaching methods
A serie of lectures with exercises
Mode of delivery (face-to-face ; distance-learning)
face-to-face
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
Assessment methods and criteria
Written report on application of a data assimilation method on a simple model
Work placement(s)
Organizational remarks
Contacts
a.barth@ulg.ac.be