Study Programmes 2016-2017
OCEA0097-1  
Data assimilation and inverse methods
Duration :
30h Th
Number of credits :
Master in oceanography (120 ECTS)3
Lecturer :
Alexander Barth
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