2019-2020 / OCEA0097-1

Data assimilation and inverse methods


30h Th

Number of credits

 Master in oceanography (120 ECTS)3 crédits 


Alexander Barth

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

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)


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