This course is organized and funded by the Doctoral school for "Earth, Planetary and Environmental Sciences" of Grenoble Alpes University. It is given in English upon request at the beginning of the session.

It takes place from 7 to 11 January 2019 and is composed of 14 lectures of 1:30 and 1 numerical training session (3 hours), or 12 lectures with 2 training sessions (this is under study). The course starts at 1:30pm the first day and end at 12pm the last day.

The course is given by Eric Blayo (UGA, LJK), Emmanuel Cosme (UGA, IGE), and Arthur Vidard (INRIA, LJK).

Some lecture notes are available here. Some jupyter notebooks illustrating the Kalman filter and related topics can be found here, and we are currently fighting against our agendas to provide the equivalent for variational methods.

Part 1: Data assimilation based on estimation theory (10.5h)

1. Introduction to ensemble data assimilation
2. Basic notions in probability and statistics
3. Ingredients of data assimilation
4. Particle filtering
5. Kalman filtering
6. Ensemble Kalman filters

Part 2: Data assimilation based on control theory (10.5h)

1. Introduction to variational data assimilation
2. Variational data assimilation for time-independent problems
3. The adjoint method
4. Variational Data assimilation : Practical aspect
5. Adjoint coding


Necessary background for the course

- Basic notions in probability and statistics (Expectation, variance, covariance matrix)
- Basic notions in linear algebra
- Basic notions in differential calculus