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 will take place from 8 to 12 January 2018. is composed of 14 lectures of 1:30 and 1 numerical training session (3 hours). Jupyter notebooks will be made available for personal exploration.

It will be given by Eric Blayo (UGA, LJK), Emmanuel Cosme (UGA, IGE), and Arthur Vidard (INRIA, LJK).

Some lecture notes and old scilab scripts are available for download at this location. Some jupyter notebooks illustrating the Kalman filter and related topics can be found here.

**NEW:** The wednesday morning classes will be given by Maëlle Nodet. And there are new documents available here: Maëlle lecture notes, Eric and Emmanuel's slides.

**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