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From 6 June 2017 to 9 June 2017

Data assimilation

nasri |  

Deadline for registration: 15 days before the starting date of each training
Duration : 4 days / (28 hours)



Data assimilation has become an important component of modelling for a growing number of applications in the geosciences and in engineering. This training course will provide an overview of the theory and practical methods of data assimilation. First the basic concepts from statistical estimation theory and nonlinear optimization will be given. The classical variational and Kalman filtering approaches to data assimilation will then be described. The lectures will also cover more specialized topics including covariance modelling and estimation, advanced minimization algorithms, preconditioning, and hybrid ensemble-variational methods. The lectures on the theory will be complemented by both practical exercises and presentations on specific applications at CERFACS in the geosciences (oceanography, atmospheric chemistry and hydrology/hydraulics).

Target participants

This training session is for engineers, physicists, computer scientists and numerical analysts wishing to learn the fundamentals of data assimilation and the numerical methods to develop data assimilation applications.


Good knowledge of linear algebra and numerical analysis.

Scientific contact : Selime GUROL & Anthony WEAVER


  • Trainees/PhDs/PostDocs : 200 €
  • CERFACS shareholders/CNRS/INRIA : 600 €
  • Public : 1200 €


(Every day from 9h to 17h30)

Day 1:
Basic concepts of data assimilation with examples
Introduction to estimation theory
Introduction to nonlinear-least squares
Exercises with Matlab

Day 2:
Variational assimilation
Introduction to covariance modelling
Applications from Earth sciences
Exercises with Matlab

Day 3:

The Kalman filter and its variants
Advanced covariance modelling and estimation
Applications from Earth sciences
Exercises with Matlab

Day 4:
The Kalman filter and its variants
Hybrid variational-ensemble methods
Applications from Earth sciences
Exercises with Matlab


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