Data Assimilation

The Ensemble Kalman Filter

  • GeirĀ Evensen

Table of contents

  1. Front Matter
    Pages i-xix
  2. Geir Evensen
    Pages 1-4
  3. Geir Evensen
    Pages 5-12
  4. Geir Evensen
    Pages 13-25
  5. Geir Evensen
    Pages 27-45
  6. Geir Evensen
    Pages 47-69
  7. Geir Evensen
    Pages 71-93
  8. Geir Evensen
    Pages 95-101
  9. Geir Evensen
    Pages 103-117
  10. Geir Evensen
    Pages 119-137
  11. Geir Evensen
    Pages 139-155
  12. Geir Evensen
    Pages 157-176
  13. Geir Evensen
    Pages 177-196
  14. Geir Evensen
    Pages 197-209
  15. Geir Evensen
    Pages 211-236
  16. Geir Evensen
    Pages 255-261
  17. Geir Evensen
    Pages 263-272
  18. Back Matter
    Pages 1-33

About this book


Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples.

It presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page.

The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time.

The 2nd edition includes a partial rewrite of Chapters 13 an 14, and the Appendix.  In addition, there is a completely new Chapter on "Spurious correlations, localization and inflation", and an updated and improved sampling discussion in Chap 11.


Data assimilation Ensemble Kalman Filter Ensemble Kalman Smoother Measure bayesian statistics inverse methods parameter estimation

Authors and affiliations

  • GeirĀ Evensen
    • 1
  1. 1.Norsk HydroBergenNorway

Bibliographic information