Principles of Data Assimilation

A unique combination of both theoretical and practical aspects of data assimilation with examples and exercises for students.

Seon Ki Park (Author), Milija Zupanski (Author)

9781108831765, Cambridge University Press

Hardback, published 29 September 2022

400 pages
25.1 x 17.8 x 2.4 cm, 0.885 kg

Data assimilation is theoretically founded on probability, statistics, control theory, information theory, linear algebra, and functional analysis. At the same time, data assimilation is a very practical subject, given its goal of estimating the posterior probability density function in realistic high-dimensional applications. This puts data assimilation at the intersection between the contrasting requirements of theory and practice. Based on over twenty years of teaching courses in data assimilation, Principles of Data Assimilation introduces a unique perspective that is firmly based on mathematical theories, but also acknowledges practical limitations of the theory. With the inclusion of numerous examples and practical case studies throughout, this new perspective will help students and researchers to competently interpret data assimilation results and to identify critical challenges of developing data assimilation algorithms. The benefit of information theory also introduces new pathways for further development, understanding, and improvement of data assimilation methods.

Part I. General Background: 1. Data assimilation: general background
2. Probability and Bayesian approach
3. Filters and smoothers
Part I.: Practical Tools: 4. Tangent linear and adjoint model
5. Automatic differentiation
6. Numerical minimization process
Part III. Methods and Issues: 7. Variational data assimilation
8. Ensemble and hybrid data assimilation
9. Coupled data assimilation
10. Dynamics and data assimilation
Part IV. Applications: 11. Sensitivity analysis and adaptive observation
12. Satellite data assimilation
Index.

Subject Areas: Meteorology & climatology [RBP]