Principles of Data Assimilation
- Authors:
- Seon Ki Park, Ewha Womans University, Republic of Korea
- Milija Zupanski, Colorado State University
- Date Published: September 2022
- availability: Available
- format: Hardback
- isbn: 9781108831765
Hardback
-
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.
Read more- Includes exercises and worked examples throughout, to facilitate hands-on learning of data assimilation methods for readers
- Provides a unique perspective to show how practical requirements of data assimilation often impact the direction of theoretical development
- Introduces alternative views of data assimilation based on Shannon information theory that can benefit future development of data assimilation methods
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×Product details
- Date Published: September 2022
- format: Hardback
- isbn: 9781108831765
- length: 400 pages
- dimensions: 251 x 178 x 24 mm
- weight: 0.885kg
- availability: Available
Table of Contents
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.
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