Data Assimilation for the Geosciences. From Theory to Application – Ebook PDF Instant Delivery – ISBN(s): 9780128044841
Product details:
- ISBN-10 : 0128044446
- ISBN-13 : 978-0128044445
- Author: Steven J. Fletcher
Data Assimilation for the Geosciences: From Theory to Application brings together all of the mathematical,statistical, and probability background knowledge needed to formulate data assimilation systems in one place. It includes practical exercises for understanding theoretical formulation and presents some aspects of coding the theory with a toy problem.
The book also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to the atmosphere, oceans, as well as the land surface and other geophysical situations. It offers a comprehensive presentation of the subject, from basic principles to advanced methods, such as Particle Filters and Markov-Chain Monte-Carlo methods. Additionally, Data Assimilation for the Geosciences: From Theory to Application covers the applications of data assimilation techniques in various disciplines of the geosciences, making the book useful to students, teachers, and research scientists.
Table of contents:
1. Introduction
2. Overview of Linear Algebra
3. Univariate Distribution Theory
4. Multivariate Distribution Theory
5. Introduction to Calculus of Variation
6. Introduction to Control Theory
7. Optimal Control Theory
8. Numerical Solutions to Initial Value Problems
9. Numerical Solutions to Boundary Value Problems
10. Introduction to Semi-Lagrangian Advection Methods
11. Introduction to Finite Element Modeling
12. Numerical Modeling on the Sphere
13. Tangent Linear Modeling and Adjoints
14. Observations
15. Non-variational Sequential Data Assimilation Methods
16. Variational Data Assimilation
17. Subcomponents of Variational Data Assimilation
18. Observation Space Variational Data Assimilation Methods
19. Kalman Filter and Smoother
20. Ensemble-Based Data Assimilation
21. Non-Gaussian Variational Data Assimilation
22. Markov Chain Monte Carlo and Particle Filter Methods
23. Machine Learning Artificial Intelligence with Data Assimilation
24. Applications of Data Assimilation in the Geosciences
25. Solutions to Select Exercise
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