Data Assimilation for the Geosciences: From Theory to Application
Autor Steven J. Fletcheren Limba Engleză Paperback – 18 noi 2022
- Includes practical exercises and solutions enabling readers to apply theory in both a theoretical formulation as well as enabling them to code theory
- Provides the mathematical and statistical background knowledge needed to formulate data assimilation systems into one place
- New to this edition: covers new topics such as Observing System Experiments (OSE) and Observing System Simulation Experiments; and expanded approaches for machine learning and artificial intelligence
| Toate formatele și edițiile | Preț | Express |
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| ELSEVIER SCIENCE – 15 mar 2017 | 730.70 lei 5-7 săpt. | |
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Specificații
ISBN-13: 9780323917209
ISBN-10: 0323917208
Pagini: 1128
Dimensiuni: 191 x 235 x 59 mm
Greutate: 1.9 kg
Ediția:2
Editura: ELSEVIER SCIENCE
ISBN-10: 0323917208
Pagini: 1128
Dimensiuni: 191 x 235 x 59 mm
Greutate: 1.9 kg
Ediția:2
Editura: ELSEVIER SCIENCE
Public țintă
This book is focused on three different sets of readerships: The first is students at the graduate level so that they can understand what Data Assimilation is, how it comes about but also how to expand on the theory of the different forms of DA. The second readership is those starting to work on DA at a research of operational prediction center that do not a have a DA background. The third group are those who are starting to work in industry where they need to understand what DA does and how to implement a version for their requirements.Cuprins
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
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