Data Assimilation for the Geosciences
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Data Assimilation for the Geosciences
From Theory to Application
Fletcher, Steven J.
Elsevier - Health Sciences Division
11/2022
1128
Mole
Inglês
9780323917209
15 a 20 dias
Descrição não disponível.
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Variational; Ensemble; non-Gaussian; Calculus of Variation; Distribution theory; Bayes Theorem; Numerical modelling; Particle filters; Observing System Experiments; Machine Learning; Artificial Intelligence
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
