Data Assimilation for the Geosciences
From Theory to Application
- 2 Edición - 16 de noviembre de 2022
- Última edición
- Autor: Steven J. Fletcher
- Idioma: Inglés
Data Assimilation for the Geosciences: From Theory to Application, Second Edition brings together all of the mathematical and statistical background knowledge needed to formul… Leer más
Descripción
Descripción
Data Assimilation for the Geosciences: From Theory to Application, Second Edition brings together all of the mathematical and statistical background knowledge needed to formulate data assimilation systems into one place. It includes practical exercises enabling readers to apply theory in both a theoretical formulation as well as teach them how to code the theory with toy problems to verify their understanding. It also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to land surface, the atmosphere, ocean and other geophysical situations. The second edition of Data Assimilation for the Geosciences has been revised with up to date research that is going on in data assimilation, as well as how to apply the techniques. The new edition features an introduction of how machine learning and artificial intelligence are interfacing and aiding data assimilation. In addition to appealing to students and researchers across the geosciences, this now also appeals to new students and scientists in the field of data assimilation as it will now have even more information on the techniques, research, and applications, consolidated into one source.
Puntos claves
Puntos claves
- 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
De interès para
De interès para
Students at the graduate level, those starting to work on DA at a research of operational prediction center that do not a have a DA background, 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
Índice
Índice
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
Detalles del producto
Detalles del producto
- Edición: 2
- Última edición
- Publicado: 18 de noviembre de 2022
- Idioma: Inglés
Sobre el autor
Sobre el autor
SF
Steven J. Fletcher
Steven J. Fletcher is a Research Scientist III at the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University, where he is the lead scientist on the development of non-Gaussian based data assimilation theory for variational, PSAS, and hybrid systems. He has worked extensively with the Naval Research Laboratory in Monterey in development of their data assimilation system, as well as working with the National Atmospheric and Oceanic Administration (NOAA)’s Environmental Prediction Centers (EMC) data assimilation system. Dr. Fletcher is extensively involved with the American Geophysical Union (AGU)’s Fall meeting planning committee, having served on the committee since 2013 as the representative of the Nonlinear Geophysics section. He has also been the lead organizer and science program committee member for the Joint Center for Satellite Data Assimilation Summer Colloquium on Satellite Data Assimilation since 2016. Dr. Fletcher is the author of Data Assimilation for the Geosciences: From Theory to Application (Elsevier, 2017). In 2017 Dr. Fletcher became a fellow of the Royal Meteorological Society.
Afiliaciones y experiencia
Research Scientist III, Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University – Fort Collins, Colorado, USAVer libro en ScienceDirect
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