Data Assimilation for the Geosciences. From Theory to Application Steven 0128044845 9780128044841
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Product details:
- ISBN 10: 0128044845
- ISBN 13: 9780128044841
- 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:
Chapter 1: Introduction
Chapter 2: Overview of Linear Algebra
Chapter 3: Univariate Distribution Theory
Chapter 4: Multivariate Distribution Theory
Chapter 5: Introduction to Calculus of Variation
Chapter 6: Introduction to Control Theory
Chapter 7: Optimal Control Theory
Chapter 8: Numerical Solutions to Initial Value Problems
Chapter 9: Numerical Solutions to Boundary Value Problems
Chapter 10: Introduction to Semi-Lagrangian Advection Methods
Chapter 11: Introduction to Finite Element Modeling
Chapter 12: Numerical Modeling on the Sphere
Chapter 13: Tangent Linear Modeling and Adjoints
Chapter 14: Observations
Chapter 15: Non-variational Sequential Data Assimilation Methods
Chapter 16: Variational Data Assimilation
Chapter 17: Subcomponents of Variational Data Assimilation
Chapter 18: Observation Space Variational Data Assimilation Methods
Chapter 19: Kalman Filter and Smoother
Chapter 20: Ensemble-Based Data Assimilation
Chapter 21: Non-Gaussian Variational Data Assimilation
Chapter 22: Markov Chain Monte Carlo and Particle Filter Methods
Chapter 23: Applications of Data Assimilation in the Geosciences
Chapter 24: Solutions to Select Exercise
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