## Browse content

### Table of contents

#### Actions for selected chapters

- Full text access
- Book chapterAbstract only
#### Chapter 1 - Introduction

Pages 1-6 - Book chapterAbstract only
#### Chapter 2 - Overview of Linear Algebra

Pages 7-29 - Book chapterAbstract only
#### Chapter 3 - Univariate Distribution Theory

Pages 31-132 - Book chapterAbstract only
#### Chapter 4 - Multivariate Distribution Theory

Pages 133-173 - Book chapterAbstract only
#### Chapter 5 - Introduction to Calculus of Variation

Pages 175-208 - Book chapterAbstract only
#### Chapter 6 - Introduction to Control Theory

Pages 209-246 - Book chapterAbstract only
#### Chapter 7 - Optimal Control Theory

Pages 247-284 - Book chapterAbstract only
#### Chapter 8 - Numerical Solutions to Initial Value Problems

Pages 285-326 - Book chapterAbstract only
#### Chapter 9 - Numerical Solutions to Boundary Value Problems

Pages 327-370 - Book chapterAbstract only
#### Chapter 10 - Introduction to Semi-Lagrangian Advection Methods

Pages 371-443 - Book chapterAbstract only
#### Chapter 11 - Introduction to Finite Element Modeling

Pages 445-484 - Book chapterAbstract only
#### Chapter 12 - Numerical Modeling on the Sphere

Pages 485-555 - Book chapterAbstract only
#### Chapter 13 - Tangent Linear Modeling and Adjoints

Pages 557-599 - Book chapterAbstract only
#### Chapter 14 - Observations

Pages 601-629 - Book chapterAbstract only
#### Chapter 15 - Non-Variational Sequential Data Assimilation Methods

Pages 631-675 - Book chapterAbstract only
#### Chapter 16 - Variational Data Assimilation

Pages 677-733 - Book chapterAbstract only
#### Chapter 17 - Subcomponents of Variational Data Assimilation

Pages 735-784 - Book chapterAbstract only
#### Chapter 18 - Observation Space Variational Data Assimilation Methods

Pages 785-795 - Book chapterAbstract only
#### Chapter 19 - Kalman Filter and Smoother

Pages 797-813 - Book chapterAbstract only
#### Chapter 20 - Ensemble-Based Data Assimilation

Pages 815-863 - Book chapterAbstract only
#### Chapter 21 - Non-Gaussian Based Data Assimilation

Pages 865-929 - Book chapterAbstract only
#### Chapter 22 - Markov Chain Monte Carlo, Particle Filters, Particle Smoothers, and Sigma Point Filters

Pages 931-963 - Book chapterAbstract only
#### Chapter 23 - Lagrangian Data Assimilation

Pages 965-983 - Book chapterAbstract only
#### Chapter 24 - Artificial Intelligence and Data Assimilation

Pages 985-1017 - Book chapterAbstract only
#### Chapter 25 - Applications of Data Assimilation in the Geosciences

Pages 1019-1065 - Book chapterNo access
#### Chapter 26 - Solutions to Select Exercise

Pages 1067-1071 - Book chapterNo access
#### Bibliography

Pages 1073-1094 - Book chapterNo access
#### Index

Pages 1095-1108

## About the book

### Description

*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.

*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.

### Key Features

- 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

- 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

## Details

### ISBN

978-0-323-91720-9

### Language

English

### Published

2022

### Copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

### Imprint

Elsevier