Cover for Data Assimilation for the Geosciences

Data Assimilation for the Geosciences

From Theory to Application

Book • Second Edition2022

Author:

Steven J. Fletcher

Data Assimilation for the Geosciences

From Theory to Application

Book • Second Edition2022

 

Cover for Data Assimilation for the Geosciences

Author:

Steven J. Fletcher

About the book

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Data Assimilation for the Geosciences: From Theory to Application, Second Edition brings together all of the mathematical and statistical background knowledge needed to formulate d ... read full description

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  2. Book chapterAbstract only

    Chapter 1 - Introduction

    Pages 1-6

  3. Book chapterAbstract only

    Chapter 2 - Overview of Linear Algebra

    Pages 7-29

  4. Book chapterAbstract only

    Chapter 3 - Univariate Distribution Theory

    Pages 31-132

  5. Book chapterAbstract only

    Chapter 4 - Multivariate Distribution Theory

    Pages 133-173

  6. Book chapterAbstract only

    Chapter 5 - Introduction to Calculus of Variation

    Pages 175-208

  7. Book chapterAbstract only

    Chapter 6 - Introduction to Control Theory

    Pages 209-246

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    Chapter 7 - Optimal Control Theory

    Pages 247-284

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    Chapter 8 - Numerical Solutions to Initial Value Problems

    Pages 285-326

  10. Book chapterAbstract only

    Chapter 9 - Numerical Solutions to Boundary Value Problems

    Pages 327-370

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    Chapter 10 - Introduction to Semi-Lagrangian Advection Methods

    Pages 371-443

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    Chapter 11 - Introduction to Finite Element Modeling

    Pages 445-484

  13. Book chapterAbstract only

    Chapter 12 - Numerical Modeling on the Sphere

    Pages 485-555

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    Chapter 13 - Tangent Linear Modeling and Adjoints

    Pages 557-599

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    Chapter 14 - Observations

    Pages 601-629

  16. Book chapterAbstract only

    Chapter 15 - Non-Variational Sequential Data Assimilation Methods

    Pages 631-675

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    Chapter 16 - Variational Data Assimilation

    Pages 677-733

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    Chapter 17 - Subcomponents of Variational Data Assimilation

    Pages 735-784

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    Chapter 18 - Observation Space Variational Data Assimilation Methods

    Pages 785-795

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    Chapter 19 - Kalman Filter and Smoother

    Pages 797-813

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    Chapter 20 - Ensemble-Based Data Assimilation

    Pages 815-863

  22. Book chapterAbstract only

    Chapter 21 - Non-Gaussian Based Data Assimilation

    Pages 865-929

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    Chapter 22 - Markov Chain Monte Carlo, Particle Filters, Particle Smoothers, and Sigma Point Filters

    Pages 931-963

  24. Book chapterAbstract only

    Chapter 23 - Lagrangian Data Assimilation

    Pages 965-983

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    Chapter 24 - Artificial Intelligence and Data Assimilation

    Pages 985-1017

  26. Book chapterAbstract only

    Chapter 25 - Applications of Data Assimilation in the Geosciences

    Pages 1019-1065

  27. Book chapterNo access

    Chapter 26 - Solutions to Select Exercise

    Pages 1067-1071

  28. Book chapterNo access

    Bibliography

    Pages 1073-1094

  29. 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

Authors

Steven J. Fletcher

Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, CO, United States