Dedication

pp v-vi

Contents

pp vii-x

Preface

pp xi-xiv

Part I - General Background

pp 1-2

1 - Data Assimilation: General Background

pp 3-34

2 - Probability and the Bayesian Approach

pp 35-53

3 - Filters and Smoothers

pp 54-80

Part II - Practical Tools

pp 81-82

4 - Tangent Linear and Adjoint Models

pp 83-110

5 - Automatic Differentiation

pp 111-127

6 - Numerical Minimization Process

pp 128-160

Part III - Methods and Issues

pp 161-162

7 - Variational Data Assimilation

pp 163-209

8 - Ensemble and Hybrid Data Assimilation

pp 210-231

9 - Coupled Data Assimilation

pp 232-250

10 - Dynamics and Data Assimilation

pp 251-262

Part IV - Applications

pp 263-264

11 - Sensitivity Analysis and Adaptive Observation

pp 265-304

12 - Satellite Data Assimilation

pp 305-330

Part V - Appendices

pp 331-332

Appendix A - Linear Algebra and Functional Analysis

pp 333-346

Appendix B - Discretization of Partial Differential Equations

pp 347-352

Appendix C - Lab Practice I

pp 353-360

Appendix D - Lab Practice II

pp 361-383

Index

pp 384-398