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This book summarizes about 75 years of the authors’accumulated experience within the formulation, development, and practical use of advanceddata-assimilation methods. We realize that no available texts discuss the multitudeof data-assimilation methods and problems in a“unified”manner and with a unifiednotation. Thus, we believe this book will serve as an essential resource for anyoneworking or planning to work in data assimilation.We also believe this book is very suitable for an advanced course in dataassimilation. The mathematical level is modest, and we explain all derivations inquite some detail. Furthermore, the book connects and gives a nearly completeoverview and introduction to today’s most popular data-assimilation methods.
Mathematical Formulation
Problem Formulation
Maximum a Posteriori Solution
Strong-Constraint 4DVar
Weak Constraint 4DVar
Kalman Filters and 3DVar
Randomized-Maximum-Likelihood Sampling
Low-Rank Ensemble Methods
Fully Nonlinear Data Assimilation
Localization and Inflation
Methods’Summary
Examples and Applications
A Kalman Filter with the Roessler Model
Linear EnKF Update
EnKF with the Lorenz Equations
3Dvar and SC-4DVar for the Lorenz 63 Model
Representer Method with an Ekman-Flow Model
Comparison of Methods on a Scalar Model
Particle Filter for Seismic-Cycle Estimation
Particle Flow for a Quasi-Geostrophic Model
EnRML for History Matching Petroleum Models
ESMDA with a SARS-COV-2 Pandemic Model
Final Summary