Data Assimilation Fundamentals A Unified Formulation of the State and Parameter Estimation Problem /
This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts fro...
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| Main Authors: | , , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2022.
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| Edition: | 1st ed. 2022. |
| Series: | Springer Textbooks in Earth Sciences, Geography and Environment,
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| Subjects: | |
| Online Access: | Link to Metadata |
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Table of Contents:
- Introduction
- Part I 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
- Part II Examples and Applications: A Kalman filter with the Roessler model
- Linear EnKF update
- EnKF for an advection equation
- 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
- References
- Index. .