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Data Assimilation for the Geosciences
From Theory to Application
- 1st Edition - March 10, 2017
- Author: Steven J. Fletcher
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 4 4 4 4 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 4 4 8 4 - 1
Data Assimilation for the Geosciences: From Theory to Application brings together all of the mathematical,statistical, and probability background knowledge needed to formulate… Read more
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Request a sales quoteData Assimilation for the Geosciences: From Theory to Application brings together all of the mathematical,statistical, and probability background knowledge needed to formulate data assimilation systems in one place. It includes practical exercises for understanding theoretical formulation and presents some aspects of coding the theory with a toy problem.
The book also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to the atmosphere, oceans, as well as the land surface and other geophysical situations. It offers a comprehensive presentation of the subject, from basic principles to advanced methods, such as Particle Filters and Markov-Chain Monte-Carlo methods. Additionally, Data Assimilation for the Geosciences: From Theory to Application covers the applications of data assimilation techniques in various disciplines of the geosciences, making the book useful to students, teachers, and research scientists.
- Includes practical exercises, enabling readers to apply concepts in a theoretical formulation
- Offers explanations for how to code certain parts of the theory
- Presents a step-by-step guide on how, and why, data assimilation works and can be used
All geoscientists especially geophysicists, atmospheric scientists and mathematicians who are learning about data assimilation
Chapter 1: Introduction
- Abstract
Chapter 2: Overview of Linear Algebra
- Abstract
- 2.1 Properties of Matrices
- 2.2 Matrix and Vector Norms
- 2.3 Eigenvalues and Eigenvectors
- 2.4 Matrix Decompositions
- 2.5 Sherman-Morrison-Woodbury Formula
- 2.6 Summary
Chapter 3: Univariate Distribution Theory
- Abstract
- 3.1 Random Variables
- 3.2 Discrete Probability Theory
- 3.3 Continuous Probability Theory
- 3.4 Discrete Distribution Theory
- 3.5 Expectation and Variance of Discrete Random Variables
- 3.6 Moments and Moment-Generating Functions
- 3.7 Continuous Distribution Theory
- 3.8 Lognormal Distribution
- 3.9 Exponential Distribution
- 3.10 Gamma Distribution
- 3.11 Beta Distribution
- 3.12 Chi-Squared (χ2) Distribution
- 3.13 Rayleigh Distribution
- 3.14 Weibull Distribution
- 3.15 Gumbel Distribution
- 3.16 Summary of the Descriptive Statistics, Moment-Generating Functions, and Moments for the Univariate Distribution
- 3.17 Summary
Chapter 4: Multivariate Distribution Theory
- Abstract
- 4.1 Descriptive Statistics for Multivariate Density Functions
- 4.2 Gaussian Distribution
- 4.3 Lognormal Distribution
- 4.4 Mixed Gaussian-Lognormal Distribution
- 4.5 Multivariate Mixed Gaussian-Lognormal Distribution
- 4.6 Gamma Distribution
- 4.7 Summary
Chapter 5: Introduction to Calculus of Variation
- Abstract
- 5.1 Examples of Calculus of Variation Problems
- 5.2 Solving Calculus of Variation Problems
- 5.3 Functional With Higher-Order Derivatives
- 5.4 Three-Dimensional Problems
- 5.5 Functionals With Constraints
- 5.6 Functional With Extremals That Are Functions of Two or More Variables
- 5.7 Summary
Chapter 6: Introduction to Control Theory
- Abstract
- 6.1 The Control Problem
- 6.2 The Uncontrolled Problem
- 6.3 The Controlled Problem
- 6.4 Observability
- 6.5 Duality
- 6.6 Stability
- 6.7 Feedback
- 6.8 Summary
Chapter 7: Optimal Control Theory
- Abstract
- 7.1 Optimizing Scalar Control Problems
- 7.2 Multivariate Case
- 7.3 Autonomous (Time-Invariant) Problem
- 7.4 Extension to General Boundary Conditions
- 7.5 Free End Time Optimal Control Problems
- 7.6 Piecewise Smooth Calculus of Variation Problems
- 7.7 Maximization of Constrained Control Problems
- 7.8 Two Classical Optimal Control Problems
- 7.9 Summary
Chapter 8: Numerical Solutions to Initial Value Problems
- Abstract
- 8.1 Local and Truncation Errors
- 8.2 Linear Multistep Methods
- 8.3 Stability
- 8.4 Convergence
- 8.5 Runge-Kutta Schemes
- 8.6 Numerical Solutions to Initial Value Partial Differential Equations
- 8.7 Wave Equation
- 8.8 Courant Friedrichs Lewy Condition
- 8.9 Summary
Chapter 9: Numerical Solutions to Boundary Value Problems
- Abstract
- 9.1 Types of Differential Equations
- 9.2 Shooting Methods
- 9.3 Finite Difference Methods
- 9.4 Self-Adjoint Problems
- 9.5 Error Analysis
- 9.6 Partial Differential Equations
- 9.7 Self-Adjoint Problem in Two Dimensions
- 9.8 Periodic Boundary Conditions
- 9.9 Summary
Chapter 10: Introduction to Semi-Lagrangian Advection Methods
- Abstract
- 10.1 History of Semi-Lagrangian Approaches
- 10.2 Derivation of Semi-Lagrangian Approach
- 10.3 Interpolation Polynomials
- 10.4 Stability of Semi-Lagrangian Schemes
- 10.5 Consistency Analysis of Semi-Lagrangian Schemes
- 10.6 Semi-Lagrangian Schemes for Non-Constant Advection Velocity
- 10.7 Semi-Lagrangian Scheme for Non-Zero Forcing
- 10.8 Example: 2D Quasi-Geostrophic Potential Vorticity (Eady Model)
- 10.9 Summary
Chapter 11: Introduction to Finite Element Modeling
- Abstract
- 11.1 Solving the Boundary Value Problem
- 11.2 Weak Solutions of Differential Equation
- 11.3 Accuracy of the Finite Element Approach
- 11.4 Pin Tong
- 11.5 Finite Element Basis Functions
- 11.6 Coding Finite Element Approximations for Triangle Elements
- 11.7 Isoparametric Elements
- 11.8 Summary
Chapter 12: Numerical Modeling on the Sphere
- Abstract
- 12.1 Vector Operators in Spherical Coordinates
- 12.2 Spherical Vector Derivative Operators
- 12.3 Finite Differencing on the Sphere
- 12.4 Introduction to Fourier Analysis
- 12.5 Spectral Modeling
- 12.6 Summary
Chapter 13: Tangent Linear Modeling and Adjoints
- Abstract
- 13.1 Additive Tangent Linear and Adjoint Modeling Theory
- 13.2 Multiplicative Tangent Linear and Adjoint Modeling Theory
- 13.3 Examples of Adjoint Derivations
- 13.4 Perturbation Forecast Modeling
- 13.5 Adjoint Sensitivities
- 13.6 Singular Vectors
- 13.7 Summary
Chapter 14: Observations
- Abstract
- 14.1 Conventional Observations
- 14.2 Remote Sensing
- 14.3 Quality Control
- 14.4 Summary
Chapter 15: Non-variational Sequential Data Assimilation Methods
- Abstract
- 15.1 Direct Insertion
- 15.2 Nudging
- 15.3 Successive Correction
- 15.4 Linear and Nonlinear Least Squares
- 15.5 Regression
- 15.6 Optimal (Optimum) Interpolation/Statistical Interpolation/Analysis Correction
- 15.7 Summary
Chapter 16: Variational Data Assimilation
- Abstract
- 16.1 Sasaki and the Strong and Weak Constraints
- 16.2 Three-Dimensional Data Assimilation
- 16.3 Four-Dimensional Data Assimilation
- 16.4 Incremental VAR
- 16.5 Weak Constraint—Model Error 4D VAR
- 16.6 Observational Errors
- 16.7 4D VAR as an Optimal Control Problem
- 16.8 Summary
Chapter 17: Subcomponents of Variational Data Assimilation
- Abstract
- 17.1 Balance
- 17.2 Control Variable Transforms
- 17.3 Background Error Covariance Modeling
- 17.4 Preconditioning
- 17.5 Minimization Algorithms
- 17.6 Performance Metrics
- 17.7 Summary
Chapter 18: Observation Space Variational Data Assimilation Methods
- Abstract
- 18.1 Derivation of Observation Space-Based 3D VAR
- 18.2 4D VAR in Observation Space
- 18.3 Duality of the VAR and PSAS Systems
- 18.4 Summary
Chapter 19: Kalman Filter and Smoother
- Abstract
- 19.1 Derivation of the Kalman Filter
- 19.2 Kalman Filter Derivation from a Statistical Approach
- 19.3 Extended Kalman Filter
- 19.4 Square Root Kalman Filter
- 19.5 Smoother
- 19.6 Properties and Equivalencies of the Kalman Filter and Smoother
- 19.7 Summary
Chapter 20: Ensemble-Based Data Assimilation
- Abstract
- 20.1 Stochastic Dynamical Modeling
- 20.2 Ensemble Kalman Filter
- 20.3 Ensemble Square Root Filters
- 20.4 Ensemble and Local Ensemble Transform Kalman Filter
- 20.5 Maximum Likelihood Ensemble Filter
- 20.6 Hybrid Ensemble and Variational Data Assimilation Methods
- 20.7 NDEnVAR
- 20.8 Ensemble Kalman Smoother
- 20.9 Ensemble Sensitivity
- 20.10 Summary
Chapter 21: Non-Gaussian Variational Data Assimilation
- Abstract
- 21.1 Error Definitions
- 21.2 Full Field Lognormal 3D VAR
- 21.3 Logarithmic Transforms
- 21.4 Mixed Gaussian-Lognormal 3D VAR
- 21.5 Lognormal Calculus of Variation-Based 4D VAR
- 21.6 Bayesian-Based 4D VAR
- 21.7 Bayesian Networks Formulation of Weak Constraint/Model Error 4D VAR
- 21.8 Results of the Lorenz 1963 Model for 4D VAR
- 21.9 Incremental Lognormal and Mixed 3D and 4D VAR
- 21.10 Regions of Optimality for Lognormal Descriptive Statistics
- 21.11 Summary
Chapter 22: Markov Chain Monte Carlo and Particle Filter Methods
- Abstract
- 22.1 Markov Chain Monte Carlo Methods
- 22.2 Particle Filters
- 22.3 Summary
Chapter 23: Applications of Data Assimilation in the Geosciences
- Abstract
- 23.1 Atmospheric Science
- 23.2 Oceans
- 23.3 Hydrological Applications
- 23.4 Coupled Data Assimilation
- 23.5 Reanalysis
- 23.6 Ionospheric Data Assimilation
- 23.7 Renewable Energy Data Application
- 23.8 Oil and Natural Gas
- 23.9 Biogeoscience Application of Data Assimilation
- 23.10 Other Applications of Data Assimilation
- 23.11 Summary
Chapter 24: Solutions to Select Exercise
- Chapter 2
- Chapter 3
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 9
- No. of pages: 976
- Language: English
- Edition: 1
- Published: March 10, 2017
- Imprint: Elsevier
- Paperback ISBN: 9780128044445
- eBook ISBN: 9780128044841
SF
Steven J. Fletcher
Steven J. Fletcher is a Research Scientist III at the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University, where he is the lead scientist on the development of non-Gaussian based data assimilation theory for variational, PSAS, and hybrid systems. He has worked extensively with the Naval Research Laboratory in Monterey in development of their data assimilation system, as well as working with the National Atmospheric and Oceanic Administration (NOAA)’s Environmental Prediction Centers (EMC) data assimilation system. Dr. Fletcher is extensively involved with the American Geophysical Union (AGU)’s Fall meeting planning committee, having served on the committee since 2013 as the representative of the Nonlinear Geophysics section. He has also been the lead organizer and science program committee member for the Joint Center for Satellite Data Assimilation Summer Colloquium on Satellite Data Assimilation since 2016. Dr. Fletcher is the author of Data Assimilation for the Geosciences: From Theory to Application (Elsevier, 2017). In 2017 Dr. Fletcher became a fellow of the Royal Meteorological Society.
Affiliations and expertise
Research Scientist III, Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University – Fort Collins, Colorado, USARead Data Assimilation for the Geosciences on ScienceDirect