Dynamic Data Assimilation

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4 STUDENTS

Introduction:Data Mining, Data Assimilation, Inverse problems and Prediction – Static vs. dynamic and deterministic vs. stochastic problems- formulation & classification;Mathematical tools:Finite dimensional vector space – basic concepts – Overview of properties and operations on matrices – Special classes of matrices, Eigen decomposition, and matrix square root – Gradient, Jacobian, Hessian, Quadratic forms and their properties;Static, deterministic models: least Squares method – formulation and properties – Linear least squares (LLS) – over determined case, weighted and unweighted formulation, orthogonal and oblique projections – LLS- Underdetermined case -Lagrangian multiplier – Nonlinear least squares problem (NLS) – formulation – Approximation – first and second-order methods for solving NLS – Examples of LLS and NLS – satellite retrieval

Matrix methods solving LLS:Normal equations – symmetric positive definite (SPD) systems – multiplicative matrix decomposition – Cholesky decomposition- matrix square root – Gramm-Schmidt orthogonalization process – QR decomposition – Singular value decomposition (SVD) – Solution of retrieval problems;Direct minimization methods for solving LLS:LLS as a quadratic minimization problem – Gradient method, its properties – Convergence and speed of convergence of gradient method – Conjugate gradient and Quasi-Newton methods – Practice problems and programming exercises;Deterministic, dynamic models:adjoint method – Dynamic models, role of observations, and least squares objective function, estimation of initial condition (IC) and parameters, adjoint sensitivity – A straight line problem – a warm up – Linear model, first-order adjoint dynamics and computation of the gradient of the least squares objective function – Nonlinear model and first-order adjoint dynamics – Illustrative examples and practice problems, programming exercises

Deterministic, Dynamic models:Other methods – Forward sensitivity method for estimation of IC and parameters, forward sensitivity dynamics – Example of Carbon dynamics,Relation between adjoint and forward sensitivity, Predictability, Lyapunov index – Method of nudging and overview of nudging methods;Static, stochastic models:Bayesian framework – Bayesian method – linear, Gaussian case – Linear minimum variance estimation (LMVE) and prelude to Kalman filter – Model space vs. observation space formulation -Duality between Bayesian and LMVE;Dynamic, Stochastic models:Kalman filtering – Derivation of the Kalman filter equations – Derivation of Nonlinear filter – Computational requirements – Ensemble Kalman filtering;Dynamic stochastic models:Other methods – Unscented Kalman filtering – Particle filtering – An overview and assessment of methods

Course Curriculum

An Overview Details 1:16
Data Mining, Data assimilation and prediction Details 1:4:56
A classification of forecast errors Details 27:8
Finite Dimensional Vector Space Details 48:31
Matrices Details 1:17:30
Matrices Continued Details 45:11
Multi-variate Calculus Details 50:15
Optimization in Finite Dimensional Vector spaces Details 59:46
Deterministic, Static, linear Inverse (well-posed) Problems Details 1:3:27
Deterministic, Static, Linear Inverse (Ill-posed) Problems Details 31:21
A Geometric View – Projections Details 33:21
Deterministic, Static, nonlinear Inverse Problems Details 35:22
On-line Least Squares Details 37:20
Examples of static inverse problems Details 50:28
Interlude and a Way Forward Details 14:29
Matrix Decomposition Algorithms Details 1:3:1
Matrix Decomposition Algorithms Continued Details 50:51
Minimization algorithms Details 1:10:48
Minimization algorithms Continued Details 1:6:46
Inverse problems in deterministic Details 1:10:50
Inverse problems in deterministic Continued Details 54:25
Forward sensitivity method Details 1:2:7
Relation between FSM and 4DVAR Details 44:28
Statistical Estimation Details 1:26:41
Statistical Least Squares Details 52:29
Maximum Likelihood Method Details 28:59
Bayesian Estimation Details 1:17:34
From Gauss to Kalman-Linear Minimum Variance Estimation Details 1:9:41
Initialization Classical Method Details 1:13:59
Optimal interpolations Details 59:7
A Bayesian Formation-3D-VAR methods Details 54:1
Linear Stochastic Dynamics – Kalman Filter Details 1:4:55
Linear Stochastic Dynamics – Kalman Filter Continued Details 27:52
Linear Stochastic Dynamics – Kalman Filter Continued. Details 39:48
Covariance Square Root Filter Details 49:49
Nonlinear Filtering Details 2:30:54
Ensemble Reduced Rank Filter Details 1:37:2
Basic nudging methods Details 1:5:22
Deterministic predictability Details 1:20:29
Predictability A stochastic view and Summary Details 1:17:19

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