Dynamic Data Assimilation: An Introduction


Lecture 1 - An Overview


Lecture 2 - Data Mining, Data assimilation and prediction


Lecture 3 - A classification of forecast errors


Lecture 4 - Finite Dimensional Vector Space


Lecture 5 - Matrices


Lecture 6 - Matrices (Continued...)


Lecture 7 - Multi-variate Calculus


Lecture 8 - Optimization in Finite Dimensional Vector spaces


Lecture 9 - Deterministic, Static, linear Inverse (well-posed) Problems


Lecture 10 - Deterministic, Static, Linear Inverse (Ill-posed) Problems


Lecture 11 - A Geometric View – Projections


Lecture 12 - Deterministic, Static, nonlinear Inverse Problems


Lecture 13 - On-line Least Squares


Lecture 14 - Examples of static inverse problems


Lecture 15 - Interlude and a Way Forward


Lecture 16 - Matrix Decomposition Algorithms


Lecture 17 - Matrix Decomposition Algorithms (Continued...)


Lecture 18 - Minimization algorithms


Lecture 19 - Minimization algorithms (Continued...)


Lecture 20 - Inverse problems in deterministic


Lecture 21 - Inverse problems in deterministic (Continued...)


Lecture 22 - Forward sensitivity method


Lecture 23 - Relation between FSM and 4DVAR


Lecture 24 - Statistical Estimation


Lecture 25 - Statistical Least Squares


Lecture 26 - Maximum Likelihood Method


Lecture 27 - Bayesian Estimation


Lecture 28 - From Gauss to Kalman-Linear Minimum Variance Estimation


Lecture 29 - Initialization Classical Method


Lecture 30 - Optimal interpolations


Lecture 31 - A Bayesian Formation-3D-VAR methods


Lecture 32 - Linear Stochastic Dynamics - Kalman Filter


Lecture 33 - Linear Stochastic Dynamics - Kalman Filter (Continued...)


Lecture 34 - Linear Stochastic Dynamics - Kalman Filter (Continued...)


Lecture 35 - Covariance Square Root Filter


Lecture 36 - Nonlinear Filtering


Lecture 37 - Ensemble Reduced Rank Filter


Lecture 38 - Basic nudging methods


Lecture 39 - Deterministic predictability


Lecture 40 - Predictability A stochastic view and Summary