Dynamic Data Assimilation: An Introduction (USB)
Media Storage Type : 32 GB USB Stick
NPTEL Subject Matter Expert : Prof. S. Lakshmivarahan
NPTEL Co-ordinating Institute : IIT Madras
NPTEL Lecture Count : 40
NPTEL Course Size : 19 GB
NPTEL PDF Text Transcription : Available and Included
NPTEL Subtitle Transcription : Available and Included (SRT)
Lecture Titles:
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