Dynamic Data Assimilation: An Introduction

Video Lectures

Displaying all 40 video lectures.
I. Introduction
Lecture 1
An Overview
Play Video		 An Overview
Lecture 2
Data Mining, Data assimilation and prediction
Play Video		 Data Mining, Data assimilation and prediction
II. Mathematical Tools
Lecture 3
A classification of forecast errors
Play Video		 A classification of forecast errors
Lecture 4
Finite Dimensional Vector Space
Play Video		 Finite Dimensional Vector Space
Lecture 5
Matrices
Play Video		 Matrices
Lecture 6
Matrices Continued
Play Video		 Matrices Continued
III. Static & Deterministic Models
Lecture 7
Multi-variate Calculus
Play Video		 Multi-variate Calculus
Lecture 8
Optimization in Finite Dimensional Vector spaces
Play Video		 Optimization in Finite Dimensional Vector spaces
Lecture 9
Deterministic, Static, linear Inverse (well-posed) Problems
Play Video		 Deterministic, Static, linear Inverse (well-posed) Problems
Lecture 10
Deterministic, Static, Linear Inverse (Ill-posed) Problems
Play Video		 Deterministic, Static, Linear Inverse (Ill-posed) Problems
Lecture 11
A Geometric View - Projections
Play Video		 A Geometric View - Projections
IV. Matrix Methods Solving LLS
Lecture 12
Deterministic, Static, nonlinear Inverse Problems
Play Video		 Deterministic, Static, nonlinear Inverse Problems
Lecture 13
On-line Least Squares
Play Video		 On-line Least Squares
Lecture 14
Examples of static inverse problems
Play Video		 Examples of static inverse problems
Lecture 15
Interlude and a Way Forward
Play Video		 Interlude and a Way Forward
Lecture 16
Matrix Decomposition Algorithms
Play Video		 Matrix Decomposition Algorithms
Lecture 17
Matrix Decomposition Algorithms Continued
Play Video		 Matrix Decomposition Algorithms Continued
V. Direct Minimization Methods for Solving LLS
Lecture 18
Minimization algorithms
Play Video		 Minimization algorithms
Lecture 19
Minimization algorithms Continued
Play Video		 Minimization algorithms Continued
Lecture 20
Inverse problems in deterministic
Play Video		 Inverse problems in deterministic
Lecture 21
Inverse problems in deterministic Continued
Play Video		 Inverse problems in deterministic Continued
Lecture 22
Forward sensitivity method
Play Video		 Forward sensitivity method
VI. Deterministic & Dynamic Models: Adjoint Method
Lecture 23
Relation between FSM and 4DVAR
Play Video		 Relation between FSM and 4DVAR
Lecture 24
Statistical Estimation
Play Video		 Statistical Estimation
Lecture 25
Statistical Least Squares
Play Video		 Statistical Least Squares
Lecture 26
Maximum Likelihood Method
Play Video		 Maximum Likelihood Method
Lecture 27
Bayesian Estimation
Play Video		 Bayesian Estimation
VII. Deterministic & Dynamic models: Other Methods
Lecture 28
From Gauss to Kalman-Linear Minimum Variance Estimation
Play Video		 From Gauss to Kalman-Linear Minimum Variance Estimation
Lecture 29
Initialization Classical Method
Play Video		 Initialization Classical Method
Lecture 30
Optimal interpolations
Play Video		 Optimal interpolations
VIII. Static & Stochastic Models: Bayesian Framework
Lecture 31
A Bayesian Formation-3D-VAR methods
Play Video		 A Bayesian Formation-3D-VAR methods
Lecture 32
Linear Stochastic Dynamics - Kalman Filter
Play Video		 Linear Stochastic Dynamics - Kalman Filter
Lecture 33
Linear Stochastic Dynamics - Kalman Filter Continued
Play Video		 Linear Stochastic Dynamics - Kalman Filter Continued
IX. Dynamic & Stochastic Models: Kalman Filtering
Lecture 34
Linear Stochastic Dynamics - Kalman Filter Continued.
Play Video		 Linear Stochastic Dynamics - Kalman Filter Continued.
Lecture 35
Covariance Square Root Filter
Play Video		 Covariance Square Root Filter
Lecture 36
Nonlinear Filtering
Play Video		 Nonlinear Filtering
Lecture 37
Ensemble Reduced Rank Filter
Play Video		 Ensemble Reduced Rank Filter
X. Dynamic Stochastic Models: Other Methods
Lecture 38
Basic nudging methods
Play Video		 Basic nudging methods
Lecture 39
Deterministic predictability
Play Video		 Deterministic predictability
Lecture 40
Predictability: A stochastic view and summary
Play Video		 Predictability: A stochastic view and summary